From ca514e579f49d830b34beae43fe288ba9ee188ed Mon Sep 17 00:00:00 2001 From: Gabriel Luiz Freitas Almeida Date: Thu, 29 Feb 2024 23:07:10 -0300 Subject: [PATCH 001/701] Add branching to GenericNode --- .../components/parameterComponent/index.tsx | 8 ++ .../src/CustomNodes/GenericNode/index.tsx | 110 +++++++++++++----- src/frontend/src/types/api/index.ts | 1 + src/frontend/src/types/components/index.ts | 1 + src/frontend/src/types/flow/index.ts | 1 + 5 files changed, 90 insertions(+), 31 deletions(-) diff --git a/src/frontend/src/CustomNodes/GenericNode/components/parameterComponent/index.tsx b/src/frontend/src/CustomNodes/GenericNode/components/parameterComponent/index.tsx index ef8ad8d80..272aad5ba 100644 --- a/src/frontend/src/CustomNodes/GenericNode/components/parameterComponent/index.tsx +++ b/src/frontend/src/CustomNodes/GenericNode/components/parameterComponent/index.tsx @@ -15,6 +15,7 @@ import KeypairListComponent from "../../../../components/keypairListComponent"; import PromptAreaComponent from "../../../../components/promptComponent"; import TextAreaComponent from "../../../../components/textAreaComponent"; import ToggleShadComponent from "../../../../components/toggleShadComponent"; +import { Badge } from "../../../../components/ui/badge"; import { Button } from "../../../../components/ui/button"; import { LANGFLOW_SUPPORTED_TYPES, @@ -50,6 +51,7 @@ export default function ParameterComponent({ data, tooltipTitle, title, + conditionPath, color, type, name = "", @@ -315,6 +317,12 @@ export default function ParameterComponent({ (info !== "" ? " flex items-center" : "") } > + {" "} + {conditionPath && ( + + {conditionPath} + + )} {proxy ? ( {proxy.id}}> {title} diff --git a/src/frontend/src/CustomNodes/GenericNode/index.tsx b/src/frontend/src/CustomNodes/GenericNode/index.tsx index c9844a47d..3729ac418 100644 --- a/src/frontend/src/CustomNodes/GenericNode/index.tsx +++ b/src/frontend/src/CustomNodes/GenericNode/index.tsx @@ -664,37 +664,85 @@ export default function GenericNode({ > {" "} - {data.node!.base_classes.length > 0 && ( - 0 - ? nodeColors[data.node.output_types[0]] ?? - nodeColors[types[data.node.output_types[0]]] - : nodeColors[types[data.type]]) ?? nodeColors.unknown - } - title={ - data.node?.output_types && data.node.output_types.length > 0 - ? data.node.output_types.join(" | ") - : data.type - } - tooltipTitle={data.node?.base_classes.join("\n")} - id={{ - baseClasses: data.node!.base_classes, - id: data.id, - dataType: data.type, - }} - type={data.node?.base_classes.join("|")} - left={false} - showNode={showNode} - /> - )} +
+ {data.node!.base_classes.length > 0 && ( + 0 + ? nodeColors[data.node.output_types[0]] ?? + nodeColors[types[data.node.output_types[0]]] + : nodeColors[types[data.type]]) ?? nodeColors.unknown + } + title={ + data.node?.output_types && + data.node.output_types.length > 0 + ? data.node.output_types.join(" | ") + : data.type + } + conditionPath={data.node?.is_conditional ? "True" : null} + tooltipTitle={data.node?.base_classes.join("\n")} + id={{ + baseClasses: data.node!.base_classes, + id: data.id, + dataType: data.type, + // First parameter component should be true + // Second should be false + conditionalPath: data.node!.is_conditional ? true : null, + }} + // Type should be base_classes if it's not a conditional node + // else it should be true in the first parameter component + type={data.node?.base_classes.join("|")} + left={false} + showNode={showNode} + /> + )} + {data.node!.is_conditional && ( + 0 + ? nodeColors[data.node.output_types[0]] ?? + nodeColors[types[data.node.output_types[0]]] + : nodeColors[types[data.type]]) ?? nodeColors.unknown + } + title={ + data.node?.output_types && + data.node.output_types.length > 0 + ? data.node.output_types.join(" | ") + : data.type + } + conditionPath={data.node?.is_conditional ? "False" : null} + tooltipTitle={data.node?.base_classes.join("\n")} + id={{ + baseClasses: data.node!.base_classes, + id: data.id, + dataType: data.type, + // condition should be null if it's not a conditional node + // false if it's a conditional node and the condition is false + // so we should check if it's a conditional node. + conditionalPath: data.node!.is_conditional ? false : null, + }} + type={data.node?.base_classes.join("|")} + left={false} + showNode={showNode} + /> + )} +
)} diff --git a/src/frontend/src/types/api/index.ts b/src/frontend/src/types/api/index.ts index 6663692dc..646941bd9 100644 --- a/src/frontend/src/types/api/index.ts +++ b/src/frontend/src/types/api/index.ts @@ -20,6 +20,7 @@ export type APIClassType = { icon?: string; is_input?: boolean; is_output?: boolean; + is_conditional?: boolean; input_types?: Array; output_types?: Array; custom_fields?: CustomFieldsType; diff --git a/src/frontend/src/types/components/index.ts b/src/frontend/src/types/components/index.ts index 4f5d1bb60..7236998bd 100644 --- a/src/frontend/src/types/components/index.ts +++ b/src/frontend/src/types/components/index.ts @@ -40,6 +40,7 @@ export type DropDownComponentType = { export type ParameterComponentType = { data: NodeDataType; title: string; + conditionPath?: string | null; id: sourceHandleType | targetHandleType; color: string; left: boolean; diff --git a/src/frontend/src/types/flow/index.ts b/src/frontend/src/types/flow/index.ts index 967d4e424..02861cf35 100644 --- a/src/frontend/src/types/flow/index.ts +++ b/src/frontend/src/types/flow/index.ts @@ -52,6 +52,7 @@ export type sourceHandleType = { dataType: string; id: string; baseClasses: string[]; + conditionalPath?: boolean | null; }; //left side export type targetHandleType = { From ac2758c0acec7562017aed582a73e40101ce329c Mon Sep 17 00:00:00 2001 From: Gabriel Luiz Freitas Almeida Date: Thu, 29 Feb 2024 23:07:27 -0300 Subject: [PATCH 002/701] Add is_conditional attribute to CustomComponent and FrontendNode classes --- .../langflow/interface/custom/attributes.py | 1 + .../custom_component/custom_component.py | 2 ++ .../langflow/template/frontend_node/base.py | 29 +++++++++++++++---- 3 files changed, 26 insertions(+), 6 deletions(-) diff --git a/src/backend/langflow/interface/custom/attributes.py b/src/backend/langflow/interface/custom/attributes.py index 9b91af43c..071d3f2b2 100644 --- a/src/backend/langflow/interface/custom/attributes.py +++ b/src/backend/langflow/interface/custom/attributes.py @@ -40,4 +40,5 @@ ATTR_FUNC_MAPPING: dict[str, Callable] = { "pinned": getattr_return_bool, "is_input": getattr_return_bool, "is_output": getattr_return_bool, + "is_conditional": getattr_return_bool, } diff --git a/src/backend/langflow/interface/custom/custom_component/custom_component.py b/src/backend/langflow/interface/custom/custom_component/custom_component.py index c3b06f90a..2c1ac57fc 100644 --- a/src/backend/langflow/interface/custom/custom_component/custom_component.py +++ b/src/backend/langflow/interface/custom/custom_component/custom_component.py @@ -66,6 +66,8 @@ class CustomComponent(Component): """The selected output type of the component. Defaults to None.""" vertex: Optional["Vertex"] = None """The edge target parameter of the component. Defaults to None.""" + is_conditional: Optional[bool] = False + """The conditional state of the component. Defaults to False.""" code_class_base_inheritance: ClassVar[str] = "CustomComponent" function_entrypoint_name: ClassVar[str] = "build" function: Optional[Callable] = None diff --git a/src/backend/langflow/template/frontend_node/base.py b/src/backend/langflow/template/frontend_node/base.py index 2a19ec9c9..2d79da2f3 100644 --- a/src/backend/langflow/template/frontend_node/base.py +++ b/src/backend/langflow/template/frontend_node/base.py @@ -73,6 +73,8 @@ class FrontendNode(BaseModel): """Field formatters for the frontend node.""" pinned: bool = False """Whether the frontend node is pinned.""" + is_conditional: bool = False + """Whether the frontend node is conditional. This is used for the frontend node to show two output handles.""" beta: bool = False error: Optional[str] = None @@ -171,7 +173,9 @@ class FrontendNode(BaseModel): return _type @staticmethod - def handle_special_field(field, key: str, _type: str, SPECIAL_FIELD_HANDLERS) -> str: + def handle_special_field( + field, key: str, _type: str, SPECIAL_FIELD_HANDLERS + ) -> str: """Handles special field by using the respective handler if present.""" handler = SPECIAL_FIELD_HANDLERS.get(key) return handler(field) if handler else _type @@ -182,7 +186,11 @@ class FrontendNode(BaseModel): if "dict" in _type.lower() and field.name == "dict_": field.field_type = "file" field.file_types = [".json", ".yaml", ".yml"] - elif _type.startswith("Dict") or _type.startswith("Mapping") or _type.startswith("dict"): + elif ( + _type.startswith("Dict") + or _type.startswith("Mapping") + or _type.startswith("dict") + ): field.field_type = "dict" return _type @@ -193,7 +201,9 @@ class FrontendNode(BaseModel): field.value = value["default"] @staticmethod - def handle_specific_field_values(field: TemplateField, key: str, name: Optional[str] = None) -> None: + def handle_specific_field_values( + field: TemplateField, key: str, name: Optional[str] = None + ) -> None: """Handles specific field values for certain fields.""" if key == "headers": field.value = """{"Authorization": "Bearer "}""" @@ -201,7 +211,9 @@ class FrontendNode(BaseModel): FrontendNode._handle_api_key_specific_field_values(field, key, name) @staticmethod - def _handle_model_specific_field_values(field: TemplateField, key: str, name: Optional[str] = None) -> None: + def _handle_model_specific_field_values( + field: TemplateField, key: str, name: Optional[str] = None + ) -> None: """Handles specific field values related to models.""" model_dict = { "OpenAI": constants.OPENAI_MODELS, @@ -214,7 +226,9 @@ class FrontendNode(BaseModel): field.is_list = True @staticmethod - def _handle_api_key_specific_field_values(field: TemplateField, key: str, name: Optional[str] = None) -> None: + def _handle_api_key_specific_field_values( + field: TemplateField, key: str, name: Optional[str] = None + ) -> None: """Handles specific field values related to API keys.""" if "api_key" in key and "OpenAI" in str(name): field.display_name = "OpenAI API Key" @@ -254,7 +268,10 @@ class FrontendNode(BaseModel): @staticmethod def should_be_password(key: str, show: bool) -> bool: """Determines whether the field should be a password field.""" - return any(text in key.lower() for text in {"password", "token", "api", "key"}) and show + return ( + any(text in key.lower() for text in {"password", "token", "api", "key"}) + and show + ) @staticmethod def should_be_multiline(key: str) -> bool: From 1366967232f9491c1ba10c454f41c5c0814140fd Mon Sep 17 00:00:00 2001 From: Gabriel Luiz Freitas Almeida Date: Thu, 29 Feb 2024 23:08:21 -0300 Subject: [PATCH 003/701] Add UnbuiltResult import and update vertex result type --- src/backend/langflow/api/v1/chat.py | 3 ++- src/backend/langflow/graph/graph/base.py | 10 +++++----- src/backend/langflow/graph/vertex/base.py | 13 +++++++------ 3 files changed, 14 insertions(+), 12 deletions(-) diff --git a/src/backend/langflow/api/v1/chat.py b/src/backend/langflow/api/v1/chat.py index 258277bca..0c8719527 100644 --- a/src/backend/langflow/api/v1/chat.py +++ b/src/backend/langflow/api/v1/chat.py @@ -18,6 +18,7 @@ from langflow.api.v1.schemas import ( VertexBuildResponse, VerticesOrderResponse, ) +from langflow.graph.utils import UnbuiltResult from langflow.services.auth.utils import get_current_active_user from langflow.services.chat.service import ChatService from langflow.services.deps import get_chat_service, get_session, get_session_service @@ -116,7 +117,7 @@ async def build_vertex( inputs_dict = inputs.model_dump() if inputs else {} await vertex.build(user_id=current_user.id, inputs=inputs_dict) - if vertex.result is not None: + if not isinstance(vertex.result, UnbuiltResult): params = vertex._built_object_repr() valid = True result_dict = vertex.result diff --git a/src/backend/langflow/graph/graph/base.py b/src/backend/langflow/graph/graph/base.py index 453076e1b..51dd53775 100644 --- a/src/backend/langflow/graph/graph/base.py +++ b/src/backend/langflow/graph/graph/base.py @@ -10,7 +10,7 @@ from langflow.graph.graph.constants import lazy_load_vertex_dict from langflow.graph.graph.state_manager import GraphStateManager from langflow.graph.graph.utils import process_flow from langflow.graph.schema import INPUT_FIELD_NAME, InterfaceComponentTypes -from langflow.graph.vertex.base import Vertex +from langflow.graph.vertex.base import Vertex, VertexStates from langflow.graph.vertex.types import ( ChatVertex, FileToolVertex, @@ -148,17 +148,17 @@ class Graph: def reset_inactive_vertices(self): self.inactive_vertices = set() - def mark_all_vertices(self, state: str): + def mark_all_vertices(self, state: "VertexStates"): """Marks all vertices in the graph.""" for vertex in self.vertices: vertex.set_state(state) - def mark_vertex(self, vertex_id: str, state: str): + def mark_vertex(self, vertex_id: str, state: "VertexStates"): """Marks a vertex in the graph.""" vertex = self.get_vertex(vertex_id) vertex.set_state(state) - def mark_branch(self, vertex_id: str, state: str): + def mark_branch(self, vertex_id: str, state: "VertexStates"): """Marks a branch of the graph.""" self.mark_vertex(vertex_id, state) for child_id in self.parent_child_map[vertex_id]: @@ -552,7 +552,7 @@ class Graph: node_name = node_id.split("-")[0] if node_name in ["ChatOutput", "ChatInput"]: return ChatVertex - elif node_name in ["ShouldRunNext"]: + elif node_name in ["ShouldRunNext", "Branch"]: return RoutingVertex elif node_base_type in lazy_load_vertex_dict.VERTEX_TYPE_MAP: return lazy_load_vertex_dict.VERTEX_TYPE_MAP[node_base_type] diff --git a/src/backend/langflow/graph/vertex/base.py b/src/backend/langflow/graph/vertex/base.py index 6425f5e08..25e8188f3 100644 --- a/src/backend/langflow/graph/vertex/base.py +++ b/src/backend/langflow/graph/vertex/base.py @@ -2,7 +2,7 @@ import ast import inspect import types from enum import Enum -from typing import TYPE_CHECKING, Any, Callable, Coroutine, Dict, List, Optional +from typing import TYPE_CHECKING, Any, Callable, Coroutine, Dict, List, Optional, Union from loguru import logger @@ -75,7 +75,7 @@ class Vertex: self.parent_is_top_level = False self.layer = None self.should_run = True - self.result: Optional[ResultData] = None + self.result: Union[ResultData, UnbuiltResult] = UnbuiltResult() try: self.is_interface_component = self.vertex_type in InterfaceComponentTypes except ValueError: @@ -95,11 +95,12 @@ class Vertex: else: self.graph_state[key] = new_state - def set_state(self, state: str): - self.state = VertexStates[state] + def set_state(self, state: "VertexStates"): + self.state = state if ( - self.state == VertexStates.INACTIVE - and self.graph.in_degree_map[self.id] < 2 + self.state + == VertexStates.INACTIVE + # and self.graph.in_degree_map[self.id] < 2 ): # If the vertex is inactive and has only one in degree # it means that it is not a merge point in the graph From 1a144a2fc160b3aa545b63beadcba29df5e4a3e5 Mon Sep 17 00:00:00 2001 From: Gabriel Luiz Freitas Almeida Date: Thu, 29 Feb 2024 23:08:32 -0300 Subject: [PATCH 004/701] Refactor edge validation logic and add conditional path support --- src/backend/langflow/graph/edge/base.py | 46 ++++++++++++++++++++----- 1 file changed, 37 insertions(+), 9 deletions(-) diff --git a/src/backend/langflow/graph/edge/base.py b/src/backend/langflow/graph/edge/base.py index 4992d0b47..1171fba57 100644 --- a/src/backend/langflow/graph/edge/base.py +++ b/src/backend/langflow/graph/edge/base.py @@ -13,15 +13,22 @@ if TYPE_CHECKING: class SourceHandle(BaseModel): - baseClasses: List[str] = Field(..., description="List of base classes for the source handle.") + baseClasses: List[str] = Field( + ..., description="List of base classes for the source handle." + ) dataType: str = Field(..., description="Data type for the source handle.") id: str = Field(..., description="Unique identifier for the source handle.") + conditionalPath: Optional[bool] = Field( + None, description="Conditional path for the source handle." + ) class TargetHandle(BaseModel): fieldName: str = Field(..., description="Field name for the target handle.") id: str = Field(..., description="Unique identifier for the target handle.") - inputTypes: Optional[List[str]] = Field(None, description="List of input types for the target handle.") + inputTypes: Optional[List[str]] = Field( + None, description="List of input types for the target handle." + ) type: str = Field(..., description="Type of the target handle.") @@ -50,16 +57,24 @@ class Edge: def validate_handles(self, source, target) -> None: if self.target_handle.inputTypes is None: - self.valid_handles = self.target_handle.type in self.source_handle.baseClasses + self.valid_handles = ( + self.target_handle.type in self.source_handle.baseClasses + ) else: self.valid_handles = ( - any(baseClass in self.target_handle.inputTypes for baseClass in self.source_handle.baseClasses) + any( + baseClass in self.target_handle.inputTypes + for baseClass in self.source_handle.baseClasses + ) or self.target_handle.type in self.source_handle.baseClasses ) if not self.valid_handles: logger.debug(self.source_handle) logger.debug(self.target_handle) - raise ValueError(f"Edge between {source.vertex_type} and {target.vertex_type} " f"has invalid handles") + raise ValueError( + f"Edge between {source.vertex_type} and {target.vertex_type} " + f"has invalid handles" + ) def __setstate__(self, state): self.source_id = state["source_id"] @@ -76,7 +91,11 @@ class Edge: # Both lists contain strings and sometimes a string contains the value we are # looking for e.g. comgin_out=["Chain"] and target_reqs=["LLMChain"] # so we need to check if any of the strings in source_types is in target_reqs - self.valid = any(output in target_req for output in self.source_types for target_req in self.target_reqs) + self.valid = any( + output in target_req + for output in self.source_types + for target_req in self.target_reqs + ) # Get what type of input the target node is expecting self.matched_type = next( @@ -87,7 +106,10 @@ class Edge: if no_matched_type: logger.debug(self.source_types) logger.debug(self.target_reqs) - raise ValueError(f"Edge between {source.vertex_type} and {target.vertex_type} " f"has no matched type") + raise ValueError( + f"Edge between {source.vertex_type} and {target.vertex_type} " + f"has no matched type" + ) def __repr__(self) -> str: return ( @@ -99,7 +121,11 @@ class Edge: return hash(self.__repr__()) def __eq__(self, __value: object) -> bool: - return self.__repr__() == __value.__repr__() if isinstance(__value, Edge) else False + return ( + self.__repr__() == __value.__repr__() + if isinstance(__value, Edge) + else False + ) class ContractEdge(Edge): @@ -156,7 +182,9 @@ class ContractEdge(Edge): return f"{self.source_id} -[{self.target_param}]-> {self.target_id}" -def log_transaction(edge: ContractEdge, source: "Vertex", target: "Vertex", status, error=None): +def log_transaction( + edge: ContractEdge, source: "Vertex", target: "Vertex", status, error=None +): try: monitor_service = get_monitor_service() clean_params = build_clean_params(target) From 803d51ac257b7ee23f88c3658a9e476be3a8183b Mon Sep 17 00:00:00 2001 From: Gabriel Luiz Freitas Almeida Date: Thu, 29 Feb 2024 23:09:00 -0300 Subject: [PATCH 005/701] Add branches functionality to RoutingVertex --- src/backend/langflow/graph/vertex/types.py | 110 ++++++++++++++++----- 1 file changed, 86 insertions(+), 24 deletions(-) diff --git a/src/backend/langflow/graph/vertex/types.py b/src/backend/langflow/graph/vertex/types.py index c4f33df40..11b529fd3 100644 --- a/src/backend/langflow/graph/vertex/types.py +++ b/src/backend/langflow/graph/vertex/types.py @@ -1,5 +1,6 @@ import ast import json +from collections import defaultdict from typing import AsyncIterator, Callable, Dict, Iterator, List, Optional, Union import yaml @@ -8,7 +9,7 @@ from loguru import logger from langflow.graph.schema import INPUT_FIELD_NAME from langflow.graph.utils import UnbuiltObject, flatten_list, serialize_field -from langflow.graph.vertex.base import StatefulVertex, StatelessVertex +from langflow.graph.vertex.base import StatefulVertex, StatelessVertex, VertexStates from langflow.interface.utils import extract_input_variables_from_prompt from langflow.schema import Record from langflow.services.monitor.utils import log_vertex_build @@ -123,9 +124,11 @@ class DocumentLoaderVertex(StatefulVertex): # show how many documents are in the list? if not isinstance(self._built_object, UnbuiltObject): - avg_length = sum(len(doc.page_content) for doc in self._built_object if hasattr(doc, "page_content")) / len( - self._built_object - ) + avg_length = sum( + len(doc.page_content) + for doc in self._built_object + if hasattr(doc, "page_content") + ) / len(self._built_object) return f"""{self.display_name}({len(self._built_object)} documents) \nAvg. Document Length (characters): {int(avg_length)} Documents: {self._built_object[:3]}...""" @@ -198,7 +201,9 @@ class TextSplitterVertex(StatefulVertex): # show how many documents are in the list? if not isinstance(self._built_object, UnbuiltObject): - avg_length = sum(len(doc.page_content) for doc in self._built_object) / len(self._built_object) + avg_length = sum(len(doc.page_content) for doc in self._built_object) / len( + self._built_object + ) return f"""{self.vertex_type}({len(self._built_object)} documents) \nAvg. Document Length (characters): {int(avg_length)} \nDocuments: {self._built_object[:3]}...""" @@ -245,18 +250,27 @@ class PromptVertex(StatelessVertex): user_id = kwargs.get("user_id", None) tools = kwargs.get("tools", []) if not self._built or force: - if "input_variables" not in self.params or self.params["input_variables"] is None: + if ( + "input_variables" not in self.params + or self.params["input_variables"] is None + ): self.params["input_variables"] = [] # Check if it is a ZeroShotPrompt and needs a tool if "ShotPrompt" in self.vertex_type: - tools = [tool_node.build(user_id=user_id) for tool_node in tools] if tools is not None else [] + tools = ( + [tool_node.build(user_id=user_id) for tool_node in tools] + if tools is not None + else [] + ) # flatten the list of tools if it is a list of lists # first check if it is a list if tools and isinstance(tools, list) and isinstance(tools[0], list): tools = flatten_list(tools) self.params["tools"] = tools prompt_params = [ - key for key, value in self.params.items() if isinstance(value, str) and key != "format_instructions" + key + for key, value in self.params.items() + if isinstance(value, str) and key != "format_instructions" ] else: prompt_params = ["template"] @@ -266,14 +280,20 @@ class PromptVertex(StatelessVertex): prompt_text = self.params[param] variables = extract_input_variables_from_prompt(prompt_text) self.params["input_variables"].extend(variables) - self.params["input_variables"] = list(set(self.params["input_variables"])) + self.params["input_variables"] = list( + set(self.params["input_variables"]) + ) elif isinstance(self.params, dict): self.params.pop("input_variables", None) await self._build(user_id=user_id) def _built_object_repr(self): - if not self.artifacts or self._built_object is None or not hasattr(self._built_object, "format"): + if ( + not self.artifacts + or self._built_object is None + or not hasattr(self._built_object, "format") + ): return super()._built_object_repr() elif isinstance(self._built_object, UnbuiltObject): return super()._built_object_repr() @@ -285,7 +305,9 @@ class PromptVertex(StatelessVertex): # so the prompt format doesn't break artifacts.pop("handle_keys", None) try: - if not hasattr(self._built_object, "template") and hasattr(self._built_object, "prompt"): + if not hasattr(self._built_object, "template") and hasattr( + self._built_object, "prompt" + ): template = self._built_object.prompt.template else: template = self._built_object.template @@ -293,7 +315,11 @@ class PromptVertex(StatelessVertex): if value: replace_key = "{" + key + "}" template = template.replace(replace_key, value) - return template if isinstance(template, str) else f"{self.vertex_type}({template})" + return ( + template + if isinstance(template, str) + else f"{self.vertex_type}({template})" + ) except KeyError: return str(self._built_object) @@ -436,6 +462,24 @@ class RoutingVertex(StatelessVertex): super().__init__(data, graph=graph, base_type="custom_components") self.use_result = True self.steps = [self._build, self._run] + self._branches = defaultdict(set) + + def build_branches(self): + if self._branches: + return + for edge in self.edges: + if edge.target_id == self.id: + continue + if edge.source_handle.conditionalPath is not None: + self._branches[edge.source_handle.conditionalPath].add(edge.target_id) + + @property + def true_branch(self): + return self._branches.get(True, set()) + + @property + def false_branch(self): + return self._branches.get(False, set()) def _built_object_repr(self): if self.artifacts and "repr" in self.artifacts: @@ -443,18 +487,36 @@ class RoutingVertex(StatelessVertex): return super()._built_object_repr() def _run(self, *args, **kwargs): - if self._built_object: - condition = self._built_object.get("condition") - result = self._built_object.get("result") - if condition is None: - raise ValueError("Condition is required for the routing vertex.") - if result is None: - raise ValueError("Result is required for the routing vertex.") - if condition is True: - self._built_result = result - else: - self.graph.mark_branch(self.id, "INACTIVE") - self._built_result = None + + self.build_branches() + condition_path = self._built_object.get("path") + result = self._built_object.get("result") + try: + # check if bool + condition_path = bool(condition_path) + except ValueError: + raise ValueError("'path' must be a boolean value.") + + # Validate necessary components are present + if not isinstance(condition_path, bool): + raise ValueError("Condition is required for the routing vertex.") + if result is None: + raise ValueError("Result is required for the routing vertex.") + if self._branches: + # Deactivate the branch not taken + self._deactivate_branch(condition_path) + self._built_result = result + elif condition_path is True: + self._built_result = result + else: + self.graph.mark_branch(self.id, VertexStates.INACTIVE) + + def _deactivate_branch(self, condition_path: bool): + """Deactivates the branch not taken based on the condition.""" + # self.graph.mark_branch(target_id, "INACTIVE") + branch_to_deactivate = self.false_branch if condition_path else self.true_branch + for target_id in branch_to_deactivate: + self.graph.mark_branch(target_id, VertexStates.INACTIVE) def dict_to_codeblock(d: dict) -> str: From 603bca019ceaae807bc87185d54ef9af5c35320c Mon Sep 17 00:00:00 2001 From: Gabriel Luiz Freitas Almeida Date: Thu, 29 Feb 2024 23:09:42 -0300 Subject: [PATCH 006/701] Refactor ShouldRunNext component to handle boolean response properly --- src/backend/langflow/components/utilities/ShouldRunNext.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/src/backend/langflow/components/utilities/ShouldRunNext.py b/src/backend/langflow/components/utilities/ShouldRunNext.py index b9ae3b048..995f1cb43 100644 --- a/src/backend/langflow/components/utilities/ShouldRunNext.py +++ b/src/backend/langflow/components/utilities/ShouldRunNext.py @@ -1,5 +1,6 @@ # Implement ShouldRunNext component from typing import Text + from langchain_core.prompts import PromptTemplate from langflow import CustomComponent @@ -42,8 +43,10 @@ class ShouldRunNext(CustomComponent): result = result.get("response") if result.lower() not in ["true", "false"]: - raise ValueError("The prompt should generate a boolean response (True or False).") + raise ValueError( + "The prompt should generate a boolean response (True or False)." + ) # The string should be the words true or false # if not raise an error bool_result = result.lower() == "true" - return {"condition": bool_result, "result": kwargs} + return {"path": bool_result, "result": kwargs} From ef78b5eeb69d0cf3329f43c4eb0a06b4f42c6cf8 Mon Sep 17 00:00:00 2001 From: Gabriel Luiz Freitas Almeida Date: Thu, 29 Feb 2024 23:09:49 -0300 Subject: [PATCH 007/701] Add BranchComponent to utilities package --- .../langflow/components/utilities/Branch.py | 14 ++++++++++++++ 1 file changed, 14 insertions(+) create mode 100644 src/backend/langflow/components/utilities/Branch.py diff --git a/src/backend/langflow/components/utilities/Branch.py b/src/backend/langflow/components/utilities/Branch.py new file mode 100644 index 000000000..daa103e09 --- /dev/null +++ b/src/backend/langflow/components/utilities/Branch.py @@ -0,0 +1,14 @@ +from langflow import CustomComponent +from langflow.field_typing import Text + + +class BranchComponent(CustomComponent): + display_name: str = "Branch Component" + documentation: str = "http://docs.langflow.org/components/custom" + is_conditional = True + + def build_config(self): + return {"param": {"display_name": "Parameter"}} + + def build(self, param: Text) -> Text: + return {"path": True, "result": param} From ba93c25ff4d5044d901a0bc2405649f1aee71edd Mon Sep 17 00:00:00 2001 From: Gabriel Luiz Freitas Almeida Date: Thu, 29 Feb 2024 23:16:45 -0300 Subject: [PATCH 008/701] Add Decision class for decision making components --- .../langflow/components/utilities/Branch.py | 4 +++- .../components/utilities/ShouldRunNext.py | 4 +++- src/backend/langflow/schema.py | 17 +++++++++++++++++ 3 files changed, 23 insertions(+), 2 deletions(-) diff --git a/src/backend/langflow/components/utilities/Branch.py b/src/backend/langflow/components/utilities/Branch.py index daa103e09..3e786b7b9 100644 --- a/src/backend/langflow/components/utilities/Branch.py +++ b/src/backend/langflow/components/utilities/Branch.py @@ -1,5 +1,6 @@ from langflow import CustomComponent from langflow.field_typing import Text +from langflow.schema import Decision class BranchComponent(CustomComponent): @@ -11,4 +12,5 @@ class BranchComponent(CustomComponent): return {"param": {"display_name": "Parameter"}} def build(self, param: Text) -> Text: - return {"path": True, "result": param} + + return Decision(path=True, result=param) diff --git a/src/backend/langflow/components/utilities/ShouldRunNext.py b/src/backend/langflow/components/utilities/ShouldRunNext.py index 995f1cb43..bdc328884 100644 --- a/src/backend/langflow/components/utilities/ShouldRunNext.py +++ b/src/backend/langflow/components/utilities/ShouldRunNext.py @@ -5,6 +5,7 @@ from langchain_core.prompts import PromptTemplate from langflow import CustomComponent from langflow.field_typing import BaseLanguageModel, Prompt +from langflow.schema import Decision class ShouldRunNext(CustomComponent): @@ -49,4 +50,5 @@ class ShouldRunNext(CustomComponent): # The string should be the words true or false # if not raise an error bool_result = result.lower() == "true" - return {"path": bool_result, "result": kwargs} + + return Decision(path=bool_result, result=kwargs) diff --git a/src/backend/langflow/schema.py b/src/backend/langflow/schema.py index e9f437038..bfc8f8efa 100644 --- a/src/backend/langflow/schema.py +++ b/src/backend/langflow/schema.py @@ -68,3 +68,20 @@ def docs_to_records(documents: list[Document]) -> list[Record]: list[Record]: The converted list of Records. """ return [Record.from_document(document) for document in documents] + + +# {"path": bool_result, "result": kwargs} +# Create a class for the above dictionary +# with a good name that fits the context of +# a decision making component +class Decision(BaseModel): + """ + Represents a decision made in the Graph. + + Attributes: + path (bool): The path taken in the Graph. + result (dict): The result of the decision. + """ + + path: bool + result: Any From 3fcedc8a07cd16037cc055c6c2502c0795674101 Mon Sep 17 00:00:00 2001 From: Gabriel Luiz Freitas Almeida Date: Fri, 1 Mar 2024 08:00:30 -0300 Subject: [PATCH 009/701] Refactor parameter component in GenericNode --- .../src/CustomNodes/GenericNode/index.tsx | 146 +++++++++--------- src/frontend/src/types/api/index.ts | 2 +- src/frontend/src/types/flow/index.ts | 2 +- 3 files changed, 72 insertions(+), 78 deletions(-) diff --git a/src/frontend/src/CustomNodes/GenericNode/index.tsx b/src/frontend/src/CustomNodes/GenericNode/index.tsx index 3729ac418..bb343a147 100644 --- a/src/frontend/src/CustomNodes/GenericNode/index.tsx +++ b/src/frontend/src/CustomNodes/GenericNode/index.tsx @@ -162,6 +162,56 @@ export default function GenericNode({ ); }; + const buildParameterComponent = ({ + data, + conditionalPath, + showNode, + left, + }: { + data: NodeDataType; + conditionalPath: string | null; + showNode: boolean; + left: boolean; + }) => { + return ( + 0 + ? nodeColors[data.node.output_types[0]] ?? + nodeColors[types[data.node.output_types[0]]] + : nodeColors[types[data.type]]) ?? nodeColors.unknown + } + title={ + data.node?.output_types && data.node.output_types.length > 0 + ? data.node.output_types.join(" | ") + : data.type + } + conditionPath={conditionalPath} + tooltipTitle={data.node?.base_classes.join("\n")} + id={{ + baseClasses: data.node!.base_classes, + id: data.id, + dataType: data.type, + // First parameter component should be true + // Second should be false + conditionalPath: conditionalPath, + }} + // Type should be base_classes if it's not a conditional node + // else it should be true in the first parameter component + type={data.node?.base_classes.join("|")} + left={left} + showNode={showNode} + /> + ); + }; + const isDark = useDarkStore((state) => state.dark); const renderIconStatus = ( buildStatus: BuildStatus | undefined, @@ -665,83 +715,27 @@ export default function GenericNode({ {" "}
- {data.node!.base_classes.length > 0 && ( - 0 && + // if conditionalPaths in data.node.conditionalPaths + // then buildParameterComponent for each conditionalPath + // else buildParameterComponent for each data.node.base_classes + // first we check + data.node!.conditionalPaths && + data.node!.conditionalPaths.length > 1 + ? data.node!.conditionalPaths.map((conditionalPath) => + buildParameterComponent({ + data, + conditionalPath, + showNode, + left: false, + }) + ) + : buildParameterComponent({ + data, + conditionalPath: null, + showNode, + left: false, })} - data={data} - color={ - (data.node?.output_types && - data.node.output_types.length > 0 - ? nodeColors[data.node.output_types[0]] ?? - nodeColors[types[data.node.output_types[0]]] - : nodeColors[types[data.type]]) ?? nodeColors.unknown - } - title={ - data.node?.output_types && - data.node.output_types.length > 0 - ? data.node.output_types.join(" | ") - : data.type - } - conditionPath={data.node?.is_conditional ? "True" : null} - tooltipTitle={data.node?.base_classes.join("\n")} - id={{ - baseClasses: data.node!.base_classes, - id: data.id, - dataType: data.type, - // First parameter component should be true - // Second should be false - conditionalPath: data.node!.is_conditional ? true : null, - }} - // Type should be base_classes if it's not a conditional node - // else it should be true in the first parameter component - type={data.node?.base_classes.join("|")} - left={false} - showNode={showNode} - /> - )} - {data.node!.is_conditional && ( - 0 - ? nodeColors[data.node.output_types[0]] ?? - nodeColors[types[data.node.output_types[0]]] - : nodeColors[types[data.type]]) ?? nodeColors.unknown - } - title={ - data.node?.output_types && - data.node.output_types.length > 0 - ? data.node.output_types.join(" | ") - : data.type - } - conditionPath={data.node?.is_conditional ? "False" : null} - tooltipTitle={data.node?.base_classes.join("\n")} - id={{ - baseClasses: data.node!.base_classes, - id: data.id, - dataType: data.type, - // condition should be null if it's not a conditional node - // false if it's a conditional node and the condition is false - // so we should check if it's a conditional node. - conditionalPath: data.node!.is_conditional ? false : null, - }} - type={data.node?.base_classes.join("|")} - left={false} - showNode={showNode} - /> - )}
diff --git a/src/frontend/src/types/api/index.ts b/src/frontend/src/types/api/index.ts index 646941bd9..d2735e7ed 100644 --- a/src/frontend/src/types/api/index.ts +++ b/src/frontend/src/types/api/index.ts @@ -20,7 +20,7 @@ export type APIClassType = { icon?: string; is_input?: boolean; is_output?: boolean; - is_conditional?: boolean; + conditionalPaths?: Array; input_types?: Array; output_types?: Array; custom_fields?: CustomFieldsType; diff --git a/src/frontend/src/types/flow/index.ts b/src/frontend/src/types/flow/index.ts index 02861cf35..781528043 100644 --- a/src/frontend/src/types/flow/index.ts +++ b/src/frontend/src/types/flow/index.ts @@ -52,7 +52,7 @@ export type sourceHandleType = { dataType: string; id: string; baseClasses: string[]; - conditionalPath?: boolean | null; + conditionalPath?: string | null; }; //left side export type targetHandleType = { From b450e6604679aafb2a5948a597751d7afeaa28ce Mon Sep 17 00:00:00 2001 From: Gabriel Luiz Freitas Almeida Date: Fri, 1 Mar 2024 08:00:46 -0300 Subject: [PATCH 010/701] Update conditional paths in BranchComponent and ShouldRunNext --- .../langflow/components/utilities/Branch.py | 4 ++-- .../components/utilities/ShouldRunNext.py | 7 +++++-- .../langflow/interface/custom/attributes.py | 8 ++++++- .../custom_component/custom_component.py | 4 ++-- src/backend/langflow/schema.py | 21 ++++++++++++++++--- .../langflow/template/frontend_node/base.py | 5 ++--- 6 files changed, 36 insertions(+), 13 deletions(-) diff --git a/src/backend/langflow/components/utilities/Branch.py b/src/backend/langflow/components/utilities/Branch.py index 3e786b7b9..fc6d414ab 100644 --- a/src/backend/langflow/components/utilities/Branch.py +++ b/src/backend/langflow/components/utilities/Branch.py @@ -6,11 +6,11 @@ from langflow.schema import Decision class BranchComponent(CustomComponent): display_name: str = "Branch Component" documentation: str = "http://docs.langflow.org/components/custom" - is_conditional = True + conditional_paths: list[str] = ["True", "False"] def build_config(self): return {"param": {"display_name": "Parameter"}} def build(self, param: Text) -> Text: - return Decision(path=True, result=param) + return Decision(path="True", result=param) diff --git a/src/backend/langflow/components/utilities/ShouldRunNext.py b/src/backend/langflow/components/utilities/ShouldRunNext.py index bdc328884..5270e4d34 100644 --- a/src/backend/langflow/components/utilities/ShouldRunNext.py +++ b/src/backend/langflow/components/utilities/ShouldRunNext.py @@ -49,6 +49,9 @@ class ShouldRunNext(CustomComponent): ) # The string should be the words true or false # if not raise an error - bool_result = result.lower() == "true" + if result.lower() not in ["true", "false"]: + raise ValueError( + "The prompt should generate a boolean response (True or False)." + ) - return Decision(path=bool_result, result=kwargs) + return Decision(path=result, result=kwargs) diff --git a/src/backend/langflow/interface/custom/attributes.py b/src/backend/langflow/interface/custom/attributes.py index 071d3f2b2..2524c0677 100644 --- a/src/backend/langflow/interface/custom/attributes.py +++ b/src/backend/langflow/interface/custom/attributes.py @@ -31,6 +31,12 @@ def getattr_return_bool(value): return value +def getattr_return_list_of_str(value): + if isinstance(value, list): + return [str(val) for val in value] + return [] + + ATTR_FUNC_MAPPING: dict[str, Callable] = { "display_name": getattr_return_str, "description": getattr_return_str, @@ -40,5 +46,5 @@ ATTR_FUNC_MAPPING: dict[str, Callable] = { "pinned": getattr_return_bool, "is_input": getattr_return_bool, "is_output": getattr_return_bool, - "is_conditional": getattr_return_bool, + "conditional_paths": getattr_return_list_of_str, } diff --git a/src/backend/langflow/interface/custom/custom_component/custom_component.py b/src/backend/langflow/interface/custom/custom_component/custom_component.py index 2c1ac57fc..57ded0d10 100644 --- a/src/backend/langflow/interface/custom/custom_component/custom_component.py +++ b/src/backend/langflow/interface/custom/custom_component/custom_component.py @@ -66,8 +66,8 @@ class CustomComponent(Component): """The selected output type of the component. Defaults to None.""" vertex: Optional["Vertex"] = None """The edge target parameter of the component. Defaults to None.""" - is_conditional: Optional[bool] = False - """The conditional state of the component. Defaults to False.""" + conditional_paths: Optional[List[str]] = None + """The conditional paths of the component. Defaults to None.""" code_class_base_inheritance: ClassVar[str] = "CustomComponent" function_entrypoint_name: ClassVar[str] = "build" function: Optional[Callable] = None diff --git a/src/backend/langflow/schema.py b/src/backend/langflow/schema.py index bfc8f8efa..44ee4532d 100644 --- a/src/backend/langflow/schema.py +++ b/src/backend/langflow/schema.py @@ -1,7 +1,7 @@ from typing import Any from langchain_core.documents import Document -from pydantic import BaseModel +from pydantic import BaseModel, field_validator class Record(BaseModel): @@ -79,9 +79,24 @@ class Decision(BaseModel): Represents a decision made in the Graph. Attributes: - path (bool): The path taken in the Graph. + path (str): The path to take as a result of the decision. result (dict): The result of the decision. """ - path: bool + path: str result: Any + + @field_validator("path") + def validate_path(cls, value: str) -> str: + """ + Validates the path. + + Args: + value (str): The path to validate. + + Returns: + str: The validated path. + """ + if isinstance(value, str): + return value + return str(value) diff --git a/src/backend/langflow/template/frontend_node/base.py b/src/backend/langflow/template/frontend_node/base.py index 2d79da2f3..c0f74ed0e 100644 --- a/src/backend/langflow/template/frontend_node/base.py +++ b/src/backend/langflow/template/frontend_node/base.py @@ -73,9 +73,8 @@ class FrontendNode(BaseModel): """Field formatters for the frontend node.""" pinned: bool = False """Whether the frontend node is pinned.""" - is_conditional: bool = False - """Whether the frontend node is conditional. This is used for the frontend node to show two output handles.""" - + conditional_paths: List[str] = [] + """List of conditional paths for the frontend node.""" beta: bool = False error: Optional[str] = None From 6ab07d7ca345f1428e688c8891143c0a0b82ed50 Mon Sep 17 00:00:00 2001 From: Gabriel Luiz Freitas Almeida Date: Fri, 1 Mar 2024 08:06:48 -0300 Subject: [PATCH 011/701] Update ShouldRunNext and GenericNode components --- .../langflow/components/utilities/ShouldRunNext.py | 9 ++------- src/frontend/src/CustomNodes/GenericNode/index.tsx | 10 +++++----- src/frontend/src/types/api/index.ts | 2 +- 3 files changed, 8 insertions(+), 13 deletions(-) diff --git a/src/backend/langflow/components/utilities/ShouldRunNext.py b/src/backend/langflow/components/utilities/ShouldRunNext.py index 5270e4d34..8b2350cfb 100644 --- a/src/backend/langflow/components/utilities/ShouldRunNext.py +++ b/src/backend/langflow/components/utilities/ShouldRunNext.py @@ -11,6 +11,7 @@ from langflow.schema import Decision class ShouldRunNext(CustomComponent): display_name = "Should Run Next" description = "Decides whether to run the next component." + conditional_paths = ["True"] def build_config(self): return { @@ -43,13 +44,7 @@ class ShouldRunNext(CustomComponent): else: result = result.get("response") - if result.lower() not in ["true", "false"]: - raise ValueError( - "The prompt should generate a boolean response (True or False)." - ) - # The string should be the words true or false - # if not raise an error - if result.lower() not in ["true", "false"]: + if result.lower() not in self.conditional_paths: raise ValueError( "The prompt should generate a boolean response (True or False)." ) diff --git a/src/frontend/src/CustomNodes/GenericNode/index.tsx b/src/frontend/src/CustomNodes/GenericNode/index.tsx index bb343a147..d52908953 100644 --- a/src/frontend/src/CustomNodes/GenericNode/index.tsx +++ b/src/frontend/src/CustomNodes/GenericNode/index.tsx @@ -716,13 +716,13 @@ export default function GenericNode({
{data.node!.base_classes.length > 0 && - // if conditionalPaths in data.node.conditionalPaths + // if conditional_paths in data.node.conditional_paths // then buildParameterComponent for each conditionalPath // else buildParameterComponent for each data.node.base_classes - // first we check - data.node!.conditionalPaths && - data.node!.conditionalPaths.length > 1 - ? data.node!.conditionalPaths.map((conditionalPath) => + // first we check if there are any conditional paths + data.node!.conditional_paths && + data.node!.conditional_paths.length > 0 + ? data.node!.conditional_paths.map((conditionalPath) => buildParameterComponent({ data, conditionalPath, diff --git a/src/frontend/src/types/api/index.ts b/src/frontend/src/types/api/index.ts index d2735e7ed..3423a84c9 100644 --- a/src/frontend/src/types/api/index.ts +++ b/src/frontend/src/types/api/index.ts @@ -20,7 +20,7 @@ export type APIClassType = { icon?: string; is_input?: boolean; is_output?: boolean; - conditionalPaths?: Array; + conditional_paths?: Array; input_types?: Array; output_types?: Array; custom_fields?: CustomFieldsType; From 3e5bcf42fff77d8229df6a704db1062d40af2212 Mon Sep 17 00:00:00 2001 From: Lucas Oliveira Date: Wed, 15 May 2024 18:37:25 +0200 Subject: [PATCH 012/701] Made Output Types dropdown work --- .../components/parameterComponent/index.tsx | 58 ++++++++++++++++--- .../src/customNodes/genericNode/index.tsx | 34 +++++------ src/frontend/src/types/flow/index.ts | 1 + 3 files changed, 68 insertions(+), 25 deletions(-) diff --git a/src/frontend/src/customNodes/genericNode/components/parameterComponent/index.tsx b/src/frontend/src/customNodes/genericNode/components/parameterComponent/index.tsx index dbcb1e3dc..fee39fce4 100644 --- a/src/frontend/src/customNodes/genericNode/components/parameterComponent/index.tsx +++ b/src/frontend/src/customNodes/genericNode/components/parameterComponent/index.tsx @@ -5,7 +5,9 @@ import CodeAreaComponent from "../../../../components/codeAreaComponent"; import DictComponent from "../../../../components/dictComponent"; import Dropdown from "../../../../components/dropdownComponent"; import FloatComponent from "../../../../components/floatComponent"; -import { default as IconComponent } from "../../../../components/genericIconComponent"; +import ForwardedIconComponent, { + default as IconComponent, +} from "../../../../components/genericIconComponent"; import InputFileComponent from "../../../../components/inputFileComponent"; import InputGlobalComponent from "../../../../components/inputGlobalComponent"; import InputListComponent from "../../../../components/inputListComponent"; @@ -16,6 +18,12 @@ import ShadTooltip from "../../../../components/shadTooltipComponent"; import TextAreaComponent from "../../../../components/textAreaComponent"; import ToggleShadComponent from "../../../../components/toggleShadComponent"; import { Button } from "../../../../components/ui/button"; +import { + DropdownMenu, + DropdownMenuContent, + DropdownMenuItem, + DropdownMenuTrigger, +} from "../../../../components/ui/dropdown-menu"; import { RefreshButton } from "../../../../components/ui/refreshButton"; import { INPUT_HANDLER_HOVER, @@ -49,7 +57,7 @@ import { nodeIconsLucide, nodeNames, } from "../../../../utils/styleUtils"; -import { classNames, groupByFamily } from "../../../../utils/utils"; +import { classNames, cn, groupByFamily } from "../../../../utils/utils"; export default function ParameterComponent({ left, @@ -257,6 +265,44 @@ export default function ParameterComponent({ ); }, [info]); + function renderTitle() { + const output_types = title.split("|"); + const displayTitle = data.selected_output_type ?? output_types[0]; + return !left && output_types.length > 1 ? ( + + + + {displayTitle} + + + + + {output_types.map((type) => ( + { + setNode(data.id, (node) => ({ + ...node, + data: { ...node.data, selected_output_type: type }, + })); + }} + > + {type} + + ))} + + + ) : ( + + {title} + + ); + } + function renderTooltips() { let groupedObj: any = groupByFamily(myData, tooltipTitle!, left, flow!); groupedEdge.current = groupedObj; @@ -434,14 +480,10 @@ export default function ParameterComponent({ )} {proxy ? ( {proxy.id}}> - - {title} - + {renderTitle()} ) : ( - - {title} - + renderTitle() )} {required ? "*" : ""} diff --git a/src/frontend/src/customNodes/genericNode/index.tsx b/src/frontend/src/customNodes/genericNode/index.tsx index 044d8cca4..f4059120a 100644 --- a/src/frontend/src/customNodes/genericNode/index.tsx +++ b/src/frontend/src/customNodes/genericNode/index.tsx @@ -54,14 +54,14 @@ export default function GenericNode({ const [nodeName, setNodeName] = useState(data.node!.display_name); const [inputDescription, setInputDescription] = useState(false); const [nodeDescription, setNodeDescription] = useState( - data.node?.description! + data.node?.description!, ); const [isOutdated, setIsOutdated] = useState(false); const buildStatus = useFlowStore( - (state) => state.flowBuildStatus[data.id]?.status + (state) => state.flowBuildStatus[data.id]?.status, ); const lastRunTime = useFlowStore( - (state) => state.flowBuildStatus[data.id]?.timestamp + (state) => state.flowBuildStatus[data.id]?.timestamp, ); const [validationStatus, setValidationStatus] = useState(null); @@ -118,7 +118,7 @@ export default function GenericNode({ updateNodeInternals(data.id); }, - [data.id, data.node, setNode, setIsOutdated] + [data.id, data.node, setNode, setIsOutdated], ); if (!data.node!.template) { @@ -258,7 +258,7 @@ export default function GenericNode({ const isDark = useDarkStore((state) => state.dark); const renderIconStatus = ( buildStatus: BuildStatus | undefined, - validationStatus: validationStatusType | null + validationStatus: validationStatusType | null, ) => { if (buildStatus === BuildStatus.BUILDING) { return ; @@ -299,7 +299,7 @@ export default function GenericNode({ }; const getSpecificClassFromBuildStatus = ( buildStatus: BuildStatus | undefined, - validationStatus: validationStatusType | null + validationStatus: validationStatusType | null, ) => { let isInvalid = validationStatus && !validationStatus.valid; @@ -323,11 +323,11 @@ export default function GenericNode({ selected: boolean, showNode: boolean, buildStatus: BuildStatus | undefined, - validationStatus: validationStatusType | null + validationStatus: validationStatusType | null, ) => { const specificClassFromBuildStatus = getSpecificClassFromBuildStatus( buildStatus, - validationStatus + validationStatus, ); const baseBorderClass = getBaseBorderClass(selected); const nodeSizeClass = getNodeSizeClass(showNode); @@ -335,7 +335,7 @@ export default function GenericNode({ baseBorderClass, nodeSizeClass, "generic-node-div", - specificClassFromBuildStatus + specificClassFromBuildStatus, ); }; @@ -392,7 +392,7 @@ export default function GenericNode({ selected, showNode, buildStatus, - validationStatus + validationStatus, )} > {data.node?.beta && showNode && ( @@ -537,7 +537,7 @@ export default function GenericNode({ } title={getFieldTitle( data.node?.template!, - templateField + templateField, )} info={data.node?.template[templateField].info} name={templateField} @@ -565,7 +565,7 @@ export default function GenericNode({ proxy={data.node?.template[templateField].proxy} showNode={showNode} /> - ) + ), )} 0 - ? data.node.output_types.join(" | ") + ? data.node.output_types.join("|") : data.type } tooltipTitle={data.node?.base_classes.join("\n")} @@ -722,7 +722,7 @@ export default function GenericNode({ !data.node?.description) && nameEditable ? "font-light italic" - : "" + : "", )} onDoubleClick={(e) => { setInputDescription(true); @@ -784,13 +784,13 @@ export default function GenericNode({ } title={getFieldTitle( data.node?.template!, - templateField + templateField, )} info={data.node?.template[templateField].info} name={templateField} tooltipTitle={ data.node?.template[templateField].input_types?.join( - "\n" + "\n", ) ?? data.node?.template[templateField].type } required={data.node!.template[templateField].required} @@ -817,7 +817,7 @@ export default function GenericNode({
{" "} diff --git a/src/frontend/src/types/flow/index.ts b/src/frontend/src/types/flow/index.ts index 8b259186a..0d453b4b2 100644 --- a/src/frontend/src/types/flow/index.ts +++ b/src/frontend/src/types/flow/index.ts @@ -33,6 +33,7 @@ export type NodeDataType = { node?: APIClassType; id: string; output_types?: string[]; + selected_output_type?: string; buildStatus?: BuildStatus; }; // FlowStyleType is the type of the style object that is used to style the From 64c42a9280189852967105ea5d6569fa110294b9 Mon Sep 17 00:00:00 2001 From: Gabriel Luiz Freitas Almeida Date: Thu, 16 May 2024 15:48:49 -0700 Subject: [PATCH 013/701] feat: Update selected_output_type handling in Vertex class This commit updates the handling of the `selected_output_type` attribute in the `Vertex` class. Previously, the attribute was assigned directly from the `data` dictionary, which could result in unexpected behavior. Now, the attribute is properly stripped and converted to a string before assignment, ensuring consistent behavior. This change improves the reliability and accuracy of the `selected_output_type` attribute in the `Vertex` class. --- src/backend/base/langflow/graph/vertex/base.py | 4 +++- src/backend/base/langflow/interface/initialize/loading.py | 1 - src/backend/base/langflow/utils/validate.py | 3 +++ 3 files changed, 6 insertions(+), 2 deletions(-) diff --git a/src/backend/base/langflow/graph/vertex/base.py b/src/backend/base/langflow/graph/vertex/base.py index e250e9419..27110a510 100644 --- a/src/backend/base/langflow/graph/vertex/base.py +++ b/src/backend/base/langflow/graph/vertex/base.py @@ -201,7 +201,9 @@ class Vertex: self.description = self.data["node"].get("description", "") self.frozen = self.data["node"].get("frozen", False) - self.selected_output_type = self.data["node"].get("selected_output_type") + self.selected_output_type = ( + str(self.data.get("selected_output_type")).strip() if self.data.get("selected_output_type") else None + ) self.is_input = self.data["node"].get("is_input") or self.is_input self.is_output = self.data["node"].get("is_output") or self.is_output template_dicts = {key: value for key, value in self.data["node"]["template"].items() if isinstance(value, dict)} diff --git a/src/backend/base/langflow/interface/initialize/loading.py b/src/backend/base/langflow/interface/initialize/loading.py index c1014ef90..96fe8832f 100644 --- a/src/backend/base/langflow/interface/initialize/loading.py +++ b/src/backend/base/langflow/interface/initialize/loading.py @@ -2,7 +2,6 @@ import inspect import json from typing import TYPE_CHECKING, Any, Callable, Dict, Sequence, Type - import orjson from langchain.agents import agent as agent_module from langchain.agents.agent import AgentExecutor diff --git a/src/backend/base/langflow/utils/validate.py b/src/backend/base/langflow/utils/validate.py index 0871dbd82..bd7827199 100644 --- a/src/backend/base/langflow/utils/validate.py +++ b/src/backend/base/langflow/utils/validate.py @@ -253,6 +253,9 @@ def build_class_constructor(compiled_class, exec_globals, class_name): globals()[module_name] = module instance = exec_globals[class_name](*args, **kwargs) + # Get selected type from global scope + if instance.selected_output_type in exec_globals: + instance.selected_output_type = exec_globals[instance.selected_output_type] return instance build_custom_class.__globals__.update(exec_globals) From 6595702e73ea9f62102b17b1c45ceb662ecfaaa0 Mon Sep 17 00:00:00 2001 From: anovazzi1 Date: Fri, 24 May 2024 17:42:31 -0300 Subject: [PATCH 014/701] feat(frontend): add ComponentOutput component to display output options for generic nodes feat(types): add outputComponentType to define props for ComponentOutput component --- .../components/ComponentOutput/index.tsx | 54 +++++++++++++++++++ src/frontend/src/types/components/index.ts | 7 +++ 2 files changed, 61 insertions(+) create mode 100644 src/frontend/src/customNodes/genericNode/components/ComponentOutput/index.tsx diff --git a/src/frontend/src/customNodes/genericNode/components/ComponentOutput/index.tsx b/src/frontend/src/customNodes/genericNode/components/ComponentOutput/index.tsx new file mode 100644 index 000000000..6f872afe2 --- /dev/null +++ b/src/frontend/src/customNodes/genericNode/components/ComponentOutput/index.tsx @@ -0,0 +1,54 @@ +import { + DropdownMenu, + DropdownMenuTrigger, + DropdownMenuContent, + DropdownMenuItem, +} from "@radix-ui/react-dropdown-menu"; +import ForwardedIconComponent from "../../../../components/genericIconComponent"; +import { outputComponentType } from "../../../../types/components"; +import { cn } from "../../../../utils/utils"; +import useFlowStore from "../../../../stores/flowStore"; + +export default function ComponentOutput({ + selected, + types, + frozen = false, + nodeId, +}: outputComponentType) { + const setNode = useFlowStore((state) => state.setNode); + let displayTitle = selected ?? types[0]; + + if (types.length < 2) { + return ( + {displayTitle} + ); + } + + return ( + + + + {displayTitle} + + + + + {types.map((type) => ( + { + // TODO: UDPDATE SET NODE TO NEW NODE FORM + setNode(nodeId, (node) => ({ + ...node, + data: { ...node.data, selected: type }, + })); + }} + > + {type} + + ))} + + + ); +} diff --git a/src/frontend/src/types/components/index.ts b/src/frontend/src/types/components/index.ts index e23557a84..758b13510 100644 --- a/src/frontend/src/types/components/index.ts +++ b/src/frontend/src/types/components/index.ts @@ -111,6 +111,13 @@ export type TextAreaComponentType = { readonly?: boolean; }; +export type outputComponentType = { + types: string[]; + selected: string; + nodeId: string; + frozen?: boolean; +}; + export type PromptAreaComponentType = { field_name?: string; nodeClass?: APIClassType; From 70a43c394752897e0b5ab1ab0ba12342e1115fb3 Mon Sep 17 00:00:00 2001 From: anovazzi1 Date: Mon, 27 May 2024 11:44:47 -0300 Subject: [PATCH 015/701] refactor handle tooltip to it's own component --- .../HandleTooltipComponent/index.tsx | 31 +++++++++ .../components/componentOutputs/index.tsx | 17 +++++ .../components/parameterComponent/index.tsx | 64 ++++++++----------- .../src/customNodes/genericNode/index.tsx | 4 ++ .../hooks/use-fetch-data-on-mount.tsx | 2 - .../hooks/use-handle-new-value.tsx | 3 - .../hooks/use-handle-node-class.tsx | 3 - .../hooks/use-handle-refresh-buttons.tsx | 3 +- src/frontend/src/types/api/index.ts | 4 +- 9 files changed, 81 insertions(+), 50 deletions(-) create mode 100644 src/frontend/src/customNodes/genericNode/components/HandleTooltipComponent/index.tsx create mode 100644 src/frontend/src/customNodes/genericNode/components/componentOutputs/index.tsx diff --git a/src/frontend/src/customNodes/genericNode/components/HandleTooltipComponent/index.tsx b/src/frontend/src/customNodes/genericNode/components/HandleTooltipComponent/index.tsx new file mode 100644 index 000000000..2dddabbb5 --- /dev/null +++ b/src/frontend/src/customNodes/genericNode/components/HandleTooltipComponent/index.tsx @@ -0,0 +1,31 @@ +import { useRef } from "react"; +import { TOOLTIP_EMPTY } from "../../../../constants/constants"; +import { groupByFamily } from "../../../../utils/utils"; +import TooltipRenderComponent from "../tooltipRenderComponent"; +import { useTypesStore } from "../../../../stores/typesStore"; +import { NodeType } from "../../../../types/flow"; +import useFlowStore from "../../../../stores/flowStore"; + +export default function HandleTooltips({ + left, + tooltipTitle, +}: { + left: boolean; + nodes: NodeType[]; + tooltipTitle: string; +}) { + const myData = useTypesStore((state) => state.data); + const nodes = useFlowStore((state) => state.nodes); + + let groupedObj: any = groupByFamily(myData, tooltipTitle!, left, nodes!); + + if (groupedObj && groupedObj.length > 0) { + //@ts-ignore + return groupedObj.map((item, index) => { + return ; + }); + } else { + //@ts-ignore + return {TOOLTIP_EMPTY}; + } +} diff --git a/src/frontend/src/customNodes/genericNode/components/componentOutputs/index.tsx b/src/frontend/src/customNodes/genericNode/components/componentOutputs/index.tsx new file mode 100644 index 000000000..6cdc72839 --- /dev/null +++ b/src/frontend/src/customNodes/genericNode/components/componentOutputs/index.tsx @@ -0,0 +1,17 @@ +import { NodeDataType } from "../../../../types/flow"; +import ComponentOutput from "../ComponentOutput"; + +export default function ComponentOutputs({ data }: { data: NodeDataType }) { + return ( +
+ {data.node?.outputs?.map((output) => ( + + ))} +
+ ); +} diff --git a/src/frontend/src/customNodes/genericNode/components/parameterComponent/index.tsx b/src/frontend/src/customNodes/genericNode/components/parameterComponent/index.tsx index 2503567fa..e1f737ad0 100644 --- a/src/frontend/src/customNodes/genericNode/components/parameterComponent/index.tsx +++ b/src/frontend/src/customNodes/genericNode/components/parameterComponent/index.tsx @@ -54,6 +54,7 @@ import useHandleOnNewValue from "../../../hooks/use-handle-new-value"; import useHandleNodeClass from "../../../hooks/use-handle-node-class"; import useHandleRefreshButtonPress from "../../../hooks/use-handle-refresh-buttons"; import TooltipRenderComponent from "../tooltipRenderComponent"; +import HandleTooltips from "../HandleTooltipComponent"; export default function ParameterComponent({ left, @@ -72,7 +73,6 @@ export default function ParameterComponent({ index = "", }: ParameterComponentType): JSX.Element { const ref = useRef(null); - const refHtml = useRef(null); const infoHtml = useRef(null); const setErrorData = useAlertStore((state) => state.setErrorData); const currentFlow = useFlowsManagerStore((state) => state.currentFlow); @@ -84,8 +84,6 @@ export default function ParameterComponent({ const [isLoading, setIsLoading] = useState(false); const updateNodeInternals = useUpdateNodeInternals(); const [errorDuplicateKey, setErrorDuplicateKey] = useState(false); - const flow = currentFlow?.data?.nodes ?? null; - const groupedEdge = useRef(null); const setFilterEdge = useFlowStore((state) => state.setFilterEdge); const { handleOnNewValue: handleOnNewValueHook } = useHandleOnNewValue( @@ -95,7 +93,6 @@ export default function ParameterComponent({ handleUpdateValues, debouncedHandleUpdateValues, setNode, - renderTooltips, isLoading, setIsLoading, ); @@ -106,11 +103,10 @@ export default function ParameterComponent({ takeSnapshot, setNode, updateNodeInternals, - renderTooltips, ); const { handleRefreshButtonPress: handleRefreshButtonPressHook } = - useHandleRefreshButtonPress(setIsLoading, setNode, renderTooltips); + useHandleRefreshButtonPress(setIsLoading, setNode); let disabled = edges.some( @@ -122,14 +118,7 @@ export default function ParameterComponent({ handleRefreshButtonPressHook(name, data); }; - useFetchDataOnMount( - data, - name, - handleUpdateValues, - setNode, - renderTooltips, - setIsLoading, - ); + useFetchDataOnMount(data, name, handleUpdateValues, setNode, setIsLoading); const handleOnNewValue = async ( newValue: string | string[] | boolean | Object[], @@ -193,32 +182,11 @@ export default function ParameterComponent({ ); } - function renderTooltips() { - let groupedObj: any = groupByFamily(myData, tooltipTitle!, left, flow!); - groupedEdge.current = groupedObj; - - if (groupedObj && groupedObj.length > 0) { - //@ts-ignore - refHtml.current = groupedObj.map((item, index) => { - return ; - }); - } else { - //@ts-ignore - refHtml.current = ( - {TOOLTIP_EMPTY} - ); - } - } - // If optionalHandle is an empty list, then it is not an optional handle if (optionalHandle && optionalHandle.length === 0) { optionalHandle = null; } - useEffect(() => { - renderTooltips(); - }, [tooltipTitle, flow]); - return !showNode ? ( left && LANGFLOW_SUPPORTED_TYPES.has(type ?? "") && !optionalHandle ? ( <> @@ -228,7 +196,13 @@ export default function ParameterComponent({ + } side={left ? "left" : "right"} > { - setFilterEdge(groupedEdge.current); + setFilterEdge( + groupByFamily(myData, tooltipTitle!, left, nodes!), + ); }} > @@ -322,7 +298,13 @@ export default function ParameterComponent({ + } side={left ? "left" : "right"} > setFilterEdge(groupedEdge.current)} + onClick={() => { + setFilterEdge( + groupByFamily(myData, tooltipTitle!, left, nodes!), + ); + }} />
diff --git a/src/frontend/src/customNodes/genericNode/index.tsx b/src/frontend/src/customNodes/genericNode/index.tsx index ec7bbd9f5..baf5948f4 100644 --- a/src/frontend/src/customNodes/genericNode/index.tsx +++ b/src/frontend/src/customNodes/genericNode/index.tsx @@ -31,6 +31,7 @@ import { classNames, cn } from "../../utils/utils"; import ParameterComponent from "./components/parameterComponent"; import getFieldTitle from "../utils/get-field-title"; import sortFields from "../utils/sort-fields"; +import ComponentOutputs from "./components/componentOutputs"; export default function GenericNode({ data, @@ -824,6 +825,9 @@ export default function GenericNode({ > {" "}
+ {data.node!.outputs && data.node!.outputs.length > 0 && ( + + )} {data.node!.base_classes.length > 0 && ( { const setErrorData = useAlertStore((state) => state.setErrorData); @@ -44,7 +43,6 @@ const useFetchDataOnMount = ( }); } setIsLoading(false); - renderTooltips(); } } fetchData(); diff --git a/src/frontend/src/customNodes/hooks/use-handle-new-value.tsx b/src/frontend/src/customNodes/hooks/use-handle-new-value.tsx index 7de830eda..c6f026f51 100644 --- a/src/frontend/src/customNodes/hooks/use-handle-new-value.tsx +++ b/src/frontend/src/customNodes/hooks/use-handle-new-value.tsx @@ -9,7 +9,6 @@ const useHandleOnNewValue = ( handleUpdateValues, debouncedHandleUpdateValues, setNode, - renderTooltips, isLoading, setIsLoading, ) => { @@ -65,8 +64,6 @@ const useHandleOnNewValue = ( return newNode; }); - - renderTooltips(); }; return { handleOnNewValue }; diff --git a/src/frontend/src/customNodes/hooks/use-handle-node-class.tsx b/src/frontend/src/customNodes/hooks/use-handle-node-class.tsx index 412658d77..6fb78ce10 100644 --- a/src/frontend/src/customNodes/hooks/use-handle-node-class.tsx +++ b/src/frontend/src/customNodes/hooks/use-handle-node-class.tsx @@ -6,7 +6,6 @@ const useHandleNodeClass = ( takeSnapshot, setNode, updateNodeInternals, - renderTooltips, ) => { const handleNodeClass = (newNodeClass, code) => { if (!data.node) return; @@ -30,8 +29,6 @@ const useHandleNodeClass = ( }); updateNodeInternals(data.id); - - renderTooltips(); }; return { handleNodeClass }; diff --git a/src/frontend/src/customNodes/hooks/use-handle-refresh-buttons.tsx b/src/frontend/src/customNodes/hooks/use-handle-refresh-buttons.tsx index 19f2a3c29..0101e8ee0 100644 --- a/src/frontend/src/customNodes/hooks/use-handle-refresh-buttons.tsx +++ b/src/frontend/src/customNodes/hooks/use-handle-refresh-buttons.tsx @@ -3,7 +3,7 @@ import useAlertStore from "../../stores/alertStore"; import { ResponseErrorDetailAPI } from "../../types/api"; import { handleUpdateValues } from "../../utils/parameterUtils"; -const useHandleRefreshButtonPress = (setIsLoading, setNode, renderTooltips) => { +const useHandleRefreshButtonPress = (setIsLoading, setNode) => { const setErrorData = useAlertStore((state) => state.setErrorData); const handleRefreshButtonPress = async (name, data) => { @@ -30,7 +30,6 @@ const useHandleRefreshButtonPress = (setIsLoading, setNode, renderTooltips) => { }); } setIsLoading(false); - renderTooltips(); }; return { handleRefreshButtonPress }; diff --git a/src/frontend/src/types/api/index.ts b/src/frontend/src/types/api/index.ts index faef230a5..c9f09bd40 100644 --- a/src/frontend/src/types/api/index.ts +++ b/src/frontend/src/types/api/index.ts @@ -27,6 +27,7 @@ export type APIClassType = { documentation: string; error?: string; official?: boolean; + outputs?: Array<{ types: Array; selected?: string }>; frozen?: boolean; flow?: FlowType; field_order?: string[]; @@ -38,7 +39,8 @@ export type APIClassType = { | FlowType | CustomFieldsType | boolean - | undefined; + | undefined + | Array<{ types: Array; selected?: string }>; }; export type TemplateVariableType = { From 596ab2f282fc375229488a68ebf0c62fdcc8c51a Mon Sep 17 00:00:00 2001 From: anovazzi1 Date: Mon, 27 May 2024 16:08:34 -0300 Subject: [PATCH 016/701] feat(frontend): add new OutputComponent to handle dropdown menu for selecting output types in generic nodes feat(frontend): remove ComponentOutputs component and integrate its functionality into ParameterComponent feat(frontend): update ParameterComponent to use OutputComponent for displaying output types feat(frontend): update GenericNode to use OutputComponent for displaying output types and remove ComponentOutputs feat(types): update ParameterComponentType to use number type for index instead of string --- .../index.tsx | 22 +++--- .../components/componentOutputs/index.tsx | 17 ----- .../components/parameterComponent/index.tsx | 49 +++---------- .../src/customNodes/genericNode/index.tsx | 69 +++++++++---------- src/frontend/src/types/components/index.ts | 3 +- 5 files changed, 57 insertions(+), 103 deletions(-) rename src/frontend/src/customNodes/genericNode/components/{ComponentOutput => OutputComponent}/index.tsx (70%) delete mode 100644 src/frontend/src/customNodes/genericNode/components/componentOutputs/index.tsx diff --git a/src/frontend/src/customNodes/genericNode/components/ComponentOutput/index.tsx b/src/frontend/src/customNodes/genericNode/components/OutputComponent/index.tsx similarity index 70% rename from src/frontend/src/customNodes/genericNode/components/ComponentOutput/index.tsx rename to src/frontend/src/customNodes/genericNode/components/OutputComponent/index.tsx index 6f872afe2..2dbdc7167 100644 --- a/src/frontend/src/customNodes/genericNode/components/ComponentOutput/index.tsx +++ b/src/frontend/src/customNodes/genericNode/components/OutputComponent/index.tsx @@ -8,20 +8,20 @@ import ForwardedIconComponent from "../../../../components/genericIconComponent" import { outputComponentType } from "../../../../types/components"; import { cn } from "../../../../utils/utils"; import useFlowStore from "../../../../stores/flowStore"; +import { NodeDataType } from "../../../../types/flow"; +import { cloneDeep } from "lodash"; -export default function ComponentOutput({ +export default function OutputComponent({ selected, types, frozen = false, nodeId, + idx, }: outputComponentType) { const setNode = useFlowStore((state) => state.setNode); - let displayTitle = selected ?? types[0]; if (types.length < 2) { - return ( - {displayTitle} - ); + return {selected}; } return ( @@ -30,7 +30,7 @@ export default function ComponentOutput({ - {displayTitle} + {selected} @@ -39,10 +39,12 @@ export default function ComponentOutput({ { // TODO: UDPDATE SET NODE TO NEW NODE FORM - setNode(nodeId, (node) => ({ - ...node, - data: { ...node.data, selected: type }, - })); + setNode(nodeId, (node) => { + const newNode = cloneDeep(node); + (newNode.data as NodeDataType).node!.outputs![idx].selected = + type; + return newNode; + }); }} > {type} diff --git a/src/frontend/src/customNodes/genericNode/components/componentOutputs/index.tsx b/src/frontend/src/customNodes/genericNode/components/componentOutputs/index.tsx deleted file mode 100644 index 6cdc72839..000000000 --- a/src/frontend/src/customNodes/genericNode/components/componentOutputs/index.tsx +++ /dev/null @@ -1,17 +0,0 @@ -import { NodeDataType } from "../../../../types/flow"; -import ComponentOutput from "../ComponentOutput"; - -export default function ComponentOutputs({ data }: { data: NodeDataType }) { - return ( -
- {data.node?.outputs?.map((output) => ( - - ))} -
- ); -} diff --git a/src/frontend/src/customNodes/genericNode/components/parameterComponent/index.tsx b/src/frontend/src/customNodes/genericNode/components/parameterComponent/index.tsx index e1f737ad0..fe84645a6 100644 --- a/src/frontend/src/customNodes/genericNode/components/parameterComponent/index.tsx +++ b/src/frontend/src/customNodes/genericNode/components/parameterComponent/index.tsx @@ -55,6 +55,7 @@ import useHandleNodeClass from "../../../hooks/use-handle-node-class"; import useHandleRefreshButtonPress from "../../../hooks/use-handle-refresh-buttons"; import TooltipRenderComponent from "../tooltipRenderComponent"; import HandleTooltips from "../HandleTooltipComponent"; +import OutputComponent from "../OutputComponent"; export default function ParameterComponent({ left, @@ -70,12 +71,9 @@ export default function ParameterComponent({ info = "", proxy, showNode, - index = "", + index, }: ParameterComponentType): JSX.Element { - const ref = useRef(null); const infoHtml = useRef(null); - const setErrorData = useAlertStore((state) => state.setErrorData); - const currentFlow = useFlowsManagerStore((state) => state.currentFlow); const nodes = useFlowStore((state) => state.nodes); const edges = useFlowStore((state) => state.edges); const setNode = useFlowStore((state) => state.setNode); @@ -145,40 +143,16 @@ export default function ParameterComponent({ }, [info]); function renderTitle() { - const output_types = title.split("|"); - const displayTitle = data.selected_output_type ?? output_types[0]; - return !left && output_types.length > 1 ? ( - - - - {displayTitle} - - - - - {output_types.map((type) => ( - { - setNode(data.id, (node) => ({ - ...node, - data: { ...node.data, selected_output_type: type }, - })); - }} - > - {type} - - ))} - - + return !left ? ( + ) : ( - - {title} - + {title} ); } @@ -244,7 +218,6 @@ export default function ParameterComponent({ ) ) : (
{" "}
- {data.node!.outputs && data.node!.outputs.length > 0 && ( - - )} - {data.node!.base_classes.length > 0 && ( - 0 - ? nodeColors[data.node.output_types[0]] ?? - nodeColors[types[data.node.output_types[0]]] - : nodeColors[types[data.type]]) ?? nodeColors.unknown - } - title={ - data.node?.output_types && data.node.output_types.length > 0 - ? data.node.output_types.join(" | ") - : data.type - } - tooltipTitle={data.node?.base_classes.join("\n")} - id={{ - baseClasses: data.node!.base_classes, - id: data.id, - dataType: data.type, - }} - type={data.node?.base_classes.join("|")} - left={false} - showNode={showNode} - /> - )} + {data.node!.outputs && + data.node!.outputs.length > 0 && + data.node!.outputs.map((output, idx) => ( + + ))} )} diff --git a/src/frontend/src/types/components/index.ts b/src/frontend/src/types/components/index.ts index 758b13510..bc4f65ed7 100644 --- a/src/frontend/src/types/components/index.ts +++ b/src/frontend/src/types/components/index.ts @@ -67,7 +67,7 @@ export type ParameterComponentType = { info?: string; proxy?: { field: string; id: string }; showNode?: boolean; - index?: string; + index: number; onCloseModal?: (close: boolean) => void; }; export type InputListComponentType = { @@ -116,6 +116,7 @@ export type outputComponentType = { selected: string; nodeId: string; frozen?: boolean; + idx: number; }; export type PromptAreaComponentType = { From 354e1bc126492b319dca359013bf7e89e3d768d8 Mon Sep 17 00:00:00 2001 From: anovazzi1 Date: Mon, 27 May 2024 17:57:22 -0300 Subject: [PATCH 017/701] =?UTF-8?q?funcionando=20mas=20ainda=20n=C3=A3o=20?= =?UTF-8?q?ideal?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- src/frontend/src/stores/flowsManagerStore.ts | 9 ++-- src/frontend/src/utils/reactflowUtils.ts | 45 +++++++++++++++++++- 2 files changed, 47 insertions(+), 7 deletions(-) diff --git a/src/frontend/src/stores/flowsManagerStore.ts b/src/frontend/src/stores/flowsManagerStore.ts index 81637fba0..90bf82169 100644 --- a/src/frontend/src/stores/flowsManagerStore.ts +++ b/src/frontend/src/stores/flowsManagerStore.ts @@ -199,11 +199,10 @@ const useFlowsManagerStore = create((set, get) => ({ position?: XYPosition, fromDragAndDrop?: boolean, ): Promise => { + let flowData = flow + ? processDataFromFlow(flow) + : { nodes: [], edges: [], viewport: { zoom: 1, x: 0, y: 0 } }; if (newProject) { - let flowData = flow - ? processDataFromFlow(flow) - : { nodes: [], edges: [], viewport: { zoom: 1, x: 0, y: 0 } }; - // Create a new flow with a default name if no flow is provided. const folder_id = useFolderStore.getState().folderUrl; const my_collection_id = useFolderStore.getState().myCollectionId; @@ -272,7 +271,7 @@ const useFlowsManagerStore = create((set, get) => ({ useFlowStore .getState() .paste( - { nodes: flow!.data!.nodes, edges: flow!.data!.edges }, + { nodes: flowData?.nodes, edges: flowData?.edges }, position ?? { x: 10, y: 10 }, ); } diff --git a/src/frontend/src/utils/reactflowUtils.ts b/src/frontend/src/utils/reactflowUtils.ts index b379b445e..97384e714 100644 --- a/src/frontend/src/utils/reactflowUtils.ts +++ b/src/frontend/src/utils/reactflowUtils.ts @@ -191,8 +191,8 @@ export const processDataFromFlow = (flow: FlowType, refreshIds = true) => { let data = flow?.data ? flow.data : null; if (data) { processFlowEdges(flow); - //prevent node update for now - // processFlowNodes(flow); + //add dropdown option to nodeOutputs + processFlowNodes(flow); //add animation to text type edges updateEdges(data.edges); // updateNodes(data.nodes, data.edges); @@ -410,6 +410,34 @@ export function updateEdgesHandleIds({ return newEdges; } +export function updateNewOutput({ nodes, edges }: updateEdgesHandleIdsType) { + let newEdges = cloneDeep(edges); + let newNodes = cloneDeep(nodes); + newNodes.forEach((node) => { + if ( + !node.data.node?.outputs && + (node.data.node?.base_classes ?? []).length > 0 + ) { + const selected = node.data.node?.base_classes[0]!; + node.data.node!.outputs = [ + { types: node.data.node!.base_classes, selected: selected }, + ]; + newEdges.forEach((edge) => { + if (edge.source === node.id && edge.sourceHandle) { + let newSourceHandle: sourceHandleType = scapeJSONParse( + edge.sourceHandle, + ); + newSourceHandle.baseClasses = [selected]; + edge.sourceHandle = scapedJSONStringfy(newSourceHandle); + edge.data.sourceHandle = newSourceHandle; + } + }); + } + }); + + return { nodes: newNodes, edges: newEdges }; +} + export function handleKeyDown( e: | React.KeyboardEvent @@ -561,6 +589,10 @@ export function checkOldEdgesHandles(edges: Edge[]): boolean { ); } +export function checkOldNodesOutput(nodes: NodeType[]): boolean { + return nodes.some((node) => !node.data.node?.outputs); +} + export function customStringify(obj: any): string { if (typeof obj === "undefined") { return "null"; @@ -1017,6 +1049,15 @@ export function processFlowEdges(flow: FlowType) { }); } +export function processFlowNodes(flow: FlowType) { + if (!flow.data || !flow.data.nodes) return; + if (checkOldNodesOutput(flow.data.nodes)) { + const { nodes, edges } = updateNewOutput(flow.data); + flow.data.nodes = nodes; + flow.data.edges = edges; + } +} + export function expandGroupNode( id: string, flow: FlowType, From 9d38ccc15db7febfef48f6c8cf5306e4b0344794 Mon Sep 17 00:00:00 2001 From: anovazzi1 Date: Mon, 27 May 2024 19:00:26 -0300 Subject: [PATCH 018/701] feat(frontend): refactor OutputComponent to use external dropdown menu component --- .../genericNode/components/OutputComponent/index.tsx | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/src/frontend/src/customNodes/genericNode/components/OutputComponent/index.tsx b/src/frontend/src/customNodes/genericNode/components/OutputComponent/index.tsx index 2dbdc7167..d76f30f63 100644 --- a/src/frontend/src/customNodes/genericNode/components/OutputComponent/index.tsx +++ b/src/frontend/src/customNodes/genericNode/components/OutputComponent/index.tsx @@ -1,15 +1,15 @@ -import { - DropdownMenu, - DropdownMenuTrigger, - DropdownMenuContent, - DropdownMenuItem, -} from "@radix-ui/react-dropdown-menu"; import ForwardedIconComponent from "../../../../components/genericIconComponent"; import { outputComponentType } from "../../../../types/components"; import { cn } from "../../../../utils/utils"; import useFlowStore from "../../../../stores/flowStore"; import { NodeDataType } from "../../../../types/flow"; import { cloneDeep } from "lodash"; +import { + DropdownMenu, + DropdownMenuContent, + DropdownMenuItem, + DropdownMenuTrigger, +} from "../../../../components/ui/dropdown-menu"; export default function OutputComponent({ selected, From 3f43958f089909beb8e9d38065139b5c58ad5f48 Mon Sep 17 00:00:00 2001 From: anovazzi1 Date: Tue, 28 May 2024 08:43:48 -0300 Subject: [PATCH 019/701] fix dropdown bug --- .../genericNode/components/OutputComponent/index.tsx | 3 +++ 1 file changed, 3 insertions(+) diff --git a/src/frontend/src/customNodes/genericNode/components/OutputComponent/index.tsx b/src/frontend/src/customNodes/genericNode/components/OutputComponent/index.tsx index d76f30f63..69ca3965c 100644 --- a/src/frontend/src/customNodes/genericNode/components/OutputComponent/index.tsx +++ b/src/frontend/src/customNodes/genericNode/components/OutputComponent/index.tsx @@ -10,6 +10,7 @@ import { DropdownMenuItem, DropdownMenuTrigger, } from "../../../../components/ui/dropdown-menu"; +import { useUpdateNodeInternals } from "reactflow"; export default function OutputComponent({ selected, @@ -19,6 +20,7 @@ export default function OutputComponent({ idx, }: outputComponentType) { const setNode = useFlowStore((state) => state.setNode); + const updateNodeInternals = useUpdateNodeInternals(); if (types.length < 2) { return {selected}; @@ -45,6 +47,7 @@ export default function OutputComponent({ type; return newNode; }); + updateNodeInternals(nodeId); }} > {type} From 77cb85b7f27dc946db2742960bc206ae32abd829 Mon Sep 17 00:00:00 2001 From: anovazzi1 Date: Tue, 28 May 2024 15:35:38 -0300 Subject: [PATCH 020/701] feat(frontend): refactor OutputComponent to use external dropdown menu component --- src/frontend/src/utils/reactflowUtils.ts | 53 +++++++++++++++--------- 1 file changed, 33 insertions(+), 20 deletions(-) diff --git a/src/frontend/src/utils/reactflowUtils.ts b/src/frontend/src/utils/reactflowUtils.ts index 97384e714..b3e172be2 100644 --- a/src/frontend/src/utils/reactflowUtils.ts +++ b/src/frontend/src/utils/reactflowUtils.ts @@ -16,6 +16,8 @@ import { specialCharsRegex, } from "../constants/constants"; import { downloadFlowsFromDatabase } from "../controllers/API"; +import getFieldTitle from "../customNodes/utils/get-field-title"; +import { DESCRIPTIONS } from "../flow_constants"; import { APIClassType, APIKindType, @@ -37,8 +39,6 @@ import { updateEdgesHandleIdsType, } from "../types/utils/reactflowUtils"; import { createRandomKey, toTitleCase } from "./utils"; -import { DESCRIPTIONS } from "../flow_constants"; -import getFieldTitle from "../customNodes/utils/get-field-title"; const uid = new ShortUniqueId({ length: 5 }); export function checkChatInput(nodes: Node[]) { @@ -413,25 +413,38 @@ export function updateEdgesHandleIds({ export function updateNewOutput({ nodes, edges }: updateEdgesHandleIdsType) { let newEdges = cloneDeep(edges); let newNodes = cloneDeep(nodes); - newNodes.forEach((node) => { - if ( - !node.data.node?.outputs && - (node.data.node?.base_classes ?? []).length > 0 - ) { - const selected = node.data.node?.base_classes[0]!; - node.data.node!.outputs = [ - { types: node.data.node!.base_classes, selected: selected }, - ]; - newEdges.forEach((edge) => { - if (edge.source === node.id && edge.sourceHandle) { - let newSourceHandle: sourceHandleType = scapeJSONParse( - edge.sourceHandle, - ); - newSourceHandle.baseClasses = [selected]; - edge.sourceHandle = scapedJSONStringfy(newSourceHandle); - edge.data.sourceHandle = newSourceHandle; + newEdges.forEach((edge) => { + if (edge.sourceHandle && edge.targetHandle) { + let newSourceHandle: sourceHandleType = scapeJSONParse(edge.sourceHandle); + let newTargetHandle: targetHandleType = scapeJSONParse(edge.targetHandle); + let intersection; + if (newTargetHandle.inputTypes && newTargetHandle.inputTypes.length > 0) { + //conjuction subtraction + intersection = newSourceHandle.baseClasses.filter((type) => + newTargetHandle.inputTypes!.includes(type), + ); + } else { + intersection = newSourceHandle.baseClasses.filter( + (type) => type === newTargetHandle.type, + ); + } + const selected = intersection[0]; + newSourceHandle.baseClasses = [selected]; + const id = newSourceHandle.id; + const sourceNodeIndex = newNodes.findIndex((node) => node.id === id); + if (sourceNodeIndex > -1) { + const sourceNode = newNodes[sourceNodeIndex]; + if ( + !sourceNode.data.node?.outputs || + sourceNode.data.node!.outputs!.length === 0 + ) { + sourceNode.data.node!.outputs = [ + { types: sourceNode.data.node!.base_classes, selected: selected }, + ]; } - }); + } + edge.sourceHandle = scapedJSONStringfy(newSourceHandle); + edge.data.sourceHandle = newSourceHandle; } }); From 45011e8fda8fc6b70bd78db5c56873f6bcd077ed Mon Sep 17 00:00:00 2001 From: anovazzi1 Date: Tue, 28 May 2024 17:57:52 -0300 Subject: [PATCH 021/701] refactor: add idx property to handle types in GenericNode and reactflowUtils --- src/frontend/src/customNodes/genericNode/index.tsx | 2 ++ src/frontend/src/types/flow/index.ts | 1 + src/frontend/src/utils/reactflowUtils.ts | 3 +++ 3 files changed, 6 insertions(+) diff --git a/src/frontend/src/customNodes/genericNode/index.tsx b/src/frontend/src/customNodes/genericNode/index.tsx index 5eb2c9c39..febf12b39 100644 --- a/src/frontend/src/customNodes/genericNode/index.tsx +++ b/src/frontend/src/customNodes/genericNode/index.tsx @@ -589,6 +589,7 @@ export default function GenericNode({ baseClasses: data.node!.base_classes, id: data.id, dataType: data.type, + idx: 0, }} type={data.node?.base_classes.join("|")} left={false} @@ -848,6 +849,7 @@ export default function GenericNode({ baseClasses: [output.selected ?? output.types[0]], id: data.id, dataType: data.type, + idx: idx, }} type={output.types.join("|")} left={false} diff --git a/src/frontend/src/types/flow/index.ts b/src/frontend/src/types/flow/index.ts index d50f8def6..005bb6197 100644 --- a/src/frontend/src/types/flow/index.ts +++ b/src/frontend/src/types/flow/index.ts @@ -58,6 +58,7 @@ export type sourceHandleType = { dataType: string; id: string; baseClasses: string[]; + idx: number; }; //left side export type targetHandleType = { diff --git a/src/frontend/src/utils/reactflowUtils.ts b/src/frontend/src/utils/reactflowUtils.ts index b3e172be2..758c60d6d 100644 --- a/src/frontend/src/utils/reactflowUtils.ts +++ b/src/frontend/src/utils/reactflowUtils.ts @@ -79,6 +79,8 @@ export function cleanEdges(nodes: Node[], edges: Edge[]) { id: sourceNode.data.id, baseClasses: sourceNode.data.node!.base_classes, dataType: sourceNode.data.type, + idx: + sourceNode.data.node!.outputs[scapeJSONParse(sourceHandle).idx] ?? 0, }; if (scapedJSONStringfy(id) !== sourceHandle) { newEdges = newEdges.filter((e) => e.id !== edge.id); @@ -397,6 +399,7 @@ export function updateEdgesHandleIds({ id: sourceNode.data.id, baseClasses: sourceNode.data.node!.base_classes, dataType: sourceNode.data.type, + idx: 0, }; } edge.sourceHandle = scapedJSONStringfy(newSource!); From 02a1624bf4df61cbc1e633497136dad455775a72 Mon Sep 17 00:00:00 2001 From: anovazzi1 Date: Tue, 28 May 2024 18:29:07 -0300 Subject: [PATCH 022/701] update cleanEdges and fix updateNewOutput --- src/frontend/src/utils/reactflowUtils.ts | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/src/frontend/src/utils/reactflowUtils.ts b/src/frontend/src/utils/reactflowUtils.ts index 758c60d6d..a51baee23 100644 --- a/src/frontend/src/utils/reactflowUtils.ts +++ b/src/frontend/src/utils/reactflowUtils.ts @@ -46,6 +46,7 @@ export function checkChatInput(nodes: Node[]) { } export function cleanEdges(nodes: Node[], edges: Edge[]) { + console.log("cleanEdges"); let newEdges = cloneDeep(edges); edges.forEach((edge) => { // check if the source and target node still exists @@ -75,12 +76,12 @@ export function cleanEdges(nodes: Node[], edges: Edge[]) { } } if (sourceHandle) { + const index = scapeJSONParse(sourceHandle).idx ?? 0; const id: sourceHandleType = { id: sourceNode.data.id, - baseClasses: sourceNode.data.node!.base_classes, + baseClasses: [sourceNode.data.node.outputs[index].selected], dataType: sourceNode.data.type, - idx: - sourceNode.data.node!.outputs[scapeJSONParse(sourceHandle).idx] ?? 0, + idx: index, }; if (scapedJSONStringfy(id) !== sourceHandle) { newEdges = newEdges.filter((e) => e.id !== edge.id); @@ -414,6 +415,7 @@ export function updateEdgesHandleIds({ } export function updateNewOutput({ nodes, edges }: updateEdgesHandleIdsType) { + console.log("updateNewOutput"); let newEdges = cloneDeep(edges); let newNodes = cloneDeep(nodes); newEdges.forEach((edge) => { @@ -434,6 +436,7 @@ export function updateNewOutput({ nodes, edges }: updateEdgesHandleIdsType) { const selected = intersection[0]; newSourceHandle.baseClasses = [selected]; const id = newSourceHandle.id; + newSourceHandle.idx = 0; const sourceNodeIndex = newNodes.findIndex((node) => node.id === id); if (sourceNodeIndex > -1) { const sourceNode = newNodes[sourceNodeIndex]; From 6732cf94f69a5b6231e746c648e69d71c3cadc0a Mon Sep 17 00:00:00 2001 From: ogabrielluiz Date: Tue, 28 May 2024 23:04:37 -0300 Subject: [PATCH 023/701] feat: Add Decision class to langflow schema --- src/backend/base/langflow/schema/__init__.py | 3 +- src/backend/base/langflow/schema/decision.py | 30 ++++++++++++++++++++ 2 files changed, 32 insertions(+), 1 deletion(-) create mode 100644 src/backend/base/langflow/schema/decision.py diff --git a/src/backend/base/langflow/schema/__init__.py b/src/backend/base/langflow/schema/__init__.py index 14230578c..9c374a730 100644 --- a/src/backend/base/langflow/schema/__init__.py +++ b/src/backend/base/langflow/schema/__init__.py @@ -1,4 +1,5 @@ from .dotdict import dotdict from .schema import Record +from .decision import Decision -__all__ = ["Record", "dotdict"] +__all__ = ["Record", "dotdict", "Decision"] diff --git a/src/backend/base/langflow/schema/decision.py b/src/backend/base/langflow/schema/decision.py new file mode 100644 index 000000000..bb4f0206b --- /dev/null +++ b/src/backend/base/langflow/schema/decision.py @@ -0,0 +1,30 @@ +from pydantic import field_validator, BaseModel +from typing import Any + + +class Decision(BaseModel): + """ + Represents a decision made in the Graph. + + Attributes: + path (str): The path to take as a result of the decision. + result (dict): The result of the decision. + """ + + path: str + result: Any + + @field_validator("path") + def validate_path(cls, value: str) -> str: + """ + Validates the path. + + Args: + value (str): The path to validate. + + Returns: + str: The validated path. + """ + if isinstance(value, str): + return value + return str(value) From ebded206b3cf0f41ed69132cd1a160950e724d4a Mon Sep 17 00:00:00 2001 From: ogabrielluiz Date: Wed, 29 May 2024 11:19:37 -0300 Subject: [PATCH 024/701] format --- .../starter_projects/VectorStore-RAG-Flows.json | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/src/backend/base/langflow/initial_setup/starter_projects/VectorStore-RAG-Flows.json b/src/backend/base/langflow/initial_setup/starter_projects/VectorStore-RAG-Flows.json index 097fdbbc2..c629cdb3e 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/VectorStore-RAG-Flows.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/VectorStore-RAG-Flows.json @@ -358,7 +358,7 @@ "list": false, "show": true, "multiline": true, - "value": "from typing import Any, Dict, List, Optional\n\nfrom langchain_openai.embeddings.base import OpenAIEmbeddings\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.field_typing import Embeddings, NestedDict\nfrom langflow.interface.custom.custom_component import CustomComponent\n\n\nclass OpenAIEmbeddingsComponent(CustomComponent):\n display_name = \"OpenAI Embeddings\"\n description = \"Generate embeddings using OpenAI models.\"\n\n def build_config(self):\n return {\n \"allowed_special\": {\n \"display_name\": \"Allowed Special\",\n \"advanced\": True,\n \"field_type\": \"str\",\n \"is_list\": True,\n },\n \"default_headers\": {\n \"display_name\": \"Default Headers\",\n \"advanced\": True,\n \"field_type\": \"dict\",\n },\n \"default_query\": {\n \"display_name\": \"Default Query\",\n \"advanced\": True,\n \"field_type\": \"NestedDict\",\n },\n \"disallowed_special\": {\n \"display_name\": \"Disallowed Special\",\n \"advanced\": True,\n \"field_type\": \"str\",\n \"is_list\": True,\n },\n \"chunk_size\": {\"display_name\": \"Chunk Size\", \"advanced\": True},\n \"client\": {\"display_name\": \"Client\", \"advanced\": True},\n \"deployment\": {\"display_name\": \"Deployment\", \"advanced\": True},\n \"embedding_ctx_length\": {\n \"display_name\": \"Embedding Context Length\",\n \"advanced\": True,\n },\n \"max_retries\": {\"display_name\": \"Max Retries\", \"advanced\": True},\n \"model\": {\n \"display_name\": \"Model\",\n \"advanced\": False,\n \"options\": [\n \"text-embedding-3-small\",\n \"text-embedding-3-large\",\n \"text-embedding-ada-002\",\n ],\n },\n \"model_kwargs\": {\"display_name\": \"Model Kwargs\", \"advanced\": True},\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"password\": True,\n \"advanced\": True,\n },\n \"openai_api_key\": {\"display_name\": \"OpenAI API Key\", \"password\": True},\n \"openai_api_type\": {\n \"display_name\": \"OpenAI API Type\",\n \"advanced\": True,\n \"password\": True,\n },\n \"openai_api_version\": {\n \"display_name\": \"OpenAI API Version\",\n \"advanced\": True,\n },\n \"openai_organization\": {\n \"display_name\": \"OpenAI Organization\",\n \"advanced\": True,\n },\n \"openai_proxy\": {\"display_name\": \"OpenAI Proxy\", \"advanced\": True},\n \"request_timeout\": {\"display_name\": \"Request Timeout\", \"advanced\": True},\n \"show_progress_bar\": {\n \"display_name\": \"Show Progress Bar\",\n \"advanced\": True,\n },\n \"skip_empty\": {\"display_name\": \"Skip Empty\", \"advanced\": True},\n \"tiktoken_model_name\": {\n \"display_name\": \"TikToken Model Name\",\n \"advanced\": True,\n },\n \"tiktoken_enable\": {\"display_name\": \"TikToken Enable\", \"advanced\": True},\n }\n\n def build(\n self,\n openai_api_key: str,\n default_headers: Optional[Dict[str, str]] = None,\n default_query: Optional[NestedDict] = {},\n allowed_special: List[str] = [],\n disallowed_special: List[str] = [\"all\"],\n chunk_size: int = 1000,\n client: Optional[Any] = None,\n deployment: str = \"text-embedding-ada-002\",\n embedding_ctx_length: int = 8191,\n max_retries: int = 6,\n model: str = \"text-embedding-ada-002\",\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n openai_api_type: Optional[str] = None,\n openai_api_version: Optional[str] = None,\n openai_organization: Optional[str] = None,\n openai_proxy: Optional[str] = None,\n request_timeout: Optional[float] = None,\n show_progress_bar: bool = False,\n skip_empty: bool = False,\n tiktoken_enable: bool = True,\n tiktoken_model_name: Optional[str] = None,\n ) -> Embeddings:\n # This is to avoid errors with Vector Stores (e.g Chroma)\n if disallowed_special == [\"all\"]:\n disallowed_special = \"all\" # type: ignore\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n return OpenAIEmbeddings(\n tiktoken_enabled=tiktoken_enable,\n default_headers=default_headers,\n default_query=default_query,\n allowed_special=set(allowed_special),\n disallowed_special=\"all\",\n chunk_size=chunk_size,\n client=client,\n deployment=deployment,\n embedding_ctx_length=embedding_ctx_length,\n max_retries=max_retries,\n model=model,\n model_kwargs=model_kwargs,\n base_url=openai_api_base,\n api_key=api_key,\n openai_api_type=openai_api_type,\n api_version=openai_api_version,\n organization=openai_organization,\n openai_proxy=openai_proxy,\n timeout=request_timeout,\n show_progress_bar=show_progress_bar,\n skip_empty=skip_empty,\n tiktoken_model_name=tiktoken_model_name,\n )\n", + "value": "from typing import Dict, List, Optional\n\nfrom langchain_openai.embeddings.base import OpenAIEmbeddings\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Embeddings, NestedDict\n\n\nclass OpenAIEmbeddingsComponent(CustomComponent):\n display_name = \"OpenAI Embeddings\"\n description = \"Generate embeddings using OpenAI models.\"\n\n def build_config(self):\n return {\n \"allowed_special\": {\n \"display_name\": \"Allowed Special\",\n \"advanced\": True,\n \"field_type\": \"str\",\n \"is_list\": True,\n },\n \"default_headers\": {\n \"display_name\": \"Default Headers\",\n \"advanced\": True,\n \"field_type\": \"dict\",\n },\n \"default_query\": {\n \"display_name\": \"Default Query\",\n \"advanced\": True,\n \"field_type\": \"NestedDict\",\n },\n \"disallowed_special\": {\n \"display_name\": \"Disallowed Special\",\n \"advanced\": True,\n \"field_type\": \"str\",\n \"is_list\": True,\n },\n \"chunk_size\": {\"display_name\": \"Chunk Size\", \"advanced\": True},\n \"client\": {\"display_name\": \"Client\", \"advanced\": True},\n \"deployment\": {\"display_name\": \"Deployment\", \"advanced\": True},\n \"embedding_ctx_length\": {\n \"display_name\": \"Embedding Context Length\",\n \"advanced\": True,\n },\n \"max_retries\": {\"display_name\": \"Max Retries\", \"advanced\": True},\n \"model\": {\n \"display_name\": \"Model\",\n \"advanced\": False,\n \"options\": [\n \"text-embedding-3-small\",\n \"text-embedding-3-large\",\n \"text-embedding-ada-002\",\n ],\n },\n \"model_kwargs\": {\"display_name\": \"Model Kwargs\", \"advanced\": True},\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"password\": True,\n \"advanced\": True,\n },\n \"openai_api_key\": {\"display_name\": \"OpenAI API Key\", \"password\": True},\n \"openai_api_type\": {\n \"display_name\": \"OpenAI API Type\",\n \"advanced\": True,\n \"password\": True,\n },\n \"openai_api_version\": {\n \"display_name\": \"OpenAI API Version\",\n \"advanced\": True,\n },\n \"openai_organization\": {\n \"display_name\": \"OpenAI Organization\",\n \"advanced\": True,\n },\n \"openai_proxy\": {\"display_name\": \"OpenAI Proxy\", \"advanced\": True},\n \"request_timeout\": {\"display_name\": \"Request Timeout\", \"advanced\": True},\n \"show_progress_bar\": {\n \"display_name\": \"Show Progress Bar\",\n \"advanced\": True,\n },\n \"skip_empty\": {\"display_name\": \"Skip Empty\", \"advanced\": True},\n \"tiktoken_model_name\": {\n \"display_name\": \"TikToken Model Name\",\n \"advanced\": True,\n },\n \"tiktoken_enable\": {\"display_name\": \"TikToken Enable\", \"advanced\": True},\n }\n\n def build(\n self,\n openai_api_key: str,\n default_headers: Optional[Dict[str, str]] = None,\n default_query: Optional[NestedDict] = {},\n allowed_special: List[str] = [],\n disallowed_special: List[str] = [\"all\"],\n chunk_size: int = 1000,\n deployment: str = \"text-embedding-ada-002\",\n embedding_ctx_length: int = 8191,\n max_retries: int = 6,\n model: str = \"text-embedding-ada-002\",\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n openai_api_type: Optional[str] = None,\n openai_api_version: Optional[str] = None,\n openai_organization: Optional[str] = None,\n openai_proxy: Optional[str] = None,\n request_timeout: Optional[float] = None,\n show_progress_bar: bool = False,\n skip_empty: bool = False,\n tiktoken_enable: bool = True,\n tiktoken_model_name: Optional[str] = None,\n ) -> Embeddings:\n # This is to avoid errors with Vector Stores (e.g Chroma)\n if disallowed_special == [\"all\"]:\n disallowed_special = \"all\" # type: ignore\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n return OpenAIEmbeddings(\n tiktoken_enabled=tiktoken_enable,\n default_headers=default_headers,\n default_query=default_query,\n allowed_special=set(allowed_special),\n disallowed_special=\"all\",\n chunk_size=chunk_size,\n deployment=deployment,\n embedding_ctx_length=embedding_ctx_length,\n max_retries=max_retries,\n model=model,\n model_kwargs=model_kwargs,\n base_url=openai_api_base,\n api_key=api_key,\n openai_api_type=openai_api_type,\n api_version=openai_api_version,\n organization=openai_organization,\n openai_proxy=openai_proxy,\n timeout=request_timeout,\n show_progress_bar=show_progress_bar,\n skip_empty=skip_empty,\n tiktoken_model_name=tiktoken_model_name,\n )\n", "fileTypes": [], "file_path": "", "password": false, @@ -851,7 +851,7 @@ "list": false, "show": true, "multiline": true, - "value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\"},\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": True,\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": MODEL_NAMES,\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"openai_api_key\": {\n \"display_name\": \"OpenAI API Key\",\n \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n \"advanced\": False,\n \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float,\n model_name: str = \"gpt-4o\",\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n output = ChatOpenAI(\n max_tokens=max_tokens,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n", + "value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\"},\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": True,\n \"info\": \"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": MODEL_NAMES,\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"openai_api_key\": {\n \"display_name\": \"OpenAI API Key\",\n \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n \"advanced\": False,\n \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float,\n model_name: str = \"gpt-4o\",\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n output = ChatOpenAI(\n max_tokens=max_tokens or None,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n", "fileTypes": [], "file_path": "", "password": false, @@ -877,7 +877,7 @@ "display_name": "Max Tokens", "advanced": true, "dynamic": false, - "info": "", + "info": "The maximum number of tokens to generate. Set to 0 for unlimited tokens.", "load_from_db": false, "title_case": false }, @@ -1106,7 +1106,7 @@ "list": false, "show": true, "multiline": true, - "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.field_typing import Prompt, TemplateField, Text\nfrom langflow.interface.custom.custom_component import CustomComponent\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": TemplateField(display_name=\"Template\"),\n \"code\": TemplateField(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n", + "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Prompt, TemplateField, Text\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": TemplateField(display_name=\"Template\"),\n \"code\": TemplateField(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n", "fileTypes": [], "file_path": "", "password": false, @@ -1491,7 +1491,7 @@ "list": false, "show": true, "multiline": true, - "value": "from pathlib import Path\nfrom typing import Any, Dict\n\nfrom langflow.base.data.utils import TEXT_FILE_TYPES, parse_text_file_to_record\nfrom langflow.interface.custom.custom_component import CustomComponent\nfrom langflow.schema import Record\n\n\nclass FileComponent(CustomComponent):\n display_name = \"File\"\n description = \"A generic file loader.\"\n icon = \"file-text\"\n\n def build_config(self) -> Dict[str, Any]:\n return {\n \"path\": {\n \"display_name\": \"Path\",\n \"field_type\": \"file\",\n \"file_types\": TEXT_FILE_TYPES,\n \"info\": f\"Supported file types: {', '.join(TEXT_FILE_TYPES)}\",\n },\n \"silent_errors\": {\n \"display_name\": \"Silent Errors\",\n \"advanced\": True,\n \"info\": \"If true, errors will not raise an exception.\",\n },\n }\n\n def load_file(self, path: str, silent_errors: bool = False) -> Record:\n resolved_path = self.resolve_path(path)\n path_obj = Path(resolved_path)\n extension = path_obj.suffix[1:].lower()\n if extension == \"doc\":\n raise ValueError(\"doc files are not supported. Please save as .docx\")\n if extension not in TEXT_FILE_TYPES:\n raise ValueError(f\"Unsupported file type: {extension}\")\n record = parse_text_file_to_record(resolved_path, silent_errors)\n self.status = record if record else \"No data\"\n return record or Record()\n\n def build(\n self,\n path: str,\n silent_errors: bool = False,\n ) -> Record:\n record = self.load_file(path, silent_errors)\n self.status = record\n return record\n", + "value": "from pathlib import Path\nfrom typing import Any, Dict\n\nfrom langflow.base.data.utils import TEXT_FILE_TYPES, parse_text_file_to_record\nfrom langflow.custom import CustomComponent\nfrom langflow.schema import Record\n\n\nclass FileComponent(CustomComponent):\n display_name = \"File\"\n description = \"A generic file loader.\"\n icon = \"file-text\"\n\n def build_config(self) -> Dict[str, Any]:\n return {\n \"path\": {\n \"display_name\": \"Path\",\n \"field_type\": \"file\",\n \"file_types\": TEXT_FILE_TYPES,\n \"info\": f\"Supported file types: {', '.join(TEXT_FILE_TYPES)}\",\n },\n \"silent_errors\": {\n \"display_name\": \"Silent Errors\",\n \"advanced\": True,\n \"info\": \"If true, errors will not raise an exception.\",\n },\n }\n\n def load_file(self, path: str, silent_errors: bool = False) -> Record:\n resolved_path = self.resolve_path(path)\n path_obj = Path(resolved_path)\n extension = path_obj.suffix[1:].lower()\n if extension == \"doc\":\n raise ValueError(\"doc files are not supported. Please save as .docx\")\n if extension not in TEXT_FILE_TYPES:\n raise ValueError(f\"Unsupported file type: {extension}\")\n record = parse_text_file_to_record(resolved_path, silent_errors)\n self.status = record if record else \"No data\"\n return record or Record()\n\n def build(\n self,\n path: str,\n silent_errors: bool = False,\n ) -> Record:\n record = self.load_file(path, silent_errors)\n self.status = record\n return record\n", "fileTypes": [], "file_path": "", "password": false, @@ -1631,7 +1631,7 @@ "list": false, "show": true, "multiline": true, - "value": "from typing import Optional\nfrom langchain_core.documents import Document\n\nfrom langflow.interface.custom.custom_component import CustomComponent\nfrom langflow.schema import Record\nfrom langflow.utils.util import build_loader_repr_from_records, unescape_string\nfrom langchain_text_splitters import RecursiveCharacterTextSplitter\n\n\nclass RecursiveCharacterTextSplitterComponent(CustomComponent):\n display_name: str = \"Recursive Character Text Splitter\"\n description: str = \"Split text into chunks of a specified length.\"\n documentation: str = \"https://docs.langflow.org/components/text-splitters#recursivecharactertextsplitter\"\n\n def build_config(self):\n return {\n \"inputs\": {\n \"display_name\": \"Input\",\n \"info\": \"The texts to split.\",\n \"input_types\": [\"Document\", \"Record\"],\n },\n \"separators\": {\n \"display_name\": \"Separators\",\n \"info\": 'The characters to split on.\\nIf left empty defaults to [\"\\\\n\\\\n\", \"\\\\n\", \" \", \"\"].',\n \"is_list\": True,\n },\n \"chunk_size\": {\n \"display_name\": \"Chunk Size\",\n \"info\": \"The maximum length of each chunk.\",\n \"field_type\": \"int\",\n \"value\": 1000,\n },\n \"chunk_overlap\": {\n \"display_name\": \"Chunk Overlap\",\n \"info\": \"The amount of overlap between chunks.\",\n \"field_type\": \"int\",\n \"value\": 200,\n },\n \"code\": {\"show\": False},\n }\n\n def build(\n self,\n inputs: list[Document],\n separators: Optional[list[str]] = None,\n chunk_size: Optional[int] = 1000,\n chunk_overlap: Optional[int] = 200,\n ) -> list[Record]:\n \"\"\"\n Split text into chunks of a specified length.\n\n Args:\n separators (list[str]): The characters to split on.\n chunk_size (int): The maximum length of each chunk.\n chunk_overlap (int): The amount of overlap between chunks.\n length_function (function): The function to use to calculate the length of the text.\n\n Returns:\n list[str]: The chunks of text.\n \"\"\"\n\n if separators == \"\":\n separators = None\n elif separators:\n # check if the separators list has escaped characters\n # if there are escaped characters, unescape them\n separators = [unescape_string(x) for x in separators]\n\n # Make sure chunk_size and chunk_overlap are ints\n if isinstance(chunk_size, str):\n chunk_size = int(chunk_size)\n if isinstance(chunk_overlap, str):\n chunk_overlap = int(chunk_overlap)\n splitter = RecursiveCharacterTextSplitter(\n separators=separators,\n chunk_size=chunk_size,\n chunk_overlap=chunk_overlap,\n )\n documents = []\n for _input in inputs:\n if isinstance(_input, Record):\n documents.append(_input.to_lc_document())\n else:\n documents.append(_input)\n docs = splitter.split_documents(documents)\n records = self.to_records(docs)\n self.repr_value = build_loader_repr_from_records(records)\n return records\n", + "value": "from typing import Optional\n\nfrom langchain_core.documents import Document\nfrom langchain_text_splitters import RecursiveCharacterTextSplitter\n\nfrom langflow.custom import CustomComponent\nfrom langflow.schema import Record\nfrom langflow.utils.util import build_loader_repr_from_records, unescape_string\n\n\nclass RecursiveCharacterTextSplitterComponent(CustomComponent):\n display_name: str = \"Recursive Character Text Splitter\"\n description: str = \"Split text into chunks of a specified length.\"\n documentation: str = \"https://docs.langflow.org/components/text-splitters#recursivecharactertextsplitter\"\n\n def build_config(self):\n return {\n \"inputs\": {\n \"display_name\": \"Input\",\n \"info\": \"The texts to split.\",\n \"input_types\": [\"Document\", \"Record\"],\n },\n \"separators\": {\n \"display_name\": \"Separators\",\n \"info\": 'The characters to split on.\\nIf left empty defaults to [\"\\\\n\\\\n\", \"\\\\n\", \" \", \"\"].',\n \"is_list\": True,\n },\n \"chunk_size\": {\n \"display_name\": \"Chunk Size\",\n \"info\": \"The maximum length of each chunk.\",\n \"field_type\": \"int\",\n \"value\": 1000,\n },\n \"chunk_overlap\": {\n \"display_name\": \"Chunk Overlap\",\n \"info\": \"The amount of overlap between chunks.\",\n \"field_type\": \"int\",\n \"value\": 200,\n },\n \"code\": {\"show\": False},\n }\n\n def build(\n self,\n inputs: list[Document],\n separators: Optional[list[str]] = None,\n chunk_size: Optional[int] = 1000,\n chunk_overlap: Optional[int] = 200,\n ) -> list[Record]:\n \"\"\"\n Split text into chunks of a specified length.\n\n Args:\n separators (list[str]): The characters to split on.\n chunk_size (int): The maximum length of each chunk.\n chunk_overlap (int): The amount of overlap between chunks.\n length_function (function): The function to use to calculate the length of the text.\n\n Returns:\n list[str]: The chunks of text.\n \"\"\"\n\n if separators == \"\":\n separators = None\n elif separators:\n # check if the separators list has escaped characters\n # if there are escaped characters, unescape them\n separators = [unescape_string(x) for x in separators]\n\n # Make sure chunk_size and chunk_overlap are ints\n if isinstance(chunk_size, str):\n chunk_size = int(chunk_size)\n if isinstance(chunk_overlap, str):\n chunk_overlap = int(chunk_overlap)\n splitter = RecursiveCharacterTextSplitter(\n separators=separators,\n chunk_size=chunk_size,\n chunk_overlap=chunk_overlap,\n )\n documents = []\n for _input in inputs:\n if isinstance(_input, Record):\n documents.append(_input.to_lc_document())\n else:\n documents.append(_input)\n docs = splitter.split_documents(documents)\n records = self.to_records(docs)\n self.repr_value = build_loader_repr_from_records(records)\n return records\n", "fileTypes": [], "file_path": "", "password": false, @@ -2632,7 +2632,7 @@ "list": false, "show": true, "multiline": true, - "value": "from typing import Any, Dict, List, Optional\n\nfrom langchain_openai.embeddings.base import OpenAIEmbeddings\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.field_typing import Embeddings, NestedDict\nfrom langflow.interface.custom.custom_component import CustomComponent\n\n\nclass OpenAIEmbeddingsComponent(CustomComponent):\n display_name = \"OpenAI Embeddings\"\n description = \"Generate embeddings using OpenAI models.\"\n\n def build_config(self):\n return {\n \"allowed_special\": {\n \"display_name\": \"Allowed Special\",\n \"advanced\": True,\n \"field_type\": \"str\",\n \"is_list\": True,\n },\n \"default_headers\": {\n \"display_name\": \"Default Headers\",\n \"advanced\": True,\n \"field_type\": \"dict\",\n },\n \"default_query\": {\n \"display_name\": \"Default Query\",\n \"advanced\": True,\n \"field_type\": \"NestedDict\",\n },\n \"disallowed_special\": {\n \"display_name\": \"Disallowed Special\",\n \"advanced\": True,\n \"field_type\": \"str\",\n \"is_list\": True,\n },\n \"chunk_size\": {\"display_name\": \"Chunk Size\", \"advanced\": True},\n \"client\": {\"display_name\": \"Client\", \"advanced\": True},\n \"deployment\": {\"display_name\": \"Deployment\", \"advanced\": True},\n \"embedding_ctx_length\": {\n \"display_name\": \"Embedding Context Length\",\n \"advanced\": True,\n },\n \"max_retries\": {\"display_name\": \"Max Retries\", \"advanced\": True},\n \"model\": {\n \"display_name\": \"Model\",\n \"advanced\": False,\n \"options\": [\n \"text-embedding-3-small\",\n \"text-embedding-3-large\",\n \"text-embedding-ada-002\",\n ],\n },\n \"model_kwargs\": {\"display_name\": \"Model Kwargs\", \"advanced\": True},\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"password\": True,\n \"advanced\": True,\n },\n \"openai_api_key\": {\"display_name\": \"OpenAI API Key\", \"password\": True},\n \"openai_api_type\": {\n \"display_name\": \"OpenAI API Type\",\n \"advanced\": True,\n \"password\": True,\n },\n \"openai_api_version\": {\n \"display_name\": \"OpenAI API Version\",\n \"advanced\": True,\n },\n \"openai_organization\": {\n \"display_name\": \"OpenAI Organization\",\n \"advanced\": True,\n },\n \"openai_proxy\": {\"display_name\": \"OpenAI Proxy\", \"advanced\": True},\n \"request_timeout\": {\"display_name\": \"Request Timeout\", \"advanced\": True},\n \"show_progress_bar\": {\n \"display_name\": \"Show Progress Bar\",\n \"advanced\": True,\n },\n \"skip_empty\": {\"display_name\": \"Skip Empty\", \"advanced\": True},\n \"tiktoken_model_name\": {\n \"display_name\": \"TikToken Model Name\",\n \"advanced\": True,\n },\n \"tiktoken_enable\": {\"display_name\": \"TikToken Enable\", \"advanced\": True},\n }\n\n def build(\n self,\n openai_api_key: str,\n default_headers: Optional[Dict[str, str]] = None,\n default_query: Optional[NestedDict] = {},\n allowed_special: List[str] = [],\n disallowed_special: List[str] = [\"all\"],\n chunk_size: int = 1000,\n client: Optional[Any] = None,\n deployment: str = \"text-embedding-ada-002\",\n embedding_ctx_length: int = 8191,\n max_retries: int = 6,\n model: str = \"text-embedding-ada-002\",\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n openai_api_type: Optional[str] = None,\n openai_api_version: Optional[str] = None,\n openai_organization: Optional[str] = None,\n openai_proxy: Optional[str] = None,\n request_timeout: Optional[float] = None,\n show_progress_bar: bool = False,\n skip_empty: bool = False,\n tiktoken_enable: bool = True,\n tiktoken_model_name: Optional[str] = None,\n ) -> Embeddings:\n # This is to avoid errors with Vector Stores (e.g Chroma)\n if disallowed_special == [\"all\"]:\n disallowed_special = \"all\" # type: ignore\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n return OpenAIEmbeddings(\n tiktoken_enabled=tiktoken_enable,\n default_headers=default_headers,\n default_query=default_query,\n allowed_special=set(allowed_special),\n disallowed_special=\"all\",\n chunk_size=chunk_size,\n client=client,\n deployment=deployment,\n embedding_ctx_length=embedding_ctx_length,\n max_retries=max_retries,\n model=model,\n model_kwargs=model_kwargs,\n base_url=openai_api_base,\n api_key=api_key,\n openai_api_type=openai_api_type,\n api_version=openai_api_version,\n organization=openai_organization,\n openai_proxy=openai_proxy,\n timeout=request_timeout,\n show_progress_bar=show_progress_bar,\n skip_empty=skip_empty,\n tiktoken_model_name=tiktoken_model_name,\n )\n", + "value": "from typing import Dict, List, Optional\n\nfrom langchain_openai.embeddings.base import OpenAIEmbeddings\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Embeddings, NestedDict\n\n\nclass OpenAIEmbeddingsComponent(CustomComponent):\n display_name = \"OpenAI Embeddings\"\n description = \"Generate embeddings using OpenAI models.\"\n\n def build_config(self):\n return {\n \"allowed_special\": {\n \"display_name\": \"Allowed Special\",\n \"advanced\": True,\n \"field_type\": \"str\",\n \"is_list\": True,\n },\n \"default_headers\": {\n \"display_name\": \"Default Headers\",\n \"advanced\": True,\n \"field_type\": \"dict\",\n },\n \"default_query\": {\n \"display_name\": \"Default Query\",\n \"advanced\": True,\n \"field_type\": \"NestedDict\",\n },\n \"disallowed_special\": {\n \"display_name\": \"Disallowed Special\",\n \"advanced\": True,\n \"field_type\": \"str\",\n \"is_list\": True,\n },\n \"chunk_size\": {\"display_name\": \"Chunk Size\", \"advanced\": True},\n \"client\": {\"display_name\": \"Client\", \"advanced\": True},\n \"deployment\": {\"display_name\": \"Deployment\", \"advanced\": True},\n \"embedding_ctx_length\": {\n \"display_name\": \"Embedding Context Length\",\n \"advanced\": True,\n },\n \"max_retries\": {\"display_name\": \"Max Retries\", \"advanced\": True},\n \"model\": {\n \"display_name\": \"Model\",\n \"advanced\": False,\n \"options\": [\n \"text-embedding-3-small\",\n \"text-embedding-3-large\",\n \"text-embedding-ada-002\",\n ],\n },\n \"model_kwargs\": {\"display_name\": \"Model Kwargs\", \"advanced\": True},\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"password\": True,\n \"advanced\": True,\n },\n \"openai_api_key\": {\"display_name\": \"OpenAI API Key\", \"password\": True},\n \"openai_api_type\": {\n \"display_name\": \"OpenAI API Type\",\n \"advanced\": True,\n \"password\": True,\n },\n \"openai_api_version\": {\n \"display_name\": \"OpenAI API Version\",\n \"advanced\": True,\n },\n \"openai_organization\": {\n \"display_name\": \"OpenAI Organization\",\n \"advanced\": True,\n },\n \"openai_proxy\": {\"display_name\": \"OpenAI Proxy\", \"advanced\": True},\n \"request_timeout\": {\"display_name\": \"Request Timeout\", \"advanced\": True},\n \"show_progress_bar\": {\n \"display_name\": \"Show Progress Bar\",\n \"advanced\": True,\n },\n \"skip_empty\": {\"display_name\": \"Skip Empty\", \"advanced\": True},\n \"tiktoken_model_name\": {\n \"display_name\": \"TikToken Model Name\",\n \"advanced\": True,\n },\n \"tiktoken_enable\": {\"display_name\": \"TikToken Enable\", \"advanced\": True},\n }\n\n def build(\n self,\n openai_api_key: str,\n default_headers: Optional[Dict[str, str]] = None,\n default_query: Optional[NestedDict] = {},\n allowed_special: List[str] = [],\n disallowed_special: List[str] = [\"all\"],\n chunk_size: int = 1000,\n deployment: str = \"text-embedding-ada-002\",\n embedding_ctx_length: int = 8191,\n max_retries: int = 6,\n model: str = \"text-embedding-ada-002\",\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n openai_api_type: Optional[str] = None,\n openai_api_version: Optional[str] = None,\n openai_organization: Optional[str] = None,\n openai_proxy: Optional[str] = None,\n request_timeout: Optional[float] = None,\n show_progress_bar: bool = False,\n skip_empty: bool = False,\n tiktoken_enable: bool = True,\n tiktoken_model_name: Optional[str] = None,\n ) -> Embeddings:\n # This is to avoid errors with Vector Stores (e.g Chroma)\n if disallowed_special == [\"all\"]:\n disallowed_special = \"all\" # type: ignore\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n return OpenAIEmbeddings(\n tiktoken_enabled=tiktoken_enable,\n default_headers=default_headers,\n default_query=default_query,\n allowed_special=set(allowed_special),\n disallowed_special=\"all\",\n chunk_size=chunk_size,\n deployment=deployment,\n embedding_ctx_length=embedding_ctx_length,\n max_retries=max_retries,\n model=model,\n model_kwargs=model_kwargs,\n base_url=openai_api_base,\n api_key=api_key,\n openai_api_type=openai_api_type,\n api_version=openai_api_version,\n organization=openai_organization,\n openai_proxy=openai_proxy,\n timeout=request_timeout,\n show_progress_bar=show_progress_bar,\n skip_empty=skip_empty,\n tiktoken_model_name=tiktoken_model_name,\n )\n", "fileTypes": [], "file_path": "", "password": false, From cc4975733344e7bf2aa610eaa7587e8c8f3a0b8a Mon Sep 17 00:00:00 2001 From: ogabrielluiz Date: Thu, 30 May 2024 16:42:01 -0300 Subject: [PATCH 025/701] feat: Update API modal utils to use InputFieldType The code changes in this commit update the API modal utils to use the `InputFieldType` type instead of the deprecated `TemplateVariableType`. This change ensures that the codebase is up to date with the latest API types and improves the accuracy and clarity of the code. --- .../apiModal/utils/get-changes-types.ts | 4 +- .../src/modals/apiModal/utils/get-value.ts | 6 +- .../src/modals/apiModal/views/index.tsx | 4 +- src/frontend/src/types/api/index.ts | 5 +- src/frontend/src/types/components/index.ts | 8 +- src/frontend/src/utils/reactflowUtils.ts | 112 +++++++++--------- src/frontend/src/utils/utils.ts | 26 ++-- 7 files changed, 82 insertions(+), 83 deletions(-) diff --git a/src/frontend/src/modals/apiModal/utils/get-changes-types.ts b/src/frontend/src/modals/apiModal/utils/get-changes-types.ts index e8e912ff3..6a35446d5 100644 --- a/src/frontend/src/modals/apiModal/utils/get-changes-types.ts +++ b/src/frontend/src/modals/apiModal/utils/get-changes-types.ts @@ -1,9 +1,9 @@ -import { TemplateVariableType } from "../../../types/api"; +import { InputFieldType } from "../../../types/api"; import { convertArrayToObj } from "../../../utils/reactflowUtils"; export const getChangesType = ( changes: string | string[] | boolean | number | Object[] | Object, - template: TemplateVariableType, + template: InputFieldType ) => { if (typeof changes === "string" && template.type === "float") { changes = parseFloat(changes); diff --git a/src/frontend/src/modals/apiModal/utils/get-value.ts b/src/frontend/src/modals/apiModal/utils/get-value.ts index df8e5bdde..108ac09e3 100644 --- a/src/frontend/src/modals/apiModal/utils/get-value.ts +++ b/src/frontend/src/modals/apiModal/utils/get-value.ts @@ -1,11 +1,11 @@ -import { TemplateVariableType } from "../../../types/api"; +import { InputFieldType } from "../../../types/api"; import { NodeType } from "../../../types/flow"; export const getValue = ( value: string, node: NodeType, - template: TemplateVariableType, - tweak: Object[], + template: InputFieldType, + tweak: Object[] ) => { let returnValue = value ?? ""; diff --git a/src/frontend/src/modals/apiModal/views/index.tsx b/src/frontend/src/modals/apiModal/views/index.tsx index a0a614b06..7ab4b504c 100644 --- a/src/frontend/src/modals/apiModal/views/index.tsx +++ b/src/frontend/src/modals/apiModal/views/index.tsx @@ -10,7 +10,7 @@ import IconComponent from "../../../components/genericIconComponent"; import { EXPORT_CODE_DIALOG } from "../../../constants/constants"; import { AuthContext } from "../../../contexts/authContext"; import { useTweaksStore } from "../../../stores/tweaksStore"; -import { TemplateVariableType } from "../../../types/api"; +import { InputFieldType } from "../../../types/api"; import { uniqueTweakType } from "../../../types/components"; import { FlowType } from "../../../types/flow/index"; import BaseModal from "../../baseModal"; @@ -128,7 +128,7 @@ const ApiModal = forwardRef( async function buildTweakObject( tw: string, changes: string | string[] | boolean | number | Object[] | Object, - template: TemplateVariableType + template: InputFieldType ) { changes = getChangesType(changes, template); diff --git a/src/frontend/src/types/api/index.ts b/src/frontend/src/types/api/index.ts index 0fb0595b2..cdc4da207 100644 --- a/src/frontend/src/types/api/index.ts +++ b/src/frontend/src/types/api/index.ts @@ -5,7 +5,7 @@ export type APIDataType = { [key: string]: APIKindType }; export type APIObjectType = { [key: string]: APIKindType }; export type APIKindType = { [key: string]: APIClassType }; export type APITemplateType = { - [key: string]: TemplateVariableType; + [key: string]: InputFieldType; }; export type CustomFieldsType = { @@ -44,7 +44,7 @@ export type APIClassType = { | Array<{ types: Array; selected?: string }>; }; -export type TemplateVariableType = { +export type InputFieldType = { type: string; required: boolean; placeholder?: string; @@ -63,6 +63,7 @@ export type TemplateVariableType = { refresh_button_text?: string; [key: string]: any; }; + export type sendAllProps = { nodes: Node[]; edges: Edge[]; diff --git a/src/frontend/src/types/components/index.ts b/src/frontend/src/types/components/index.ts index 5838ac73d..054ed7543 100644 --- a/src/frontend/src/types/components/index.ts +++ b/src/frontend/src/types/components/index.ts @@ -1,7 +1,7 @@ import { ReactElement, ReactNode, SetStateAction } from "react"; import { ReactFlowJsonObject } from "reactflow"; import { InputOutput } from "../../constants/enums"; -import { APIClassType, APITemplateType, TemplateVariableType } from "../api"; +import { APIClassType, APITemplateType, InputFieldType } from "../api"; import { ChatMessageType } from "../chat"; import { FlowStyleType, FlowType, NodeDataType, NodeType } from "../flow/index"; import { sourceHandleType, targetHandleType } from "./../flow/index"; @@ -669,13 +669,13 @@ export type codeTabsPropsType = { getValue?: ( value: string, node: NodeType, - template: TemplateVariableType, + template: InputFieldType, tweak: tweakType ) => string; buildTweakObject?: ( tw: string, changes: string | string[] | boolean | number | Object[] | Object, - template: TemplateVariableType + template: InputFieldType ) => Promise; }; activeTweaks?: boolean; @@ -742,7 +742,7 @@ export type chatViewProps = { }; export type IOFileInputProps = { - field: TemplateVariableType; + field: InputFieldType; updateValue: (e: any, type: string) => void; }; diff --git a/src/frontend/src/utils/reactflowUtils.ts b/src/frontend/src/utils/reactflowUtils.ts index a51baee23..963063eff 100644 --- a/src/frontend/src/utils/reactflowUtils.ts +++ b/src/frontend/src/utils/reactflowUtils.ts @@ -23,7 +23,7 @@ import { APIKindType, APIObjectType, APITemplateType, - TemplateVariableType, + InputFieldType, } from "../types/api"; import { FlowType, @@ -102,18 +102,18 @@ export function unselectAllNodes({ updateNodes, data }: unselectAllNodesType) { export function isValidConnection( { source, target, sourceHandle, targetHandle }: Connection, nodes: Node[], - edges: Edge[], + edges: Edge[] ) { const targetHandleObject: targetHandleType = scapeJSONParse(targetHandle!); const sourceHandleObject: sourceHandleType = scapeJSONParse(sourceHandle!); if ( targetHandleObject.inputTypes?.some( - (n) => n === sourceHandleObject.dataType, + (n) => n === sourceHandleObject.dataType ) || sourceHandleObject.baseClasses.some( (t) => targetHandleObject.inputTypes?.some((n) => n === t) || - t === targetHandleObject.type, + t === targetHandleObject.type ) ) { let targetNode = nodes.find((node) => node.id === target!)?.data?.node; @@ -146,7 +146,7 @@ export function removeApiKeys(flow: FlowType): FlowType { export function updateTemplate( reference: APITemplateType, - objectToUpdate: APITemplateType, + objectToUpdate: APITemplateType ): APITemplateType { let clonedObject: APITemplateType = cloneDeep(reference); @@ -206,7 +206,7 @@ export const processDataFromFlow = (flow: FlowType, refreshIds = true) => { export function updateIds( { edges, nodes }: { edges: Edge[]; nodes: Node[] }, - selection?: { edges: Edge[]; nodes: Node[] }, + selection?: { edges: Edge[]; nodes: Node[] } ) { let idsMap = {}; const selectionIds = selection?.nodes.map((n) => n.id); @@ -234,7 +234,7 @@ export function updateIds( edge.source = idsMap[edge.source]; edge.target = idsMap[edge.target]; const sourceHandleObject: sourceHandleType = scapeJSONParse( - edge.sourceHandle!, + edge.sourceHandle! ); edge.sourceHandle = scapedJSONStringfy({ ...sourceHandleObject, @@ -244,7 +244,7 @@ export function updateIds( edge.data.sourceHandle.id = edge.source; } const targetHandleObject: targetHandleType = scapeJSONParse( - edge.targetHandle!, + edge.targetHandle! ); edge.targetHandle = scapedJSONStringfy({ ...targetHandleObject, @@ -290,11 +290,11 @@ export function validateNode(node: NodeType, edges: Edge[]): Array { (scapeJSONParse(edge.targetHandle!) as targetHandleType).fieldName === t && (scapeJSONParse(edge.targetHandle!) as targetHandleType).id === - node.id, + node.id ) ) { errors.push( - `${displayName || type} is missing ${getFieldTitle(template, t)}.`, + `${displayName || type} is missing ${getFieldTitle(template, t)}.` ); } else if ( template[t].type === "dict" && @@ -308,15 +308,15 @@ export function validateNode(node: NodeType, edges: Edge[]): Array { errors.push( `${displayName || type} (${getFieldTitle( template, - t, - )}) contains duplicate keys with the same values.`, + t + )}) contains duplicate keys with the same values.` ); if (hasEmptyKey(template[t].value)) errors.push( `${displayName || type} (${getFieldTitle( template, - t, - )}) field must not be empty.`, + t + )}) field must not be empty.` ); } return errors; @@ -325,7 +325,7 @@ export function validateNode(node: NodeType, edges: Edge[]): Array { export function validateNodes( nodes: Node[], - edges: Edge[], + edges: Edge[] ): // this returns an array of tuples with the node id and the errors Array<{ id: string; errors: Array }> { if (nodes.length === 0) { @@ -346,7 +346,7 @@ export function updateEdges(edges: Edge[]) { if (edges) edges.forEach((edge) => { const targetHandleObject: targetHandleType = scapeJSONParse( - edge.targetHandle!, + edge.targetHandle! ); edge.className = "stroke-gray-900 stroke-connection"; }); @@ -426,11 +426,11 @@ export function updateNewOutput({ nodes, edges }: updateEdgesHandleIdsType) { if (newTargetHandle.inputTypes && newTargetHandle.inputTypes.length > 0) { //conjuction subtraction intersection = newSourceHandle.baseClasses.filter((type) => - newTargetHandle.inputTypes!.includes(type), + newTargetHandle.inputTypes!.includes(type) ); } else { intersection = newSourceHandle.baseClasses.filter( - (type) => type === newTargetHandle.type, + (type) => type === newTargetHandle.type ); } const selected = intersection[0]; @@ -462,7 +462,7 @@ export function handleKeyDown( | React.KeyboardEvent | React.KeyboardEvent, inputValue: string | string[] | null, - block: string, + block: string ) { //condition to fix bug control+backspace on Windows/Linux if ( @@ -487,7 +487,7 @@ export function handleKeyDown( } export function handleOnlyIntegerInput( - event: React.KeyboardEvent, + event: React.KeyboardEvent ) { if ( event.key === "." || @@ -503,7 +503,7 @@ export function handleOnlyIntegerInput( export function getConnectedNodes( edge: Edge, - nodes: Array, + nodes: Array ): Array { const sourceId = edge.source; const targetId = edge.target; @@ -604,7 +604,7 @@ export function checkOldEdgesHandles(edges: Edge[]): boolean { !edge.sourceHandle || !edge.targetHandle || !edge.sourceHandle.includes("{") || - !edge.targetHandle.includes("{"), + !edge.targetHandle.includes("{") ); } @@ -631,7 +631,7 @@ export function customStringify(obj: any): string { const keys = Object.keys(obj).sort(); const keyValuePairs = keys.map( - (key) => `"${key}":${customStringify(obj[key])}`, + (key) => `"${key}":${customStringify(obj[key])}` ); return `{${keyValuePairs.join(",")}}`; } @@ -660,7 +660,7 @@ export function getHandleId( source: string, sourceHandle: string, target: string, - targetHandle: string, + targetHandle: string ) { return ( "reactflow__edge-" + source + sourceHandle + "-" + target + targetHandle @@ -671,7 +671,7 @@ export function generateFlow( selection: OnSelectionChangeParams, nodes: Node[], edges: Edge[], - name: string, + name: string ): generateFlowType { const newFlowData = { nodes, edges, viewport: { zoom: 1, x: 0, y: 0 } }; const uid = new ShortUniqueId({ length: 5 }); @@ -680,7 +680,7 @@ export function generateFlow( newFlowData.edges = selection.edges.filter( (edge) => selection.nodes.some((node) => node.id === edge.target) && - selection.nodes.some((node) => node.id === edge.source), + selection.nodes.some((node) => node.id === edge.source) ); newFlowData.nodes = selection.nodes; @@ -701,7 +701,7 @@ export function generateFlow( (edge) => (selection.nodes.some((node) => node.id === edge.target) || selection.nodes.some((node) => node.id === edge.source)) && - newFlowData.edges.every((e) => e.id !== edge.id), + newFlowData.edges.every((e) => e.id !== edge.id) ), }; } @@ -712,13 +712,13 @@ export function reconnectEdges(groupNode: NodeType, excludedEdges: Edge[]) { const { nodes, edges } = groupNode.data.node!.flow!.data!; const lastNode = findLastNode(groupNode.data.node!.flow!.data!); newEdges = newEdges.filter( - (e) => !(nodes.some((n) => n.id === e.source) && e.source !== lastNode?.id), + (e) => !(nodes.some((n) => n.id === e.source) && e.source !== lastNode?.id) ); newEdges.forEach((edge) => { if (lastNode && edge.source === lastNode.id) { edge.source = groupNode.id; let newSourceHandle: sourceHandleType = scapeJSONParse( - edge.sourceHandle!, + edge.sourceHandle! ); newSourceHandle.id = groupNode.id; edge.sourceHandle = scapedJSONStringfy(newSourceHandle); @@ -745,7 +745,7 @@ export function reconnectEdges(groupNode: NodeType, excludedEdges: Edge[]) { export function filterFlow( selection: OnSelectionChangeParams, setNodes: (update: Node[] | ((oldState: Node[]) => Node[])) => void, - setEdges: (update: Edge[] | ((oldState: Edge[]) => Edge[])) => void, + setEdges: (update: Edge[] | ((oldState: Edge[]) => Edge[])) => void ) { setNodes((nodes) => nodes.filter((node) => !selection.nodes.includes(node))); setEdges((edges) => edges.filter((edge) => !selection.edges.includes(edge))); @@ -783,7 +783,7 @@ export function updateFlowPosition(NewPosition: XYPosition, flow: FlowType) { export function concatFlows( flow: FlowType, setNodes: (update: Node[] | ((oldState: Node[]) => Node[])) => void, - setEdges: (update: Edge[] | ((oldState: Edge[]) => Edge[])) => void, + setEdges: (update: Edge[] | ((oldState: Edge[]) => Edge[])) => void ) { const { nodes, edges } = flow.data!; setNodes((old) => [...old, ...nodes]); @@ -792,7 +792,7 @@ export function concatFlows( export function validateSelection( selection: OnSelectionChangeParams, - edges: Edge[], + edges: Edge[] ): Array { const clonedSelection = cloneDeep(selection); const clonedEdges = cloneDeep(edges); @@ -806,7 +806,7 @@ export function validateSelection( let nodesSet = new Set(clonedSelection.nodes.map((n) => n.id)); // then filter the edges that are connected to the nodes in the set let connectedEdges = clonedSelection.edges.filter( - (e) => nodesSet.has(e.source) && nodesSet.has(e.target), + (e) => nodesSet.has(e.source) && nodesSet.has(e.target) ); // add the edges to the selection clonedSelection.edges = connectedEdges; @@ -820,17 +820,17 @@ export function validateSelection( clonedSelection.nodes.some( (node) => isInputNode(node.data as NodeDataType) || - isOutputNode(node.data as NodeDataType), + isOutputNode(node.data as NodeDataType) ) ) { errorsArray.push( - "Please select only nodes that are not input or output nodes", + "Please select only nodes that are not input or output nodes" ); } //check if there are two or more nodes with free outputs if ( clonedSelection.nodes.filter( - (n) => !clonedSelection.edges.some((e) => e.source === n.id), + (n) => !clonedSelection.edges.some((e) => e.source === n.id) ).length > 1 ) { errorsArray.push("Please select only one node with free outputs"); @@ -841,7 +841,7 @@ export function validateSelection( clonedSelection.nodes.some( (node) => !clonedSelection.edges.some((edge) => edge.target === node.id) && - !clonedSelection.edges.some((edge) => edge.source === node.id), + !clonedSelection.edges.some((edge) => edge.source === node.id) ) ) { errorsArray.push("Please select only nodes that are connected"); @@ -898,8 +898,8 @@ export function mergeNodeTemplates({ nodeTemplate[key].display_name ? nodeTemplate[key].display_name : nodeTemplate[key].name - ? toTitleCase(nodeTemplate[key].name) - : toTitleCase(key); + ? toTitleCase(nodeTemplate[key].name) + : toTitleCase(key); } } }); @@ -909,8 +909,8 @@ export function mergeNodeTemplates({ function isHandleConnected( edges: Edge[], key: string, - field: TemplateVariableType, - nodeId: string, + field: InputFieldType, + nodeId: string ) { /* this function receives a flow and a handleId and check if there is a connection with this handle @@ -926,7 +926,7 @@ function isHandleConnected( id: nodeId, proxy: { id: field.proxy!.id, field: field.proxy!.field }, inputTypes: field.input_types, - } as targetHandleType), + } as targetHandleType) ) ) { return true; @@ -941,7 +941,7 @@ function isHandleConnected( fieldName: key, id: nodeId, inputTypes: field.input_types, - } as targetHandleType), + } as targetHandleType) ) ) { return true; @@ -964,7 +964,7 @@ export function generateNodeTemplate(Flow: FlowType) { export function generateNodeFromFlow( flow: FlowType, - getNodeId: (type: string) => string, + getNodeId: (type: string) => string ): NodeType { const { nodes } = flow.data!; const outputNode = cloneDeep(findLastNode(flow.data!)); @@ -995,7 +995,7 @@ export function generateNodeFromFlow( export function connectedInputNodesOnHandle( nodeId: string, handleId: string, - { nodes, edges }: { nodes: NodeType[]; edges: Edge[] }, + { nodes, edges }: { nodes: NodeType[]; edges: Edge[] } ) { const connectedNodes: Array<{ name: string; id: string; isGroup: boolean }> = []; @@ -1032,7 +1032,7 @@ export function connectedInputNodesOnHandle( export function updateProxyIdsOnTemplate( template: APITemplateType, - idsMap: { [key: string]: string }, + idsMap: { [key: string]: string } ) { Object.keys(template).forEach((key) => { if (template[key].proxy && idsMap[template[key].proxy!.id]) { @@ -1043,7 +1043,7 @@ export function updateProxyIdsOnTemplate( export function updateEdgesIds( edges: Edge[], - idsMap: { [key: string]: string }, + idsMap: { [key: string]: string } ) { edges.forEach((edge) => { let targetHandle: targetHandleType = edge.data.targetHandle; @@ -1084,7 +1084,7 @@ export function expandGroupNode( nodes: Node[], edges: Edge[], setNodes: (update: Node[] | ((oldState: Node[]) => Node[])) => void, - setEdges: (update: Edge[] | ((oldState: Edge[]) => Edge[])) => void, + setEdges: (update: Edge[] | ((oldState: Edge[]) => Edge[])) => void ) { const idsMap = updateIds(flow!.data!); updateProxyIdsOnTemplate(template, idsMap); @@ -1127,7 +1127,7 @@ export function expandGroupNode( const lastNode = cloneDeep(findLastNode(flow!.data!)); newEdge.source = lastNode!.id; let newSourceHandle: sourceHandleType = scapeJSONParse( - newEdge.sourceHandle!, + newEdge.sourceHandle! ); newSourceHandle.id = lastNode!.id; newEdge.data.sourceHandle = newSourceHandle; @@ -1184,7 +1184,7 @@ export function expandGroupNode( export function getGroupStatus( flow: FlowType, - ssData: { [key: string]: { valid: boolean; params: string } }, + ssData: { [key: string]: { valid: boolean; params: string } } ) { let status = { valid: true, params: SUCCESS_BUILD }; const { nodes } = flow.data!; @@ -1203,7 +1203,7 @@ export function getGroupStatus( export function createFlowComponent( nodeData: NodeDataType, - version: string, + version: string ): FlowType { const flowNode: FlowType = { data: { @@ -1239,7 +1239,7 @@ export function downloadNode(NodeFLow: FlowType) { export function updateComponentNameAndType( data: any, - component: NodeDataType, + component: NodeDataType ) {} export function removeFileNameFromComponents(flow: FlowType) { @@ -1313,7 +1313,7 @@ export function extractFieldsFromComponenents(data: APIObjectType) { export function downloadFlow( flow: FlowType, flowName: string, - flowDescription?: string, + flowDescription?: string ) { let clonedFlow = cloneDeep(flow); removeFileNameFromComponents(clonedFlow); @@ -1323,7 +1323,7 @@ export function downloadFlow( ...clonedFlow, name: flowName, description: flowDescription, - }), + }) )}`; // create a link element and set its properties @@ -1338,7 +1338,7 @@ export function downloadFlow( export function downloadFlows() { downloadFlowsFromDatabase().then((flows) => { const jsonString = `data:text/json;chatset=utf-8,${encodeURIComponent( - JSON.stringify(flows), + JSON.stringify(flows) )}`; // create a link element and set its properties @@ -1362,7 +1362,7 @@ export function getRandomDescription(): string { export const createNewFlow = ( flowData: ReactFlowJsonObject, flow: FlowType, - folderId: string, + folderId: string ) => { return { description: flow?.description ?? getRandomDescription(), diff --git a/src/frontend/src/utils/utils.ts b/src/frontend/src/utils/utils.ts index 223586986..34a7f1f42 100644 --- a/src/frontend/src/utils/utils.ts +++ b/src/frontend/src/utils/utils.ts @@ -2,9 +2,7 @@ import { ColDef, ColGroupDef } from "ag-grid-community"; import clsx, { ClassValue } from "clsx"; import { twMerge } from "tailwind-merge"; import TableAutoCellRender from "../components/tableAutoCellRender"; -import { priorityFields } from "../constants/constants"; -import { ADJECTIVES, DESCRIPTIONS, NOUNS } from "../flow_constants"; -import { APIDataType, TemplateVariableType } from "../types/api"; +import { APIDataType, InputFieldType } from "../types/api"; import { groupedObjType, nodeGroupedObjType, @@ -57,7 +55,7 @@ export function normalCaseToSnakeCase(str: string): string { export function toTitleCase( str: string | undefined, - isNodeField?: boolean, + isNodeField?: boolean ): string { if (!str) return ""; let result = str @@ -66,7 +64,7 @@ export function toTitleCase( if (isNodeField) return word; if (index === 0) { return checkUpperWords( - word[0].toUpperCase() + word.slice(1).toLowerCase(), + word[0].toUpperCase() + word.slice(1).toLowerCase() ); } return checkUpperWords(word.toLowerCase()); @@ -79,7 +77,7 @@ export function toTitleCase( if (isNodeField) return word; if (index === 0) { return checkUpperWords( - word[0].toUpperCase() + word.slice(1).toLowerCase(), + word[0].toUpperCase() + word.slice(1).toLowerCase() ); } return checkUpperWords(word.toLowerCase()); @@ -183,7 +181,7 @@ export function checkLocalStorageKey(key: string): boolean { export function IncrementObjectKey( object: object, - key: string, + key: string ): { newKey: string; increment: number } { let count = 1; const type = removeCountFromString(key); @@ -218,7 +216,7 @@ export function groupByFamily( data: APIDataType, baseClasses: string, left: boolean, - flow?: NodeType[], + flow?: NodeType[] ): groupedObjType[] { const baseClassesSet = new Set(baseClasses.split("\n")); let arrOfPossibleInputs: Array<{ @@ -236,7 +234,7 @@ export function groupByFamily( let checkedNodes = new Map(); const excludeTypes = new Set(["bool", "float", "code", "file", "int"]); - const checkBaseClass = (template: TemplateVariableType) => { + const checkBaseClass = (template: InputFieldType) => { return ( template.type && template.show && @@ -244,7 +242,7 @@ export function groupByFamily( baseClassesSet.has(template.type)) || (template.input_types && template.input_types.some((inputType) => - baseClassesSet.has(inputType), + baseClassesSet.has(inputType) ))) ); }; @@ -264,7 +262,7 @@ export function groupByFamily( hasBaseClassInBaseClasses: foundNode?.hasBaseClassInBaseClasses || nodeData.node!.base_classes.some((baseClass) => - baseClassesSet.has(baseClass), + baseClassesSet.has(baseClass) ), //seta como anterior ou verifica se o node tem base class displayName: nodeData.node?.display_name, }); @@ -281,10 +279,10 @@ export function groupByFamily( if (!foundNode) { foundNode = { hasBaseClassInTemplate: Object.values(node!.template).some( - checkBaseClass, + checkBaseClass ), hasBaseClassInBaseClasses: node!.base_classes.some((baseClass) => - baseClassesSet.has(baseClass), + baseClassesSet.has(baseClass) ), displayName: node?.display_name, }; @@ -353,7 +351,7 @@ export function isTimeStampString(str: string): boolean { export function extractColumnsFromRows( rows: object[], - mode: "intersection" | "union", + mode: "intersection" | "union" ): (ColDef | ColGroupDef)[] { const columnsKeys: { [key: string]: ColDef | ColGroupDef } = {}; if (rows.length === 0) { From 32726cba305f64f6d084532fb4df80b3f106057e Mon Sep 17 00:00:00 2001 From: ogabrielluiz Date: Thu, 30 May 2024 16:43:03 -0300 Subject: [PATCH 026/701] added OutputFieldType --- src/frontend/src/types/api/index.ts | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/src/frontend/src/types/api/index.ts b/src/frontend/src/types/api/index.ts index cdc4da207..273da960d 100644 --- a/src/frontend/src/types/api/index.ts +++ b/src/frontend/src/types/api/index.ts @@ -28,7 +28,7 @@ export type APIClassType = { documentation: string; error?: string; official?: boolean; - outputs?: Array<{ types: Array; selected?: string }>; + outputs?: Array; frozen?: boolean; flow?: FlowType; field_order?: string[]; @@ -63,7 +63,11 @@ export type InputFieldType = { refresh_button_text?: string; [key: string]: any; }; - +export type OutputFieldType = { + types: Array; + selected?: string; + name: string; +}; export type sendAllProps = { nodes: Node[]; edges: Edge[]; From 5c818f0b6019de6c155403f6067a4ac37c54adcd Mon Sep 17 00:00:00 2001 From: ogabrielluiz Date: Thu, 30 May 2024 16:46:45 -0300 Subject: [PATCH 027/701] feat: Update field types in prompt and formatter modules This commit updates the field types in the `prompt.py` and `formatter/base.py` modules. The `DefaultPromptField` class in `prompt.py` now inherits from `InputField` instead of `TemplateField`. Similarly, the `format` method in the `FieldFormatter` class in `formatter/base.py` now accepts an `InputField` parameter instead of a `TemplateField` parameter. These changes ensure consistency and improve the accuracy of the code. --- docs/static/data/AstraDB-RAG-Flows.json | 2 +- .../components/experimental/SubFlow.py | 6 +- .../components/helpers/CreateRecord.py | 4 +- .../base/langflow/components/inputs/Prompt.py | 6 +- src/backend/base/langflow/custom/utils.py | 20 +- .../base/langflow/field_typing/__init__.py | 8 +- .../Basic Prompting (Hello, world!).json | 2 +- .../Langflow Blog Writter.json | 2 +- .../Langflow Document QA.json | 2 +- .../Langflow Memory Conversation.json | 2 +- .../Langflow Prompt Chaining.json | 4 +- .../VectorStore-RAG-Flows.json | 2 +- .../base/langflow/template/field/base.py | 16 +- .../base/langflow/template/field/prompt.py | 4 +- .../langflow/template/frontend_node/base.py | 22 +- .../frontend_node/custom_components.py | 4 +- .../template/frontend_node/formatter/base.py | 4 +- .../formatter/field_formatters.py | 32 +- .../base/langflow/template/template/base.py | 12 +- src/frontend/harFiles/langflow.har | 1475 +++++++++++++---- tests/data/component_with_templatefield.py | 4 +- tests/test_frontend_nodes.py | 13 +- 22 files changed, 1275 insertions(+), 371 deletions(-) diff --git a/docs/static/data/AstraDB-RAG-Flows.json b/docs/static/data/AstraDB-RAG-Flows.json index 10dafa85f..2b5ac607e 100644 --- a/docs/static/data/AstraDB-RAG-Flows.json +++ b/docs/static/data/AstraDB-RAG-Flows.json @@ -1108,7 +1108,7 @@ "list": false, "show": true, "multiline": true, - "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.field_typing import Prompt, TemplateField, Text\nfrom langflow.interface.custom.custom_component import CustomComponent\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": TemplateField(display_name=\"Template\"),\n \"code\": TemplateField(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n", + "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.field_typing import Prompt, InputField, Text\nfrom langflow.interface.custom.custom_component import CustomComponent\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": InputField(display_name=\"Template\"),\n \"code\": InputField(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n", "fileTypes": [], "file_path": "", "password": false, diff --git a/src/backend/base/langflow/components/experimental/SubFlow.py b/src/backend/base/langflow/components/experimental/SubFlow.py index 76a9538a4..86deaf8ba 100644 --- a/src/backend/base/langflow/components/experimental/SubFlow.py +++ b/src/backend/base/langflow/components/experimental/SubFlow.py @@ -10,7 +10,7 @@ from langflow.graph.vertex.base import Vertex from langflow.helpers.flow import get_flow_inputs from langflow.schema import Record from langflow.schema.dotdict import dotdict -from langflow.template.field.base import TemplateField +from langflow.template.field.base import InputField class SubFlowComponent(CustomComponent): @@ -54,9 +54,9 @@ class SubFlowComponent(CustomComponent): return build_config def add_inputs_to_build_config(self, inputs: List[Vertex], build_config: dotdict): - new_fields: list[TemplateField] = [] + new_fields: list[InputField] = [] for vertex in inputs: - field = TemplateField( + field = InputField( display_name=vertex.display_name, name=vertex.id, info=vertex.description, diff --git a/src/backend/base/langflow/components/helpers/CreateRecord.py b/src/backend/base/langflow/components/helpers/CreateRecord.py index a4a02e76b..e37569b6b 100644 --- a/src/backend/base/langflow/components/helpers/CreateRecord.py +++ b/src/backend/base/langflow/components/helpers/CreateRecord.py @@ -4,7 +4,7 @@ from langflow.custom import CustomComponent from langflow.field_typing.range_spec import RangeSpec from langflow.schema import Record from langflow.schema.dotdict import dotdict -from langflow.template.field.base import TemplateField +from langflow.template.field.base import InputField class CreateRecordComponent(CustomComponent): @@ -35,7 +35,7 @@ class CreateRecordComponent(CustomComponent): field = existing_fields[key] build_config[key] = field else: - field = TemplateField( + field = InputField( display_name=f"Field {i}", name=key, info=f"Key for field {i}.", diff --git a/src/backend/base/langflow/components/inputs/Prompt.py b/src/backend/base/langflow/components/inputs/Prompt.py index 2c76e6132..b0f7930db 100644 --- a/src/backend/base/langflow/components/inputs/Prompt.py +++ b/src/backend/base/langflow/components/inputs/Prompt.py @@ -1,7 +1,7 @@ from langchain_core.prompts import PromptTemplate from langflow.custom import CustomComponent -from langflow.field_typing import Prompt, TemplateField, Text +from langflow.field_typing import InputField, Prompt, Text class PromptComponent(CustomComponent): @@ -11,8 +11,8 @@ class PromptComponent(CustomComponent): def build_config(self): return { - "template": TemplateField(display_name="Template"), - "code": TemplateField(advanced=True), + "template": InputField(display_name="Template"), + "code": InputField(advanced=True), } def build( diff --git a/src/backend/base/langflow/custom/utils.py b/src/backend/base/langflow/custom/utils.py index 5f7af956e..d2a769aea 100644 --- a/src/backend/base/langflow/custom/utils.py +++ b/src/backend/base/langflow/custom/utils.py @@ -22,7 +22,7 @@ from langflow.custom.eval import eval_custom_component_code from langflow.custom.schema import MissingDefault from langflow.field_typing.range_spec import RangeSpec from langflow.schema import dotdict -from langflow.template.field.base import TemplateField +from langflow.template.field.base import InputField from langflow.template.frontend_node.custom_components import CustomComponentFrontendNode from langflow.utils import validate from langflow.utils.util import get_base_classes @@ -169,7 +169,7 @@ def add_new_custom_field( required = field_config.pop("required", field_required) placeholder = field_config.pop("placeholder", "") - new_field = TemplateField( + new_field = InputField( name=field_name, field_type=field_type, value=field_value, @@ -231,9 +231,9 @@ def add_extra_fields(frontend_node, field_config, function_args): ) -def get_field_dict(field: Union[TemplateField, dict]): - """Get the field dictionary from a TemplateField or a dict""" - if isinstance(field, TemplateField): +def get_field_dict(field: Union[InputField, dict]): + """Get the field dictionary from a InputField or a dict""" + if isinstance(field, InputField): return dotdict(field.model_dump(by_alias=True, exclude_none=True)) return field @@ -266,8 +266,8 @@ def run_build_config( build_config: Dict = custom_instance.build_config() for field_name, field in build_config.copy().items(): - # Allow user to build TemplateField as well - # as a dict with the same keys as TemplateField + # Allow user to build InputField as well + # as a dict with the same keys as InputField field_dict = get_field_dict(field) # Let's check if "rangeSpec" is a RangeSpec object if "rangeSpec" in field_dict and isinstance(field_dict["rangeSpec"], RangeSpec): @@ -305,7 +305,7 @@ def build_frontend_node(template_config): def add_code_field(frontend_node: CustomComponentFrontendNode, raw_code, field_config): - code_field = TemplateField( + code_field = InputField( dynamic=True, required=True, placeholder="", @@ -429,9 +429,9 @@ def update_field_dict( return build_config -def sanitize_field_config(field_config: Union[Dict, TemplateField]): +def sanitize_field_config(field_config: Union[Dict, InputField]): # If any of the already existing keys are in field_config, remove them - if isinstance(field_config, TemplateField): + if isinstance(field_config, InputField): field_dict = field_config.to_dict() else: field_dict = field_config diff --git a/src/backend/base/langflow/field_typing/__init__.py b/src/backend/base/langflow/field_typing/__init__.py index 15ce03693..d037aa371 100644 --- a/src/backend/base/langflow/field_typing/__init__.py +++ b/src/backend/base/langflow/field_typing/__init__.py @@ -30,14 +30,14 @@ from .range_spec import RangeSpec def _import_template_field(): - from langflow.template.field.base import TemplateField + from langflow.template.field.base import InputField - return TemplateField + return InputField def __getattr__(name: str) -> Any: # This is to avoid circular imports - if name == "TemplateField": + if name == "InputField": return _import_template_field() elif name == "RangeSpec": return RangeSpec @@ -73,6 +73,6 @@ __all__ = [ "ChatPromptTemplate", "Prompt", "RangeSpec", - "TemplateField", + "InputField", "Code", ] diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting (Hello, world!).json b/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting (Hello, world!).json index bdc6da29d..786eac776 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting (Hello, world!).json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting (Hello, world!).json @@ -20,7 +20,7 @@ "list": false, "show": true, "multiline": true, - "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Prompt, TemplateField, Text\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": TemplateField(display_name=\"Template\"),\n \"code\": TemplateField(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n", + "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import InputField, Prompt, Text\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": InputField(display_name=\"Template\"),\n \"code\": InputField(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n", "fileTypes": [], "file_path": "", "password": false, diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Blog Writter.json b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Blog Writter.json index bd6013ad5..9f4b98176 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Blog Writter.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Blog Writter.json @@ -20,7 +20,7 @@ "list": false, "show": true, "multiline": true, - "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Prompt, TemplateField, Text\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": TemplateField(display_name=\"Template\"),\n \"code\": TemplateField(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n", + "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import InputField, Prompt, Text\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": InputField(display_name=\"Template\"),\n \"code\": InputField(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n", "fileTypes": [], "file_path": "", "password": false, diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Document QA.json b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Document QA.json index b7228b9e7..c14d21d1a 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Document QA.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Document QA.json @@ -20,7 +20,7 @@ "list": false, "show": true, "multiline": true, - "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Prompt, TemplateField, Text\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": TemplateField(display_name=\"Template\"),\n \"code\": TemplateField(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n", + "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import InputField, Prompt, Text\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": InputField(display_name=\"Template\"),\n \"code\": InputField(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n", "fileTypes": [], "file_path": "", "password": false, diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Memory Conversation.json b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Memory Conversation.json index 235af8f6f..50142c4a1 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Memory Conversation.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Memory Conversation.json @@ -583,7 +583,7 @@ "list": false, "show": true, "multiline": true, - "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Prompt, TemplateField, Text\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": TemplateField(display_name=\"Template\"),\n \"code\": TemplateField(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n", + "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import InputField, Prompt, Text\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": InputField(display_name=\"Template\"),\n \"code\": InputField(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n", "fileTypes": [], "file_path": "", "password": false, diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Prompt Chaining.json b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Prompt Chaining.json index dd1b1307f..e7cb1b021 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Prompt Chaining.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Prompt Chaining.json @@ -20,7 +20,7 @@ "list": false, "show": true, "multiline": true, - "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Prompt, TemplateField, Text\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": TemplateField(display_name=\"Template\"),\n \"code\": TemplateField(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n", + "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import InputField, Prompt, Text\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": InputField(display_name=\"Template\"),\n \"code\": InputField(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n", "fileTypes": [], "file_path": "", "password": false, @@ -140,7 +140,7 @@ "list": false, "show": true, "multiline": true, - "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Prompt, TemplateField, Text\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": TemplateField(display_name=\"Template\"),\n \"code\": TemplateField(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n", + "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import InputField, Prompt, Text\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": InputField(display_name=\"Template\"),\n \"code\": InputField(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n", "fileTypes": [], "file_path": "", "password": false, diff --git a/src/backend/base/langflow/initial_setup/starter_projects/VectorStore-RAG-Flows.json b/src/backend/base/langflow/initial_setup/starter_projects/VectorStore-RAG-Flows.json index c629cdb3e..4bbb1aab3 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/VectorStore-RAG-Flows.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/VectorStore-RAG-Flows.json @@ -1106,7 +1106,7 @@ "list": false, "show": true, "multiline": true, - "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Prompt, TemplateField, Text\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": TemplateField(display_name=\"Template\"),\n \"code\": TemplateField(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n", + "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import InputField, Prompt, Text\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": InputField(display_name=\"Template\"),\n \"code\": InputField(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n", "fileTypes": [], "file_path": "", "password": false, diff --git a/src/backend/base/langflow/template/field/base.py b/src/backend/base/langflow/template/field/base.py index c68a5c476..3aa4c020d 100644 --- a/src/backend/base/langflow/template/field/base.py +++ b/src/backend/base/langflow/template/field/base.py @@ -5,7 +5,7 @@ from pydantic import BaseModel, ConfigDict, Field, field_serializer, field_valid from langflow.field_typing.range_spec import RangeSpec -class TemplateField(BaseModel): +class InputField(BaseModel): model_config = ConfigDict() field_type: str = Field(default="str", serialization_alias="type") @@ -128,3 +128,17 @@ class TemplateField(BaseModel): (f".{file_type}" if isinstance(file_type, str) and not file_type.startswith(".") else file_type) for file_type in value ] + + +class OutputField(BaseModel): + types: list[str] = Field(default=[], serialization_alias="types") + """List of output types for the field.""" + + selected: Optional[str] = Field(default=None, serialization_alias="selected") + """The selected output type for the field.""" + + name: str = Field(default="", serialization_alias="name") + """The name of the field.""" + + def to_dict(self): + return self.model_dump(by_alias=True, exclude_none=True) diff --git a/src/backend/base/langflow/template/field/prompt.py b/src/backend/base/langflow/template/field/prompt.py index ccc5d01a0..ecca72503 100644 --- a/src/backend/base/langflow/template/field/prompt.py +++ b/src/backend/base/langflow/template/field/prompt.py @@ -1,9 +1,9 @@ from typing import Optional -from langflow.template.field.base import TemplateField +from langflow.template.field.base import InputField -class DefaultPromptField(TemplateField): +class DefaultPromptField(InputField): name: str display_name: Optional[str] = None field_type: str = "str" diff --git a/src/backend/base/langflow/template/frontend_node/base.py b/src/backend/base/langflow/template/frontend_node/base.py index 7b04ee821..2b3771db9 100644 --- a/src/backend/base/langflow/template/frontend_node/base.py +++ b/src/backend/base/langflow/template/frontend_node/base.py @@ -4,7 +4,7 @@ from typing import ClassVar, Dict, List, Optional, Union from pydantic import BaseModel, Field, field_serializer, model_serializer -from langflow.template.field.base import TemplateField +from langflow.template.field.base import InputField from langflow.template.frontend_node.constants import FORCE_SHOW_FIELDS from langflow.template.frontend_node.formatter import field_formatters from langflow.template.template.base import Template @@ -30,7 +30,7 @@ class FieldFormatters(BaseModel): "model_fields": field_formatters.ModelSpecificFieldFormatter(), } - def format(self, field: TemplateField, name: Optional[str] = None) -> None: + def format(self, field: InputField, name: Optional[str] = None) -> None: for key, formatter in self.base_formatters.items(): formatter.format(field, name) @@ -145,7 +145,7 @@ class FrontendNode(BaseModel): self.output_types.extend(output_type) @staticmethod - def format_field(field: TemplateField, name: Optional[str] = None) -> None: + def format_field(field: InputField, name: Optional[str] = None) -> None: """Formats a given field based on its attributes and value.""" FrontendNode.get_field_formatters().format(field, name) @@ -184,7 +184,7 @@ class FrontendNode(BaseModel): return handler(field) if handler else _type @staticmethod - def handle_dict_type(field: TemplateField, _type: str) -> str: + def handle_dict_type(field: InputField, _type: str) -> str: """Handles 'dict' type by replacing it with 'code' or 'file' based on the field name.""" if "dict" in _type.lower() and field.name == "dict_": field.field_type = "file" @@ -194,13 +194,13 @@ class FrontendNode(BaseModel): return _type @staticmethod - def replace_default_value(field: TemplateField, value: dict) -> None: + def replace_default_value(field: InputField, value: dict) -> None: """Replaces default value with actual value if 'default' is present in value.""" if "default" in value: field.value = value["default"] @staticmethod - def handle_specific_field_values(field: TemplateField, key: str, name: Optional[str] = None) -> None: + def handle_specific_field_values(field: InputField, key: str, name: Optional[str] = None) -> None: """Handles specific field values for certain fields.""" if key == "headers": field.value = """{"Authorization": "Bearer "}""" @@ -208,7 +208,7 @@ class FrontendNode(BaseModel): FrontendNode._handle_api_key_specific_field_values(field, key, name) @staticmethod - def _handle_model_specific_field_values(field: TemplateField, key: str, name: Optional[str] = None) -> None: + def _handle_model_specific_field_values(field: InputField, key: str, name: Optional[str] = None) -> None: """Handles specific field values related to models.""" model_dict = { "OpenAI": constants.OPENAI_MODELS, @@ -221,7 +221,7 @@ class FrontendNode(BaseModel): field.is_list = True @staticmethod - def _handle_api_key_specific_field_values(field: TemplateField, key: str, name: Optional[str] = None) -> None: + def _handle_api_key_specific_field_values(field: InputField, key: str, name: Optional[str] = None) -> None: """Handles specific field values related to API keys.""" if "api_key" in key and "OpenAI" in str(name): field.display_name = "OpenAI API Key" @@ -230,7 +230,7 @@ class FrontendNode(BaseModel): field.value = "" @staticmethod - def handle_kwargs_field(field: TemplateField) -> None: + def handle_kwargs_field(field: InputField) -> None: """Handles kwargs field by setting certain attributes.""" if "kwargs" in (field.name or "").lower(): @@ -239,7 +239,7 @@ class FrontendNode(BaseModel): field.show = False @staticmethod - def handle_api_key_field(field: TemplateField, key: str) -> None: + def handle_api_key_field(field: InputField, key: str) -> None: """Handles api key field by setting certain attributes.""" if "api" in key.lower() and "key" in key.lower(): field.required = False @@ -277,7 +277,7 @@ class FrontendNode(BaseModel): } @staticmethod - def set_field_default_value(field: TemplateField, value: dict, key: str) -> None: + def set_field_default_value(field: InputField, value: dict, key: str) -> None: """Sets the field value with the default value if present.""" if "default" in value: field.value = value["default"] diff --git a/src/backend/base/langflow/template/frontend_node/custom_components.py b/src/backend/base/langflow/template/frontend_node/custom_components.py index 932d30799..d218c85a1 100644 --- a/src/backend/base/langflow/template/frontend_node/custom_components.py +++ b/src/backend/base/langflow/template/frontend_node/custom_components.py @@ -1,6 +1,6 @@ from typing import Optional -from langflow.template.field.base import TemplateField +from langflow.template.field.base import InputField from langflow.template.frontend_node.base import FrontendNode from langflow.template.template.base import Template @@ -52,7 +52,7 @@ class CustomComponentFrontendNode(FrontendNode): template: Template = Template( type_name="CustomComponent", fields=[ - TemplateField( + InputField( field_type="code", required=True, placeholder="", diff --git a/src/backend/base/langflow/template/frontend_node/formatter/base.py b/src/backend/base/langflow/template/frontend_node/formatter/base.py index 20ba64eae..dce85d003 100644 --- a/src/backend/base/langflow/template/frontend_node/formatter/base.py +++ b/src/backend/base/langflow/template/frontend_node/formatter/base.py @@ -3,10 +3,10 @@ from typing import Optional from pydantic import BaseModel -from langflow.template.field.base import TemplateField +from langflow.template.field.base import InputField class FieldFormatter(BaseModel, ABC): @abstractmethod - def format(self, field: TemplateField, name: Optional[str]) -> None: + def format(self, field: InputField, name: Optional[str]) -> None: pass diff --git a/src/backend/base/langflow/template/frontend_node/formatter/field_formatters.py b/src/backend/base/langflow/template/frontend_node/formatter/field_formatters.py index 42d3321ff..3ae5ece65 100644 --- a/src/backend/base/langflow/template/frontend_node/formatter/field_formatters.py +++ b/src/backend/base/langflow/template/frontend_node/formatter/field_formatters.py @@ -1,14 +1,14 @@ import re from typing import ClassVar, Dict, Optional -from langflow.template.field.base import TemplateField +from langflow.template.field.base import InputField from langflow.template.frontend_node.constants import FORCE_SHOW_FIELDS from langflow.template.frontend_node.formatter.base import FieldFormatter from langflow.utils.constants import ANTHROPIC_MODELS, CHAT_OPENAI_MODELS, OPENAI_MODELS class OpenAIAPIKeyFormatter(FieldFormatter): - def format(self, field: TemplateField, name: Optional[str] = None) -> None: + def format(self, field: InputField, name: Optional[str] = None) -> None: if field.name and "api_key" in field.name and "OpenAI" in str(name): field.display_name = "OpenAI API Key" field.required = False @@ -24,14 +24,14 @@ class ModelSpecificFieldFormatter(FieldFormatter): "ChatAnthropic": ANTHROPIC_MODELS, } - def format(self, field: TemplateField, name: Optional[str] = None) -> None: + def format(self, field: InputField, name: Optional[str] = None) -> None: if field.name and name in self.MODEL_DICT and field.name == "model_name": field.options = self.MODEL_DICT[name] field.is_list = True class KwargsFormatter(FieldFormatter): - def format(self, field: TemplateField, name: Optional[str] = None) -> None: + def format(self, field: InputField, name: Optional[str] = None) -> None: if field.name and "kwargs" in field.name.lower(): field.advanced = True field.required = False @@ -39,7 +39,7 @@ class KwargsFormatter(FieldFormatter): class APIKeyFormatter(FieldFormatter): - def format(self, field: TemplateField, name: Optional[str] = None) -> None: + def format(self, field: InputField, name: Optional[str] = None) -> None: if field.name and "api" in field.name.lower() and "key" in field.name.lower(): field.required = False field.advanced = False @@ -49,13 +49,13 @@ class APIKeyFormatter(FieldFormatter): class RemoveOptionalFormatter(FieldFormatter): - def format(self, field: TemplateField, name: Optional[str] = None) -> None: + def format(self, field: InputField, name: Optional[str] = None) -> None: _type = field.field_type field.field_type = re.sub(r"Optional\[(.*)\]", r"\1", _type) class ListTypeFormatter(FieldFormatter): - def format(self, field: TemplateField, name: Optional[str] = None) -> None: + def format(self, field: InputField, name: Optional[str] = None) -> None: _type = field.field_type is_list = "List" in _type or "Sequence" in _type if is_list: @@ -65,14 +65,14 @@ class ListTypeFormatter(FieldFormatter): class DictTypeFormatter(FieldFormatter): - def format(self, field: TemplateField, name: Optional[str] = None) -> None: + def format(self, field: InputField, name: Optional[str] = None) -> None: _type = field.field_type _type = _type.replace("Mapping", "dict") field.field_type = _type class UnionTypeFormatter(FieldFormatter): - def format(self, field: TemplateField, name: Optional[str] = None) -> None: + def format(self, field: InputField, name: Optional[str] = None) -> None: _type = field.field_type if "Union" in _type: _type = _type.replace("Union[", "")[:-1] @@ -87,13 +87,13 @@ class SpecialFieldFormatter(FieldFormatter): "max_value_length": lambda field: "int", } - def format(self, field: TemplateField, name: Optional[str] = None) -> None: + def format(self, field: InputField, name: Optional[str] = None) -> None: handler = self.SPECIAL_FIELD_HANDLERS.get(field.name) field.field_type = handler(field) if handler else field.field_type class ShowFieldFormatter(FieldFormatter): - def format(self, field: TemplateField, name: Optional[str] = None) -> None: + def format(self, field: InputField, name: Optional[str] = None) -> None: key = field.name or "" required = field.required field.show = ( @@ -105,7 +105,7 @@ class ShowFieldFormatter(FieldFormatter): class PasswordFieldFormatter(FieldFormatter): - def format(self, field: TemplateField, name: Optional[str] = None) -> None: + def format(self, field: InputField, name: Optional[str] = None) -> None: key = field.name or "" show = field.show if any(text in key.lower() for text in {"password", "token", "api", "key"}) and show: @@ -113,7 +113,7 @@ class PasswordFieldFormatter(FieldFormatter): class MultilineFieldFormatter(FieldFormatter): - def format(self, field: TemplateField, name: Optional[str] = None) -> None: + def format(self, field: InputField, name: Optional[str] = None) -> None: key = field.name or "" if key in { "suffix", @@ -128,21 +128,21 @@ class MultilineFieldFormatter(FieldFormatter): class DefaultValueFormatter(FieldFormatter): - def format(self, field: TemplateField, name: Optional[str] = None) -> None: + def format(self, field: InputField, name: Optional[str] = None) -> None: value = field.model_dump(by_alias=True, exclude_none=True) if "default" in value: field.value = value["default"] class HeadersDefaultValueFormatter(FieldFormatter): - def format(self, field: TemplateField, name: Optional[str] = None) -> None: + def format(self, field: InputField, name: Optional[str] = None) -> None: key = field.name if key == "headers": field.value = """{"Authorization": "Bearer "}""" class DictCodeFileFormatter(FieldFormatter): - def format(self, field: TemplateField, name: Optional[str] = None) -> None: + def format(self, field: InputField, name: Optional[str] = None) -> None: key = field.name value = field.model_dump(by_alias=True, exclude_none=True) _type = value["type"] diff --git a/src/backend/base/langflow/template/template/base.py b/src/backend/base/langflow/template/template/base.py index d7632e239..1db5e6911 100644 --- a/src/backend/base/langflow/template/template/base.py +++ b/src/backend/base/langflow/template/template/base.py @@ -2,13 +2,13 @@ from typing import Callable, Union from pydantic import BaseModel, model_serializer -from langflow.template.field.base import TemplateField +from langflow.template.field.base import InputField from langflow.utils.constants import DIRECT_TYPES class Template(BaseModel): type_name: str - fields: list[TemplateField] + fields: list[InputField] def process_fields( self, @@ -38,17 +38,17 @@ class Template(BaseModel): self.sort_fields() return self.model_dump(by_alias=True, exclude_none=True, exclude={"fields"}) - def add_field(self, field: TemplateField) -> None: + def add_field(self, field: InputField) -> None: self.fields.append(field) - def get_field(self, field_name: str) -> TemplateField: + def get_field(self, field_name: str) -> InputField: """Returns the field with the given name.""" field = next((field for field in self.fields if field.name == field_name), None) if field is None: raise ValueError(f"Field {field_name} not found in template {self.type_name}") return field - def update_field(self, field_name: str, field: TemplateField) -> None: + def update_field(self, field_name: str, field: InputField) -> None: """Updates the field with the given name.""" for idx, template_field in enumerate(self.fields): if template_field.name == field_name: @@ -56,7 +56,7 @@ class Template(BaseModel): return raise ValueError(f"Field {field_name} not found in template {self.type_name}") - def upsert_field(self, field_name: str, field: TemplateField) -> None: + def upsert_field(self, field_name: str, field: InputField) -> None: """Updates the field with the given name or adds it if it doesn't exist.""" try: self.update_field(field_name, field) diff --git a/src/frontend/harFiles/langflow.har b/src/frontend/harFiles/langflow.har index d6fef50cd..dcd0b23aa 100644 --- a/src/frontend/harFiles/langflow.har +++ b/src/frontend/harFiles/langflow.har @@ -19,19 +19,58 @@ "httpVersion": "HTTP/1.1", "cookies": [], "headers": [ - { "name": "Accept", "value": "application/json, text/plain, */*" }, - { "name": "Accept-Encoding", "value": "gzip, deflate, br, zstd" }, - { "name": "Accept-Language", "value": "en-US,en;q=0.9" }, - { "name": "Connection", "value": "keep-alive" }, - { "name": "Host", "value": "localhost:3000" }, - { "name": "Referer", "value": "http://localhost:3000/" }, - { "name": "Sec-Fetch-Dest", "value": "empty" }, - { "name": "Sec-Fetch-Mode", "value": "cors" }, - { "name": "Sec-Fetch-Site", "value": "same-origin" }, - { "name": "User-Agent", "value": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/123.0.0.0 Safari/537.36" }, - { "name": "sec-ch-ua", "value": "\"Chromium\";v=\"123\", \"Not:A-Brand\";v=\"8\"" }, - { "name": "sec-ch-ua-mobile", "value": "?0" }, - { "name": "sec-ch-ua-platform", "value": "\"Linux\"" } + { + "name": "Accept", + "value": "application/json, text/plain, */*" + }, + { + "name": "Accept-Encoding", + "value": "gzip, deflate, br, zstd" + }, + { + "name": "Accept-Language", + "value": "en-US,en;q=0.9" + }, + { + "name": "Connection", + "value": "keep-alive" + }, + { + "name": "Host", + "value": "localhost:3000" + }, + { + "name": "Referer", + "value": "http://localhost:3000/" + }, + { + "name": "Sec-Fetch-Dest", + "value": "empty" + }, + { + "name": "Sec-Fetch-Mode", + "value": "cors" + }, + { + "name": "Sec-Fetch-Site", + "value": "same-origin" + }, + { + "name": "User-Agent", + "value": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/123.0.0.0 Safari/537.36" + }, + { + "name": "sec-ch-ua", + "value": "\"Chromium\";v=\"123\", \"Not:A-Brand\";v=\"8\"" + }, + { + "name": "sec-ch-ua-mobile", + "value": "?0" + }, + { + "name": "sec-ch-ua-platform", + "value": "\"Linux\"" + } ], "queryString": [], "headersSize": -1, @@ -43,12 +82,30 @@ "httpVersion": "HTTP/1.1", "cookies": [], "headers": [ - { "name": "Access-Control-Allow-Origin", "value": "*" }, - { "name": "connection", "value": "close" }, - { "name": "content-length", "value": "19" }, - { "name": "content-type", "value": "application/json" }, - { "name": "date", "value": "Wed, 28 Feb 2024 14:32:30 GMT" }, - { "name": "server", "value": "uvicorn" } + { + "name": "Access-Control-Allow-Origin", + "value": "*" + }, + { + "name": "connection", + "value": "close" + }, + { + "name": "content-length", + "value": "19" + }, + { + "name": "content-type", + "value": "application/json" + }, + { + "name": "date", + "value": "Wed, 28 Feb 2024 14:32:30 GMT" + }, + { + "name": "server", + "value": "uvicorn" + } ], "content": { "size": -1, @@ -60,7 +117,11 @@ "redirectURL": "" }, "cache": {}, - "timings": { "send": -1, "wait": -1, "receive": 0.77 } + "timings": { + "send": -1, + "wait": -1, + "receive": 0.77 + } }, { "startedDateTime": "2024-02-28T14:32:30.859Z", @@ -71,19 +132,58 @@ "httpVersion": "HTTP/1.1", "cookies": [], "headers": [ - { "name": "Accept", "value": "application/json, text/plain, */*" }, - { "name": "Accept-Encoding", "value": "gzip, deflate, br, zstd" }, - { "name": "Accept-Language", "value": "en-US,en;q=0.9" }, - { "name": "Connection", "value": "keep-alive" }, - { "name": "Host", "value": "localhost:3000" }, - { "name": "Referer", "value": "http://localhost:3000/" }, - { "name": "Sec-Fetch-Dest", "value": "empty" }, - { "name": "Sec-Fetch-Mode", "value": "cors" }, - { "name": "Sec-Fetch-Site", "value": "same-origin" }, - { "name": "User-Agent", "value": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/123.0.0.0 Safari/537.36" }, - { "name": "sec-ch-ua", "value": "\"Chromium\";v=\"123\", \"Not:A-Brand\";v=\"8\"" }, - { "name": "sec-ch-ua-mobile", "value": "?0" }, - { "name": "sec-ch-ua-platform", "value": "\"Linux\"" } + { + "name": "Accept", + "value": "application/json, text/plain, */*" + }, + { + "name": "Accept-Encoding", + "value": "gzip, deflate, br, zstd" + }, + { + "name": "Accept-Language", + "value": "en-US,en;q=0.9" + }, + { + "name": "Connection", + "value": "keep-alive" + }, + { + "name": "Host", + "value": "localhost:3000" + }, + { + "name": "Referer", + "value": "http://localhost:3000/" + }, + { + "name": "Sec-Fetch-Dest", + "value": "empty" + }, + { + "name": "Sec-Fetch-Mode", + "value": "cors" + }, + { + "name": "Sec-Fetch-Site", + "value": "same-origin" + }, + { + "name": "User-Agent", + "value": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/123.0.0.0 Safari/537.36" + }, + { + "name": "sec-ch-ua", + "value": "\"Chromium\";v=\"123\", \"Not:A-Brand\";v=\"8\"" + }, + { + "name": "sec-ch-ua-mobile", + "value": "?0" + }, + { + "name": "sec-ch-ua-platform", + "value": "\"Linux\"" + } ], "queryString": [], "headersSize": -1, @@ -95,13 +195,34 @@ "httpVersion": "HTTP/1.1", "cookies": [], "headers": [ - { "name": "Access-Control-Allow-Origin", "value": "*" }, - { "name": "connection", "value": "close" }, - { "name": "content-length", "value": "227" }, - { "name": "content-type", "value": "application/json" }, - { "name": "date", "value": "Wed, 28 Feb 2024 14:32:30 GMT" }, - { "name": "server", "value": "uvicorn" }, - { "name": "set-cookie", "value": "access_token_lf=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIwMzg5ZmIyOS1kYWE2LTQwOGMtYjhjYi1iOGZmOGQxNzM0M2EiLCJleHAiOjE3NDA2NjY3NTB9.ef5W5jwNOeVzU3JZ7ylLYf2MLEJcVxC4-fF7EK9Ecdc; Path=/; SameSite=none; Secure" } + { + "name": "Access-Control-Allow-Origin", + "value": "*" + }, + { + "name": "connection", + "value": "close" + }, + { + "name": "content-length", + "value": "227" + }, + { + "name": "content-type", + "value": "application/json" + }, + { + "name": "date", + "value": "Wed, 28 Feb 2024 14:32:30 GMT" + }, + { + "name": "server", + "value": "uvicorn" + }, + { + "name": "set-cookie", + "value": "access_token_lf=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIwMzg5ZmIyOS1kYWE2LTQwOGMtYjhjYi1iOGZmOGQxNzM0M2EiLCJleHAiOjE3NDA2NjY3NTB9.ef5W5jwNOeVzU3JZ7ylLYf2MLEJcVxC4-fF7EK9Ecdc; Path=/; SameSite=none; Secure" + } ], "content": { "size": -1, @@ -113,7 +234,11 @@ "redirectURL": "" }, "cache": {}, - "timings": { "send": -1, "wait": -1, "receive": 0.894 } + "timings": { + "send": -1, + "wait": -1, + "receive": 0.894 + } }, { "startedDateTime": "2024-02-28T14:32:30.937Z", @@ -124,21 +249,66 @@ "httpVersion": "HTTP/1.1", "cookies": [], "headers": [ - { "name": "Accept", "value": "application/json, text/plain, */*" }, - { "name": "Accept-Encoding", "value": "gzip, deflate, br, zstd" }, - { "name": "Accept-Language", "value": "en-US,en;q=0.9" }, - { "name": "Authorization", "value": "Bearer eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIwMzg5ZmIyOS1kYWE2LTQwOGMtYjhjYi1iOGZmOGQxNzM0M2EiLCJleHAiOjE3NDA2NjY3NTB9.ef5W5jwNOeVzU3JZ7ylLYf2MLEJcVxC4-fF7EK9Ecdc" }, - { "name": "Connection", "value": "keep-alive" }, - { "name": "Cookie", "value": "access_token_lf=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIwMzg5ZmIyOS1kYWE2LTQwOGMtYjhjYi1iOGZmOGQxNzM0M2EiLCJleHAiOjE3NDA2NjY3NTB9.ef5W5jwNOeVzU3JZ7ylLYf2MLEJcVxC4-fF7EK9Ecdc" }, - { "name": "Host", "value": "localhost:3000" }, - { "name": "Referer", "value": "http://localhost:3000/" }, - { "name": "Sec-Fetch-Dest", "value": "empty" }, - { "name": "Sec-Fetch-Mode", "value": "cors" }, - { "name": "Sec-Fetch-Site", "value": "same-origin" }, - { "name": "User-Agent", "value": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/123.0.0.0 Safari/537.36" }, - { "name": "sec-ch-ua", "value": "\"Chromium\";v=\"123\", \"Not:A-Brand\";v=\"8\"" }, - { "name": "sec-ch-ua-mobile", "value": "?0" }, - { "name": "sec-ch-ua-platform", "value": "\"Linux\"" } + { + "name": "Accept", + "value": "application/json, text/plain, */*" + }, + { + "name": "Accept-Encoding", + "value": "gzip, deflate, br, zstd" + }, + { + "name": "Accept-Language", + "value": "en-US,en;q=0.9" + }, + { + "name": "Authorization", + "value": "Bearer eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIwMzg5ZmIyOS1kYWE2LTQwOGMtYjhjYi1iOGZmOGQxNzM0M2EiLCJleHAiOjE3NDA2NjY3NTB9.ef5W5jwNOeVzU3JZ7ylLYf2MLEJcVxC4-fF7EK9Ecdc" + }, + { + "name": "Connection", + "value": "keep-alive" + }, + { + "name": "Cookie", + "value": "access_token_lf=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIwMzg5ZmIyOS1kYWE2LTQwOGMtYjhjYi1iOGZmOGQxNzM0M2EiLCJleHAiOjE3NDA2NjY3NTB9.ef5W5jwNOeVzU3JZ7ylLYf2MLEJcVxC4-fF7EK9Ecdc" + }, + { + "name": "Host", + "value": "localhost:3000" + }, + { + "name": "Referer", + "value": "http://localhost:3000/" + }, + { + "name": "Sec-Fetch-Dest", + "value": "empty" + }, + { + "name": "Sec-Fetch-Mode", + "value": "cors" + }, + { + "name": "Sec-Fetch-Site", + "value": "same-origin" + }, + { + "name": "User-Agent", + "value": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/123.0.0.0 Safari/537.36" + }, + { + "name": "sec-ch-ua", + "value": "\"Chromium\";v=\"123\", \"Not:A-Brand\";v=\"8\"" + }, + { + "name": "sec-ch-ua-mobile", + "value": "?0" + }, + { + "name": "sec-ch-ua-platform", + "value": "\"Linux\"" + } ], "queryString": [], "headersSize": -1, @@ -150,12 +320,30 @@ "httpVersion": "HTTP/1.1", "cookies": [], "headers": [ - { "name": "Access-Control-Allow-Origin", "value": "*" }, - { "name": "connection", "value": "close" }, - { "name": "content-length", "value": "253" }, - { "name": "content-type", "value": "application/json" }, - { "name": "date", "value": "Wed, 28 Feb 2024 14:32:30 GMT" }, - { "name": "server", "value": "uvicorn" } + { + "name": "Access-Control-Allow-Origin", + "value": "*" + }, + { + "name": "connection", + "value": "close" + }, + { + "name": "content-length", + "value": "253" + }, + { + "name": "content-type", + "value": "application/json" + }, + { + "name": "date", + "value": "Wed, 28 Feb 2024 14:32:30 GMT" + }, + { + "name": "server", + "value": "uvicorn" + } ], "content": { "size": -1, @@ -167,7 +355,11 @@ "redirectURL": "" }, "cache": {}, - "timings": { "send": -1, "wait": -1, "receive": 0.944 } + "timings": { + "send": -1, + "wait": -1, + "receive": 0.944 + } }, { "startedDateTime": "2024-02-28T14:32:30.976Z", @@ -178,21 +370,66 @@ "httpVersion": "HTTP/1.1", "cookies": [], "headers": [ - { "name": "Accept", "value": "application/json, text/plain, */*" }, - { "name": "Accept-Encoding", "value": "gzip, deflate, br, zstd" }, - { "name": "Accept-Language", "value": "en-US,en;q=0.9" }, - { "name": "Authorization", "value": "Bearer eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIwMzg5ZmIyOS1kYWE2LTQwOGMtYjhjYi1iOGZmOGQxNzM0M2EiLCJleHAiOjE3NDA2NjY3NTB9.ef5W5jwNOeVzU3JZ7ylLYf2MLEJcVxC4-fF7EK9Ecdc" }, - { "name": "Connection", "value": "keep-alive" }, - { "name": "Cookie", "value": "access_token_lf=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIwMzg5ZmIyOS1kYWE2LTQwOGMtYjhjYi1iOGZmOGQxNzM0M2EiLCJleHAiOjE3NDA2NjY3NTB9.ef5W5jwNOeVzU3JZ7ylLYf2MLEJcVxC4-fF7EK9Ecdc" }, - { "name": "Host", "value": "localhost:3000" }, - { "name": "Referer", "value": "http://localhost:3000/" }, - { "name": "Sec-Fetch-Dest", "value": "empty" }, - { "name": "Sec-Fetch-Mode", "value": "cors" }, - { "name": "Sec-Fetch-Site", "value": "same-origin" }, - { "name": "User-Agent", "value": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/123.0.0.0 Safari/537.36" }, - { "name": "sec-ch-ua", "value": "\"Chromium\";v=\"123\", \"Not:A-Brand\";v=\"8\"" }, - { "name": "sec-ch-ua-mobile", "value": "?0" }, - { "name": "sec-ch-ua-platform", "value": "\"Linux\"" } + { + "name": "Accept", + "value": "application/json, text/plain, */*" + }, + { + "name": "Accept-Encoding", + "value": "gzip, deflate, br, zstd" + }, + { + "name": "Accept-Language", + "value": "en-US,en;q=0.9" + }, + { + "name": "Authorization", + "value": "Bearer eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIwMzg5ZmIyOS1kYWE2LTQwOGMtYjhjYi1iOGZmOGQxNzM0M2EiLCJleHAiOjE3NDA2NjY3NTB9.ef5W5jwNOeVzU3JZ7ylLYf2MLEJcVxC4-fF7EK9Ecdc" + }, + { + "name": "Connection", + "value": "keep-alive" + }, + { + "name": "Cookie", + "value": "access_token_lf=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIwMzg5ZmIyOS1kYWE2LTQwOGMtYjhjYi1iOGZmOGQxNzM0M2EiLCJleHAiOjE3NDA2NjY3NTB9.ef5W5jwNOeVzU3JZ7ylLYf2MLEJcVxC4-fF7EK9Ecdc" + }, + { + "name": "Host", + "value": "localhost:3000" + }, + { + "name": "Referer", + "value": "http://localhost:3000/" + }, + { + "name": "Sec-Fetch-Dest", + "value": "empty" + }, + { + "name": "Sec-Fetch-Mode", + "value": "cors" + }, + { + "name": "Sec-Fetch-Site", + "value": "same-origin" + }, + { + "name": "User-Agent", + "value": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/123.0.0.0 Safari/537.36" + }, + { + "name": "sec-ch-ua", + "value": "\"Chromium\";v=\"123\", \"Not:A-Brand\";v=\"8\"" + }, + { + "name": "sec-ch-ua-mobile", + "value": "?0" + }, + { + "name": "sec-ch-ua-platform", + "value": "\"Linux\"" + } ], "queryString": [], "headersSize": -1, @@ -204,12 +441,30 @@ "httpVersion": "HTTP/1.1", "cookies": [], "headers": [ - { "name": "Access-Control-Allow-Origin", "value": "*" }, - { "name": "connection", "value": "close" }, - { "name": "content-length", "value": "19" }, - { "name": "content-type", "value": "application/json" }, - { "name": "date", "value": "Wed, 28 Feb 2024 14:32:30 GMT" }, - { "name": "server", "value": "uvicorn" } + { + "name": "Access-Control-Allow-Origin", + "value": "*" + }, + { + "name": "connection", + "value": "close" + }, + { + "name": "content-length", + "value": "19" + }, + { + "name": "content-type", + "value": "application/json" + }, + { + "name": "date", + "value": "Wed, 28 Feb 2024 14:32:30 GMT" + }, + { + "name": "server", + "value": "uvicorn" + } ], "content": { "size": -1, @@ -221,7 +476,11 @@ "redirectURL": "" }, "cache": {}, - "timings": { "send": -1, "wait": -1, "receive": 0.697 } + "timings": { + "send": -1, + "wait": -1, + "receive": 0.697 + } }, { "startedDateTime": "2024-02-28T14:32:30.976Z", @@ -232,21 +491,66 @@ "httpVersion": "HTTP/1.1", "cookies": [], "headers": [ - { "name": "Accept", "value": "application/json, text/plain, */*" }, - { "name": "Accept-Encoding", "value": "gzip, deflate, br, zstd" }, - { "name": "Accept-Language", "value": "en-US,en;q=0.9" }, - { "name": "Authorization", "value": "Bearer eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIwMzg5ZmIyOS1kYWE2LTQwOGMtYjhjYi1iOGZmOGQxNzM0M2EiLCJleHAiOjE3NDA2NjY3NTB9.ef5W5jwNOeVzU3JZ7ylLYf2MLEJcVxC4-fF7EK9Ecdc" }, - { "name": "Connection", "value": "keep-alive" }, - { "name": "Cookie", "value": "access_token_lf=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIwMzg5ZmIyOS1kYWE2LTQwOGMtYjhjYi1iOGZmOGQxNzM0M2EiLCJleHAiOjE3NDA2NjY3NTB9.ef5W5jwNOeVzU3JZ7ylLYf2MLEJcVxC4-fF7EK9Ecdc" }, - { "name": "Host", "value": "localhost:3000" }, - { "name": "Referer", "value": "http://localhost:3000/" }, - { "name": "Sec-Fetch-Dest", "value": "empty" }, - { "name": "Sec-Fetch-Mode", "value": "cors" }, - { "name": "Sec-Fetch-Site", "value": "same-origin" }, - { "name": "User-Agent", "value": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/123.0.0.0 Safari/537.36" }, - { "name": "sec-ch-ua", "value": "\"Chromium\";v=\"123\", \"Not:A-Brand\";v=\"8\"" }, - { "name": "sec-ch-ua-mobile", "value": "?0" }, - { "name": "sec-ch-ua-platform", "value": "\"Linux\"" } + { + "name": "Accept", + "value": "application/json, text/plain, */*" + }, + { + "name": "Accept-Encoding", + "value": "gzip, deflate, br, zstd" + }, + { + "name": "Accept-Language", + "value": "en-US,en;q=0.9" + }, + { + "name": "Authorization", + "value": "Bearer eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIwMzg5ZmIyOS1kYWE2LTQwOGMtYjhjYi1iOGZmOGQxNzM0M2EiLCJleHAiOjE3NDA2NjY3NTB9.ef5W5jwNOeVzU3JZ7ylLYf2MLEJcVxC4-fF7EK9Ecdc" + }, + { + "name": "Connection", + "value": "keep-alive" + }, + { + "name": "Cookie", + "value": "access_token_lf=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIwMzg5ZmIyOS1kYWE2LTQwOGMtYjhjYi1iOGZmOGQxNzM0M2EiLCJleHAiOjE3NDA2NjY3NTB9.ef5W5jwNOeVzU3JZ7ylLYf2MLEJcVxC4-fF7EK9Ecdc" + }, + { + "name": "Host", + "value": "localhost:3000" + }, + { + "name": "Referer", + "value": "http://localhost:3000/" + }, + { + "name": "Sec-Fetch-Dest", + "value": "empty" + }, + { + "name": "Sec-Fetch-Mode", + "value": "cors" + }, + { + "name": "Sec-Fetch-Site", + "value": "same-origin" + }, + { + "name": "User-Agent", + "value": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/123.0.0.0 Safari/537.36" + }, + { + "name": "sec-ch-ua", + "value": "\"Chromium\";v=\"123\", \"Not:A-Brand\";v=\"8\"" + }, + { + "name": "sec-ch-ua-mobile", + "value": "?0" + }, + { + "name": "sec-ch-ua-platform", + "value": "\"Linux\"" + } ], "queryString": [], "headersSize": -1, @@ -258,24 +562,46 @@ "httpVersion": "HTTP/1.1", "cookies": [], "headers": [ - { "name": "Access-Control-Allow-Origin", "value": "*" }, - { "name": "connection", "value": "close" }, - { "name": "content-length", "value": "633593" }, - { "name": "content-type", "value": "application/json" }, - { "name": "date", "value": "Wed, 28 Feb 2024 14:32:30 GMT" }, - { "name": "server", "value": "uvicorn" } + { + "name": "Access-Control-Allow-Origin", + "value": "*" + }, + { + "name": "connection", + "value": "close" + }, + { + "name": "content-length", + "value": "633593" + }, + { + "name": "content-type", + "value": "application/json" + }, + { + "name": "date", + "value": "Wed, 28 Feb 2024 14:32:30 GMT" + }, + { + "name": "server", + "value": "uvicorn" + } ], "content": { "size": -1, "mimeType": "application/json", - "text": "{\"chains\":{\"ConversationalRetrievalChain\":{\"template\":{\"callbacks\":{\"type\":\"Callbacks\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"callbacks\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"condense_question_llm\":{\"type\":\"BaseLanguageModel\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"condense_question_llm\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"condense_question_prompt\":{\"type\":\"BasePromptTemplate\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":{\"name\":null,\"input_variables\":[\"chat_history\",\"question\"],\"input_types\":{},\"output_parser\":null,\"partial_variables\":{},\"metadata\":null,\"tags\":null,\"template\":\"Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question, in its original language.\\n\\nChat History:\\n{chat_history}\\nFollow Up Input: {question}\\nStandalone question:\",\"template_format\":\"f-string\",\"validate_template\":false},\"fileTypes\":[],\"password\":false,\"name\":\"condense_question_prompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"llm\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"memory\":{\"type\":\"BaseChatMemory\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"retriever\":{\"type\":\"BaseRetriever\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"retriever\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"chain_type\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"stuff\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"stuff\",\"map_reduce\",\"map_rerank\",\"refine\"],\"name\":\"chain_type\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"combine_docs_chain_kwargs\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"combine_docs_chain_kwargs\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"return_source_documents\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":true,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"return_source_documents\",\"display_name\":\"Return source documents\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"verbose\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"password\":false,\"name\":\"verbose\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"ConversationalRetrievalChain\"},\"description\":\"Convenience method to load chain from LLM and retriever.\",\"base_classes\":[\"BaseConversationalRetrievalChain\",\"Runnable\",\"Chain\",\"Generic\",\"Text\",\"RunnableSerializable\",\"Serializable\",\"object\",\"ConversationalRetrievalChain\",\"Callable\"],\"display_name\":\"ConversationalRetrievalChain\",\"documentation\":\"https://python.langchain.com/docs/modules/chains/popular/chat_vector_db\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":false,\"beta\":false,\"output_type\":\"Chain\"},\"LLMCheckerChain\":{\"template\":{\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Callable, Union\\n\\nfrom langchain.chains import LLMCheckerChain\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import BaseLanguageModel, Chain\\n\\n\\nclass LLMCheckerChainComponent(CustomComponent):\\n display_name = \\\"LLMCheckerChain\\\"\\n description = \\\"\\\"\\n documentation = \\\"https://python.langchain.com/docs/modules/chains/additional/llm_checker\\\"\\n\\n def build_config(self):\\n return {\\n \\\"llm\\\": {\\\"display_name\\\": \\\"LLM\\\"},\\n }\\n\\n def build(\\n self,\\n llm: BaseLanguageModel,\\n ) -> Union[Chain, Callable]:\\n return LLMCheckerChain.from_llm(llm=llm)\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"\",\"base_classes\":[\"Runnable\",\"Chain\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"object\",\"Callable\"],\"display_name\":\"LLMCheckerChain\",\"documentation\":\"https://python.langchain.com/docs/modules/chains/additional/llm_checker\",\"custom_fields\":{\"llm\":null},\"output_types\":[\"Chain\",\"Callable\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"LLMMathChain\":{\"template\":{\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"llm_chain\":{\"type\":\"LLMChain\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm_chain\",\"display_name\":\"LLM Chain\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"memory\":{\"type\":\"BaseMemory\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"memory\",\"display_name\":\"Memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Callable, Optional, Union\\n\\nfrom langchain.chains import LLMChain, LLMMathChain\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import BaseLanguageModel, BaseMemory, Chain\\n\\n\\nclass LLMMathChainComponent(CustomComponent):\\n display_name = \\\"LLMMathChain\\\"\\n description = \\\"Chain that interprets a prompt and executes python code to do math.\\\"\\n documentation = \\\"https://python.langchain.com/docs/modules/chains/additional/llm_math\\\"\\n\\n def build_config(self):\\n return {\\n \\\"llm\\\": {\\\"display_name\\\": \\\"LLM\\\"},\\n \\\"llm_chain\\\": {\\\"display_name\\\": \\\"LLM Chain\\\"},\\n \\\"memory\\\": {\\\"display_name\\\": \\\"Memory\\\"},\\n \\\"input_key\\\": {\\\"display_name\\\": \\\"Input Key\\\"},\\n \\\"output_key\\\": {\\\"display_name\\\": \\\"Output Key\\\"},\\n }\\n\\n def build(\\n self,\\n llm: BaseLanguageModel,\\n llm_chain: LLMChain,\\n input_key: str = \\\"question\\\",\\n output_key: str = \\\"answer\\\",\\n memory: Optional[BaseMemory] = None,\\n ) -> Union[LLMMathChain, Callable, Chain]:\\n return LLMMathChain(llm=llm, llm_chain=llm_chain, input_key=input_key, output_key=output_key, memory=memory)\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"input_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"question\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"input_key\",\"display_name\":\"Input Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"output_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"answer\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"output_key\",\"display_name\":\"Output Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Chain that interprets a prompt and executes python code to do math.\",\"base_classes\":[\"LLMMathChain\",\"Runnable\",\"Chain\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"object\",\"Callable\"],\"display_name\":\"LLMMathChain\",\"documentation\":\"https://python.langchain.com/docs/modules/chains/additional/llm_math\",\"custom_fields\":{\"llm\":null,\"llm_chain\":null,\"input_key\":null,\"output_key\":null,\"memory\":null},\"output_types\":[\"LLMMathChain\",\"Callable\",\"Chain\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"RetrievalQA\":{\"template\":{\"combine_documents_chain\":{\"type\":\"BaseCombineDocumentsChain\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"combine_documents_chain\",\"display_name\":\"Combine Documents Chain\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"memory\":{\"type\":\"BaseMemory\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"memory\",\"display_name\":\"Memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"retriever\":{\"type\":\"BaseRetriever\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"retriever\",\"display_name\":\"Retriever\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Callable, Optional, Union\\n\\nfrom langchain.chains.combine_documents.base import BaseCombineDocumentsChain\\nfrom langchain.chains.retrieval_qa.base import BaseRetrievalQA, RetrievalQA\\nfrom langchain_core.documents import Document\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import BaseMemory, BaseRetriever, Text\\n\\n\\nclass RetrievalQAComponent(CustomComponent):\\n display_name = \\\"Retrieval QA\\\"\\n description = \\\"Chain for question-answering against an index.\\\"\\n\\n def build_config(self):\\n return {\\n \\\"combine_documents_chain\\\": {\\\"display_name\\\": \\\"Combine Documents Chain\\\"},\\n \\\"retriever\\\": {\\\"display_name\\\": \\\"Retriever\\\"},\\n \\\"memory\\\": {\\\"display_name\\\": \\\"Memory\\\", \\\"required\\\": False},\\n \\\"input_key\\\": {\\\"display_name\\\": \\\"Input Key\\\", \\\"advanced\\\": True},\\n \\\"output_key\\\": {\\\"display_name\\\": \\\"Output Key\\\", \\\"advanced\\\": True},\\n \\\"return_source_documents\\\": {\\\"display_name\\\": \\\"Return Source Documents\\\"},\\n \\\"inputs\\\": {\\\"display_name\\\": \\\"Input\\\", \\\"input_types\\\": [\\\"Text\\\", \\\"Document\\\"]},\\n }\\n\\n def build(\\n self,\\n combine_documents_chain: BaseCombineDocumentsChain,\\n retriever: BaseRetriever,\\n inputs: str = \\\"\\\",\\n memory: Optional[BaseMemory] = None,\\n input_key: str = \\\"query\\\",\\n output_key: str = \\\"result\\\",\\n return_source_documents: bool = True,\\n ) -> Union[BaseRetrievalQA, Callable, Text]:\\n runnable = RetrievalQA(\\n combine_documents_chain=combine_documents_chain,\\n retriever=retriever,\\n memory=memory,\\n input_key=input_key,\\n output_key=output_key,\\n return_source_documents=return_source_documents,\\n )\\n if isinstance(inputs, Document):\\n inputs = inputs.page_content\\n self.status = runnable\\n result = runnable.invoke({input_key: inputs})\\n result = result.content if hasattr(result, \\\"content\\\") else result\\n # Result is a dict with keys \\\"query\\\", \\\"result\\\" and \\\"source_documents\\\"\\n # for now we just return the result\\n records = self.to_records(result.get(\\\"source_documents\\\"))\\n references_str = \\\"\\\"\\n if return_source_documents:\\n references_str = self.create_references_from_records(records)\\n result_str = result.get(\\\"result\\\")\\n final_result = \\\"\\\\n\\\".join([result_str, references_str])\\n self.status = final_result\\n return final_result\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"input_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"query\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"input_key\",\"display_name\":\"Input Key\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"inputs\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"inputs\",\"display_name\":\"Input\",\"advanced\":false,\"input_types\":[\"Text\",\"Document\",\"Text\"],\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"output_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"result\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"output_key\",\"display_name\":\"Output Key\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"return_source_documents\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":true,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"return_source_documents\",\"display_name\":\"Return Source Documents\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Chain for question-answering against an index.\",\"base_classes\":[\"Runnable\",\"Chain\",\"BaseRetrievalQA\",\"Generic\",\"Text\",\"RunnableSerializable\",\"Serializable\",\"object\",\"Callable\"],\"display_name\":\"Retrieval QA\",\"documentation\":\"\",\"custom_fields\":{\"combine_documents_chain\":null,\"retriever\":null,\"inputs\":null,\"memory\":null,\"input_key\":null,\"output_key\":null,\"return_source_documents\":null},\"output_types\":[\"BaseRetrievalQA\",\"Callable\",\"Text\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"RetrievalQAWithSourcesChain\":{\"template\":{\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"memory\":{\"type\":\"BaseMemory\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"memory\",\"display_name\":\"Memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"retriever\":{\"type\":\"BaseRetriever\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"retriever\",\"display_name\":\"Retriever\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"chain_type\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"stuff\",\"map_reduce\",\"map_rerank\",\"refine\"],\"name\":\"chain_type\",\"display_name\":\"Chain Type\",\"advanced\":false,\"dynamic\":false,\"info\":\"The type of chain to use to combined Documents.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain.chains import RetrievalQAWithSourcesChain\\nfrom langchain_core.documents import Document\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import BaseLanguageModel, BaseMemory, BaseRetriever, Text\\n\\n\\nclass RetrievalQAWithSourcesChainComponent(CustomComponent):\\n display_name = \\\"RetrievalQAWithSourcesChain\\\"\\n description = \\\"Question-answering with sources over an index.\\\"\\n\\n def build_config(self):\\n return {\\n \\\"llm\\\": {\\\"display_name\\\": \\\"LLM\\\"},\\n \\\"chain_type\\\": {\\n \\\"display_name\\\": \\\"Chain Type\\\",\\n \\\"options\\\": [\\\"stuff\\\", \\\"map_reduce\\\", \\\"map_rerank\\\", \\\"refine\\\"],\\n \\\"info\\\": \\\"The type of chain to use to combined Documents.\\\",\\n },\\n \\\"memory\\\": {\\\"display_name\\\": \\\"Memory\\\"},\\n \\\"return_source_documents\\\": {\\\"display_name\\\": \\\"Return Source Documents\\\"},\\n \\\"retriever\\\": {\\\"display_name\\\": \\\"Retriever\\\"},\\n }\\n\\n def build(\\n self,\\n inputs: str,\\n retriever: BaseRetriever,\\n llm: BaseLanguageModel,\\n chain_type: str,\\n memory: Optional[BaseMemory] = None,\\n return_source_documents: Optional[bool] = True,\\n ) -> Text:\\n runnable = RetrievalQAWithSourcesChain.from_chain_type(\\n llm=llm,\\n chain_type=chain_type,\\n memory=memory,\\n return_source_documents=return_source_documents,\\n retriever=retriever,\\n )\\n if isinstance(inputs, Document):\\n inputs = inputs.page_content\\n self.status = runnable\\n input_key = runnable.input_keys[0]\\n result = runnable.invoke({input_key: inputs})\\n result = result.content if hasattr(result, \\\"content\\\") else result\\n # Result is a dict with keys \\\"query\\\", \\\"result\\\" and \\\"source_documents\\\"\\n # for now we just return the result\\n records = self.to_records(result.get(\\\"source_documents\\\"))\\n references_str = \\\"\\\"\\n if return_source_documents:\\n references_str = self.create_references_from_records(records)\\n result_str = result.get(\\\"answer\\\")\\n final_result = \\\"\\\\n\\\".join([result_str, references_str])\\n self.status = final_result\\n return final_result\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"inputs\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"inputs\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"return_source_documents\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":true,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"return_source_documents\",\"display_name\":\"Return Source Documents\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Question-answering with sources over an index.\",\"base_classes\":[\"Text\",\"object\"],\"display_name\":\"RetrievalQAWithSourcesChain\",\"documentation\":\"\",\"custom_fields\":{\"inputs\":null,\"retriever\":null,\"llm\":null,\"chain_type\":null,\"memory\":null,\"return_source_documents\":null},\"output_types\":[\"Text\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"SQLDatabaseChain\":{\"template\":{\"db\":{\"type\":\"SQLDatabase\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"db\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"llm\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"prompt\":{\"type\":\"BasePromptTemplate\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"prompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"SQLDatabaseChain\"},\"description\":\"Create a SQLDatabaseChain from an LLM and a database connection.\",\"base_classes\":[\"Runnable\",\"Chain\",\"Generic\",\"Text\",\"RunnableSerializable\",\"Serializable\",\"object\",\"SQLDatabaseChain\",\"Callable\"],\"display_name\":\"SQLDatabaseChain\",\"documentation\":\"\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":false,\"beta\":false,\"output_type\":\"Chain\"},\"CombineDocsChain\":{\"template\":{\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"chain_type\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"stuff\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"stuff\",\"map_reduce\",\"map_rerank\",\"refine\"],\"name\":\"chain_type\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"load_qa_chain\"},\"description\":\"Load question answering chain.\",\"base_classes\":[\"function\",\"BaseCombineDocumentsChain\"],\"display_name\":\"CombineDocsChain\",\"documentation\":\"\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":false,\"beta\":false,\"output_type\":\"Chain\"},\"SeriesCharacterChain\":{\"template\":{\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"character\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"character\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"series\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"series\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"SeriesCharacterChain\"},\"description\":\"SeriesCharacterChain is a chain you can use to have a conversation with a character from a series.\",\"base_classes\":[\"Chain\",\"BaseCustomChain\",\"ConversationChain\",\"function\",\"SeriesCharacterChain\",\"LLMChain\"],\"display_name\":\"SeriesCharacterChain\",\"documentation\":\"\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":false,\"beta\":false,\"output_type\":\"Chain\"},\"MidJourneyPromptChain\":{\"template\":{\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"memory\":{\"type\":\"BaseChatMemory\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"MidJourneyPromptChain\"},\"description\":\"MidJourneyPromptChain is a chain you can use to generate new MidJourney prompts.\",\"base_classes\":[\"Chain\",\"BaseCustomChain\",\"MidJourneyPromptChain\",\"ConversationChain\",\"LLMChain\"],\"display_name\":\"MidJourneyPromptChain\",\"documentation\":\"\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":false,\"beta\":false,\"output_type\":\"Chain\"},\"TimeTravelGuideChain\":{\"template\":{\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"memory\":{\"type\":\"BaseChatMemory\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"TimeTravelGuideChain\"},\"description\":\"Time travel guide chain.\",\"base_classes\":[\"Chain\",\"BaseCustomChain\",\"ConversationChain\",\"TimeTravelGuideChain\",\"LLMChain\"],\"display_name\":\"TimeTravelGuideChain\",\"documentation\":\"\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":false,\"beta\":false,\"output_type\":\"Chain\"},\"LLMChain\":{\"template\":{\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"memory\":{\"type\":\"BaseMemory\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"memory\",\"display_name\":\"Memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"prompt\":{\"type\":\"BasePromptTemplate\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"prompt\",\"display_name\":\"Prompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain.chains import LLMChain\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import (\\n BaseLanguageModel,\\n BaseMemory,\\n BasePromptTemplate,\\n Text,\\n)\\n\\n\\nclass LLMChainComponent(CustomComponent):\\n display_name = \\\"LLMChain\\\"\\n description = \\\"Chain to run queries against LLMs\\\"\\n\\n def build_config(self):\\n return {\\n \\\"prompt\\\": {\\\"display_name\\\": \\\"Prompt\\\"},\\n \\\"llm\\\": {\\\"display_name\\\": \\\"LLM\\\"},\\n \\\"memory\\\": {\\\"display_name\\\": \\\"Memory\\\"},\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(\\n self,\\n prompt: BasePromptTemplate,\\n llm: BaseLanguageModel,\\n memory: Optional[BaseMemory] = None,\\n ) -> Text:\\n runnable = LLMChain(prompt=prompt, llm=llm, memory=memory)\\n result_dict = runnable.invoke({})\\n output_key = runnable.output_key\\n result = result_dict[output_key]\\n self.status = result\\n return result\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Chain to run queries against LLMs\",\"base_classes\":[\"Text\",\"object\"],\"display_name\":\"LLMChain\",\"documentation\":\"\",\"custom_fields\":{\"prompt\":null,\"llm\":null,\"memory\":null},\"output_types\":[\"Text\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"SQLGenerator\":{\"template\":{\"db\":{\"type\":\"SQLDatabase\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"db\",\"display_name\":\"Database\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"inputs\":{\"type\":\"Text\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"inputs\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"prompt\":{\"type\":\"PromptTemplate\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"prompt\",\"display_name\":\"Prompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"The prompt must contain `{question}`.\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain.chains import create_sql_query_chain\\nfrom langchain_community.utilities.sql_database import SQLDatabase\\nfrom langchain_core.prompts import PromptTemplate\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import BaseLanguageModel, Text\\n\\n\\nclass SQLGeneratorComponent(CustomComponent):\\n display_name = \\\"Natural Language to SQL\\\"\\n description = \\\"Generate SQL from natural language.\\\"\\n\\n def build_config(self):\\n return {\\n \\\"db\\\": {\\\"display_name\\\": \\\"Database\\\"},\\n \\\"llm\\\": {\\\"display_name\\\": \\\"LLM\\\"},\\n \\\"prompt\\\": {\\n \\\"display_name\\\": \\\"Prompt\\\",\\n \\\"info\\\": \\\"The prompt must contain `{question}`.\\\",\\n },\\n \\\"top_k\\\": {\\n \\\"display_name\\\": \\\"Top K\\\",\\n \\\"info\\\": \\\"The number of results per select statement to return. If 0, no limit.\\\",\\n },\\n }\\n\\n def build(\\n self,\\n inputs: Text,\\n db: SQLDatabase,\\n llm: BaseLanguageModel,\\n top_k: int = 5,\\n prompt: Optional[PromptTemplate] = None,\\n ) -> Text:\\n if top_k > 0:\\n kwargs = {\\n \\\"k\\\": top_k,\\n }\\n if not prompt:\\n sql_query_chain = create_sql_query_chain(llm=llm, db=db, **kwargs)\\n else:\\n template = prompt.template if hasattr(prompt, \\\"template\\\") else prompt\\n # Check if {question} is in the prompt\\n if \\\"{question}\\\" not in template or \\\"question\\\" not in template.input_variables:\\n raise ValueError(\\\"Prompt must contain `{question}` to be used with Natural Language to SQL.\\\")\\n sql_query_chain = create_sql_query_chain(llm=llm, db=db, prompt=prompt, **kwargs)\\n query_writer = sql_query_chain | {\\\"query\\\": lambda x: x.replace(\\\"SQLQuery:\\\", \\\"\\\").strip()}\\n response = query_writer.invoke({\\\"question\\\": inputs})\\n query = response.get(\\\"query\\\")\\n self.status = query\\n return query\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"top_k\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":5,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_k\",\"display_name\":\"Top K\",\"advanced\":false,\"dynamic\":false,\"info\":\"The number of results per select statement to return. If 0, no limit.\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Generate SQL from natural language.\",\"base_classes\":[\"Text\",\"object\"],\"display_name\":\"Natural Language to SQL\",\"documentation\":\"\",\"custom_fields\":{\"inputs\":null,\"db\":null,\"llm\":null,\"top_k\":null,\"prompt\":null},\"output_types\":[\"Text\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"ConversationChain\":{\"template\":{\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"memory\":{\"type\":\"BaseMemory\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"memory\",\"display_name\":\"Memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"Memory to load context from. If none is provided, a ConversationBufferMemory will be used.\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Callable, Optional, Union\\n\\nfrom langchain.chains import ConversationChain\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import BaseLanguageModel, BaseMemory, Chain, Text\\n\\n\\nclass ConversationChainComponent(CustomComponent):\\n display_name = \\\"ConversationChain\\\"\\n description = \\\"Chain to have a conversation and load context from memory.\\\"\\n\\n def build_config(self):\\n return {\\n \\\"prompt\\\": {\\\"display_name\\\": \\\"Prompt\\\"},\\n \\\"llm\\\": {\\\"display_name\\\": \\\"LLM\\\"},\\n \\\"memory\\\": {\\n \\\"display_name\\\": \\\"Memory\\\",\\n \\\"info\\\": \\\"Memory to load context from. If none is provided, a ConversationBufferMemory will be used.\\\",\\n },\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(\\n self,\\n inputs: str,\\n llm: BaseLanguageModel,\\n memory: Optional[BaseMemory] = None,\\n ) -> Union[Chain, Callable, Text]:\\n if memory is None:\\n chain = ConversationChain(llm=llm)\\n else:\\n chain = ConversationChain(llm=llm, memory=memory)\\n result = chain.invoke(inputs)\\n # result is an AIMessage which is a subclass of BaseMessage\\n # We need to check if it is a string or a BaseMessage\\n if hasattr(result, \\\"content\\\") and isinstance(result.content, str):\\n self.status = \\\"is message\\\"\\n result = result.content\\n elif isinstance(result, str):\\n self.status = \\\"is_string\\\"\\n result = result\\n else:\\n # is dict\\n result = result.get(\\\"response\\\")\\n self.status = result\\n return result\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"inputs\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"inputs\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Chain to have a conversation and load context from memory.\",\"base_classes\":[\"Runnable\",\"Chain\",\"Generic\",\"Text\",\"RunnableSerializable\",\"Serializable\",\"object\",\"Callable\"],\"display_name\":\"ConversationChain\",\"documentation\":\"\",\"custom_fields\":{\"inputs\":null,\"llm\":null,\"memory\":null},\"output_types\":[\"Chain\",\"Callable\",\"Text\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true}},\"agents\":{\"ZeroShotAgent\":{\"template\":{\"callback_manager\":{\"type\":\"BaseCallbackManager\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"callback_manager\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"llm\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"output_parser\":{\"type\":\"AgentOutputParser\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"output_parser\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"tools\":{\"type\":\"BaseTool\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"tools\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"format_instructions\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":true,\"value\":\"Use the following format:\\n\\nQuestion: the input question you must answer\\nThought: you should always think about what to do\\nAction: the action to take, should be one of [{tool_names}]\\nAction Input: the input to the action\\nObservation: the result of the action\\n... (this Thought/Action/Action Input/Observation can repeat N times)\\nThought: I now know the final answer\\nFinal Answer: the final answer to the original input question\",\"fileTypes\":[],\"password\":false,\"name\":\"format_instructions\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"input_variables\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"input_variables\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"prefix\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"Answer the following questions as best you can. You have access to the following tools:\",\"fileTypes\":[],\"password\":false,\"name\":\"prefix\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"suffix\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"Begin!\\n\\nQuestion: {input}\\nThought:{agent_scratchpad}\",\"fileTypes\":[],\"password\":false,\"name\":\"suffix\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"ZeroShotAgent\"},\"description\":\"Construct an agent from an LLM and tools.\",\"base_classes\":[\"ZeroShotAgent\",\"Callable\",\"BaseSingleActionAgent\",\"Agent\"],\"display_name\":\"ZeroShotAgent\",\"documentation\":\"https://python.langchain.com/docs/modules/agents/how_to/custom_mrkl_agent\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"JsonAgent\":{\"template\":{\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"toolkit\":{\"type\":\"JsonToolkit\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"toolkit\",\"display_name\":\"Toolkit\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langchain.agents import AgentExecutor, create_json_agent\\nfrom langflow.field_typing import (\\n BaseLanguageModel,\\n)\\nfrom langchain_community.agent_toolkits.json.toolkit import JsonToolkit\\n\\n\\nclass JsonAgentComponent(CustomComponent):\\n display_name = \\\"JsonAgent\\\"\\n description = \\\"Construct a json agent from an LLM and tools.\\\"\\n\\n def build_config(self):\\n return {\\n \\\"llm\\\": {\\\"display_name\\\": \\\"LLM\\\"},\\n \\\"toolkit\\\": {\\\"display_name\\\": \\\"Toolkit\\\"},\\n }\\n\\n def build(\\n self,\\n llm: BaseLanguageModel,\\n toolkit: JsonToolkit,\\n ) -> AgentExecutor:\\n return create_json_agent(llm=llm, toolkit=toolkit)\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Construct a json agent from an LLM and tools.\",\"base_classes\":[\"Runnable\",\"Chain\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"AgentExecutor\",\"object\"],\"display_name\":\"JsonAgent\",\"documentation\":\"\",\"custom_fields\":{\"llm\":null,\"toolkit\":null},\"output_types\":[\"AgentExecutor\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"CSVAgent\":{\"template\":{\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"path\":{\"type\":\"file\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[\".csv\"],\"file_path\":\"\",\"password\":false,\"name\":\"path\",\"display_name\":\"Path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langflow.field_typing import BaseLanguageModel, AgentExecutor\\nfrom langchain_experimental.agents.agent_toolkits.csv.base import create_csv_agent\\n\\n\\nclass CSVAgentComponent(CustomComponent):\\n display_name = \\\"CSVAgent\\\"\\n description = \\\"Construct a CSV agent from a CSV and tools.\\\"\\n documentation = \\\"https://python.langchain.com/docs/modules/agents/toolkits/csv\\\"\\n\\n def build_config(self):\\n return {\\n \\\"llm\\\": {\\\"display_name\\\": \\\"LLM\\\", \\\"type\\\": BaseLanguageModel},\\n \\\"path\\\": {\\\"display_name\\\": \\\"Path\\\", \\\"field_type\\\": \\\"file\\\", \\\"suffixes\\\": [\\\".csv\\\"], \\\"file_types\\\": [\\\".csv\\\"]},\\n }\\n\\n def build(\\n self,\\n llm: BaseLanguageModel,\\n path: str,\\n ) -> AgentExecutor:\\n # Instantiate and return the CSV agent class with the provided llm and path\\n return create_csv_agent(llm=llm, path=path)\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Construct a CSV agent from a CSV and tools.\",\"base_classes\":[\"Runnable\",\"Chain\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"AgentExecutor\",\"object\"],\"display_name\":\"CSVAgent\",\"documentation\":\"https://python.langchain.com/docs/modules/agents/toolkits/csv\",\"custom_fields\":{\"llm\":null,\"path\":null},\"output_types\":[\"AgentExecutor\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"VectorStoreAgent\":{\"template\":{\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"vector_store_toolkit\":{\"type\":\"VectorStoreToolkit\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"vector_store_toolkit\",\"display_name\":\"Vector Store Info\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langchain.agents import AgentExecutor, create_vectorstore_agent\\nfrom langchain.agents.agent_toolkits.vectorstore.toolkit import VectorStoreToolkit\\nfrom typing import Union, Callable\\nfrom langflow.field_typing import BaseLanguageModel\\n\\n\\nclass VectorStoreAgentComponent(CustomComponent):\\n display_name = \\\"VectorStoreAgent\\\"\\n description = \\\"Construct an agent from a Vector Store.\\\"\\n\\n def build_config(self):\\n return {\\n \\\"llm\\\": {\\\"display_name\\\": \\\"LLM\\\"},\\n \\\"vector_store_toolkit\\\": {\\\"display_name\\\": \\\"Vector Store Info\\\"},\\n }\\n\\n def build(\\n self,\\n llm: BaseLanguageModel,\\n vector_store_toolkit: VectorStoreToolkit,\\n ) -> Union[AgentExecutor, Callable]:\\n return create_vectorstore_agent(llm=llm, toolkit=vector_store_toolkit)\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Construct an agent from a Vector Store.\",\"base_classes\":[\"Runnable\",\"Chain\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"AgentExecutor\",\"object\",\"Callable\"],\"display_name\":\"VectorStoreAgent\",\"documentation\":\"\",\"custom_fields\":{\"llm\":null,\"vector_store_toolkit\":null},\"output_types\":[\"AgentExecutor\",\"Callable\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"VectorStoreRouterAgent\":{\"template\":{\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"vectorstoreroutertoolkit\":{\"type\":\"VectorStoreRouterToolkit\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"vectorstoreroutertoolkit\",\"display_name\":\"Vector Store Router Toolkit\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langchain_core.language_models.base import BaseLanguageModel\\nfrom langchain.agents.agent_toolkits.vectorstore.toolkit import VectorStoreRouterToolkit\\nfrom langchain.agents import create_vectorstore_router_agent\\nfrom typing import Callable\\n\\n\\nclass VectorStoreRouterAgentComponent(CustomComponent):\\n display_name = \\\"VectorStoreRouterAgent\\\"\\n description = \\\"Construct an agent from a Vector Store Router.\\\"\\n\\n def build_config(self):\\n return {\\n \\\"llm\\\": {\\\"display_name\\\": \\\"LLM\\\"},\\n \\\"vectorstoreroutertoolkit\\\": {\\\"display_name\\\": \\\"Vector Store Router Toolkit\\\"},\\n }\\n\\n def build(self, llm: BaseLanguageModel, vectorstoreroutertoolkit: VectorStoreRouterToolkit) -> Callable:\\n return create_vectorstore_router_agent(llm=llm, toolkit=vectorstoreroutertoolkit)\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Construct an agent from a Vector Store Router.\",\"base_classes\":[\"Callable\"],\"display_name\":\"VectorStoreRouterAgent\",\"documentation\":\"\",\"custom_fields\":{\"llm\":null,\"vectorstoreroutertoolkit\":null},\"output_types\":[\"Callable\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"SQLAgent\":{\"template\":{\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom typing import Union, Callable\\nfrom langchain.agents import AgentExecutor\\nfrom langflow.field_typing import BaseLanguageModel\\nfrom langchain_community.agent_toolkits.sql.base import create_sql_agent\\nfrom langchain.sql_database import SQLDatabase\\nfrom langchain_community.agent_toolkits import SQLDatabaseToolkit\\n\\n\\nclass SQLAgentComponent(CustomComponent):\\n display_name = \\\"SQLAgent\\\"\\n description = \\\"Construct an SQL agent from an LLM and tools.\\\"\\n\\n def build_config(self):\\n return {\\n \\\"llm\\\": {\\\"display_name\\\": \\\"LLM\\\"},\\n \\\"database_uri\\\": {\\\"display_name\\\": \\\"Database URI\\\"},\\n \\\"verbose\\\": {\\\"display_name\\\": \\\"Verbose\\\", \\\"value\\\": False, \\\"advanced\\\": True},\\n }\\n\\n def build(\\n self,\\n llm: BaseLanguageModel,\\n database_uri: str,\\n verbose: bool = False,\\n ) -> Union[AgentExecutor, Callable]:\\n db = SQLDatabase.from_uri(database_uri)\\n toolkit = SQLDatabaseToolkit(db=db, llm=llm)\\n return create_sql_agent(llm=llm, toolkit=toolkit)\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"database_uri\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"database_uri\",\"display_name\":\"Database URI\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"verbose\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"verbose\",\"display_name\":\"Verbose\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Construct an SQL agent from an LLM and tools.\",\"base_classes\":[\"Runnable\",\"Chain\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"AgentExecutor\",\"object\",\"Callable\"],\"display_name\":\"SQLAgent\",\"documentation\":\"\",\"custom_fields\":{\"llm\":null,\"database_uri\":null,\"verbose\":null},\"output_types\":[\"AgentExecutor\",\"Callable\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"OpenAIConversationalAgent\":{\"template\":{\"memory\":{\"type\":\"BaseMemory\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"memory\",\"display_name\":\"Memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"system_message\":{\"type\":\"SystemMessagePromptTemplate\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"system_message\",\"display_name\":\"System Message\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"tools\":{\"type\":\"Tool\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"tools\",\"display_name\":\"Tools\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import List, Optional\\n\\nfrom langchain.agents.agent import AgentExecutor\\nfrom langchain.agents.agent_toolkits.conversational_retrieval.openai_functions import _get_default_system_message\\nfrom langchain.agents.openai_functions_agent.base import OpenAIFunctionsAgent\\nfrom langchain.memory.token_buffer import ConversationTokenBufferMemory\\nfrom langchain.prompts import SystemMessagePromptTemplate\\nfrom langchain.prompts.chat import MessagesPlaceholder\\nfrom langchain.schema.memory import BaseMemory\\nfrom langchain.tools import Tool\\nfrom langchain_community.chat_models import ChatOpenAI\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing.range_spec import RangeSpec\\n\\n\\nclass ConversationalAgent(CustomComponent):\\n display_name: str = \\\"OpenAI Conversational Agent\\\"\\n description: str = \\\"Conversational Agent that can use OpenAI's function calling API\\\"\\n\\n def build_config(self):\\n openai_function_models = [\\n \\\"gpt-4-turbo-preview\\\",\\n \\\"gpt-4-0125-preview\\\",\\n \\\"gpt-4-1106-preview\\\",\\n \\\"gpt-4-vision-preview\\\",\\n \\\"gpt-3.5-turbo-0125\\\",\\n \\\"gpt-3.5-turbo-1106\\\",\\n ]\\n return {\\n \\\"tools\\\": {\\\"display_name\\\": \\\"Tools\\\"},\\n \\\"memory\\\": {\\\"display_name\\\": \\\"Memory\\\"},\\n \\\"system_message\\\": {\\\"display_name\\\": \\\"System Message\\\"},\\n \\\"max_token_limit\\\": {\\\"display_name\\\": \\\"Max Token Limit\\\"},\\n \\\"model_name\\\": {\\n \\\"display_name\\\": \\\"Model Name\\\",\\n \\\"options\\\": openai_function_models,\\n \\\"value\\\": openai_function_models[0],\\n },\\n \\\"code\\\": {\\\"show\\\": False},\\n \\\"temperature\\\": {\\n \\\"display_name\\\": \\\"Temperature\\\",\\n \\\"value\\\": 0.2,\\n \\\"range_spec\\\": RangeSpec(min=0, max=2, step=0.1),\\n },\\n }\\n\\n def build(\\n self,\\n model_name: str,\\n openai_api_key: str,\\n tools: List[Tool],\\n openai_api_base: Optional[str] = None,\\n memory: Optional[BaseMemory] = None,\\n system_message: Optional[SystemMessagePromptTemplate] = None,\\n max_token_limit: int = 2000,\\n temperature: float = 0.9,\\n ) -> AgentExecutor:\\n llm = ChatOpenAI(\\n model=model_name,\\n api_key=openai_api_key,\\n base_url=openai_api_base,\\n max_tokens=max_token_limit,\\n temperature=temperature,\\n )\\n if not memory:\\n memory_key = \\\"chat_history\\\"\\n memory = ConversationTokenBufferMemory(\\n memory_key=memory_key,\\n return_messages=True,\\n output_key=\\\"output\\\",\\n llm=llm,\\n max_token_limit=max_token_limit,\\n )\\n else:\\n memory_key = memory.memory_key # type: ignore\\n\\n _system_message = system_message or _get_default_system_message()\\n prompt = OpenAIFunctionsAgent.create_prompt(\\n system_message=_system_message, # type: ignore\\n extra_prompt_messages=[MessagesPlaceholder(variable_name=memory_key)],\\n )\\n agent = OpenAIFunctionsAgent(\\n llm=llm,\\n tools=tools,\\n prompt=prompt, # type: ignore\\n )\\n return AgentExecutor(\\n agent=agent,\\n tools=tools, # type: ignore\\n memory=memory,\\n verbose=True,\\n return_intermediate_steps=True,\\n handle_parsing_errors=True,\\n )\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"max_token_limit\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":2000,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_token_limit\",\"display_name\":\"Max Token Limit\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"model_name\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"gpt-4-turbo-preview\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"gpt-4-turbo-preview\",\"gpt-4-0125-preview\",\"gpt-4-1106-preview\",\"gpt-4-vision-preview\",\"gpt-3.5-turbo-0125\",\"gpt-3.5-turbo-1106\"],\"name\":\"model_name\",\"display_name\":\"Model Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"openai_api_base\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"openai_api_base\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"openai_api_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"openai_api_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"temperature\":{\"type\":\"float\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.2,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":0.0,\"max\":2.0,\"step\":0.1},\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Conversational Agent that can use OpenAI's function calling API\",\"base_classes\":[\"Runnable\",\"Chain\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"AgentExecutor\",\"object\"],\"display_name\":\"OpenAI Conversational Agent\",\"documentation\":\"\",\"custom_fields\":{\"model_name\":null,\"openai_api_key\":null,\"tools\":null,\"openai_api_base\":null,\"memory\":null,\"system_message\":null,\"max_token_limit\":null,\"temperature\":null},\"output_types\":[\"AgentExecutor\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"AgentInitializer\":{\"template\":{\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm\",\"display_name\":\"Language Model\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"memory\":{\"type\":\"BaseChatMemory\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"memory\",\"display_name\":\"Memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"tools\":{\"type\":\"Tool\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"tools\",\"display_name\":\"Tools\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"agent\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"zero-shot-react-description\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"zero-shot-react-description\",\"react-docstore\",\"self-ask-with-search\",\"conversational-react-description\",\"chat-zero-shot-react-description\",\"chat-conversational-react-description\",\"structured-chat-zero-shot-react-description\",\"openai-functions\",\"openai-multi-functions\",\"JsonAgent\",\"CSVAgent\",\"VectorStoreAgent\",\"VectorStoreRouterAgent\",\"SQLAgent\"],\"name\":\"agent\",\"display_name\":\"Agent Type\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Callable, List, Optional, Union\\n\\nfrom langchain.agents import AgentExecutor, AgentType, initialize_agent, types\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import BaseChatMemory, BaseLanguageModel, Tool\\n\\n\\nclass AgentInitializerComponent(CustomComponent):\\n display_name: str = \\\"Agent Initializer\\\"\\n description: str = \\\"Initialize a Langchain Agent.\\\"\\n documentation: str = \\\"https://python.langchain.com/docs/modules/agents/agent_types/\\\"\\n\\n def build_config(self):\\n agents = list(types.AGENT_TO_CLASS.keys())\\n # field_type and required are optional\\n return {\\n \\\"agent\\\": {\\\"options\\\": agents, \\\"value\\\": agents[0], \\\"display_name\\\": \\\"Agent Type\\\"},\\n \\\"max_iterations\\\": {\\\"display_name\\\": \\\"Max Iterations\\\", \\\"value\\\": 10},\\n \\\"memory\\\": {\\\"display_name\\\": \\\"Memory\\\"},\\n \\\"tools\\\": {\\\"display_name\\\": \\\"Tools\\\"},\\n \\\"llm\\\": {\\\"display_name\\\": \\\"Language Model\\\"},\\n \\\"code\\\": {\\\"advanced\\\": True},\\n }\\n\\n def build(\\n self,\\n agent: str,\\n llm: BaseLanguageModel,\\n tools: List[Tool],\\n max_iterations: int,\\n memory: Optional[BaseChatMemory] = None,\\n ) -> Union[AgentExecutor, Callable]:\\n agent = AgentType(agent)\\n if memory:\\n return initialize_agent(\\n tools=tools,\\n llm=llm,\\n agent=agent,\\n memory=memory,\\n return_intermediate_steps=True,\\n handle_parsing_errors=True,\\n max_iterations=max_iterations,\\n )\\n return initialize_agent(\\n tools=tools,\\n llm=llm,\\n agent=agent,\\n return_intermediate_steps=True,\\n handle_parsing_errors=True,\\n max_iterations=max_iterations,\\n )\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":true,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"max_iterations\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":10,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_iterations\",\"display_name\":\"Max Iterations\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Initialize a Langchain Agent.\",\"base_classes\":[\"Runnable\",\"Chain\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"AgentExecutor\",\"object\",\"Callable\"],\"display_name\":\"Agent Initializer\",\"documentation\":\"https://python.langchain.com/docs/modules/agents/agent_types/\",\"custom_fields\":{\"agent\":null,\"llm\":null,\"tools\":null,\"max_iterations\":null,\"memory\":null},\"output_types\":[\"AgentExecutor\",\"Callable\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true}},\"memories\":{\"ConversationBufferMemory\":{\"template\":{\"chat_memory\":{\"type\":\"BaseChatMessageHistory\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"chat_memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"ai_prefix\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"AI\",\"fileTypes\":[],\"password\":false,\"name\":\"ai_prefix\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"human_prefix\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"Human\",\"fileTypes\":[],\"password\":false,\"name\":\"human_prefix\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"input_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"input_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"The variable to be used as Chat Input when more than one variable is available.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"memory_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"chat_history\",\"fileTypes\":[],\"password\":false,\"name\":\"memory_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"output_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"output_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"The variable to be used as Chat Output (e.g. answer in a ConversationalRetrievalChain)\",\"title_case\":false,\"input_types\":[\"Text\"]},\"return_messages\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"return_messages\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"ConversationBufferMemory\"},\"description\":\"Buffer for storing conversation memory.\",\"base_classes\":[\"BaseMemory\",\"BaseChatMemory\",\"ConversationBufferMemory\",\"Serializable\"],\"display_name\":\"ConversationBufferMemory\",\"documentation\":\"https://python.langchain.com/docs/modules/memory/how_to/buffer\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":true,\"beta\":false},\"ConversationBufferWindowMemory\":{\"template\":{\"chat_memory\":{\"type\":\"BaseChatMessageHistory\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"chat_memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"ai_prefix\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"AI\",\"fileTypes\":[],\"password\":false,\"name\":\"ai_prefix\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"human_prefix\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"Human\",\"fileTypes\":[],\"password\":false,\"name\":\"human_prefix\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"input_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"input_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"The variable to be used as Chat Input when more than one variable is available.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"k\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":10,\"fileTypes\":[],\"password\":false,\"name\":\"k\",\"display_name\":\"Memory Size\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"memory_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"chat_history\",\"fileTypes\":[],\"password\":false,\"name\":\"memory_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"output_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"output_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"The variable to be used as Chat Output (e.g. answer in a ConversationalRetrievalChain)\",\"title_case\":false,\"input_types\":[\"Text\"]},\"return_messages\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"return_messages\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"ConversationBufferWindowMemory\"},\"description\":\"Buffer for storing conversation memory inside a limited size window.\",\"base_classes\":[\"ConversationBufferWindowMemory\",\"BaseMemory\",\"BaseChatMemory\",\"Serializable\"],\"display_name\":\"ConversationBufferWindowMemory\",\"documentation\":\"https://python.langchain.com/docs/modules/memory/how_to/buffer_window\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":true,\"beta\":false},\"ConversationEntityMemory\":{\"template\":{\"chat_memory\":{\"type\":\"BaseChatMessageHistory\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"chat_memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"entity_extraction_prompt\":{\"type\":\"BasePromptTemplate\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"entity_extraction_prompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"entity_store\":{\"type\":\"BaseEntityStore\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"entity_store\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"entity_summarization_prompt\":{\"type\":\"BasePromptTemplate\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"entity_summarization_prompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"llm\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"ai_prefix\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"AI\",\"fileTypes\":[],\"password\":false,\"name\":\"ai_prefix\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"chat_history_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"history\",\"fileTypes\":[],\"password\":false,\"name\":\"chat_history_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"entity_cache\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"entity_cache\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"human_prefix\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"Human\",\"fileTypes\":[],\"password\":false,\"name\":\"human_prefix\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"input_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"input_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"The variable to be used as Chat Input when more than one variable is available.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"k\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":10,\"fileTypes\":[],\"password\":false,\"name\":\"k\",\"display_name\":\"Memory Size\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"output_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"output_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"The variable to be used as Chat Output (e.g. answer in a ConversationalRetrievalChain)\",\"title_case\":false,\"input_types\":[\"Text\"]},\"return_messages\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"return_messages\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"ConversationEntityMemory\"},\"description\":\"Entity extractor & summarizer memory.\",\"base_classes\":[\"ConversationEntityMemory\",\"BaseMemory\",\"BaseChatMemory\",\"Serializable\"],\"display_name\":\"ConversationEntityMemory\",\"documentation\":\"https://python.langchain.com/docs/modules/memory/integrations/entity_memory_with_sqlite\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":true,\"beta\":false},\"ConversationKGMemory\":{\"template\":{\"chat_memory\":{\"type\":\"BaseChatMessageHistory\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"chat_memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"entity_extraction_prompt\":{\"type\":\"BasePromptTemplate\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"entity_extraction_prompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"kg\":{\"type\":\"NetworkxEntityGraph\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"kg\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"knowledge_extraction_prompt\":{\"type\":\"BasePromptTemplate\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"knowledge_extraction_prompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"llm\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"summary_message_cls\":{\"type\":\"Type[langchain_core.messages.base.BaseMessage]\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"summary_message_cls\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"ai_prefix\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"AI\",\"fileTypes\":[],\"password\":false,\"name\":\"ai_prefix\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"human_prefix\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"Human\",\"fileTypes\":[],\"password\":false,\"name\":\"human_prefix\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"input_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"input_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"The variable to be used as Chat Input when more than one variable is available.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"k\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":10,\"fileTypes\":[],\"password\":false,\"name\":\"k\",\"display_name\":\"Memory Size\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"memory_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"chat_history\",\"fileTypes\":[],\"password\":false,\"name\":\"memory_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"output_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"output_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"The variable to be used as Chat Output (e.g. answer in a ConversationalRetrievalChain)\",\"title_case\":false,\"input_types\":[\"Text\"]},\"return_messages\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"return_messages\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"ConversationKGMemory\"},\"description\":\"Knowledge graph conversation memory.\",\"base_classes\":[\"BaseChatMemory\",\"BaseMemory\",\"ConversationKGMemory\",\"Serializable\"],\"display_name\":\"ConversationKGMemory\",\"documentation\":\"https://python.langchain.com/docs/modules/memory/how_to/kg\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":true,\"beta\":false},\"ConversationSummaryMemory\":{\"template\":{\"chat_memory\":{\"type\":\"BaseChatMessageHistory\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"chat_memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"llm\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"prompt\":{\"type\":\"BasePromptTemplate\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"prompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"summary_message_cls\":{\"type\":\"Type[langchain_core.messages.base.BaseMessage]\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"summary_message_cls\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"ai_prefix\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"AI\",\"fileTypes\":[],\"password\":false,\"name\":\"ai_prefix\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"buffer\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"password\":false,\"name\":\"buffer\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"human_prefix\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"Human\",\"fileTypes\":[],\"password\":false,\"name\":\"human_prefix\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"input_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"input_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"The variable to be used as Chat Input when more than one variable is available.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"memory_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"chat_history\",\"fileTypes\":[],\"password\":false,\"name\":\"memory_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"output_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"output_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"The variable to be used as Chat Output (e.g. answer in a ConversationalRetrievalChain)\",\"title_case\":false,\"input_types\":[\"Text\"]},\"return_messages\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"return_messages\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"ConversationSummaryMemory\"},\"description\":\"Conversation summarizer to chat memory.\",\"base_classes\":[\"BaseChatMemory\",\"Serializable\",\"ConversationSummaryMemory\",\"BaseMemory\",\"SummarizerMixin\"],\"display_name\":\"ConversationSummaryMemory\",\"documentation\":\"https://python.langchain.com/docs/modules/memory/how_to/summary\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":true,\"beta\":false},\"MongoDBChatMessageHistory\":{\"template\":{\"collection_name\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"message_store\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"collection_name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"connection_string\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"connection_string\",\"advanced\":false,\"dynamic\":false,\"info\":\"MongoDB connection string (e.g mongodb://mongo_user:password123@mongo:27017)\",\"title_case\":false,\"input_types\":[\"Text\"]},\"database_name\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"chat_history\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"database_name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"session_id\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"session_id\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"MongoDBChatMessageHistory\"},\"description\":\"Memory store with MongoDB\",\"base_classes\":[\"BaseChatMessageHistory\",\"MongoDBChatMessageHistory\"],\"display_name\":\"MongoDBChatMessageHistory\",\"documentation\":\"https://python.langchain.com/docs/modules/memory/integrations/mongodb_chat_message_history\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":true,\"beta\":false},\"MotorheadMemory\":{\"template\":{\"chat_memory\":{\"type\":\"BaseChatMessageHistory\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"chat_memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"api_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"api_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"client_id\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"client_id\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"context\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"context\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"input_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"input_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"The variable to be used as Chat Input when more than one variable is available.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"memory_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"chat_history\",\"fileTypes\":[],\"password\":false,\"name\":\"memory_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"output_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"output_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"The variable to be used as Chat Output (e.g. answer in a ConversationalRetrievalChain)\",\"title_case\":false,\"input_types\":[\"Text\"]},\"return_messages\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"return_messages\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"session_id\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"session_id\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"timeout\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"timeout\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"url\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"https://api.getmetal.io/v1/motorhead\",\"fileTypes\":[],\"password\":false,\"name\":\"url\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"MotorheadMemory\"},\"description\":\"Chat message memory backed by Motorhead service.\",\"base_classes\":[\"MotorheadMemory\",\"BaseChatMemory\",\"BaseMemory\",\"Serializable\"],\"display_name\":\"MotorheadMemory\",\"documentation\":\"https://python.langchain.com/docs/integrations/memory/motorhead_memory\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":true,\"beta\":false},\"PostgresChatMessageHistory\":{\"template\":{\"connection_string\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"postgresql://postgres:mypassword@localhost/chat_history\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"connection_string\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"session_id\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"session_id\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"table_name\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"message_store\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"table_name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"PostgresChatMessageHistory\"},\"description\":\"Memory store with Postgres\",\"base_classes\":[\"BaseChatMessageHistory\",\"PostgresChatMessageHistory\"],\"display_name\":\"PostgresChatMessageHistory\",\"documentation\":\"https://python.langchain.com/docs/modules/memory/integrations/postgres_chat_message_history\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":true,\"beta\":false},\"VectorStoreRetrieverMemory\":{\"template\":{\"retriever\":{\"type\":\"VectorStoreRetriever\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"retriever\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"exclude_input_keys\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"exclude_input_keys\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"input_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"input_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"The variable to be used as Chat Input when more than one variable is available.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"memory_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"chat_history\",\"fileTypes\":[],\"password\":false,\"name\":\"memory_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"return_docs\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"return_docs\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"return_messages\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"return_messages\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"VectorStoreRetrieverMemory\"},\"description\":\"VectorStoreRetriever-backed memory.\",\"base_classes\":[\"BaseMemory\",\"VectorStoreRetrieverMemory\",\"Serializable\"],\"display_name\":\"VectorStoreRetrieverMemory\",\"documentation\":\"https://python.langchain.com/docs/modules/memory/how_to/vectorstore_retriever_memory\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":true,\"beta\":false}},\"tools\":{\"Calculator\":{\"template\":{\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"Calculator\"},\"description\":\"Useful for when you need to answer questions about math.\",\"base_classes\":[\"BaseTool\",\"Tool\"],\"display_name\":\"Calculator\",\"documentation\":\"\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"Search\":{\"template\":{\"aiosession\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"aiosession\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"serpapi_api_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"serpapi_api_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"Search\"},\"description\":\"A search engine. Useful for when you need to answer questions about current events. Input should be a search query.\",\"base_classes\":[\"BaseTool\",\"Tool\"],\"display_name\":\"Search\",\"documentation\":\"\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"Tool\":{\"template\":{\"func\":{\"type\":\"Callable\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"func\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"description\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"description\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"name\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"return_direct\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"return_direct\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"Tool\"},\"description\":\"Converts a chain, agent or function into a tool.\",\"base_classes\":[\"BaseTool\",\"Tool\"],\"display_name\":\"Tool\",\"documentation\":\"\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"PythonFunctionTool\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"\\ndef python_function(text: str) -> str:\\n \\\"\\\"\\\"This is a default python function that returns the input text\\\"\\\"\\\"\\n return text\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"description\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"description\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"name\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"return_direct\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"return_direct\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"PythonFunctionTool\"},\"description\":\"Python function to be executed.\",\"base_classes\":[\"BaseTool\",\"Tool\"],\"display_name\":\"PythonFunctionTool\",\"documentation\":\"\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"PythonFunction\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"\\ndef python_function(text: str) -> str:\\n \\\"\\\"\\\"This is a default python function that returns the input text\\\"\\\"\\\"\\n return text\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"PythonFunction\"},\"description\":\"Python function to be executed.\",\"base_classes\":[\"Callable\"],\"display_name\":\"PythonFunction\",\"documentation\":\"\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"JsonSpec\":{\"template\":{\"path\":{\"type\":\"file\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[\".json\",\".yaml\",\".yml\"],\"file_path\":\"\",\"password\":false,\"name\":\"path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"max_value_length\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_value_length\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"JsonSpec\"},\"description\":\"\",\"base_classes\":[\"JsonSpec\",\"BaseTool\",\"Tool\"],\"display_name\":\"JsonSpec\",\"documentation\":\"\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"BingSearchRun\":{\"template\":{\"api_wrapper\":{\"type\":\"BingSearchAPIWrapper\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"api_wrapper\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"args_schema\":{\"type\":\"Type[pydantic.v1.main.BaseModel]\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"args_schema\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"callbacks\":{\"type\":\"langchain_core.callbacks.base.BaseCallbackHandler\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"callbacks\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"handle_tool_error\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"handle_tool_error\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"handle_validation_error\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"handle_validation_error\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"tags\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"tags\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"BingSearchRun\"},\"description\":\"\",\"base_classes\":[\"Runnable\",\"BingSearchRun\",\"Generic\",\"BaseTool\",\"RunnableSerializable\",\"Tool\",\"Serializable\",\"object\"],\"display_name\":\"BingSearchRun\",\"documentation\":\"\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"GoogleSearchResults\":{\"template\":{\"api_wrapper\":{\"type\":\"GoogleSearchAPIWrapper\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"api_wrapper\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"args_schema\":{\"type\":\"Type[pydantic.v1.main.BaseModel]\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"args_schema\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"callbacks\":{\"type\":\"langchain_core.callbacks.base.BaseCallbackHandler\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"callbacks\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"handle_tool_error\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"handle_tool_error\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"handle_validation_error\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"handle_validation_error\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"num_results\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":4,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"num_results\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"tags\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"tags\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"GoogleSearchResults\"},\"description\":\"\",\"base_classes\":[\"GoogleSearchResults\",\"Runnable\",\"Generic\",\"BaseTool\",\"RunnableSerializable\",\"Tool\",\"Serializable\",\"object\"],\"display_name\":\"GoogleSearchResults\",\"documentation\":\"\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"GoogleSearchRun\":{\"template\":{\"api_wrapper\":{\"type\":\"GoogleSearchAPIWrapper\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"api_wrapper\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"args_schema\":{\"type\":\"Type[pydantic.v1.main.BaseModel]\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"args_schema\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"callbacks\":{\"type\":\"langchain_core.callbacks.base.BaseCallbackHandler\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"callbacks\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"handle_tool_error\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"handle_tool_error\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"handle_validation_error\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"handle_validation_error\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"tags\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"tags\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"GoogleSearchRun\"},\"description\":\"\",\"base_classes\":[\"Runnable\",\"Generic\",\"BaseTool\",\"RunnableSerializable\",\"Tool\",\"GoogleSearchRun\",\"Serializable\",\"object\"],\"display_name\":\"GoogleSearchRun\",\"documentation\":\"\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"GoogleSerperRun\":{\"template\":{\"api_wrapper\":{\"type\":\"GoogleSerperAPIWrapper\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"api_wrapper\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"args_schema\":{\"type\":\"Type[pydantic.v1.main.BaseModel]\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"args_schema\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"callbacks\":{\"type\":\"langchain_core.callbacks.base.BaseCallbackHandler\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"callbacks\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"handle_tool_error\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"handle_tool_error\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"handle_validation_error\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"handle_validation_error\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"tags\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"tags\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"GoogleSerperRun\"},\"description\":\"\",\"base_classes\":[\"Runnable\",\"Generic\",\"BaseTool\",\"RunnableSerializable\",\"Tool\",\"Serializable\",\"GoogleSerperRun\",\"object\"],\"display_name\":\"GoogleSerperRun\",\"documentation\":\"\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"InfoSQLDatabaseTool\":{\"template\":{\"args_schema\":{\"type\":\"Type[pydantic.v1.main.BaseModel]\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"args_schema\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"callbacks\":{\"type\":\"langchain_core.callbacks.base.BaseCallbackHandler\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"callbacks\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"db\":{\"type\":\"SQLDatabase\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"db\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"handle_tool_error\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"handle_tool_error\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"handle_validation_error\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"handle_validation_error\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"tags\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"tags\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"InfoSQLDatabaseTool\"},\"description\":\"\",\"base_classes\":[\"InfoSQLDatabaseTool\",\"Runnable\",\"BaseSQLDatabaseTool\",\"Generic\",\"BaseTool\",\"RunnableSerializable\",\"Tool\",\"Serializable\",\"object\"],\"display_name\":\"InfoSQLDatabaseTool\",\"documentation\":\"\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"JsonGetValueTool\":{\"template\":{\"args_schema\":{\"type\":\"Type[pydantic.v1.main.BaseModel]\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"args_schema\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"callbacks\":{\"type\":\"langchain_core.callbacks.base.BaseCallbackHandler\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"callbacks\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"spec\":{\"type\":\"JsonSpec\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"spec\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"handle_tool_error\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"handle_tool_error\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"handle_validation_error\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"handle_validation_error\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"tags\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"tags\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"JsonGetValueTool\"},\"description\":\"\",\"base_classes\":[\"Runnable\",\"Generic\",\"BaseTool\",\"RunnableSerializable\",\"Tool\",\"Serializable\",\"object\",\"JsonGetValueTool\"],\"display_name\":\"JsonGetValueTool\",\"documentation\":\"\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"JsonListKeysTool\":{\"template\":{\"args_schema\":{\"type\":\"Type[pydantic.v1.main.BaseModel]\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"args_schema\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"callbacks\":{\"type\":\"langchain_core.callbacks.base.BaseCallbackHandler\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"callbacks\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"spec\":{\"type\":\"JsonSpec\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"spec\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"handle_tool_error\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"handle_tool_error\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"handle_validation_error\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"handle_validation_error\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"tags\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"tags\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"JsonListKeysTool\"},\"description\":\"\",\"base_classes\":[\"Runnable\",\"Generic\",\"BaseTool\",\"RunnableSerializable\",\"Tool\",\"Serializable\",\"object\",\"JsonListKeysTool\"],\"display_name\":\"JsonListKeysTool\",\"documentation\":\"\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"ListSQLDatabaseTool\":{\"template\":{\"args_schema\":{\"type\":\"Type[pydantic.v1.main.BaseModel]\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"args_schema\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"callbacks\":{\"type\":\"langchain_core.callbacks.base.BaseCallbackHandler\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"callbacks\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"db\":{\"type\":\"SQLDatabase\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"db\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"handle_tool_error\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"handle_tool_error\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"handle_validation_error\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"handle_validation_error\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"tags\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"tags\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"ListSQLDatabaseTool\"},\"description\":\"\",\"base_classes\":[\"Runnable\",\"BaseSQLDatabaseTool\",\"Generic\",\"BaseTool\",\"RunnableSerializable\",\"Tool\",\"Serializable\",\"object\",\"ListSQLDatabaseTool\"],\"display_name\":\"ListSQLDatabaseTool\",\"documentation\":\"\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"QuerySQLDataBaseTool\":{\"template\":{\"args_schema\":{\"type\":\"Type[pydantic.v1.main.BaseModel]\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"args_schema\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"callbacks\":{\"type\":\"langchain_core.callbacks.base.BaseCallbackHandler\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"callbacks\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"db\":{\"type\":\"SQLDatabase\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"db\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"handle_tool_error\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"handle_tool_error\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"handle_validation_error\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"handle_validation_error\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"tags\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"tags\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"QuerySQLDataBaseTool\"},\"description\":\"\",\"base_classes\":[\"Runnable\",\"BaseSQLDatabaseTool\",\"Generic\",\"BaseTool\",\"RunnableSerializable\",\"Tool\",\"QuerySQLDataBaseTool\",\"Serializable\",\"object\"],\"display_name\":\"QuerySQLDataBaseTool\",\"documentation\":\"\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"RequestsDeleteTool\":{\"template\":{\"args_schema\":{\"type\":\"Type[pydantic.v1.main.BaseModel]\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"args_schema\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"callbacks\":{\"type\":\"langchain_core.callbacks.base.BaseCallbackHandler\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"callbacks\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"requests_wrapper\":{\"type\":\"GenericRequestsWrapper\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"requests_wrapper\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"handle_tool_error\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"handle_tool_error\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"handle_validation_error\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"handle_validation_error\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"tags\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"tags\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"RequestsDeleteTool\"},\"description\":\"\",\"base_classes\":[\"Runnable\",\"Generic\",\"BaseTool\",\"RunnableSerializable\",\"Tool\",\"Serializable\",\"object\",\"RequestsDeleteTool\",\"BaseRequestsTool\"],\"display_name\":\"RequestsDeleteTool\",\"documentation\":\"\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"RequestsGetTool\":{\"template\":{\"args_schema\":{\"type\":\"Type[pydantic.v1.main.BaseModel]\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"args_schema\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"callbacks\":{\"type\":\"langchain_core.callbacks.base.BaseCallbackHandler\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"callbacks\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"requests_wrapper\":{\"type\":\"GenericRequestsWrapper\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"requests_wrapper\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"handle_tool_error\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"handle_tool_error\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"handle_validation_error\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"handle_validation_error\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"tags\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"tags\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"RequestsGetTool\"},\"description\":\"\",\"base_classes\":[\"Runnable\",\"Generic\",\"BaseTool\",\"RunnableSerializable\",\"Tool\",\"Serializable\",\"object\",\"RequestsGetTool\",\"BaseRequestsTool\"],\"display_name\":\"RequestsGetTool\",\"documentation\":\"\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"RequestsPatchTool\":{\"template\":{\"args_schema\":{\"type\":\"Type[pydantic.v1.main.BaseModel]\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"args_schema\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"callbacks\":{\"type\":\"langchain_core.callbacks.base.BaseCallbackHandler\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"callbacks\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"requests_wrapper\":{\"type\":\"GenericRequestsWrapper\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"requests_wrapper\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"handle_tool_error\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"handle_tool_error\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"handle_validation_error\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"handle_validation_error\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"tags\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"tags\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"RequestsPatchTool\"},\"description\":\"\",\"base_classes\":[\"Runnable\",\"RequestsPatchTool\",\"Generic\",\"BaseTool\",\"RunnableSerializable\",\"Tool\",\"Serializable\",\"object\",\"BaseRequestsTool\"],\"display_name\":\"RequestsPatchTool\",\"documentation\":\"\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"RequestsPostTool\":{\"template\":{\"args_schema\":{\"type\":\"Type[pydantic.v1.main.BaseModel]\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"args_schema\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"callbacks\":{\"type\":\"langchain_core.callbacks.base.BaseCallbackHandler\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"callbacks\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"requests_wrapper\":{\"type\":\"GenericRequestsWrapper\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"requests_wrapper\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"handle_tool_error\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"handle_tool_error\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"handle_validation_error\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"handle_validation_error\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"tags\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"tags\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"RequestsPostTool\"},\"description\":\"\",\"base_classes\":[\"Runnable\",\"Generic\",\"BaseTool\",\"RunnableSerializable\",\"Tool\",\"Serializable\",\"RequestsPostTool\",\"object\",\"BaseRequestsTool\"],\"display_name\":\"RequestsPostTool\",\"documentation\":\"\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"RequestsPutTool\":{\"template\":{\"args_schema\":{\"type\":\"Type[pydantic.v1.main.BaseModel]\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"args_schema\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"callbacks\":{\"type\":\"langchain_core.callbacks.base.BaseCallbackHandler\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"callbacks\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"requests_wrapper\":{\"type\":\"GenericRequestsWrapper\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"requests_wrapper\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"handle_tool_error\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"handle_tool_error\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"handle_validation_error\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"handle_validation_error\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"tags\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"tags\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"RequestsPutTool\"},\"description\":\"\",\"base_classes\":[\"RequestsPutTool\",\"Runnable\",\"Generic\",\"BaseTool\",\"RunnableSerializable\",\"Tool\",\"Serializable\",\"object\",\"BaseRequestsTool\"],\"display_name\":\"RequestsPutTool\",\"documentation\":\"\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"WikipediaQueryRun\":{\"template\":{\"api_wrapper\":{\"type\":\"WikipediaAPIWrapper\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"api_wrapper\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"args_schema\":{\"type\":\"Type[pydantic.v1.main.BaseModel]\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"args_schema\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"callbacks\":{\"type\":\"langchain_core.callbacks.base.BaseCallbackHandler\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"callbacks\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"handle_tool_error\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"handle_tool_error\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"handle_validation_error\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"handle_validation_error\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"tags\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"tags\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"WikipediaQueryRun\"},\"description\":\"\",\"base_classes\":[\"Runnable\",\"Generic\",\"BaseTool\",\"RunnableSerializable\",\"Tool\",\"WikipediaQueryRun\",\"Serializable\",\"object\"],\"display_name\":\"WikipediaQueryRun\",\"documentation\":\"\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"WolframAlphaQueryRun\":{\"template\":{\"api_wrapper\":{\"type\":\"WolframAlphaAPIWrapper\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"api_wrapper\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"args_schema\":{\"type\":\"Type[pydantic.v1.main.BaseModel]\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"args_schema\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"callbacks\":{\"type\":\"langchain_core.callbacks.base.BaseCallbackHandler\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"callbacks\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"handle_tool_error\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"handle_tool_error\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"handle_validation_error\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"handle_validation_error\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"tags\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"tags\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"WolframAlphaQueryRun\"},\"description\":\"\",\"base_classes\":[\"Runnable\",\"WolframAlphaQueryRun\",\"Generic\",\"BaseTool\",\"RunnableSerializable\",\"Tool\",\"Serializable\",\"object\"],\"display_name\":\"WolframAlphaQueryRun\",\"documentation\":\"\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":false,\"beta\":false}},\"toolkits\":{\"JsonToolkit\":{\"template\":{\"spec\":{\"type\":\"JsonSpec\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"spec\",\"display_name\":\"Spec\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langchain_community.tools.json.tool import JsonSpec\\nfrom langchain_community.agent_toolkits.json.toolkit import JsonToolkit\\n\\n\\nclass JsonToolkitComponent(CustomComponent):\\n display_name = \\\"JsonToolkit\\\"\\n description = \\\"Toolkit for interacting with a JSON spec.\\\"\\n\\n def build_config(self):\\n return {\\n \\\"spec\\\": {\\\"display_name\\\": \\\"Spec\\\", \\\"type\\\": JsonSpec},\\n }\\n\\n def build(self, spec: JsonSpec) -> JsonToolkit:\\n return JsonToolkit(spec=spec)\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Toolkit for interacting with a JSON spec.\",\"base_classes\":[\"BaseToolkit\",\"JsonToolkit\"],\"display_name\":\"JsonToolkit\",\"documentation\":\"\",\"custom_fields\":{\"spec\":null},\"output_types\":[\"JsonToolkit\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"OpenAPIToolkit\":{\"template\":{\"json_agent\":{\"type\":\"AgentExecutor\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"json_agent\",\"display_name\":\"JSON Agent\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"requests_wrapper\":{\"type\":\"TextRequestsWrapper\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"requests_wrapper\",\"display_name\":\"Text Requests Wrapper\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from langchain_community.agent_toolkits.openapi.toolkit import BaseToolkit, OpenAPIToolkit\\nfrom langchain_community.utilities.requests import TextRequestsWrapper\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import AgentExecutor\\n\\n\\nclass OpenAPIToolkitComponent(CustomComponent):\\n display_name = \\\"OpenAPIToolkit\\\"\\n description = \\\"Toolkit for interacting with an OpenAPI API.\\\"\\n\\n def build_config(self):\\n return {\\n \\\"json_agent\\\": {\\\"display_name\\\": \\\"JSON Agent\\\"},\\n \\\"requests_wrapper\\\": {\\\"display_name\\\": \\\"Text Requests Wrapper\\\"},\\n }\\n\\n def build(\\n self,\\n json_agent: AgentExecutor,\\n requests_wrapper: TextRequestsWrapper,\\n ) -> BaseToolkit:\\n return OpenAPIToolkit(json_agent=json_agent, requests_wrapper=requests_wrapper)\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Toolkit for interacting with an OpenAPI API.\",\"base_classes\":[\"BaseToolkit\"],\"display_name\":\"OpenAPIToolkit\",\"documentation\":\"\",\"custom_fields\":{\"json_agent\":null,\"requests_wrapper\":null},\"output_types\":[\"BaseToolkit\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"VectorStoreInfo\":{\"template\":{\"vectorstore\":{\"type\":\"VectorStore\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"vectorstore\",\"display_name\":\"VectorStore\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Callable, Union\\n\\nfrom langchain.agents.agent_toolkits.vectorstore.toolkit import VectorStoreInfo\\nfrom langchain_community.vectorstores import VectorStore\\n\\nfrom langflow import CustomComponent\\n\\n\\nclass VectorStoreInfoComponent(CustomComponent):\\n display_name = \\\"VectorStoreInfo\\\"\\n description = \\\"Information about a VectorStore\\\"\\n\\n def build_config(self):\\n return {\\n \\\"vectorstore\\\": {\\\"display_name\\\": \\\"VectorStore\\\"},\\n \\\"description\\\": {\\\"display_name\\\": \\\"Description\\\", \\\"multiline\\\": True},\\n \\\"name\\\": {\\\"display_name\\\": \\\"Name\\\"},\\n }\\n\\n def build(\\n self,\\n vectorstore: VectorStore,\\n description: str,\\n name: str,\\n ) -> Union[VectorStoreInfo, Callable]:\\n return VectorStoreInfo(vectorstore=vectorstore, description=description, name=name)\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"description\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"description\",\"display_name\":\"Description\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"name\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"name\",\"display_name\":\"Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Information about a VectorStore\",\"base_classes\":[\"Callable\",\"VectorStoreInfo\"],\"display_name\":\"VectorStoreInfo\",\"documentation\":\"\",\"custom_fields\":{\"vectorstore\":null,\"description\":null,\"name\":null},\"output_types\":[\"VectorStoreInfo\",\"Callable\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"VectorStoreRouterToolkit\":{\"template\":{\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"vectorstores\":{\"type\":\"VectorStoreInfo\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"vectorstores\",\"display_name\":\"Vector Stores\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom typing import List, Union\\nfrom langchain.agents.agent_toolkits.vectorstore.toolkit import VectorStoreRouterToolkit\\nfrom langchain.agents.agent_toolkits.vectorstore.toolkit import VectorStoreInfo\\nfrom langflow.field_typing import BaseLanguageModel, Tool\\n\\n\\nclass VectorStoreRouterToolkitComponent(CustomComponent):\\n display_name = \\\"VectorStoreRouterToolkit\\\"\\n description = \\\"Toolkit for routing between Vector Stores.\\\"\\n\\n def build_config(self):\\n return {\\n \\\"vectorstores\\\": {\\\"display_name\\\": \\\"Vector Stores\\\"},\\n \\\"llm\\\": {\\\"display_name\\\": \\\"LLM\\\"},\\n }\\n\\n def build(\\n self, vectorstores: List[VectorStoreInfo], llm: BaseLanguageModel\\n ) -> Union[Tool, VectorStoreRouterToolkit]:\\n print(\\\"vectorstores\\\", vectorstores)\\n print(\\\"llm\\\", llm)\\n return VectorStoreRouterToolkit(vectorstores=vectorstores, llm=llm)\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Toolkit for routing between Vector Stores.\",\"base_classes\":[\"Runnable\",\"BaseToolkit\",\"Generic\",\"VectorStoreRouterToolkit\",\"BaseTool\",\"RunnableSerializable\",\"Tool\",\"Serializable\",\"object\"],\"display_name\":\"VectorStoreRouterToolkit\",\"documentation\":\"\",\"custom_fields\":{\"vectorstores\":null,\"llm\":null},\"output_types\":[\"Tool\",\"VectorStoreRouterToolkit\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"VectorStoreToolkit\":{\"template\":{\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"vectorstore_info\":{\"type\":\"VectorStoreInfo\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"vectorstore_info\",\"display_name\":\"Vector Store Info\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langchain.agents.agent_toolkits.vectorstore.toolkit import VectorStoreToolkit\\nfrom langchain.agents.agent_toolkits.vectorstore.toolkit import VectorStoreInfo\\nfrom langflow.field_typing import (\\n BaseLanguageModel,\\n)\\nfrom langflow.field_typing import (\\n Tool,\\n)\\nfrom typing import Union\\n\\n\\nclass VectorStoreToolkitComponent(CustomComponent):\\n display_name = \\\"VectorStoreToolkit\\\"\\n description = \\\"Toolkit for interacting with a Vector Store.\\\"\\n\\n def build_config(self):\\n return {\\n \\\"vectorstore_info\\\": {\\\"display_name\\\": \\\"Vector Store Info\\\"},\\n \\\"llm\\\": {\\\"display_name\\\": \\\"LLM\\\"},\\n }\\n\\n def build(\\n self,\\n vectorstore_info: VectorStoreInfo,\\n llm: BaseLanguageModel,\\n ) -> Union[Tool, VectorStoreToolkit]:\\n return VectorStoreToolkit(vectorstore_info=vectorstore_info, llm=llm)\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Toolkit for interacting with a Vector Store.\",\"base_classes\":[\"Runnable\",\"BaseToolkit\",\"Generic\",\"BaseTool\",\"RunnableSerializable\",\"Tool\",\"Serializable\",\"object\",\"VectorStoreToolkit\"],\"display_name\":\"VectorStoreToolkit\",\"documentation\":\"\",\"custom_fields\":{\"vectorstore_info\":null,\"llm\":null},\"output_types\":[\"Tool\",\"VectorStoreToolkit\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"Metaphor\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import List, Union\\n\\nfrom langchain.agents import tool\\nfrom langchain.agents.agent_toolkits.base import BaseToolkit\\nfrom langchain.tools import Tool\\nfrom metaphor_python import Metaphor # type: ignore\\n\\nfrom langflow import CustomComponent\\n\\n\\nclass MetaphorToolkit(CustomComponent):\\n display_name: str = \\\"Metaphor\\\"\\n description: str = \\\"Metaphor Toolkit\\\"\\n documentation = \\\"https://python.langchain.com/docs/integrations/tools/metaphor_search\\\"\\n beta: bool = True\\n # api key should be password = True\\n field_config = {\\n \\\"metaphor_api_key\\\": {\\\"display_name\\\": \\\"Metaphor API Key\\\", \\\"password\\\": True},\\n \\\"code\\\": {\\\"advanced\\\": True},\\n }\\n\\n def build(\\n self,\\n metaphor_api_key: str,\\n use_autoprompt: bool = True,\\n search_num_results: int = 5,\\n similar_num_results: int = 5,\\n ) -> Union[Tool, BaseToolkit]:\\n # If documents, then we need to create a Vectara instance using .from_documents\\n client = Metaphor(api_key=metaphor_api_key)\\n\\n @tool\\n def search(query: str):\\n \\\"\\\"\\\"Call search engine with a query.\\\"\\\"\\\"\\n return client.search(query, use_autoprompt=use_autoprompt, num_results=search_num_results)\\n\\n @tool\\n def get_contents(ids: List[str]):\\n \\\"\\\"\\\"Get contents of a webpage.\\n\\n The ids passed in should be a list of ids as fetched from `search`.\\n \\\"\\\"\\\"\\n return client.get_contents(ids)\\n\\n @tool\\n def find_similar(url: str):\\n \\\"\\\"\\\"Get search results similar to a given URL.\\n\\n The url passed in should be a URL returned from `search`\\n \\\"\\\"\\\"\\n return client.find_similar(url, num_results=similar_num_results)\\n\\n return [search, get_contents, find_similar] # type: ignore\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":true,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"metaphor_api_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"metaphor_api_key\",\"display_name\":\"Metaphor API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"search_num_results\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":5,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"search_num_results\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"similar_num_results\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":5,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"similar_num_results\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"use_autoprompt\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":true,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"use_autoprompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Metaphor Toolkit\",\"base_classes\":[\"Runnable\",\"BaseToolkit\",\"Generic\",\"BaseTool\",\"RunnableSerializable\",\"Tool\",\"Serializable\",\"object\"],\"display_name\":\"Metaphor\",\"documentation\":\"https://python.langchain.com/docs/integrations/tools/metaphor_search\",\"custom_fields\":{\"metaphor_api_key\":null,\"use_autoprompt\":null,\"search_num_results\":null,\"similar_num_results\":null},\"output_types\":[\"Tool\",\"BaseToolkit\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true}},\"wrappers\":{\"TextRequestsWrapper\":{\"template\":{\"aiosession\":{\"type\":\"ClientSession\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"aiosession\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"auth\":{\"type\":\"Any\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"auth\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"headers\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"{\\\"Authorization\\\": \\\"Bearer \\\"}\",\"fileTypes\":[],\"password\":false,\"name\":\"headers\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"response_content_type\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"text\",\"fileTypes\":[],\"password\":false,\"name\":\"response_content_type\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"TextRequestsWrapper\"},\"description\":\"Lightweight wrapper around requests library, with async support.\",\"base_classes\":[\"TextRequestsWrapper\",\"GenericRequestsWrapper\"],\"display_name\":\"TextRequestsWrapper\",\"documentation\":\"\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":false,\"beta\":false}},\"embeddings\":{\"OpenAIEmbeddings\":{\"template\":{\"allowed_special\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":[],\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"allowed_special\",\"display_name\":\"Allowed Special\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"chunk_size\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":1000,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"chunk_size\",\"display_name\":\"Chunk Size\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"client\":{\"type\":\"Any\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"client\",\"display_name\":\"Client\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Any, Callable, Dict, List, Optional, Union\\n\\nfrom langchain_openai.embeddings.base import OpenAIEmbeddings\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import NestedDict\\nfrom pydantic.v1.types import SecretStr\\n\\n\\nclass OpenAIEmbeddingsComponent(CustomComponent):\\n display_name = \\\"OpenAIEmbeddings\\\"\\n description = \\\"OpenAI embedding models\\\"\\n\\n def build_config(self):\\n return {\\n \\\"allowed_special\\\": {\\n \\\"display_name\\\": \\\"Allowed Special\\\",\\n \\\"advanced\\\": True,\\n \\\"field_type\\\": \\\"str\\\",\\n \\\"is_list\\\": True,\\n },\\n \\\"default_headers\\\": {\\n \\\"display_name\\\": \\\"Default Headers\\\",\\n \\\"advanced\\\": True,\\n \\\"field_type\\\": \\\"dict\\\",\\n },\\n \\\"default_query\\\": {\\n \\\"display_name\\\": \\\"Default Query\\\",\\n \\\"advanced\\\": True,\\n \\\"field_type\\\": \\\"NestedDict\\\",\\n },\\n \\\"disallowed_special\\\": {\\n \\\"display_name\\\": \\\"Disallowed Special\\\",\\n \\\"advanced\\\": True,\\n \\\"field_type\\\": \\\"str\\\",\\n \\\"is_list\\\": True,\\n },\\n \\\"chunk_size\\\": {\\\"display_name\\\": \\\"Chunk Size\\\", \\\"advanced\\\": True},\\n \\\"client\\\": {\\\"display_name\\\": \\\"Client\\\", \\\"advanced\\\": True},\\n \\\"deployment\\\": {\\\"display_name\\\": \\\"Deployment\\\", \\\"advanced\\\": True},\\n \\\"embedding_ctx_length\\\": {\\n \\\"display_name\\\": \\\"Embedding Context Length\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"max_retries\\\": {\\\"display_name\\\": \\\"Max Retries\\\", \\\"advanced\\\": True},\\n \\\"model\\\": {\\n \\\"display_name\\\": \\\"Model\\\",\\n \\\"advanced\\\": False,\\n \\\"options\\\": [\\\"text-embedding-3-small\\\", \\\"text-embedding-3-large\\\", \\\"text-embedding-ada-002\\\"],\\n },\\n \\\"model_kwargs\\\": {\\\"display_name\\\": \\\"Model Kwargs\\\", \\\"advanced\\\": True},\\n \\\"openai_api_base\\\": {\\\"display_name\\\": \\\"OpenAI API Base\\\", \\\"password\\\": True, \\\"advanced\\\": True},\\n \\\"openai_api_key\\\": {\\\"display_name\\\": \\\"OpenAI API Key\\\", \\\"password\\\": True},\\n \\\"openai_api_type\\\": {\\\"display_name\\\": \\\"OpenAI API Type\\\", \\\"advanced\\\": True, \\\"password\\\": True},\\n \\\"openai_api_version\\\": {\\n \\\"display_name\\\": \\\"OpenAI API Version\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"openai_organization\\\": {\\n \\\"display_name\\\": \\\"OpenAI Organization\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"openai_proxy\\\": {\\\"display_name\\\": \\\"OpenAI Proxy\\\", \\\"advanced\\\": True},\\n \\\"request_timeout\\\": {\\\"display_name\\\": \\\"Request Timeout\\\", \\\"advanced\\\": True},\\n \\\"show_progress_bar\\\": {\\n \\\"display_name\\\": \\\"Show Progress Bar\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"skip_empty\\\": {\\\"display_name\\\": \\\"Skip Empty\\\", \\\"advanced\\\": True},\\n \\\"tiktoken_model_name\\\": {\\\"display_name\\\": \\\"TikToken Model Name\\\"},\\n \\\"tikToken_enable\\\": {\\\"display_name\\\": \\\"TikToken Enable\\\", \\\"advanced\\\": True},\\n }\\n\\n def build(\\n self,\\n default_headers: Optional[Dict[str, str]] = None,\\n default_query: Optional[NestedDict] = {},\\n allowed_special: List[str] = [],\\n disallowed_special: List[str] = [\\\"all\\\"],\\n chunk_size: int = 1000,\\n client: Optional[Any] = None,\\n deployment: str = \\\"text-embedding-3-small\\\",\\n embedding_ctx_length: int = 8191,\\n max_retries: int = 6,\\n model: str = \\\"text-embedding-3-small\\\",\\n model_kwargs: NestedDict = {},\\n openai_api_base: Optional[str] = None,\\n openai_api_key: Optional[str] = \\\"\\\",\\n openai_api_type: Optional[str] = None,\\n openai_api_version: Optional[str] = None,\\n openai_organization: Optional[str] = None,\\n openai_proxy: Optional[str] = None,\\n request_timeout: Optional[float] = None,\\n show_progress_bar: bool = False,\\n skip_empty: bool = False,\\n tiktoken_enable: bool = True,\\n tiktoken_model_name: Optional[str] = None,\\n ) -> Union[OpenAIEmbeddings, Callable]:\\n # This is to avoid errors with Vector Stores (e.g Chroma)\\n if disallowed_special == [\\\"all\\\"]:\\n disallowed_special = \\\"all\\\" # type: ignore\\n\\n api_key = SecretStr(openai_api_key) if openai_api_key else None\\n\\n return OpenAIEmbeddings(\\n tiktoken_enabled=tiktoken_enable,\\n default_headers=default_headers,\\n default_query=default_query,\\n allowed_special=set(allowed_special),\\n disallowed_special=\\\"all\\\",\\n chunk_size=chunk_size,\\n client=client,\\n deployment=deployment,\\n embedding_ctx_length=embedding_ctx_length,\\n max_retries=max_retries,\\n model=model,\\n model_kwargs=model_kwargs,\\n base_url=openai_api_base,\\n api_key=api_key,\\n openai_api_type=openai_api_type,\\n api_version=openai_api_version,\\n organization=openai_organization,\\n openai_proxy=openai_proxy,\\n timeout=request_timeout,\\n show_progress_bar=show_progress_bar,\\n skip_empty=skip_empty,\\n tiktoken_model_name=tiktoken_model_name,\\n )\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"default_headers\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"default_headers\",\"display_name\":\"Default Headers\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"default_query\":{\"type\":\"NestedDict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"default_query\",\"display_name\":\"Default Query\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"deployment\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"text-embedding-3-small\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"deployment\",\"display_name\":\"Deployment\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"disallowed_special\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":[\"all\"],\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"disallowed_special\",\"display_name\":\"Disallowed Special\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"embedding_ctx_length\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":8191,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"embedding_ctx_length\",\"display_name\":\"Embedding Context Length\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"max_retries\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":6,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_retries\",\"display_name\":\"Max Retries\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"model\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"text-embedding-3-small\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"text-embedding-3-small\",\"text-embedding-3-large\",\"text-embedding-ada-002\"],\"name\":\"model\",\"display_name\":\"Model\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"model_kwargs\":{\"type\":\"NestedDict\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model_kwargs\",\"display_name\":\"Model Kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"openai_api_base\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"openai_api_base\",\"display_name\":\"OpenAI API Base\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"openai_api_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"openai_api_key\",\"display_name\":\"OpenAI API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"openai_api_type\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"openai_api_type\",\"display_name\":\"OpenAI API Type\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"openai_api_version\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"openai_api_version\",\"display_name\":\"OpenAI API Version\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"openai_organization\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"openai_organization\",\"display_name\":\"OpenAI Organization\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"openai_proxy\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"openai_proxy\",\"display_name\":\"OpenAI Proxy\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"request_timeout\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"request_timeout\",\"display_name\":\"Request Timeout\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"show_progress_bar\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"show_progress_bar\",\"display_name\":\"Show Progress Bar\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"skip_empty\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"skip_empty\",\"display_name\":\"Skip Empty\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"tiktoken_enable\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":true,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"tiktoken_enable\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"tiktoken_model_name\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"tiktoken_model_name\",\"display_name\":\"TikToken Model Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"OpenAI embedding models\",\"base_classes\":[\"Embeddings\",\"OpenAIEmbeddings\",\"Callable\"],\"display_name\":\"OpenAIEmbeddings\",\"documentation\":\"\",\"custom_fields\":{\"default_headers\":null,\"default_query\":null,\"allowed_special\":null,\"disallowed_special\":null,\"chunk_size\":null,\"client\":null,\"deployment\":null,\"embedding_ctx_length\":null,\"max_retries\":null,\"model\":null,\"model_kwargs\":null,\"openai_api_base\":null,\"openai_api_key\":null,\"openai_api_type\":null,\"openai_api_version\":null,\"openai_organization\":null,\"openai_proxy\":null,\"request_timeout\":null,\"show_progress_bar\":null,\"skip_empty\":null,\"tiktoken_enable\":null,\"tiktoken_model_name\":null},\"output_types\":[\"OpenAIEmbeddings\",\"Callable\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"CohereEmbeddings\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain_community.embeddings.cohere import CohereEmbeddings\\nfrom langflow import CustomComponent\\n\\n\\nclass CohereEmbeddingsComponent(CustomComponent):\\n display_name = \\\"CohereEmbeddings\\\"\\n description = \\\"Cohere embedding models.\\\"\\n\\n def build_config(self):\\n return {\\n \\\"cohere_api_key\\\": {\\\"display_name\\\": \\\"Cohere API Key\\\", \\\"password\\\": True},\\n \\\"model\\\": {\\\"display_name\\\": \\\"Model\\\", \\\"default\\\": \\\"embed-english-v2.0\\\", \\\"advanced\\\": True},\\n \\\"truncate\\\": {\\\"display_name\\\": \\\"Truncate\\\", \\\"advanced\\\": True},\\n \\\"max_retries\\\": {\\\"display_name\\\": \\\"Max Retries\\\", \\\"advanced\\\": True},\\n \\\"user_agent\\\": {\\\"display_name\\\": \\\"User Agent\\\", \\\"advanced\\\": True},\\n }\\n\\n def build(\\n self,\\n request_timeout: Optional[float] = None,\\n cohere_api_key: str = \\\"\\\",\\n max_retries: Optional[int] = None,\\n model: str = \\\"embed-english-v2.0\\\",\\n truncate: Optional[str] = None,\\n user_agent: str = \\\"langchain\\\",\\n ) -> CohereEmbeddings:\\n return CohereEmbeddings( # type: ignore\\n max_retries=max_retries,\\n user_agent=user_agent,\\n request_timeout=request_timeout,\\n cohere_api_key=cohere_api_key,\\n model=model,\\n truncate=truncate,\\n )\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"cohere_api_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"cohere_api_key\",\"display_name\":\"Cohere API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"max_retries\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_retries\",\"display_name\":\"Max Retries\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"model\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"embed-english-v2.0\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model\",\"display_name\":\"Model\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"request_timeout\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"request_timeout\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"truncate\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"truncate\",\"display_name\":\"Truncate\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"user_agent\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"langchain\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"user_agent\",\"display_name\":\"User Agent\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Cohere embedding models.\",\"base_classes\":[\"Embeddings\",\"CohereEmbeddings\"],\"display_name\":\"CohereEmbeddings\",\"documentation\":\"\",\"custom_fields\":{\"request_timeout\":null,\"cohere_api_key\":null,\"max_retries\":null,\"model\":null,\"truncate\":null,\"user_agent\":null},\"output_types\":[\"CohereEmbeddings\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"HuggingFaceEmbeddings\":{\"template\":{\"cache_folder\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"cache_folder\",\"display_name\":\"Cache Folder\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom typing import Optional, Dict\\nfrom langchain_community.embeddings.huggingface import HuggingFaceEmbeddings\\n\\n\\nclass HuggingFaceEmbeddingsComponent(CustomComponent):\\n display_name = \\\"HuggingFaceEmbeddings\\\"\\n description = \\\"HuggingFace sentence_transformers embedding models.\\\"\\n documentation = (\\n \\\"https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/sentence_transformers\\\"\\n )\\n\\n def build_config(self):\\n return {\\n \\\"cache_folder\\\": {\\\"display_name\\\": \\\"Cache Folder\\\", \\\"advanced\\\": True},\\n \\\"encode_kwargs\\\": {\\\"display_name\\\": \\\"Encode Kwargs\\\", \\\"advanced\\\": True, \\\"field_type\\\": \\\"dict\\\"},\\n \\\"model_kwargs\\\": {\\\"display_name\\\": \\\"Model Kwargs\\\", \\\"field_type\\\": \\\"dict\\\", \\\"advanced\\\": True},\\n \\\"model_name\\\": {\\\"display_name\\\": \\\"Model Name\\\"},\\n \\\"multi_process\\\": {\\\"display_name\\\": \\\"Multi Process\\\", \\\"advanced\\\": True},\\n }\\n\\n def build(\\n self,\\n cache_folder: Optional[str] = None,\\n encode_kwargs: Optional[Dict] = {},\\n model_kwargs: Optional[Dict] = {},\\n model_name: str = \\\"sentence-transformers/all-mpnet-base-v2\\\",\\n multi_process: bool = False,\\n ) -> HuggingFaceEmbeddings:\\n return HuggingFaceEmbeddings(\\n cache_folder=cache_folder,\\n encode_kwargs=encode_kwargs,\\n model_kwargs=model_kwargs,\\n model_name=model_name,\\n multi_process=multi_process,\\n )\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"encode_kwargs\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"encode_kwargs\",\"display_name\":\"Encode Kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"model_kwargs\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model_kwargs\",\"display_name\":\"Model Kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"model_name\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"sentence-transformers/all-mpnet-base-v2\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model_name\",\"display_name\":\"Model Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"multi_process\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"multi_process\",\"display_name\":\"Multi Process\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"HuggingFace sentence_transformers embedding models.\",\"base_classes\":[\"Embeddings\",\"HuggingFaceEmbeddings\"],\"display_name\":\"HuggingFaceEmbeddings\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/sentence_transformers\",\"custom_fields\":{\"cache_folder\":null,\"encode_kwargs\":null,\"model_kwargs\":null,\"model_name\":null,\"multi_process\":null},\"output_types\":[\"HuggingFaceEmbeddings\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"VertexAIEmbeddings\":{\"template\":{\"credentials\":{\"type\":\"file\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[\".json\"],\"file_path\":\"\",\"password\":false,\"name\":\"credentials\",\"display_name\":\"Credentials\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langchain_community.embeddings import VertexAIEmbeddings\\nfrom typing import Optional, List\\n\\n\\nclass VertexAIEmbeddingsComponent(CustomComponent):\\n display_name = \\\"VertexAIEmbeddings\\\"\\n description = \\\"Google Cloud VertexAI embedding models.\\\"\\n\\n def build_config(self):\\n return {\\n \\\"credentials\\\": {\\n \\\"display_name\\\": \\\"Credentials\\\",\\n \\\"value\\\": \\\"\\\",\\n \\\"file_types\\\": [\\\".json\\\"],\\n \\\"field_type\\\": \\\"file\\\",\\n },\\n \\\"instance\\\": {\\n \\\"display_name\\\": \\\"instance\\\",\\n \\\"advanced\\\": True,\\n \\\"field_type\\\": \\\"dict\\\",\\n },\\n \\\"location\\\": {\\n \\\"display_name\\\": \\\"Location\\\",\\n \\\"value\\\": \\\"us-central1\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"max_output_tokens\\\": {\\\"display_name\\\": \\\"Max Output Tokens\\\", \\\"value\\\": 128},\\n \\\"max_retries\\\": {\\n \\\"display_name\\\": \\\"Max Retries\\\",\\n \\\"value\\\": 6,\\n \\\"advanced\\\": True,\\n },\\n \\\"model_name\\\": {\\n \\\"display_name\\\": \\\"Model Name\\\",\\n \\\"value\\\": \\\"textembedding-gecko\\\",\\n },\\n \\\"n\\\": {\\\"display_name\\\": \\\"N\\\", \\\"value\\\": 1, \\\"advanced\\\": True},\\n \\\"project\\\": {\\\"display_name\\\": \\\"Project\\\", \\\"advanced\\\": True},\\n \\\"request_parallelism\\\": {\\n \\\"display_name\\\": \\\"Request Parallelism\\\",\\n \\\"value\\\": 5,\\n \\\"advanced\\\": True,\\n },\\n \\\"stop\\\": {\\\"display_name\\\": \\\"Stop\\\", \\\"advanced\\\": True},\\n \\\"streaming\\\": {\\n \\\"display_name\\\": \\\"Streaming\\\",\\n \\\"value\\\": False,\\n \\\"advanced\\\": True,\\n },\\n \\\"temperature\\\": {\\\"display_name\\\": \\\"Temperature\\\", \\\"value\\\": 0.0},\\n \\\"top_k\\\": {\\\"display_name\\\": \\\"Top K\\\", \\\"value\\\": 40, \\\"advanced\\\": True},\\n \\\"top_p\\\": {\\\"display_name\\\": \\\"Top P\\\", \\\"value\\\": 0.95, \\\"advanced\\\": True},\\n }\\n\\n def build(\\n self,\\n instance: Optional[str] = None,\\n credentials: Optional[str] = None,\\n location: str = \\\"us-central1\\\",\\n max_output_tokens: int = 128,\\n max_retries: int = 6,\\n model_name: str = \\\"textembedding-gecko\\\",\\n n: int = 1,\\n project: Optional[str] = None,\\n request_parallelism: int = 5,\\n stop: Optional[List[str]] = None,\\n streaming: bool = False,\\n temperature: float = 0.0,\\n top_k: int = 40,\\n top_p: float = 0.95,\\n ) -> VertexAIEmbeddings:\\n return VertexAIEmbeddings(\\n instance=instance,\\n credentials=credentials,\\n location=location,\\n max_output_tokens=max_output_tokens,\\n max_retries=max_retries,\\n model_name=model_name,\\n n=n,\\n project=project,\\n request_parallelism=request_parallelism,\\n stop=stop,\\n streaming=streaming,\\n temperature=temperature,\\n top_k=top_k,\\n top_p=top_p,\\n )\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"instance\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"instance\",\"display_name\":\"instance\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"location\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"us-central1\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"location\",\"display_name\":\"Location\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"max_output_tokens\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":128,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_output_tokens\",\"display_name\":\"Max Output Tokens\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"max_retries\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":6,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_retries\",\"display_name\":\"Max Retries\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"model_name\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"textembedding-gecko\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model_name\",\"display_name\":\"Model Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"n\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":1,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"n\",\"display_name\":\"N\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"project\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"project\",\"display_name\":\"Project\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"request_parallelism\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":5,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"request_parallelism\",\"display_name\":\"Request Parallelism\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"stop\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"stop\",\"display_name\":\"Stop\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"streaming\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"streaming\",\"display_name\":\"Streaming\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"temperature\":{\"type\":\"float\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.0,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"top_k\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":40,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_k\",\"display_name\":\"Top K\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"top_p\":{\"type\":\"float\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.95,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_p\",\"display_name\":\"Top P\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Google Cloud VertexAI embedding models.\",\"base_classes\":[\"_VertexAICommon\",\"Embeddings\",\"_VertexAIBase\",\"VertexAIEmbeddings\"],\"display_name\":\"VertexAIEmbeddings\",\"documentation\":\"\",\"custom_fields\":{\"instance\":null,\"credentials\":null,\"location\":null,\"max_output_tokens\":null,\"max_retries\":null,\"model_name\":null,\"n\":null,\"project\":null,\"request_parallelism\":null,\"stop\":null,\"streaming\":null,\"temperature\":null,\"top_k\":null,\"top_p\":null},\"output_types\":[\"VertexAIEmbeddings\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"OllamaEmbeddings\":{\"template\":{\"base_url\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"http://localhost:11434\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"base_url\",\"display_name\":\"Ollama Base URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langflow import CustomComponent\\nfrom langchain.embeddings.base import Embeddings\\nfrom langchain_community.embeddings import OllamaEmbeddings\\n\\n\\nclass OllamaEmbeddingsComponent(CustomComponent):\\n \\\"\\\"\\\"\\n A custom component for implementing an Embeddings Model using Ollama.\\n \\\"\\\"\\\"\\n\\n display_name: str = \\\"Ollama Embeddings\\\"\\n description: str = \\\"Embeddings model from Ollama.\\\"\\n documentation = \\\"https://python.langchain.com/docs/integrations/text_embedding/ollama\\\"\\n beta = True\\n\\n def build_config(self):\\n return {\\n \\\"model\\\": {\\n \\\"display_name\\\": \\\"Ollama Model\\\",\\n },\\n \\\"base_url\\\": {\\\"display_name\\\": \\\"Ollama Base URL\\\"},\\n \\\"temperature\\\": {\\\"display_name\\\": \\\"Model Temperature\\\"},\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(\\n self,\\n model: str = \\\"llama2\\\",\\n base_url: str = \\\"http://localhost:11434\\\",\\n temperature: Optional[float] = None,\\n ) -> Embeddings:\\n try:\\n output = OllamaEmbeddings(model=model, base_url=base_url, temperature=temperature) # type: ignore\\n except Exception as e:\\n raise ValueError(\\\"Could not connect to Ollama API.\\\") from e\\n return output\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"model\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"llama2\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model\",\"display_name\":\"Ollama Model\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"temperature\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Model Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Embeddings model from Ollama.\",\"base_classes\":[\"Embeddings\"],\"display_name\":\"Ollama Embeddings\",\"documentation\":\"https://python.langchain.com/docs/integrations/text_embedding/ollama\",\"custom_fields\":{\"model\":null,\"base_url\":null,\"temperature\":null},\"output_types\":[\"Embeddings\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"AmazonBedrockEmbeddings\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain.embeddings.base import Embeddings\\nfrom langchain_community.embeddings import BedrockEmbeddings\\n\\n\\nfrom langflow import CustomComponent\\n\\n\\nclass AmazonBedrockEmeddingsComponent(CustomComponent):\\n \\\"\\\"\\\"\\n A custom component for implementing an Embeddings Model using Amazon Bedrock.\\n \\\"\\\"\\\"\\n\\n display_name: str = \\\"Amazon Bedrock Embeddings\\\"\\n description: str = \\\"Embeddings model from Amazon Bedrock.\\\"\\n documentation = \\\"https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/bedrock\\\"\\n beta = True\\n\\n def build_config(self):\\n return {\\n \\\"model_id\\\": {\\n \\\"display_name\\\": \\\"Model Id\\\",\\n \\\"options\\\": [\\\"amazon.titan-embed-text-v1\\\"],\\n },\\n \\\"credentials_profile_name\\\": {\\\"display_name\\\": \\\"Credentials Profile Name\\\"},\\n \\\"endpoint_url\\\": {\\\"display_name\\\": \\\"Bedrock Endpoint URL\\\"},\\n \\\"region_name\\\": {\\\"display_name\\\": \\\"AWS Region\\\"},\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(\\n self,\\n model_id: str = \\\"amazon.titan-embed-text-v1\\\",\\n credentials_profile_name: Optional[str] = None,\\n endpoint_url: Optional[str] = None,\\n region_name: Optional[str] = None,\\n ) -> Embeddings:\\n try:\\n output = BedrockEmbeddings(\\n credentials_profile_name=credentials_profile_name,\\n model_id=model_id,\\n endpoint_url=endpoint_url,\\n region_name=region_name,\\n ) # type: ignore\\n except Exception as e:\\n raise ValueError(\\\"Could not connect to AmazonBedrock API.\\\") from e\\n return output\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"credentials_profile_name\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"credentials_profile_name\",\"display_name\":\"Credentials Profile Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"endpoint_url\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"endpoint_url\",\"display_name\":\"Bedrock Endpoint URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"model_id\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"amazon.titan-embed-text-v1\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"amazon.titan-embed-text-v1\"],\"name\":\"model_id\",\"display_name\":\"Model Id\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"region_name\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"region_name\",\"display_name\":\"AWS Region\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Embeddings model from Amazon Bedrock.\",\"base_classes\":[\"Embeddings\"],\"display_name\":\"Amazon Bedrock Embeddings\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/bedrock\",\"custom_fields\":{\"model_id\":null,\"credentials_profile_name\":null,\"endpoint_url\":null,\"region_name\":null},\"output_types\":[\"Embeddings\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"AzureOpenAIEmbeddings\":{\"template\":{\"api_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"api_key\",\"display_name\":\"API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"api_version\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"2023-08-01-preview\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"2022-12-01\",\"2023-03-15-preview\",\"2023-05-15\",\"2023-06-01-preview\",\"2023-07-01-preview\",\"2023-08-01-preview\"],\"name\":\"api_version\",\"display_name\":\"API Version\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"azure_deployment\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"azure_deployment\",\"display_name\":\"Deployment Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"azure_endpoint\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"azure_endpoint\",\"display_name\":\"Azure Endpoint\",\"advanced\":false,\"dynamic\":false,\"info\":\"Your Azure endpoint, including the resource.. Example: `https://example-resource.azure.openai.com/`\",\"title_case\":false,\"input_types\":[\"Text\"]},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from langchain.embeddings.base import Embeddings\\nfrom langchain_community.embeddings import AzureOpenAIEmbeddings\\n\\nfrom langflow import CustomComponent\\n\\n\\nclass AzureOpenAIEmbeddingsComponent(CustomComponent):\\n display_name: str = \\\"AzureOpenAIEmbeddings\\\"\\n description: str = \\\"Embeddings model from Azure OpenAI.\\\"\\n documentation: str = \\\"https://python.langchain.com/docs/integrations/text_embedding/azureopenai\\\"\\n beta = False\\n\\n API_VERSION_OPTIONS = [\\n \\\"2022-12-01\\\",\\n \\\"2023-03-15-preview\\\",\\n \\\"2023-05-15\\\",\\n \\\"2023-06-01-preview\\\",\\n \\\"2023-07-01-preview\\\",\\n \\\"2023-08-01-preview\\\",\\n ]\\n\\n def build_config(self):\\n return {\\n \\\"azure_endpoint\\\": {\\n \\\"display_name\\\": \\\"Azure Endpoint\\\",\\n \\\"required\\\": True,\\n \\\"info\\\": \\\"Your Azure endpoint, including the resource.. Example: `https://example-resource.azure.openai.com/`\\\",\\n },\\n \\\"azure_deployment\\\": {\\n \\\"display_name\\\": \\\"Deployment Name\\\",\\n \\\"required\\\": True,\\n },\\n \\\"api_version\\\": {\\n \\\"display_name\\\": \\\"API Version\\\",\\n \\\"options\\\": self.API_VERSION_OPTIONS,\\n \\\"value\\\": self.API_VERSION_OPTIONS[-1],\\n \\\"advanced\\\": True,\\n },\\n \\\"api_key\\\": {\\n \\\"display_name\\\": \\\"API Key\\\",\\n \\\"required\\\": True,\\n \\\"password\\\": True,\\n },\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(\\n self,\\n azure_endpoint: str,\\n azure_deployment: str,\\n api_version: str,\\n api_key: str,\\n ) -> Embeddings:\\n try:\\n embeddings = AzureOpenAIEmbeddings(\\n azure_endpoint=azure_endpoint,\\n azure_deployment=azure_deployment,\\n api_version=api_version,\\n api_key=api_key,\\n )\\n\\n except Exception as e:\\n raise ValueError(\\\"Could not connect to AzureOpenAIEmbeddings API.\\\") from e\\n\\n return embeddings\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Embeddings model from Azure OpenAI.\",\"base_classes\":[\"Embeddings\"],\"display_name\":\"AzureOpenAIEmbeddings\",\"documentation\":\"https://python.langchain.com/docs/integrations/text_embedding/azureopenai\",\"custom_fields\":{\"azure_endpoint\":null,\"azure_deployment\":null,\"api_version\":null,\"api_key\":null},\"output_types\":[\"Embeddings\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"HuggingFaceInferenceAPIEmbeddings\":{\"template\":{\"api_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"api_key\",\"display_name\":\"API Key\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"api_url\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"http://localhost:8080\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"api_url\",\"display_name\":\"API URL\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"cache_folder\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"cache_folder\",\"display_name\":\"Cache Folder\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Dict, Optional\\n\\nfrom langchain_community.embeddings.huggingface import HuggingFaceInferenceAPIEmbeddings\\nfrom langflow import CustomComponent\\nfrom pydantic.v1.types import SecretStr\\n\\n\\nclass HuggingFaceInferenceAPIEmbeddingsComponent(CustomComponent):\\n display_name = \\\"HuggingFaceInferenceAPIEmbeddings\\\"\\n description = \\\"HuggingFace sentence_transformers embedding models, API version.\\\"\\n documentation = \\\"https://github.com/huggingface/text-embeddings-inference\\\"\\n\\n def build_config(self):\\n return {\\n \\\"api_key\\\": {\\\"display_name\\\": \\\"API Key\\\", \\\"password\\\": True, \\\"advanced\\\": True},\\n \\\"api_url\\\": {\\\"display_name\\\": \\\"API URL\\\", \\\"advanced\\\": True},\\n \\\"model_name\\\": {\\\"display_name\\\": \\\"Model Name\\\"},\\n \\\"cache_folder\\\": {\\\"display_name\\\": \\\"Cache Folder\\\", \\\"advanced\\\": True},\\n \\\"encode_kwargs\\\": {\\\"display_name\\\": \\\"Encode Kwargs\\\", \\\"advanced\\\": True, \\\"field_type\\\": \\\"dict\\\"},\\n \\\"model_kwargs\\\": {\\\"display_name\\\": \\\"Model Kwargs\\\", \\\"field_type\\\": \\\"dict\\\", \\\"advanced\\\": True},\\n \\\"multi_process\\\": {\\\"display_name\\\": \\\"Multi Process\\\", \\\"advanced\\\": True},\\n }\\n\\n def build(\\n self,\\n api_key: Optional[str] = \\\"\\\",\\n api_url: str = \\\"http://localhost:8080\\\",\\n model_name: str = \\\"BAAI/bge-large-en-v1.5\\\",\\n cache_folder: Optional[str] = None,\\n encode_kwargs: Optional[Dict] = {},\\n model_kwargs: Optional[Dict] = {},\\n multi_process: bool = False,\\n ) -> HuggingFaceInferenceAPIEmbeddings:\\n if api_key:\\n secret_api_key = SecretStr(api_key)\\n else:\\n raise ValueError(\\\"API Key is required\\\")\\n return HuggingFaceInferenceAPIEmbeddings(\\n api_key=secret_api_key,\\n api_url=api_url,\\n model_name=model_name,\\n )\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"encode_kwargs\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"encode_kwargs\",\"display_name\":\"Encode Kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"model_kwargs\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model_kwargs\",\"display_name\":\"Model Kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"model_name\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"BAAI/bge-large-en-v1.5\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model_name\",\"display_name\":\"Model Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"multi_process\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"multi_process\",\"display_name\":\"Multi Process\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"HuggingFace sentence_transformers embedding models, API version.\",\"base_classes\":[\"Embeddings\",\"HuggingFaceInferenceAPIEmbeddings\"],\"display_name\":\"HuggingFaceInferenceAPIEmbeddings\",\"documentation\":\"https://github.com/huggingface/text-embeddings-inference\",\"custom_fields\":{\"api_key\":null,\"api_url\":null,\"model_name\":null,\"cache_folder\":null,\"encode_kwargs\":null,\"model_kwargs\":null,\"multi_process\":null},\"output_types\":[\"HuggingFaceInferenceAPIEmbeddings\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true}},\"documentloaders\":{\"AZLyricsLoader\":{\"template\":{\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"web_path\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"web_path\",\"display_name\":\"Web Page\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"AZLyricsLoader\"},\"description\":\"Load `AZLyrics` webpages.\",\"base_classes\":[\"Document\"],\"display_name\":\"AZLyricsLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/azlyrics\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"AirbyteJSONLoader\":{\"template\":{\"file_path\":{\"type\":\"file\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[\".json\"],\"file_path\":\"\",\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"AirbyteJSONLoader\"},\"description\":\"Load local `Airbyte` json files.\",\"base_classes\":[\"Document\"],\"display_name\":\"AirbyteJSONLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/airbyte_json\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"BSHTMLLoader\":{\"template\":{\"file_path\":{\"type\":\"file\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[\".html\"],\"file_path\":\"\",\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"BSHTMLLoader\"},\"description\":\"Load `HTML` files and parse them with `beautiful soup`.\",\"base_classes\":[\"Document\"],\"display_name\":\"BSHTMLLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/how_to/html\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"CSVLoader\":{\"template\":{\"file_path\":{\"type\":\"file\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[\".csv\"],\"file_path\":\"\",\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"CSVLoader\"},\"description\":\"Load a `CSV` file into a list of Documents.\",\"base_classes\":[\"Document\"],\"display_name\":\"CSVLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/csv\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"CoNLLULoader\":{\"template\":{\"file_path\":{\"type\":\"file\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[\".csv\"],\"file_path\":\"\",\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"CoNLLULoader\"},\"description\":\"Load `CoNLL-U` files.\",\"base_classes\":[\"Document\"],\"display_name\":\"CoNLLULoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/conll-u\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"CollegeConfidentialLoader\":{\"template\":{\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"web_path\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"web_path\",\"display_name\":\"Web Page\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CollegeConfidentialLoader\"},\"description\":\"Load `College Confidential` webpages.\",\"base_classes\":[\"Document\"],\"display_name\":\"CollegeConfidentialLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/college_confidential\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"DirectoryLoader\":{\"template\":{\"glob\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"**/*.txt\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"glob\",\"display_name\":\"glob\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"load_hidden\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"False\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"load_hidden\",\"display_name\":\"Load hidden files\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"max_concurrency\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":10,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_concurrency\",\"display_name\":\"Max concurrency\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"path\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"path\",\"display_name\":\"Local directory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"recursive\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"True\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"recursive\",\"display_name\":\"Recursive\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"silent_errors\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"False\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"silent_errors\",\"display_name\":\"Silent errors\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"use_multithreading\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"True\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"use_multithreading\",\"display_name\":\"Use multithreading\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"DirectoryLoader\"},\"description\":\"Load from a directory.\",\"base_classes\":[\"Document\"],\"display_name\":\"DirectoryLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/how_to/file_directory\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"EverNoteLoader\":{\"template\":{\"file_path\":{\"type\":\"file\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[\".xml\"],\"file_path\":\"\",\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"EverNoteLoader\"},\"description\":\"Load from `EverNote`.\",\"base_classes\":[\"Document\"],\"display_name\":\"EverNoteLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/evernote\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"FacebookChatLoader\":{\"template\":{\"file_path\":{\"type\":\"file\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[\".json\"],\"file_path\":\"\",\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"FacebookChatLoader\"},\"description\":\"Load `Facebook Chat` messages directory dump.\",\"base_classes\":[\"Document\"],\"display_name\":\"FacebookChatLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/facebook_chat\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"GitLoader\":{\"template\":{\"branch\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"branch\",\"display_name\":\"Branch\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"clone_url\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"clone_url\",\"display_name\":\"Clone URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"file_filter\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"file_filter\",\"display_name\":\"File extensions (comma-separated)\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"repo_path\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"repo_path\",\"display_name\":\"Path to repository\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"GitLoader\"},\"description\":\"Load `Git` repository files.\",\"base_classes\":[\"Document\"],\"display_name\":\"GitLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/git\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"GitbookLoader\":{\"template\":{\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"web_page\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"web_page\",\"display_name\":\"Web Page\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"GitbookLoader\"},\"description\":\"Load `GitBook` data.\",\"base_classes\":[\"Document\"],\"display_name\":\"GitbookLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/gitbook\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"GutenbergLoader\":{\"template\":{\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"web_path\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"web_path\",\"display_name\":\"Web Page\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"GutenbergLoader\"},\"description\":\"Load from `Gutenberg.org`.\",\"base_classes\":[\"Document\"],\"display_name\":\"GutenbergLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/gutenberg\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"HNLoader\":{\"template\":{\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"web_path\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"web_path\",\"display_name\":\"Web Page\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"HNLoader\"},\"description\":\"Load `Hacker News` data.\",\"base_classes\":[\"Document\"],\"display_name\":\"HNLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/hacker_news\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"IFixitLoader\":{\"template\":{\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"web_path\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"web_path\",\"display_name\":\"Web Page\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"IFixitLoader\"},\"description\":\"Load `iFixit` repair guides, device wikis and answers.\",\"base_classes\":[\"Document\"],\"display_name\":\"IFixitLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/ifixit\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"IMSDbLoader\":{\"template\":{\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"web_path\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"web_path\",\"display_name\":\"Web Page\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"IMSDbLoader\"},\"description\":\"Load `IMSDb` webpages.\",\"base_classes\":[\"Document\"],\"display_name\":\"IMSDbLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/imsdb\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"NotionDirectoryLoader\":{\"template\":{\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"path\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"path\",\"display_name\":\"Local directory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"NotionDirectoryLoader\"},\"description\":\"Load `Notion directory` dump.\",\"base_classes\":[\"Document\"],\"display_name\":\"NotionDirectoryLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/notion\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"PyPDFLoader\":{\"template\":{\"file_path\":{\"type\":\"file\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[\".pdf\"],\"file_path\":\"\",\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"PyPDFLoader\"},\"description\":\"Load PDF using pypdf into list of documents.\",\"base_classes\":[\"Document\"],\"display_name\":\"PyPDFLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/how_to/pdf\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"PyPDFDirectoryLoader\":{\"template\":{\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"path\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"path\",\"display_name\":\"Local directory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"PyPDFDirectoryLoader\"},\"description\":\"Load a directory with `PDF` files using `pypdf` and chunks at character level.\",\"base_classes\":[\"Document\"],\"display_name\":\"PyPDFDirectoryLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/how_to/pdf\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"ReadTheDocsLoader\":{\"template\":{\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"path\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"path\",\"display_name\":\"Local directory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"ReadTheDocsLoader\"},\"description\":\"Load `ReadTheDocs` documentation directory.\",\"base_classes\":[\"Document\"],\"display_name\":\"ReadTheDocsLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/readthedocs_documentation\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"SRTLoader\":{\"template\":{\"file_path\":{\"type\":\"file\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[\".srt\"],\"file_path\":\"\",\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"SRTLoader\"},\"description\":\"Load `.srt` (subtitle) files.\",\"base_classes\":[\"Document\"],\"display_name\":\"SRTLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/subtitle\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"SlackDirectoryLoader\":{\"template\":{\"zip_path\":{\"type\":\"file\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[\".zip\"],\"file_path\":\"\",\"password\":false,\"name\":\"zip_path\",\"display_name\":\"Path to zip file\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"workspace_url\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"workspace_url\",\"display_name\":\"Workspace URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"SlackDirectoryLoader\"},\"description\":\"Load from a `Slack` directory dump.\",\"base_classes\":[\"Document\"],\"display_name\":\"SlackDirectoryLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/slack\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"TextLoader\":{\"template\":{\"file_path\":{\"type\":\"file\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[\".txt\"],\"file_path\":\"\",\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"TextLoader\"},\"description\":\"Load text file.\",\"base_classes\":[\"Document\"],\"display_name\":\"TextLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"UnstructuredEmailLoader\":{\"template\":{\"file_path\":{\"type\":\"file\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[\".eml\"],\"file_path\":\"\",\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"UnstructuredEmailLoader\"},\"description\":\"Load email files using `Unstructured`.\",\"base_classes\":[\"Document\"],\"display_name\":\"UnstructuredEmailLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/email\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"UnstructuredHTMLLoader\":{\"template\":{\"file_path\":{\"type\":\"file\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[\".html\"],\"file_path\":\"\",\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"UnstructuredHTMLLoader\"},\"description\":\"Load `HTML` files using `Unstructured`.\",\"base_classes\":[\"Document\"],\"display_name\":\"UnstructuredHTMLLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/how_to/html\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"UnstructuredMarkdownLoader\":{\"template\":{\"file_path\":{\"type\":\"file\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[\".md\"],\"file_path\":\"\",\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"UnstructuredMarkdownLoader\"},\"description\":\"Load `Markdown` files using `Unstructured`.\",\"base_classes\":[\"Document\"],\"display_name\":\"UnstructuredMarkdownLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/how_to/markdown\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"UnstructuredPowerPointLoader\":{\"template\":{\"file_path\":{\"type\":\"file\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[\".pptx\",\".ppt\"],\"file_path\":\"\",\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"UnstructuredPowerPointLoader\"},\"description\":\"Load `Microsoft PowerPoint` files using `Unstructured`.\",\"base_classes\":[\"Document\"],\"display_name\":\"UnstructuredPowerPointLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/microsoft_powerpoint\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"UnstructuredWordDocumentLoader\":{\"template\":{\"file_path\":{\"type\":\"file\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[\".docx\",\".doc\"],\"file_path\":\"\",\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"UnstructuredWordDocumentLoader\"},\"description\":\"Load `Microsoft Word` file using `Unstructured`.\",\"base_classes\":[\"Document\"],\"display_name\":\"UnstructuredWordDocumentLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/microsoft_word\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"WebBaseLoader\":{\"template\":{\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"web_path\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"web_path\",\"display_name\":\"Web Page\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"WebBaseLoader\"},\"description\":\"Load HTML pages using `urllib` and parse them with `BeautifulSoup'.\",\"base_classes\":[\"Document\"],\"display_name\":\"WebBaseLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/web_base\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"FileLoader\":{\"template\":{\"file_path\":{\"type\":\"file\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[\".json\",\".txt\",\".csv\",\".jsonl\",\".html\",\".htm\",\".conllu\",\".enex\",\".msg\",\".pdf\",\".srt\",\".eml\",\".md\",\".mdx\",\".pptx\",\".docx\"],\"file_path\":\"\",\"password\":false,\"name\":\"file_path\",\"display_name\":\"File Path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from langchain_core.documents import Document\\n\\nfrom langflow import CustomComponent\\nfrom langflow.utils.constants import LOADERS_INFO\\n\\n\\nclass FileLoaderComponent(CustomComponent):\\n display_name: str = \\\"File Loader\\\"\\n description: str = \\\"Generic File Loader\\\"\\n beta = True\\n\\n def build_config(self):\\n loader_options = [\\\"Automatic\\\"] + [loader_info[\\\"name\\\"] for loader_info in LOADERS_INFO]\\n\\n file_types = []\\n suffixes = []\\n\\n for loader_info in LOADERS_INFO:\\n if \\\"allowedTypes\\\" in loader_info:\\n file_types.extend(loader_info[\\\"allowedTypes\\\"])\\n suffixes.extend([f\\\".{ext}\\\" for ext in loader_info[\\\"allowedTypes\\\"]])\\n\\n return {\\n \\\"file_path\\\": {\\n \\\"display_name\\\": \\\"File Path\\\",\\n \\\"required\\\": True,\\n \\\"field_type\\\": \\\"file\\\",\\n \\\"file_types\\\": [\\n \\\"json\\\",\\n \\\"txt\\\",\\n \\\"csv\\\",\\n \\\"jsonl\\\",\\n \\\"html\\\",\\n \\\"htm\\\",\\n \\\"conllu\\\",\\n \\\"enex\\\",\\n \\\"msg\\\",\\n \\\"pdf\\\",\\n \\\"srt\\\",\\n \\\"eml\\\",\\n \\\"md\\\",\\n \\\"mdx\\\",\\n \\\"pptx\\\",\\n \\\"docx\\\",\\n ],\\n \\\"suffixes\\\": [\\n \\\".json\\\",\\n \\\".txt\\\",\\n \\\".csv\\\",\\n \\\".jsonl\\\",\\n \\\".html\\\",\\n \\\".htm\\\",\\n \\\".conllu\\\",\\n \\\".enex\\\",\\n \\\".msg\\\",\\n \\\".pdf\\\",\\n \\\".srt\\\",\\n \\\".eml\\\",\\n \\\".md\\\",\\n \\\".mdx\\\",\\n \\\".pptx\\\",\\n \\\".docx\\\",\\n ],\\n # \\\"file_types\\\" : file_types,\\n # \\\"suffixes\\\": suffixes,\\n },\\n \\\"loader\\\": {\\n \\\"display_name\\\": \\\"Loader\\\",\\n \\\"is_list\\\": True,\\n \\\"required\\\": True,\\n \\\"options\\\": loader_options,\\n \\\"value\\\": \\\"Automatic\\\",\\n },\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(self, file_path: str, loader: str) -> Document:\\n file_type = file_path.split(\\\".\\\")[-1]\\n\\n # Map the loader to the correct loader class\\n selected_loader_info = None\\n for loader_info in LOADERS_INFO:\\n if loader_info[\\\"name\\\"] == loader:\\n selected_loader_info = loader_info\\n break\\n\\n if selected_loader_info is None and loader != \\\"Automatic\\\":\\n raise ValueError(f\\\"Loader {loader} not found in the loader info list\\\")\\n\\n if loader == \\\"Automatic\\\":\\n # Determine the loader based on the file type\\n default_loader_info = None\\n for info in LOADERS_INFO:\\n if \\\"defaultFor\\\" in info and file_type in info[\\\"defaultFor\\\"]:\\n default_loader_info = info\\n break\\n\\n if default_loader_info is None:\\n raise ValueError(f\\\"No default loader found for file type: {file_type}\\\")\\n\\n selected_loader_info = default_loader_info\\n if isinstance(selected_loader_info, dict):\\n loader_import: str = selected_loader_info[\\\"import\\\"]\\n else:\\n raise ValueError(f\\\"Loader info for {loader} is not a dict\\\\nLoader info:\\\\n{selected_loader_info}\\\")\\n module_name, class_name = loader_import.rsplit(\\\".\\\", 1)\\n\\n try:\\n # Import the loader class\\n loader_module = __import__(module_name, fromlist=[class_name])\\n loader_instance = getattr(loader_module, class_name)\\n except ImportError as e:\\n raise ValueError(f\\\"Loader {loader} could not be imported\\\\nLoader info:\\\\n{selected_loader_info}\\\") from e\\n\\n result = loader_instance(file_path=file_path)\\n return result.load()\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"loader\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"Automatic\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"Automatic\",\"Airbyte JSON (.jsonl)\",\"JSON (.json)\",\"BeautifulSoup4 HTML (.html, .htm)\",\"CSV (.csv)\",\"CoNLL-U (.conllu)\",\"EverNote (.enex)\",\"Facebook Chat (.json)\",\"Outlook Message (.msg)\",\"PyPDF (.pdf)\",\"Subtitle (.str)\",\"Text (.txt)\",\"Unstructured Email (.eml)\",\"Unstructured HTML (.html, .htm)\",\"Unstructured Markdown (.md)\",\"Unstructured PowerPoint (.pptx)\",\"Unstructured Word (.docx)\"],\"name\":\"loader\",\"display_name\":\"Loader\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Generic File Loader\",\"base_classes\":[\"Serializable\",\"Document\"],\"display_name\":\"File Loader\",\"documentation\":\"\",\"custom_fields\":{\"file_path\":null,\"loader\":null},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"UrlLoader\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import List\\n\\nfrom langchain import document_loaders\\nfrom langchain_core.documents import Document\\n\\nfrom langflow import CustomComponent\\n\\n\\nclass UrlLoaderComponent(CustomComponent):\\n display_name: str = \\\"Url Loader\\\"\\n description: str = \\\"Generic Url Loader Component\\\"\\n\\n def build_config(self):\\n return {\\n \\\"web_path\\\": {\\n \\\"display_name\\\": \\\"Url\\\",\\n \\\"required\\\": True,\\n },\\n \\\"loader\\\": {\\n \\\"display_name\\\": \\\"Loader\\\",\\n \\\"is_list\\\": True,\\n \\\"required\\\": True,\\n \\\"options\\\": [\\n \\\"AZLyricsLoader\\\",\\n \\\"CollegeConfidentialLoader\\\",\\n \\\"GitbookLoader\\\",\\n \\\"HNLoader\\\",\\n \\\"IFixitLoader\\\",\\n \\\"IMSDbLoader\\\",\\n \\\"WebBaseLoader\\\",\\n ],\\n \\\"value\\\": \\\"WebBaseLoader\\\",\\n },\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(self, web_path: str, loader: str) -> List[Document]:\\n try:\\n loader_instance = getattr(document_loaders, loader)(web_path=web_path)\\n except Exception as e:\\n raise ValueError(f\\\"No loader found for: {web_path}\\\") from e\\n docs = loader_instance.load()\\n avg_length = sum(len(doc.page_content) for doc in docs if hasattr(doc, \\\"page_content\\\")) / len(docs)\\n self.status = f\\\"\\\"\\\"{len(docs)} documents)\\n \\\\nAvg. Document Length (characters): {int(avg_length)}\\n Documents: {docs[:3]}...\\\"\\\"\\\"\\n return docs\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"loader\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"WebBaseLoader\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"AZLyricsLoader\",\"CollegeConfidentialLoader\",\"GitbookLoader\",\"HNLoader\",\"IFixitLoader\",\"IMSDbLoader\",\"WebBaseLoader\"],\"name\":\"loader\",\"display_name\":\"Loader\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"web_path\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"web_path\",\"display_name\":\"Url\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Generic Url Loader Component\",\"base_classes\":[\"Serializable\",\"Document\"],\"display_name\":\"Url Loader\",\"documentation\":\"\",\"custom_fields\":{\"web_path\":null,\"loader\":null},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"GatherRecords\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from concurrent import futures\\nfrom pathlib import Path\\nfrom typing import Any, Dict, List\\n\\nfrom langflow import CustomComponent\\nfrom langflow.schema import Record\\n\\n\\nclass GatherRecordsComponent(CustomComponent):\\n display_name = \\\"Gather Records\\\"\\n description = \\\"Gather records from a directory.\\\"\\n\\n def build_config(self) -> Dict[str, Any]:\\n return {\\n \\\"load_hidden\\\": {\\n \\\"display_name\\\": \\\"Load Hidden Files\\\",\\n \\\"value\\\": False,\\n \\\"advanced\\\": True,\\n },\\n \\\"max_concurrency\\\": {\\n \\\"display_name\\\": \\\"Max Concurrency\\\",\\n \\\"value\\\": 10,\\n \\\"advanced\\\": True,\\n },\\n \\\"path\\\": {\\\"display_name\\\": \\\"Local Directory\\\"},\\n \\\"recursive\\\": {\\\"display_name\\\": \\\"Recursive\\\", \\\"value\\\": True, \\\"advanced\\\": True},\\n \\\"use_multithreading\\\": {\\n \\\"display_name\\\": \\\"Use Multithreading\\\",\\n \\\"value\\\": True,\\n \\\"advanced\\\": True,\\n },\\n }\\n\\n def is_hidden(self, path: Path) -> bool:\\n return path.name.startswith(\\\".\\\")\\n\\n def retrieve_file_paths(\\n self,\\n path: str,\\n types: List[str],\\n load_hidden: bool,\\n recursive: bool,\\n depth: int,\\n ) -> List[str]:\\n path_obj = Path(path)\\n if not path_obj.exists() or not path_obj.is_dir():\\n raise ValueError(f\\\"Path {path} must exist and be a directory.\\\")\\n\\n def match_types(p: Path) -> bool:\\n return any(p.suffix == f\\\".{t}\\\" for t in types) if types else True\\n\\n def is_not_hidden(p: Path) -> bool:\\n return not self.is_hidden(p) or load_hidden\\n\\n def walk_level(directory: Path, max_depth: int):\\n directory = directory.resolve()\\n prefix_length = len(directory.parts)\\n for p in directory.rglob(\\\"*\\\" if recursive else \\\"[!.]*\\\"):\\n if len(p.parts) - prefix_length <= max_depth:\\n yield p\\n\\n glob = \\\"**/*\\\" if recursive else \\\"*\\\"\\n paths = walk_level(path_obj, depth) if depth else path_obj.glob(glob)\\n file_paths = [str(p) for p in paths if p.is_file() and match_types(p) and is_not_hidden(p)]\\n\\n return file_paths\\n\\n def parse_file_to_record(self, file_path: str, silent_errors: bool) -> Record:\\n # Use the partition function to load the file\\n from unstructured.partition.auto import partition\\n\\n try:\\n elements = partition(file_path)\\n except Exception as e:\\n if not silent_errors:\\n raise ValueError(f\\\"Error loading file {file_path}: {e}\\\") from e\\n return None\\n\\n # Create a Record\\n text = \\\"\\\\n\\\\n\\\".join([str(el) for el in elements])\\n metadata = elements.metadata if hasattr(elements, \\\"metadata\\\") else {}\\n metadata[\\\"file_path\\\"] = file_path\\n record = Record(text=text, data=metadata)\\n return record\\n\\n def get_elements(\\n self,\\n file_paths: List[str],\\n silent_errors: bool,\\n max_concurrency: int,\\n use_multithreading: bool,\\n ) -> List[Record]:\\n if use_multithreading:\\n records = self.parallel_load_records(file_paths, silent_errors, max_concurrency)\\n else:\\n records = [self.parse_file_to_record(file_path, silent_errors) for file_path in file_paths]\\n records = list(filter(None, records))\\n return records\\n\\n def parallel_load_records(self, file_paths: List[str], silent_errors: bool, max_concurrency: int) -> List[Record]:\\n with futures.ThreadPoolExecutor(max_workers=max_concurrency) as executor:\\n loaded_files = executor.map(\\n lambda file_path: self.parse_file_to_record(file_path, silent_errors),\\n file_paths,\\n )\\n return loaded_files\\n\\n def build(\\n self,\\n path: str,\\n types: List[str] = None,\\n depth: int = 0,\\n max_concurrency: int = 2,\\n load_hidden: bool = False,\\n recursive: bool = True,\\n silent_errors: bool = False,\\n use_multithreading: bool = True,\\n ) -> List[Record]:\\n resolved_path = self.resolve_path(path)\\n file_paths = self.retrieve_file_paths(resolved_path, types, load_hidden, recursive, depth)\\n loaded_records = []\\n\\n if use_multithreading:\\n loaded_records = self.parallel_load_records(file_paths, silent_errors, max_concurrency)\\n else:\\n loaded_records = [self.parse_file_to_record(file_path, silent_errors) for file_path in file_paths]\\n loaded_records = list(filter(None, loaded_records))\\n self.status = loaded_records\\n return loaded_records\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"depth\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"depth\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"load_hidden\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"load_hidden\",\"display_name\":\"Load Hidden Files\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"max_concurrency\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":10,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_concurrency\",\"display_name\":\"Max Concurrency\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"path\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"path\",\"display_name\":\"Local Directory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"recursive\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":true,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"recursive\",\"display_name\":\"Recursive\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"silent_errors\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"silent_errors\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"types\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"types\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"use_multithreading\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":true,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"use_multithreading\",\"display_name\":\"Use Multithreading\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Gather records from a directory.\",\"base_classes\":[\"Record\"],\"display_name\":\"Gather Records\",\"documentation\":\"\",\"custom_fields\":{\"path\":null,\"types\":null,\"depth\":null,\"max_concurrency\":null,\"load_hidden\":null,\"recursive\":null,\"silent_errors\":null,\"use_multithreading\":null},\"output_types\":[\"Record\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true}},\"textsplitters\":{\"CharacterTextSplitter\":{\"template\":{\"documents\":{\"type\":\"Document\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"documents\",\"display_name\":\"Documents\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"chunk_overlap\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":200,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"chunk_overlap\",\"display_name\":\"Chunk Overlap\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"chunk_size\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":1000,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"chunk_size\",\"display_name\":\"Chunk Size\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import List\\n\\nfrom langchain.text_splitter import CharacterTextSplitter\\nfrom langchain_core.documents.base import Document\\nfrom langflow import CustomComponent\\n\\n\\nclass CharacterTextSplitterComponent(CustomComponent):\\n display_name = \\\"CharacterTextSplitter\\\"\\n description = \\\"Splitting text that looks at characters.\\\"\\n\\n def build_config(self):\\n return {\\n \\\"documents\\\": {\\\"display_name\\\": \\\"Documents\\\"},\\n \\\"chunk_overlap\\\": {\\\"display_name\\\": \\\"Chunk Overlap\\\", \\\"default\\\": 200},\\n \\\"chunk_size\\\": {\\\"display_name\\\": \\\"Chunk Size\\\", \\\"default\\\": 1000},\\n \\\"separator\\\": {\\\"display_name\\\": \\\"Separator\\\", \\\"default\\\": \\\"\\\\n\\\"},\\n }\\n\\n def build(\\n self,\\n documents: List[Document],\\n chunk_overlap: int = 200,\\n chunk_size: int = 1000,\\n separator: str = \\\"\\\\n\\\",\\n ) -> List[Document]:\\n # separator may come escaped from the frontend\\n separator = separator.encode().decode(\\\"unicode_escape\\\")\\n docs = CharacterTextSplitter(\\n chunk_overlap=chunk_overlap,\\n chunk_size=chunk_size,\\n separator=separator,\\n ).split_documents(documents)\\n self.status = docs\\n return docs\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"separator\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\\\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"separator\",\"display_name\":\"Separator\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Splitting text that looks at characters.\",\"base_classes\":[\"Serializable\",\"Document\"],\"display_name\":\"CharacterTextSplitter\",\"documentation\":\"\",\"custom_fields\":{\"documents\":null,\"chunk_overlap\":null,\"chunk_size\":null,\"separator\":null},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"RecursiveCharacterTextSplitter\":{\"template\":{\"documents\":{\"type\":\"Document\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"documents\",\"display_name\":\"Documents\",\"advanced\":false,\"dynamic\":false,\"info\":\"The documents to split.\",\"title_case\":false},\"chunk_overlap\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":200,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"chunk_overlap\",\"display_name\":\"Chunk Overlap\",\"advanced\":false,\"dynamic\":false,\"info\":\"The amount of overlap between chunks.\",\"title_case\":false},\"chunk_size\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":1000,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"chunk_size\",\"display_name\":\"Chunk Size\",\"advanced\":false,\"dynamic\":false,\"info\":\"The maximum length of each chunk.\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain_core.documents import Document\\n\\nfrom langflow import CustomComponent\\nfrom langflow.utils.util import build_loader_repr_from_documents\\nfrom langchain.text_splitter import RecursiveCharacterTextSplitter\\n\\n\\nclass RecursiveCharacterTextSplitterComponent(CustomComponent):\\n display_name: str = \\\"Recursive Character Text Splitter\\\"\\n description: str = \\\"Split text into chunks of a specified length.\\\"\\n documentation: str = \\\"https://docs.langflow.org/components/text-splitters#recursivecharactertextsplitter\\\"\\n\\n def build_config(self):\\n return {\\n \\\"documents\\\": {\\n \\\"display_name\\\": \\\"Documents\\\",\\n \\\"info\\\": \\\"The documents to split.\\\",\\n },\\n \\\"separators\\\": {\\n \\\"display_name\\\": \\\"Separators\\\",\\n \\\"info\\\": 'The characters to split on.\\\\nIf left empty defaults to [\\\"\\\\\\\\n\\\\\\\\n\\\", \\\"\\\\\\\\n\\\", \\\" \\\", \\\"\\\"].',\\n \\\"is_list\\\": True,\\n },\\n \\\"chunk_size\\\": {\\n \\\"display_name\\\": \\\"Chunk Size\\\",\\n \\\"info\\\": \\\"The maximum length of each chunk.\\\",\\n \\\"field_type\\\": \\\"int\\\",\\n \\\"value\\\": 1000,\\n },\\n \\\"chunk_overlap\\\": {\\n \\\"display_name\\\": \\\"Chunk Overlap\\\",\\n \\\"info\\\": \\\"The amount of overlap between chunks.\\\",\\n \\\"field_type\\\": \\\"int\\\",\\n \\\"value\\\": 200,\\n },\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(\\n self,\\n documents: list[Document],\\n separators: Optional[list[str]] = None,\\n chunk_size: Optional[int] = 1000,\\n chunk_overlap: Optional[int] = 200,\\n ) -> list[Document]:\\n \\\"\\\"\\\"\\n Split text into chunks of a specified length.\\n\\n Args:\\n separators (list[str]): The characters to split on.\\n chunk_size (int): The maximum length of each chunk.\\n chunk_overlap (int): The amount of overlap between chunks.\\n length_function (function): The function to use to calculate the length of the text.\\n\\n Returns:\\n list[str]: The chunks of text.\\n \\\"\\\"\\\"\\n\\n if separators == \\\"\\\":\\n separators = None\\n elif separators:\\n # check if the separators list has escaped characters\\n # if there are escaped characters, unescape them\\n separators = [x.encode().decode(\\\"unicode-escape\\\") for x in separators]\\n\\n # Make sure chunk_size and chunk_overlap are ints\\n if isinstance(chunk_size, str):\\n chunk_size = int(chunk_size)\\n if isinstance(chunk_overlap, str):\\n chunk_overlap = int(chunk_overlap)\\n splitter = RecursiveCharacterTextSplitter(\\n separators=separators,\\n chunk_size=chunk_size,\\n chunk_overlap=chunk_overlap,\\n )\\n\\n docs = splitter.split_documents(documents)\\n self.repr_value = build_loader_repr_from_documents(docs)\\n return docs\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"separators\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"separators\",\"display_name\":\"Separators\",\"advanced\":false,\"dynamic\":false,\"info\":\"The characters to split on.\\nIf left empty defaults to [\\\"\\\\n\\\\n\\\", \\\"\\\\n\\\", \\\" \\\", \\\"\\\"].\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Split text into chunks of a specified length.\",\"base_classes\":[\"Serializable\",\"Document\"],\"display_name\":\"Recursive Character Text Splitter\",\"documentation\":\"https://docs.langflow.org/components/text-splitters#recursivecharactertextsplitter\",\"custom_fields\":{\"documents\":null,\"separators\":null,\"chunk_size\":null,\"chunk_overlap\":null},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"LanguageRecursiveTextSplitter\":{\"template\":{\"documents\":{\"type\":\"Document\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"documents\",\"display_name\":\"Documents\",\"advanced\":false,\"dynamic\":false,\"info\":\"The documents to split.\",\"title_case\":false},\"chunk_overlap\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":200,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"chunk_overlap\",\"display_name\":\"Chunk Overlap\",\"advanced\":false,\"dynamic\":false,\"info\":\"The amount of overlap between chunks.\",\"title_case\":false},\"chunk_size\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":1000,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"chunk_size\",\"display_name\":\"Chunk Size\",\"advanced\":false,\"dynamic\":false,\"info\":\"The maximum length of each chunk.\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain.text_splitter import Language\\nfrom langchain_core.documents import Document\\n\\nfrom langflow import CustomComponent\\n\\n\\nclass LanguageRecursiveTextSplitterComponent(CustomComponent):\\n display_name: str = \\\"Language Recursive Text Splitter\\\"\\n description: str = \\\"Split text into chunks of a specified length based on language.\\\"\\n documentation: str = \\\"https://docs.langflow.org/components/text-splitters#languagerecursivetextsplitter\\\"\\n\\n def build_config(self):\\n options = [x.value for x in Language]\\n return {\\n \\\"documents\\\": {\\n \\\"display_name\\\": \\\"Documents\\\",\\n \\\"info\\\": \\\"The documents to split.\\\",\\n },\\n \\\"separator_type\\\": {\\n \\\"display_name\\\": \\\"Separator Type\\\",\\n \\\"info\\\": \\\"The type of separator to use.\\\",\\n \\\"field_type\\\": \\\"str\\\",\\n \\\"options\\\": options,\\n \\\"value\\\": \\\"Python\\\",\\n },\\n \\\"separators\\\": {\\n \\\"display_name\\\": \\\"Separators\\\",\\n \\\"info\\\": \\\"The characters to split on.\\\",\\n \\\"is_list\\\": True,\\n },\\n \\\"chunk_size\\\": {\\n \\\"display_name\\\": \\\"Chunk Size\\\",\\n \\\"info\\\": \\\"The maximum length of each chunk.\\\",\\n \\\"field_type\\\": \\\"int\\\",\\n \\\"value\\\": 1000,\\n },\\n \\\"chunk_overlap\\\": {\\n \\\"display_name\\\": \\\"Chunk Overlap\\\",\\n \\\"info\\\": \\\"The amount of overlap between chunks.\\\",\\n \\\"field_type\\\": \\\"int\\\",\\n \\\"value\\\": 200,\\n },\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(\\n self,\\n documents: list[Document],\\n chunk_size: Optional[int] = 1000,\\n chunk_overlap: Optional[int] = 200,\\n separator_type: str = \\\"Python\\\",\\n ) -> list[Document]:\\n \\\"\\\"\\\"\\n Split text into chunks of a specified length.\\n\\n Args:\\n separators (list[str]): The characters to split on.\\n chunk_size (int): The maximum length of each chunk.\\n chunk_overlap (int): The amount of overlap between chunks.\\n length_function (function): The function to use to calculate the length of the text.\\n\\n Returns:\\n list[str]: The chunks of text.\\n \\\"\\\"\\\"\\n from langchain.text_splitter import RecursiveCharacterTextSplitter\\n\\n # Make sure chunk_size and chunk_overlap are ints\\n if isinstance(chunk_size, str):\\n chunk_size = int(chunk_size)\\n if isinstance(chunk_overlap, str):\\n chunk_overlap = int(chunk_overlap)\\n\\n splitter = RecursiveCharacterTextSplitter.from_language(\\n language=Language(separator_type),\\n chunk_size=chunk_size,\\n chunk_overlap=chunk_overlap,\\n )\\n\\n docs = splitter.split_documents(documents)\\n return docs\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"separator_type\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"Python\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"cpp\",\"go\",\"java\",\"kotlin\",\"js\",\"ts\",\"php\",\"proto\",\"python\",\"rst\",\"ruby\",\"rust\",\"scala\",\"swift\",\"markdown\",\"latex\",\"html\",\"sol\",\"csharp\",\"cobol\",\"c\",\"lua\",\"perl\"],\"name\":\"separator_type\",\"display_name\":\"Separator Type\",\"advanced\":false,\"dynamic\":false,\"info\":\"The type of separator to use.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Split text into chunks of a specified length based on language.\",\"base_classes\":[\"Serializable\",\"Document\"],\"display_name\":\"Language Recursive Text Splitter\",\"documentation\":\"https://docs.langflow.org/components/text-splitters#languagerecursivetextsplitter\",\"custom_fields\":{\"documents\":null,\"chunk_size\":null,\"chunk_overlap\":null,\"separator_type\":null},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true}},\"utilities\":{\"BingSearchAPIWrapper\":{\"template\":{\"bing_search_url\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"bing_search_url\",\"display_name\":\"Bing Search URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"bing_subscription_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"bing_subscription_key\",\"display_name\":\"Bing Subscription Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\n\\n# Assuming `BingSearchAPIWrapper` is a class that exists in the context\\n# and has the appropriate methods and attributes.\\n# We need to make sure this class is importable from the context where this code will be running.\\nfrom langchain_community.utilities.bing_search import BingSearchAPIWrapper\\n\\n\\nclass BingSearchAPIWrapperComponent(CustomComponent):\\n display_name = \\\"BingSearchAPIWrapper\\\"\\n description = \\\"Wrapper for Bing Search API.\\\"\\n\\n def build_config(self):\\n return {\\n \\\"bing_search_url\\\": {\\\"display_name\\\": \\\"Bing Search URL\\\"},\\n \\\"bing_subscription_key\\\": {\\n \\\"display_name\\\": \\\"Bing Subscription Key\\\",\\n \\\"password\\\": True,\\n },\\n \\\"k\\\": {\\\"display_name\\\": \\\"Number of results\\\", \\\"advanced\\\": True},\\n # 'k' is not included as it is not shown (show=False)\\n }\\n\\n def build(\\n self,\\n bing_search_url: str,\\n bing_subscription_key: str,\\n k: int = 10,\\n ) -> BingSearchAPIWrapper:\\n # 'k' has a default value and is not shown (show=False), so it is hardcoded here\\n return BingSearchAPIWrapper(bing_search_url=bing_search_url, bing_subscription_key=bing_subscription_key, k=k)\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"k\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":10,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"k\",\"display_name\":\"Number of results\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Wrapper for Bing Search API.\",\"base_classes\":[\"BingSearchAPIWrapper\"],\"display_name\":\"BingSearchAPIWrapper\",\"documentation\":\"\",\"custom_fields\":{\"bing_search_url\":null,\"bing_subscription_key\":null,\"k\":null},\"output_types\":[\"BingSearchAPIWrapper\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"GoogleSearchAPIWrapper\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Callable, Union\\n\\nfrom langchain_community.utilities.google_search import GoogleSearchAPIWrapper\\nfrom langflow import CustomComponent\\n\\n\\nclass GoogleSearchAPIWrapperComponent(CustomComponent):\\n display_name = \\\"GoogleSearchAPIWrapper\\\"\\n description = \\\"Wrapper for Google Search API.\\\"\\n\\n def build_config(self):\\n return {\\n \\\"google_api_key\\\": {\\\"display_name\\\": \\\"Google API Key\\\", \\\"password\\\": True},\\n \\\"google_cse_id\\\": {\\\"display_name\\\": \\\"Google CSE ID\\\", \\\"password\\\": True},\\n }\\n\\n def build(\\n self,\\n google_api_key: str,\\n google_cse_id: str,\\n ) -> Union[GoogleSearchAPIWrapper, Callable]:\\n return GoogleSearchAPIWrapper(google_api_key=google_api_key, google_cse_id=google_cse_id) # type: ignore\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"google_api_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"google_api_key\",\"display_name\":\"Google API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"google_cse_id\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"google_cse_id\",\"display_name\":\"Google CSE ID\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Wrapper for Google Search API.\",\"base_classes\":[\"GoogleSearchAPIWrapper\",\"Callable\"],\"display_name\":\"GoogleSearchAPIWrapper\",\"documentation\":\"\",\"custom_fields\":{\"google_api_key\":null,\"google_cse_id\":null},\"output_types\":[\"GoogleSearchAPIWrapper\",\"Callable\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"GoogleSerperAPIWrapper\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Dict\\n\\n# Assuming the existence of GoogleSerperAPIWrapper class in the serper module\\n# If this class does not exist, you would need to create it or import the appropriate class from another module\\nfrom langchain_community.utilities.google_serper import GoogleSerperAPIWrapper\\n\\nfrom langflow import CustomComponent\\n\\n\\nclass GoogleSerperAPIWrapperComponent(CustomComponent):\\n display_name = \\\"GoogleSerperAPIWrapper\\\"\\n description = \\\"Wrapper around the Serper.dev Google Search API.\\\"\\n\\n def build_config(self) -> Dict[str, Dict]:\\n return {\\n \\\"result_key_for_type\\\": {\\n \\\"display_name\\\": \\\"Result Key for Type\\\",\\n \\\"show\\\": True,\\n \\\"multiline\\\": False,\\n \\\"password\\\": False,\\n \\\"advanced\\\": False,\\n \\\"dynamic\\\": False,\\n \\\"info\\\": \\\"\\\",\\n \\\"field_type\\\": \\\"dict\\\",\\n \\\"list\\\": False,\\n \\\"value\\\": {\\n \\\"news\\\": \\\"news\\\",\\n \\\"places\\\": \\\"places\\\",\\n \\\"images\\\": \\\"images\\\",\\n \\\"search\\\": \\\"organic\\\",\\n },\\n },\\n \\\"serper_api_key\\\": {\\n \\\"display_name\\\": \\\"Serper API Key\\\",\\n \\\"show\\\": True,\\n \\\"multiline\\\": False,\\n \\\"password\\\": True,\\n \\\"advanced\\\": False,\\n \\\"dynamic\\\": False,\\n \\\"info\\\": \\\"\\\",\\n \\\"type\\\": \\\"str\\\",\\n \\\"list\\\": False,\\n },\\n }\\n\\n def build(\\n self,\\n serper_api_key: str,\\n ) -> GoogleSerperAPIWrapper:\\n return GoogleSerperAPIWrapper(serper_api_key=serper_api_key)\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"serper_api_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"serper_api_key\",\"display_name\":\"Serper API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Wrapper around the Serper.dev Google Search API.\",\"base_classes\":[\"GoogleSerperAPIWrapper\"],\"display_name\":\"GoogleSerperAPIWrapper\",\"documentation\":\"\",\"custom_fields\":{\"serper_api_key\":null},\"output_types\":[\"GoogleSerperAPIWrapper\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"SearxSearchWrapper\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom typing import Optional, Dict\\nfrom langchain_community.utilities.searx_search import SearxSearchWrapper\\n\\n\\nclass SearxSearchWrapperComponent(CustomComponent):\\n display_name = \\\"SearxSearchWrapper\\\"\\n description = \\\"Wrapper for Searx API.\\\"\\n\\n def build_config(self):\\n return {\\n \\\"headers\\\": {\\n \\\"field_type\\\": \\\"dict\\\",\\n \\\"display_name\\\": \\\"Headers\\\",\\n \\\"multiline\\\": True,\\n \\\"value\\\": '{\\\"Authorization\\\": \\\"Bearer \\\"}',\\n },\\n \\\"k\\\": {\\\"display_name\\\": \\\"k\\\", \\\"advanced\\\": True, \\\"field_type\\\": \\\"int\\\", \\\"value\\\": 10},\\n \\\"searx_host\\\": {\\n \\\"display_name\\\": \\\"Searx Host\\\",\\n \\\"field_type\\\": \\\"str\\\",\\n \\\"value\\\": \\\"https://searx.example.com\\\",\\n \\\"advanced\\\": True,\\n },\\n }\\n\\n def build(\\n self,\\n k: int = 10,\\n headers: Optional[Dict[str, str]] = None,\\n searx_host: str = \\\"https://searx.example.com\\\",\\n ) -> SearxSearchWrapper:\\n return SearxSearchWrapper(headers=headers, k=k, searx_host=searx_host)\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"headers\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"{\\\"Authorization\\\": \\\"Bearer \\\"}\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"headers\",\"display_name\":\"Headers\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"k\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":10,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"k\",\"display_name\":\"k\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"searx_host\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"https://searx.example.com\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"searx_host\",\"display_name\":\"Searx Host\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Wrapper for Searx API.\",\"base_classes\":[\"SearxSearchWrapper\"],\"display_name\":\"SearxSearchWrapper\",\"documentation\":\"\",\"custom_fields\":{\"k\":null,\"headers\":null,\"searx_host\":null},\"output_types\":[\"SearxSearchWrapper\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"SerpAPIWrapper\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Callable, Union\\n\\nfrom langchain_community.utilities.serpapi import SerpAPIWrapper\\nfrom langflow import CustomComponent\\n\\n\\nclass SerpAPIWrapperComponent(CustomComponent):\\n display_name = \\\"SerpAPIWrapper\\\"\\n description = \\\"Wrapper around SerpAPI\\\"\\n\\n def build_config(self):\\n return {\\n \\\"serpapi_api_key\\\": {\\\"display_name\\\": \\\"SerpAPI API Key\\\", \\\"type\\\": \\\"str\\\", \\\"password\\\": True},\\n \\\"params\\\": {\\n \\\"display_name\\\": \\\"Parameters\\\",\\n \\\"type\\\": \\\"dict\\\",\\n \\\"advanced\\\": True,\\n \\\"multiline\\\": True,\\n \\\"value\\\": '{\\\"engine\\\": \\\"google\\\",\\\"google_domain\\\": \\\"google.com\\\",\\\"gl\\\": \\\"us\\\",\\\"hl\\\": \\\"en\\\"}',\\n },\\n }\\n\\n def build(\\n self,\\n serpapi_api_key: str,\\n params: dict,\\n ) -> Union[SerpAPIWrapper, Callable]: # Removed quotes around SerpAPIWrapper\\n return SerpAPIWrapper( # type: ignore\\n serpapi_api_key=serpapi_api_key,\\n params=params,\\n )\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"params\":{\"type\":\"dict\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"{\\\"engine\\\": \\\"google\\\",\\\"google_domain\\\": \\\"google.com\\\",\\\"gl\\\": \\\"us\\\",\\\"hl\\\": \\\"en\\\"}\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"params\",\"display_name\":\"Parameters\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"serpapi_api_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"serpapi_api_key\",\"display_name\":\"SerpAPI API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Wrapper around SerpAPI\",\"base_classes\":[\"Callable\",\"SerpAPIWrapper\"],\"display_name\":\"SerpAPIWrapper\",\"documentation\":\"\",\"custom_fields\":{\"serpapi_api_key\":null,\"params\":null},\"output_types\":[\"SerpAPIWrapper\",\"Callable\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"WikipediaAPIWrapper\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Callable, Union\\n\\nfrom langchain_community.utilities.wikipedia import WikipediaAPIWrapper\\nfrom langflow import CustomComponent\\n\\n# Assuming WikipediaAPIWrapper is a class that needs to be imported.\\n# The import statement is not included as it is not provided in the JSON\\n# and the actual implementation details are unknown.\\n\\n\\nclass WikipediaAPIWrapperComponent(CustomComponent):\\n display_name = \\\"WikipediaAPIWrapper\\\"\\n description = \\\"Wrapper around WikipediaAPI.\\\"\\n\\n def build_config(self):\\n return {}\\n\\n def build(\\n self,\\n top_k_results: int = 3,\\n lang: str = \\\"en\\\",\\n load_all_available_meta: bool = False,\\n doc_content_chars_max: int = 4000,\\n ) -> Union[WikipediaAPIWrapper, Callable]:\\n return WikipediaAPIWrapper( # type: ignore\\n top_k_results=top_k_results,\\n lang=lang,\\n load_all_available_meta=load_all_available_meta,\\n doc_content_chars_max=doc_content_chars_max,\\n )\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"doc_content_chars_max\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":4000,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"doc_content_chars_max\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"lang\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"en\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"lang\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"load_all_available_meta\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"load_all_available_meta\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"top_k_results\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":3,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_k_results\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Wrapper around WikipediaAPI.\",\"base_classes\":[\"WikipediaAPIWrapper\",\"Callable\"],\"display_name\":\"WikipediaAPIWrapper\",\"documentation\":\"\",\"custom_fields\":{\"top_k_results\":null,\"lang\":null,\"load_all_available_meta\":null,\"doc_content_chars_max\":null},\"output_types\":[\"WikipediaAPIWrapper\",\"Callable\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"WolframAlphaAPIWrapper\":{\"template\":{\"appid\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"appid\",\"display_name\":\"App ID\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Callable, Union\\n\\nfrom langchain_community.utilities.wolfram_alpha import WolframAlphaAPIWrapper\\nfrom langflow import CustomComponent\\n\\n# Since all the fields in the JSON have show=False, we will only create a basic component\\n# without any configurable fields.\\n\\n\\nclass WolframAlphaAPIWrapperComponent(CustomComponent):\\n display_name = \\\"WolframAlphaAPIWrapper\\\"\\n description = \\\"Wrapper for Wolfram Alpha.\\\"\\n\\n def build_config(self):\\n return {\\\"appid\\\": {\\\"display_name\\\": \\\"App ID\\\", \\\"type\\\": \\\"str\\\", \\\"password\\\": True}}\\n\\n def build(self, appid: str) -> Union[Callable, WolframAlphaAPIWrapper]:\\n return WolframAlphaAPIWrapper(wolfram_alpha_appid=appid) # type: ignore\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Wrapper for Wolfram Alpha.\",\"base_classes\":[\"WolframAlphaAPIWrapper\",\"Callable\"],\"display_name\":\"WolframAlphaAPIWrapper\",\"documentation\":\"\",\"custom_fields\":{\"appid\":null},\"output_types\":[\"Callable\",\"WolframAlphaAPIWrapper\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"RunnableExecutor\":{\"template\":{\"runnable\":{\"type\":\"Runnable\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"runnable\",\"display_name\":\"Runnable\",\"advanced\":false,\"dynamic\":false,\"info\":\"The runnable to execute.\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from langchain_core.runnables import Runnable\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Text\\n\\n\\nclass RunnableExecComponent(CustomComponent):\\n documentation: str = \\\"http://docs.langflow.org/components/custom\\\"\\n display_name = \\\"Runnable Executor\\\"\\n beta = True\\n\\n def build_config(self):\\n return {\\n \\\"input_key\\\": {\\n \\\"display_name\\\": \\\"Input Key\\\",\\n \\\"info\\\": \\\"The key to use for the input.\\\",\\n },\\n \\\"inputs\\\": {\\n \\\"display_name\\\": \\\"Inputs\\\",\\n \\\"info\\\": \\\"The inputs to pass to the runnable.\\\",\\n },\\n \\\"runnable\\\": {\\n \\\"display_name\\\": \\\"Runnable\\\",\\n \\\"info\\\": \\\"The runnable to execute.\\\",\\n },\\n \\\"output_key\\\": {\\n \\\"display_name\\\": \\\"Output Key\\\",\\n \\\"info\\\": \\\"The key to use for the output.\\\",\\n },\\n }\\n\\n def build(\\n self,\\n input_key: str,\\n inputs: str,\\n runnable: Runnable,\\n output_key: str = \\\"output\\\",\\n ) -> Text:\\n result = runnable.invoke({input_key: inputs})\\n result = result.get(output_key)\\n self.status = result\\n return result\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"input_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"input_key\",\"display_name\":\"Input Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"The key to use for the input.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"inputs\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"inputs\",\"display_name\":\"Inputs\",\"advanced\":false,\"dynamic\":false,\"info\":\"The inputs to pass to the runnable.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"output_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"output\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"output_key\",\"display_name\":\"Output Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"The key to use for the output.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"base_classes\":[\"Text\",\"object\"],\"display_name\":\"Runnable Executor\",\"documentation\":\"http://docs.langflow.org/components/custom\",\"custom_fields\":{\"input_key\":null,\"inputs\":null,\"runnable\":null,\"output_key\":null},\"output_types\":[\"Text\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"DocumentToRecord\":{\"template\":{\"documents\":{\"type\":\"Document\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"documents\",\"display_name\":\"Documents\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import List\\n\\nfrom langchain_core.documents import Document\\n\\nfrom langflow import CustomComponent\\nfrom langflow.schema import Record\\n\\n\\nclass DocumentToRecordComponent(CustomComponent):\\n display_name = \\\"Documents to Records\\\"\\n description = \\\"Convert documents to records.\\\"\\n\\n field_config = {\\n \\\"documents\\\": {\\\"display_name\\\": \\\"Documents\\\"},\\n }\\n\\n def build(self, documents: List[Document]) -> List[Record]:\\n if isinstance(documents, Document):\\n documents = [documents]\\n records = [Record.from_document(document) for document in documents]\\n self.status = records\\n return records\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Convert documents to records.\",\"base_classes\":[\"Record\"],\"display_name\":\"Documents to Records\",\"documentation\":\"\",\"custom_fields\":{\"documents\":null},\"output_types\":[\"Record\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"GetRequest\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nimport requests\\nfrom langchain_core.documents import Document\\nfrom langflow import CustomComponent\\nfrom langflow.services.database.models.base import orjson_dumps\\n\\n\\nclass GetRequest(CustomComponent):\\n display_name: str = \\\"GET Request\\\"\\n description: str = \\\"Make a GET request to the given URL.\\\"\\n output_types: list[str] = [\\\"Document\\\"]\\n documentation: str = \\\"https://docs.langflow.org/components/utilities#get-request\\\"\\n beta: bool = True\\n field_config = {\\n \\\"url\\\": {\\n \\\"display_name\\\": \\\"URL\\\",\\n \\\"info\\\": \\\"The URL to make the request to\\\",\\n \\\"is_list\\\": True,\\n },\\n \\\"headers\\\": {\\n \\\"display_name\\\": \\\"Headers\\\",\\n \\\"info\\\": \\\"The headers to send with the request.\\\",\\n },\\n \\\"code\\\": {\\\"show\\\": False},\\n \\\"timeout\\\": {\\n \\\"display_name\\\": \\\"Timeout\\\",\\n \\\"field_type\\\": \\\"int\\\",\\n \\\"info\\\": \\\"The timeout to use for the request.\\\",\\n \\\"value\\\": 5,\\n },\\n }\\n\\n def get_document(self, session: requests.Session, url: str, headers: Optional[dict], timeout: int) -> Document:\\n try:\\n response = session.get(url, headers=headers, timeout=int(timeout))\\n try:\\n response_json = response.json()\\n result = orjson_dumps(response_json, indent_2=False)\\n except Exception:\\n result = response.text\\n self.repr_value = result\\n return Document(\\n page_content=result,\\n metadata={\\n \\\"source\\\": url,\\n \\\"headers\\\": headers,\\n \\\"status_code\\\": response.status_code,\\n },\\n )\\n except requests.Timeout:\\n return Document(\\n page_content=\\\"Request Timed Out\\\",\\n metadata={\\\"source\\\": url, \\\"headers\\\": headers, \\\"status_code\\\": 408},\\n )\\n except Exception as exc:\\n return Document(\\n page_content=str(exc),\\n metadata={\\\"source\\\": url, \\\"headers\\\": headers, \\\"status_code\\\": 500},\\n )\\n\\n def build(\\n self,\\n url: str,\\n headers: Optional[dict] = None,\\n timeout: int = 5,\\n ) -> list[Document]:\\n if headers is None:\\n headers = {}\\n urls = url if isinstance(url, list) else [url]\\n with requests.Session() as session:\\n documents = [self.get_document(session, u, headers, timeout) for u in urls]\\n self.repr_value = documents\\n return documents\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"headers\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"headers\",\"display_name\":\"Headers\",\"advanced\":false,\"dynamic\":false,\"info\":\"The headers to send with the request.\",\"title_case\":false},\"timeout\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":5,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"timeout\",\"display_name\":\"Timeout\",\"advanced\":false,\"dynamic\":false,\"info\":\"The timeout to use for the request.\",\"title_case\":false},\"url\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"url\",\"display_name\":\"URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"The URL to make the request to\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Make a GET request to the given URL.\",\"base_classes\":[\"Serializable\",\"Document\"],\"display_name\":\"GET Request\",\"documentation\":\"https://docs.langflow.org/components/utilities#get-request\",\"custom_fields\":{\"url\":null,\"headers\":null,\"timeout\":null},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"SQLExecutor\":{\"template\":{\"database\":{\"type\":\"SQLDatabase\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"database\",\"display_name\":\"Database\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"add_error\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"add_error\",\"display_name\":\"Add Error\",\"advanced\":false,\"dynamic\":false,\"info\":\"Add the error to the result.\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from langchain_community.tools.sql_database.tool import QuerySQLDataBaseTool\\nfrom langchain_experimental.sql.base import SQLDatabase\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Text\\n\\n\\nclass SQLExecutorComponent(CustomComponent):\\n display_name = \\\"SQL Executor\\\"\\n description = \\\"Execute SQL query.\\\"\\n\\n def build_config(self):\\n return {\\n \\\"database\\\": {\\\"display_name\\\": \\\"Database\\\"},\\n \\\"include_columns\\\": {\\n \\\"display_name\\\": \\\"Include Columns\\\",\\n \\\"info\\\": \\\"Include columns in the result.\\\",\\n },\\n \\\"passthrough\\\": {\\n \\\"display_name\\\": \\\"Passthrough\\\",\\n \\\"info\\\": \\\"If an error occurs, return the query instead of raising an exception.\\\",\\n },\\n \\\"add_error\\\": {\\n \\\"display_name\\\": \\\"Add Error\\\",\\n \\\"info\\\": \\\"Add the error to the result.\\\",\\n },\\n }\\n\\n def build(\\n self,\\n query: str,\\n database: SQLDatabase,\\n include_columns: bool = False,\\n passthrough: bool = False,\\n add_error: bool = False,\\n ) -> Text:\\n error = None\\n try:\\n tool = QuerySQLDataBaseTool(db=database)\\n result = tool.run(query, include_columns=include_columns)\\n self.status = result\\n except Exception as e:\\n result = str(e)\\n self.status = result\\n if not passthrough:\\n raise e\\n error = repr(e)\\n\\n if add_error and error is not None:\\n result = f\\\"{result}\\\\n\\\\nError: {error}\\\\n\\\\nQuery: {query}\\\"\\n elif error is not None:\\n # Then we won't add the error to the result\\n # but since we are in passthrough mode, we will return the query\\n result = query\\n\\n return result\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"include_columns\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"include_columns\",\"display_name\":\"Include Columns\",\"advanced\":false,\"dynamic\":false,\"info\":\"Include columns in the result.\",\"title_case\":false},\"passthrough\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"passthrough\",\"display_name\":\"Passthrough\",\"advanced\":false,\"dynamic\":false,\"info\":\"If an error occurs, return the query instead of raising an exception.\",\"title_case\":false},\"query\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"query\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Execute SQL query.\",\"base_classes\":[\"Text\",\"object\"],\"display_name\":\"SQL Executor\",\"documentation\":\"\",\"custom_fields\":{\"query\":null,\"database\":null,\"include_columns\":null,\"passthrough\":null,\"add_error\":null},\"output_types\":[\"Text\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"ShouldRunNext\":{\"template\":{\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"The language model to use for the decision.\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"# Implement ShouldRunNext component\\nfrom langchain_core.prompts import PromptTemplate\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import BaseLanguageModel, Prompt\\n\\n\\nclass ShouldRunNext(CustomComponent):\\n display_name = \\\"Should Run Next\\\"\\n description = \\\"Decides whether to run the next component.\\\"\\n\\n def build_config(self):\\n return {\\n \\\"prompt\\\": {\\n \\\"display_name\\\": \\\"Prompt\\\",\\n \\\"info\\\": \\\"The prompt to use for the decision. It should generate a boolean response (True or False).\\\",\\n },\\n \\\"llm\\\": {\\n \\\"display_name\\\": \\\"LLM\\\",\\n \\\"info\\\": \\\"The language model to use for the decision.\\\",\\n },\\n }\\n\\n def build(self, template: Prompt, llm: BaseLanguageModel, **kwargs) -> dict:\\n # This is a simple component that always returns True\\n prompt_template = PromptTemplate.from_template(template)\\n\\n attributes_to_check = [\\\"text\\\", \\\"page_content\\\"]\\n for key, value in kwargs.items():\\n for attribute in attributes_to_check:\\n if hasattr(value, attribute):\\n kwargs[key] = getattr(value, attribute)\\n\\n chain = prompt_template | llm\\n result = chain.invoke(kwargs)\\n if hasattr(result, \\\"content\\\") and isinstance(result.content, str):\\n result = result.content\\n elif isinstance(result, str):\\n result = result\\n else:\\n result = result.get(\\\"response\\\")\\n\\n if result.lower() not in [\\\"true\\\", \\\"false\\\"]:\\n raise ValueError(\\\"The prompt should generate a boolean response (True or False).\\\")\\n # The string should be the words true or false\\n # if not raise an error\\n bool_result = result.lower() == \\\"true\\\"\\n return {\\\"condition\\\": bool_result, \\\"result\\\": kwargs}\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"template\":{\"type\":\"prompt\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"template\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Decides whether to run the next component.\",\"base_classes\":[\"object\",\"dict\"],\"display_name\":\"Should Run Next\",\"documentation\":\"\",\"custom_fields\":{\"template\":null,\"llm\":null},\"output_types\":[\"dict\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"PythonFunction\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Callable\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Code\\nfrom langflow.interface.custom.utils import get_function\\n\\n\\nclass PythonFunctionComponent(CustomComponent):\\n display_name = \\\"Python Function\\\"\\n description = \\\"Define a Python function.\\\"\\n\\n def build_config(self):\\n return {\\n \\\"function_code\\\": {\\n \\\"display_name\\\": \\\"Code\\\",\\n \\\"info\\\": \\\"The code for the function.\\\",\\n \\\"show\\\": True,\\n },\\n }\\n\\n def build(self, function_code: Code) -> Callable:\\n self.status = function_code\\n func = get_function(function_code)\\n return func\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"function_code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"function_code\",\"display_name\":\"Code\",\"advanced\":false,\"dynamic\":false,\"info\":\"The code for the function.\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Define a Python function.\",\"base_classes\":[\"Callable\"],\"display_name\":\"Python Function\",\"documentation\":\"\",\"custom_fields\":{\"function_code\":null},\"output_types\":[\"Callable\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"PostRequest\":{\"template\":{\"document\":{\"type\":\"Document\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"document\",\"display_name\":\"Document\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nimport requests\\nfrom langchain_core.documents import Document\\nfrom langflow import CustomComponent\\nfrom langflow.services.database.models.base import orjson_dumps\\n\\n\\nclass PostRequest(CustomComponent):\\n display_name: str = \\\"POST Request\\\"\\n description: str = \\\"Make a POST request to the given URL.\\\"\\n output_types: list[str] = [\\\"Document\\\"]\\n documentation: str = \\\"https://docs.langflow.org/components/utilities#post-request\\\"\\n beta: bool = True\\n field_config = {\\n \\\"url\\\": {\\\"display_name\\\": \\\"URL\\\", \\\"info\\\": \\\"The URL to make the request to.\\\"},\\n \\\"headers\\\": {\\n \\\"display_name\\\": \\\"Headers\\\",\\n \\\"info\\\": \\\"The headers to send with the request.\\\",\\n },\\n \\\"code\\\": {\\\"show\\\": False},\\n \\\"document\\\": {\\\"display_name\\\": \\\"Document\\\"},\\n }\\n\\n def post_document(\\n self,\\n session: requests.Session,\\n document: Document,\\n url: str,\\n headers: Optional[dict] = None,\\n ) -> Document:\\n try:\\n response = session.post(url, headers=headers, data=document.page_content)\\n try:\\n response_json = response.json()\\n result = orjson_dumps(response_json, indent_2=False)\\n except Exception:\\n result = response.text\\n self.repr_value = result\\n return Document(\\n page_content=result,\\n metadata={\\n \\\"source\\\": url,\\n \\\"headers\\\": headers,\\n \\\"status_code\\\": response,\\n },\\n )\\n except Exception as exc:\\n return Document(\\n page_content=str(exc),\\n metadata={\\n \\\"source\\\": url,\\n \\\"headers\\\": headers,\\n \\\"status_code\\\": 500,\\n },\\n )\\n\\n def build(\\n self,\\n document: Document,\\n url: str,\\n headers: Optional[dict] = None,\\n ) -> list[Document]:\\n if headers is None:\\n headers = {}\\n\\n if not isinstance(document, list) and isinstance(document, Document):\\n documents: list[Document] = [document]\\n elif isinstance(document, list) and all(isinstance(doc, Document) for doc in document):\\n documents = document\\n else:\\n raise ValueError(\\\"document must be a Document or a list of Documents\\\")\\n\\n with requests.Session() as session:\\n documents = [self.post_document(session, doc, url, headers) for doc in documents]\\n self.repr_value = documents\\n return documents\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"headers\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"headers\",\"display_name\":\"Headers\",\"advanced\":false,\"dynamic\":false,\"info\":\"The headers to send with the request.\",\"title_case\":false},\"url\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"url\",\"display_name\":\"URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"The URL to make the request to.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Make a POST request to the given URL.\",\"base_classes\":[\"Serializable\",\"Document\"],\"display_name\":\"POST Request\",\"documentation\":\"https://docs.langflow.org/components/utilities#post-request\",\"custom_fields\":{\"document\":null,\"url\":null,\"headers\":null},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"IDGenerator\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"import uuid\\n\\nfrom langflow import CustomComponent\\n\\n\\nclass UUIDGeneratorComponent(CustomComponent):\\n documentation: str = \\\"http://docs.langflow.org/components/custom\\\"\\n display_name = \\\"Unique ID Generator\\\"\\n description = \\\"Generates a unique ID.\\\"\\n\\n def generate(self, *args, **kwargs):\\n return str(uuid.uuid4().hex)\\n\\n def build_config(self):\\n return {\\\"unique_id\\\": {\\\"display_name\\\": \\\"Value\\\", \\\"value\\\": self.generate}}\\n\\n def build(self, unique_id: str) -> str:\\n return unique_id\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"unique_id\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"a62d43140aba4c799af4ddc400295790\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"unique_id\",\"display_name\":\"Value\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"refresh\":true,\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Generates a unique ID.\",\"base_classes\":[\"object\",\"str\"],\"display_name\":\"Unique ID Generator\",\"documentation\":\"http://docs.langflow.org/components/custom\",\"custom_fields\":{\"unique_id\":null},\"output_types\":[\"str\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"SQLDatabase\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from langchain_experimental.sql.base import SQLDatabase\\n\\nfrom langflow import CustomComponent\\n\\n\\nclass SQLDatabaseComponent(CustomComponent):\\n display_name = \\\"SQLDatabase\\\"\\n description = \\\"SQL Database\\\"\\n\\n def build_config(self):\\n return {\\n \\\"uri\\\": {\\\"display_name\\\": \\\"URI\\\", \\\"info\\\": \\\"URI to the database.\\\"},\\n }\\n\\n def clean_up_uri(self, uri: str) -> str:\\n if uri.startswith(\\\"postgresql://\\\"):\\n uri = uri.replace(\\\"postgresql://\\\", \\\"postgres://\\\")\\n return uri.strip()\\n\\n def build(self, uri: str) -> SQLDatabase:\\n uri = self.clean_up_uri(uri)\\n return SQLDatabase.from_uri(uri)\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"uri\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"uri\",\"display_name\":\"URI\",\"advanced\":false,\"dynamic\":false,\"info\":\"URI to the database.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"SQL Database\",\"base_classes\":[\"object\",\"SQLDatabase\"],\"display_name\":\"SQLDatabase\",\"documentation\":\"\",\"custom_fields\":{\"uri\":null},\"output_types\":[\"SQLDatabase\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"RecordsAsText\":{\"template\":{\"records\":{\"type\":\"Record\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"records\",\"display_name\":\"Records\",\"advanced\":false,\"dynamic\":false,\"info\":\"The records to convert to text.\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langflow.field_typing import Text\\nfrom langflow.schema import Record\\n\\n\\nclass RecordsAsTextComponent(CustomComponent):\\n display_name = \\\"Records to Text\\\"\\n description = \\\"Converts Records a list of Records to text using a template.\\\"\\n\\n def build_config(self):\\n return {\\n \\\"records\\\": {\\n \\\"display_name\\\": \\\"Records\\\",\\n \\\"info\\\": \\\"The records to convert to text.\\\",\\n },\\n \\\"template\\\": {\\n \\\"display_name\\\": \\\"Template\\\",\\n \\\"info\\\": \\\"The template to use for formatting the records. It must contain the keys {text} and {data}.\\\",\\n },\\n }\\n\\n def build(\\n self,\\n records: list[Record],\\n template: str = \\\"Text: {text}\\\\nData: {data}\\\",\\n ) -> Text:\\n if isinstance(records, Record):\\n records = [records]\\n\\n formated_records = [\\n template.format(text=record.text, data=record.data, **record.data)\\n for record in records\\n ]\\n result_string = \\\"\\\\n\\\".join(formated_records)\\n self.status = result_string\\n return result_string\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"template\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"Text: {text}\\\\nData: {data}\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"template\",\"display_name\":\"Template\",\"advanced\":false,\"dynamic\":false,\"info\":\"The template to use for formatting the records. It must contain the keys {text} and {data}.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Converts Records a list of Records to text using a template.\",\"base_classes\":[\"Text\",\"object\"],\"display_name\":\"Records to Text\",\"documentation\":\"\",\"custom_fields\":{\"records\":null,\"template\":null},\"output_types\":[\"Text\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"UpdateRequest\":{\"template\":{\"document\":{\"type\":\"Document\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"document\",\"display_name\":\"Document\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import List, Optional\\n\\nimport requests\\nfrom langchain_core.documents import Document\\nfrom langflow import CustomComponent\\nfrom langflow.services.database.models.base import orjson_dumps\\n\\n\\nclass UpdateRequest(CustomComponent):\\n display_name: str = \\\"Update Request\\\"\\n description: str = \\\"Make a PATCH request to the given URL.\\\"\\n output_types: list[str] = [\\\"Document\\\"]\\n documentation: str = \\\"https://docs.langflow.org/components/utilities#update-request\\\"\\n beta: bool = True\\n field_config = {\\n \\\"url\\\": {\\\"display_name\\\": \\\"URL\\\", \\\"info\\\": \\\"The URL to make the request to.\\\"},\\n \\\"headers\\\": {\\n \\\"display_name\\\": \\\"Headers\\\",\\n \\\"field_type\\\": \\\"NestedDict\\\",\\n \\\"info\\\": \\\"The headers to send with the request.\\\",\\n },\\n \\\"code\\\": {\\\"show\\\": False},\\n \\\"document\\\": {\\\"display_name\\\": \\\"Document\\\"},\\n \\\"method\\\": {\\n \\\"display_name\\\": \\\"Method\\\",\\n \\\"field_type\\\": \\\"str\\\",\\n \\\"info\\\": \\\"The HTTP method to use.\\\",\\n \\\"options\\\": [\\\"PATCH\\\", \\\"PUT\\\"],\\n \\\"value\\\": \\\"PATCH\\\",\\n },\\n }\\n\\n def update_document(\\n self,\\n session: requests.Session,\\n document: Document,\\n url: str,\\n headers: Optional[dict] = None,\\n method: str = \\\"PATCH\\\",\\n ) -> Document:\\n try:\\n if method == \\\"PATCH\\\":\\n response = session.patch(url, headers=headers, data=document.page_content)\\n elif method == \\\"PUT\\\":\\n response = session.put(url, headers=headers, data=document.page_content)\\n else:\\n raise ValueError(f\\\"Unsupported method: {method}\\\")\\n try:\\n response_json = response.json()\\n result = orjson_dumps(response_json, indent_2=False)\\n except Exception:\\n result = response.text\\n self.repr_value = result\\n return Document(\\n page_content=result,\\n metadata={\\n \\\"source\\\": url,\\n \\\"headers\\\": headers,\\n \\\"status_code\\\": response.status_code,\\n },\\n )\\n except Exception as exc:\\n return Document(\\n page_content=str(exc),\\n metadata={\\\"source\\\": url, \\\"headers\\\": headers, \\\"status_code\\\": 500},\\n )\\n\\n def build(\\n self,\\n method: str,\\n document: Document,\\n url: str,\\n headers: Optional[dict] = None,\\n ) -> List[Document]:\\n if headers is None:\\n headers = {}\\n\\n if not isinstance(document, list) and isinstance(document, Document):\\n documents: list[Document] = [document]\\n elif isinstance(document, list) and all(isinstance(doc, Document) for doc in document):\\n documents = document\\n else:\\n raise ValueError(\\\"document must be a Document or a list of Documents\\\")\\n\\n with requests.Session() as session:\\n documents = [self.update_document(session, doc, url, headers, method) for doc in documents]\\n self.repr_value = documents\\n return documents\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"headers\":{\"type\":\"NestedDict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"headers\",\"display_name\":\"Headers\",\"advanced\":false,\"dynamic\":false,\"info\":\"The headers to send with the request.\",\"title_case\":false},\"method\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"PATCH\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"PATCH\",\"PUT\"],\"name\":\"method\",\"display_name\":\"Method\",\"advanced\":false,\"dynamic\":false,\"info\":\"The HTTP method to use.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"url\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"url\",\"display_name\":\"URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"The URL to make the request to.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Make a PATCH request to the given URL.\",\"base_classes\":[\"Serializable\",\"Document\"],\"display_name\":\"Update Request\",\"documentation\":\"https://docs.langflow.org/components/utilities#update-request\",\"custom_fields\":{\"method\":null,\"document\":null,\"url\":null,\"headers\":null},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"JSONDocumentBuilder\":{\"template\":{\"document\":{\"type\":\"Document\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"document\",\"display_name\":\"Document\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"### JSON Document Builder\\n\\n# Build a Document containing a JSON object using a key and another Document page content.\\n\\n# **Params**\\n\\n# - **Key:** The key to use for the JSON object.\\n# - **Document:** The Document page to use for the JSON object.\\n\\n# **Output**\\n\\n# - **Document:** The Document containing the JSON object.\\n\\nfrom langchain_core.documents import Document\\nfrom langflow import CustomComponent\\nfrom langflow.services.database.models.base import orjson_dumps\\n\\n\\nclass JSONDocumentBuilder(CustomComponent):\\n display_name: str = \\\"JSON Document Builder\\\"\\n description: str = \\\"Build a Document containing a JSON object using a key and another Document page content.\\\"\\n output_types: list[str] = [\\\"Document\\\"]\\n beta = True\\n documentation: str = \\\"https://docs.langflow.org/components/utilities#json-document-builder\\\"\\n\\n field_config = {\\n \\\"key\\\": {\\\"display_name\\\": \\\"Key\\\"},\\n \\\"document\\\": {\\\"display_name\\\": \\\"Document\\\"},\\n }\\n\\n def build(\\n self,\\n key: str,\\n document: Document,\\n ) -> Document:\\n documents = None\\n if isinstance(document, list):\\n documents = [\\n Document(page_content=orjson_dumps({key: doc.page_content}, indent_2=False)) for doc in document\\n ]\\n elif isinstance(document, Document):\\n documents = Document(page_content=orjson_dumps({key: document.page_content}, indent_2=False))\\n else:\\n raise TypeError(f\\\"Expected Document or list of Documents, got {type(document)}\\\")\\n self.repr_value = documents\\n return documents\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"key\",\"display_name\":\"Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Build a Document containing a JSON object using a key and another Document page content.\",\"base_classes\":[\"Serializable\",\"Document\"],\"display_name\":\"JSON Document Builder\",\"documentation\":\"https://docs.langflow.org/components/utilities#json-document-builder\",\"custom_fields\":{\"key\":null,\"document\":null},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true}},\"output_parsers\":{\"ResponseSchema\":{\"template\":{\"description\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"fileTypes\":[],\"password\":false,\"name\":\"description\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"name\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"type\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"string\",\"fileTypes\":[],\"password\":false,\"name\":\"type\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"ResponseSchema\"},\"description\":\"A schema for a response from a structured output parser.\",\"base_classes\":[\"ResponseSchema\"],\"display_name\":\"ResponseSchema\",\"documentation\":\"https://python.langchain.com/docs/modules/model_io/output_parsers/structured\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"StructuredOutputParser\":{\"template\":{\"response_schemas\":{\"type\":\"ResponseSchema\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"response_schemas\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"StructuredOutputParser\"},\"description\":\"\",\"base_classes\":[\"BaseOutputParser\",\"Runnable\",\"BaseLLMOutputParser\",\"Generic\",\"RunnableSerializable\",\"StructuredOutputParser\",\"Serializable\",\"object\"],\"display_name\":\"StructuredOutputParser\",\"documentation\":\"https://python.langchain.com/docs/modules/model_io/output_parsers/structured\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":false,\"beta\":false}},\"retrievers\":{\"AmazonKendra\":{\"template\":{\"attribute_filter\":{\"type\":\"code\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"attribute_filter\",\"display_name\":\"Attribute Filter\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain.schema import BaseRetriever\\nfrom langchain_community.retrievers import AmazonKendraRetriever\\n\\nfrom langflow import CustomComponent\\n\\n\\nclass AmazonKendraRetrieverComponent(CustomComponent):\\n display_name: str = \\\"Amazon Kendra Retriever\\\"\\n description: str = \\\"Retriever that uses the Amazon Kendra API.\\\"\\n\\n def build_config(self):\\n return {\\n \\\"index_id\\\": {\\\"display_name\\\": \\\"Index ID\\\"},\\n \\\"region_name\\\": {\\\"display_name\\\": \\\"Region Name\\\"},\\n \\\"credentials_profile_name\\\": {\\\"display_name\\\": \\\"Credentials Profile Name\\\"},\\n \\\"attribute_filter\\\": {\\n \\\"display_name\\\": \\\"Attribute Filter\\\",\\n \\\"field_type\\\": \\\"code\\\",\\n },\\n \\\"top_k\\\": {\\\"display_name\\\": \\\"Top K\\\", \\\"field_type\\\": \\\"int\\\"},\\n \\\"user_context\\\": {\\n \\\"display_name\\\": \\\"User Context\\\",\\n \\\"field_type\\\": \\\"code\\\",\\n },\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(\\n self,\\n index_id: str,\\n top_k: int = 3,\\n region_name: Optional[str] = None,\\n credentials_profile_name: Optional[str] = None,\\n attribute_filter: Optional[dict] = None,\\n user_context: Optional[dict] = None,\\n ) -> BaseRetriever:\\n try:\\n output = AmazonKendraRetriever(\\n index_id=index_id,\\n top_k=top_k,\\n region_name=region_name,\\n credentials_profile_name=credentials_profile_name,\\n attribute_filter=attribute_filter,\\n user_context=user_context,\\n ) # type: ignore\\n except Exception as e:\\n raise ValueError(\\\"Could not connect to AmazonKendra API.\\\") from e\\n return output\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"credentials_profile_name\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"credentials_profile_name\",\"display_name\":\"Credentials Profile Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"index_id\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"index_id\",\"display_name\":\"Index ID\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"region_name\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"region_name\",\"display_name\":\"Region Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"top_k\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":3,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_k\",\"display_name\":\"Top K\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"user_context\":{\"type\":\"code\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"user_context\",\"display_name\":\"User Context\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Retriever that uses the Amazon Kendra API.\",\"base_classes\":[\"Runnable\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"object\",\"BaseRetriever\"],\"display_name\":\"Amazon Kendra Retriever\",\"documentation\":\"\",\"custom_fields\":{\"index_id\":null,\"top_k\":null,\"region_name\":null,\"credentials_profile_name\":null,\"attribute_filter\":null,\"user_context\":null},\"output_types\":[\"BaseRetriever\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"VectaraSelfQueryRetriver\":{\"template\":{\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"For self query retriever\",\"title_case\":false},\"vectorstore\":{\"type\":\"VectorStore\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"vectorstore\",\"display_name\":\"Vector Store\",\"advanced\":false,\"dynamic\":false,\"info\":\"Input Vectara Vectore Store\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import List\\nfrom langflow import CustomComponent\\nimport json\\nfrom langchain.schema import BaseRetriever\\nfrom langchain.schema.vectorstore import VectorStore\\nfrom langchain.base_language import BaseLanguageModel\\nfrom langchain.retrievers.self_query.base import SelfQueryRetriever\\nfrom langchain.chains.query_constructor.base import AttributeInfo\\n\\n\\nclass VectaraSelfQueryRetriverComponent(CustomComponent):\\n \\\"\\\"\\\"\\n A custom component for implementing Vectara Self Query Retriever using a vector store.\\n \\\"\\\"\\\"\\n\\n display_name: str = \\\"Vectara Self Query Retriever for Vectara Vector Store\\\"\\n description: str = \\\"Implementation of Vectara Self Query Retriever\\\"\\n documentation = \\\"https://python.langchain.com/docs/integrations/retrievers/self_query/vectara_self_query\\\"\\n beta = True\\n\\n field_config = {\\n \\\"code\\\": {\\\"show\\\": True},\\n \\\"vectorstore\\\": {\\\"display_name\\\": \\\"Vector Store\\\", \\\"info\\\": \\\"Input Vectara Vectore Store\\\"},\\n \\\"llm\\\": {\\\"display_name\\\": \\\"LLM\\\", \\\"info\\\": \\\"For self query retriever\\\"},\\n \\\"document_content_description\\\": {\\n \\\"display_name\\\": \\\"Document Content Description\\\",\\n \\\"info\\\": \\\"For self query retriever\\\",\\n },\\n \\\"metadata_field_info\\\": {\\n \\\"display_name\\\": \\\"Metadata Field Info\\\",\\n \\\"info\\\": 'Each metadata field info is a string in the form of key value pair dictionary containing additional search metadata.\\\\nExample input: {\\\"name\\\":\\\"speech\\\",\\\"description\\\":\\\"what name of the speech\\\",\\\"type\\\":\\\"string or list[string]\\\"}.\\\\nThe keys should remain constant(name, description, type)',\\n },\\n }\\n\\n def build(\\n self,\\n vectorstore: VectorStore,\\n document_content_description: str,\\n llm: BaseLanguageModel,\\n metadata_field_info: List[str],\\n ) -> BaseRetriever:\\n metadata_field_obj = []\\n\\n for meta in metadata_field_info:\\n meta_obj = json.loads(meta)\\n if \\\"name\\\" not in meta_obj or \\\"description\\\" not in meta_obj or \\\"type\\\" not in meta_obj:\\n raise Exception(\\\"Incorrect metadata field info format.\\\")\\n attribute_info = AttributeInfo(\\n name=meta_obj[\\\"name\\\"],\\n description=meta_obj[\\\"description\\\"],\\n type=meta_obj[\\\"type\\\"],\\n )\\n metadata_field_obj.append(attribute_info)\\n\\n return SelfQueryRetriever.from_llm(\\n llm, vectorstore, document_content_description, metadata_field_obj, verbose=True\\n )\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"document_content_description\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"document_content_description\",\"display_name\":\"Document Content Description\",\"advanced\":false,\"dynamic\":false,\"info\":\"For self query retriever\",\"title_case\":false,\"input_types\":[\"Text\"]},\"metadata_field_info\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata_field_info\",\"display_name\":\"Metadata Field Info\",\"advanced\":false,\"dynamic\":false,\"info\":\"Each metadata field info is a string in the form of key value pair dictionary containing additional search metadata.\\nExample input: {\\\"name\\\":\\\"speech\\\",\\\"description\\\":\\\"what name of the speech\\\",\\\"type\\\":\\\"string or list[string]\\\"}.\\nThe keys should remain constant(name, description, type)\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Implementation of Vectara Self Query Retriever\",\"base_classes\":[\"Runnable\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"object\",\"BaseRetriever\"],\"display_name\":\"Vectara Self Query Retriever for Vectara Vector Store\",\"documentation\":\"https://python.langchain.com/docs/integrations/retrievers/self_query/vectara_self_query\",\"custom_fields\":{\"vectorstore\":null,\"document_content_description\":null,\"llm\":null,\"metadata_field_info\":null},\"output_types\":[\"BaseRetriever\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"MultiQueryRetriever\":{\"template\":{\"llm\":{\"type\":\"BaseLLM\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"prompt\":{\"type\":\"PromptTemplate\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"prompt\",\"display_name\":\"Prompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"retriever\":{\"type\":\"BaseRetriever\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"retriever\",\"display_name\":\"Retriever\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Callable, Optional, Union\\n\\nfrom langchain.retrievers import MultiQueryRetriever\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import BaseLLM, BaseRetriever, PromptTemplate\\n\\n\\nclass MultiQueryRetrieverComponent(CustomComponent):\\n display_name = \\\"MultiQueryRetriever\\\"\\n description = \\\"Initialize from llm using default template.\\\"\\n documentation = \\\"https://python.langchain.com/docs/modules/data_connection/retrievers/how_to/MultiQueryRetriever\\\"\\n\\n def build_config(self):\\n return {\\n \\\"llm\\\": {\\\"display_name\\\": \\\"LLM\\\"},\\n \\\"prompt\\\": {\\n \\\"display_name\\\": \\\"Prompt\\\",\\n \\\"default\\\": {\\n \\\"input_variables\\\": [\\\"question\\\"],\\n \\\"input_types\\\": {},\\n \\\"output_parser\\\": None,\\n \\\"partial_variables\\\": {},\\n \\\"template\\\": \\\"You are an AI language model assistant. Your task is \\\\n\\\"\\n \\\"to generate 3 different versions of the given user \\\\n\\\"\\n \\\"question to retrieve relevant documents from a vector database. \\\\n\\\"\\n \\\"By generating multiple perspectives on the user question, \\\\n\\\"\\n \\\"your goal is to help the user overcome some of the limitations \\\\n\\\"\\n \\\"of distance-based similarity search. Provide these alternative \\\\n\\\"\\n \\\"questions separated by newlines. Original question: {question}\\\",\\n \\\"template_format\\\": \\\"f-string\\\",\\n \\\"validate_template\\\": False,\\n \\\"_type\\\": \\\"prompt\\\",\\n },\\n },\\n \\\"retriever\\\": {\\\"display_name\\\": \\\"Retriever\\\"},\\n \\\"parser_key\\\": {\\\"display_name\\\": \\\"Parser Key\\\", \\\"default\\\": \\\"lines\\\"},\\n }\\n\\n def build(\\n self,\\n llm: BaseLLM,\\n retriever: BaseRetriever,\\n prompt: Optional[PromptTemplate] = None,\\n parser_key: str = \\\"lines\\\",\\n ) -> Union[Callable, MultiQueryRetriever]:\\n if not prompt:\\n return MultiQueryRetriever.from_llm(llm=llm, retriever=retriever, parser_key=parser_key)\\n else:\\n return MultiQueryRetriever.from_llm(llm=llm, retriever=retriever, prompt=prompt, parser_key=parser_key)\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"parser_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"lines\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"parser_key\",\"display_name\":\"Parser Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Initialize from llm using default template.\",\"base_classes\":[],\"display_name\":\"MultiQueryRetriever\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/retrievers/how_to/MultiQueryRetriever\",\"custom_fields\":{\"llm\":null,\"retriever\":null,\"prompt\":null,\"parser_key\":null},\"output_types\":[],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"MetalRetriever\":{\"template\":{\"api_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"api_key\",\"display_name\":\"API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"client_id\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"client_id\",\"display_name\":\"Client ID\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain.schema import BaseRetriever\\nfrom langchain_community.retrievers import MetalRetriever\\nfrom metal_sdk.metal import Metal # type: ignore\\n\\nfrom langflow import CustomComponent\\n\\n\\nclass MetalRetrieverComponent(CustomComponent):\\n display_name: str = \\\"Metal Retriever\\\"\\n description: str = \\\"Retriever that uses the Metal API.\\\"\\n\\n def build_config(self):\\n return {\\n \\\"api_key\\\": {\\\"display_name\\\": \\\"API Key\\\", \\\"password\\\": True},\\n \\\"client_id\\\": {\\\"display_name\\\": \\\"Client ID\\\", \\\"password\\\": True},\\n \\\"index_id\\\": {\\\"display_name\\\": \\\"Index ID\\\"},\\n \\\"params\\\": {\\\"display_name\\\": \\\"Parameters\\\"},\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(self, api_key: str, client_id: str, index_id: str, params: Optional[dict] = None) -> BaseRetriever:\\n try:\\n metal = Metal(api_key=api_key, client_id=client_id, index_id=index_id)\\n except Exception as e:\\n raise ValueError(\\\"Could not connect to Metal API.\\\") from e\\n return MetalRetriever(client=metal, params=params or {})\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"index_id\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"index_id\",\"display_name\":\"Index ID\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"params\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"params\",\"display_name\":\"Parameters\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Retriever that uses the Metal API.\",\"base_classes\":[\"Runnable\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"object\",\"BaseRetriever\"],\"display_name\":\"Metal Retriever\",\"documentation\":\"\",\"custom_fields\":{\"api_key\":null,\"client_id\":null,\"index_id\":null,\"params\":null},\"output_types\":[\"BaseRetriever\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true}},\"custom_components\":{\"CustomComponent\":{\"template\":{\"param\":{\"type\":\"Data\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"param\",\"display_name\":\"Parameter\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langflow.field_typing import Data\\n\\n\\nclass Component(CustomComponent):\\n documentation: str = \\\"http://docs.langflow.org/components/custom\\\"\\n\\n def build_config(self):\\n return {\\\"param\\\": {\\\"display_name\\\": \\\"Parameter\\\"}}\\n\\n def build(self, param: Data) -> Data:\\n return param\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"base_classes\":[\"object\",\"Data\"],\"display_name\":\"CustomComponent\",\"documentation\":\"http://docs.langflow.org/components/custom\",\"custom_fields\":{\"param\":null},\"output_types\":[\"Data\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true}},\"vectorstores\":{\"Weaviate\":{\"template\":{\"documents\":{\"type\":\"Document\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"documents\",\"display_name\":\"Documents\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"embedding\":{\"type\":\"Embeddings\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"embedding\",\"display_name\":\"Embedding\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"api_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"api_key\",\"display_name\":\"API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"attributes\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"attributes\",\"display_name\":\"Attributes\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional, Union\\n\\nimport weaviate # type: ignore\\nfrom langchain.embeddings.base import Embeddings\\nfrom langchain.schema import BaseRetriever, Document\\nfrom langchain_community.vectorstores import VectorStore, Weaviate\\n\\nfrom langflow import CustomComponent\\n\\n\\nclass WeaviateVectorStore(CustomComponent):\\n display_name: str = \\\"Weaviate\\\"\\n description: str = \\\"Implementation of Vector Store using Weaviate\\\"\\n documentation = \\\"https://python.langchain.com/docs/integrations/vectorstores/weaviate\\\"\\n beta = True\\n field_config = {\\n \\\"url\\\": {\\\"display_name\\\": \\\"Weaviate URL\\\", \\\"value\\\": \\\"http://localhost:8080\\\"},\\n \\\"api_key\\\": {\\n \\\"display_name\\\": \\\"API Key\\\",\\n \\\"password\\\": True,\\n \\\"required\\\": False,\\n },\\n \\\"index_name\\\": {\\n \\\"display_name\\\": \\\"Index name\\\",\\n \\\"required\\\": False,\\n },\\n \\\"text_key\\\": {\\\"display_name\\\": \\\"Text Key\\\", \\\"required\\\": False, \\\"advanced\\\": True, \\\"value\\\": \\\"text\\\"},\\n \\\"documents\\\": {\\\"display_name\\\": \\\"Documents\\\", \\\"is_list\\\": True},\\n \\\"embedding\\\": {\\\"display_name\\\": \\\"Embedding\\\"},\\n \\\"attributes\\\": {\\n \\\"display_name\\\": \\\"Attributes\\\",\\n \\\"required\\\": False,\\n \\\"is_list\\\": True,\\n \\\"field_type\\\": \\\"str\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"search_by_text\\\": {\\\"display_name\\\": \\\"Search By Text\\\", \\\"field_type\\\": \\\"bool\\\", \\\"advanced\\\": True},\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(\\n self,\\n url: str,\\n search_by_text: bool = False,\\n api_key: Optional[str] = None,\\n index_name: Optional[str] = None,\\n text_key: str = \\\"text\\\",\\n embedding: Optional[Embeddings] = None,\\n documents: Optional[Document] = None,\\n attributes: Optional[list] = None,\\n ) -> Union[VectorStore, BaseRetriever]:\\n if api_key:\\n auth_config = weaviate.AuthApiKey(api_key=api_key)\\n client = weaviate.Client(url=url, auth_client_secret=auth_config)\\n else:\\n client = weaviate.Client(url=url)\\n\\n def _to_pascal_case(word: str):\\n if word and not word[0].isupper():\\n word = word.capitalize()\\n\\n if word.isidentifier():\\n return word\\n\\n word = word.replace(\\\"-\\\", \\\" \\\").replace(\\\"_\\\", \\\" \\\")\\n parts = word.split()\\n pascal_case_word = \\\"\\\".join([part.capitalize() for part in parts])\\n\\n return pascal_case_word\\n\\n index_name = _to_pascal_case(index_name) if index_name else None\\n\\n if documents is not None and embedding is not None:\\n return Weaviate.from_documents(\\n client=client,\\n index_name=index_name,\\n documents=documents,\\n embedding=embedding,\\n by_text=search_by_text,\\n )\\n\\n return Weaviate(\\n client=client,\\n index_name=index_name,\\n text_key=text_key,\\n embedding=embedding,\\n by_text=search_by_text,\\n attributes=attributes if attributes is not None else [],\\n )\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"index_name\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"index_name\",\"display_name\":\"Index name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"search_by_text\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"search_by_text\",\"display_name\":\"Search By Text\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"text_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"text\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"text_key\",\"display_name\":\"Text Key\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"url\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"http://localhost:8080\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"url\",\"display_name\":\"Weaviate URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Implementation of Vector Store using Weaviate\",\"base_classes\":[\"Runnable\",\"Generic\",\"VectorStore\",\"RunnableSerializable\",\"Serializable\",\"object\",\"BaseRetriever\"],\"display_name\":\"Weaviate\",\"documentation\":\"https://python.langchain.com/docs/integrations/vectorstores/weaviate\",\"custom_fields\":{\"url\":null,\"search_by_text\":null,\"api_key\":null,\"index_name\":null,\"text_key\":null,\"embedding\":null,\"documents\":null,\"attributes\":null},\"output_types\":[\"VectorStore\",\"BaseRetriever\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"Vectara\":{\"template\":{\"documents\":{\"type\":\"Document\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"documents\",\"display_name\":\"Documents\",\"advanced\":false,\"dynamic\":false,\"info\":\"If provided, will be upserted to corpus (optional)\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"import tempfile\\nimport urllib\\nimport urllib.request\\nfrom typing import List, Optional, Union\\n\\nfrom langchain_community.embeddings import FakeEmbeddings\\nfrom langchain_community.vectorstores.vectara import Vectara\\nfrom langchain_core.vectorstores import VectorStore\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import BaseRetriever, Document\\n\\n\\nclass VectaraComponent(CustomComponent):\\n display_name: str = \\\"Vectara\\\"\\n description: str = \\\"Implementation of Vector Store using Vectara\\\"\\n documentation = \\\"https://python.langchain.com/docs/integrations/vectorstores/vectara\\\"\\n beta = True\\n field_config = {\\n \\\"vectara_customer_id\\\": {\\n \\\"display_name\\\": \\\"Vectara Customer ID\\\",\\n },\\n \\\"vectara_corpus_id\\\": {\\n \\\"display_name\\\": \\\"Vectara Corpus ID\\\",\\n },\\n \\\"vectara_api_key\\\": {\\n \\\"display_name\\\": \\\"Vectara API Key\\\",\\n \\\"password\\\": True,\\n },\\n \\\"documents\\\": {\\\"display_name\\\": \\\"Documents\\\", \\\"info\\\": \\\"If provided, will be upserted to corpus (optional)\\\"},\\n \\\"files_url\\\": {\\n \\\"display_name\\\": \\\"Files Url\\\",\\n \\\"info\\\": \\\"Make vectara object using url of files (optional)\\\",\\n },\\n }\\n\\n def build(\\n self,\\n vectara_customer_id: str,\\n vectara_corpus_id: str,\\n vectara_api_key: str,\\n files_url: Optional[List[str]] = None,\\n documents: Optional[Document] = None,\\n ) -> Union[VectorStore, BaseRetriever]:\\n source = \\\"Langflow\\\"\\n\\n if documents is not None:\\n return Vectara.from_documents(\\n documents=documents, # type: ignore\\n embedding=FakeEmbeddings(size=768),\\n vectara_customer_id=vectara_customer_id,\\n vectara_corpus_id=vectara_corpus_id,\\n vectara_api_key=vectara_api_key,\\n source=source,\\n )\\n\\n if files_url is not None:\\n files_list = []\\n for url in files_url:\\n name = tempfile.NamedTemporaryFile().name\\n urllib.request.urlretrieve(url, name)\\n files_list.append(name)\\n\\n return Vectara.from_files(\\n files=files_list,\\n embedding=FakeEmbeddings(size=768),\\n vectara_customer_id=vectara_customer_id,\\n vectara_corpus_id=vectara_corpus_id,\\n vectara_api_key=vectara_api_key,\\n source=source,\\n )\\n\\n return Vectara(\\n vectara_customer_id=vectara_customer_id,\\n vectara_corpus_id=vectara_corpus_id,\\n vectara_api_key=vectara_api_key,\\n source=source,\\n )\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"files_url\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"files_url\",\"display_name\":\"Files Url\",\"advanced\":false,\"dynamic\":false,\"info\":\"Make vectara object using url of files (optional)\",\"title_case\":false,\"input_types\":[\"Text\"]},\"vectara_api_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"vectara_api_key\",\"display_name\":\"Vectara API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"vectara_corpus_id\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"vectara_corpus_id\",\"display_name\":\"Vectara Corpus ID\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"vectara_customer_id\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"vectara_customer_id\",\"display_name\":\"Vectara Customer ID\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Implementation of Vector Store using Vectara\",\"base_classes\":[\"Runnable\",\"Generic\",\"VectorStore\",\"RunnableSerializable\",\"Serializable\",\"object\",\"BaseRetriever\"],\"display_name\":\"Vectara\",\"documentation\":\"https://python.langchain.com/docs/integrations/vectorstores/vectara\",\"custom_fields\":{\"vectara_customer_id\":null,\"vectara_corpus_id\":null,\"vectara_api_key\":null,\"files_url\":null,\"documents\":null},\"output_types\":[\"VectorStore\",\"BaseRetriever\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"Chroma\":{\"template\":{\"documents\":{\"type\":\"Document\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"documents\",\"display_name\":\"Documents\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"embedding\":{\"type\":\"Embeddings\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"embedding\",\"display_name\":\"Embedding\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"chroma_server_cors_allow_origins\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"chroma_server_cors_allow_origins\",\"display_name\":\"Server CORS Allow Origins\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"chroma_server_grpc_port\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"chroma_server_grpc_port\",\"display_name\":\"Server gRPC Port\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"chroma_server_host\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"chroma_server_host\",\"display_name\":\"Server Host\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"chroma_server_port\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"chroma_server_port\",\"display_name\":\"Server Port\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"chroma_server_ssl_enabled\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"chroma_server_ssl_enabled\",\"display_name\":\"Server SSL Enabled\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import List, Optional, Union\\n\\nimport chromadb # type: ignore\\nfrom langchain.embeddings.base import Embeddings\\nfrom langchain.schema import BaseRetriever, Document\\nfrom langchain_community.vectorstores import VectorStore\\nfrom langchain_community.vectorstores.chroma import Chroma\\n\\nfrom langflow import CustomComponent\\n\\n\\nclass ChromaComponent(CustomComponent):\\n \\\"\\\"\\\"\\n A custom component for implementing a Vector Store using Chroma.\\n \\\"\\\"\\\"\\n\\n display_name: str = \\\"Chroma\\\"\\n description: str = \\\"Implementation of Vector Store using Chroma\\\"\\n documentation = \\\"https://python.langchain.com/docs/integrations/vectorstores/chroma\\\"\\n beta: bool = True\\n icon = \\\"Chroma\\\"\\n\\n def build_config(self):\\n \\\"\\\"\\\"\\n Builds the configuration for the component.\\n\\n Returns:\\n - dict: A dictionary containing the configuration options for the component.\\n \\\"\\\"\\\"\\n return {\\n \\\"collection_name\\\": {\\\"display_name\\\": \\\"Collection Name\\\", \\\"value\\\": \\\"langflow\\\"},\\n \\\"index_directory\\\": {\\\"display_name\\\": \\\"Persist Directory\\\"},\\n \\\"code\\\": {\\\"advanced\\\": True, \\\"display_name\\\": \\\"Code\\\"},\\n \\\"documents\\\": {\\\"display_name\\\": \\\"Documents\\\", \\\"is_list\\\": True},\\n \\\"embedding\\\": {\\\"display_name\\\": \\\"Embedding\\\"},\\n \\\"chroma_server_cors_allow_origins\\\": {\\n \\\"display_name\\\": \\\"Server CORS Allow Origins\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"chroma_server_host\\\": {\\\"display_name\\\": \\\"Server Host\\\", \\\"advanced\\\": True},\\n \\\"chroma_server_port\\\": {\\\"display_name\\\": \\\"Server Port\\\", \\\"advanced\\\": True},\\n \\\"chroma_server_grpc_port\\\": {\\n \\\"display_name\\\": \\\"Server gRPC Port\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"chroma_server_ssl_enabled\\\": {\\n \\\"display_name\\\": \\\"Server SSL Enabled\\\",\\n \\\"advanced\\\": True,\\n },\\n }\\n\\n def build(\\n self,\\n collection_name: str,\\n embedding: Embeddings,\\n chroma_server_ssl_enabled: bool,\\n index_directory: Optional[str] = None,\\n documents: Optional[List[Document]] = None,\\n chroma_server_cors_allow_origins: Optional[str] = None,\\n chroma_server_host: Optional[str] = None,\\n chroma_server_port: Optional[int] = None,\\n chroma_server_grpc_port: Optional[int] = None,\\n ) -> Union[VectorStore, BaseRetriever]:\\n \\\"\\\"\\\"\\n Builds the Vector Store or BaseRetriever object.\\n\\n Args:\\n - collection_name (str): The name of the collection.\\n - index_directory (Optional[str]): The directory to persist the Vector Store to.\\n - chroma_server_ssl_enabled (bool): Whether to enable SSL for the Chroma server.\\n - embedding (Optional[Embeddings]): The embeddings to use for the Vector Store.\\n - documents (Optional[Document]): The documents to use for the Vector Store.\\n - chroma_server_cors_allow_origins (Optional[str]): The CORS allow origins for the Chroma server.\\n - chroma_server_host (Optional[str]): The host for the Chroma server.\\n - chroma_server_port (Optional[int]): The port for the Chroma server.\\n - chroma_server_grpc_port (Optional[int]): The gRPC port for the Chroma server.\\n\\n Returns:\\n - Union[VectorStore, BaseRetriever]: The Vector Store or BaseRetriever object.\\n \\\"\\\"\\\"\\n\\n # Chroma settings\\n chroma_settings = None\\n\\n if chroma_server_host is not None:\\n chroma_settings = chromadb.config.Settings(\\n chroma_server_cors_allow_origins=chroma_server_cors_allow_origins\\n or None,\\n chroma_server_host=chroma_server_host,\\n chroma_server_port=chroma_server_port or None,\\n chroma_server_grpc_port=chroma_server_grpc_port or None,\\n chroma_server_ssl_enabled=chroma_server_ssl_enabled,\\n )\\n\\n # If documents, then we need to create a Chroma instance using .from_documents\\n\\n # Check index_directory and expand it if it is a relative path\\n\\n index_directory = self.resolve_path(index_directory)\\n\\n if documents is not None and embedding is not None:\\n if len(documents) == 0:\\n raise ValueError(\\n \\\"If documents are provided, there must be at least one document.\\\"\\n )\\n chroma = Chroma.from_documents(\\n documents=documents, # type: ignore\\n persist_directory=index_directory,\\n collection_name=collection_name,\\n embedding=embedding,\\n client_settings=chroma_settings,\\n )\\n else:\\n chroma = Chroma(\\n persist_directory=index_directory,\\n client_settings=chroma_settings,\\n embedding_function=embedding,\\n )\\n return chroma\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":true,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"collection_name\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"langflow\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"collection_name\",\"display_name\":\"Collection Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"index_directory\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"index_directory\",\"display_name\":\"Persist Directory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Implementation of Vector Store using Chroma\",\"icon\":\"Chroma\",\"base_classes\":[\"Runnable\",\"Generic\",\"VectorStore\",\"RunnableSerializable\",\"Serializable\",\"object\",\"BaseRetriever\"],\"display_name\":\"Chroma\",\"documentation\":\"https://python.langchain.com/docs/integrations/vectorstores/chroma\",\"custom_fields\":{\"collection_name\":null,\"embedding\":null,\"chroma_server_ssl_enabled\":null,\"index_directory\":null,\"documents\":null,\"chroma_server_cors_allow_origins\":null,\"chroma_server_host\":null,\"chroma_server_port\":null,\"chroma_server_grpc_port\":null},\"output_types\":[\"VectorStore\",\"BaseRetriever\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"SupabaseVectorStore\":{\"template\":{\"documents\":{\"type\":\"Document\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"documents\",\"display_name\":\"Documents\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"embedding\":{\"type\":\"Embeddings\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"embedding\",\"display_name\":\"Embedding\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import List, Union\\n\\nfrom langchain.schema import BaseRetriever\\nfrom langchain_community.vectorstores import VectorStore\\nfrom langchain_community.vectorstores.supabase import SupabaseVectorStore\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Document, Embeddings, NestedDict\\nfrom supabase.client import Client, create_client\\n\\n\\nclass SupabaseComponent(CustomComponent):\\n display_name = \\\"Supabase\\\"\\n description = \\\"Return VectorStore initialized from texts and embeddings.\\\"\\n\\n def build_config(self):\\n return {\\n \\\"documents\\\": {\\\"display_name\\\": \\\"Documents\\\"},\\n \\\"embedding\\\": {\\\"display_name\\\": \\\"Embedding\\\"},\\n \\\"query_name\\\": {\\\"display_name\\\": \\\"Query Name\\\"},\\n \\\"search_kwargs\\\": {\\\"display_name\\\": \\\"Search Kwargs\\\", \\\"advanced\\\": True},\\n \\\"supabase_service_key\\\": {\\\"display_name\\\": \\\"Supabase Service Key\\\"},\\n \\\"supabase_url\\\": {\\\"display_name\\\": \\\"Supabase URL\\\"},\\n \\\"table_name\\\": {\\\"display_name\\\": \\\"Table Name\\\", \\\"advanced\\\": True},\\n }\\n\\n def build(\\n self,\\n embedding: Embeddings,\\n documents: List[Document],\\n query_name: str = \\\"\\\",\\n search_kwargs: NestedDict = {},\\n supabase_service_key: str = \\\"\\\",\\n supabase_url: str = \\\"\\\",\\n table_name: str = \\\"\\\",\\n ) -> Union[VectorStore, SupabaseVectorStore, BaseRetriever]:\\n supabase: Client = create_client(supabase_url, supabase_key=supabase_service_key)\\n return SupabaseVectorStore.from_documents(\\n documents=documents,\\n embedding=embedding,\\n query_name=query_name,\\n search_kwargs=search_kwargs,\\n client=supabase,\\n table_name=table_name,\\n )\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"query_name\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"query_name\",\"display_name\":\"Query Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"search_kwargs\":{\"type\":\"NestedDict\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"search_kwargs\",\"display_name\":\"Search Kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"supabase_service_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"supabase_service_key\",\"display_name\":\"Supabase Service Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"supabase_url\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"supabase_url\",\"display_name\":\"Supabase URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"table_name\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"table_name\",\"display_name\":\"Table Name\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Return VectorStore initialized from texts and embeddings.\",\"base_classes\":[\"Runnable\",\"Generic\",\"VectorStore\",\"SupabaseVectorStore\",\"RunnableSerializable\",\"Serializable\",\"object\",\"BaseRetriever\"],\"display_name\":\"Supabase\",\"documentation\":\"\",\"custom_fields\":{\"embedding\":null,\"documents\":null,\"query_name\":null,\"search_kwargs\":null,\"supabase_service_key\":null,\"supabase_url\":null,\"table_name\":null},\"output_types\":[\"VectorStore\",\"SupabaseVectorStore\",\"BaseRetriever\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"Redis\":{\"template\":{\"documents\":{\"type\":\"Document\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"documents\",\"display_name\":\"Documents\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"embedding\":{\"type\":\"Embeddings\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"embedding\",\"display_name\":\"Embedding\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional, Union\\n\\nfrom langchain.embeddings.base import Embeddings\\nfrom langchain_community.vectorstores import VectorStore\\nfrom langchain_community.vectorstores.redis import Redis\\nfrom langchain_core.documents import Document\\nfrom langchain_core.retrievers import BaseRetriever\\nfrom langflow import CustomComponent\\n\\n\\nclass RedisComponent(CustomComponent):\\n \\\"\\\"\\\"\\n A custom component for implementing a Vector Store using Redis.\\n \\\"\\\"\\\"\\n\\n display_name: str = \\\"Redis\\\"\\n description: str = \\\"Implementation of Vector Store using Redis\\\"\\n documentation = \\\"https://python.langchain.com/docs/integrations/vectorstores/redis\\\"\\n beta = True\\n\\n def build_config(self):\\n \\\"\\\"\\\"\\n Builds the configuration for the component.\\n\\n Returns:\\n - dict: A dictionary containing the configuration options for the component.\\n \\\"\\\"\\\"\\n return {\\n \\\"index_name\\\": {\\\"display_name\\\": \\\"Index Name\\\", \\\"value\\\": \\\"your_index\\\"},\\n \\\"code\\\": {\\\"show\\\": False, \\\"display_name\\\": \\\"Code\\\"},\\n \\\"documents\\\": {\\\"display_name\\\": \\\"Documents\\\", \\\"is_list\\\": True},\\n \\\"embedding\\\": {\\\"display_name\\\": \\\"Embedding\\\"},\\n \\\"schema\\\": {\\\"display_name\\\": \\\"Schema\\\", \\\"file_types\\\": [\\\".yaml\\\"]},\\n \\\"redis_server_url\\\": {\\n \\\"display_name\\\": \\\"Redis Server Connection String\\\",\\n \\\"advanced\\\": False,\\n },\\n \\\"redis_index_name\\\": {\\\"display_name\\\": \\\"Redis Index\\\", \\\"advanced\\\": False},\\n }\\n\\n def build(\\n self,\\n embedding: Embeddings,\\n redis_server_url: str,\\n redis_index_name: str,\\n schema: Optional[str] = None,\\n documents: Optional[Document] = None,\\n ) -> Union[VectorStore, BaseRetriever]:\\n \\\"\\\"\\\"\\n Builds the Vector Store or BaseRetriever object.\\n\\n Args:\\n - embedding (Embeddings): The embeddings to use for the Vector Store.\\n - documents (Optional[Document]): The documents to use for the Vector Store.\\n - redis_index_name (str): The name of the Redis index.\\n - redis_server_url (str): The URL for the Redis server.\\n\\n Returns:\\n - VectorStore: The Vector Store object.\\n \\\"\\\"\\\"\\n if documents is None:\\n if schema is None:\\n raise ValueError(\\\"If no documents are provided, a schema must be provided.\\\")\\n redis_vs = Redis.from_existing_index(\\n embedding=embedding,\\n index_name=redis_index_name,\\n schema=schema,\\n key_prefix=None,\\n redis_url=redis_server_url,\\n )\\n else:\\n redis_vs = Redis.from_documents(\\n documents=documents, # type: ignore\\n embedding=embedding,\\n redis_url=redis_server_url,\\n index_name=redis_index_name,\\n )\\n return redis_vs\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"redis_index_name\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"redis_index_name\",\"display_name\":\"Redis Index\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"redis_server_url\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"redis_server_url\",\"display_name\":\"Redis Server Connection String\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"schema\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[\".yaml\"],\"file_path\":\"\",\"password\":false,\"name\":\"schema\",\"display_name\":\"Schema\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Implementation of Vector Store using Redis\",\"base_classes\":[\"Runnable\",\"Generic\",\"VectorStore\",\"RunnableSerializable\",\"Serializable\",\"object\",\"BaseRetriever\"],\"display_name\":\"Redis\",\"documentation\":\"https://python.langchain.com/docs/integrations/vectorstores/redis\",\"custom_fields\":{\"embedding\":null,\"redis_server_url\":null,\"redis_index_name\":null,\"schema\":null,\"documents\":null},\"output_types\":[\"VectorStore\",\"BaseRetriever\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"pgvector\":{\"template\":{\"documents\":{\"type\":\"Document\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"documents\",\"display_name\":\"Documents\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"embedding\":{\"type\":\"Embeddings\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"embedding\",\"display_name\":\"Embedding\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional, Union\\n\\nfrom langchain.embeddings.base import Embeddings\\nfrom langchain_community.vectorstores import VectorStore\\nfrom langchain_community.vectorstores.pgvector import PGVector\\nfrom langchain_core.documents import Document\\nfrom langchain_core.retrievers import BaseRetriever\\nfrom langflow import CustomComponent\\n\\n\\nclass PGVectorComponent(CustomComponent):\\n \\\"\\\"\\\"\\n A custom component for implementing a Vector Store using PostgreSQL.\\n \\\"\\\"\\\"\\n\\n display_name: str = \\\"PGVector\\\"\\n description: str = \\\"Implementation of Vector Store using PostgreSQL\\\"\\n documentation = \\\"https://python.langchain.com/docs/integrations/vectorstores/pgvector\\\"\\n\\n def build_config(self):\\n \\\"\\\"\\\"\\n Builds the configuration for the component.\\n\\n Returns:\\n - dict: A dictionary containing the configuration options for the component.\\n \\\"\\\"\\\"\\n return {\\n \\\"code\\\": {\\\"show\\\": False},\\n \\\"documents\\\": {\\\"display_name\\\": \\\"Documents\\\", \\\"is_list\\\": True},\\n \\\"embedding\\\": {\\\"display_name\\\": \\\"Embedding\\\"},\\n \\\"pg_server_url\\\": {\\n \\\"display_name\\\": \\\"PostgreSQL Server Connection String\\\",\\n \\\"advanced\\\": False,\\n },\\n \\\"collection_name\\\": {\\\"display_name\\\": \\\"Table\\\", \\\"advanced\\\": False},\\n }\\n\\n def build(\\n self,\\n embedding: Embeddings,\\n pg_server_url: str,\\n collection_name: str,\\n documents: Optional[Document] = None,\\n ) -> Union[VectorStore, BaseRetriever]:\\n \\\"\\\"\\\"\\n Builds the Vector Store or BaseRetriever object.\\n\\n Args:\\n - embedding (Embeddings): The embeddings to use for the Vector Store.\\n - documents (Optional[Document]): The documents to use for the Vector Store.\\n - collection_name (str): The name of the PG table.\\n - pg_server_url (str): The URL for the PG server.\\n\\n Returns:\\n - VectorStore: The Vector Store object.\\n \\\"\\\"\\\"\\n\\n try:\\n if documents is None:\\n vector_store = PGVector.from_existing_index(\\n embedding=embedding,\\n collection_name=collection_name,\\n connection_string=pg_server_url,\\n )\\n else:\\n vector_store = PGVector.from_documents(\\n embedding=embedding,\\n documents=documents, # type: ignore\\n collection_name=collection_name,\\n connection_string=pg_server_url,\\n )\\n except Exception as e:\\n raise RuntimeError(f\\\"Failed to build PGVector: {e}\\\")\\n return vector_store\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"collection_name\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"collection_name\",\"display_name\":\"Table\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"pg_server_url\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"pg_server_url\",\"display_name\":\"PostgreSQL Server Connection String\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Implementation of Vector Store using PostgreSQL\",\"base_classes\":[\"Runnable\",\"Generic\",\"VectorStore\",\"RunnableSerializable\",\"Serializable\",\"object\",\"BaseRetriever\"],\"display_name\":\"PGVector\",\"documentation\":\"https://python.langchain.com/docs/integrations/vectorstores/pgvector\",\"custom_fields\":{\"embedding\":null,\"pg_server_url\":null,\"collection_name\":null,\"documents\":null},\"output_types\":[\"VectorStore\",\"BaseRetriever\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"Pinecone\":{\"template\":{\"documents\":{\"type\":\"Document\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"documents\",\"display_name\":\"Documents\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"embedding\":{\"type\":\"Embeddings\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"embedding\",\"display_name\":\"Embedding\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"import os\\nfrom typing import List, Optional, Union\\n\\nimport pinecone # type: ignore\\nfrom langchain.schema import BaseRetriever\\nfrom langchain_community.vectorstores import VectorStore\\nfrom langchain_community.vectorstores.pinecone import Pinecone\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Document, Embeddings\\n\\n\\nclass PineconeComponent(CustomComponent):\\n display_name = \\\"Pinecone\\\"\\n description = \\\"Construct Pinecone wrapper from raw documents.\\\"\\n\\n def build_config(self):\\n return {\\n \\\"documents\\\": {\\\"display_name\\\": \\\"Documents\\\"},\\n \\\"embedding\\\": {\\\"display_name\\\": \\\"Embedding\\\"},\\n \\\"index_name\\\": {\\\"display_name\\\": \\\"Index Name\\\"},\\n \\\"namespace\\\": {\\\"display_name\\\": \\\"Namespace\\\"},\\n \\\"pinecone_api_key\\\": {\\\"display_name\\\": \\\"Pinecone API Key\\\", \\\"default\\\": \\\"\\\", \\\"password\\\": True, \\\"required\\\": True},\\n \\\"pinecone_env\\\": {\\\"display_name\\\": \\\"Pinecone Environment\\\", \\\"default\\\": \\\"\\\", \\\"required\\\": True},\\n \\\"search_kwargs\\\": {\\\"display_name\\\": \\\"Search Kwargs\\\", \\\"default\\\": \\\"{}\\\"},\\n \\\"pool_threads\\\": {\\\"display_name\\\": \\\"Pool Threads\\\", \\\"default\\\": 1, \\\"advanced\\\": True},\\n }\\n\\n def build(\\n self,\\n embedding: Embeddings,\\n pinecone_env: str,\\n documents: List[Document],\\n text_key: str = \\\"text\\\",\\n pool_threads: int = 4,\\n index_name: Optional[str] = None,\\n pinecone_api_key: Optional[str] = None,\\n namespace: Optional[str] = \\\"default\\\",\\n ) -> Union[VectorStore, Pinecone, BaseRetriever]:\\n if pinecone_api_key is None or pinecone_env is None:\\n raise ValueError(\\\"Pinecone API Key and Environment are required.\\\")\\n if os.getenv(\\\"PINECONE_API_KEY\\\") is None and pinecone_api_key is None:\\n raise ValueError(\\\"Pinecone API Key is required.\\\")\\n\\n pinecone.init(api_key=pinecone_api_key, environment=pinecone_env) # type: ignore\\n if not index_name:\\n raise ValueError(\\\"Index Name is required.\\\")\\n if documents:\\n return Pinecone.from_documents(\\n documents=documents,\\n embedding=embedding,\\n index_name=index_name,\\n pool_threads=pool_threads,\\n namespace=namespace,\\n text_key=text_key,\\n )\\n\\n return Pinecone.from_existing_index(\\n index_name=index_name,\\n embedding=embedding,\\n text_key=text_key,\\n namespace=namespace,\\n pool_threads=pool_threads,\\n )\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"index_name\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"index_name\",\"display_name\":\"Index Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"namespace\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"default\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"namespace\",\"display_name\":\"Namespace\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"pinecone_api_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"pinecone_api_key\",\"display_name\":\"Pinecone API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"pinecone_env\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"pinecone_env\",\"display_name\":\"Pinecone Environment\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"pool_threads\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":4,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"pool_threads\",\"display_name\":\"Pool Threads\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"text_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"text\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"text_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Construct Pinecone wrapper from raw documents.\",\"base_classes\":[\"Runnable\",\"Generic\",\"VectorStore\",\"Pinecone\",\"RunnableSerializable\",\"Serializable\",\"object\",\"BaseRetriever\"],\"display_name\":\"Pinecone\",\"documentation\":\"\",\"custom_fields\":{\"embedding\":null,\"pinecone_env\":null,\"documents\":null,\"text_key\":null,\"pool_threads\":null,\"index_name\":null,\"pinecone_api_key\":null,\"namespace\":null},\"output_types\":[\"VectorStore\",\"Pinecone\",\"BaseRetriever\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"Qdrant\":{\"template\":{\"documents\":{\"type\":\"Document\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"documents\",\"display_name\":\"Documents\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"embedding\":{\"type\":\"Embeddings\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"embedding\",\"display_name\":\"Embedding\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"api_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"api_key\",\"display_name\":\"API Key\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional, Union\\n\\nfrom langchain.schema import BaseRetriever\\nfrom langchain_community.vectorstores import VectorStore\\nfrom langchain_community.vectorstores.qdrant import Qdrant\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Document, Embeddings, NestedDict\\n\\n\\nclass QdrantComponent(CustomComponent):\\n display_name = \\\"Qdrant\\\"\\n description = \\\"Construct Qdrant wrapper from a list of texts.\\\"\\n\\n def build_config(self):\\n return {\\n \\\"documents\\\": {\\\"display_name\\\": \\\"Documents\\\"},\\n \\\"embedding\\\": {\\\"display_name\\\": \\\"Embedding\\\"},\\n \\\"api_key\\\": {\\\"display_name\\\": \\\"API Key\\\", \\\"password\\\": True, \\\"advanced\\\": True},\\n \\\"collection_name\\\": {\\\"display_name\\\": \\\"Collection Name\\\"},\\n \\\"content_payload_key\\\": {\\\"display_name\\\": \\\"Content Payload Key\\\", \\\"advanced\\\": True},\\n \\\"distance_func\\\": {\\\"display_name\\\": \\\"Distance Function\\\", \\\"advanced\\\": True},\\n \\\"grpc_port\\\": {\\\"display_name\\\": \\\"gRPC Port\\\", \\\"advanced\\\": True},\\n \\\"host\\\": {\\\"display_name\\\": \\\"Host\\\", \\\"advanced\\\": True},\\n \\\"https\\\": {\\\"display_name\\\": \\\"HTTPS\\\", \\\"advanced\\\": True},\\n \\\"location\\\": {\\\"display_name\\\": \\\"Location\\\", \\\"advanced\\\": True},\\n \\\"metadata_payload_key\\\": {\\\"display_name\\\": \\\"Metadata Payload Key\\\", \\\"advanced\\\": True},\\n \\\"path\\\": {\\\"display_name\\\": \\\"Path\\\", \\\"advanced\\\": True},\\n \\\"port\\\": {\\\"display_name\\\": \\\"Port\\\", \\\"advanced\\\": True},\\n \\\"prefer_grpc\\\": {\\\"display_name\\\": \\\"Prefer gRPC\\\", \\\"advanced\\\": True},\\n \\\"prefix\\\": {\\\"display_name\\\": \\\"Prefix\\\", \\\"advanced\\\": True},\\n \\\"search_kwargs\\\": {\\\"display_name\\\": \\\"Search Kwargs\\\", \\\"advanced\\\": True},\\n \\\"timeout\\\": {\\\"display_name\\\": \\\"Timeout\\\", \\\"advanced\\\": True},\\n \\\"url\\\": {\\\"display_name\\\": \\\"URL\\\", \\\"advanced\\\": True},\\n }\\n\\n def build(\\n self,\\n embedding: Embeddings,\\n collection_name: str,\\n documents: Optional[Document] = None,\\n api_key: Optional[str] = None,\\n content_payload_key: str = \\\"page_content\\\",\\n distance_func: str = \\\"Cosine\\\",\\n grpc_port: int = 6334,\\n https: bool = False,\\n host: Optional[str] = None,\\n location: Optional[str] = None,\\n metadata_payload_key: str = \\\"metadata\\\",\\n path: Optional[str] = None,\\n port: Optional[int] = 6333,\\n prefer_grpc: bool = False,\\n prefix: Optional[str] = None,\\n search_kwargs: Optional[NestedDict] = None,\\n timeout: Optional[int] = None,\\n url: Optional[str] = None,\\n ) -> Union[VectorStore, Qdrant, BaseRetriever]:\\n if documents is None:\\n from qdrant_client import QdrantClient\\n\\n client = QdrantClient(\\n location=location,\\n url=host,\\n port=port,\\n grpc_port=grpc_port,\\n https=https,\\n prefix=prefix,\\n timeout=timeout,\\n prefer_grpc=prefer_grpc,\\n metadata_payload_key=metadata_payload_key,\\n content_payload_key=content_payload_key,\\n api_key=api_key,\\n collection_name=collection_name,\\n host=host,\\n path=path,\\n )\\n vs = Qdrant(\\n client=client,\\n collection_name=collection_name,\\n embeddings=embedding,\\n )\\n return vs\\n else:\\n vs = Qdrant.from_documents(\\n documents=documents, # type: ignore\\n embedding=embedding,\\n api_key=api_key,\\n collection_name=collection_name,\\n content_payload_key=content_payload_key,\\n distance_func=distance_func,\\n grpc_port=grpc_port,\\n host=host,\\n https=https,\\n location=location,\\n metadata_payload_key=metadata_payload_key,\\n path=path,\\n port=port,\\n prefer_grpc=prefer_grpc,\\n prefix=prefix,\\n search_kwargs=search_kwargs,\\n timeout=timeout,\\n url=url,\\n )\\n return vs\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"collection_name\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"collection_name\",\"display_name\":\"Collection Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"content_payload_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"page_content\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"content_payload_key\",\"display_name\":\"Content Payload Key\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"distance_func\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"Cosine\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"distance_func\",\"display_name\":\"Distance Function\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"grpc_port\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":6334,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"grpc_port\",\"display_name\":\"gRPC Port\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"host\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"host\",\"display_name\":\"Host\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"https\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"https\",\"display_name\":\"HTTPS\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"location\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"location\",\"display_name\":\"Location\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"metadata_payload_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"metadata\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata_payload_key\",\"display_name\":\"Metadata Payload Key\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"path\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"path\",\"display_name\":\"Path\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"port\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":6333,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"port\",\"display_name\":\"Port\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"prefer_grpc\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"prefer_grpc\",\"display_name\":\"Prefer gRPC\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"prefix\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"prefix\",\"display_name\":\"Prefix\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"search_kwargs\":{\"type\":\"NestedDict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"search_kwargs\",\"display_name\":\"Search Kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"timeout\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"timeout\",\"display_name\":\"Timeout\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"url\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"url\",\"display_name\":\"URL\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Construct Qdrant wrapper from a list of texts.\",\"base_classes\":[\"Runnable\",\"Generic\",\"VectorStore\",\"Qdrant\",\"RunnableSerializable\",\"Serializable\",\"object\",\"BaseRetriever\"],\"display_name\":\"Qdrant\",\"documentation\":\"\",\"custom_fields\":{\"embedding\":null,\"collection_name\":null,\"documents\":null,\"api_key\":null,\"content_payload_key\":null,\"distance_func\":null,\"grpc_port\":null,\"https\":null,\"host\":null,\"location\":null,\"metadata_payload_key\":null,\"path\":null,\"port\":null,\"prefer_grpc\":null,\"prefix\":null,\"search_kwargs\":null,\"timeout\":null,\"url\":null},\"output_types\":[\"VectorStore\",\"Qdrant\",\"BaseRetriever\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"MongoDBAtlasVectorSearch\":{\"template\":{\"documents\":{\"type\":\"Document\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"documents\",\"display_name\":\"Documents\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"embedding\":{\"type\":\"Embeddings\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"embedding\",\"display_name\":\"Embedding\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import List, Optional\\n\\nfrom langchain_community.vectorstores import MongoDBAtlasVectorSearch\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import (\\n Document,\\n Embeddings,\\n NestedDict,\\n)\\n\\n\\nclass MongoDBAtlasComponent(CustomComponent):\\n display_name = \\\"MongoDB Atlas\\\"\\n description = \\\"Construct a `MongoDB Atlas Vector Search` vector store from raw documents.\\\"\\n\\n def build_config(self):\\n return {\\n \\\"documents\\\": {\\\"display_name\\\": \\\"Documents\\\"},\\n \\\"embedding\\\": {\\\"display_name\\\": \\\"Embedding\\\"},\\n \\\"collection_name\\\": {\\\"display_name\\\": \\\"Collection Name\\\"},\\n \\\"db_name\\\": {\\\"display_name\\\": \\\"Database Name\\\"},\\n \\\"index_name\\\": {\\\"display_name\\\": \\\"Index Name\\\"},\\n \\\"mongodb_atlas_cluster_uri\\\": {\\\"display_name\\\": \\\"MongoDB Atlas Cluster URI\\\"},\\n \\\"search_kwargs\\\": {\\\"display_name\\\": \\\"Search Kwargs\\\", \\\"advanced\\\": True},\\n }\\n\\n def build(\\n self,\\n documents: List[Document],\\n embedding: Embeddings,\\n collection_name: str = \\\"\\\",\\n db_name: str = \\\"\\\",\\n index_name: str = \\\"\\\",\\n mongodb_atlas_cluster_uri: str = \\\"\\\",\\n search_kwargs: Optional[NestedDict] = None,\\n ) -> MongoDBAtlasVectorSearch:\\n search_kwargs = search_kwargs or {}\\n return MongoDBAtlasVectorSearch(\\n documents=documents,\\n embedding=embedding,\\n collection_name=collection_name,\\n db_name=db_name,\\n index_name=index_name,\\n mongodb_atlas_cluster_uri=mongodb_atlas_cluster_uri,\\n search_kwargs=search_kwargs,\\n )\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"collection_name\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"collection_name\",\"display_name\":\"Collection Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"db_name\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"db_name\",\"display_name\":\"Database Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"index_name\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"index_name\",\"display_name\":\"Index Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"mongodb_atlas_cluster_uri\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"mongodb_atlas_cluster_uri\",\"display_name\":\"MongoDB Atlas Cluster URI\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"search_kwargs\":{\"type\":\"NestedDict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"search_kwargs\",\"display_name\":\"Search Kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Construct a `MongoDB Atlas Vector Search` vector store from raw documents.\",\"base_classes\":[\"VectorStore\",\"MongoDBAtlasVectorSearch\"],\"display_name\":\"MongoDB Atlas\",\"documentation\":\"\",\"custom_fields\":{\"documents\":null,\"embedding\":null,\"collection_name\":null,\"db_name\":null,\"index_name\":null,\"mongodb_atlas_cluster_uri\":null,\"search_kwargs\":null},\"output_types\":[\"MongoDBAtlasVectorSearch\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"ChromaSearch\":{\"template\":{\"embedding\":{\"type\":\"Embeddings\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"embedding\",\"display_name\":\"Embedding\",\"advanced\":false,\"dynamic\":false,\"info\":\"Embedding model to vectorize inputs (make sure to use same as index)\",\"title_case\":false},\"inputs\":{\"type\":\"Text\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"inputs\",\"display_name\":\"Input\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"chroma_server_cors_allow_origins\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"chroma_server_cors_allow_origins\",\"display_name\":\"Server CORS Allow Origins\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"chroma_server_grpc_port\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"chroma_server_grpc_port\",\"display_name\":\"Server gRPC Port\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"chroma_server_host\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"chroma_server_host\",\"display_name\":\"Server Host\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"chroma_server_port\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"chroma_server_port\",\"display_name\":\"Server Port\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"chroma_server_ssl_enabled\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"chroma_server_ssl_enabled\",\"display_name\":\"Server SSL Enabled\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import List, Optional\\n\\nimport chromadb # type: ignore\\nfrom langchain_community.vectorstores.chroma import Chroma\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Embeddings, Text\\nfrom langflow.schema import Record, docs_to_records\\n\\n\\nclass ChromaSearchComponent(CustomComponent):\\n \\\"\\\"\\\"\\n A custom component for implementing a Vector Store using Chroma.\\n \\\"\\\"\\\"\\n\\n display_name: str = \\\"Chroma Search\\\"\\n description: str = \\\"Search a Chroma collection for similar documents.\\\"\\n beta: bool = True\\n icon = \\\"Chroma\\\"\\n\\n def build_config(self):\\n \\\"\\\"\\\"\\n Builds the configuration for the component.\\n\\n Returns:\\n - dict: A dictionary containing the configuration options for the component.\\n \\\"\\\"\\\"\\n return {\\n \\\"inputs\\\": {\\\"display_name\\\": \\\"Input\\\"},\\n \\\"search_type\\\": {\\n \\\"display_name\\\": \\\"Search Type\\\",\\n \\\"options\\\": [\\\"Similarity\\\", \\\"MMR\\\"],\\n },\\n \\\"collection_name\\\": {\\\"display_name\\\": \\\"Collection Name\\\", \\\"value\\\": \\\"langflow\\\"},\\n # \\\"persist\\\": {\\\"display_name\\\": \\\"Persist\\\"},\\n \\\"index_directory\\\": {\\\"display_name\\\": \\\"Index Directory\\\"},\\n \\\"code\\\": {\\\"show\\\": False, \\\"display_name\\\": \\\"Code\\\"},\\n \\\"documents\\\": {\\\"display_name\\\": \\\"Documents\\\", \\\"is_list\\\": True},\\n \\\"embedding\\\": {\\n \\\"display_name\\\": \\\"Embedding\\\",\\n \\\"info\\\": \\\"Embedding model to vectorize inputs (make sure to use same as index)\\\",\\n },\\n \\\"chroma_server_cors_allow_origins\\\": {\\n \\\"display_name\\\": \\\"Server CORS Allow Origins\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"chroma_server_host\\\": {\\\"display_name\\\": \\\"Server Host\\\", \\\"advanced\\\": True},\\n \\\"chroma_server_port\\\": {\\\"display_name\\\": \\\"Server Port\\\", \\\"advanced\\\": True},\\n \\\"chroma_server_grpc_port\\\": {\\n \\\"display_name\\\": \\\"Server gRPC Port\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"chroma_server_ssl_enabled\\\": {\\n \\\"display_name\\\": \\\"Server SSL Enabled\\\",\\n \\\"advanced\\\": True,\\n },\\n }\\n\\n def build(\\n self,\\n inputs: Text,\\n search_type: str,\\n collection_name: str,\\n embedding: Embeddings,\\n chroma_server_ssl_enabled: bool,\\n index_directory: Optional[str] = None,\\n chroma_server_cors_allow_origins: Optional[str] = None,\\n chroma_server_host: Optional[str] = None,\\n chroma_server_port: Optional[int] = None,\\n chroma_server_grpc_port: Optional[int] = None,\\n ) -> List[Record]:\\n \\\"\\\"\\\"\\n Builds the Vector Store or BaseRetriever object.\\n\\n Args:\\n - collection_name (str): The name of the collection.\\n - persist_directory (Optional[str]): The directory to persist the Vector Store to.\\n - chroma_server_ssl_enabled (bool): Whether to enable SSL for the Chroma server.\\n - persist (bool): Whether to persist the Vector Store or not.\\n - embedding (Optional[Embeddings]): The embeddings to use for the Vector Store.\\n - documents (Optional[Document]): The documents to use for the Vector Store.\\n - chroma_server_cors_allow_origins (Optional[str]): The CORS allow origins for the Chroma server.\\n - chroma_server_host (Optional[str]): The host for the Chroma server.\\n - chroma_server_port (Optional[int]): The port for the Chroma server.\\n - chroma_server_grpc_port (Optional[int]): The gRPC port for the Chroma server.\\n\\n Returns:\\n - Union[VectorStore, BaseRetriever]: The Vector Store or BaseRetriever object.\\n \\\"\\\"\\\"\\n\\n # Chroma settings\\n chroma_settings = None\\n\\n if chroma_server_host is not None:\\n chroma_settings = chromadb.config.Settings(\\n chroma_server_cors_allow_origins=chroma_server_cors_allow_origins or None,\\n chroma_server_host=chroma_server_host,\\n chroma_server_port=chroma_server_port or None,\\n chroma_server_grpc_port=chroma_server_grpc_port or None,\\n chroma_server_ssl_enabled=chroma_server_ssl_enabled,\\n )\\n index_directory = self.resolve_path(index_directory)\\n chroma = Chroma(\\n embedding_function=embedding,\\n collection_name=collection_name,\\n persist_directory=index_directory,\\n client_settings=chroma_settings,\\n )\\n\\n # Validate the inputs\\n docs = []\\n if inputs and isinstance(inputs, str):\\n docs = chroma.search(query=inputs, search_type=search_type.lower())\\n else:\\n raise ValueError(\\\"Invalid inputs provided.\\\")\\n return docs_to_records(docs)\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"collection_name\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"langflow\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"collection_name\",\"display_name\":\"Collection Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"index_directory\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"index_directory\",\"display_name\":\"Index Directory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"search_type\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"Similarity\",\"MMR\"],\"name\":\"search_type\",\"display_name\":\"Search Type\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Search a Chroma collection for similar documents.\",\"icon\":\"Chroma\",\"base_classes\":[\"Record\"],\"display_name\":\"Chroma Search\",\"documentation\":\"\",\"custom_fields\":{\"inputs\":null,\"search_type\":null,\"collection_name\":null,\"embedding\":null,\"chroma_server_ssl_enabled\":null,\"index_directory\":null,\"chroma_server_cors_allow_origins\":null,\"chroma_server_host\":null,\"chroma_server_port\":null,\"chroma_server_grpc_port\":null},\"output_types\":[\"Record\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"FAISS\":{\"template\":{\"documents\":{\"type\":\"Document\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"documents\",\"display_name\":\"Documents\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"embedding\":{\"type\":\"Embeddings\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"embedding\",\"display_name\":\"Embedding\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import List, Union\\n\\nfrom langchain.schema import BaseRetriever\\nfrom langchain_community.vectorstores import VectorStore\\nfrom langchain_community.vectorstores.faiss import FAISS\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Document, Embeddings\\n\\n\\nclass FAISSComponent(CustomComponent):\\n display_name = \\\"FAISS\\\"\\n description = \\\"Construct FAISS wrapper from raw documents.\\\"\\n documentation = \\\"https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/faiss\\\"\\n\\n def build_config(self):\\n return {\\n \\\"documents\\\": {\\\"display_name\\\": \\\"Documents\\\"},\\n \\\"embedding\\\": {\\\"display_name\\\": \\\"Embedding\\\"},\\n }\\n\\n def build(\\n self,\\n embedding: Embeddings,\\n documents: List[Document],\\n ) -> Union[VectorStore, FAISS, BaseRetriever]:\\n return FAISS.from_documents(documents=documents, embedding=embedding)\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Construct FAISS wrapper from raw documents.\",\"base_classes\":[\"Runnable\",\"FAISS\",\"Generic\",\"VectorStore\",\"RunnableSerializable\",\"Serializable\",\"object\",\"BaseRetriever\"],\"display_name\":\"FAISS\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/faiss\",\"custom_fields\":{\"embedding\":null,\"documents\":null},\"output_types\":[\"VectorStore\",\"FAISS\",\"BaseRetriever\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true}},\"models\":{\"LlamaCppModel\":{\"template\":{\"metadata\":{\"type\":\"Dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"model_kwargs\":{\"type\":\"Dict\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model_kwargs\",\"display_name\":\"Model Kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"model_path\":{\"type\":\"file\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[\".bin\"],\"file_path\":\"\",\"password\":false,\"name\":\"model_path\",\"display_name\":\"Model Path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"cache\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"cache\",\"display_name\":\"Cache\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"client\":{\"type\":\"Any\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"client\",\"display_name\":\"Client\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Any, Dict, List, Optional\\n\\nfrom langchain_community.llms.llamacpp import LlamaCpp\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Text\\n\\n\\nclass LlamaCppComponent(CustomComponent):\\n display_name = \\\"LlamaCppModel\\\"\\n description = \\\"Generate text using llama.cpp model.\\\"\\n documentation = \\\"https://python.langchain.com/docs/modules/model_io/models/llms/integrations/llamacpp\\\"\\n\\n def build_config(self):\\n return {\\n \\\"grammar\\\": {\\\"display_name\\\": \\\"Grammar\\\", \\\"advanced\\\": True},\\n \\\"cache\\\": {\\\"display_name\\\": \\\"Cache\\\", \\\"advanced\\\": True},\\n \\\"client\\\": {\\\"display_name\\\": \\\"Client\\\", \\\"advanced\\\": True},\\n \\\"echo\\\": {\\\"display_name\\\": \\\"Echo\\\", \\\"advanced\\\": True},\\n \\\"f16_kv\\\": {\\\"display_name\\\": \\\"F16 KV\\\", \\\"advanced\\\": True},\\n \\\"grammar_path\\\": {\\\"display_name\\\": \\\"Grammar Path\\\", \\\"advanced\\\": True},\\n \\\"last_n_tokens_size\\\": {\\n \\\"display_name\\\": \\\"Last N Tokens Size\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"logits_all\\\": {\\\"display_name\\\": \\\"Logits All\\\", \\\"advanced\\\": True},\\n \\\"logprobs\\\": {\\\"display_name\\\": \\\"Logprobs\\\", \\\"advanced\\\": True},\\n \\\"lora_base\\\": {\\\"display_name\\\": \\\"Lora Base\\\", \\\"advanced\\\": True},\\n \\\"lora_path\\\": {\\\"display_name\\\": \\\"Lora Path\\\", \\\"advanced\\\": True},\\n \\\"max_tokens\\\": {\\\"display_name\\\": \\\"Max Tokens\\\", \\\"advanced\\\": True},\\n \\\"metadata\\\": {\\\"display_name\\\": \\\"Metadata\\\", \\\"advanced\\\": True},\\n \\\"model_kwargs\\\": {\\\"display_name\\\": \\\"Model Kwargs\\\", \\\"advanced\\\": True},\\n \\\"model_path\\\": {\\n \\\"display_name\\\": \\\"Model Path\\\",\\n \\\"field_type\\\": \\\"file\\\",\\n \\\"file_types\\\": [\\\".bin\\\"],\\n \\\"required\\\": True,\\n },\\n \\\"n_batch\\\": {\\\"display_name\\\": \\\"N Batch\\\", \\\"advanced\\\": True},\\n \\\"n_ctx\\\": {\\\"display_name\\\": \\\"N Ctx\\\", \\\"advanced\\\": True},\\n \\\"n_gpu_layers\\\": {\\\"display_name\\\": \\\"N GPU Layers\\\", \\\"advanced\\\": True},\\n \\\"n_parts\\\": {\\\"display_name\\\": \\\"N Parts\\\", \\\"advanced\\\": True},\\n \\\"n_threads\\\": {\\\"display_name\\\": \\\"N Threads\\\", \\\"advanced\\\": True},\\n \\\"repeat_penalty\\\": {\\\"display_name\\\": \\\"Repeat Penalty\\\", \\\"advanced\\\": True},\\n \\\"rope_freq_base\\\": {\\\"display_name\\\": \\\"Rope Freq Base\\\", \\\"advanced\\\": True},\\n \\\"rope_freq_scale\\\": {\\\"display_name\\\": \\\"Rope Freq Scale\\\", \\\"advanced\\\": True},\\n \\\"seed\\\": {\\\"display_name\\\": \\\"Seed\\\", \\\"advanced\\\": True},\\n \\\"stop\\\": {\\\"display_name\\\": \\\"Stop\\\", \\\"advanced\\\": True},\\n \\\"streaming\\\": {\\\"display_name\\\": \\\"Streaming\\\", \\\"advanced\\\": True},\\n \\\"suffix\\\": {\\\"display_name\\\": \\\"Suffix\\\", \\\"advanced\\\": True},\\n \\\"tags\\\": {\\\"display_name\\\": \\\"Tags\\\", \\\"advanced\\\": True},\\n \\\"temperature\\\": {\\\"display_name\\\": \\\"Temperature\\\"},\\n \\\"top_k\\\": {\\\"display_name\\\": \\\"Top K\\\", \\\"advanced\\\": True},\\n \\\"top_p\\\": {\\\"display_name\\\": \\\"Top P\\\", \\\"advanced\\\": True},\\n \\\"use_mlock\\\": {\\\"display_name\\\": \\\"Use Mlock\\\", \\\"advanced\\\": True},\\n \\\"use_mmap\\\": {\\\"display_name\\\": \\\"Use Mmap\\\", \\\"advanced\\\": True},\\n \\\"verbose\\\": {\\\"display_name\\\": \\\"Verbose\\\", \\\"advanced\\\": True},\\n \\\"vocab_only\\\": {\\\"display_name\\\": \\\"Vocab Only\\\", \\\"advanced\\\": True},\\n \\\"inputs\\\": {\\\"display_name\\\": \\\"Input\\\"},\\n }\\n\\n def build(\\n self,\\n model_path: str,\\n inputs: str,\\n grammar: Optional[str] = None,\\n cache: Optional[bool] = None,\\n client: Optional[Any] = None,\\n echo: Optional[bool] = False,\\n f16_kv: bool = True,\\n grammar_path: Optional[str] = None,\\n last_n_tokens_size: Optional[int] = 64,\\n logits_all: bool = False,\\n logprobs: Optional[int] = None,\\n lora_base: Optional[str] = None,\\n lora_path: Optional[str] = None,\\n max_tokens: Optional[int] = 256,\\n metadata: Optional[Dict] = None,\\n model_kwargs: Dict = {},\\n n_batch: Optional[int] = 8,\\n n_ctx: int = 512,\\n n_gpu_layers: Optional[int] = 1,\\n n_parts: int = -1,\\n n_threads: Optional[int] = 1,\\n repeat_penalty: Optional[float] = 1.1,\\n rope_freq_base: float = 10000.0,\\n rope_freq_scale: float = 1.0,\\n seed: int = -1,\\n stop: Optional[List[str]] = [],\\n streaming: bool = True,\\n suffix: Optional[str] = \\\"\\\",\\n tags: Optional[List[str]] = [],\\n temperature: Optional[float] = 0.8,\\n top_k: Optional[int] = 40,\\n top_p: Optional[float] = 0.95,\\n use_mlock: bool = False,\\n use_mmap: Optional[bool] = True,\\n verbose: bool = True,\\n vocab_only: bool = False,\\n ) -> Text:\\n output = LlamaCpp(\\n model_path=model_path,\\n grammar=grammar,\\n cache=cache,\\n client=client,\\n echo=echo,\\n f16_kv=f16_kv,\\n grammar_path=grammar_path,\\n last_n_tokens_size=last_n_tokens_size,\\n logits_all=logits_all,\\n logprobs=logprobs,\\n lora_base=lora_base,\\n lora_path=lora_path,\\n max_tokens=max_tokens,\\n metadata=metadata,\\n model_kwargs=model_kwargs,\\n n_batch=n_batch,\\n n_ctx=n_ctx,\\n n_gpu_layers=n_gpu_layers,\\n n_parts=n_parts,\\n n_threads=n_threads,\\n repeat_penalty=repeat_penalty,\\n rope_freq_base=rope_freq_base,\\n rope_freq_scale=rope_freq_scale,\\n seed=seed,\\n stop=stop,\\n streaming=streaming,\\n suffix=suffix,\\n tags=tags,\\n temperature=temperature,\\n top_k=top_k,\\n top_p=top_p,\\n use_mlock=use_mlock,\\n use_mmap=use_mmap,\\n verbose=verbose,\\n vocab_only=vocab_only,\\n )\\n message = output.invoke(inputs)\\n result = message.content if hasattr(message, \\\"content\\\") else message\\n self.status = result\\n return result\\n self.status = result\\n return result\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"echo\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"echo\",\"display_name\":\"Echo\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"f16_kv\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":true,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"f16_kv\",\"display_name\":\"F16 KV\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"grammar\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"grammar\",\"display_name\":\"Grammar\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"grammar_path\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"grammar_path\",\"display_name\":\"Grammar Path\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"inputs\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"inputs\",\"display_name\":\"Input\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"last_n_tokens_size\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":64,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"last_n_tokens_size\",\"display_name\":\"Last N Tokens Size\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"logits_all\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"logits_all\",\"display_name\":\"Logits All\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"logprobs\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"logprobs\",\"display_name\":\"Logprobs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"lora_base\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"lora_base\",\"display_name\":\"Lora Base\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"lora_path\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"lora_path\",\"display_name\":\"Lora Path\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"max_tokens\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":256,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_tokens\",\"display_name\":\"Max Tokens\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"n_batch\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":8,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"n_batch\",\"display_name\":\"N Batch\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"n_ctx\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":512,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"n_ctx\",\"display_name\":\"N Ctx\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"n_gpu_layers\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":1,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"n_gpu_layers\",\"display_name\":\"N GPU Layers\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"n_parts\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":-1,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"n_parts\",\"display_name\":\"N Parts\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"n_threads\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":1,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"n_threads\",\"display_name\":\"N Threads\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"repeat_penalty\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":1.1,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"repeat_penalty\",\"display_name\":\"Repeat Penalty\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"rope_freq_base\":{\"type\":\"float\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":10000.0,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"rope_freq_base\",\"display_name\":\"Rope Freq Base\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"rope_freq_scale\":{\"type\":\"float\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":1.0,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"rope_freq_scale\",\"display_name\":\"Rope Freq Scale\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"seed\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":-1,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"seed\",\"display_name\":\"Seed\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"stop\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":[],\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"stop\",\"display_name\":\"Stop\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"streaming\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":true,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"streaming\",\"display_name\":\"Streaming\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"suffix\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"suffix\",\"display_name\":\"Suffix\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"tags\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":[],\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"tags\",\"display_name\":\"Tags\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"temperature\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.8,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"top_k\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":40,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_k\",\"display_name\":\"Top K\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"top_p\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.95,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_p\",\"display_name\":\"Top P\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"use_mlock\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"use_mlock\",\"display_name\":\"Use Mlock\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"use_mmap\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":true,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"use_mmap\",\"display_name\":\"Use Mmap\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"verbose\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":true,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"verbose\",\"display_name\":\"Verbose\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"vocab_only\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"vocab_only\",\"display_name\":\"Vocab Only\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Generate text using llama.cpp model.\",\"base_classes\":[\"Text\",\"object\"],\"display_name\":\"LlamaCppModel\",\"documentation\":\"https://python.langchain.com/docs/modules/model_io/models/llms/integrations/llamacpp\",\"custom_fields\":{\"model_path\":null,\"inputs\":null,\"grammar\":null,\"cache\":null,\"client\":null,\"echo\":null,\"f16_kv\":null,\"grammar_path\":null,\"last_n_tokens_size\":null,\"logits_all\":null,\"logprobs\":null,\"lora_base\":null,\"lora_path\":null,\"max_tokens\":null,\"metadata\":null,\"model_kwargs\":null,\"n_batch\":null,\"n_ctx\":null,\"n_gpu_layers\":null,\"n_parts\":null,\"n_threads\":null,\"repeat_penalty\":null,\"rope_freq_base\":null,\"rope_freq_scale\":null,\"seed\":null,\"stop\":null,\"streaming\":null,\"suffix\":null,\"tags\":null,\"temperature\":null,\"top_k\":null,\"top_p\":null,\"use_mlock\":null,\"use_mmap\":null,\"verbose\":null,\"vocab_only\":null},\"output_types\":[\"Text\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"BaiduQianfanChatModel\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain_community.chat_models.baidu_qianfan_endpoint import QianfanChatEndpoint\\nfrom pydantic.v1 import SecretStr\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Text\\n\\n\\nclass QianfanChatEndpointComponent(CustomComponent):\\n display_name: str = \\\"QianfanChat Model\\\"\\n description: str = (\\n \\\"Generate text using Baidu Qianfan chat models. Get more detail from \\\"\\n \\\"https://python.langchain.com/docs/integrations/chat/baidu_qianfan_endpoint.\\\"\\n )\\n\\n def build_config(self):\\n return {\\n \\\"model\\\": {\\n \\\"display_name\\\": \\\"Model Name\\\",\\n \\\"options\\\": [\\n \\\"ERNIE-Bot\\\",\\n \\\"ERNIE-Bot-turbo\\\",\\n \\\"BLOOMZ-7B\\\",\\n \\\"Llama-2-7b-chat\\\",\\n \\\"Llama-2-13b-chat\\\",\\n \\\"Llama-2-70b-chat\\\",\\n \\\"Qianfan-BLOOMZ-7B-compressed\\\",\\n \\\"Qianfan-Chinese-Llama-2-7B\\\",\\n \\\"ChatGLM2-6B-32K\\\",\\n \\\"AquilaChat-7B\\\",\\n ],\\n \\\"info\\\": \\\"https://python.langchain.com/docs/integrations/chat/baidu_qianfan_endpoint\\\",\\n \\\"required\\\": True,\\n },\\n \\\"qianfan_ak\\\": {\\n \\\"display_name\\\": \\\"Qianfan Ak\\\",\\n \\\"required\\\": True,\\n \\\"password\\\": True,\\n \\\"info\\\": \\\"which you could get from https://cloud.baidu.com/product/wenxinworkshop\\\",\\n },\\n \\\"qianfan_sk\\\": {\\n \\\"display_name\\\": \\\"Qianfan Sk\\\",\\n \\\"required\\\": True,\\n \\\"password\\\": True,\\n \\\"info\\\": \\\"which you could get from https://cloud.baidu.com/product/wenxinworkshop\\\",\\n },\\n \\\"top_p\\\": {\\n \\\"display_name\\\": \\\"Top p\\\",\\n \\\"field_type\\\": \\\"float\\\",\\n \\\"info\\\": \\\"Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo\\\",\\n \\\"value\\\": 0.8,\\n },\\n \\\"temperature\\\": {\\n \\\"display_name\\\": \\\"Temperature\\\",\\n \\\"field_type\\\": \\\"float\\\",\\n \\\"info\\\": \\\"Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo\\\",\\n \\\"value\\\": 0.95,\\n },\\n \\\"penalty_score\\\": {\\n \\\"display_name\\\": \\\"Penalty Score\\\",\\n \\\"field_type\\\": \\\"float\\\",\\n \\\"info\\\": \\\"Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo\\\",\\n \\\"value\\\": 1.0,\\n },\\n \\\"endpoint\\\": {\\n \\\"display_name\\\": \\\"Endpoint\\\",\\n \\\"info\\\": \\\"Endpoint of the Qianfan LLM, required if custom model used.\\\",\\n },\\n \\\"code\\\": {\\\"show\\\": False},\\n \\\"inputs\\\": {\\\"display_name\\\": \\\"Input\\\"},\\n }\\n\\n def build(\\n self,\\n inputs: str,\\n model: str = \\\"ERNIE-Bot-turbo\\\",\\n qianfan_ak: Optional[str] = None,\\n qianfan_sk: Optional[str] = None,\\n top_p: Optional[float] = None,\\n temperature: Optional[float] = None,\\n penalty_score: Optional[float] = None,\\n endpoint: Optional[str] = None,\\n ) -> Text:\\n try:\\n output = QianfanChatEndpoint( # type: ignore\\n model=model,\\n qianfan_ak=SecretStr(qianfan_ak) if qianfan_ak else None,\\n qianfan_sk=SecretStr(qianfan_sk) if qianfan_sk else None,\\n top_p=top_p,\\n temperature=temperature,\\n penalty_score=penalty_score,\\n endpoint=endpoint,\\n )\\n except Exception as e:\\n raise ValueError(\\\"Could not connect to Baidu Qianfan API.\\\") from e\\n message = output.invoke(inputs)\\n result = message.content if hasattr(message, \\\"content\\\") else message\\n self.status = result\\n return result\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"endpoint\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"endpoint\",\"display_name\":\"Endpoint\",\"advanced\":false,\"dynamic\":false,\"info\":\"Endpoint of the Qianfan LLM, required if custom model used.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"inputs\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"inputs\",\"display_name\":\"Input\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"model\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"ERNIE-Bot-turbo\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"ERNIE-Bot\",\"ERNIE-Bot-turbo\",\"BLOOMZ-7B\",\"Llama-2-7b-chat\",\"Llama-2-13b-chat\",\"Llama-2-70b-chat\",\"Qianfan-BLOOMZ-7B-compressed\",\"Qianfan-Chinese-Llama-2-7B\",\"ChatGLM2-6B-32K\",\"AquilaChat-7B\"],\"name\":\"model\",\"display_name\":\"Model Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"https://python.langchain.com/docs/integrations/chat/baidu_qianfan_endpoint\",\"title_case\":false,\"input_types\":[\"Text\"]},\"penalty_score\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":1.0,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"penalty_score\",\"display_name\":\"Penalty Score\",\"advanced\":false,\"dynamic\":false,\"info\":\"Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"qianfan_ak\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"qianfan_ak\",\"display_name\":\"Qianfan Ak\",\"advanced\":false,\"dynamic\":false,\"info\":\"which you could get from https://cloud.baidu.com/product/wenxinworkshop\",\"title_case\":false,\"input_types\":[\"Text\"]},\"qianfan_sk\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"qianfan_sk\",\"display_name\":\"Qianfan Sk\",\"advanced\":false,\"dynamic\":false,\"info\":\"which you could get from https://cloud.baidu.com/product/wenxinworkshop\",\"title_case\":false,\"input_types\":[\"Text\"]},\"temperature\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.95,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"top_p\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.8,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_p\",\"display_name\":\"Top p\",\"advanced\":false,\"dynamic\":false,\"info\":\"Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Generate text using Baidu Qianfan chat models. Get more detail from https://python.langchain.com/docs/integrations/chat/baidu_qianfan_endpoint.\",\"base_classes\":[\"Text\",\"object\"],\"display_name\":\"QianfanChat Model\",\"documentation\":\"\",\"custom_fields\":{\"inputs\":null,\"model\":null,\"qianfan_ak\":null,\"qianfan_sk\":null,\"top_p\":null,\"temperature\":null,\"penalty_score\":null,\"endpoint\":null},\"output_types\":[\"Text\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"GoogleGenerativeAIModel\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain_google_genai import ChatGoogleGenerativeAI # type: ignore\\nfrom pydantic.v1.types import SecretStr\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import RangeSpec, Text\\n\\n\\nclass GoogleGenerativeAIComponent(CustomComponent):\\n display_name: str = \\\"Google Generative AIModel\\\"\\n description: str = \\\"Generate text using Google Generative AI to generate text.\\\"\\n documentation: str = \\\"http://docs.langflow.org/components/custom\\\"\\n\\n def build_config(self):\\n return {\\n \\\"google_api_key\\\": {\\n \\\"display_name\\\": \\\"Google API Key\\\",\\n \\\"info\\\": \\\"The Google API Key to use for the Google Generative AI.\\\",\\n },\\n \\\"max_output_tokens\\\": {\\n \\\"display_name\\\": \\\"Max Output Tokens\\\",\\n \\\"info\\\": \\\"The maximum number of tokens to generate.\\\",\\n },\\n \\\"temperature\\\": {\\n \\\"display_name\\\": \\\"Temperature\\\",\\n \\\"info\\\": \\\"Run inference with this temperature. Must by in the closed interval [0.0, 1.0].\\\",\\n },\\n \\\"top_k\\\": {\\n \\\"display_name\\\": \\\"Top K\\\",\\n \\\"info\\\": \\\"Decode using top-k sampling: consider the set of top_k most probable tokens. Must be positive.\\\",\\n \\\"range_spec\\\": RangeSpec(min=0, max=2, step=0.1),\\n \\\"advanced\\\": True,\\n },\\n \\\"top_p\\\": {\\n \\\"display_name\\\": \\\"Top P\\\",\\n \\\"info\\\": \\\"The maximum cumulative probability of tokens to consider when sampling.\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"n\\\": {\\n \\\"display_name\\\": \\\"N\\\",\\n \\\"info\\\": \\\"Number of chat completions to generate for each prompt. Note that the API may not return the full n completions if duplicates are generated.\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"model\\\": {\\n \\\"display_name\\\": \\\"Model\\\",\\n \\\"info\\\": \\\"The name of the model to use. Supported examples: gemini-pro\\\",\\n \\\"options\\\": [\\\"gemini-pro\\\", \\\"gemini-pro-vision\\\"],\\n },\\n \\\"code\\\": {\\n \\\"advanced\\\": True,\\n },\\n \\\"inputs\\\": {\\\"display_name\\\": \\\"Input\\\"},\\n }\\n\\n def build(\\n self,\\n google_api_key: str,\\n model: str,\\n inputs: str,\\n max_output_tokens: Optional[int] = None,\\n temperature: float = 0.1,\\n top_k: Optional[int] = None,\\n top_p: Optional[float] = None,\\n n: Optional[int] = 1,\\n ) -> Text:\\n output = ChatGoogleGenerativeAI(\\n model=model,\\n max_output_tokens=max_output_tokens or None, # type: ignore\\n temperature=temperature,\\n top_k=top_k or None,\\n top_p=top_p or None, # type: ignore\\n n=n or 1,\\n google_api_key=SecretStr(google_api_key),\\n )\\n message = output.invoke(inputs)\\n result = message.content if hasattr(message, \\\"content\\\") else message\\n self.status = result\\n return result\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":true,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"google_api_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"google_api_key\",\"display_name\":\"Google API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"The Google API Key to use for the Google Generative AI.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"inputs\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"inputs\",\"display_name\":\"Input\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"max_output_tokens\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_output_tokens\",\"display_name\":\"Max Output Tokens\",\"advanced\":false,\"dynamic\":false,\"info\":\"The maximum number of tokens to generate.\",\"title_case\":false},\"model\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"gemini-pro\",\"gemini-pro-vision\"],\"name\":\"model\",\"display_name\":\"Model\",\"advanced\":false,\"dynamic\":false,\"info\":\"The name of the model to use. Supported examples: gemini-pro\",\"title_case\":false,\"input_types\":[\"Text\"]},\"n\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":1,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"n\",\"display_name\":\"N\",\"advanced\":true,\"dynamic\":false,\"info\":\"Number of chat completions to generate for each prompt. Note that the API may not return the full n completions if duplicates are generated.\",\"title_case\":false},\"temperature\":{\"type\":\"float\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.1,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"Run inference with this temperature. Must by in the closed interval [0.0, 1.0].\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"top_k\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_k\",\"display_name\":\"Top K\",\"advanced\":true,\"dynamic\":false,\"info\":\"Decode using top-k sampling: consider the set of top_k most probable tokens. Must be positive.\",\"rangeSpec\":{\"min\":0.0,\"max\":2.0,\"step\":0.1},\"title_case\":false},\"top_p\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_p\",\"display_name\":\"Top P\",\"advanced\":true,\"dynamic\":false,\"info\":\"The maximum cumulative probability of tokens to consider when sampling.\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Generate text using Google Generative AI to generate text.\",\"base_classes\":[\"Text\",\"object\"],\"display_name\":\"Google Generative AIModel\",\"documentation\":\"http://docs.langflow.org/components/custom\",\"custom_fields\":{\"google_api_key\":null,\"model\":null,\"inputs\":null,\"max_output_tokens\":null,\"temperature\":null,\"top_k\":null,\"top_p\":null,\"n\":null},\"output_types\":[\"Text\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"CTransformersModel\":{\"template\":{\"model_file\":{\"type\":\"file\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[\".bin\"],\"file_path\":\"\",\"password\":false,\"name\":\"model_file\",\"display_name\":\"Model File\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Dict, Optional\\n\\nfrom langchain_community.llms.ctransformers import CTransformers\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Text\\n\\n\\nclass CTransformersComponent(CustomComponent):\\n display_name = \\\"CTransformersModel\\\"\\n description = \\\"Generate text using CTransformers LLM models\\\"\\n documentation = \\\"https://python.langchain.com/docs/modules/model_io/models/llms/integrations/ctransformers\\\"\\n\\n def build_config(self):\\n return {\\n \\\"model\\\": {\\\"display_name\\\": \\\"Model\\\", \\\"required\\\": True},\\n \\\"model_file\\\": {\\n \\\"display_name\\\": \\\"Model File\\\",\\n \\\"required\\\": False,\\n \\\"field_type\\\": \\\"file\\\",\\n \\\"file_types\\\": [\\\".bin\\\"],\\n },\\n \\\"model_type\\\": {\\\"display_name\\\": \\\"Model Type\\\", \\\"required\\\": True},\\n \\\"config\\\": {\\n \\\"display_name\\\": \\\"Config\\\",\\n \\\"advanced\\\": True,\\n \\\"required\\\": False,\\n \\\"field_type\\\": \\\"dict\\\",\\n \\\"value\\\": '{\\\"top_k\\\":40,\\\"top_p\\\":0.95,\\\"temperature\\\":0.8,\\\"repetition_penalty\\\":1.1,\\\"last_n_tokens\\\":64,\\\"seed\\\":-1,\\\"max_new_tokens\\\":256,\\\"stop\\\":\\\"\\\",\\\"stream\\\":\\\"False\\\",\\\"reset\\\":\\\"True\\\",\\\"batch_size\\\":8,\\\"threads\\\":-1,\\\"context_length\\\":-1,\\\"gpu_layers\\\":0}',\\n },\\n \\\"inputs\\\": {\\\"display_name\\\": \\\"Input\\\"},\\n }\\n\\n def build(\\n self,\\n model: str,\\n model_file: str,\\n inputs: str,\\n model_type: str,\\n config: Optional[Dict] = None,\\n ) -> Text:\\n output = CTransformers(model=model, model_file=model_file, model_type=model_type, config=config)\\n message = output.invoke(inputs)\\n result = message.content if hasattr(message, \\\"content\\\") else message\\n self.status = result\\n return result\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"config\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"{\\\"top_k\\\":40,\\\"top_p\\\":0.95,\\\"temperature\\\":0.8,\\\"repetition_penalty\\\":1.1,\\\"last_n_tokens\\\":64,\\\"seed\\\":-1,\\\"max_new_tokens\\\":256,\\\"stop\\\":\\\"\\\",\\\"stream\\\":\\\"False\\\",\\\"reset\\\":\\\"True\\\",\\\"batch_size\\\":8,\\\"threads\\\":-1,\\\"context_length\\\":-1,\\\"gpu_layers\\\":0}\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"config\",\"display_name\":\"Config\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"inputs\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"inputs\",\"display_name\":\"Input\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"model\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model\",\"display_name\":\"Model\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"model_type\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model_type\",\"display_name\":\"Model Type\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Generate text using CTransformers LLM models\",\"base_classes\":[\"Text\",\"object\"],\"display_name\":\"CTransformersModel\",\"documentation\":\"https://python.langchain.com/docs/modules/model_io/models/llms/integrations/ctransformers\",\"custom_fields\":{\"model\":null,\"model_file\":null,\"inputs\":null,\"model_type\":null,\"config\":null},\"output_types\":[\"Text\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"VertexAiModel\":{\"template\":{\"credentials\":{\"type\":\"file\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[\".json\"],\"password\":false,\"name\":\"credentials\",\"display_name\":\"Credentials\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"examples\":{\"type\":\"BaseMessage\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":true,\"value\":[],\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"examples\",\"display_name\":\"Examples\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import List, Optional\\n\\nfrom langchain_core.messages.base import BaseMessage\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Text\\n\\n\\nclass ChatVertexAIComponent(CustomComponent):\\n display_name = \\\"ChatVertexAIModel\\\"\\n description = \\\"Generate text using Vertex AI Chat large language models API.\\\"\\n\\n def build_config(self):\\n return {\\n \\\"credentials\\\": {\\n \\\"display_name\\\": \\\"Credentials\\\",\\n \\\"field_type\\\": \\\"file\\\",\\n \\\"file_types\\\": [\\\".json\\\"],\\n \\\"file_path\\\": None,\\n },\\n \\\"examples\\\": {\\n \\\"display_name\\\": \\\"Examples\\\",\\n \\\"multiline\\\": True,\\n },\\n \\\"location\\\": {\\n \\\"display_name\\\": \\\"Location\\\",\\n \\\"value\\\": \\\"us-central1\\\",\\n },\\n \\\"max_output_tokens\\\": {\\n \\\"display_name\\\": \\\"Max Output Tokens\\\",\\n \\\"value\\\": 128,\\n \\\"advanced\\\": True,\\n },\\n \\\"model_name\\\": {\\n \\\"display_name\\\": \\\"Model Name\\\",\\n \\\"value\\\": \\\"chat-bison\\\",\\n },\\n \\\"project\\\": {\\n \\\"display_name\\\": \\\"Project\\\",\\n },\\n \\\"temperature\\\": {\\n \\\"display_name\\\": \\\"Temperature\\\",\\n \\\"value\\\": 0.0,\\n },\\n \\\"top_k\\\": {\\n \\\"display_name\\\": \\\"Top K\\\",\\n \\\"value\\\": 40,\\n \\\"advanced\\\": True,\\n },\\n \\\"top_p\\\": {\\n \\\"display_name\\\": \\\"Top P\\\",\\n \\\"value\\\": 0.95,\\n \\\"advanced\\\": True,\\n },\\n \\\"verbose\\\": {\\n \\\"display_name\\\": \\\"Verbose\\\",\\n \\\"value\\\": False,\\n \\\"advanced\\\": True,\\n },\\n \\\"inputs\\\": {\\\"display_name\\\": \\\"Input\\\"},\\n }\\n\\n def build(\\n self,\\n inputs: str,\\n credentials: Optional[str],\\n project: str,\\n examples: Optional[List[BaseMessage]] = [],\\n location: str = \\\"us-central1\\\",\\n max_output_tokens: int = 128,\\n model_name: str = \\\"chat-bison\\\",\\n temperature: float = 0.0,\\n top_k: int = 40,\\n top_p: float = 0.95,\\n verbose: bool = False,\\n ) -> Text:\\n try:\\n from langchain_google_vertexai import ChatVertexAI\\n except ImportError:\\n raise ImportError(\\n \\\"To use the ChatVertexAI model, you need to install the langchain-google-vertexai package.\\\"\\n )\\n output = ChatVertexAI(\\n credentials=credentials,\\n examples=examples,\\n location=location,\\n max_output_tokens=max_output_tokens,\\n model_name=model_name,\\n project=project,\\n temperature=temperature,\\n top_k=top_k,\\n top_p=top_p,\\n verbose=verbose,\\n )\\n message = output.invoke(inputs)\\n result = message.content if hasattr(message, \\\"content\\\") else message\\n self.status = result\\n return result\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"inputs\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"inputs\",\"display_name\":\"Input\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"location\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"us-central1\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"location\",\"display_name\":\"Location\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"max_output_tokens\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":128,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_output_tokens\",\"display_name\":\"Max Output Tokens\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"model_name\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"chat-bison\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model_name\",\"display_name\":\"Model Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"project\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"project\",\"display_name\":\"Project\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"temperature\":{\"type\":\"float\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.0,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"top_k\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":40,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_k\",\"display_name\":\"Top K\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"top_p\":{\"type\":\"float\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.95,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_p\",\"display_name\":\"Top P\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"verbose\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"verbose\",\"display_name\":\"Verbose\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Generate text using Vertex AI Chat large language models API.\",\"base_classes\":[\"Text\",\"object\"],\"display_name\":\"ChatVertexAIModel\",\"documentation\":\"\",\"custom_fields\":{\"inputs\":null,\"credentials\":null,\"project\":null,\"examples\":null,\"location\":null,\"max_output_tokens\":null,\"model_name\":null,\"temperature\":null,\"top_k\":null,\"top_p\":null,\"verbose\":null},\"output_types\":[\"Text\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"OllamaModel\":{\"template\":{\"metadata\":{\"type\":\"Dict[str, Any]\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":true,\"dynamic\":false,\"info\":\"Metadata to add to the run trace.\",\"title_case\":false},\"stop\":{\"type\":\"list\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"stop\",\"display_name\":\"Stop Tokens\",\"advanced\":true,\"dynamic\":false,\"info\":\"List of tokens to signal the model to stop generating text.\",\"title_case\":false},\"tags\":{\"type\":\"list\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"tags\",\"display_name\":\"Tags\",\"advanced\":true,\"dynamic\":false,\"info\":\"Tags to add to the run trace.\",\"title_case\":false},\"base_url\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"base_url\",\"display_name\":\"Base URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"Endpoint of the Ollama API. Defaults to 'http://localhost:11434' if not specified.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"cache\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"cache\",\"display_name\":\"Cache\",\"advanced\":true,\"dynamic\":false,\"info\":\"Enable or disable caching.\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Any, Dict, List, Optional\\n\\n# from langchain_community.chat_models import ChatOllama\\nfrom langchain_community.chat_models import ChatOllama\\n\\n# from langchain.chat_models import ChatOllama\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Text\\n\\n# whe When a callback component is added to Langflow, the comment must be uncommented.\\n# from langchain.callbacks.manager import CallbackManager\\n\\n\\nclass ChatOllamaComponent(CustomComponent):\\n display_name = \\\"ChatOllamaModel\\\"\\n description = \\\"Generate text using Local LLM for chat with Ollama.\\\"\\n\\n def build_config(self) -> dict:\\n return {\\n \\\"base_url\\\": {\\n \\\"display_name\\\": \\\"Base URL\\\",\\n \\\"info\\\": \\\"Endpoint of the Ollama API. Defaults to 'http://localhost:11434' if not specified.\\\",\\n },\\n \\\"model\\\": {\\n \\\"display_name\\\": \\\"Model Name\\\",\\n \\\"value\\\": \\\"llama2\\\",\\n \\\"info\\\": \\\"Refer to https://ollama.ai/library for more models.\\\",\\n },\\n \\\"temperature\\\": {\\n \\\"display_name\\\": \\\"Temperature\\\",\\n \\\"field_type\\\": \\\"float\\\",\\n \\\"value\\\": 0.8,\\n \\\"info\\\": \\\"Controls the creativity of model responses.\\\",\\n },\\n \\\"cache\\\": {\\n \\\"display_name\\\": \\\"Cache\\\",\\n \\\"field_type\\\": \\\"bool\\\",\\n \\\"info\\\": \\\"Enable or disable caching.\\\",\\n \\\"advanced\\\": True,\\n \\\"value\\\": False,\\n },\\n ### When a callback component is added to Langflow, the comment must be uncommented. ###\\n # \\\"callback_manager\\\": {\\n # \\\"display_name\\\": \\\"Callback Manager\\\",\\n # \\\"info\\\": \\\"Optional callback manager for additional functionality.\\\",\\n # \\\"advanced\\\": True,\\n # },\\n # \\\"callbacks\\\": {\\n # \\\"display_name\\\": \\\"Callbacks\\\",\\n # \\\"info\\\": \\\"Callbacks to execute during model runtime.\\\",\\n # \\\"advanced\\\": True,\\n # },\\n ########################################################################################\\n \\\"format\\\": {\\n \\\"display_name\\\": \\\"Format\\\",\\n \\\"field_type\\\": \\\"str\\\",\\n \\\"info\\\": \\\"Specify the format of the output (e.g., json).\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"metadata\\\": {\\n \\\"display_name\\\": \\\"Metadata\\\",\\n \\\"info\\\": \\\"Metadata to add to the run trace.\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"mirostat\\\": {\\n \\\"display_name\\\": \\\"Mirostat\\\",\\n \\\"options\\\": [\\\"Disabled\\\", \\\"Mirostat\\\", \\\"Mirostat 2.0\\\"],\\n \\\"info\\\": \\\"Enable/disable Mirostat sampling for controlling perplexity.\\\",\\n \\\"value\\\": \\\"Disabled\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"mirostat_eta\\\": {\\n \\\"display_name\\\": \\\"Mirostat Eta\\\",\\n \\\"field_type\\\": \\\"float\\\",\\n \\\"info\\\": \\\"Learning rate for Mirostat algorithm. (Default: 0.1)\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"mirostat_tau\\\": {\\n \\\"display_name\\\": \\\"Mirostat Tau\\\",\\n \\\"field_type\\\": \\\"float\\\",\\n \\\"info\\\": \\\"Controls the balance between coherence and diversity of the output. (Default: 5.0)\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"num_ctx\\\": {\\n \\\"display_name\\\": \\\"Context Window Size\\\",\\n \\\"field_type\\\": \\\"int\\\",\\n \\\"info\\\": \\\"Size of the context window for generating tokens. (Default: 2048)\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"num_gpu\\\": {\\n \\\"display_name\\\": \\\"Number of GPUs\\\",\\n \\\"field_type\\\": \\\"int\\\",\\n \\\"info\\\": \\\"Number of GPUs to use for computation. (Default: 1 on macOS, 0 to disable)\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"num_thread\\\": {\\n \\\"display_name\\\": \\\"Number of Threads\\\",\\n \\\"field_type\\\": \\\"int\\\",\\n \\\"info\\\": \\\"Number of threads to use during computation. (Default: detected for optimal performance)\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"repeat_last_n\\\": {\\n \\\"display_name\\\": \\\"Repeat Last N\\\",\\n \\\"field_type\\\": \\\"int\\\",\\n \\\"info\\\": \\\"How far back the model looks to prevent repetition. (Default: 64, 0 = disabled, -1 = num_ctx)\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"repeat_penalty\\\": {\\n \\\"display_name\\\": \\\"Repeat Penalty\\\",\\n \\\"field_type\\\": \\\"float\\\",\\n \\\"info\\\": \\\"Penalty for repetitions in generated text. (Default: 1.1)\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"tfs_z\\\": {\\n \\\"display_name\\\": \\\"TFS Z\\\",\\n \\\"field_type\\\": \\\"float\\\",\\n \\\"info\\\": \\\"Tail free sampling value. (Default: 1)\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"timeout\\\": {\\n \\\"display_name\\\": \\\"Timeout\\\",\\n \\\"field_type\\\": \\\"int\\\",\\n \\\"info\\\": \\\"Timeout for the request stream.\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"top_k\\\": {\\n \\\"display_name\\\": \\\"Top K\\\",\\n \\\"field_type\\\": \\\"int\\\",\\n \\\"info\\\": \\\"Limits token selection to top K. (Default: 40)\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"top_p\\\": {\\n \\\"display_name\\\": \\\"Top P\\\",\\n \\\"field_type\\\": \\\"float\\\",\\n \\\"info\\\": \\\"Works together with top-k. (Default: 0.9)\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"verbose\\\": {\\n \\\"display_name\\\": \\\"Verbose\\\",\\n \\\"field_type\\\": \\\"bool\\\",\\n \\\"info\\\": \\\"Whether to print out response text.\\\",\\n },\\n \\\"tags\\\": {\\n \\\"display_name\\\": \\\"Tags\\\",\\n \\\"field_type\\\": \\\"list\\\",\\n \\\"info\\\": \\\"Tags to add to the run trace.\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"stop\\\": {\\n \\\"display_name\\\": \\\"Stop Tokens\\\",\\n \\\"field_type\\\": \\\"list\\\",\\n \\\"info\\\": \\\"List of tokens to signal the model to stop generating text.\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"system\\\": {\\n \\\"display_name\\\": \\\"System\\\",\\n \\\"field_type\\\": \\\"str\\\",\\n \\\"info\\\": \\\"System to use for generating text.\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"template\\\": {\\n \\\"display_name\\\": \\\"Template\\\",\\n \\\"field_type\\\": \\\"str\\\",\\n \\\"info\\\": \\\"Template to use for generating text.\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"inputs\\\": {\\\"display_name\\\": \\\"Input\\\"},\\n }\\n\\n def build(\\n self,\\n base_url: Optional[str],\\n model: str,\\n inputs: str,\\n mirostat: Optional[str],\\n mirostat_eta: Optional[float] = None,\\n mirostat_tau: Optional[float] = None,\\n ### When a callback component is added to Langflow, the comment must be uncommented.###\\n # callback_manager: Optional[CallbackManager] = None,\\n # callbacks: Optional[List[Callbacks]] = None,\\n #######################################################################################\\n repeat_last_n: Optional[int] = None,\\n verbose: Optional[bool] = None,\\n cache: Optional[bool] = None,\\n num_ctx: Optional[int] = None,\\n num_gpu: Optional[int] = None,\\n format: Optional[str] = None,\\n metadata: Optional[Dict[str, Any]] = None,\\n num_thread: Optional[int] = None,\\n repeat_penalty: Optional[float] = None,\\n stop: Optional[List[str]] = None,\\n system: Optional[str] = None,\\n tags: Optional[List[str]] = None,\\n temperature: Optional[float] = None,\\n template: Optional[str] = None,\\n tfs_z: Optional[float] = None,\\n timeout: Optional[int] = None,\\n top_k: Optional[int] = None,\\n top_p: Optional[int] = None,\\n ) -> Text:\\n if not base_url:\\n base_url = \\\"http://localhost:11434\\\"\\n\\n # Mapping mirostat settings to their corresponding values\\n mirostat_options = {\\\"Mirostat\\\": 1, \\\"Mirostat 2.0\\\": 2}\\n\\n # Default to 0 for 'Disabled'\\n mirostat_value = mirostat_options.get(mirostat, 0) # type: ignore\\n\\n # Set mirostat_eta and mirostat_tau to None if mirostat is disabled\\n if mirostat_value == 0:\\n mirostat_eta = None\\n mirostat_tau = None\\n\\n # Mapping system settings to their corresponding values\\n llm_params = {\\n \\\"base_url\\\": base_url,\\n \\\"cache\\\": cache,\\n \\\"model\\\": model,\\n \\\"mirostat\\\": mirostat_value,\\n \\\"format\\\": format,\\n \\\"metadata\\\": metadata,\\n \\\"tags\\\": tags,\\n ## When a callback component is added to Langflow, the comment must be uncommented.##\\n # \\\"callback_manager\\\": callback_manager,\\n # \\\"callbacks\\\": callbacks,\\n #####################################################################################\\n \\\"mirostat_eta\\\": mirostat_eta,\\n \\\"mirostat_tau\\\": mirostat_tau,\\n \\\"num_ctx\\\": num_ctx,\\n \\\"num_gpu\\\": num_gpu,\\n \\\"num_thread\\\": num_thread,\\n \\\"repeat_last_n\\\": repeat_last_n,\\n \\\"repeat_penalty\\\": repeat_penalty,\\n \\\"temperature\\\": temperature,\\n \\\"stop\\\": stop,\\n \\\"system\\\": system,\\n \\\"template\\\": template,\\n \\\"tfs_z\\\": tfs_z,\\n \\\"timeout\\\": timeout,\\n \\\"top_k\\\": top_k,\\n \\\"top_p\\\": top_p,\\n \\\"verbose\\\": verbose,\\n }\\n\\n # None Value remove\\n llm_params = {k: v for k, v in llm_params.items() if v is not None}\\n\\n try:\\n output = ChatOllama(**llm_params) # type: ignore\\n except Exception as e:\\n raise ValueError(\\\"Could not initialize Ollama LLM.\\\") from e\\n message = output.invoke(inputs)\\n result = message.content if hasattr(message, \\\"content\\\") else message\\n self.status = result\\n return result\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"format\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"format\",\"display_name\":\"Format\",\"advanced\":true,\"dynamic\":false,\"info\":\"Specify the format of the output (e.g., json).\",\"title_case\":false,\"input_types\":[\"Text\"]},\"inputs\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"inputs\",\"display_name\":\"Input\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"mirostat\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"Disabled\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"Disabled\",\"Mirostat\",\"Mirostat 2.0\"],\"name\":\"mirostat\",\"display_name\":\"Mirostat\",\"advanced\":true,\"dynamic\":false,\"info\":\"Enable/disable Mirostat sampling for controlling perplexity.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"mirostat_eta\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"mirostat_eta\",\"display_name\":\"Mirostat Eta\",\"advanced\":true,\"dynamic\":false,\"info\":\"Learning rate for Mirostat algorithm. (Default: 0.1)\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"mirostat_tau\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"mirostat_tau\",\"display_name\":\"Mirostat Tau\",\"advanced\":true,\"dynamic\":false,\"info\":\"Controls the balance between coherence and diversity of the output. (Default: 5.0)\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"model\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"llama2\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model\",\"display_name\":\"Model Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"Refer to https://ollama.ai/library for more models.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"num_ctx\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"num_ctx\",\"display_name\":\"Context Window Size\",\"advanced\":true,\"dynamic\":false,\"info\":\"Size of the context window for generating tokens. (Default: 2048)\",\"title_case\":false},\"num_gpu\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"num_gpu\",\"display_name\":\"Number of GPUs\",\"advanced\":true,\"dynamic\":false,\"info\":\"Number of GPUs to use for computation. (Default: 1 on macOS, 0 to disable)\",\"title_case\":false},\"num_thread\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"num_thread\",\"display_name\":\"Number of Threads\",\"advanced\":true,\"dynamic\":false,\"info\":\"Number of threads to use during computation. (Default: detected for optimal performance)\",\"title_case\":false},\"repeat_last_n\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"repeat_last_n\",\"display_name\":\"Repeat Last N\",\"advanced\":true,\"dynamic\":false,\"info\":\"How far back the model looks to prevent repetition. (Default: 64, 0 = disabled, -1 = num_ctx)\",\"title_case\":false},\"repeat_penalty\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"repeat_penalty\",\"display_name\":\"Repeat Penalty\",\"advanced\":true,\"dynamic\":false,\"info\":\"Penalty for repetitions in generated text. (Default: 1.1)\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"system\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"system\",\"display_name\":\"System\",\"advanced\":true,\"dynamic\":false,\"info\":\"System to use for generating text.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"temperature\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.8,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"Controls the creativity of model responses.\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"template\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"template\",\"display_name\":\"Template\",\"advanced\":true,\"dynamic\":false,\"info\":\"Template to use for generating text.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"tfs_z\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"tfs_z\",\"display_name\":\"TFS Z\",\"advanced\":true,\"dynamic\":false,\"info\":\"Tail free sampling value. (Default: 1)\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"timeout\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"timeout\",\"display_name\":\"Timeout\",\"advanced\":true,\"dynamic\":false,\"info\":\"Timeout for the request stream.\",\"title_case\":false},\"top_k\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_k\",\"display_name\":\"Top K\",\"advanced\":true,\"dynamic\":false,\"info\":\"Limits token selection to top K. (Default: 40)\",\"title_case\":false},\"top_p\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_p\",\"display_name\":\"Top P\",\"advanced\":true,\"dynamic\":false,\"info\":\"Works together with top-k. (Default: 0.9)\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"verbose\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"verbose\",\"display_name\":\"Verbose\",\"advanced\":false,\"dynamic\":false,\"info\":\"Whether to print out response text.\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Generate text using Local LLM for chat with Ollama.\",\"base_classes\":[\"Text\",\"object\"],\"display_name\":\"ChatOllamaModel\",\"documentation\":\"\",\"custom_fields\":{\"base_url\":null,\"model\":null,\"inputs\":null,\"mirostat\":null,\"mirostat_eta\":null,\"mirostat_tau\":null,\"repeat_last_n\":null,\"verbose\":null,\"cache\":null,\"num_ctx\":null,\"num_gpu\":null,\"format\":null,\"metadata\":null,\"num_thread\":null,\"repeat_penalty\":null,\"stop\":null,\"system\":null,\"tags\":null,\"temperature\":null,\"template\":null,\"tfs_z\":null,\"timeout\":null,\"top_k\":null,\"top_p\":null},\"output_types\":[\"Text\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"AnthropicModel\":{\"template\":{\"anthropic_api_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"anthropic_api_key\",\"display_name\":\"Anthropic API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"Your Anthropic API key.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"api_endpoint\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"api_endpoint\",\"display_name\":\"API Endpoint\",\"advanced\":false,\"dynamic\":false,\"info\":\"Endpoint of the Anthropic API. Defaults to 'https://api.anthropic.com' if not specified.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain_community.chat_models.anthropic import ChatAnthropic\\nfrom pydantic.v1 import SecretStr\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Text\\n\\n\\nclass AnthropicLLM(CustomComponent):\\n display_name: str = \\\"AnthropicModel\\\"\\n description: str = \\\"Generate text using Anthropic Chat&Completion large language models.\\\"\\n\\n def build_config(self):\\n return {\\n \\\"model\\\": {\\n \\\"display_name\\\": \\\"Model Name\\\",\\n \\\"options\\\": [\\n \\\"claude-2.1\\\",\\n \\\"claude-2.0\\\",\\n \\\"claude-instant-1.2\\\",\\n \\\"claude-instant-1\\\",\\n # Add more models as needed\\n ],\\n \\\"info\\\": \\\"https://python.langchain.com/docs/integrations/chat/anthropic\\\",\\n \\\"required\\\": True,\\n \\\"value\\\": \\\"claude-2.1\\\",\\n },\\n \\\"anthropic_api_key\\\": {\\n \\\"display_name\\\": \\\"Anthropic API Key\\\",\\n \\\"required\\\": True,\\n \\\"password\\\": True,\\n \\\"info\\\": \\\"Your Anthropic API key.\\\",\\n },\\n \\\"max_tokens\\\": {\\n \\\"display_name\\\": \\\"Max Tokens\\\",\\n \\\"field_type\\\": \\\"int\\\",\\n \\\"value\\\": 256,\\n },\\n \\\"temperature\\\": {\\n \\\"display_name\\\": \\\"Temperature\\\",\\n \\\"field_type\\\": \\\"float\\\",\\n \\\"value\\\": 0.7,\\n },\\n \\\"api_endpoint\\\": {\\n \\\"display_name\\\": \\\"API Endpoint\\\",\\n \\\"info\\\": \\\"Endpoint of the Anthropic API. Defaults to 'https://api.anthropic.com' if not specified.\\\",\\n },\\n \\\"code\\\": {\\\"show\\\": False},\\n \\\"inputs\\\": {\\\"display_name\\\": \\\"Input\\\"},\\n }\\n\\n def build(\\n self,\\n model: str,\\n inputs: str,\\n anthropic_api_key: Optional[str] = None,\\n max_tokens: Optional[int] = None,\\n temperature: Optional[float] = None,\\n api_endpoint: Optional[str] = None,\\n ) -> Text:\\n # Set default API endpoint if not provided\\n if not api_endpoint:\\n api_endpoint = \\\"https://api.anthropic.com\\\"\\n\\n try:\\n output = ChatAnthropic(\\n model_name=model,\\n anthropic_api_key=(SecretStr(anthropic_api_key) if anthropic_api_key else None),\\n max_tokens_to_sample=max_tokens, # type: ignore\\n temperature=temperature,\\n anthropic_api_url=api_endpoint,\\n )\\n except Exception as e:\\n raise ValueError(\\\"Could not connect to Anthropic API.\\\") from e\\n message = output.invoke(inputs)\\n result = message.content if hasattr(message, \\\"content\\\") else message\\n self.status = result\\n return result\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"inputs\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"inputs\",\"display_name\":\"Input\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"max_tokens\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":256,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_tokens\",\"display_name\":\"Max Tokens\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"model\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"claude-2.1\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"claude-2.1\",\"claude-2.0\",\"claude-instant-1.2\",\"claude-instant-1\"],\"name\":\"model\",\"display_name\":\"Model Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"https://python.langchain.com/docs/integrations/chat/anthropic\",\"title_case\":false,\"input_types\":[\"Text\"]},\"temperature\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.7,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Generate text using Anthropic Chat&Completion large language models.\",\"base_classes\":[\"Text\",\"object\"],\"display_name\":\"AnthropicModel\",\"documentation\":\"\",\"custom_fields\":{\"model\":null,\"inputs\":null,\"anthropic_api_key\":null,\"max_tokens\":null,\"temperature\":null,\"api_endpoint\":null},\"output_types\":[\"Text\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"OpenAIModel\":{\"template\":{\"inputs\":{\"type\":\"Text\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"inputs\",\"display_name\":\"Input\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain_openai import ChatOpenAI\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import NestedDict, Text\\n\\n\\nclass OpenAIModelComponent(CustomComponent):\\n display_name = \\\"OpenAI Model\\\"\\n description = \\\"Generates text using OpenAI's models.\\\"\\n\\n def build_config(self):\\n return {\\n \\\"inputs\\\": {\\\"display_name\\\": \\\"Input\\\"},\\n \\\"max_tokens\\\": {\\n \\\"display_name\\\": \\\"Max Tokens\\\",\\n \\\"advanced\\\": False,\\n \\\"required\\\": False,\\n },\\n \\\"model_kwargs\\\": {\\n \\\"display_name\\\": \\\"Model Kwargs\\\",\\n \\\"advanced\\\": True,\\n \\\"required\\\": False,\\n },\\n \\\"model_name\\\": {\\n \\\"display_name\\\": \\\"Model Name\\\",\\n \\\"advanced\\\": False,\\n \\\"required\\\": False,\\n \\\"options\\\": [\\n \\\"gpt-4-turbo-preview\\\",\\n \\\"gpt-4-0125-preview\\\",\\n \\\"gpt-4-1106-preview\\\",\\n \\\"gpt-4-vision-preview\\\",\\n \\\"gpt-3.5-turbo-0125\\\",\\n \\\"gpt-3.5-turbo-1106\\\",\\n ],\\n },\\n \\\"openai_api_base\\\": {\\n \\\"display_name\\\": \\\"OpenAI API Base\\\",\\n \\\"advanced\\\": False,\\n \\\"required\\\": False,\\n \\\"info\\\": (\\n \\\"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\\\n\\\\n\\\"\\n \\\"You can change this to use other APIs like JinaChat, LocalAI and Prem.\\\"\\n ),\\n },\\n \\\"openai_api_key\\\": {\\n \\\"display_name\\\": \\\"OpenAI API Key\\\",\\n \\\"advanced\\\": False,\\n \\\"required\\\": False,\\n \\\"password\\\": True,\\n },\\n \\\"temperature\\\": {\\n \\\"display_name\\\": \\\"Temperature\\\",\\n \\\"advanced\\\": False,\\n \\\"required\\\": False,\\n \\\"value\\\": 0.7,\\n },\\n }\\n\\n def build(\\n self,\\n inputs: Text,\\n max_tokens: Optional[int] = 256,\\n model_kwargs: NestedDict = {},\\n model_name: str = \\\"gpt-4-1106-preview\\\",\\n openai_api_base: Optional[str] = None,\\n openai_api_key: Optional[str] = None,\\n temperature: float = 0.7,\\n ) -> Text:\\n if not openai_api_base:\\n openai_api_base = \\\"https://api.openai.com/v1\\\"\\n model = ChatOpenAI(\\n max_tokens=max_tokens,\\n model_kwargs=model_kwargs,\\n model=model_name,\\n base_url=openai_api_base,\\n api_key=openai_api_key,\\n temperature=temperature,\\n )\\n\\n message = model.invoke(inputs)\\n result = message.content if hasattr(message, \\\"content\\\") else message\\n self.status = result\\n return result\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"max_tokens\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":256,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_tokens\",\"display_name\":\"Max Tokens\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"model_kwargs\":{\"type\":\"NestedDict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model_kwargs\",\"display_name\":\"Model Kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"model_name\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"gpt-4-1106-preview\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"gpt-4-turbo-preview\",\"gpt-4-0125-preview\",\"gpt-4-1106-preview\",\"gpt-4-vision-preview\",\"gpt-3.5-turbo-0125\",\"gpt-3.5-turbo-1106\"],\"name\":\"model_name\",\"display_name\":\"Model Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"openai_api_base\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"openai_api_base\",\"display_name\":\"OpenAI API Base\",\"advanced\":false,\"dynamic\":false,\"info\":\"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"openai_api_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"openai_api_key\",\"display_name\":\"OpenAI API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"temperature\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.7,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Generates text using OpenAI's models.\",\"base_classes\":[\"Text\",\"object\"],\"display_name\":\"OpenAI Model\",\"documentation\":\"\",\"custom_fields\":{\"inputs\":null,\"max_tokens\":null,\"model_kwargs\":null,\"model_name\":null,\"openai_api_base\":null,\"openai_api_key\":null,\"temperature\":null},\"output_types\":[\"Text\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"HuggingFaceModel\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain_community.chat_models.huggingface import ChatHuggingFace\\nfrom langchain_community.llms.huggingface_endpoint import HuggingFaceEndpoint\\n\\nfrom langflow import CustomComponent\\n\\nfrom langflow.field_typing import Text\\n\\n\\nclass HuggingFaceEndpointsComponent(CustomComponent):\\n display_name: str = \\\"Hugging Face Inference API models\\\"\\n description: str = \\\"Generate text using LLM model from Hugging Face Inference API.\\\"\\n\\n def build_config(self):\\n return {\\n \\\"endpoint_url\\\": {\\\"display_name\\\": \\\"Endpoint URL\\\", \\\"password\\\": True},\\n \\\"task\\\": {\\n \\\"display_name\\\": \\\"Task\\\",\\n \\\"options\\\": [\\\"text2text-generation\\\", \\\"text-generation\\\", \\\"summarization\\\"],\\n },\\n \\\"huggingfacehub_api_token\\\": {\\\"display_name\\\": \\\"API token\\\", \\\"password\\\": True},\\n \\\"model_kwargs\\\": {\\n \\\"display_name\\\": \\\"Model Keyword Arguments\\\",\\n \\\"field_type\\\": \\\"code\\\",\\n },\\n \\\"code\\\": {\\\"show\\\": False},\\n \\\"inputs\\\": {\\\"display_name\\\": \\\"Input\\\"},\\n }\\n\\n def build(\\n self,\\n inputs: str,\\n endpoint_url: str,\\n task: str = \\\"text2text-generation\\\",\\n huggingfacehub_api_token: Optional[str] = None,\\n model_kwargs: Optional[dict] = None,\\n ) -> Text:\\n try:\\n llm = HuggingFaceEndpoint(\\n endpoint_url=endpoint_url,\\n task=task,\\n huggingfacehub_api_token=huggingfacehub_api_token,\\n model_kwargs=model_kwargs,\\n )\\n except Exception as e:\\n raise ValueError(\\\"Could not connect to HuggingFace Endpoints API.\\\") from e\\n output = ChatHuggingFace(llm=llm)\\n message = output.invoke(inputs)\\n result = message.content if hasattr(message, \\\"content\\\") else message\\n self.status = result\\n return result\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"endpoint_url\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"endpoint_url\",\"display_name\":\"Endpoint URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"huggingfacehub_api_token\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"huggingfacehub_api_token\",\"display_name\":\"API token\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"inputs\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"inputs\",\"display_name\":\"Input\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"model_kwargs\":{\"type\":\"code\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model_kwargs\",\"display_name\":\"Model Keyword Arguments\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"task\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"text2text-generation\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"text2text-generation\",\"text-generation\",\"summarization\"],\"name\":\"task\",\"display_name\":\"Task\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Generate text using LLM model from Hugging Face Inference API.\",\"base_classes\":[\"Text\",\"object\"],\"display_name\":\"Hugging Face Inference API models\",\"documentation\":\"\",\"custom_fields\":{\"inputs\":null,\"endpoint_url\":null,\"task\":null,\"huggingfacehub_api_token\":null,\"model_kwargs\":null},\"output_types\":[\"Text\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"AzureOpenAIModel\":{\"template\":{\"api_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"api_key\",\"display_name\":\"API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"api_version\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"2023-12-01-preview\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"2023-03-15-preview\",\"2023-05-15\",\"2023-06-01-preview\",\"2023-07-01-preview\",\"2023-08-01-preview\",\"2023-09-01-preview\",\"2023-12-01-preview\"],\"name\":\"api_version\",\"display_name\":\"API Version\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"azure_deployment\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"azure_deployment\",\"display_name\":\"Deployment Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"azure_endpoint\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"azure_endpoint\",\"display_name\":\"Azure Endpoint\",\"advanced\":false,\"dynamic\":false,\"info\":\"Your Azure endpoint, including the resource.. Example: `https://example-resource.azure.openai.com/`\",\"title_case\":false,\"input_types\":[\"Text\"]},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain.llms.base import BaseLanguageModel\\nfrom langchain_openai import AzureChatOpenAI\\n\\nfrom langflow import CustomComponent\\n\\n\\nclass AzureChatOpenAIComponent(CustomComponent):\\n display_name: str = \\\"AzureOpenAI Model\\\"\\n description: str = \\\"Generate text using LLM model from Azure OpenAI.\\\"\\n documentation: str = \\\"https://python.langchain.com/docs/integrations/llms/azure_openai\\\"\\n beta = False\\n\\n AZURE_OPENAI_MODELS = [\\n \\\"gpt-35-turbo\\\",\\n \\\"gpt-35-turbo-16k\\\",\\n \\\"gpt-35-turbo-instruct\\\",\\n \\\"gpt-4\\\",\\n \\\"gpt-4-32k\\\",\\n \\\"gpt-4-vision\\\",\\n ]\\n\\n AZURE_OPENAI_API_VERSIONS = [\\n \\\"2023-03-15-preview\\\",\\n \\\"2023-05-15\\\",\\n \\\"2023-06-01-preview\\\",\\n \\\"2023-07-01-preview\\\",\\n \\\"2023-08-01-preview\\\",\\n \\\"2023-09-01-preview\\\",\\n \\\"2023-12-01-preview\\\",\\n ]\\n\\n def build_config(self):\\n return {\\n \\\"model\\\": {\\n \\\"display_name\\\": \\\"Model Name\\\",\\n \\\"value\\\": self.AZURE_OPENAI_MODELS[0],\\n \\\"options\\\": self.AZURE_OPENAI_MODELS,\\n \\\"required\\\": True,\\n },\\n \\\"azure_endpoint\\\": {\\n \\\"display_name\\\": \\\"Azure Endpoint\\\",\\n \\\"required\\\": True,\\n \\\"info\\\": \\\"Your Azure endpoint, including the resource.. Example: `https://example-resource.azure.openai.com/`\\\",\\n },\\n \\\"azure_deployment\\\": {\\n \\\"display_name\\\": \\\"Deployment Name\\\",\\n \\\"required\\\": True,\\n },\\n \\\"api_version\\\": {\\n \\\"display_name\\\": \\\"API Version\\\",\\n \\\"options\\\": self.AZURE_OPENAI_API_VERSIONS,\\n \\\"value\\\": self.AZURE_OPENAI_API_VERSIONS[-1],\\n \\\"required\\\": True,\\n \\\"advanced\\\": True,\\n },\\n \\\"api_key\\\": {\\\"display_name\\\": \\\"API Key\\\", \\\"required\\\": True, \\\"password\\\": True},\\n \\\"temperature\\\": {\\n \\\"display_name\\\": \\\"Temperature\\\",\\n \\\"value\\\": 0.7,\\n \\\"field_type\\\": \\\"float\\\",\\n \\\"required\\\": False,\\n },\\n \\\"max_tokens\\\": {\\n \\\"display_name\\\": \\\"Max Tokens\\\",\\n \\\"value\\\": 1000,\\n \\\"required\\\": False,\\n \\\"field_type\\\": \\\"int\\\",\\n \\\"advanced\\\": True,\\n \\\"info\\\": \\\"Maximum number of tokens to generate.\\\",\\n },\\n \\\"code\\\": {\\\"show\\\": False},\\n \\\"inputs\\\": {\\\"display_name\\\": \\\"Input\\\"},\\n }\\n\\n def build(\\n self,\\n model: str,\\n azure_endpoint: str,\\n inputs: str,\\n azure_deployment: str,\\n api_key: str,\\n api_version: str,\\n temperature: float = 0.7,\\n max_tokens: Optional[int] = 1000,\\n ) -> BaseLanguageModel:\\n try:\\n output = AzureChatOpenAI(\\n model=model,\\n azure_endpoint=azure_endpoint,\\n azure_deployment=azure_deployment,\\n api_version=api_version,\\n api_key=api_key,\\n temperature=temperature,\\n max_tokens=max_tokens,\\n )\\n except Exception as e:\\n raise ValueError(\\\"Could not connect to AzureOpenAI API.\\\") from e\\n message = output.invoke(inputs)\\n result = message.content if hasattr(message, \\\"content\\\") else message\\n self.status = result\\n return result\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"inputs\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"inputs\",\"display_name\":\"Input\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"max_tokens\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":1000,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_tokens\",\"display_name\":\"Max Tokens\",\"advanced\":true,\"dynamic\":false,\"info\":\"Maximum number of tokens to generate.\",\"title_case\":false},\"model\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"gpt-35-turbo\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"gpt-35-turbo\",\"gpt-35-turbo-16k\",\"gpt-35-turbo-instruct\",\"gpt-4\",\"gpt-4-32k\",\"gpt-4-vision\"],\"name\":\"model\",\"display_name\":\"Model Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"temperature\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.7,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Generate text using LLM model from Azure OpenAI.\",\"base_classes\":[\"BaseLanguageModel\",\"Runnable\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"object\"],\"display_name\":\"AzureOpenAI Model\",\"documentation\":\"https://python.langchain.com/docs/integrations/llms/azure_openai\",\"custom_fields\":{\"model\":null,\"azure_endpoint\":null,\"inputs\":null,\"azure_deployment\":null,\"api_key\":null,\"api_version\":null,\"temperature\":null,\"max_tokens\":null},\"output_types\":[\"BaseLanguageModel\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"AmazonBedrockModel\":{\"template\":{\"cache\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"cache\",\"display_name\":\"Cache\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain_community.chat_models.bedrock import BedrockChat\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Text\\n\\n\\nclass AmazonBedrockComponent(CustomComponent):\\n display_name: str = \\\"Amazon Bedrock Model\\\"\\n description: str = \\\"Generate text using LLM model from Amazon Bedrock.\\\"\\n\\n def build_config(self):\\n return {\\n \\\"model_id\\\": {\\n \\\"display_name\\\": \\\"Model Id\\\",\\n \\\"options\\\": [\\n \\\"ai21.j2-grande-instruct\\\",\\n \\\"ai21.j2-jumbo-instruct\\\",\\n \\\"ai21.j2-mid\\\",\\n \\\"ai21.j2-mid-v1\\\",\\n \\\"ai21.j2-ultra\\\",\\n \\\"ai21.j2-ultra-v1\\\",\\n \\\"anthropic.claude-instant-v1\\\",\\n \\\"anthropic.claude-v1\\\",\\n \\\"anthropic.claude-v2\\\",\\n \\\"cohere.command-text-v14\\\",\\n ],\\n },\\n \\\"credentials_profile_name\\\": {\\\"display_name\\\": \\\"Credentials Profile Name\\\"},\\n \\\"streaming\\\": {\\\"display_name\\\": \\\"Streaming\\\", \\\"field_type\\\": \\\"bool\\\"},\\n \\\"endpoint_url\\\": {\\\"display_name\\\": \\\"Endpoint URL\\\"},\\n \\\"region_name\\\": {\\\"display_name\\\": \\\"Region Name\\\"},\\n \\\"model_kwargs\\\": {\\\"display_name\\\": \\\"Model Kwargs\\\"},\\n \\\"cache\\\": {\\\"display_name\\\": \\\"Cache\\\"},\\n \\\"code\\\": {\\\"advanced\\\": True},\\n \\\"inputs\\\": {\\\"display_name\\\": \\\"Input\\\"},\\n }\\n\\n def build(\\n self,\\n inputs: str,\\n model_id: str = \\\"anthropic.claude-instant-v1\\\",\\n credentials_profile_name: Optional[str] = None,\\n region_name: Optional[str] = None,\\n model_kwargs: Optional[dict] = None,\\n endpoint_url: Optional[str] = None,\\n streaming: bool = False,\\n cache: Optional[bool] = None,\\n ) -> Text:\\n try:\\n output = BedrockChat(\\n credentials_profile_name=credentials_profile_name,\\n model_id=model_id,\\n region_name=region_name,\\n model_kwargs=model_kwargs,\\n endpoint_url=endpoint_url,\\n streaming=streaming,\\n cache=cache,\\n ) # type: ignore\\n except Exception as e:\\n raise ValueError(\\\"Could not connect to AmazonBedrock API.\\\") from e\\n message = output.invoke(inputs)\\n result = message.content if hasattr(message, \\\"content\\\") else message\\n self.status = result\\n return result\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":true,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"credentials_profile_name\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"credentials_profile_name\",\"display_name\":\"Credentials Profile Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"endpoint_url\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"endpoint_url\",\"display_name\":\"Endpoint URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"inputs\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"inputs\",\"display_name\":\"Input\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"model_id\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"anthropic.claude-instant-v1\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"ai21.j2-grande-instruct\",\"ai21.j2-jumbo-instruct\",\"ai21.j2-mid\",\"ai21.j2-mid-v1\",\"ai21.j2-ultra\",\"ai21.j2-ultra-v1\",\"anthropic.claude-instant-v1\",\"anthropic.claude-v1\",\"anthropic.claude-v2\",\"cohere.command-text-v14\"],\"name\":\"model_id\",\"display_name\":\"Model Id\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"model_kwargs\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model_kwargs\",\"display_name\":\"Model Kwargs\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"region_name\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"region_name\",\"display_name\":\"Region Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"streaming\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"streaming\",\"display_name\":\"Streaming\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Generate text using LLM model from Amazon Bedrock.\",\"base_classes\":[\"Text\",\"object\"],\"display_name\":\"Amazon Bedrock Model\",\"documentation\":\"\",\"custom_fields\":{\"inputs\":null,\"model_id\":null,\"credentials_profile_name\":null,\"region_name\":null,\"model_kwargs\":null,\"endpoint_url\":null,\"streaming\":null,\"cache\":null},\"output_types\":[\"Text\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"CohereModel\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from langchain_community.chat_models.cohere import ChatCohere\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Text\\n\\n\\nclass CohereComponent(CustomComponent):\\n display_name = \\\"CohereModel\\\"\\n description = \\\"Generate text using Cohere large language models.\\\"\\n documentation = \\\"https://python.langchain.com/docs/modules/model_io/models/llms/integrations/cohere\\\"\\n\\n def build_config(self):\\n return {\\n \\\"cohere_api_key\\\": {\\n \\\"display_name\\\": \\\"Cohere API Key\\\",\\n \\\"type\\\": \\\"password\\\",\\n \\\"password\\\": True,\\n },\\n \\\"max_tokens\\\": {\\n \\\"display_name\\\": \\\"Max Tokens\\\",\\n \\\"default\\\": 256,\\n \\\"type\\\": \\\"int\\\",\\n \\\"show\\\": True,\\n },\\n \\\"temperature\\\": {\\n \\\"display_name\\\": \\\"Temperature\\\",\\n \\\"default\\\": 0.75,\\n \\\"type\\\": \\\"float\\\",\\n \\\"show\\\": True,\\n },\\n \\\"inputs\\\": {\\\"display_name\\\": \\\"Input\\\"},\\n }\\n\\n def build(\\n self,\\n cohere_api_key: str,\\n inputs: str,\\n max_tokens: int = 256,\\n temperature: float = 0.75,\\n ) -> Text:\\n output = ChatCohere(\\n cohere_api_key=cohere_api_key,\\n max_tokens=max_tokens,\\n temperature=temperature,\\n )\\n message = output.invoke(inputs)\\n result = message.content if hasattr(message, \\\"content\\\") else message\\n self.status = result\\n return result\\n return result\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"cohere_api_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"cohere_api_key\",\"display_name\":\"Cohere API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"inputs\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"inputs\",\"display_name\":\"Input\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"max_tokens\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":256,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_tokens\",\"display_name\":\"Max Tokens\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"temperature\":{\"type\":\"float\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.75,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Generate text using Cohere large language models.\",\"base_classes\":[\"Text\",\"object\"],\"display_name\":\"CohereModel\",\"documentation\":\"https://python.langchain.com/docs/modules/model_io/models/llms/integrations/cohere\",\"custom_fields\":{\"cohere_api_key\":null,\"inputs\":null,\"max_tokens\":null,\"temperature\":null},\"output_types\":[\"Text\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true}},\"model_specs\":{\"AmazonBedrockSpecs\":{\"template\":{\"cache\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"cache\",\"display_name\":\"Cache\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain.llms.base import BaseLLM\\nfrom langchain_community.llms.bedrock import Bedrock\\n\\n\\nfrom langflow import CustomComponent\\n\\n\\nclass AmazonBedrockComponent(CustomComponent):\\n display_name: str = \\\"Amazon Bedrock\\\"\\n description: str = \\\"LLM model from Amazon Bedrock.\\\"\\n\\n def build_config(self):\\n return {\\n \\\"model_id\\\": {\\n \\\"display_name\\\": \\\"Model Id\\\",\\n \\\"options\\\": [\\n \\\"ai21.j2-grande-instruct\\\",\\n \\\"ai21.j2-jumbo-instruct\\\",\\n \\\"ai21.j2-mid\\\",\\n \\\"ai21.j2-mid-v1\\\",\\n \\\"ai21.j2-ultra\\\",\\n \\\"ai21.j2-ultra-v1\\\",\\n \\\"anthropic.claude-instant-v1\\\",\\n \\\"anthropic.claude-v1\\\",\\n \\\"anthropic.claude-v2\\\",\\n \\\"cohere.command-text-v14\\\",\\n ],\\n },\\n \\\"credentials_profile_name\\\": {\\\"display_name\\\": \\\"Credentials Profile Name\\\"},\\n \\\"streaming\\\": {\\\"display_name\\\": \\\"Streaming\\\", \\\"field_type\\\": \\\"bool\\\"},\\n \\\"endpoint_url\\\": {\\\"display_name\\\": \\\"Endpoint URL\\\"},\\n \\\"region_name\\\": {\\\"display_name\\\": \\\"Region Name\\\"},\\n \\\"model_kwargs\\\": {\\\"display_name\\\": \\\"Model Kwargs\\\"},\\n \\\"cache\\\": {\\\"display_name\\\": \\\"Cache\\\"},\\n \\\"code\\\": {\\\"advanced\\\": True},\\n }\\n\\n def build(\\n self,\\n model_id: str = \\\"anthropic.claude-instant-v1\\\",\\n credentials_profile_name: Optional[str] = None,\\n region_name: Optional[str] = None,\\n model_kwargs: Optional[dict] = None,\\n endpoint_url: Optional[str] = None,\\n streaming: bool = False,\\n cache: Optional[bool] = None,\\n ) -> BaseLLM:\\n try:\\n output = Bedrock(\\n credentials_profile_name=credentials_profile_name,\\n model_id=model_id,\\n region_name=region_name,\\n model_kwargs=model_kwargs,\\n endpoint_url=endpoint_url,\\n streaming=streaming,\\n cache=cache,\\n ) # type: ignore\\n except Exception as e:\\n raise ValueError(\\\"Could not connect to AmazonBedrock API.\\\") from e\\n return output\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":true,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"credentials_profile_name\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"credentials_profile_name\",\"display_name\":\"Credentials Profile Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"endpoint_url\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"endpoint_url\",\"display_name\":\"Endpoint URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"model_id\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"anthropic.claude-instant-v1\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"ai21.j2-grande-instruct\",\"ai21.j2-jumbo-instruct\",\"ai21.j2-mid\",\"ai21.j2-mid-v1\",\"ai21.j2-ultra\",\"ai21.j2-ultra-v1\",\"anthropic.claude-instant-v1\",\"anthropic.claude-v1\",\"anthropic.claude-v2\",\"cohere.command-text-v14\"],\"name\":\"model_id\",\"display_name\":\"Model Id\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"model_kwargs\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model_kwargs\",\"display_name\":\"Model Kwargs\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"region_name\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"region_name\",\"display_name\":\"Region Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"streaming\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"streaming\",\"display_name\":\"Streaming\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"LLM model from Amazon Bedrock.\",\"base_classes\":[\"BaseLanguageModel\",\"Runnable\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"object\",\"BaseLLM\"],\"display_name\":\"Amazon Bedrock\",\"documentation\":\"\",\"custom_fields\":{\"model_id\":null,\"credentials_profile_name\":null,\"region_name\":null,\"model_kwargs\":null,\"endpoint_url\":null,\"streaming\":null,\"cache\":null},\"output_types\":[\"BaseLLM\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"ChatVertexAISpecs\":{\"template\":{\"credentials\":{\"type\":\"file\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[\".json\"],\"password\":false,\"name\":\"credentials\",\"display_name\":\"Credentials\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"examples\":{\"type\":\"BaseMessage\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":true,\"value\":[],\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"examples\",\"display_name\":\"Examples\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import List, Optional, Union\\n\\nfrom langchain.llms import BaseLLM\\nfrom langchain_community.chat_models.vertexai import ChatVertexAI\\nfrom langchain_core.messages.base import BaseMessage\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import BaseLanguageModel\\n\\n\\nclass ChatVertexAIComponent(CustomComponent):\\n display_name = \\\"ChatVertexAI\\\"\\n description = \\\"`Vertex AI` Chat large language models API.\\\"\\n\\n def build_config(self):\\n return {\\n \\\"credentials\\\": {\\n \\\"display_name\\\": \\\"Credentials\\\",\\n \\\"field_type\\\": \\\"file\\\",\\n \\\"file_types\\\": [\\\".json\\\"],\\n \\\"file_path\\\": None,\\n },\\n \\\"examples\\\": {\\n \\\"display_name\\\": \\\"Examples\\\",\\n \\\"multiline\\\": True,\\n },\\n \\\"location\\\": {\\n \\\"display_name\\\": \\\"Location\\\",\\n \\\"value\\\": \\\"us-central1\\\",\\n },\\n \\\"max_output_tokens\\\": {\\n \\\"display_name\\\": \\\"Max Output Tokens\\\",\\n \\\"value\\\": 128,\\n \\\"advanced\\\": True,\\n },\\n \\\"model_name\\\": {\\n \\\"display_name\\\": \\\"Model Name\\\",\\n \\\"value\\\": \\\"chat-bison\\\",\\n },\\n \\\"project\\\": {\\n \\\"display_name\\\": \\\"Project\\\",\\n },\\n \\\"temperature\\\": {\\n \\\"display_name\\\": \\\"Temperature\\\",\\n \\\"value\\\": 0.0,\\n },\\n \\\"top_k\\\": {\\n \\\"display_name\\\": \\\"Top K\\\",\\n \\\"value\\\": 40,\\n \\\"advanced\\\": True,\\n },\\n \\\"top_p\\\": {\\n \\\"display_name\\\": \\\"Top P\\\",\\n \\\"value\\\": 0.95,\\n \\\"advanced\\\": True,\\n },\\n \\\"verbose\\\": {\\n \\\"display_name\\\": \\\"Verbose\\\",\\n \\\"value\\\": False,\\n \\\"advanced\\\": True,\\n },\\n }\\n\\n def build(\\n self,\\n credentials: Optional[str],\\n project: str,\\n examples: Optional[List[BaseMessage]] = [],\\n location: str = \\\"us-central1\\\",\\n max_output_tokens: int = 128,\\n model_name: str = \\\"chat-bison\\\",\\n temperature: float = 0.0,\\n top_k: int = 40,\\n top_p: float = 0.95,\\n verbose: bool = False,\\n ) -> Union[BaseLanguageModel, BaseLLM]:\\n return ChatVertexAI(\\n credentials=credentials,\\n examples=examples,\\n location=location,\\n max_output_tokens=max_output_tokens,\\n model_name=model_name,\\n project=project,\\n temperature=temperature,\\n top_k=top_k,\\n top_p=top_p,\\n verbose=verbose,\\n )\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"location\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"us-central1\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"location\",\"display_name\":\"Location\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"max_output_tokens\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":128,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_output_tokens\",\"display_name\":\"Max Output Tokens\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"model_name\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"chat-bison\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model_name\",\"display_name\":\"Model Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"project\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"project\",\"display_name\":\"Project\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"temperature\":{\"type\":\"float\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.0,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"top_k\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":40,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_k\",\"display_name\":\"Top K\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"top_p\":{\"type\":\"float\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.95,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_p\",\"display_name\":\"Top P\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"verbose\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"verbose\",\"display_name\":\"Verbose\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"`Vertex AI` Chat large language models API.\",\"base_classes\":[\"BaseLanguageModel\",\"Runnable\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"object\",\"BaseLLM\"],\"display_name\":\"ChatVertexAI\",\"documentation\":\"\",\"custom_fields\":{\"credentials\":null,\"project\":null,\"examples\":null,\"location\":null,\"max_output_tokens\":null,\"model_name\":null,\"temperature\":null,\"top_k\":null,\"top_p\":null,\"verbose\":null},\"output_types\":[\"BaseLanguageModel\",\"BaseLLM\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"VertexAISpecs\":{\"template\":{\"credentials\":{\"type\":\"file\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[\".json\"],\"file_path\":\"\",\"password\":false,\"name\":\"credentials\",\"display_name\":\"Credentials\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langchain.llms import BaseLLM\\nfrom typing import Optional, Union, Callable, Dict\\nfrom langchain_community.llms.vertexai import VertexAI\\n\\n\\nclass VertexAIComponent(CustomComponent):\\n display_name = \\\"VertexAI\\\"\\n description = \\\"Google Vertex AI large language models\\\"\\n\\n def build_config(self):\\n return {\\n \\\"credentials\\\": {\\n \\\"display_name\\\": \\\"Credentials\\\",\\n \\\"field_type\\\": \\\"file\\\",\\n \\\"file_types\\\": [\\\".json\\\"],\\n \\\"required\\\": False,\\n \\\"value\\\": None,\\n },\\n \\\"location\\\": {\\n \\\"display_name\\\": \\\"Location\\\",\\n \\\"type\\\": \\\"str\\\",\\n \\\"advanced\\\": True,\\n \\\"value\\\": \\\"us-central1\\\",\\n \\\"required\\\": False,\\n },\\n \\\"max_output_tokens\\\": {\\n \\\"display_name\\\": \\\"Max Output Tokens\\\",\\n \\\"field_type\\\": \\\"int\\\",\\n \\\"value\\\": 128,\\n \\\"required\\\": False,\\n \\\"advanced\\\": True,\\n },\\n \\\"max_retries\\\": {\\n \\\"display_name\\\": \\\"Max Retries\\\",\\n \\\"type\\\": \\\"int\\\",\\n \\\"value\\\": 6,\\n \\\"required\\\": False,\\n \\\"advanced\\\": True,\\n },\\n \\\"metadata\\\": {\\n \\\"display_name\\\": \\\"Metadata\\\",\\n \\\"field_type\\\": \\\"dict\\\",\\n \\\"required\\\": False,\\n \\\"default\\\": {},\\n },\\n \\\"model_name\\\": {\\n \\\"display_name\\\": \\\"Model Name\\\",\\n \\\"type\\\": \\\"str\\\",\\n \\\"value\\\": \\\"text-bison\\\",\\n \\\"required\\\": False,\\n },\\n \\\"n\\\": {\\n \\\"advanced\\\": True,\\n \\\"display_name\\\": \\\"N\\\",\\n \\\"field_type\\\": \\\"int\\\",\\n \\\"value\\\": 1,\\n \\\"required\\\": False,\\n },\\n \\\"project\\\": {\\n \\\"display_name\\\": \\\"Project\\\",\\n \\\"type\\\": \\\"str\\\",\\n \\\"required\\\": False,\\n \\\"default\\\": None,\\n },\\n \\\"request_parallelism\\\": {\\n \\\"display_name\\\": \\\"Request Parallelism\\\",\\n \\\"field_type\\\": \\\"int\\\",\\n \\\"value\\\": 5,\\n \\\"required\\\": False,\\n \\\"advanced\\\": True,\\n },\\n \\\"streaming\\\": {\\n \\\"display_name\\\": \\\"Streaming\\\",\\n \\\"field_type\\\": \\\"bool\\\",\\n \\\"value\\\": False,\\n \\\"required\\\": False,\\n \\\"advanced\\\": True,\\n },\\n \\\"temperature\\\": {\\n \\\"display_name\\\": \\\"Temperature\\\",\\n \\\"field_type\\\": \\\"float\\\",\\n \\\"value\\\": 0.0,\\n \\\"required\\\": False,\\n \\\"advanced\\\": True,\\n },\\n \\\"top_k\\\": {\\\"display_name\\\": \\\"Top K\\\", \\\"type\\\": \\\"int\\\", \\\"default\\\": 40, \\\"required\\\": False, \\\"advanced\\\": True},\\n \\\"top_p\\\": {\\n \\\"display_name\\\": \\\"Top P\\\",\\n \\\"field_type\\\": \\\"float\\\",\\n \\\"value\\\": 0.95,\\n \\\"required\\\": False,\\n \\\"advanced\\\": True,\\n },\\n \\\"tuned_model_name\\\": {\\n \\\"display_name\\\": \\\"Tuned Model Name\\\",\\n \\\"type\\\": \\\"str\\\",\\n \\\"required\\\": False,\\n \\\"value\\\": None,\\n \\\"advanced\\\": True,\\n },\\n \\\"verbose\\\": {\\n \\\"display_name\\\": \\\"Verbose\\\",\\n \\\"field_type\\\": \\\"bool\\\",\\n \\\"value\\\": False,\\n \\\"required\\\": False,\\n },\\n \\\"name\\\": {\\\"display_name\\\": \\\"Name\\\", \\\"field_type\\\": \\\"str\\\"},\\n }\\n\\n def build(\\n self,\\n credentials: Optional[str] = None,\\n location: str = \\\"us-central1\\\",\\n max_output_tokens: int = 128,\\n max_retries: int = 6,\\n metadata: Dict = {},\\n model_name: str = \\\"text-bison\\\",\\n n: int = 1,\\n name: Optional[str] = None,\\n project: Optional[str] = None,\\n request_parallelism: int = 5,\\n streaming: bool = False,\\n temperature: float = 0.0,\\n top_k: int = 40,\\n top_p: float = 0.95,\\n tuned_model_name: Optional[str] = None,\\n verbose: bool = False,\\n ) -> Union[BaseLLM, Callable]:\\n return VertexAI(\\n credentials=credentials,\\n location=location,\\n max_output_tokens=max_output_tokens,\\n max_retries=max_retries,\\n metadata=metadata,\\n model_name=model_name,\\n n=n,\\n name=name,\\n project=project,\\n request_parallelism=request_parallelism,\\n streaming=streaming,\\n temperature=temperature,\\n top_k=top_k,\\n top_p=top_p,\\n tuned_model_name=tuned_model_name,\\n verbose=verbose,\\n )\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"location\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"us-central1\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"location\",\"display_name\":\"Location\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"max_output_tokens\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":128,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_output_tokens\",\"display_name\":\"Max Output Tokens\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"max_retries\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":6,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_retries\",\"display_name\":\"Max Retries\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"model_name\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"text-bison\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model_name\",\"display_name\":\"Model Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"n\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":1,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"n\",\"display_name\":\"N\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"name\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"name\",\"display_name\":\"Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"project\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"project\",\"display_name\":\"Project\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"request_parallelism\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":5,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"request_parallelism\",\"display_name\":\"Request Parallelism\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"streaming\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"streaming\",\"display_name\":\"Streaming\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"temperature\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.0,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"top_k\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":40,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_k\",\"display_name\":\"Top K\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"top_p\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.95,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_p\",\"display_name\":\"Top P\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"tuned_model_name\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"tuned_model_name\",\"display_name\":\"Tuned Model Name\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"verbose\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"verbose\",\"display_name\":\"Verbose\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Google Vertex AI large language models\",\"base_classes\":[\"BaseLanguageModel\",\"Runnable\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"object\",\"BaseLLM\",\"Callable\"],\"display_name\":\"VertexAI\",\"documentation\":\"\",\"custom_fields\":{\"credentials\":null,\"location\":null,\"max_output_tokens\":null,\"max_retries\":null,\"metadata\":null,\"model_name\":null,\"n\":null,\"name\":null,\"project\":null,\"request_parallelism\":null,\"streaming\":null,\"temperature\":null,\"top_k\":null,\"top_p\":null,\"tuned_model_name\":null,\"verbose\":null},\"output_types\":[\"BaseLLM\",\"Callable\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"ChatAnthropicSpecs\":{\"template\":{\"anthropic_api_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"anthropic_api_key\",\"display_name\":\"Anthropic API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"anthropic_api_url\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"anthropic_api_url\",\"display_name\":\"Anthropic API URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from pydantic.v1.types import SecretStr\\nfrom langflow import CustomComponent\\nfrom typing import Optional, Union, Callable\\nfrom langflow.field_typing import BaseLanguageModel\\nfrom langchain_community.chat_models.anthropic import ChatAnthropic\\n\\n\\nclass ChatAnthropicComponent(CustomComponent):\\n display_name = \\\"ChatAnthropic\\\"\\n description = \\\"`Anthropic` chat large language models.\\\"\\n documentation = \\\"https://python.langchain.com/docs/modules/model_io/models/chat/integrations/anthropic\\\"\\n\\n def build_config(self):\\n return {\\n \\\"anthropic_api_key\\\": {\\n \\\"display_name\\\": \\\"Anthropic API Key\\\",\\n \\\"field_type\\\": \\\"str\\\",\\n \\\"password\\\": True,\\n },\\n \\\"anthropic_api_url\\\": {\\n \\\"display_name\\\": \\\"Anthropic API URL\\\",\\n \\\"field_type\\\": \\\"str\\\",\\n },\\n \\\"model_kwargs\\\": {\\n \\\"display_name\\\": \\\"Model Kwargs\\\",\\n \\\"field_type\\\": \\\"dict\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"temperature\\\": {\\n \\\"display_name\\\": \\\"Temperature\\\",\\n \\\"field_type\\\": \\\"float\\\",\\n },\\n }\\n\\n def build(\\n self,\\n anthropic_api_key: str,\\n anthropic_api_url: Optional[str] = None,\\n model_kwargs: dict = {},\\n temperature: Optional[float] = None,\\n ) -> Union[BaseLanguageModel, Callable]:\\n return ChatAnthropic(\\n anthropic_api_key=SecretStr(anthropic_api_key),\\n anthropic_api_url=anthropic_api_url,\\n model_kwargs=model_kwargs,\\n temperature=temperature,\\n )\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"model_kwargs\":{\"type\":\"dict\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model_kwargs\",\"display_name\":\"Model Kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"temperature\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"`Anthropic` chat large language models.\",\"base_classes\":[\"BaseLanguageModel\",\"Runnable\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"object\",\"Callable\"],\"display_name\":\"ChatAnthropic\",\"documentation\":\"https://python.langchain.com/docs/modules/model_io/models/chat/integrations/anthropic\",\"custom_fields\":{\"anthropic_api_key\":null,\"anthropic_api_url\":null,\"model_kwargs\":null,\"temperature\":null},\"output_types\":[\"BaseLanguageModel\",\"Callable\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"AzureChatOpenAISpecs\":{\"template\":{\"api_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"api_key\",\"display_name\":\"API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"api_version\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"2023-12-01-preview\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"2023-03-15-preview\",\"2023-05-15\",\"2023-06-01-preview\",\"2023-07-01-preview\",\"2023-08-01-preview\",\"2023-09-01-preview\",\"2023-12-01-preview\"],\"name\":\"api_version\",\"display_name\":\"API Version\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"azure_deployment\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"azure_deployment\",\"display_name\":\"Deployment Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"azure_endpoint\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"azure_endpoint\",\"display_name\":\"Azure Endpoint\",\"advanced\":false,\"dynamic\":false,\"info\":\"Your Azure endpoint, including the resource.. Example: `https://example-resource.azure.openai.com/`\",\"title_case\":false,\"input_types\":[\"Text\"]},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain.llms.base import BaseLanguageModel\\nfrom langchain_community.chat_models.azure_openai import AzureChatOpenAI\\n\\nfrom langflow import CustomComponent\\n\\n\\nclass AzureChatOpenAISpecsComponent(CustomComponent):\\n display_name: str = \\\"AzureChatOpenAI\\\"\\n description: str = \\\"LLM model from Azure OpenAI.\\\"\\n documentation: str = \\\"https://python.langchain.com/docs/integrations/llms/azure_openai\\\"\\n beta = False\\n\\n AZURE_OPENAI_MODELS = [\\n \\\"gpt-35-turbo\\\",\\n \\\"gpt-35-turbo-16k\\\",\\n \\\"gpt-35-turbo-instruct\\\",\\n \\\"gpt-4\\\",\\n \\\"gpt-4-32k\\\",\\n \\\"gpt-4-vision\\\",\\n ]\\n\\n AZURE_OPENAI_API_VERSIONS = [\\n \\\"2023-03-15-preview\\\",\\n \\\"2023-05-15\\\",\\n \\\"2023-06-01-preview\\\",\\n \\\"2023-07-01-preview\\\",\\n \\\"2023-08-01-preview\\\",\\n \\\"2023-09-01-preview\\\",\\n \\\"2023-12-01-preview\\\",\\n ]\\n\\n def build_config(self):\\n return {\\n \\\"model\\\": {\\n \\\"display_name\\\": \\\"Model Name\\\",\\n \\\"value\\\": self.AZURE_OPENAI_MODELS[0],\\n \\\"options\\\": self.AZURE_OPENAI_MODELS,\\n \\\"required\\\": True,\\n },\\n \\\"azure_endpoint\\\": {\\n \\\"display_name\\\": \\\"Azure Endpoint\\\",\\n \\\"required\\\": True,\\n \\\"info\\\": \\\"Your Azure endpoint, including the resource.. Example: `https://example-resource.azure.openai.com/`\\\",\\n },\\n \\\"azure_deployment\\\": {\\n \\\"display_name\\\": \\\"Deployment Name\\\",\\n \\\"required\\\": True,\\n },\\n \\\"api_version\\\": {\\n \\\"display_name\\\": \\\"API Version\\\",\\n \\\"options\\\": self.AZURE_OPENAI_API_VERSIONS,\\n \\\"value\\\": self.AZURE_OPENAI_API_VERSIONS[-1],\\n \\\"required\\\": True,\\n \\\"advanced\\\": True,\\n },\\n \\\"api_key\\\": {\\\"display_name\\\": \\\"API Key\\\", \\\"required\\\": True, \\\"password\\\": True},\\n \\\"temperature\\\": {\\n \\\"display_name\\\": \\\"Temperature\\\",\\n \\\"value\\\": 0.7,\\n \\\"field_type\\\": \\\"float\\\",\\n \\\"required\\\": False,\\n },\\n \\\"max_tokens\\\": {\\n \\\"display_name\\\": \\\"Max Tokens\\\",\\n \\\"value\\\": 1000,\\n \\\"required\\\": False,\\n \\\"field_type\\\": \\\"int\\\",\\n \\\"advanced\\\": True,\\n \\\"info\\\": \\\"Maximum number of tokens to generate.\\\",\\n },\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(\\n self,\\n model: str,\\n azure_endpoint: str,\\n azure_deployment: str,\\n api_key: str,\\n api_version: str,\\n temperature: float = 0.7,\\n max_tokens: Optional[int] = 1000,\\n ) -> BaseLanguageModel:\\n try:\\n llm = AzureChatOpenAI(\\n model=model,\\n azure_endpoint=azure_endpoint,\\n azure_deployment=azure_deployment,\\n api_version=api_version,\\n api_key=api_key,\\n temperature=temperature,\\n max_tokens=max_tokens,\\n )\\n except Exception as e:\\n raise ValueError(\\\"Could not connect to AzureOpenAI API.\\\") from e\\n return llm\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"max_tokens\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":1000,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_tokens\",\"display_name\":\"Max Tokens\",\"advanced\":true,\"dynamic\":false,\"info\":\"Maximum number of tokens to generate.\",\"title_case\":false},\"model\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"gpt-35-turbo\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"gpt-35-turbo\",\"gpt-35-turbo-16k\",\"gpt-35-turbo-instruct\",\"gpt-4\",\"gpt-4-32k\",\"gpt-4-vision\"],\"name\":\"model\",\"display_name\":\"Model Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"temperature\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.7,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"LLM model from Azure OpenAI.\",\"base_classes\":[\"BaseLanguageModel\",\"Runnable\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"object\"],\"display_name\":\"AzureChatOpenAI\",\"documentation\":\"https://python.langchain.com/docs/integrations/llms/azure_openai\",\"custom_fields\":{\"model\":null,\"azure_endpoint\":null,\"azure_deployment\":null,\"api_key\":null,\"api_version\":null,\"temperature\":null,\"max_tokens\":null},\"output_types\":[\"BaseLanguageModel\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"ChatOllamaEndpointSpecs\":{\"template\":{\"metadata\":{\"type\":\"Dict[str, Any]\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":true,\"dynamic\":false,\"info\":\"Metadata to add to the run trace.\",\"title_case\":false},\"stop\":{\"type\":\"list\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"stop\",\"display_name\":\"Stop Tokens\",\"advanced\":true,\"dynamic\":false,\"info\":\"List of tokens to signal the model to stop generating text.\",\"title_case\":false},\"tags\":{\"type\":\"list\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"tags\",\"display_name\":\"Tags\",\"advanced\":true,\"dynamic\":false,\"info\":\"Tags to add to the run trace.\",\"title_case\":false},\"base_url\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"base_url\",\"display_name\":\"Base URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"Endpoint of the Ollama API. Defaults to 'http://localhost:11434' if not specified.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"cache\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"cache\",\"display_name\":\"Cache\",\"advanced\":true,\"dynamic\":false,\"info\":\"Enable or disable caching.\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Any, Dict, List, Optional\\n\\n# from langchain_community.chat_models import ChatOllama\\nfrom langchain_community.chat_models import ChatOllama\\nfrom langchain_core.language_models.chat_models import BaseChatModel\\n\\n# from langchain.chat_models import ChatOllama\\nfrom langflow import CustomComponent\\n\\n# whe When a callback component is added to Langflow, the comment must be uncommented.\\n# from langchain.callbacks.manager import CallbackManager\\n\\n\\nclass ChatOllamaComponent(CustomComponent):\\n display_name = \\\"ChatOllama\\\"\\n description = \\\"Local LLM for chat with Ollama.\\\"\\n\\n def build_config(self) -> dict:\\n return {\\n \\\"base_url\\\": {\\n \\\"display_name\\\": \\\"Base URL\\\",\\n \\\"info\\\": \\\"Endpoint of the Ollama API. Defaults to 'http://localhost:11434' if not specified.\\\",\\n },\\n \\\"model\\\": {\\n \\\"display_name\\\": \\\"Model Name\\\",\\n \\\"value\\\": \\\"llama2\\\",\\n \\\"info\\\": \\\"Refer to https://ollama.ai/library for more models.\\\",\\n },\\n \\\"temperature\\\": {\\n \\\"display_name\\\": \\\"Temperature\\\",\\n \\\"field_type\\\": \\\"float\\\",\\n \\\"value\\\": 0.8,\\n \\\"info\\\": \\\"Controls the creativity of model responses.\\\",\\n },\\n \\\"cache\\\": {\\n \\\"display_name\\\": \\\"Cache\\\",\\n \\\"field_type\\\": \\\"bool\\\",\\n \\\"info\\\": \\\"Enable or disable caching.\\\",\\n \\\"advanced\\\": True,\\n \\\"value\\\": False,\\n },\\n ### When a callback component is added to Langflow, the comment must be uncommented. ###\\n # \\\"callback_manager\\\": {\\n # \\\"display_name\\\": \\\"Callback Manager\\\",\\n # \\\"info\\\": \\\"Optional callback manager for additional functionality.\\\",\\n # \\\"advanced\\\": True,\\n # },\\n # \\\"callbacks\\\": {\\n # \\\"display_name\\\": \\\"Callbacks\\\",\\n # \\\"info\\\": \\\"Callbacks to execute during model runtime.\\\",\\n # \\\"advanced\\\": True,\\n # },\\n ########################################################################################\\n \\\"format\\\": {\\n \\\"display_name\\\": \\\"Format\\\",\\n \\\"field_type\\\": \\\"str\\\",\\n \\\"info\\\": \\\"Specify the format of the output (e.g., json).\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"metadata\\\": {\\n \\\"display_name\\\": \\\"Metadata\\\",\\n \\\"info\\\": \\\"Metadata to add to the run trace.\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"mirostat\\\": {\\n \\\"display_name\\\": \\\"Mirostat\\\",\\n \\\"options\\\": [\\\"Disabled\\\", \\\"Mirostat\\\", \\\"Mirostat 2.0\\\"],\\n \\\"info\\\": \\\"Enable/disable Mirostat sampling for controlling perplexity.\\\",\\n \\\"value\\\": \\\"Disabled\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"mirostat_eta\\\": {\\n \\\"display_name\\\": \\\"Mirostat Eta\\\",\\n \\\"field_type\\\": \\\"float\\\",\\n \\\"info\\\": \\\"Learning rate for Mirostat algorithm. (Default: 0.1)\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"mirostat_tau\\\": {\\n \\\"display_name\\\": \\\"Mirostat Tau\\\",\\n \\\"field_type\\\": \\\"float\\\",\\n \\\"info\\\": \\\"Controls the balance between coherence and diversity of the output. (Default: 5.0)\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"num_ctx\\\": {\\n \\\"display_name\\\": \\\"Context Window Size\\\",\\n \\\"field_type\\\": \\\"int\\\",\\n \\\"info\\\": \\\"Size of the context window for generating tokens. (Default: 2048)\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"num_gpu\\\": {\\n \\\"display_name\\\": \\\"Number of GPUs\\\",\\n \\\"field_type\\\": \\\"int\\\",\\n \\\"info\\\": \\\"Number of GPUs to use for computation. (Default: 1 on macOS, 0 to disable)\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"num_thread\\\": {\\n \\\"display_name\\\": \\\"Number of Threads\\\",\\n \\\"field_type\\\": \\\"int\\\",\\n \\\"info\\\": \\\"Number of threads to use during computation. (Default: detected for optimal performance)\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"repeat_last_n\\\": {\\n \\\"display_name\\\": \\\"Repeat Last N\\\",\\n \\\"field_type\\\": \\\"int\\\",\\n \\\"info\\\": \\\"How far back the model looks to prevent repetition. (Default: 64, 0 = disabled, -1 = num_ctx)\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"repeat_penalty\\\": {\\n \\\"display_name\\\": \\\"Repeat Penalty\\\",\\n \\\"field_type\\\": \\\"float\\\",\\n \\\"info\\\": \\\"Penalty for repetitions in generated text. (Default: 1.1)\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"tfs_z\\\": {\\n \\\"display_name\\\": \\\"TFS Z\\\",\\n \\\"field_type\\\": \\\"float\\\",\\n \\\"info\\\": \\\"Tail free sampling value. (Default: 1)\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"timeout\\\": {\\n \\\"display_name\\\": \\\"Timeout\\\",\\n \\\"field_type\\\": \\\"int\\\",\\n \\\"info\\\": \\\"Timeout for the request stream.\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"top_k\\\": {\\n \\\"display_name\\\": \\\"Top K\\\",\\n \\\"field_type\\\": \\\"int\\\",\\n \\\"info\\\": \\\"Limits token selection to top K. (Default: 40)\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"top_p\\\": {\\n \\\"display_name\\\": \\\"Top P\\\",\\n \\\"field_type\\\": \\\"float\\\",\\n \\\"info\\\": \\\"Works together with top-k. (Default: 0.9)\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"verbose\\\": {\\n \\\"display_name\\\": \\\"Verbose\\\",\\n \\\"field_type\\\": \\\"bool\\\",\\n \\\"info\\\": \\\"Whether to print out response text.\\\",\\n },\\n \\\"tags\\\": {\\n \\\"display_name\\\": \\\"Tags\\\",\\n \\\"field_type\\\": \\\"list\\\",\\n \\\"info\\\": \\\"Tags to add to the run trace.\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"stop\\\": {\\n \\\"display_name\\\": \\\"Stop Tokens\\\",\\n \\\"field_type\\\": \\\"list\\\",\\n \\\"info\\\": \\\"List of tokens to signal the model to stop generating text.\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"system\\\": {\\n \\\"display_name\\\": \\\"System\\\",\\n \\\"field_type\\\": \\\"str\\\",\\n \\\"info\\\": \\\"System to use for generating text.\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"template\\\": {\\n \\\"display_name\\\": \\\"Template\\\",\\n \\\"field_type\\\": \\\"str\\\",\\n \\\"info\\\": \\\"Template to use for generating text.\\\",\\n \\\"advanced\\\": True,\\n },\\n }\\n\\n def build(\\n self,\\n base_url: Optional[str],\\n model: str,\\n mirostat: Optional[str],\\n mirostat_eta: Optional[float] = None,\\n mirostat_tau: Optional[float] = None,\\n ### When a callback component is added to Langflow, the comment must be uncommented.###\\n # callback_manager: Optional[CallbackManager] = None,\\n # callbacks: Optional[List[Callbacks]] = None,\\n #######################################################################################\\n repeat_last_n: Optional[int] = None,\\n verbose: Optional[bool] = None,\\n cache: Optional[bool] = None,\\n num_ctx: Optional[int] = None,\\n num_gpu: Optional[int] = None,\\n format: Optional[str] = None,\\n metadata: Optional[Dict[str, Any]] = None,\\n num_thread: Optional[int] = None,\\n repeat_penalty: Optional[float] = None,\\n stop: Optional[List[str]] = None,\\n system: Optional[str] = None,\\n tags: Optional[List[str]] = None,\\n temperature: Optional[float] = None,\\n template: Optional[str] = None,\\n tfs_z: Optional[float] = None,\\n timeout: Optional[int] = None,\\n top_k: Optional[int] = None,\\n top_p: Optional[int] = None,\\n ) -> BaseChatModel:\\n if not base_url:\\n base_url = \\\"http://localhost:11434\\\"\\n\\n # Mapping mirostat settings to their corresponding values\\n mirostat_options = {\\\"Mirostat\\\": 1, \\\"Mirostat 2.0\\\": 2}\\n\\n # Default to 0 for 'Disabled'\\n mirostat_value = mirostat_options.get(mirostat, 0) # type: ignore\\n\\n # Set mirostat_eta and mirostat_tau to None if mirostat is disabled\\n if mirostat_value == 0:\\n mirostat_eta = None\\n mirostat_tau = None\\n\\n # Mapping system settings to their corresponding values\\n llm_params = {\\n \\\"base_url\\\": base_url,\\n \\\"cache\\\": cache,\\n \\\"model\\\": model,\\n \\\"mirostat\\\": mirostat_value,\\n \\\"format\\\": format,\\n \\\"metadata\\\": metadata,\\n \\\"tags\\\": tags,\\n ## When a callback component is added to Langflow, the comment must be uncommented.##\\n # \\\"callback_manager\\\": callback_manager,\\n # \\\"callbacks\\\": callbacks,\\n #####################################################################################\\n \\\"mirostat_eta\\\": mirostat_eta,\\n \\\"mirostat_tau\\\": mirostat_tau,\\n \\\"num_ctx\\\": num_ctx,\\n \\\"num_gpu\\\": num_gpu,\\n \\\"num_thread\\\": num_thread,\\n \\\"repeat_last_n\\\": repeat_last_n,\\n \\\"repeat_penalty\\\": repeat_penalty,\\n \\\"temperature\\\": temperature,\\n \\\"stop\\\": stop,\\n \\\"system\\\": system,\\n \\\"template\\\": template,\\n \\\"tfs_z\\\": tfs_z,\\n \\\"timeout\\\": timeout,\\n \\\"top_k\\\": top_k,\\n \\\"top_p\\\": top_p,\\n \\\"verbose\\\": verbose,\\n }\\n\\n # None Value remove\\n llm_params = {k: v for k, v in llm_params.items() if v is not None}\\n\\n try:\\n output = ChatOllama(**llm_params) # type: ignore\\n except Exception as e:\\n raise ValueError(\\\"Could not initialize Ollama LLM.\\\") from e\\n\\n return output # type: ignore\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"format\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"format\",\"display_name\":\"Format\",\"advanced\":true,\"dynamic\":false,\"info\":\"Specify the format of the output (e.g., json).\",\"title_case\":false,\"input_types\":[\"Text\"]},\"mirostat\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"Disabled\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"Disabled\",\"Mirostat\",\"Mirostat 2.0\"],\"name\":\"mirostat\",\"display_name\":\"Mirostat\",\"advanced\":true,\"dynamic\":false,\"info\":\"Enable/disable Mirostat sampling for controlling perplexity.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"mirostat_eta\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"mirostat_eta\",\"display_name\":\"Mirostat Eta\",\"advanced\":true,\"dynamic\":false,\"info\":\"Learning rate for Mirostat algorithm. (Default: 0.1)\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"mirostat_tau\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"mirostat_tau\",\"display_name\":\"Mirostat Tau\",\"advanced\":true,\"dynamic\":false,\"info\":\"Controls the balance between coherence and diversity of the output. (Default: 5.0)\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"model\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"llama2\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model\",\"display_name\":\"Model Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"Refer to https://ollama.ai/library for more models.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"num_ctx\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"num_ctx\",\"display_name\":\"Context Window Size\",\"advanced\":true,\"dynamic\":false,\"info\":\"Size of the context window for generating tokens. (Default: 2048)\",\"title_case\":false},\"num_gpu\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"num_gpu\",\"display_name\":\"Number of GPUs\",\"advanced\":true,\"dynamic\":false,\"info\":\"Number of GPUs to use for computation. (Default: 1 on macOS, 0 to disable)\",\"title_case\":false},\"num_thread\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"num_thread\",\"display_name\":\"Number of Threads\",\"advanced\":true,\"dynamic\":false,\"info\":\"Number of threads to use during computation. (Default: detected for optimal performance)\",\"title_case\":false},\"repeat_last_n\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"repeat_last_n\",\"display_name\":\"Repeat Last N\",\"advanced\":true,\"dynamic\":false,\"info\":\"How far back the model looks to prevent repetition. (Default: 64, 0 = disabled, -1 = num_ctx)\",\"title_case\":false},\"repeat_penalty\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"repeat_penalty\",\"display_name\":\"Repeat Penalty\",\"advanced\":true,\"dynamic\":false,\"info\":\"Penalty for repetitions in generated text. (Default: 1.1)\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"system\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"system\",\"display_name\":\"System\",\"advanced\":true,\"dynamic\":false,\"info\":\"System to use for generating text.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"temperature\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.8,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"Controls the creativity of model responses.\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"template\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"template\",\"display_name\":\"Template\",\"advanced\":true,\"dynamic\":false,\"info\":\"Template to use for generating text.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"tfs_z\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"tfs_z\",\"display_name\":\"TFS Z\",\"advanced\":true,\"dynamic\":false,\"info\":\"Tail free sampling value. (Default: 1)\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"timeout\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"timeout\",\"display_name\":\"Timeout\",\"advanced\":true,\"dynamic\":false,\"info\":\"Timeout for the request stream.\",\"title_case\":false},\"top_k\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_k\",\"display_name\":\"Top K\",\"advanced\":true,\"dynamic\":false,\"info\":\"Limits token selection to top K. (Default: 40)\",\"title_case\":false},\"top_p\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_p\",\"display_name\":\"Top P\",\"advanced\":true,\"dynamic\":false,\"info\":\"Works together with top-k. (Default: 0.9)\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"verbose\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"verbose\",\"display_name\":\"Verbose\",\"advanced\":false,\"dynamic\":false,\"info\":\"Whether to print out response text.\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Local LLM for chat with Ollama.\",\"base_classes\":[\"BaseLanguageModel\",\"Runnable\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"object\",\"BaseChatModel\"],\"display_name\":\"ChatOllama\",\"documentation\":\"\",\"custom_fields\":{\"base_url\":null,\"model\":null,\"mirostat\":null,\"mirostat_eta\":null,\"mirostat_tau\":null,\"repeat_last_n\":null,\"verbose\":null,\"cache\":null,\"num_ctx\":null,\"num_gpu\":null,\"format\":null,\"metadata\":null,\"num_thread\":null,\"repeat_penalty\":null,\"stop\":null,\"system\":null,\"tags\":null,\"temperature\":null,\"template\":null,\"tfs_z\":null,\"timeout\":null,\"top_k\":null,\"top_p\":null},\"output_types\":[\"BaseChatModel\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"BaiduQianfanChatEndpointsSpecs\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain_community.chat_models.baidu_qianfan_endpoint import QianfanChatEndpoint\\nfrom langchain.llms.base import BaseLLM\\nfrom pydantic.v1 import SecretStr\\n\\nfrom langflow import CustomComponent\\n\\n\\nclass QianfanChatEndpointComponent(CustomComponent):\\n display_name: str = \\\"QianfanChatEndpoint\\\"\\n description: str = (\\n \\\"Baidu Qianfan chat models. Get more detail from \\\"\\n \\\"https://python.langchain.com/docs/integrations/chat/baidu_qianfan_endpoint.\\\"\\n )\\n\\n def build_config(self):\\n return {\\n \\\"model\\\": {\\n \\\"display_name\\\": \\\"Model Name\\\",\\n \\\"options\\\": [\\n \\\"ERNIE-Bot\\\",\\n \\\"ERNIE-Bot-turbo\\\",\\n \\\"BLOOMZ-7B\\\",\\n \\\"Llama-2-7b-chat\\\",\\n \\\"Llama-2-13b-chat\\\",\\n \\\"Llama-2-70b-chat\\\",\\n \\\"Qianfan-BLOOMZ-7B-compressed\\\",\\n \\\"Qianfan-Chinese-Llama-2-7B\\\",\\n \\\"ChatGLM2-6B-32K\\\",\\n \\\"AquilaChat-7B\\\",\\n ],\\n \\\"info\\\": \\\"https://python.langchain.com/docs/integrations/chat/baidu_qianfan_endpoint\\\",\\n \\\"required\\\": True,\\n },\\n \\\"qianfan_ak\\\": {\\n \\\"display_name\\\": \\\"Qianfan Ak\\\",\\n \\\"required\\\": True,\\n \\\"password\\\": True,\\n \\\"info\\\": \\\"which you could get from https://cloud.baidu.com/product/wenxinworkshop\\\",\\n },\\n \\\"qianfan_sk\\\": {\\n \\\"display_name\\\": \\\"Qianfan Sk\\\",\\n \\\"required\\\": True,\\n \\\"password\\\": True,\\n \\\"info\\\": \\\"which you could get from https://cloud.baidu.com/product/wenxinworkshop\\\",\\n },\\n \\\"top_p\\\": {\\n \\\"display_name\\\": \\\"Top p\\\",\\n \\\"field_type\\\": \\\"float\\\",\\n \\\"info\\\": \\\"Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo\\\",\\n \\\"value\\\": 0.8,\\n },\\n \\\"temperature\\\": {\\n \\\"display_name\\\": \\\"Temperature\\\",\\n \\\"field_type\\\": \\\"float\\\",\\n \\\"info\\\": \\\"Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo\\\",\\n \\\"value\\\": 0.95,\\n },\\n \\\"penalty_score\\\": {\\n \\\"display_name\\\": \\\"Penalty Score\\\",\\n \\\"field_type\\\": \\\"float\\\",\\n \\\"info\\\": \\\"Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo\\\",\\n \\\"value\\\": 1.0,\\n },\\n \\\"endpoint\\\": {\\n \\\"display_name\\\": \\\"Endpoint\\\",\\n \\\"info\\\": \\\"Endpoint of the Qianfan LLM, required if custom model used.\\\",\\n },\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(\\n self,\\n model: str = \\\"ERNIE-Bot-turbo\\\",\\n qianfan_ak: Optional[str] = None,\\n qianfan_sk: Optional[str] = None,\\n top_p: Optional[float] = None,\\n temperature: Optional[float] = None,\\n penalty_score: Optional[float] = None,\\n endpoint: Optional[str] = None,\\n ) -> BaseLLM:\\n try:\\n output = QianfanChatEndpoint( # type: ignore\\n model=model,\\n qianfan_ak=SecretStr(qianfan_ak) if qianfan_ak else None,\\n qianfan_sk=SecretStr(qianfan_sk) if qianfan_sk else None,\\n top_p=top_p,\\n temperature=temperature,\\n penalty_score=penalty_score,\\n endpoint=endpoint,\\n )\\n except Exception as e:\\n raise ValueError(\\\"Could not connect to Baidu Qianfan API.\\\") from e\\n return output # type: ignore\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"endpoint\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"endpoint\",\"display_name\":\"Endpoint\",\"advanced\":false,\"dynamic\":false,\"info\":\"Endpoint of the Qianfan LLM, required if custom model used.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"model\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"ERNIE-Bot-turbo\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"ERNIE-Bot\",\"ERNIE-Bot-turbo\",\"BLOOMZ-7B\",\"Llama-2-7b-chat\",\"Llama-2-13b-chat\",\"Llama-2-70b-chat\",\"Qianfan-BLOOMZ-7B-compressed\",\"Qianfan-Chinese-Llama-2-7B\",\"ChatGLM2-6B-32K\",\"AquilaChat-7B\"],\"name\":\"model\",\"display_name\":\"Model Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"https://python.langchain.com/docs/integrations/chat/baidu_qianfan_endpoint\",\"title_case\":false,\"input_types\":[\"Text\"]},\"penalty_score\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":1.0,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"penalty_score\",\"display_name\":\"Penalty Score\",\"advanced\":false,\"dynamic\":false,\"info\":\"Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"qianfan_ak\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"qianfan_ak\",\"display_name\":\"Qianfan Ak\",\"advanced\":false,\"dynamic\":false,\"info\":\"which you could get from https://cloud.baidu.com/product/wenxinworkshop\",\"title_case\":false,\"input_types\":[\"Text\"]},\"qianfan_sk\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"qianfan_sk\",\"display_name\":\"Qianfan Sk\",\"advanced\":false,\"dynamic\":false,\"info\":\"which you could get from https://cloud.baidu.com/product/wenxinworkshop\",\"title_case\":false,\"input_types\":[\"Text\"]},\"temperature\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.95,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"top_p\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.8,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_p\",\"display_name\":\"Top p\",\"advanced\":false,\"dynamic\":false,\"info\":\"Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Baidu Qianfan chat models. Get more detail from https://python.langchain.com/docs/integrations/chat/baidu_qianfan_endpoint.\",\"base_classes\":[\"BaseLanguageModel\",\"Runnable\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"object\",\"BaseLLM\"],\"display_name\":\"QianfanChatEndpoint\",\"documentation\":\"\",\"custom_fields\":{\"model\":null,\"qianfan_ak\":null,\"qianfan_sk\":null,\"top_p\":null,\"temperature\":null,\"penalty_score\":null,\"endpoint\":null},\"output_types\":[\"BaseLLM\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"LlamaCppSpecs\":{\"template\":{\"metadata\":{\"type\":\"Dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"model_kwargs\":{\"type\":\"Dict\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model_kwargs\",\"display_name\":\"Model Kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"model_path\":{\"type\":\"file\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[\".bin\"],\"file_path\":\"\",\"password\":false,\"name\":\"model_path\",\"display_name\":\"Model Path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"cache\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"cache\",\"display_name\":\"Cache\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"client\":{\"type\":\"Any\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"client\",\"display_name\":\"Client\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional, List, Dict, Any\\nfrom langflow import CustomComponent\\nfrom langchain_community.llms.llamacpp import LlamaCpp\\n\\n\\nclass LlamaCppComponent(CustomComponent):\\n display_name = \\\"LlamaCpp\\\"\\n description = \\\"llama.cpp model.\\\"\\n documentation = \\\"https://python.langchain.com/docs/modules/model_io/models/llms/integrations/llamacpp\\\"\\n\\n def build_config(self):\\n return {\\n \\\"grammar\\\": {\\\"display_name\\\": \\\"Grammar\\\", \\\"advanced\\\": True},\\n \\\"cache\\\": {\\\"display_name\\\": \\\"Cache\\\", \\\"advanced\\\": True},\\n \\\"client\\\": {\\\"display_name\\\": \\\"Client\\\", \\\"advanced\\\": True},\\n \\\"echo\\\": {\\\"display_name\\\": \\\"Echo\\\", \\\"advanced\\\": True},\\n \\\"f16_kv\\\": {\\\"display_name\\\": \\\"F16 KV\\\", \\\"advanced\\\": True},\\n \\\"grammar_path\\\": {\\\"display_name\\\": \\\"Grammar Path\\\", \\\"advanced\\\": True},\\n \\\"last_n_tokens_size\\\": {\\\"display_name\\\": \\\"Last N Tokens Size\\\", \\\"advanced\\\": True},\\n \\\"logits_all\\\": {\\\"display_name\\\": \\\"Logits All\\\", \\\"advanced\\\": True},\\n \\\"logprobs\\\": {\\\"display_name\\\": \\\"Logprobs\\\", \\\"advanced\\\": True},\\n \\\"lora_base\\\": {\\\"display_name\\\": \\\"Lora Base\\\", \\\"advanced\\\": True},\\n \\\"lora_path\\\": {\\\"display_name\\\": \\\"Lora Path\\\", \\\"advanced\\\": True},\\n \\\"max_tokens\\\": {\\\"display_name\\\": \\\"Max Tokens\\\", \\\"advanced\\\": True},\\n \\\"metadata\\\": {\\\"display_name\\\": \\\"Metadata\\\", \\\"advanced\\\": True},\\n \\\"model_kwargs\\\": {\\\"display_name\\\": \\\"Model Kwargs\\\", \\\"advanced\\\": True},\\n \\\"model_path\\\": {\\n \\\"display_name\\\": \\\"Model Path\\\",\\n \\\"field_type\\\": \\\"file\\\",\\n \\\"file_types\\\": [\\\".bin\\\"],\\n \\\"required\\\": True,\\n },\\n \\\"n_batch\\\": {\\\"display_name\\\": \\\"N Batch\\\", \\\"advanced\\\": True},\\n \\\"n_ctx\\\": {\\\"display_name\\\": \\\"N Ctx\\\", \\\"advanced\\\": True},\\n \\\"n_gpu_layers\\\": {\\\"display_name\\\": \\\"N GPU Layers\\\", \\\"advanced\\\": True},\\n \\\"n_parts\\\": {\\\"display_name\\\": \\\"N Parts\\\", \\\"advanced\\\": True},\\n \\\"n_threads\\\": {\\\"display_name\\\": \\\"N Threads\\\", \\\"advanced\\\": True},\\n \\\"repeat_penalty\\\": {\\\"display_name\\\": \\\"Repeat Penalty\\\", \\\"advanced\\\": True},\\n \\\"rope_freq_base\\\": {\\\"display_name\\\": \\\"Rope Freq Base\\\", \\\"advanced\\\": True},\\n \\\"rope_freq_scale\\\": {\\\"display_name\\\": \\\"Rope Freq Scale\\\", \\\"advanced\\\": True},\\n \\\"seed\\\": {\\\"display_name\\\": \\\"Seed\\\", \\\"advanced\\\": True},\\n \\\"stop\\\": {\\\"display_name\\\": \\\"Stop\\\", \\\"advanced\\\": True},\\n \\\"streaming\\\": {\\\"display_name\\\": \\\"Streaming\\\", \\\"advanced\\\": True},\\n \\\"suffix\\\": {\\\"display_name\\\": \\\"Suffix\\\", \\\"advanced\\\": True},\\n \\\"tags\\\": {\\\"display_name\\\": \\\"Tags\\\", \\\"advanced\\\": True},\\n \\\"temperature\\\": {\\\"display_name\\\": \\\"Temperature\\\"},\\n \\\"top_k\\\": {\\\"display_name\\\": \\\"Top K\\\", \\\"advanced\\\": True},\\n \\\"top_p\\\": {\\\"display_name\\\": \\\"Top P\\\", \\\"advanced\\\": True},\\n \\\"use_mlock\\\": {\\\"display_name\\\": \\\"Use Mlock\\\", \\\"advanced\\\": True},\\n \\\"use_mmap\\\": {\\\"display_name\\\": \\\"Use Mmap\\\", \\\"advanced\\\": True},\\n \\\"verbose\\\": {\\\"display_name\\\": \\\"Verbose\\\", \\\"advanced\\\": True},\\n \\\"vocab_only\\\": {\\\"display_name\\\": \\\"Vocab Only\\\", \\\"advanced\\\": True},\\n }\\n\\n def build(\\n self,\\n model_path: str,\\n grammar: Optional[str] = None,\\n cache: Optional[bool] = None,\\n client: Optional[Any] = None,\\n echo: Optional[bool] = False,\\n f16_kv: bool = True,\\n grammar_path: Optional[str] = None,\\n last_n_tokens_size: Optional[int] = 64,\\n logits_all: bool = False,\\n logprobs: Optional[int] = None,\\n lora_base: Optional[str] = None,\\n lora_path: Optional[str] = None,\\n max_tokens: Optional[int] = 256,\\n metadata: Optional[Dict] = None,\\n model_kwargs: Dict = {},\\n n_batch: Optional[int] = 8,\\n n_ctx: int = 512,\\n n_gpu_layers: Optional[int] = 1,\\n n_parts: int = -1,\\n n_threads: Optional[int] = 1,\\n repeat_penalty: Optional[float] = 1.1,\\n rope_freq_base: float = 10000.0,\\n rope_freq_scale: float = 1.0,\\n seed: int = -1,\\n stop: Optional[List[str]] = [],\\n streaming: bool = True,\\n suffix: Optional[str] = \\\"\\\",\\n tags: Optional[List[str]] = [],\\n temperature: Optional[float] = 0.8,\\n top_k: Optional[int] = 40,\\n top_p: Optional[float] = 0.95,\\n use_mlock: bool = False,\\n use_mmap: Optional[bool] = True,\\n verbose: bool = True,\\n vocab_only: bool = False,\\n ) -> LlamaCpp:\\n return LlamaCpp(\\n model_path=model_path,\\n grammar=grammar,\\n cache=cache,\\n client=client,\\n echo=echo,\\n f16_kv=f16_kv,\\n grammar_path=grammar_path,\\n last_n_tokens_size=last_n_tokens_size,\\n logits_all=logits_all,\\n logprobs=logprobs,\\n lora_base=lora_base,\\n lora_path=lora_path,\\n max_tokens=max_tokens,\\n metadata=metadata,\\n model_kwargs=model_kwargs,\\n n_batch=n_batch,\\n n_ctx=n_ctx,\\n n_gpu_layers=n_gpu_layers,\\n n_parts=n_parts,\\n n_threads=n_threads,\\n repeat_penalty=repeat_penalty,\\n rope_freq_base=rope_freq_base,\\n rope_freq_scale=rope_freq_scale,\\n seed=seed,\\n stop=stop,\\n streaming=streaming,\\n suffix=suffix,\\n tags=tags,\\n temperature=temperature,\\n top_k=top_k,\\n top_p=top_p,\\n use_mlock=use_mlock,\\n use_mmap=use_mmap,\\n verbose=verbose,\\n vocab_only=vocab_only,\\n )\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"echo\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"echo\",\"display_name\":\"Echo\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"f16_kv\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":true,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"f16_kv\",\"display_name\":\"F16 KV\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"grammar\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"grammar\",\"display_name\":\"Grammar\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"grammar_path\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"grammar_path\",\"display_name\":\"Grammar Path\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"last_n_tokens_size\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":64,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"last_n_tokens_size\",\"display_name\":\"Last N Tokens Size\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"logits_all\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"logits_all\",\"display_name\":\"Logits All\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"logprobs\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"logprobs\",\"display_name\":\"Logprobs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"lora_base\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"lora_base\",\"display_name\":\"Lora Base\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"lora_path\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"lora_path\",\"display_name\":\"Lora Path\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"max_tokens\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":256,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_tokens\",\"display_name\":\"Max Tokens\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"n_batch\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":8,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"n_batch\",\"display_name\":\"N Batch\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"n_ctx\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":512,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"n_ctx\",\"display_name\":\"N Ctx\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"n_gpu_layers\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":1,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"n_gpu_layers\",\"display_name\":\"N GPU Layers\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"n_parts\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":-1,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"n_parts\",\"display_name\":\"N Parts\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"n_threads\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":1,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"n_threads\",\"display_name\":\"N Threads\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"repeat_penalty\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":1.1,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"repeat_penalty\",\"display_name\":\"Repeat Penalty\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"rope_freq_base\":{\"type\":\"float\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":10000.0,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"rope_freq_base\",\"display_name\":\"Rope Freq Base\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"rope_freq_scale\":{\"type\":\"float\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":1.0,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"rope_freq_scale\",\"display_name\":\"Rope Freq Scale\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"seed\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":-1,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"seed\",\"display_name\":\"Seed\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"stop\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":[],\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"stop\",\"display_name\":\"Stop\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"streaming\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":true,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"streaming\",\"display_name\":\"Streaming\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"suffix\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"suffix\",\"display_name\":\"Suffix\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"tags\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":[],\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"tags\",\"display_name\":\"Tags\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"temperature\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.8,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"top_k\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":40,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_k\",\"display_name\":\"Top K\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"top_p\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.95,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_p\",\"display_name\":\"Top P\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"use_mlock\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"use_mlock\",\"display_name\":\"Use Mlock\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"use_mmap\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":true,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"use_mmap\",\"display_name\":\"Use Mmap\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"verbose\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":true,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"verbose\",\"display_name\":\"Verbose\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"vocab_only\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"vocab_only\",\"display_name\":\"Vocab Only\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"llama.cpp model.\",\"base_classes\":[\"BaseLanguageModel\",\"Runnable\",\"Generic\",\"LlamaCpp\",\"RunnableSerializable\",\"Serializable\",\"object\",\"BaseLLM\",\"LLM\"],\"display_name\":\"LlamaCpp\",\"documentation\":\"https://python.langchain.com/docs/modules/model_io/models/llms/integrations/llamacpp\",\"custom_fields\":{\"model_path\":null,\"grammar\":null,\"cache\":null,\"client\":null,\"echo\":null,\"f16_kv\":null,\"grammar_path\":null,\"last_n_tokens_size\":null,\"logits_all\":null,\"logprobs\":null,\"lora_base\":null,\"lora_path\":null,\"max_tokens\":null,\"metadata\":null,\"model_kwargs\":null,\"n_batch\":null,\"n_ctx\":null,\"n_gpu_layers\":null,\"n_parts\":null,\"n_threads\":null,\"repeat_penalty\":null,\"rope_freq_base\":null,\"rope_freq_scale\":null,\"seed\":null,\"stop\":null,\"streaming\":null,\"suffix\":null,\"tags\":null,\"temperature\":null,\"top_k\":null,\"top_p\":null,\"use_mlock\":null,\"use_mmap\":null,\"verbose\":null,\"vocab_only\":null},\"output_types\":[\"LlamaCpp\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"AnthropicSpecs\":{\"template\":{\"anthropic_api_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"anthropic_api_key\",\"display_name\":\"Anthropic API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"anthropic_api_url\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"anthropic_api_url\",\"display_name\":\"Anthropic API URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain_community.llms.anthropic import Anthropic\\nfrom pydantic.v1 import SecretStr\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import BaseLanguageModel, NestedDict\\n\\n\\nclass AnthropicComponent(CustomComponent):\\n display_name = \\\"Anthropic\\\"\\n description = \\\"Anthropic large language models.\\\"\\n\\n def build_config(self):\\n return {\\n \\\"anthropic_api_key\\\": {\\n \\\"display_name\\\": \\\"Anthropic API Key\\\",\\n \\\"type\\\": str,\\n \\\"password\\\": True,\\n },\\n \\\"anthropic_api_url\\\": {\\n \\\"display_name\\\": \\\"Anthropic API URL\\\",\\n \\\"type\\\": str,\\n },\\n \\\"model_kwargs\\\": {\\n \\\"display_name\\\": \\\"Model Kwargs\\\",\\n \\\"field_type\\\": \\\"NestedDict\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"temperature\\\": {\\n \\\"display_name\\\": \\\"Temperature\\\",\\n \\\"field_type\\\": \\\"float\\\",\\n },\\n }\\n\\n def build(\\n self,\\n anthropic_api_key: str,\\n anthropic_api_url: str,\\n model_kwargs: NestedDict = {},\\n temperature: Optional[float] = None,\\n ) -> BaseLanguageModel:\\n return Anthropic(\\n anthropic_api_key=SecretStr(anthropic_api_key),\\n anthropic_api_url=anthropic_api_url,\\n model_kwargs=model_kwargs,\\n temperature=temperature,\\n )\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"model_kwargs\":{\"type\":\"NestedDict\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model_kwargs\",\"display_name\":\"Model Kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"temperature\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Anthropic large language models.\",\"base_classes\":[\"BaseLanguageModel\",\"Runnable\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"object\"],\"display_name\":\"Anthropic\",\"documentation\":\"\",\"custom_fields\":{\"anthropic_api_key\":null,\"anthropic_api_url\":null,\"model_kwargs\":null,\"temperature\":null},\"output_types\":[\"BaseLanguageModel\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"AnthropicLLMSpecs\":{\"template\":{\"anthropic_api_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"anthropic_api_key\",\"display_name\":\"Anthropic API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"Your Anthropic API key.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"api_endpoint\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"api_endpoint\",\"display_name\":\"API Endpoint\",\"advanced\":false,\"dynamic\":false,\"info\":\"Endpoint of the Anthropic API. Defaults to 'https://api.anthropic.com' if not specified.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain_community.chat_models.anthropic import ChatAnthropic\\nfrom langchain.llms.base import BaseLanguageModel\\nfrom pydantic.v1 import SecretStr\\n\\nfrom langflow import CustomComponent\\n\\n\\nclass AnthropicLLM(CustomComponent):\\n display_name: str = \\\"AnthropicLLM\\\"\\n description: str = \\\"Anthropic Chat&Completion large language models.\\\"\\n\\n def build_config(self):\\n return {\\n \\\"model\\\": {\\n \\\"display_name\\\": \\\"Model Name\\\",\\n \\\"options\\\": [\\n \\\"claude-2.1\\\",\\n \\\"claude-2.0\\\",\\n \\\"claude-instant-1.2\\\",\\n \\\"claude-instant-1\\\",\\n # Add more models as needed\\n ],\\n \\\"info\\\": \\\"https://python.langchain.com/docs/integrations/chat/anthropic\\\",\\n \\\"required\\\": True,\\n \\\"value\\\": \\\"claude-2.1\\\",\\n },\\n \\\"anthropic_api_key\\\": {\\n \\\"display_name\\\": \\\"Anthropic API Key\\\",\\n \\\"required\\\": True,\\n \\\"password\\\": True,\\n \\\"info\\\": \\\"Your Anthropic API key.\\\",\\n },\\n \\\"max_tokens\\\": {\\n \\\"display_name\\\": \\\"Max Tokens\\\",\\n \\\"field_type\\\": \\\"int\\\",\\n \\\"value\\\": 256,\\n },\\n \\\"temperature\\\": {\\n \\\"display_name\\\": \\\"Temperature\\\",\\n \\\"field_type\\\": \\\"float\\\",\\n \\\"value\\\": 0.7,\\n },\\n \\\"api_endpoint\\\": {\\n \\\"display_name\\\": \\\"API Endpoint\\\",\\n \\\"info\\\": \\\"Endpoint of the Anthropic API. Defaults to 'https://api.anthropic.com' if not specified.\\\",\\n },\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(\\n self,\\n model: str,\\n anthropic_api_key: Optional[str] = None,\\n max_tokens: Optional[int] = None,\\n temperature: Optional[float] = None,\\n api_endpoint: Optional[str] = None,\\n ) -> BaseLanguageModel:\\n # Set default API endpoint if not provided\\n if not api_endpoint:\\n api_endpoint = \\\"https://api.anthropic.com\\\"\\n\\n try:\\n output = ChatAnthropic(\\n model_name=model,\\n anthropic_api_key=SecretStr(anthropic_api_key) if anthropic_api_key else None,\\n max_tokens_to_sample=max_tokens, # type: ignore\\n temperature=temperature,\\n anthropic_api_url=api_endpoint,\\n )\\n except Exception as e:\\n raise ValueError(\\\"Could not connect to Anthropic API.\\\") from e\\n return output\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"max_tokens\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":256,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_tokens\",\"display_name\":\"Max Tokens\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"model\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"claude-2.1\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"claude-2.1\",\"claude-2.0\",\"claude-instant-1.2\",\"claude-instant-1\"],\"name\":\"model\",\"display_name\":\"Model Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"https://python.langchain.com/docs/integrations/chat/anthropic\",\"title_case\":false,\"input_types\":[\"Text\"]},\"temperature\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.7,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Anthropic Chat&Completion large language models.\",\"base_classes\":[\"BaseLanguageModel\",\"Runnable\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"object\"],\"display_name\":\"AnthropicLLM\",\"documentation\":\"\",\"custom_fields\":{\"model\":null,\"anthropic_api_key\":null,\"max_tokens\":null,\"temperature\":null,\"api_endpoint\":null},\"output_types\":[\"BaseLanguageModel\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"CohereSpecs\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from langchain_community.llms.cohere import Cohere\\nfrom langchain_core.language_models.base import BaseLanguageModel\\nfrom langflow import CustomComponent\\n\\n\\nclass CohereComponent(CustomComponent):\\n display_name = \\\"Cohere\\\"\\n description = \\\"Cohere large language models.\\\"\\n documentation = \\\"https://python.langchain.com/docs/modules/model_io/models/llms/integrations/cohere\\\"\\n\\n def build_config(self):\\n return {\\n \\\"cohere_api_key\\\": {\\\"display_name\\\": \\\"Cohere API Key\\\", \\\"type\\\": \\\"password\\\", \\\"password\\\": True},\\n \\\"max_tokens\\\": {\\\"display_name\\\": \\\"Max Tokens\\\", \\\"default\\\": 256, \\\"type\\\": \\\"int\\\", \\\"show\\\": True},\\n \\\"temperature\\\": {\\\"display_name\\\": \\\"Temperature\\\", \\\"default\\\": 0.75, \\\"type\\\": \\\"float\\\", \\\"show\\\": True},\\n }\\n\\n def build(\\n self,\\n cohere_api_key: str,\\n max_tokens: int = 256,\\n temperature: float = 0.75,\\n ) -> BaseLanguageModel:\\n return Cohere(cohere_api_key=cohere_api_key, max_tokens=max_tokens, temperature=temperature) # type: ignore\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"cohere_api_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"cohere_api_key\",\"display_name\":\"Cohere API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"max_tokens\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":256,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_tokens\",\"display_name\":\"Max Tokens\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"temperature\":{\"type\":\"float\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.75,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Cohere large language models.\",\"base_classes\":[\"BaseLanguageModel\",\"Runnable\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"object\"],\"display_name\":\"Cohere\",\"documentation\":\"https://python.langchain.com/docs/modules/model_io/models/llms/integrations/cohere\",\"custom_fields\":{\"cohere_api_key\":null,\"max_tokens\":null,\"temperature\":null},\"output_types\":[\"BaseLanguageModel\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"GoogleGenerativeAISpecs\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain_google_genai import ChatGoogleGenerativeAI # type: ignore\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import BaseLanguageModel, RangeSpec\\nfrom pydantic.v1.types import SecretStr\\n\\n\\nclass GoogleGenerativeAIComponent(CustomComponent):\\n display_name: str = \\\"Google Generative AI\\\"\\n description: str = \\\"A component that uses Google Generative AI to generate text.\\\"\\n documentation: str = \\\"http://docs.langflow.org/components/custom\\\"\\n\\n def build_config(self):\\n return {\\n \\\"google_api_key\\\": {\\n \\\"display_name\\\": \\\"Google API Key\\\",\\n \\\"info\\\": \\\"The Google API Key to use for the Google Generative AI.\\\",\\n },\\n \\\"max_output_tokens\\\": {\\n \\\"display_name\\\": \\\"Max Output Tokens\\\",\\n \\\"info\\\": \\\"The maximum number of tokens to generate.\\\",\\n },\\n \\\"temperature\\\": {\\n \\\"display_name\\\": \\\"Temperature\\\",\\n \\\"info\\\": \\\"Run inference with this temperature. Must by in the closed interval [0.0, 1.0].\\\",\\n },\\n \\\"top_k\\\": {\\n \\\"display_name\\\": \\\"Top K\\\",\\n \\\"info\\\": \\\"Decode using top-k sampling: consider the set of top_k most probable tokens. Must be positive.\\\",\\n \\\"range_spec\\\": RangeSpec(min=0, max=2, step=0.1),\\n \\\"advanced\\\": True,\\n },\\n \\\"top_p\\\": {\\n \\\"display_name\\\": \\\"Top P\\\",\\n \\\"info\\\": \\\"The maximum cumulative probability of tokens to consider when sampling.\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"n\\\": {\\n \\\"display_name\\\": \\\"N\\\",\\n \\\"info\\\": \\\"Number of chat completions to generate for each prompt. Note that the API may not return the full n completions if duplicates are generated.\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"model\\\": {\\n \\\"display_name\\\": \\\"Model\\\",\\n \\\"info\\\": \\\"The name of the model to use. Supported examples: gemini-pro\\\",\\n \\\"options\\\": [\\\"gemini-pro\\\", \\\"gemini-pro-vision\\\"],\\n },\\n \\\"code\\\": {\\n \\\"advanced\\\": True,\\n },\\n }\\n\\n def build(\\n self,\\n google_api_key: str,\\n model: str,\\n max_output_tokens: Optional[int] = None,\\n temperature: float = 0.1,\\n top_k: Optional[int] = None,\\n top_p: Optional[float] = None,\\n n: Optional[int] = 1,\\n ) -> BaseLanguageModel:\\n return ChatGoogleGenerativeAI(\\n model=model,\\n max_output_tokens=max_output_tokens or None, # type: ignore\\n temperature=temperature,\\n top_k=top_k or None,\\n top_p=top_p or None, # type: ignore\\n n=n or 1,\\n google_api_key=SecretStr(google_api_key),\\n )\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":true,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"google_api_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"google_api_key\",\"display_name\":\"Google API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"The Google API Key to use for the Google Generative AI.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"max_output_tokens\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_output_tokens\",\"display_name\":\"Max Output Tokens\",\"advanced\":false,\"dynamic\":false,\"info\":\"The maximum number of tokens to generate.\",\"title_case\":false},\"model\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"gemini-pro\",\"gemini-pro-vision\"],\"name\":\"model\",\"display_name\":\"Model\",\"advanced\":false,\"dynamic\":false,\"info\":\"The name of the model to use. Supported examples: gemini-pro\",\"title_case\":false,\"input_types\":[\"Text\"]},\"n\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":1,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"n\",\"display_name\":\"N\",\"advanced\":true,\"dynamic\":false,\"info\":\"Number of chat completions to generate for each prompt. Note that the API may not return the full n completions if duplicates are generated.\",\"title_case\":false},\"temperature\":{\"type\":\"float\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.1,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"Run inference with this temperature. Must by in the closed interval [0.0, 1.0].\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"top_k\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_k\",\"display_name\":\"Top K\",\"advanced\":true,\"dynamic\":false,\"info\":\"Decode using top-k sampling: consider the set of top_k most probable tokens. Must be positive.\",\"rangeSpec\":{\"min\":0.0,\"max\":2.0,\"step\":0.1},\"title_case\":false},\"top_p\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_p\",\"display_name\":\"Top P\",\"advanced\":true,\"dynamic\":false,\"info\":\"The maximum cumulative probability of tokens to consider when sampling.\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"A component that uses Google Generative AI to generate text.\",\"base_classes\":[\"BaseLanguageModel\",\"Runnable\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"object\"],\"display_name\":\"Google Generative AI\",\"documentation\":\"http://docs.langflow.org/components/custom\",\"custom_fields\":{\"google_api_key\":null,\"model\":null,\"max_output_tokens\":null,\"temperature\":null,\"top_k\":null,\"top_p\":null,\"n\":null},\"output_types\":[\"BaseLanguageModel\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"BaiduQianfanLLMEndpointsSpecs\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\nfrom langflow import CustomComponent\\nfrom langchain.llms.baidu_qianfan_endpoint import QianfanLLMEndpoint\\nfrom langchain.llms.base import BaseLLM\\n\\n\\nclass QianfanLLMEndpointComponent(CustomComponent):\\n display_name: str = \\\"QianfanLLMEndpoint\\\"\\n description: str = (\\n \\\"Baidu Qianfan hosted open source or customized models. \\\"\\n \\\"Get more detail from https://python.langchain.com/docs/integrations/chat/baidu_qianfan_endpoint\\\"\\n )\\n\\n def build_config(self):\\n return {\\n \\\"model\\\": {\\n \\\"display_name\\\": \\\"Model Name\\\",\\n \\\"options\\\": [\\n \\\"ERNIE-Bot\\\",\\n \\\"ERNIE-Bot-turbo\\\",\\n \\\"BLOOMZ-7B\\\",\\n \\\"Llama-2-7b-chat\\\",\\n \\\"Llama-2-13b-chat\\\",\\n \\\"Llama-2-70b-chat\\\",\\n \\\"Qianfan-BLOOMZ-7B-compressed\\\",\\n \\\"Qianfan-Chinese-Llama-2-7B\\\",\\n \\\"ChatGLM2-6B-32K\\\",\\n \\\"AquilaChat-7B\\\",\\n ],\\n \\\"info\\\": \\\"https://python.langchain.com/docs/integrations/chat/baidu_qianfan_endpoint\\\",\\n \\\"required\\\": True,\\n },\\n \\\"qianfan_ak\\\": {\\n \\\"display_name\\\": \\\"Qianfan Ak\\\",\\n \\\"required\\\": True,\\n \\\"password\\\": True,\\n \\\"info\\\": \\\"which you could get from https://cloud.baidu.com/product/wenxinworkshop\\\",\\n },\\n \\\"qianfan_sk\\\": {\\n \\\"display_name\\\": \\\"Qianfan Sk\\\",\\n \\\"required\\\": True,\\n \\\"password\\\": True,\\n \\\"info\\\": \\\"which you could get from https://cloud.baidu.com/product/wenxinworkshop\\\",\\n },\\n \\\"top_p\\\": {\\n \\\"display_name\\\": \\\"Top p\\\",\\n \\\"field_type\\\": \\\"float\\\",\\n \\\"info\\\": \\\"Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo\\\",\\n \\\"value\\\": 0.8,\\n },\\n \\\"temperature\\\": {\\n \\\"display_name\\\": \\\"Temperature\\\",\\n \\\"field_type\\\": \\\"float\\\",\\n \\\"info\\\": \\\"Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo\\\",\\n \\\"value\\\": 0.95,\\n },\\n \\\"penalty_score\\\": {\\n \\\"display_name\\\": \\\"Penalty Score\\\",\\n \\\"field_type\\\": \\\"float\\\",\\n \\\"info\\\": \\\"Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo\\\",\\n \\\"value\\\": 1.0,\\n },\\n \\\"endpoint\\\": {\\n \\\"display_name\\\": \\\"Endpoint\\\",\\n \\\"info\\\": \\\"Endpoint of the Qianfan LLM, required if custom model used.\\\",\\n },\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(\\n self,\\n model: str = \\\"ERNIE-Bot-turbo\\\",\\n qianfan_ak: Optional[str] = None,\\n qianfan_sk: Optional[str] = None,\\n top_p: Optional[float] = None,\\n temperature: Optional[float] = None,\\n penalty_score: Optional[float] = None,\\n endpoint: Optional[str] = None,\\n ) -> BaseLLM:\\n try:\\n output = QianfanLLMEndpoint( # type: ignore\\n model=model,\\n qianfan_ak=qianfan_ak,\\n qianfan_sk=qianfan_sk,\\n top_p=top_p,\\n temperature=temperature,\\n penalty_score=penalty_score,\\n endpoint=endpoint,\\n )\\n except Exception as e:\\n raise ValueError(\\\"Could not connect to Baidu Qianfan API.\\\") from e\\n return output # type: ignore\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"endpoint\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"endpoint\",\"display_name\":\"Endpoint\",\"advanced\":false,\"dynamic\":false,\"info\":\"Endpoint of the Qianfan LLM, required if custom model used.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"model\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"ERNIE-Bot-turbo\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"ERNIE-Bot\",\"ERNIE-Bot-turbo\",\"BLOOMZ-7B\",\"Llama-2-7b-chat\",\"Llama-2-13b-chat\",\"Llama-2-70b-chat\",\"Qianfan-BLOOMZ-7B-compressed\",\"Qianfan-Chinese-Llama-2-7B\",\"ChatGLM2-6B-32K\",\"AquilaChat-7B\"],\"name\":\"model\",\"display_name\":\"Model Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"https://python.langchain.com/docs/integrations/chat/baidu_qianfan_endpoint\",\"title_case\":false,\"input_types\":[\"Text\"]},\"penalty_score\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":1.0,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"penalty_score\",\"display_name\":\"Penalty Score\",\"advanced\":false,\"dynamic\":false,\"info\":\"Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"qianfan_ak\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"qianfan_ak\",\"display_name\":\"Qianfan Ak\",\"advanced\":false,\"dynamic\":false,\"info\":\"which you could get from https://cloud.baidu.com/product/wenxinworkshop\",\"title_case\":false,\"input_types\":[\"Text\"]},\"qianfan_sk\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"qianfan_sk\",\"display_name\":\"Qianfan Sk\",\"advanced\":false,\"dynamic\":false,\"info\":\"which you could get from https://cloud.baidu.com/product/wenxinworkshop\",\"title_case\":false,\"input_types\":[\"Text\"]},\"temperature\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.95,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"top_p\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.8,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_p\",\"display_name\":\"Top p\",\"advanced\":false,\"dynamic\":false,\"info\":\"Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Baidu Qianfan hosted open source or customized models. Get more detail from https://python.langchain.com/docs/integrations/chat/baidu_qianfan_endpoint\",\"base_classes\":[\"BaseLanguageModel\",\"Runnable\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"object\",\"BaseLLM\"],\"display_name\":\"QianfanLLMEndpoint\",\"documentation\":\"\",\"custom_fields\":{\"model\":null,\"qianfan_ak\":null,\"qianfan_sk\":null,\"top_p\":null,\"temperature\":null,\"penalty_score\":null,\"endpoint\":null},\"output_types\":[\"BaseLLM\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"ChatLiteLLMSpecs\":{\"template\":{\"api_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"api_key\",\"display_name\":\"API key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Any, Callable, Dict, Optional, Union\\n\\nfrom langchain_community.chat_models.litellm import ChatLiteLLM, ChatLiteLLMException\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import BaseLanguageModel\\n\\n\\nclass ChatLiteLLMComponent(CustomComponent):\\n display_name = \\\"ChatLiteLLM\\\"\\n description = \\\"`LiteLLM` collection of large language models.\\\"\\n documentation = \\\"https://python.langchain.com/docs/integrations/chat/litellm\\\"\\n\\n def build_config(self):\\n return {\\n \\\"model\\\": {\\n \\\"display_name\\\": \\\"Model name\\\",\\n \\\"field_type\\\": \\\"str\\\",\\n \\\"advanced\\\": False,\\n \\\"required\\\": True,\\n \\\"info\\\": \\\"The name of the model to use. For example, `gpt-3.5-turbo`.\\\",\\n },\\n \\\"api_key\\\": {\\n \\\"display_name\\\": \\\"API key\\\",\\n \\\"field_type\\\": \\\"str\\\",\\n \\\"advanced\\\": False,\\n \\\"required\\\": False,\\n \\\"password\\\": True,\\n },\\n \\\"provider\\\": {\\n \\\"display_name\\\": \\\"Provider\\\",\\n \\\"info\\\": \\\"The provider of the API key.\\\",\\n \\\"options\\\": [\\n \\\"OpenAI\\\",\\n \\\"Azure\\\",\\n \\\"Anthropic\\\",\\n \\\"Replicate\\\",\\n \\\"Cohere\\\",\\n \\\"OpenRouter\\\",\\n ],\\n },\\n \\\"streaming\\\": {\\n \\\"display_name\\\": \\\"Streaming\\\",\\n \\\"field_type\\\": \\\"bool\\\",\\n \\\"advanced\\\": True,\\n \\\"required\\\": False,\\n \\\"default\\\": True,\\n },\\n \\\"temperature\\\": {\\n \\\"display_name\\\": \\\"Temperature\\\",\\n \\\"field_type\\\": \\\"float\\\",\\n \\\"advanced\\\": False,\\n \\\"required\\\": False,\\n \\\"default\\\": 0.7,\\n },\\n \\\"model_kwargs\\\": {\\n \\\"display_name\\\": \\\"Model kwargs\\\",\\n \\\"field_type\\\": \\\"dict\\\",\\n \\\"advanced\\\": True,\\n \\\"required\\\": False,\\n \\\"default\\\": {},\\n },\\n \\\"top_p\\\": {\\n \\\"display_name\\\": \\\"Top p\\\",\\n \\\"field_type\\\": \\\"float\\\",\\n \\\"advanced\\\": True,\\n \\\"required\\\": False,\\n },\\n \\\"top_k\\\": {\\n \\\"display_name\\\": \\\"Top k\\\",\\n \\\"field_type\\\": \\\"int\\\",\\n \\\"advanced\\\": True,\\n \\\"required\\\": False,\\n },\\n \\\"n\\\": {\\n \\\"display_name\\\": \\\"N\\\",\\n \\\"field_type\\\": \\\"int\\\",\\n \\\"advanced\\\": True,\\n \\\"required\\\": False,\\n \\\"info\\\": \\\"Number of chat completions to generate for each prompt. \\\"\\n \\\"Note that the API may not return the full n completions if duplicates are generated.\\\",\\n \\\"default\\\": 1,\\n },\\n \\\"max_tokens\\\": {\\n \\\"display_name\\\": \\\"Max tokens\\\",\\n \\\"field_type\\\": \\\"int\\\",\\n \\\"advanced\\\": False,\\n \\\"required\\\": False,\\n \\\"default\\\": 256,\\n \\\"info\\\": \\\"The maximum number of tokens to generate for each chat completion.\\\",\\n },\\n \\\"max_retries\\\": {\\n \\\"display_name\\\": \\\"Max retries\\\",\\n \\\"field_type\\\": \\\"int\\\",\\n \\\"advanced\\\": True,\\n \\\"required\\\": False,\\n \\\"default\\\": 6,\\n },\\n \\\"verbose\\\": {\\n \\\"display_name\\\": \\\"Verbose\\\",\\n \\\"field_type\\\": \\\"bool\\\",\\n \\\"advanced\\\": True,\\n \\\"required\\\": False,\\n \\\"default\\\": False,\\n },\\n }\\n\\n def build(\\n self,\\n model: str,\\n provider: str,\\n api_key: Optional[str] = None,\\n streaming: bool = True,\\n temperature: Optional[float] = 0.7,\\n model_kwargs: Optional[Dict[str, Any]] = {},\\n top_p: Optional[float] = None,\\n top_k: Optional[int] = None,\\n n: int = 1,\\n max_tokens: int = 256,\\n max_retries: int = 6,\\n verbose: bool = False,\\n ) -> Union[BaseLanguageModel, Callable]:\\n try:\\n import litellm # type: ignore\\n\\n litellm.drop_params = True\\n litellm.set_verbose = verbose\\n except ImportError:\\n raise ChatLiteLLMException(\\n \\\"Could not import litellm python package. \\\" \\\"Please install it with `pip install litellm`\\\"\\n )\\n provider_map = {\\n \\\"OpenAI\\\": \\\"openai_api_key\\\",\\n \\\"Azure\\\": \\\"azure_api_key\\\",\\n \\\"Anthropic\\\": \\\"anthropic_api_key\\\",\\n \\\"Replicate\\\": \\\"replicate_api_key\\\",\\n \\\"Cohere\\\": \\\"cohere_api_key\\\",\\n \\\"OpenRouter\\\": \\\"openrouter_api_key\\\",\\n }\\n # Set the API key based on the provider\\n kwarg = {provider_map[provider]: api_key}\\n\\n LLM = ChatLiteLLM(\\n model=model,\\n client=None,\\n streaming=streaming,\\n temperature=temperature,\\n model_kwargs=model_kwargs if model_kwargs is not None else {},\\n top_p=top_p,\\n top_k=top_k,\\n n=n,\\n max_tokens=max_tokens,\\n max_retries=max_retries,\\n **kwarg,\\n )\\n return LLM\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"max_retries\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":6,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_retries\",\"display_name\":\"Max retries\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"max_tokens\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":256,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_tokens\",\"display_name\":\"Max tokens\",\"advanced\":false,\"dynamic\":false,\"info\":\"The maximum number of tokens to generate for each chat completion.\",\"title_case\":false},\"model\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model\",\"display_name\":\"Model name\",\"advanced\":false,\"dynamic\":false,\"info\":\"The name of the model to use. For example, `gpt-3.5-turbo`.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"model_kwargs\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model_kwargs\",\"display_name\":\"Model kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"n\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":1,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"n\",\"display_name\":\"N\",\"advanced\":true,\"dynamic\":false,\"info\":\"Number of chat completions to generate for each prompt. Note that the API may not return the full n completions if duplicates are generated.\",\"title_case\":false},\"provider\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"OpenAI\",\"Azure\",\"Anthropic\",\"Replicate\",\"Cohere\",\"OpenRouter\"],\"name\":\"provider\",\"display_name\":\"Provider\",\"advanced\":false,\"dynamic\":false,\"info\":\"The provider of the API key.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"streaming\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":true,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"streaming\",\"display_name\":\"Streaming\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"temperature\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.7,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"top_k\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_k\",\"display_name\":\"Top k\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"top_p\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_p\",\"display_name\":\"Top p\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"verbose\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"verbose\",\"display_name\":\"Verbose\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"`LiteLLM` collection of large language models.\",\"base_classes\":[\"BaseLanguageModel\",\"Runnable\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"object\",\"Callable\"],\"display_name\":\"ChatLiteLLM\",\"documentation\":\"https://python.langchain.com/docs/integrations/chat/litellm\",\"custom_fields\":{\"model\":null,\"provider\":null,\"api_key\":null,\"streaming\":null,\"temperature\":null,\"model_kwargs\":null,\"top_p\":null,\"top_k\":null,\"n\":null,\"max_tokens\":null,\"max_retries\":null,\"verbose\":null},\"output_types\":[\"BaseLanguageModel\",\"Callable\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"CTransformersSpecs\":{\"template\":{\"model_file\":{\"type\":\"file\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[\".bin\"],\"file_path\":\"\",\"password\":false,\"name\":\"model_file\",\"display_name\":\"Model File\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Dict, Optional\\n\\nfrom langchain_community.llms.ctransformers import CTransformers\\n\\nfrom langflow import CustomComponent\\n\\n\\nclass CTransformersComponent(CustomComponent):\\n display_name = \\\"CTransformers\\\"\\n description = \\\"C Transformers LLM models\\\"\\n documentation = \\\"https://python.langchain.com/docs/modules/model_io/models/llms/integrations/ctransformers\\\"\\n\\n def build_config(self):\\n return {\\n \\\"model\\\": {\\\"display_name\\\": \\\"Model\\\", \\\"required\\\": True},\\n \\\"model_file\\\": {\\n \\\"display_name\\\": \\\"Model File\\\",\\n \\\"required\\\": False,\\n \\\"field_type\\\": \\\"file\\\",\\n \\\"file_types\\\": [\\\".bin\\\"],\\n },\\n \\\"model_type\\\": {\\\"display_name\\\": \\\"Model Type\\\", \\\"required\\\": True},\\n \\\"config\\\": {\\n \\\"display_name\\\": \\\"Config\\\",\\n \\\"advanced\\\": True,\\n \\\"required\\\": False,\\n \\\"field_type\\\": \\\"dict\\\",\\n \\\"value\\\": '{\\\"top_k\\\":40,\\\"top_p\\\":0.95,\\\"temperature\\\":0.8,\\\"repetition_penalty\\\":1.1,\\\"last_n_tokens\\\":64,\\\"seed\\\":-1,\\\"max_new_tokens\\\":256,\\\"stop\\\":\\\"\\\",\\\"stream\\\":\\\"False\\\",\\\"reset\\\":\\\"True\\\",\\\"batch_size\\\":8,\\\"threads\\\":-1,\\\"context_length\\\":-1,\\\"gpu_layers\\\":0}',\\n },\\n }\\n\\n def build(self, model: str, model_file: str, model_type: str, config: Optional[Dict] = None) -> CTransformers:\\n return CTransformers(model=model, model_file=model_file, model_type=model_type, config=config) # type: ignore\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"config\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"{\\\"top_k\\\":40,\\\"top_p\\\":0.95,\\\"temperature\\\":0.8,\\\"repetition_penalty\\\":1.1,\\\"last_n_tokens\\\":64,\\\"seed\\\":-1,\\\"max_new_tokens\\\":256,\\\"stop\\\":\\\"\\\",\\\"stream\\\":\\\"False\\\",\\\"reset\\\":\\\"True\\\",\\\"batch_size\\\":8,\\\"threads\\\":-1,\\\"context_length\\\":-1,\\\"gpu_layers\\\":0}\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"config\",\"display_name\":\"Config\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"model\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model\",\"display_name\":\"Model\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"model_type\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model_type\",\"display_name\":\"Model Type\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"C Transformers LLM models\",\"base_classes\":[\"BaseLanguageModel\",\"Runnable\",\"Generic\",\"CTransformers\",\"RunnableSerializable\",\"Serializable\",\"object\",\"BaseLLM\",\"LLM\"],\"display_name\":\"CTransformers\",\"documentation\":\"https://python.langchain.com/docs/modules/model_io/models/llms/integrations/ctransformers\",\"custom_fields\":{\"model\":null,\"model_file\":null,\"model_type\":null,\"config\":null},\"output_types\":[\"CTransformers\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"HuggingFaceEndpointsSpecs\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain.llms.base import BaseLLM\\nfrom langchain.llms.huggingface_endpoint import HuggingFaceEndpoint\\nfrom langflow import CustomComponent\\n\\n\\nclass HuggingFaceEndpointsComponent(CustomComponent):\\n display_name: str = \\\"Hugging Face Inference API\\\"\\n description: str = \\\"LLM model from Hugging Face Inference API.\\\"\\n\\n def build_config(self):\\n return {\\n \\\"endpoint_url\\\": {\\\"display_name\\\": \\\"Endpoint URL\\\", \\\"password\\\": True},\\n \\\"task\\\": {\\n \\\"display_name\\\": \\\"Task\\\",\\n \\\"options\\\": [\\\"text2text-generation\\\", \\\"text-generation\\\", \\\"summarization\\\"],\\n },\\n \\\"huggingfacehub_api_token\\\": {\\\"display_name\\\": \\\"API token\\\", \\\"password\\\": True},\\n \\\"model_kwargs\\\": {\\n \\\"display_name\\\": \\\"Model Keyword Arguments\\\",\\n \\\"field_type\\\": \\\"code\\\",\\n },\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(\\n self,\\n endpoint_url: str,\\n task: str = \\\"text2text-generation\\\",\\n huggingfacehub_api_token: Optional[str] = None,\\n model_kwargs: Optional[dict] = None,\\n ) -> BaseLLM:\\n try:\\n output = HuggingFaceEndpoint( # type: ignore\\n endpoint_url=endpoint_url,\\n task=task,\\n huggingfacehub_api_token=huggingfacehub_api_token,\\n model_kwargs=model_kwargs or {},\\n )\\n except Exception as e:\\n raise ValueError(\\\"Could not connect to HuggingFace Endpoints API.\\\") from e\\n return output\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"endpoint_url\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"endpoint_url\",\"display_name\":\"Endpoint URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"huggingfacehub_api_token\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"huggingfacehub_api_token\",\"display_name\":\"API token\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"model_kwargs\":{\"type\":\"code\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model_kwargs\",\"display_name\":\"Model Keyword Arguments\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"task\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"text2text-generation\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"text2text-generation\",\"text-generation\",\"summarization\"],\"name\":\"task\",\"display_name\":\"Task\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"LLM model from Hugging Face Inference API.\",\"base_classes\":[\"BaseLanguageModel\",\"Runnable\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"object\",\"BaseLLM\"],\"display_name\":\"Hugging Face Inference API\",\"documentation\":\"\",\"custom_fields\":{\"endpoint_url\":null,\"task\":null,\"huggingfacehub_api_token\":null,\"model_kwargs\":null},\"output_types\":[\"BaseLLM\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"ChatOpenAISpecs\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional, Union\\n\\nfrom langchain.llms import BaseLLM\\nfrom langchain_community.chat_models.openai import ChatOpenAI\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import BaseLanguageModel, NestedDict\\n\\n\\nclass ChatOpenAIComponent(CustomComponent):\\n display_name = \\\"ChatOpenAI\\\"\\n description = \\\"`OpenAI` Chat large language models API.\\\"\\n\\n def build_config(self):\\n return {\\n \\\"max_tokens\\\": {\\n \\\"display_name\\\": \\\"Max Tokens\\\",\\n \\\"advanced\\\": False,\\n \\\"required\\\": False,\\n },\\n \\\"model_kwargs\\\": {\\n \\\"display_name\\\": \\\"Model Kwargs\\\",\\n \\\"advanced\\\": True,\\n \\\"required\\\": False,\\n },\\n \\\"model_name\\\": {\\n \\\"display_name\\\": \\\"Model Name\\\",\\n \\\"advanced\\\": False,\\n \\\"required\\\": False,\\n \\\"options\\\": [\\n \\\"gpt-4-turbo-preview\\\",\\n \\\"gpt-4-0125-preview\\\",\\n \\\"gpt-4-1106-preview\\\",\\n \\\"gpt-4-vision-preview\\\",\\n \\\"gpt-3.5-turbo-0125\\\",\\n \\\"gpt-3.5-turbo-1106\\\",\\n ],\\n },\\n \\\"openai_api_base\\\": {\\n \\\"display_name\\\": \\\"OpenAI API Base\\\",\\n \\\"advanced\\\": False,\\n \\\"required\\\": False,\\n \\\"info\\\": (\\n \\\"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\\\n\\\\n\\\"\\n \\\"You can change this to use other APIs like JinaChat, LocalAI and Prem.\\\"\\n ),\\n },\\n \\\"openai_api_key\\\": {\\n \\\"display_name\\\": \\\"OpenAI API Key\\\",\\n \\\"advanced\\\": False,\\n \\\"required\\\": False,\\n \\\"password\\\": True,\\n },\\n \\\"temperature\\\": {\\n \\\"display_name\\\": \\\"Temperature\\\",\\n \\\"advanced\\\": False,\\n \\\"required\\\": False,\\n \\\"value\\\": 0.7,\\n },\\n }\\n\\n def build(\\n self,\\n max_tokens: Optional[int] = 256,\\n model_kwargs: NestedDict = {},\\n model_name: str = \\\"gpt-4-1106-preview\\\",\\n openai_api_base: Optional[str] = None,\\n openai_api_key: Optional[str] = None,\\n temperature: float = 0.7,\\n ) -> Union[BaseLanguageModel, BaseLLM]:\\n if not openai_api_base:\\n openai_api_base = \\\"https://api.openai.com/v1\\\"\\n return ChatOpenAI(\\n max_tokens=max_tokens,\\n model_kwargs=model_kwargs,\\n model=model_name,\\n base_url=openai_api_base,\\n api_key=openai_api_key,\\n temperature=temperature,\\n )\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"max_tokens\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":256,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_tokens\",\"display_name\":\"Max Tokens\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"model_kwargs\":{\"type\":\"NestedDict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model_kwargs\",\"display_name\":\"Model Kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"model_name\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"gpt-4-1106-preview\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"gpt-4-turbo-preview\",\"gpt-4-0125-preview\",\"gpt-4-1106-preview\",\"gpt-4-vision-preview\",\"gpt-3.5-turbo-0125\",\"gpt-3.5-turbo-1106\"],\"name\":\"model_name\",\"display_name\":\"Model Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"openai_api_base\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"openai_api_base\",\"display_name\":\"OpenAI API Base\",\"advanced\":false,\"dynamic\":false,\"info\":\"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"openai_api_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"openai_api_key\",\"display_name\":\"OpenAI API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"temperature\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.7,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"`OpenAI` Chat large language models API.\",\"base_classes\":[\"BaseLanguageModel\",\"Runnable\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"object\",\"BaseLLM\"],\"display_name\":\"ChatOpenAI\",\"documentation\":\"\",\"custom_fields\":{\"max_tokens\":null,\"model_kwargs\":null,\"model_name\":null,\"openai_api_base\":null,\"openai_api_key\":null,\"temperature\":null},\"output_types\":[\"BaseLanguageModel\",\"BaseLLM\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"OllamaLLMSpecs\":{\"template\":{\"base_url\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"base_url\",\"display_name\":\"Base URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"Endpoint of the Ollama API. Defaults to 'http://localhost:11434' if not specified.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import List, Optional\\n\\nfrom langchain.llms.base import BaseLLM\\nfrom langchain_community.llms.ollama import Ollama\\n\\nfrom langflow import CustomComponent\\n\\n\\nclass OllamaLLM(CustomComponent):\\n display_name = \\\"Ollama\\\"\\n description = \\\"Local LLM with Ollama.\\\"\\n\\n def build_config(self) -> dict:\\n return {\\n \\\"base_url\\\": {\\n \\\"display_name\\\": \\\"Base URL\\\",\\n \\\"info\\\": \\\"Endpoint of the Ollama API. Defaults to 'http://localhost:11434' if not specified.\\\",\\n },\\n \\\"model\\\": {\\n \\\"display_name\\\": \\\"Model Name\\\",\\n \\\"value\\\": \\\"llama2\\\",\\n \\\"info\\\": \\\"Refer to https://ollama.ai/library for more models.\\\",\\n },\\n \\\"temperature\\\": {\\n \\\"display_name\\\": \\\"Temperature\\\",\\n \\\"field_type\\\": \\\"float\\\",\\n \\\"value\\\": 0.8,\\n \\\"info\\\": \\\"Controls the creativity of model responses.\\\",\\n },\\n \\\"mirostat\\\": {\\n \\\"display_name\\\": \\\"Mirostat\\\",\\n \\\"options\\\": [\\\"Disabled\\\", \\\"Mirostat\\\", \\\"Mirostat 2.0\\\"],\\n \\\"info\\\": \\\"Enable/disable Mirostat sampling for controlling perplexity.\\\",\\n \\\"value\\\": \\\"Disabled\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"mirostat_eta\\\": {\\n \\\"display_name\\\": \\\"Mirostat Eta\\\",\\n \\\"field_type\\\": \\\"float\\\",\\n \\\"info\\\": \\\"Learning rate influencing the algorithm's response to feedback.\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"mirostat_tau\\\": {\\n \\\"display_name\\\": \\\"Mirostat Tau\\\",\\n \\\"field_type\\\": \\\"float\\\",\\n \\\"info\\\": \\\"Controls balance between coherence and diversity.\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"num_ctx\\\": {\\n \\\"display_name\\\": \\\"Context Window Size\\\",\\n \\\"field_type\\\": \\\"int\\\",\\n \\\"info\\\": \\\"Size of the context window for generating the next token.\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"num_gpu\\\": {\\n \\\"display_name\\\": \\\"Number of GPUs\\\",\\n \\\"field_type\\\": \\\"int\\\",\\n \\\"info\\\": \\\"Number of GPUs to use for computation.\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"num_thread\\\": {\\n \\\"display_name\\\": \\\"Number of Threads\\\",\\n \\\"field_type\\\": \\\"int\\\",\\n \\\"info\\\": \\\"Number of threads to use during computation.\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"repeat_last_n\\\": {\\n \\\"display_name\\\": \\\"Repeat Last N\\\",\\n \\\"field_type\\\": \\\"int\\\",\\n \\\"info\\\": \\\"Sets how far back the model looks to prevent repetition.\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"repeat_penalty\\\": {\\n \\\"display_name\\\": \\\"Repeat Penalty\\\",\\n \\\"field_type\\\": \\\"float\\\",\\n \\\"info\\\": \\\"Penalty for repetitions in generated text.\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"stop\\\": {\\n \\\"display_name\\\": \\\"Stop Tokens\\\",\\n \\\"info\\\": \\\"List of tokens to signal the model to stop generating text.\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"tfs_z\\\": {\\n \\\"display_name\\\": \\\"TFS Z\\\",\\n \\\"field_type\\\": \\\"float\\\",\\n \\\"info\\\": \\\"Tail free sampling to reduce impact of less probable tokens.\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"top_k\\\": {\\n \\\"display_name\\\": \\\"Top K\\\",\\n \\\"field_type\\\": \\\"int\\\",\\n \\\"info\\\": \\\"Limits token selection to top K for reducing nonsense generation.\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"top_p\\\": {\\n \\\"display_name\\\": \\\"Top P\\\",\\n \\\"field_type\\\": \\\"int\\\",\\n \\\"info\\\": \\\"Works with top-k to control diversity of generated text.\\\",\\n \\\"advanced\\\": True,\\n },\\n }\\n\\n def build(\\n self,\\n base_url: Optional[str],\\n model: str,\\n temperature: Optional[float],\\n mirostat: Optional[str],\\n mirostat_eta: Optional[float] = None,\\n mirostat_tau: Optional[float] = None,\\n num_ctx: Optional[int] = None,\\n num_gpu: Optional[int] = None,\\n num_thread: Optional[int] = None,\\n repeat_last_n: Optional[int] = None,\\n repeat_penalty: Optional[float] = None,\\n stop: Optional[List[str]] = None,\\n tfs_z: Optional[float] = None,\\n top_k: Optional[int] = None,\\n top_p: Optional[int] = None,\\n ) -> BaseLLM:\\n if not base_url:\\n base_url = \\\"http://localhost:11434\\\"\\n\\n # Mapping mirostat settings to their corresponding values\\n mirostat_options = {\\\"Mirostat\\\": 1, \\\"Mirostat 2.0\\\": 2}\\n\\n # Default to 0 for 'Disabled'\\n mirostat_value = mirostat_options.get(mirostat, 0) # type: ignore\\n\\n # Set mirostat_eta and mirostat_tau to None if mirostat is disabled\\n if mirostat_value == 0:\\n mirostat_eta = None\\n mirostat_tau = None\\n\\n try:\\n llm = Ollama(\\n base_url=base_url,\\n model=model,\\n mirostat=mirostat_value,\\n mirostat_eta=mirostat_eta,\\n mirostat_tau=mirostat_tau,\\n num_ctx=num_ctx,\\n num_gpu=num_gpu,\\n num_thread=num_thread,\\n repeat_last_n=repeat_last_n,\\n repeat_penalty=repeat_penalty,\\n temperature=temperature,\\n stop=stop,\\n tfs_z=tfs_z,\\n top_k=top_k,\\n top_p=top_p,\\n )\\n\\n except Exception as e:\\n raise ValueError(\\\"Could not connect to Ollama.\\\") from e\\n\\n return llm\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"mirostat\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"Disabled\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"Disabled\",\"Mirostat\",\"Mirostat 2.0\"],\"name\":\"mirostat\",\"display_name\":\"Mirostat\",\"advanced\":true,\"dynamic\":false,\"info\":\"Enable/disable Mirostat sampling for controlling perplexity.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"mirostat_eta\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"mirostat_eta\",\"display_name\":\"Mirostat Eta\",\"advanced\":true,\"dynamic\":false,\"info\":\"Learning rate influencing the algorithm's response to feedback.\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"mirostat_tau\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"mirostat_tau\",\"display_name\":\"Mirostat Tau\",\"advanced\":true,\"dynamic\":false,\"info\":\"Controls balance between coherence and diversity.\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"model\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"llama2\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model\",\"display_name\":\"Model Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"Refer to https://ollama.ai/library for more models.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"num_ctx\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"num_ctx\",\"display_name\":\"Context Window Size\",\"advanced\":true,\"dynamic\":false,\"info\":\"Size of the context window for generating the next token.\",\"title_case\":false},\"num_gpu\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"num_gpu\",\"display_name\":\"Number of GPUs\",\"advanced\":true,\"dynamic\":false,\"info\":\"Number of GPUs to use for computation.\",\"title_case\":false},\"num_thread\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"num_thread\",\"display_name\":\"Number of Threads\",\"advanced\":true,\"dynamic\":false,\"info\":\"Number of threads to use during computation.\",\"title_case\":false},\"repeat_last_n\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"repeat_last_n\",\"display_name\":\"Repeat Last N\",\"advanced\":true,\"dynamic\":false,\"info\":\"Sets how far back the model looks to prevent repetition.\",\"title_case\":false},\"repeat_penalty\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"repeat_penalty\",\"display_name\":\"Repeat Penalty\",\"advanced\":true,\"dynamic\":false,\"info\":\"Penalty for repetitions in generated text.\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"stop\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"stop\",\"display_name\":\"Stop Tokens\",\"advanced\":true,\"dynamic\":false,\"info\":\"List of tokens to signal the model to stop generating text.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"temperature\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.8,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"Controls the creativity of model responses.\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"tfs_z\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"tfs_z\",\"display_name\":\"TFS Z\",\"advanced\":true,\"dynamic\":false,\"info\":\"Tail free sampling to reduce impact of less probable tokens.\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"top_k\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_k\",\"display_name\":\"Top K\",\"advanced\":true,\"dynamic\":false,\"info\":\"Limits token selection to top K for reducing nonsense generation.\",\"title_case\":false},\"top_p\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_p\",\"display_name\":\"Top P\",\"advanced\":true,\"dynamic\":false,\"info\":\"Works with top-k to control diversity of generated text.\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Local LLM with Ollama.\",\"base_classes\":[\"BaseLanguageModel\",\"Runnable\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"object\",\"BaseLLM\"],\"display_name\":\"Ollama\",\"documentation\":\"\",\"custom_fields\":{\"base_url\":null,\"model\":null,\"temperature\":null,\"mirostat\":null,\"mirostat_eta\":null,\"mirostat_tau\":null,\"num_ctx\":null,\"num_gpu\":null,\"num_thread\":null,\"repeat_last_n\":null,\"repeat_penalty\":null,\"stop\":null,\"tfs_z\":null,\"top_k\":null,\"top_p\":null},\"output_types\":[\"BaseLLM\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true}},\"io\":{\"ChatOutput\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional, Union\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Text\\nfrom langflow.schema import Record\\n\\n\\nclass ChatOutput(CustomComponent):\\n display_name = \\\"Chat Output\\\"\\n description = \\\"Used to send a message to the chat.\\\"\\n\\n field_config = {\\n \\\"code\\\": {\\n \\\"show\\\": True,\\n }\\n }\\n\\n def build_config(self):\\n return {\\n \\\"message\\\": {\\\"input_types\\\": [\\\"Text\\\"], \\\"display_name\\\": \\\"Message\\\"},\\n \\\"sender\\\": {\\n \\\"options\\\": [\\\"Machine\\\", \\\"User\\\"],\\n \\\"display_name\\\": \\\"Sender Type\\\",\\n },\\n \\\"sender_name\\\": {\\\"display_name\\\": \\\"Sender Name\\\"},\\n \\\"session_id\\\": {\\n \\\"display_name\\\": \\\"Session ID\\\",\\n \\\"info\\\": \\\"Session ID of the chat history.\\\",\\n \\\"input_types\\\": [\\\"Text\\\"],\\n },\\n \\\"return_record\\\": {\\n \\\"display_name\\\": \\\"Return Record\\\",\\n \\\"info\\\": \\\"Return the message as a record containing the sender, sender_name, and session_id.\\\",\\n },\\n }\\n\\n def build(\\n self,\\n sender: Optional[str] = \\\"Machine\\\",\\n sender_name: Optional[str] = \\\"AI\\\",\\n session_id: Optional[str] = None,\\n message: Optional[str] = None,\\n return_record: Optional[bool] = False,\\n ) -> Union[Text, Record]:\\n if return_record:\\n if isinstance(message, Record):\\n # Update the data of the record\\n message.data[\\\"sender\\\"] = sender\\n message.data[\\\"sender_name\\\"] = sender_name\\n message.data[\\\"session_id\\\"] = session_id\\n else:\\n message = Record(\\n text=message,\\n data={\\n \\\"sender\\\": sender,\\n \\\"sender_name\\\": sender_name,\\n \\\"session_id\\\": session_id,\\n },\\n )\\n if not message:\\n message = \\\"\\\"\\n self.status = message\\n return message\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"message\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"message\",\"display_name\":\"Message\",\"advanced\":false,\"input_types\":[\"Text\",\"Text\"],\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"return_record\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"return_record\",\"display_name\":\"Return Record\",\"advanced\":false,\"dynamic\":false,\"info\":\"Return the message as a record containing the sender, sender_name, and session_id.\",\"title_case\":false},\"sender\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"Machine\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"Machine\",\"User\"],\"name\":\"sender\",\"display_name\":\"Sender Type\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"sender_name\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"AI\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"sender_name\",\"display_name\":\"Sender Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"session_id\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"session_id\",\"display_name\":\"Session ID\",\"advanced\":false,\"input_types\":[\"Text\",\"Text\"],\"dynamic\":false,\"info\":\"Session ID of the chat history.\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Used to send a message to the chat.\",\"base_classes\":[\"Text\",\"object\",\"Record\"],\"display_name\":\"Chat Output\",\"documentation\":\"\",\"custom_fields\":{\"sender\":null,\"sender_name\":null,\"session_id\":null,\"message\":null,\"return_record\":null},\"output_types\":[\"Text\",\"Record\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"MessageHistory\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import List, Optional\\n\\nfrom langflow import CustomComponent\\nfrom langflow.memory import get_messages\\nfrom langflow.schema import Record\\n\\n\\nclass MessageHistoryComponent(CustomComponent):\\n display_name = \\\"Message History\\\"\\n description = \\\"Used to retrieve stored messages.\\\"\\n\\n def build_config(self):\\n return {\\n \\\"sender\\\": {\\n \\\"options\\\": [\\\"Machine\\\", \\\"User\\\"],\\n \\\"display_name\\\": \\\"Sender Type\\\",\\n },\\n \\\"sender_name\\\": {\\\"display_name\\\": \\\"Sender Name\\\"},\\n \\\"file_path\\\": {\\n \\\"display_name\\\": \\\"File Path\\\",\\n \\\"info\\\": \\\"Path of the local JSON file to store the messages. It should be a unique path for each chat history.\\\",\\n },\\n \\\"n_messages\\\": {\\n \\\"display_name\\\": \\\"Number of Messages\\\",\\n \\\"info\\\": \\\"Number of messages to retrieve.\\\",\\n },\\n \\\"session_id\\\": {\\n \\\"display_name\\\": \\\"Session ID\\\",\\n \\\"info\\\": \\\"Session ID of the chat history.\\\",\\n \\\"input_types\\\": [\\\"Text\\\"],\\n },\\n }\\n\\n def build(\\n self,\\n sender: Optional[str] = None,\\n sender_name: Optional[str] = None,\\n session_id: Optional[str] = None,\\n n_messages: int = 5,\\n ) -> List[Record]:\\n messages = get_messages(\\n sender=sender,\\n sender_name=sender_name,\\n session_id=session_id,\\n limit=n_messages,\\n )\\n self.status = messages\\n return messages\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"n_messages\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":5,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"n_messages\",\"display_name\":\"Number of Messages\",\"advanced\":false,\"dynamic\":false,\"info\":\"Number of messages to retrieve.\",\"title_case\":false},\"sender\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"Machine\",\"User\"],\"name\":\"sender\",\"display_name\":\"Sender Type\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"sender_name\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"sender_name\",\"display_name\":\"Sender Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"session_id\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"session_id\",\"display_name\":\"Session ID\",\"advanced\":false,\"input_types\":[\"Text\",\"Text\"],\"dynamic\":false,\"info\":\"Session ID of the chat history.\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Used to retrieve stored messages.\",\"base_classes\":[\"Record\"],\"display_name\":\"Message History\",\"documentation\":\"\",\"custom_fields\":{\"sender\":null,\"sender_name\":null,\"session_id\":null,\"n_messages\":null},\"output_types\":[\"Record\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"TextOutput\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Text\\n\\n\\nclass TextOutput(CustomComponent):\\n display_name = \\\"Text Output\\\"\\n description = \\\"Used to pass text output to the next component.\\\"\\n\\n field_config = {\\n \\\"value\\\": {\\\"display_name\\\": \\\"Value\\\"},\\n }\\n\\n def build(self, value: Optional[str] = \\\"\\\") -> Text:\\n self.status = value\\n if not value:\\n value = \\\"\\\"\\n return value\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"value\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"value\",\"display_name\":\"Value\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Used to pass text output to the next component.\",\"base_classes\":[\"Text\",\"object\"],\"display_name\":\"Text Output\",\"documentation\":\"\",\"custom_fields\":{\"value\":null},\"output_types\":[\"Text\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"StoreMessages\":{\"template\":{\"records\":{\"type\":\"Record\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"records\",\"display_name\":\"Records\",\"advanced\":false,\"dynamic\":false,\"info\":\"The list of records to store. Each record should contain the keys 'sender', 'sender_name', and 'session_id'.\",\"title_case\":false},\"texts\":{\"type\":\"Text\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"texts\",\"display_name\":\"Texts\",\"advanced\":false,\"dynamic\":false,\"info\":\"The list of texts to store. If records is not provided, texts must be provided.\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import List, Optional\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Text\\nfrom langflow.memory import add_messages\\nfrom langflow.schema import Record\\n\\n\\nclass StoreMessages(CustomComponent):\\n display_name = \\\"Store Messages\\\"\\n description = \\\"Used to store messages.\\\"\\n\\n def build_config(self):\\n return {\\n \\\"records\\\": {\\n \\\"display_name\\\": \\\"Records\\\",\\n \\\"info\\\": \\\"The list of records to store. Each record should contain the keys 'sender', 'sender_name', and 'session_id'.\\\",\\n },\\n \\\"texts\\\": {\\n \\\"display_name\\\": \\\"Texts\\\",\\n \\\"info\\\": \\\"The list of texts to store. If records is not provided, texts must be provided.\\\",\\n },\\n \\\"session_id\\\": {\\n \\\"display_name\\\": \\\"Session ID\\\",\\n \\\"info\\\": \\\"The session ID to store.\\\",\\n },\\n \\\"sender\\\": {\\n \\\"display_name\\\": \\\"Sender\\\",\\n \\\"info\\\": \\\"The sender to store.\\\",\\n },\\n \\\"sender_name\\\": {\\n \\\"display_name\\\": \\\"Sender Name\\\",\\n \\\"info\\\": \\\"The sender name to store.\\\",\\n },\\n }\\n\\n def build(\\n self,\\n records: Optional[List[Record]] = None,\\n texts: Optional[List[Text]] = None,\\n session_id: Optional[str] = None,\\n sender: Optional[str] = None,\\n sender_name: Optional[str] = None,\\n ) -> List[Record]:\\n # Records is the main way to store messages\\n # If records is not provided, we can use texts\\n # but we need to create the records from the texts\\n # and the other parameters\\n if not texts and not records:\\n raise ValueError(\\\"Either texts or records must be provided.\\\")\\n\\n if not records:\\n records = []\\n if not session_id or not sender or not sender_name:\\n raise ValueError(\\\"If passing texts, session_id, sender, and sender_name must be provided.\\\")\\n for text in texts:\\n record = Record(\\n text=text,\\n data={\\n \\\"session_id\\\": session_id,\\n \\\"sender\\\": sender,\\n \\\"sender_name\\\": sender_name,\\n },\\n )\\n records.append(record)\\n elif isinstance(records, Record):\\n records = [records]\\n\\n self.status = records\\n records = add_messages(records)\\n return records\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"sender\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"sender\",\"display_name\":\"Sender\",\"advanced\":false,\"dynamic\":false,\"info\":\"The sender to store.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"sender_name\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"sender_name\",\"display_name\":\"Sender Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"The sender name to store.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"session_id\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"session_id\",\"display_name\":\"Session ID\",\"advanced\":false,\"dynamic\":false,\"info\":\"The session ID to store.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Used to store messages.\",\"base_classes\":[\"Record\"],\"display_name\":\"Store Messages\",\"documentation\":\"\",\"custom_fields\":{\"records\":null,\"texts\":null,\"session_id\":null,\"sender\":null,\"sender_name\":null},\"output_types\":[\"Record\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"ChatInput\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langflow import CustomComponent\\nfrom langflow.schema import Record\\n\\n\\nclass ChatInput(CustomComponent):\\n display_name = \\\"Chat Input\\\"\\n description = \\\"Used to get user input from the chat.\\\"\\n\\n def build_config(self):\\n return {\\n \\\"message\\\": {\\n \\\"input_types\\\": [\\\"Text\\\"],\\n \\\"display_name\\\": \\\"Message\\\",\\n \\\"multiline\\\": True,\\n },\\n \\\"sender\\\": {\\n \\\"options\\\": [\\\"Machine\\\", \\\"User\\\"],\\n \\\"display_name\\\": \\\"Sender Type\\\",\\n },\\n \\\"sender_name\\\": {\\\"display_name\\\": \\\"Sender Name\\\"},\\n \\\"session_id\\\": {\\n \\\"display_name\\\": \\\"Session ID\\\",\\n \\\"info\\\": \\\"Session ID of the chat history.\\\",\\n },\\n \\\"return_record\\\": {\\n \\\"display_name\\\": \\\"Return Record\\\",\\n \\\"info\\\": \\\"Return the message as a record containing the sender, sender_name, and session_id.\\\",\\n },\\n }\\n\\n def build(\\n self,\\n sender: Optional[str] = \\\"User\\\",\\n sender_name: Optional[str] = \\\"User\\\",\\n message: Optional[str] = None,\\n session_id: Optional[str] = None,\\n return_record: Optional[bool] = False,\\n ) -> Record:\\n if return_record:\\n if isinstance(message, Record):\\n # Update the data of the record\\n message.data[\\\"sender\\\"] = sender\\n message.data[\\\"sender_name\\\"] = sender_name\\n message.data[\\\"session_id\\\"] = session_id\\n else:\\n message = Record(\\n text=message,\\n data={\\n \\\"sender\\\": sender,\\n \\\"sender_name\\\": sender_name,\\n \\\"session_id\\\": session_id,\\n },\\n )\\n if not message:\\n message = \\\"\\\"\\n self.status = message\\n return message\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"message\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"message\",\"display_name\":\"Message\",\"advanced\":false,\"input_types\":[\"Text\",\"Text\"],\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"return_record\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"return_record\",\"display_name\":\"Return Record\",\"advanced\":false,\"dynamic\":false,\"info\":\"Return the message as a record containing the sender, sender_name, and session_id.\",\"title_case\":false},\"sender\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"User\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"Machine\",\"User\"],\"name\":\"sender\",\"display_name\":\"Sender Type\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"sender_name\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"User\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"sender_name\",\"display_name\":\"Sender Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"session_id\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"session_id\",\"display_name\":\"Session ID\",\"advanced\":false,\"dynamic\":false,\"info\":\"Session ID of the chat history.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Used to get user input from the chat.\",\"base_classes\":[\"Record\"],\"display_name\":\"Chat Input\",\"documentation\":\"\",\"custom_fields\":{\"sender\":null,\"sender_name\":null,\"message\":null,\"session_id\":null,\"return_record\":null},\"output_types\":[\"Record\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"TextInput\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Text\\n\\n\\nclass TextInput(CustomComponent):\\n display_name = \\\"Text Input\\\"\\n description = \\\"Used to pass text input to the next component.\\\"\\n\\n field_config = {\\n \\\"value\\\": {\\\"display_name\\\": \\\"Value\\\"},\\n }\\n\\n def build(self, value: Optional[str] = \\\"\\\") -> Text:\\n self.status = value\\n if not value:\\n value = \\\"\\\"\\n return value\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"value\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"value\",\"display_name\":\"Value\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Used to pass text input to the next component.\",\"base_classes\":[\"Text\",\"object\"],\"display_name\":\"Text Input\",\"documentation\":\"\",\"custom_fields\":{\"value\":null},\"output_types\":[\"Text\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true}},\"prompts\":{\"Prompt\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from langchain_core.prompts import PromptTemplate\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Prompt, TemplateField, Text\\n\\n\\nclass PromptComponent(CustomComponent):\\n display_name: str = \\\"Prompt\\\"\\n description: str = \\\"A component for creating prompts using templates\\\"\\n beta = True\\n\\n def build_config(self):\\n return {\\n \\\"template\\\": TemplateField(display_name=\\\"Template\\\"),\\n \\\"code\\\": TemplateField(advanced=True),\\n }\\n\\n def build(\\n self,\\n template: Prompt,\\n **kwargs,\\n ) -> Text:\\n prompt_template = PromptTemplate.from_template(template)\\n\\n attributes_to_check = [\\\"text\\\", \\\"page_content\\\"]\\n for key, value in kwargs.items():\\n for attribute in attributes_to_check:\\n if hasattr(value, attribute):\\n kwargs[key] = getattr(value, attribute)\\n\\n try:\\n formated_prompt = prompt_template.format(**kwargs)\\n except Exception as exc:\\n raise ValueError(f\\\"Error formatting prompt: {exc}\\\") from exc\\n self.status = f'Prompt: \\\"{formated_prompt}\\\"'\\n return formated_prompt\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":true,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"template\":{\"type\":\"prompt\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"template\",\"display_name\":\"Template\",\"advanced\":false,\"input_types\":[\"Text\"],\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"A component for creating prompts using templates\",\"base_classes\":[\"Text\",\"object\"],\"display_name\":\"Prompt\",\"documentation\":\"\",\"custom_fields\":{\"template\":null},\"output_types\":[\"Text\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true}}}" + "text": "{\"chains\":{\"ConversationalRetrievalChain\":{\"template\":{\"callbacks\":{\"type\":\"Callbacks\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"callbacks\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"condense_question_llm\":{\"type\":\"BaseLanguageModel\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"condense_question_llm\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"condense_question_prompt\":{\"type\":\"BasePromptTemplate\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":{\"name\":null,\"input_variables\":[\"chat_history\",\"question\"],\"input_types\":{},\"output_parser\":null,\"partial_variables\":{},\"metadata\":null,\"tags\":null,\"template\":\"Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question, in its original language.\\n\\nChat History:\\n{chat_history}\\nFollow Up Input: {question}\\nStandalone question:\",\"template_format\":\"f-string\",\"validate_template\":false},\"fileTypes\":[],\"password\":false,\"name\":\"condense_question_prompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"llm\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"memory\":{\"type\":\"BaseChatMemory\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"retriever\":{\"type\":\"BaseRetriever\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"retriever\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"chain_type\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"stuff\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"stuff\",\"map_reduce\",\"map_rerank\",\"refine\"],\"name\":\"chain_type\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"combine_docs_chain_kwargs\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"combine_docs_chain_kwargs\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"return_source_documents\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":true,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"return_source_documents\",\"display_name\":\"Return source documents\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"verbose\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"password\":false,\"name\":\"verbose\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"ConversationalRetrievalChain\"},\"description\":\"Convenience method to load chain from LLM and retriever.\",\"base_classes\":[\"BaseConversationalRetrievalChain\",\"Runnable\",\"Chain\",\"Generic\",\"Text\",\"RunnableSerializable\",\"Serializable\",\"object\",\"ConversationalRetrievalChain\",\"Callable\"],\"display_name\":\"ConversationalRetrievalChain\",\"documentation\":\"https://python.langchain.com/docs/modules/chains/popular/chat_vector_db\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":false,\"beta\":false,\"output_type\":\"Chain\"},\"LLMCheckerChain\":{\"template\":{\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Callable, Union\\n\\nfrom langchain.chains import LLMCheckerChain\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import BaseLanguageModel, Chain\\n\\n\\nclass LLMCheckerChainComponent(CustomComponent):\\n display_name = \\\"LLMCheckerChain\\\"\\n description = \\\"\\\"\\n documentation = \\\"https://python.langchain.com/docs/modules/chains/additional/llm_checker\\\"\\n\\n def build_config(self):\\n return {\\n \\\"llm\\\": {\\\"display_name\\\": \\\"LLM\\\"},\\n }\\n\\n def build(\\n self,\\n llm: BaseLanguageModel,\\n ) -> Union[Chain, Callable]:\\n return LLMCheckerChain.from_llm(llm=llm)\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"\",\"base_classes\":[\"Runnable\",\"Chain\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"object\",\"Callable\"],\"display_name\":\"LLMCheckerChain\",\"documentation\":\"https://python.langchain.com/docs/modules/chains/additional/llm_checker\",\"custom_fields\":{\"llm\":null},\"output_types\":[\"Chain\",\"Callable\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"LLMMathChain\":{\"template\":{\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"llm_chain\":{\"type\":\"LLMChain\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm_chain\",\"display_name\":\"LLM Chain\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"memory\":{\"type\":\"BaseMemory\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"memory\",\"display_name\":\"Memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Callable, Optional, Union\\n\\nfrom langchain.chains import LLMChain, LLMMathChain\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import BaseLanguageModel, BaseMemory, Chain\\n\\n\\nclass LLMMathChainComponent(CustomComponent):\\n display_name = \\\"LLMMathChain\\\"\\n description = \\\"Chain that interprets a prompt and executes python code to do math.\\\"\\n documentation = \\\"https://python.langchain.com/docs/modules/chains/additional/llm_math\\\"\\n\\n def build_config(self):\\n return {\\n \\\"llm\\\": {\\\"display_name\\\": \\\"LLM\\\"},\\n \\\"llm_chain\\\": {\\\"display_name\\\": \\\"LLM Chain\\\"},\\n \\\"memory\\\": {\\\"display_name\\\": \\\"Memory\\\"},\\n \\\"input_key\\\": {\\\"display_name\\\": \\\"Input Key\\\"},\\n \\\"output_key\\\": {\\\"display_name\\\": \\\"Output Key\\\"},\\n }\\n\\n def build(\\n self,\\n llm: BaseLanguageModel,\\n llm_chain: LLMChain,\\n input_key: str = \\\"question\\\",\\n output_key: str = \\\"answer\\\",\\n memory: Optional[BaseMemory] = None,\\n ) -> Union[LLMMathChain, Callable, Chain]:\\n return LLMMathChain(llm=llm, llm_chain=llm_chain, input_key=input_key, output_key=output_key, memory=memory)\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"input_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"question\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"input_key\",\"display_name\":\"Input Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"output_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"answer\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"output_key\",\"display_name\":\"Output Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Chain that interprets a prompt and executes python code to do math.\",\"base_classes\":[\"LLMMathChain\",\"Runnable\",\"Chain\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"object\",\"Callable\"],\"display_name\":\"LLMMathChain\",\"documentation\":\"https://python.langchain.com/docs/modules/chains/additional/llm_math\",\"custom_fields\":{\"llm\":null,\"llm_chain\":null,\"input_key\":null,\"output_key\":null,\"memory\":null},\"output_types\":[\"LLMMathChain\",\"Callable\",\"Chain\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"RetrievalQA\":{\"template\":{\"combine_documents_chain\":{\"type\":\"BaseCombineDocumentsChain\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"combine_documents_chain\",\"display_name\":\"Combine Documents Chain\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"memory\":{\"type\":\"BaseMemory\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"memory\",\"display_name\":\"Memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"retriever\":{\"type\":\"BaseRetriever\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"retriever\",\"display_name\":\"Retriever\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Callable, Optional, Union\\n\\nfrom langchain.chains.combine_documents.base import BaseCombineDocumentsChain\\nfrom langchain.chains.retrieval_qa.base import BaseRetrievalQA, RetrievalQA\\nfrom langchain_core.documents import Document\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import BaseMemory, BaseRetriever, Text\\n\\n\\nclass RetrievalQAComponent(CustomComponent):\\n display_name = \\\"Retrieval QA\\\"\\n description = \\\"Chain for question-answering against an index.\\\"\\n\\n def build_config(self):\\n return {\\n \\\"combine_documents_chain\\\": {\\\"display_name\\\": \\\"Combine Documents Chain\\\"},\\n \\\"retriever\\\": {\\\"display_name\\\": \\\"Retriever\\\"},\\n \\\"memory\\\": {\\\"display_name\\\": \\\"Memory\\\", \\\"required\\\": False},\\n \\\"input_key\\\": {\\\"display_name\\\": \\\"Input Key\\\", \\\"advanced\\\": True},\\n \\\"output_key\\\": {\\\"display_name\\\": \\\"Output Key\\\", \\\"advanced\\\": True},\\n \\\"return_source_documents\\\": {\\\"display_name\\\": \\\"Return Source Documents\\\"},\\n \\\"inputs\\\": {\\\"display_name\\\": \\\"Input\\\", \\\"input_types\\\": [\\\"Text\\\", \\\"Document\\\"]},\\n }\\n\\n def build(\\n self,\\n combine_documents_chain: BaseCombineDocumentsChain,\\n retriever: BaseRetriever,\\n inputs: str = \\\"\\\",\\n memory: Optional[BaseMemory] = None,\\n input_key: str = \\\"query\\\",\\n output_key: str = \\\"result\\\",\\n return_source_documents: bool = True,\\n ) -> Union[BaseRetrievalQA, Callable, Text]:\\n runnable = RetrievalQA(\\n combine_documents_chain=combine_documents_chain,\\n retriever=retriever,\\n memory=memory,\\n input_key=input_key,\\n output_key=output_key,\\n return_source_documents=return_source_documents,\\n )\\n if isinstance(inputs, Document):\\n inputs = inputs.page_content\\n self.status = runnable\\n result = runnable.invoke({input_key: inputs})\\n result = result.content if hasattr(result, \\\"content\\\") else result\\n # Result is a dict with keys \\\"query\\\", \\\"result\\\" and \\\"source_documents\\\"\\n # for now we just return the result\\n records = self.to_records(result.get(\\\"source_documents\\\"))\\n references_str = \\\"\\\"\\n if return_source_documents:\\n references_str = self.create_references_from_records(records)\\n result_str = result.get(\\\"result\\\")\\n final_result = \\\"\\\\n\\\".join([result_str, references_str])\\n self.status = final_result\\n return final_result\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"input_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"query\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"input_key\",\"display_name\":\"Input Key\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"inputs\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"inputs\",\"display_name\":\"Input\",\"advanced\":false,\"input_types\":[\"Text\",\"Document\",\"Text\"],\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"output_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"result\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"output_key\",\"display_name\":\"Output Key\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"return_source_documents\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":true,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"return_source_documents\",\"display_name\":\"Return Source Documents\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Chain for question-answering against an index.\",\"base_classes\":[\"Runnable\",\"Chain\",\"BaseRetrievalQA\",\"Generic\",\"Text\",\"RunnableSerializable\",\"Serializable\",\"object\",\"Callable\"],\"display_name\":\"Retrieval QA\",\"documentation\":\"\",\"custom_fields\":{\"combine_documents_chain\":null,\"retriever\":null,\"inputs\":null,\"memory\":null,\"input_key\":null,\"output_key\":null,\"return_source_documents\":null},\"output_types\":[\"BaseRetrievalQA\",\"Callable\",\"Text\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"RetrievalQAWithSourcesChain\":{\"template\":{\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"memory\":{\"type\":\"BaseMemory\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"memory\",\"display_name\":\"Memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"retriever\":{\"type\":\"BaseRetriever\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"retriever\",\"display_name\":\"Retriever\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"chain_type\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"stuff\",\"map_reduce\",\"map_rerank\",\"refine\"],\"name\":\"chain_type\",\"display_name\":\"Chain Type\",\"advanced\":false,\"dynamic\":false,\"info\":\"The type of chain to use to combined Documents.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain.chains import RetrievalQAWithSourcesChain\\nfrom langchain_core.documents import Document\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import BaseLanguageModel, BaseMemory, BaseRetriever, Text\\n\\n\\nclass RetrievalQAWithSourcesChainComponent(CustomComponent):\\n display_name = \\\"RetrievalQAWithSourcesChain\\\"\\n description = \\\"Question-answering with sources over an index.\\\"\\n\\n def build_config(self):\\n return {\\n \\\"llm\\\": {\\\"display_name\\\": \\\"LLM\\\"},\\n \\\"chain_type\\\": {\\n \\\"display_name\\\": \\\"Chain Type\\\",\\n \\\"options\\\": [\\\"stuff\\\", \\\"map_reduce\\\", \\\"map_rerank\\\", \\\"refine\\\"],\\n \\\"info\\\": \\\"The type of chain to use to combined Documents.\\\",\\n },\\n \\\"memory\\\": {\\\"display_name\\\": \\\"Memory\\\"},\\n \\\"return_source_documents\\\": {\\\"display_name\\\": \\\"Return Source Documents\\\"},\\n \\\"retriever\\\": {\\\"display_name\\\": \\\"Retriever\\\"},\\n }\\n\\n def build(\\n self,\\n inputs: str,\\n retriever: BaseRetriever,\\n llm: BaseLanguageModel,\\n chain_type: str,\\n memory: Optional[BaseMemory] = None,\\n return_source_documents: Optional[bool] = True,\\n ) -> Text:\\n runnable = RetrievalQAWithSourcesChain.from_chain_type(\\n llm=llm,\\n chain_type=chain_type,\\n memory=memory,\\n return_source_documents=return_source_documents,\\n retriever=retriever,\\n )\\n if isinstance(inputs, Document):\\n inputs = inputs.page_content\\n self.status = runnable\\n input_key = runnable.input_keys[0]\\n result = runnable.invoke({input_key: inputs})\\n result = result.content if hasattr(result, \\\"content\\\") else result\\n # Result is a dict with keys \\\"query\\\", \\\"result\\\" and \\\"source_documents\\\"\\n # for now we just return the result\\n records = self.to_records(result.get(\\\"source_documents\\\"))\\n references_str = \\\"\\\"\\n if return_source_documents:\\n references_str = self.create_references_from_records(records)\\n result_str = result.get(\\\"answer\\\")\\n final_result = \\\"\\\\n\\\".join([result_str, references_str])\\n self.status = final_result\\n return final_result\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"inputs\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"inputs\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"return_source_documents\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":true,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"return_source_documents\",\"display_name\":\"Return Source Documents\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Question-answering with sources over an index.\",\"base_classes\":[\"Text\",\"object\"],\"display_name\":\"RetrievalQAWithSourcesChain\",\"documentation\":\"\",\"custom_fields\":{\"inputs\":null,\"retriever\":null,\"llm\":null,\"chain_type\":null,\"memory\":null,\"return_source_documents\":null},\"output_types\":[\"Text\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"SQLDatabaseChain\":{\"template\":{\"db\":{\"type\":\"SQLDatabase\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"db\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"llm\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"prompt\":{\"type\":\"BasePromptTemplate\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"prompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"SQLDatabaseChain\"},\"description\":\"Create a SQLDatabaseChain from an LLM and a database connection.\",\"base_classes\":[\"Runnable\",\"Chain\",\"Generic\",\"Text\",\"RunnableSerializable\",\"Serializable\",\"object\",\"SQLDatabaseChain\",\"Callable\"],\"display_name\":\"SQLDatabaseChain\",\"documentation\":\"\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":false,\"beta\":false,\"output_type\":\"Chain\"},\"CombineDocsChain\":{\"template\":{\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"chain_type\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"stuff\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"stuff\",\"map_reduce\",\"map_rerank\",\"refine\"],\"name\":\"chain_type\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"load_qa_chain\"},\"description\":\"Load question answering chain.\",\"base_classes\":[\"function\",\"BaseCombineDocumentsChain\"],\"display_name\":\"CombineDocsChain\",\"documentation\":\"\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":false,\"beta\":false,\"output_type\":\"Chain\"},\"SeriesCharacterChain\":{\"template\":{\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"character\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"character\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"series\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"series\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"SeriesCharacterChain\"},\"description\":\"SeriesCharacterChain is a chain you can use to have a conversation with a character from a series.\",\"base_classes\":[\"Chain\",\"BaseCustomChain\",\"ConversationChain\",\"function\",\"SeriesCharacterChain\",\"LLMChain\"],\"display_name\":\"SeriesCharacterChain\",\"documentation\":\"\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":false,\"beta\":false,\"output_type\":\"Chain\"},\"MidJourneyPromptChain\":{\"template\":{\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"memory\":{\"type\":\"BaseChatMemory\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"MidJourneyPromptChain\"},\"description\":\"MidJourneyPromptChain is a chain you can use to generate new MidJourney prompts.\",\"base_classes\":[\"Chain\",\"BaseCustomChain\",\"MidJourneyPromptChain\",\"ConversationChain\",\"LLMChain\"],\"display_name\":\"MidJourneyPromptChain\",\"documentation\":\"\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":false,\"beta\":false,\"output_type\":\"Chain\"},\"TimeTravelGuideChain\":{\"template\":{\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"memory\":{\"type\":\"BaseChatMemory\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"TimeTravelGuideChain\"},\"description\":\"Time travel guide chain.\",\"base_classes\":[\"Chain\",\"BaseCustomChain\",\"ConversationChain\",\"TimeTravelGuideChain\",\"LLMChain\"],\"display_name\":\"TimeTravelGuideChain\",\"documentation\":\"\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":false,\"beta\":false,\"output_type\":\"Chain\"},\"LLMChain\":{\"template\":{\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"memory\":{\"type\":\"BaseMemory\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"memory\",\"display_name\":\"Memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"prompt\":{\"type\":\"BasePromptTemplate\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"prompt\",\"display_name\":\"Prompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain.chains import LLMChain\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import (\\n BaseLanguageModel,\\n BaseMemory,\\n BasePromptTemplate,\\n Text,\\n)\\n\\n\\nclass LLMChainComponent(CustomComponent):\\n display_name = \\\"LLMChain\\\"\\n description = \\\"Chain to run queries against LLMs\\\"\\n\\n def build_config(self):\\n return {\\n \\\"prompt\\\": {\\\"display_name\\\": \\\"Prompt\\\"},\\n \\\"llm\\\": {\\\"display_name\\\": \\\"LLM\\\"},\\n \\\"memory\\\": {\\\"display_name\\\": \\\"Memory\\\"},\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(\\n self,\\n prompt: BasePromptTemplate,\\n llm: BaseLanguageModel,\\n memory: Optional[BaseMemory] = None,\\n ) -> Text:\\n runnable = LLMChain(prompt=prompt, llm=llm, memory=memory)\\n result_dict = runnable.invoke({})\\n output_key = runnable.output_key\\n result = result_dict[output_key]\\n self.status = result\\n return result\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Chain to run queries against LLMs\",\"base_classes\":[\"Text\",\"object\"],\"display_name\":\"LLMChain\",\"documentation\":\"\",\"custom_fields\":{\"prompt\":null,\"llm\":null,\"memory\":null},\"output_types\":[\"Text\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"SQLGenerator\":{\"template\":{\"db\":{\"type\":\"SQLDatabase\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"db\",\"display_name\":\"Database\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"inputs\":{\"type\":\"Text\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"inputs\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"prompt\":{\"type\":\"PromptTemplate\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"prompt\",\"display_name\":\"Prompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"The prompt must contain `{question}`.\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain.chains import create_sql_query_chain\\nfrom langchain_community.utilities.sql_database import SQLDatabase\\nfrom langchain_core.prompts import PromptTemplate\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import BaseLanguageModel, Text\\n\\n\\nclass SQLGeneratorComponent(CustomComponent):\\n display_name = \\\"Natural Language to SQL\\\"\\n description = \\\"Generate SQL from natural language.\\\"\\n\\n def build_config(self):\\n return {\\n \\\"db\\\": {\\\"display_name\\\": \\\"Database\\\"},\\n \\\"llm\\\": {\\\"display_name\\\": \\\"LLM\\\"},\\n \\\"prompt\\\": {\\n \\\"display_name\\\": \\\"Prompt\\\",\\n \\\"info\\\": \\\"The prompt must contain `{question}`.\\\",\\n },\\n \\\"top_k\\\": {\\n \\\"display_name\\\": \\\"Top K\\\",\\n \\\"info\\\": \\\"The number of results per select statement to return. If 0, no limit.\\\",\\n },\\n }\\n\\n def build(\\n self,\\n inputs: Text,\\n db: SQLDatabase,\\n llm: BaseLanguageModel,\\n top_k: int = 5,\\n prompt: Optional[PromptTemplate] = None,\\n ) -> Text:\\n if top_k > 0:\\n kwargs = {\\n \\\"k\\\": top_k,\\n }\\n if not prompt:\\n sql_query_chain = create_sql_query_chain(llm=llm, db=db, **kwargs)\\n else:\\n template = prompt.template if hasattr(prompt, \\\"template\\\") else prompt\\n # Check if {question} is in the prompt\\n if \\\"{question}\\\" not in template or \\\"question\\\" not in template.input_variables:\\n raise ValueError(\\\"Prompt must contain `{question}` to be used with Natural Language to SQL.\\\")\\n sql_query_chain = create_sql_query_chain(llm=llm, db=db, prompt=prompt, **kwargs)\\n query_writer = sql_query_chain | {\\\"query\\\": lambda x: x.replace(\\\"SQLQuery:\\\", \\\"\\\").strip()}\\n response = query_writer.invoke({\\\"question\\\": inputs})\\n query = response.get(\\\"query\\\")\\n self.status = query\\n return query\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"top_k\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":5,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_k\",\"display_name\":\"Top K\",\"advanced\":false,\"dynamic\":false,\"info\":\"The number of results per select statement to return. If 0, no limit.\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Generate SQL from natural language.\",\"base_classes\":[\"Text\",\"object\"],\"display_name\":\"Natural Language to SQL\",\"documentation\":\"\",\"custom_fields\":{\"inputs\":null,\"db\":null,\"llm\":null,\"top_k\":null,\"prompt\":null},\"output_types\":[\"Text\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"ConversationChain\":{\"template\":{\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"memory\":{\"type\":\"BaseMemory\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"memory\",\"display_name\":\"Memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"Memory to load context from. If none is provided, a ConversationBufferMemory will be used.\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Callable, Optional, Union\\n\\nfrom langchain.chains import ConversationChain\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import BaseLanguageModel, BaseMemory, Chain, Text\\n\\n\\nclass ConversationChainComponent(CustomComponent):\\n display_name = \\\"ConversationChain\\\"\\n description = \\\"Chain to have a conversation and load context from memory.\\\"\\n\\n def build_config(self):\\n return {\\n \\\"prompt\\\": {\\\"display_name\\\": \\\"Prompt\\\"},\\n \\\"llm\\\": {\\\"display_name\\\": \\\"LLM\\\"},\\n \\\"memory\\\": {\\n \\\"display_name\\\": \\\"Memory\\\",\\n \\\"info\\\": \\\"Memory to load context from. If none is provided, a ConversationBufferMemory will be used.\\\",\\n },\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(\\n self,\\n inputs: str,\\n llm: BaseLanguageModel,\\n memory: Optional[BaseMemory] = None,\\n ) -> Union[Chain, Callable, Text]:\\n if memory is None:\\n chain = ConversationChain(llm=llm)\\n else:\\n chain = ConversationChain(llm=llm, memory=memory)\\n result = chain.invoke(inputs)\\n # result is an AIMessage which is a subclass of BaseMessage\\n # We need to check if it is a string or a BaseMessage\\n if hasattr(result, \\\"content\\\") and isinstance(result.content, str):\\n self.status = \\\"is message\\\"\\n result = result.content\\n elif isinstance(result, str):\\n self.status = \\\"is_string\\\"\\n result = result\\n else:\\n # is dict\\n result = result.get(\\\"response\\\")\\n self.status = result\\n return result\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"inputs\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"inputs\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Chain to have a conversation and load context from memory.\",\"base_classes\":[\"Runnable\",\"Chain\",\"Generic\",\"Text\",\"RunnableSerializable\",\"Serializable\",\"object\",\"Callable\"],\"display_name\":\"ConversationChain\",\"documentation\":\"\",\"custom_fields\":{\"inputs\":null,\"llm\":null,\"memory\":null},\"output_types\":[\"Chain\",\"Callable\",\"Text\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true}},\"agents\":{\"ZeroShotAgent\":{\"template\":{\"callback_manager\":{\"type\":\"BaseCallbackManager\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"callback_manager\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"llm\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"output_parser\":{\"type\":\"AgentOutputParser\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"output_parser\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"tools\":{\"type\":\"BaseTool\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"tools\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"format_instructions\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":true,\"value\":\"Use the following format:\\n\\nQuestion: the input question you must answer\\nThought: you should always think about what to do\\nAction: the action to take, should be one of [{tool_names}]\\nAction Input: the input to the action\\nObservation: the result of the action\\n... (this Thought/Action/Action Input/Observation can repeat N times)\\nThought: I now know the final answer\\nFinal Answer: the final answer to the original input question\",\"fileTypes\":[],\"password\":false,\"name\":\"format_instructions\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"input_variables\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"input_variables\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"prefix\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"Answer the following questions as best you can. You have access to the following tools:\",\"fileTypes\":[],\"password\":false,\"name\":\"prefix\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"suffix\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"Begin!\\n\\nQuestion: {input}\\nThought:{agent_scratchpad}\",\"fileTypes\":[],\"password\":false,\"name\":\"suffix\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"ZeroShotAgent\"},\"description\":\"Construct an agent from an LLM and tools.\",\"base_classes\":[\"ZeroShotAgent\",\"Callable\",\"BaseSingleActionAgent\",\"Agent\"],\"display_name\":\"ZeroShotAgent\",\"documentation\":\"https://python.langchain.com/docs/modules/agents/how_to/custom_mrkl_agent\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"JsonAgent\":{\"template\":{\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"toolkit\":{\"type\":\"JsonToolkit\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"toolkit\",\"display_name\":\"Toolkit\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langchain.agents import AgentExecutor, create_json_agent\\nfrom langflow.field_typing import (\\n BaseLanguageModel,\\n)\\nfrom langchain_community.agent_toolkits.json.toolkit import JsonToolkit\\n\\n\\nclass JsonAgentComponent(CustomComponent):\\n display_name = \\\"JsonAgent\\\"\\n description = \\\"Construct a json agent from an LLM and tools.\\\"\\n\\n def build_config(self):\\n return {\\n \\\"llm\\\": {\\\"display_name\\\": \\\"LLM\\\"},\\n \\\"toolkit\\\": {\\\"display_name\\\": \\\"Toolkit\\\"},\\n }\\n\\n def build(\\n self,\\n llm: BaseLanguageModel,\\n toolkit: JsonToolkit,\\n ) -> AgentExecutor:\\n return create_json_agent(llm=llm, toolkit=toolkit)\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Construct a json agent from an LLM and tools.\",\"base_classes\":[\"Runnable\",\"Chain\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"AgentExecutor\",\"object\"],\"display_name\":\"JsonAgent\",\"documentation\":\"\",\"custom_fields\":{\"llm\":null,\"toolkit\":null},\"output_types\":[\"AgentExecutor\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"CSVAgent\":{\"template\":{\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"path\":{\"type\":\"file\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[\".csv\"],\"file_path\":\"\",\"password\":false,\"name\":\"path\",\"display_name\":\"Path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langflow.field_typing import BaseLanguageModel, AgentExecutor\\nfrom langchain_experimental.agents.agent_toolkits.csv.base import create_csv_agent\\n\\n\\nclass CSVAgentComponent(CustomComponent):\\n display_name = \\\"CSVAgent\\\"\\n description = \\\"Construct a CSV agent from a CSV and tools.\\\"\\n documentation = \\\"https://python.langchain.com/docs/modules/agents/toolkits/csv\\\"\\n\\n def build_config(self):\\n return {\\n \\\"llm\\\": {\\\"display_name\\\": \\\"LLM\\\", \\\"type\\\": BaseLanguageModel},\\n \\\"path\\\": {\\\"display_name\\\": \\\"Path\\\", \\\"field_type\\\": \\\"file\\\", \\\"suffixes\\\": [\\\".csv\\\"], \\\"file_types\\\": [\\\".csv\\\"]},\\n }\\n\\n def build(\\n self,\\n llm: BaseLanguageModel,\\n path: str,\\n ) -> AgentExecutor:\\n # Instantiate and return the CSV agent class with the provided llm and path\\n return create_csv_agent(llm=llm, path=path)\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Construct a CSV agent from a CSV and tools.\",\"base_classes\":[\"Runnable\",\"Chain\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"AgentExecutor\",\"object\"],\"display_name\":\"CSVAgent\",\"documentation\":\"https://python.langchain.com/docs/modules/agents/toolkits/csv\",\"custom_fields\":{\"llm\":null,\"path\":null},\"output_types\":[\"AgentExecutor\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"VectorStoreAgent\":{\"template\":{\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"vector_store_toolkit\":{\"type\":\"VectorStoreToolkit\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"vector_store_toolkit\",\"display_name\":\"Vector Store Info\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langchain.agents import AgentExecutor, create_vectorstore_agent\\nfrom langchain.agents.agent_toolkits.vectorstore.toolkit import VectorStoreToolkit\\nfrom typing import Union, Callable\\nfrom langflow.field_typing import BaseLanguageModel\\n\\n\\nclass VectorStoreAgentComponent(CustomComponent):\\n display_name = \\\"VectorStoreAgent\\\"\\n description = \\\"Construct an agent from a Vector Store.\\\"\\n\\n def build_config(self):\\n return {\\n \\\"llm\\\": {\\\"display_name\\\": \\\"LLM\\\"},\\n \\\"vector_store_toolkit\\\": {\\\"display_name\\\": \\\"Vector Store Info\\\"},\\n }\\n\\n def build(\\n self,\\n llm: BaseLanguageModel,\\n vector_store_toolkit: VectorStoreToolkit,\\n ) -> Union[AgentExecutor, Callable]:\\n return create_vectorstore_agent(llm=llm, toolkit=vector_store_toolkit)\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Construct an agent from a Vector Store.\",\"base_classes\":[\"Runnable\",\"Chain\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"AgentExecutor\",\"object\",\"Callable\"],\"display_name\":\"VectorStoreAgent\",\"documentation\":\"\",\"custom_fields\":{\"llm\":null,\"vector_store_toolkit\":null},\"output_types\":[\"AgentExecutor\",\"Callable\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"VectorStoreRouterAgent\":{\"template\":{\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"vectorstoreroutertoolkit\":{\"type\":\"VectorStoreRouterToolkit\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"vectorstoreroutertoolkit\",\"display_name\":\"Vector Store Router Toolkit\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langchain_core.language_models.base import BaseLanguageModel\\nfrom langchain.agents.agent_toolkits.vectorstore.toolkit import VectorStoreRouterToolkit\\nfrom langchain.agents import create_vectorstore_router_agent\\nfrom typing import Callable\\n\\n\\nclass VectorStoreRouterAgentComponent(CustomComponent):\\n display_name = \\\"VectorStoreRouterAgent\\\"\\n description = \\\"Construct an agent from a Vector Store Router.\\\"\\n\\n def build_config(self):\\n return {\\n \\\"llm\\\": {\\\"display_name\\\": \\\"LLM\\\"},\\n \\\"vectorstoreroutertoolkit\\\": {\\\"display_name\\\": \\\"Vector Store Router Toolkit\\\"},\\n }\\n\\n def build(self, llm: BaseLanguageModel, vectorstoreroutertoolkit: VectorStoreRouterToolkit) -> Callable:\\n return create_vectorstore_router_agent(llm=llm, toolkit=vectorstoreroutertoolkit)\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Construct an agent from a Vector Store Router.\",\"base_classes\":[\"Callable\"],\"display_name\":\"VectorStoreRouterAgent\",\"documentation\":\"\",\"custom_fields\":{\"llm\":null,\"vectorstoreroutertoolkit\":null},\"output_types\":[\"Callable\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"SQLAgent\":{\"template\":{\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom typing import Union, Callable\\nfrom langchain.agents import AgentExecutor\\nfrom langflow.field_typing import BaseLanguageModel\\nfrom langchain_community.agent_toolkits.sql.base import create_sql_agent\\nfrom langchain.sql_database import SQLDatabase\\nfrom langchain_community.agent_toolkits import SQLDatabaseToolkit\\n\\n\\nclass SQLAgentComponent(CustomComponent):\\n display_name = \\\"SQLAgent\\\"\\n description = \\\"Construct an SQL agent from an LLM and tools.\\\"\\n\\n def build_config(self):\\n return {\\n \\\"llm\\\": {\\\"display_name\\\": \\\"LLM\\\"},\\n \\\"database_uri\\\": {\\\"display_name\\\": \\\"Database URI\\\"},\\n \\\"verbose\\\": {\\\"display_name\\\": \\\"Verbose\\\", \\\"value\\\": False, \\\"advanced\\\": True},\\n }\\n\\n def build(\\n self,\\n llm: BaseLanguageModel,\\n database_uri: str,\\n verbose: bool = False,\\n ) -> Union[AgentExecutor, Callable]:\\n db = SQLDatabase.from_uri(database_uri)\\n toolkit = SQLDatabaseToolkit(db=db, llm=llm)\\n return create_sql_agent(llm=llm, toolkit=toolkit)\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"database_uri\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"database_uri\",\"display_name\":\"Database URI\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"verbose\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"verbose\",\"display_name\":\"Verbose\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Construct an SQL agent from an LLM and tools.\",\"base_classes\":[\"Runnable\",\"Chain\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"AgentExecutor\",\"object\",\"Callable\"],\"display_name\":\"SQLAgent\",\"documentation\":\"\",\"custom_fields\":{\"llm\":null,\"database_uri\":null,\"verbose\":null},\"output_types\":[\"AgentExecutor\",\"Callable\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"OpenAIConversationalAgent\":{\"template\":{\"memory\":{\"type\":\"BaseMemory\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"memory\",\"display_name\":\"Memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"system_message\":{\"type\":\"SystemMessagePromptTemplate\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"system_message\",\"display_name\":\"System Message\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"tools\":{\"type\":\"Tool\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"tools\",\"display_name\":\"Tools\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import List, Optional\\n\\nfrom langchain.agents.agent import AgentExecutor\\nfrom langchain.agents.agent_toolkits.conversational_retrieval.openai_functions import _get_default_system_message\\nfrom langchain.agents.openai_functions_agent.base import OpenAIFunctionsAgent\\nfrom langchain.memory.token_buffer import ConversationTokenBufferMemory\\nfrom langchain.prompts import SystemMessagePromptTemplate\\nfrom langchain.prompts.chat import MessagesPlaceholder\\nfrom langchain.schema.memory import BaseMemory\\nfrom langchain.tools import Tool\\nfrom langchain_community.chat_models import ChatOpenAI\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing.range_spec import RangeSpec\\n\\n\\nclass ConversationalAgent(CustomComponent):\\n display_name: str = \\\"OpenAI Conversational Agent\\\"\\n description: str = \\\"Conversational Agent that can use OpenAI's function calling API\\\"\\n\\n def build_config(self):\\n openai_function_models = [\\n \\\"gpt-4-turbo-preview\\\",\\n \\\"gpt-4-0125-preview\\\",\\n \\\"gpt-4-1106-preview\\\",\\n \\\"gpt-4-vision-preview\\\",\\n \\\"gpt-3.5-turbo-0125\\\",\\n \\\"gpt-3.5-turbo-1106\\\",\\n ]\\n return {\\n \\\"tools\\\": {\\\"display_name\\\": \\\"Tools\\\"},\\n \\\"memory\\\": {\\\"display_name\\\": \\\"Memory\\\"},\\n \\\"system_message\\\": {\\\"display_name\\\": \\\"System Message\\\"},\\n \\\"max_token_limit\\\": {\\\"display_name\\\": \\\"Max Token Limit\\\"},\\n \\\"model_name\\\": {\\n \\\"display_name\\\": \\\"Model Name\\\",\\n \\\"options\\\": openai_function_models,\\n \\\"value\\\": openai_function_models[0],\\n },\\n \\\"code\\\": {\\\"show\\\": False},\\n \\\"temperature\\\": {\\n \\\"display_name\\\": \\\"Temperature\\\",\\n \\\"value\\\": 0.2,\\n \\\"range_spec\\\": RangeSpec(min=0, max=2, step=0.1),\\n },\\n }\\n\\n def build(\\n self,\\n model_name: str,\\n openai_api_key: str,\\n tools: List[Tool],\\n openai_api_base: Optional[str] = None,\\n memory: Optional[BaseMemory] = None,\\n system_message: Optional[SystemMessagePromptTemplate] = None,\\n max_token_limit: int = 2000,\\n temperature: float = 0.9,\\n ) -> AgentExecutor:\\n llm = ChatOpenAI(\\n model=model_name,\\n api_key=openai_api_key,\\n base_url=openai_api_base,\\n max_tokens=max_token_limit,\\n temperature=temperature,\\n )\\n if not memory:\\n memory_key = \\\"chat_history\\\"\\n memory = ConversationTokenBufferMemory(\\n memory_key=memory_key,\\n return_messages=True,\\n output_key=\\\"output\\\",\\n llm=llm,\\n max_token_limit=max_token_limit,\\n )\\n else:\\n memory_key = memory.memory_key # type: ignore\\n\\n _system_message = system_message or _get_default_system_message()\\n prompt = OpenAIFunctionsAgent.create_prompt(\\n system_message=_system_message, # type: ignore\\n extra_prompt_messages=[MessagesPlaceholder(variable_name=memory_key)],\\n )\\n agent = OpenAIFunctionsAgent(\\n llm=llm,\\n tools=tools,\\n prompt=prompt, # type: ignore\\n )\\n return AgentExecutor(\\n agent=agent,\\n tools=tools, # type: ignore\\n memory=memory,\\n verbose=True,\\n return_intermediate_steps=True,\\n handle_parsing_errors=True,\\n )\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"max_token_limit\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":2000,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_token_limit\",\"display_name\":\"Max Token Limit\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"model_name\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"gpt-4-turbo-preview\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"gpt-4-turbo-preview\",\"gpt-4-0125-preview\",\"gpt-4-1106-preview\",\"gpt-4-vision-preview\",\"gpt-3.5-turbo-0125\",\"gpt-3.5-turbo-1106\"],\"name\":\"model_name\",\"display_name\":\"Model Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"openai_api_base\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"openai_api_base\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"openai_api_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"openai_api_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"temperature\":{\"type\":\"float\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.2,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":0.0,\"max\":2.0,\"step\":0.1},\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Conversational Agent that can use OpenAI's function calling API\",\"base_classes\":[\"Runnable\",\"Chain\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"AgentExecutor\",\"object\"],\"display_name\":\"OpenAI Conversational Agent\",\"documentation\":\"\",\"custom_fields\":{\"model_name\":null,\"openai_api_key\":null,\"tools\":null,\"openai_api_base\":null,\"memory\":null,\"system_message\":null,\"max_token_limit\":null,\"temperature\":null},\"output_types\":[\"AgentExecutor\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"AgentInitializer\":{\"template\":{\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm\",\"display_name\":\"Language Model\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"memory\":{\"type\":\"BaseChatMemory\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"memory\",\"display_name\":\"Memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"tools\":{\"type\":\"Tool\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"tools\",\"display_name\":\"Tools\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"agent\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"zero-shot-react-description\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"zero-shot-react-description\",\"react-docstore\",\"self-ask-with-search\",\"conversational-react-description\",\"chat-zero-shot-react-description\",\"chat-conversational-react-description\",\"structured-chat-zero-shot-react-description\",\"openai-functions\",\"openai-multi-functions\",\"JsonAgent\",\"CSVAgent\",\"VectorStoreAgent\",\"VectorStoreRouterAgent\",\"SQLAgent\"],\"name\":\"agent\",\"display_name\":\"Agent Type\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Callable, List, Optional, Union\\n\\nfrom langchain.agents import AgentExecutor, AgentType, initialize_agent, types\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import BaseChatMemory, BaseLanguageModel, Tool\\n\\n\\nclass AgentInitializerComponent(CustomComponent):\\n display_name: str = \\\"Agent Initializer\\\"\\n description: str = \\\"Initialize a Langchain Agent.\\\"\\n documentation: str = \\\"https://python.langchain.com/docs/modules/agents/agent_types/\\\"\\n\\n def build_config(self):\\n agents = list(types.AGENT_TO_CLASS.keys())\\n # field_type and required are optional\\n return {\\n \\\"agent\\\": {\\\"options\\\": agents, \\\"value\\\": agents[0], \\\"display_name\\\": \\\"Agent Type\\\"},\\n \\\"max_iterations\\\": {\\\"display_name\\\": \\\"Max Iterations\\\", \\\"value\\\": 10},\\n \\\"memory\\\": {\\\"display_name\\\": \\\"Memory\\\"},\\n \\\"tools\\\": {\\\"display_name\\\": \\\"Tools\\\"},\\n \\\"llm\\\": {\\\"display_name\\\": \\\"Language Model\\\"},\\n \\\"code\\\": {\\\"advanced\\\": True},\\n }\\n\\n def build(\\n self,\\n agent: str,\\n llm: BaseLanguageModel,\\n tools: List[Tool],\\n max_iterations: int,\\n memory: Optional[BaseChatMemory] = None,\\n ) -> Union[AgentExecutor, Callable]:\\n agent = AgentType(agent)\\n if memory:\\n return initialize_agent(\\n tools=tools,\\n llm=llm,\\n agent=agent,\\n memory=memory,\\n return_intermediate_steps=True,\\n handle_parsing_errors=True,\\n max_iterations=max_iterations,\\n )\\n return initialize_agent(\\n tools=tools,\\n llm=llm,\\n agent=agent,\\n return_intermediate_steps=True,\\n handle_parsing_errors=True,\\n max_iterations=max_iterations,\\n )\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":true,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"max_iterations\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":10,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_iterations\",\"display_name\":\"Max Iterations\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Initialize a Langchain Agent.\",\"base_classes\":[\"Runnable\",\"Chain\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"AgentExecutor\",\"object\",\"Callable\"],\"display_name\":\"Agent Initializer\",\"documentation\":\"https://python.langchain.com/docs/modules/agents/agent_types/\",\"custom_fields\":{\"agent\":null,\"llm\":null,\"tools\":null,\"max_iterations\":null,\"memory\":null},\"output_types\":[\"AgentExecutor\",\"Callable\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true}},\"memories\":{\"ConversationBufferMemory\":{\"template\":{\"chat_memory\":{\"type\":\"BaseChatMessageHistory\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"chat_memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"ai_prefix\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"AI\",\"fileTypes\":[],\"password\":false,\"name\":\"ai_prefix\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"human_prefix\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"Human\",\"fileTypes\":[],\"password\":false,\"name\":\"human_prefix\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"input_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"input_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"The variable to be used as Chat Input when more than one variable is available.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"memory_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"chat_history\",\"fileTypes\":[],\"password\":false,\"name\":\"memory_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"output_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"output_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"The variable to be used as Chat Output (e.g. answer in a ConversationalRetrievalChain)\",\"title_case\":false,\"input_types\":[\"Text\"]},\"return_messages\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"return_messages\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"ConversationBufferMemory\"},\"description\":\"Buffer for storing conversation memory.\",\"base_classes\":[\"BaseMemory\",\"BaseChatMemory\",\"ConversationBufferMemory\",\"Serializable\"],\"display_name\":\"ConversationBufferMemory\",\"documentation\":\"https://python.langchain.com/docs/modules/memory/how_to/buffer\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":true,\"beta\":false},\"ConversationBufferWindowMemory\":{\"template\":{\"chat_memory\":{\"type\":\"BaseChatMessageHistory\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"chat_memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"ai_prefix\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"AI\",\"fileTypes\":[],\"password\":false,\"name\":\"ai_prefix\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"human_prefix\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"Human\",\"fileTypes\":[],\"password\":false,\"name\":\"human_prefix\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"input_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"input_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"The variable to be used as Chat Input when more than one variable is available.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"k\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":10,\"fileTypes\":[],\"password\":false,\"name\":\"k\",\"display_name\":\"Memory Size\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"memory_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"chat_history\",\"fileTypes\":[],\"password\":false,\"name\":\"memory_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"output_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"output_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"The variable to be used as Chat Output (e.g. answer in a ConversationalRetrievalChain)\",\"title_case\":false,\"input_types\":[\"Text\"]},\"return_messages\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"return_messages\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"ConversationBufferWindowMemory\"},\"description\":\"Buffer for storing conversation memory inside a limited size window.\",\"base_classes\":[\"ConversationBufferWindowMemory\",\"BaseMemory\",\"BaseChatMemory\",\"Serializable\"],\"display_name\":\"ConversationBufferWindowMemory\",\"documentation\":\"https://python.langchain.com/docs/modules/memory/how_to/buffer_window\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":true,\"beta\":false},\"ConversationEntityMemory\":{\"template\":{\"chat_memory\":{\"type\":\"BaseChatMessageHistory\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"chat_memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"entity_extraction_prompt\":{\"type\":\"BasePromptTemplate\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"entity_extraction_prompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"entity_store\":{\"type\":\"BaseEntityStore\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"entity_store\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"entity_summarization_prompt\":{\"type\":\"BasePromptTemplate\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"entity_summarization_prompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"llm\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"ai_prefix\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"AI\",\"fileTypes\":[],\"password\":false,\"name\":\"ai_prefix\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"chat_history_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"history\",\"fileTypes\":[],\"password\":false,\"name\":\"chat_history_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"entity_cache\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"entity_cache\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"human_prefix\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"Human\",\"fileTypes\":[],\"password\":false,\"name\":\"human_prefix\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"input_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"input_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"The variable to be used as Chat Input when more than one variable is available.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"k\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":10,\"fileTypes\":[],\"password\":false,\"name\":\"k\",\"display_name\":\"Memory Size\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"output_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"output_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"The variable to be used as Chat Output (e.g. answer in a ConversationalRetrievalChain)\",\"title_case\":false,\"input_types\":[\"Text\"]},\"return_messages\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"return_messages\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"ConversationEntityMemory\"},\"description\":\"Entity extractor & summarizer memory.\",\"base_classes\":[\"ConversationEntityMemory\",\"BaseMemory\",\"BaseChatMemory\",\"Serializable\"],\"display_name\":\"ConversationEntityMemory\",\"documentation\":\"https://python.langchain.com/docs/modules/memory/integrations/entity_memory_with_sqlite\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":true,\"beta\":false},\"ConversationKGMemory\":{\"template\":{\"chat_memory\":{\"type\":\"BaseChatMessageHistory\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"chat_memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"entity_extraction_prompt\":{\"type\":\"BasePromptTemplate\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"entity_extraction_prompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"kg\":{\"type\":\"NetworkxEntityGraph\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"kg\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"knowledge_extraction_prompt\":{\"type\":\"BasePromptTemplate\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"knowledge_extraction_prompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"llm\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"summary_message_cls\":{\"type\":\"Type[langchain_core.messages.base.BaseMessage]\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"summary_message_cls\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"ai_prefix\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"AI\",\"fileTypes\":[],\"password\":false,\"name\":\"ai_prefix\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"human_prefix\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"Human\",\"fileTypes\":[],\"password\":false,\"name\":\"human_prefix\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"input_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"input_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"The variable to be used as Chat Input when more than one variable is available.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"k\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":10,\"fileTypes\":[],\"password\":false,\"name\":\"k\",\"display_name\":\"Memory Size\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"memory_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"chat_history\",\"fileTypes\":[],\"password\":false,\"name\":\"memory_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"output_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"output_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"The variable to be used as Chat Output (e.g. answer in a ConversationalRetrievalChain)\",\"title_case\":false,\"input_types\":[\"Text\"]},\"return_messages\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"return_messages\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"ConversationKGMemory\"},\"description\":\"Knowledge graph conversation memory.\",\"base_classes\":[\"BaseChatMemory\",\"BaseMemory\",\"ConversationKGMemory\",\"Serializable\"],\"display_name\":\"ConversationKGMemory\",\"documentation\":\"https://python.langchain.com/docs/modules/memory/how_to/kg\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":true,\"beta\":false},\"ConversationSummaryMemory\":{\"template\":{\"chat_memory\":{\"type\":\"BaseChatMessageHistory\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"chat_memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"llm\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"prompt\":{\"type\":\"BasePromptTemplate\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"prompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"summary_message_cls\":{\"type\":\"Type[langchain_core.messages.base.BaseMessage]\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"summary_message_cls\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"ai_prefix\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"AI\",\"fileTypes\":[],\"password\":false,\"name\":\"ai_prefix\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"buffer\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"password\":false,\"name\":\"buffer\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"human_prefix\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"Human\",\"fileTypes\":[],\"password\":false,\"name\":\"human_prefix\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"input_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"input_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"The variable to be used as Chat Input when more than one variable is available.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"memory_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"chat_history\",\"fileTypes\":[],\"password\":false,\"name\":\"memory_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"output_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"output_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"The variable to be used as Chat Output (e.g. answer in a ConversationalRetrievalChain)\",\"title_case\":false,\"input_types\":[\"Text\"]},\"return_messages\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"return_messages\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"ConversationSummaryMemory\"},\"description\":\"Conversation summarizer to chat memory.\",\"base_classes\":[\"BaseChatMemory\",\"Serializable\",\"ConversationSummaryMemory\",\"BaseMemory\",\"SummarizerMixin\"],\"display_name\":\"ConversationSummaryMemory\",\"documentation\":\"https://python.langchain.com/docs/modules/memory/how_to/summary\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":true,\"beta\":false},\"MongoDBChatMessageHistory\":{\"template\":{\"collection_name\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"message_store\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"collection_name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"connection_string\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"connection_string\",\"advanced\":false,\"dynamic\":false,\"info\":\"MongoDB connection string (e.g mongodb://mongo_user:password123@mongo:27017)\",\"title_case\":false,\"input_types\":[\"Text\"]},\"database_name\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"chat_history\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"database_name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"session_id\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"session_id\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"MongoDBChatMessageHistory\"},\"description\":\"Memory store with MongoDB\",\"base_classes\":[\"BaseChatMessageHistory\",\"MongoDBChatMessageHistory\"],\"display_name\":\"MongoDBChatMessageHistory\",\"documentation\":\"https://python.langchain.com/docs/modules/memory/integrations/mongodb_chat_message_history\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":true,\"beta\":false},\"MotorheadMemory\":{\"template\":{\"chat_memory\":{\"type\":\"BaseChatMessageHistory\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"chat_memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"api_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"api_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"client_id\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"client_id\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"context\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"context\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"input_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"input_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"The variable to be used as Chat Input when more than one variable is available.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"memory_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"chat_history\",\"fileTypes\":[],\"password\":false,\"name\":\"memory_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"output_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"output_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"The variable to be used as Chat Output (e.g. answer in a ConversationalRetrievalChain)\",\"title_case\":false,\"input_types\":[\"Text\"]},\"return_messages\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"return_messages\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"session_id\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"session_id\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"timeout\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"timeout\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"url\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"https://api.getmetal.io/v1/motorhead\",\"fileTypes\":[],\"password\":false,\"name\":\"url\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"MotorheadMemory\"},\"description\":\"Chat message memory backed by Motorhead service.\",\"base_classes\":[\"MotorheadMemory\",\"BaseChatMemory\",\"BaseMemory\",\"Serializable\"],\"display_name\":\"MotorheadMemory\",\"documentation\":\"https://python.langchain.com/docs/integrations/memory/motorhead_memory\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":true,\"beta\":false},\"PostgresChatMessageHistory\":{\"template\":{\"connection_string\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"postgresql://postgres:mypassword@localhost/chat_history\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"connection_string\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"session_id\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"session_id\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"table_name\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"message_store\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"table_name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"PostgresChatMessageHistory\"},\"description\":\"Memory store with Postgres\",\"base_classes\":[\"BaseChatMessageHistory\",\"PostgresChatMessageHistory\"],\"display_name\":\"PostgresChatMessageHistory\",\"documentation\":\"https://python.langchain.com/docs/modules/memory/integrations/postgres_chat_message_history\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":true,\"beta\":false},\"VectorStoreRetrieverMemory\":{\"template\":{\"retriever\":{\"type\":\"VectorStoreRetriever\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"retriever\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"exclude_input_keys\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"exclude_input_keys\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"input_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"input_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"The variable to be used as Chat Input when more than one variable is available.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"memory_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"chat_history\",\"fileTypes\":[],\"password\":false,\"name\":\"memory_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"return_docs\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"return_docs\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"return_messages\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"return_messages\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"VectorStoreRetrieverMemory\"},\"description\":\"VectorStoreRetriever-backed memory.\",\"base_classes\":[\"BaseMemory\",\"VectorStoreRetrieverMemory\",\"Serializable\"],\"display_name\":\"VectorStoreRetrieverMemory\",\"documentation\":\"https://python.langchain.com/docs/modules/memory/how_to/vectorstore_retriever_memory\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":true,\"beta\":false}},\"tools\":{\"Calculator\":{\"template\":{\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"Calculator\"},\"description\":\"Useful for when you need to answer questions about math.\",\"base_classes\":[\"BaseTool\",\"Tool\"],\"display_name\":\"Calculator\",\"documentation\":\"\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"Search\":{\"template\":{\"aiosession\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"aiosession\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"serpapi_api_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"serpapi_api_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"Search\"},\"description\":\"A search engine. Useful for when you need to answer questions about current events. Input should be a search query.\",\"base_classes\":[\"BaseTool\",\"Tool\"],\"display_name\":\"Search\",\"documentation\":\"\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"Tool\":{\"template\":{\"func\":{\"type\":\"Callable\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"func\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"description\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"description\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"name\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"return_direct\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"return_direct\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"Tool\"},\"description\":\"Converts a chain, agent or function into a tool.\",\"base_classes\":[\"BaseTool\",\"Tool\"],\"display_name\":\"Tool\",\"documentation\":\"\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"PythonFunctionTool\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"\\ndef python_function(text: str) -> str:\\n \\\"\\\"\\\"This is a default python function that returns the input text\\\"\\\"\\\"\\n return text\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"description\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"description\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"name\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"return_direct\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"return_direct\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"PythonFunctionTool\"},\"description\":\"Python function to be executed.\",\"base_classes\":[\"BaseTool\",\"Tool\"],\"display_name\":\"PythonFunctionTool\",\"documentation\":\"\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"PythonFunction\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"\\ndef python_function(text: str) -> str:\\n \\\"\\\"\\\"This is a default python function that returns the input text\\\"\\\"\\\"\\n return text\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"PythonFunction\"},\"description\":\"Python function to be executed.\",\"base_classes\":[\"Callable\"],\"display_name\":\"PythonFunction\",\"documentation\":\"\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"JsonSpec\":{\"template\":{\"path\":{\"type\":\"file\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[\".json\",\".yaml\",\".yml\"],\"file_path\":\"\",\"password\":false,\"name\":\"path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"max_value_length\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_value_length\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"JsonSpec\"},\"description\":\"\",\"base_classes\":[\"JsonSpec\",\"BaseTool\",\"Tool\"],\"display_name\":\"JsonSpec\",\"documentation\":\"\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"BingSearchRun\":{\"template\":{\"api_wrapper\":{\"type\":\"BingSearchAPIWrapper\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"api_wrapper\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"args_schema\":{\"type\":\"Type[pydantic.v1.main.BaseModel]\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"args_schema\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"callbacks\":{\"type\":\"langchain_core.callbacks.base.BaseCallbackHandler\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"callbacks\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"handle_tool_error\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"handle_tool_error\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"handle_validation_error\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"handle_validation_error\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"tags\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"tags\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"BingSearchRun\"},\"description\":\"\",\"base_classes\":[\"Runnable\",\"BingSearchRun\",\"Generic\",\"BaseTool\",\"RunnableSerializable\",\"Tool\",\"Serializable\",\"object\"],\"display_name\":\"BingSearchRun\",\"documentation\":\"\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"GoogleSearchResults\":{\"template\":{\"api_wrapper\":{\"type\":\"GoogleSearchAPIWrapper\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"api_wrapper\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"args_schema\":{\"type\":\"Type[pydantic.v1.main.BaseModel]\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"args_schema\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"callbacks\":{\"type\":\"langchain_core.callbacks.base.BaseCallbackHandler\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"callbacks\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"handle_tool_error\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"handle_tool_error\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"handle_validation_error\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"handle_validation_error\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"num_results\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":4,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"num_results\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"tags\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"tags\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"GoogleSearchResults\"},\"description\":\"\",\"base_classes\":[\"GoogleSearchResults\",\"Runnable\",\"Generic\",\"BaseTool\",\"RunnableSerializable\",\"Tool\",\"Serializable\",\"object\"],\"display_name\":\"GoogleSearchResults\",\"documentation\":\"\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"GoogleSearchRun\":{\"template\":{\"api_wrapper\":{\"type\":\"GoogleSearchAPIWrapper\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"api_wrapper\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"args_schema\":{\"type\":\"Type[pydantic.v1.main.BaseModel]\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"args_schema\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"callbacks\":{\"type\":\"langchain_core.callbacks.base.BaseCallbackHandler\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"callbacks\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"handle_tool_error\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"handle_tool_error\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"handle_validation_error\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"handle_validation_error\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"tags\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"tags\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"GoogleSearchRun\"},\"description\":\"\",\"base_classes\":[\"Runnable\",\"Generic\",\"BaseTool\",\"RunnableSerializable\",\"Tool\",\"GoogleSearchRun\",\"Serializable\",\"object\"],\"display_name\":\"GoogleSearchRun\",\"documentation\":\"\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"GoogleSerperRun\":{\"template\":{\"api_wrapper\":{\"type\":\"GoogleSerperAPIWrapper\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"api_wrapper\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"args_schema\":{\"type\":\"Type[pydantic.v1.main.BaseModel]\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"args_schema\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"callbacks\":{\"type\":\"langchain_core.callbacks.base.BaseCallbackHandler\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"callbacks\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"handle_tool_error\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"handle_tool_error\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"handle_validation_error\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"handle_validation_error\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"tags\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"tags\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"GoogleSerperRun\"},\"description\":\"\",\"base_classes\":[\"Runnable\",\"Generic\",\"BaseTool\",\"RunnableSerializable\",\"Tool\",\"Serializable\",\"GoogleSerperRun\",\"object\"],\"display_name\":\"GoogleSerperRun\",\"documentation\":\"\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"InfoSQLDatabaseTool\":{\"template\":{\"args_schema\":{\"type\":\"Type[pydantic.v1.main.BaseModel]\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"args_schema\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"callbacks\":{\"type\":\"langchain_core.callbacks.base.BaseCallbackHandler\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"callbacks\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"db\":{\"type\":\"SQLDatabase\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"db\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"handle_tool_error\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"handle_tool_error\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"handle_validation_error\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"handle_validation_error\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"tags\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"tags\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"InfoSQLDatabaseTool\"},\"description\":\"\",\"base_classes\":[\"InfoSQLDatabaseTool\",\"Runnable\",\"BaseSQLDatabaseTool\",\"Generic\",\"BaseTool\",\"RunnableSerializable\",\"Tool\",\"Serializable\",\"object\"],\"display_name\":\"InfoSQLDatabaseTool\",\"documentation\":\"\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"JsonGetValueTool\":{\"template\":{\"args_schema\":{\"type\":\"Type[pydantic.v1.main.BaseModel]\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"args_schema\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"callbacks\":{\"type\":\"langchain_core.callbacks.base.BaseCallbackHandler\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"callbacks\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"spec\":{\"type\":\"JsonSpec\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"spec\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"handle_tool_error\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"handle_tool_error\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"handle_validation_error\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"handle_validation_error\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"tags\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"tags\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"JsonGetValueTool\"},\"description\":\"\",\"base_classes\":[\"Runnable\",\"Generic\",\"BaseTool\",\"RunnableSerializable\",\"Tool\",\"Serializable\",\"object\",\"JsonGetValueTool\"],\"display_name\":\"JsonGetValueTool\",\"documentation\":\"\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"JsonListKeysTool\":{\"template\":{\"args_schema\":{\"type\":\"Type[pydantic.v1.main.BaseModel]\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"args_schema\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"callbacks\":{\"type\":\"langchain_core.callbacks.base.BaseCallbackHandler\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"callbacks\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"spec\":{\"type\":\"JsonSpec\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"spec\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"handle_tool_error\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"handle_tool_error\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"handle_validation_error\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"handle_validation_error\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"tags\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"tags\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"JsonListKeysTool\"},\"description\":\"\",\"base_classes\":[\"Runnable\",\"Generic\",\"BaseTool\",\"RunnableSerializable\",\"Tool\",\"Serializable\",\"object\",\"JsonListKeysTool\"],\"display_name\":\"JsonListKeysTool\",\"documentation\":\"\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"ListSQLDatabaseTool\":{\"template\":{\"args_schema\":{\"type\":\"Type[pydantic.v1.main.BaseModel]\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"args_schema\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"callbacks\":{\"type\":\"langchain_core.callbacks.base.BaseCallbackHandler\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"callbacks\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"db\":{\"type\":\"SQLDatabase\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"db\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"handle_tool_error\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"handle_tool_error\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"handle_validation_error\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"handle_validation_error\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"tags\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"tags\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"ListSQLDatabaseTool\"},\"description\":\"\",\"base_classes\":[\"Runnable\",\"BaseSQLDatabaseTool\",\"Generic\",\"BaseTool\",\"RunnableSerializable\",\"Tool\",\"Serializable\",\"object\",\"ListSQLDatabaseTool\"],\"display_name\":\"ListSQLDatabaseTool\",\"documentation\":\"\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"QuerySQLDataBaseTool\":{\"template\":{\"args_schema\":{\"type\":\"Type[pydantic.v1.main.BaseModel]\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"args_schema\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"callbacks\":{\"type\":\"langchain_core.callbacks.base.BaseCallbackHandler\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"callbacks\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"db\":{\"type\":\"SQLDatabase\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"db\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"handle_tool_error\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"handle_tool_error\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"handle_validation_error\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"handle_validation_error\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"tags\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"tags\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"QuerySQLDataBaseTool\"},\"description\":\"\",\"base_classes\":[\"Runnable\",\"BaseSQLDatabaseTool\",\"Generic\",\"BaseTool\",\"RunnableSerializable\",\"Tool\",\"QuerySQLDataBaseTool\",\"Serializable\",\"object\"],\"display_name\":\"QuerySQLDataBaseTool\",\"documentation\":\"\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"RequestsDeleteTool\":{\"template\":{\"args_schema\":{\"type\":\"Type[pydantic.v1.main.BaseModel]\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"args_schema\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"callbacks\":{\"type\":\"langchain_core.callbacks.base.BaseCallbackHandler\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"callbacks\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"requests_wrapper\":{\"type\":\"GenericRequestsWrapper\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"requests_wrapper\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"handle_tool_error\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"handle_tool_error\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"handle_validation_error\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"handle_validation_error\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"tags\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"tags\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"RequestsDeleteTool\"},\"description\":\"\",\"base_classes\":[\"Runnable\",\"Generic\",\"BaseTool\",\"RunnableSerializable\",\"Tool\",\"Serializable\",\"object\",\"RequestsDeleteTool\",\"BaseRequestsTool\"],\"display_name\":\"RequestsDeleteTool\",\"documentation\":\"\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"RequestsGetTool\":{\"template\":{\"args_schema\":{\"type\":\"Type[pydantic.v1.main.BaseModel]\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"args_schema\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"callbacks\":{\"type\":\"langchain_core.callbacks.base.BaseCallbackHandler\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"callbacks\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"requests_wrapper\":{\"type\":\"GenericRequestsWrapper\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"requests_wrapper\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"handle_tool_error\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"handle_tool_error\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"handle_validation_error\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"handle_validation_error\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"tags\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"tags\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"RequestsGetTool\"},\"description\":\"\",\"base_classes\":[\"Runnable\",\"Generic\",\"BaseTool\",\"RunnableSerializable\",\"Tool\",\"Serializable\",\"object\",\"RequestsGetTool\",\"BaseRequestsTool\"],\"display_name\":\"RequestsGetTool\",\"documentation\":\"\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"RequestsPatchTool\":{\"template\":{\"args_schema\":{\"type\":\"Type[pydantic.v1.main.BaseModel]\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"args_schema\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"callbacks\":{\"type\":\"langchain_core.callbacks.base.BaseCallbackHandler\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"callbacks\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"requests_wrapper\":{\"type\":\"GenericRequestsWrapper\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"requests_wrapper\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"handle_tool_error\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"handle_tool_error\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"handle_validation_error\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"handle_validation_error\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"tags\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"tags\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"RequestsPatchTool\"},\"description\":\"\",\"base_classes\":[\"Runnable\",\"RequestsPatchTool\",\"Generic\",\"BaseTool\",\"RunnableSerializable\",\"Tool\",\"Serializable\",\"object\",\"BaseRequestsTool\"],\"display_name\":\"RequestsPatchTool\",\"documentation\":\"\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"RequestsPostTool\":{\"template\":{\"args_schema\":{\"type\":\"Type[pydantic.v1.main.BaseModel]\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"args_schema\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"callbacks\":{\"type\":\"langchain_core.callbacks.base.BaseCallbackHandler\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"callbacks\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"requests_wrapper\":{\"type\":\"GenericRequestsWrapper\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"requests_wrapper\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"handle_tool_error\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"handle_tool_error\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"handle_validation_error\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"handle_validation_error\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"tags\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"tags\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"RequestsPostTool\"},\"description\":\"\",\"base_classes\":[\"Runnable\",\"Generic\",\"BaseTool\",\"RunnableSerializable\",\"Tool\",\"Serializable\",\"RequestsPostTool\",\"object\",\"BaseRequestsTool\"],\"display_name\":\"RequestsPostTool\",\"documentation\":\"\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"RequestsPutTool\":{\"template\":{\"args_schema\":{\"type\":\"Type[pydantic.v1.main.BaseModel]\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"args_schema\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"callbacks\":{\"type\":\"langchain_core.callbacks.base.BaseCallbackHandler\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"callbacks\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"requests_wrapper\":{\"type\":\"GenericRequestsWrapper\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"requests_wrapper\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"handle_tool_error\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"handle_tool_error\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"handle_validation_error\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"handle_validation_error\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"tags\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"tags\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"RequestsPutTool\"},\"description\":\"\",\"base_classes\":[\"RequestsPutTool\",\"Runnable\",\"Generic\",\"BaseTool\",\"RunnableSerializable\",\"Tool\",\"Serializable\",\"object\",\"BaseRequestsTool\"],\"display_name\":\"RequestsPutTool\",\"documentation\":\"\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"WikipediaQueryRun\":{\"template\":{\"api_wrapper\":{\"type\":\"WikipediaAPIWrapper\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"api_wrapper\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"args_schema\":{\"type\":\"Type[pydantic.v1.main.BaseModel]\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"args_schema\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"callbacks\":{\"type\":\"langchain_core.callbacks.base.BaseCallbackHandler\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"callbacks\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"handle_tool_error\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"handle_tool_error\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"handle_validation_error\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"handle_validation_error\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"tags\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"tags\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"WikipediaQueryRun\"},\"description\":\"\",\"base_classes\":[\"Runnable\",\"Generic\",\"BaseTool\",\"RunnableSerializable\",\"Tool\",\"WikipediaQueryRun\",\"Serializable\",\"object\"],\"display_name\":\"WikipediaQueryRun\",\"documentation\":\"\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"WolframAlphaQueryRun\":{\"template\":{\"api_wrapper\":{\"type\":\"WolframAlphaAPIWrapper\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"api_wrapper\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"args_schema\":{\"type\":\"Type[pydantic.v1.main.BaseModel]\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"args_schema\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"callbacks\":{\"type\":\"langchain_core.callbacks.base.BaseCallbackHandler\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"callbacks\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"handle_tool_error\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"handle_tool_error\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"handle_validation_error\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"handle_validation_error\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"tags\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"tags\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"WolframAlphaQueryRun\"},\"description\":\"\",\"base_classes\":[\"Runnable\",\"WolframAlphaQueryRun\",\"Generic\",\"BaseTool\",\"RunnableSerializable\",\"Tool\",\"Serializable\",\"object\"],\"display_name\":\"WolframAlphaQueryRun\",\"documentation\":\"\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":false,\"beta\":false}},\"toolkits\":{\"JsonToolkit\":{\"template\":{\"spec\":{\"type\":\"JsonSpec\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"spec\",\"display_name\":\"Spec\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langchain_community.tools.json.tool import JsonSpec\\nfrom langchain_community.agent_toolkits.json.toolkit import JsonToolkit\\n\\n\\nclass JsonToolkitComponent(CustomComponent):\\n display_name = \\\"JsonToolkit\\\"\\n description = \\\"Toolkit for interacting with a JSON spec.\\\"\\n\\n def build_config(self):\\n return {\\n \\\"spec\\\": {\\\"display_name\\\": \\\"Spec\\\", \\\"type\\\": JsonSpec},\\n }\\n\\n def build(self, spec: JsonSpec) -> JsonToolkit:\\n return JsonToolkit(spec=spec)\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Toolkit for interacting with a JSON spec.\",\"base_classes\":[\"BaseToolkit\",\"JsonToolkit\"],\"display_name\":\"JsonToolkit\",\"documentation\":\"\",\"custom_fields\":{\"spec\":null},\"output_types\":[\"JsonToolkit\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"OpenAPIToolkit\":{\"template\":{\"json_agent\":{\"type\":\"AgentExecutor\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"json_agent\",\"display_name\":\"JSON Agent\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"requests_wrapper\":{\"type\":\"TextRequestsWrapper\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"requests_wrapper\",\"display_name\":\"Text Requests Wrapper\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from langchain_community.agent_toolkits.openapi.toolkit import BaseToolkit, OpenAPIToolkit\\nfrom langchain_community.utilities.requests import TextRequestsWrapper\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import AgentExecutor\\n\\n\\nclass OpenAPIToolkitComponent(CustomComponent):\\n display_name = \\\"OpenAPIToolkit\\\"\\n description = \\\"Toolkit for interacting with an OpenAPI API.\\\"\\n\\n def build_config(self):\\n return {\\n \\\"json_agent\\\": {\\\"display_name\\\": \\\"JSON Agent\\\"},\\n \\\"requests_wrapper\\\": {\\\"display_name\\\": \\\"Text Requests Wrapper\\\"},\\n }\\n\\n def build(\\n self,\\n json_agent: AgentExecutor,\\n requests_wrapper: TextRequestsWrapper,\\n ) -> BaseToolkit:\\n return OpenAPIToolkit(json_agent=json_agent, requests_wrapper=requests_wrapper)\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Toolkit for interacting with an OpenAPI API.\",\"base_classes\":[\"BaseToolkit\"],\"display_name\":\"OpenAPIToolkit\",\"documentation\":\"\",\"custom_fields\":{\"json_agent\":null,\"requests_wrapper\":null},\"output_types\":[\"BaseToolkit\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"VectorStoreInfo\":{\"template\":{\"vectorstore\":{\"type\":\"VectorStore\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"vectorstore\",\"display_name\":\"VectorStore\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Callable, Union\\n\\nfrom langchain.agents.agent_toolkits.vectorstore.toolkit import VectorStoreInfo\\nfrom langchain_community.vectorstores import VectorStore\\n\\nfrom langflow import CustomComponent\\n\\n\\nclass VectorStoreInfoComponent(CustomComponent):\\n display_name = \\\"VectorStoreInfo\\\"\\n description = \\\"Information about a VectorStore\\\"\\n\\n def build_config(self):\\n return {\\n \\\"vectorstore\\\": {\\\"display_name\\\": \\\"VectorStore\\\"},\\n \\\"description\\\": {\\\"display_name\\\": \\\"Description\\\", \\\"multiline\\\": True},\\n \\\"name\\\": {\\\"display_name\\\": \\\"Name\\\"},\\n }\\n\\n def build(\\n self,\\n vectorstore: VectorStore,\\n description: str,\\n name: str,\\n ) -> Union[VectorStoreInfo, Callable]:\\n return VectorStoreInfo(vectorstore=vectorstore, description=description, name=name)\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"description\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"description\",\"display_name\":\"Description\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"name\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"name\",\"display_name\":\"Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Information about a VectorStore\",\"base_classes\":[\"Callable\",\"VectorStoreInfo\"],\"display_name\":\"VectorStoreInfo\",\"documentation\":\"\",\"custom_fields\":{\"vectorstore\":null,\"description\":null,\"name\":null},\"output_types\":[\"VectorStoreInfo\",\"Callable\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"VectorStoreRouterToolkit\":{\"template\":{\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"vectorstores\":{\"type\":\"VectorStoreInfo\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"vectorstores\",\"display_name\":\"Vector Stores\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom typing import List, Union\\nfrom langchain.agents.agent_toolkits.vectorstore.toolkit import VectorStoreRouterToolkit\\nfrom langchain.agents.agent_toolkits.vectorstore.toolkit import VectorStoreInfo\\nfrom langflow.field_typing import BaseLanguageModel, Tool\\n\\n\\nclass VectorStoreRouterToolkitComponent(CustomComponent):\\n display_name = \\\"VectorStoreRouterToolkit\\\"\\n description = \\\"Toolkit for routing between Vector Stores.\\\"\\n\\n def build_config(self):\\n return {\\n \\\"vectorstores\\\": {\\\"display_name\\\": \\\"Vector Stores\\\"},\\n \\\"llm\\\": {\\\"display_name\\\": \\\"LLM\\\"},\\n }\\n\\n def build(\\n self, vectorstores: List[VectorStoreInfo], llm: BaseLanguageModel\\n ) -> Union[Tool, VectorStoreRouterToolkit]:\\n print(\\\"vectorstores\\\", vectorstores)\\n print(\\\"llm\\\", llm)\\n return VectorStoreRouterToolkit(vectorstores=vectorstores, llm=llm)\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Toolkit for routing between Vector Stores.\",\"base_classes\":[\"Runnable\",\"BaseToolkit\",\"Generic\",\"VectorStoreRouterToolkit\",\"BaseTool\",\"RunnableSerializable\",\"Tool\",\"Serializable\",\"object\"],\"display_name\":\"VectorStoreRouterToolkit\",\"documentation\":\"\",\"custom_fields\":{\"vectorstores\":null,\"llm\":null},\"output_types\":[\"Tool\",\"VectorStoreRouterToolkit\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"VectorStoreToolkit\":{\"template\":{\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"vectorstore_info\":{\"type\":\"VectorStoreInfo\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"vectorstore_info\",\"display_name\":\"Vector Store Info\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langchain.agents.agent_toolkits.vectorstore.toolkit import VectorStoreToolkit\\nfrom langchain.agents.agent_toolkits.vectorstore.toolkit import VectorStoreInfo\\nfrom langflow.field_typing import (\\n BaseLanguageModel,\\n)\\nfrom langflow.field_typing import (\\n Tool,\\n)\\nfrom typing import Union\\n\\n\\nclass VectorStoreToolkitComponent(CustomComponent):\\n display_name = \\\"VectorStoreToolkit\\\"\\n description = \\\"Toolkit for interacting with a Vector Store.\\\"\\n\\n def build_config(self):\\n return {\\n \\\"vectorstore_info\\\": {\\\"display_name\\\": \\\"Vector Store Info\\\"},\\n \\\"llm\\\": {\\\"display_name\\\": \\\"LLM\\\"},\\n }\\n\\n def build(\\n self,\\n vectorstore_info: VectorStoreInfo,\\n llm: BaseLanguageModel,\\n ) -> Union[Tool, VectorStoreToolkit]:\\n return VectorStoreToolkit(vectorstore_info=vectorstore_info, llm=llm)\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Toolkit for interacting with a Vector Store.\",\"base_classes\":[\"Runnable\",\"BaseToolkit\",\"Generic\",\"BaseTool\",\"RunnableSerializable\",\"Tool\",\"Serializable\",\"object\",\"VectorStoreToolkit\"],\"display_name\":\"VectorStoreToolkit\",\"documentation\":\"\",\"custom_fields\":{\"vectorstore_info\":null,\"llm\":null},\"output_types\":[\"Tool\",\"VectorStoreToolkit\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"Metaphor\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import List, Union\\n\\nfrom langchain.agents import tool\\nfrom langchain.agents.agent_toolkits.base import BaseToolkit\\nfrom langchain.tools import Tool\\nfrom metaphor_python import Metaphor # type: ignore\\n\\nfrom langflow import CustomComponent\\n\\n\\nclass MetaphorToolkit(CustomComponent):\\n display_name: str = \\\"Metaphor\\\"\\n description: str = \\\"Metaphor Toolkit\\\"\\n documentation = \\\"https://python.langchain.com/docs/integrations/tools/metaphor_search\\\"\\n beta: bool = True\\n # api key should be password = True\\n field_config = {\\n \\\"metaphor_api_key\\\": {\\\"display_name\\\": \\\"Metaphor API Key\\\", \\\"password\\\": True},\\n \\\"code\\\": {\\\"advanced\\\": True},\\n }\\n\\n def build(\\n self,\\n metaphor_api_key: str,\\n use_autoprompt: bool = True,\\n search_num_results: int = 5,\\n similar_num_results: int = 5,\\n ) -> Union[Tool, BaseToolkit]:\\n # If documents, then we need to create a Vectara instance using .from_documents\\n client = Metaphor(api_key=metaphor_api_key)\\n\\n @tool\\n def search(query: str):\\n \\\"\\\"\\\"Call search engine with a query.\\\"\\\"\\\"\\n return client.search(query, use_autoprompt=use_autoprompt, num_results=search_num_results)\\n\\n @tool\\n def get_contents(ids: List[str]):\\n \\\"\\\"\\\"Get contents of a webpage.\\n\\n The ids passed in should be a list of ids as fetched from `search`.\\n \\\"\\\"\\\"\\n return client.get_contents(ids)\\n\\n @tool\\n def find_similar(url: str):\\n \\\"\\\"\\\"Get search results similar to a given URL.\\n\\n The url passed in should be a URL returned from `search`\\n \\\"\\\"\\\"\\n return client.find_similar(url, num_results=similar_num_results)\\n\\n return [search, get_contents, find_similar] # type: ignore\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":true,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"metaphor_api_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"metaphor_api_key\",\"display_name\":\"Metaphor API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"search_num_results\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":5,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"search_num_results\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"similar_num_results\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":5,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"similar_num_results\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"use_autoprompt\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":true,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"use_autoprompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Metaphor Toolkit\",\"base_classes\":[\"Runnable\",\"BaseToolkit\",\"Generic\",\"BaseTool\",\"RunnableSerializable\",\"Tool\",\"Serializable\",\"object\"],\"display_name\":\"Metaphor\",\"documentation\":\"https://python.langchain.com/docs/integrations/tools/metaphor_search\",\"custom_fields\":{\"metaphor_api_key\":null,\"use_autoprompt\":null,\"search_num_results\":null,\"similar_num_results\":null},\"output_types\":[\"Tool\",\"BaseToolkit\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true}},\"wrappers\":{\"TextRequestsWrapper\":{\"template\":{\"aiosession\":{\"type\":\"ClientSession\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"aiosession\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"auth\":{\"type\":\"Any\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"auth\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"headers\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"{\\\"Authorization\\\": \\\"Bearer \\\"}\",\"fileTypes\":[],\"password\":false,\"name\":\"headers\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"response_content_type\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"text\",\"fileTypes\":[],\"password\":false,\"name\":\"response_content_type\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"TextRequestsWrapper\"},\"description\":\"Lightweight wrapper around requests library, with async support.\",\"base_classes\":[\"TextRequestsWrapper\",\"GenericRequestsWrapper\"],\"display_name\":\"TextRequestsWrapper\",\"documentation\":\"\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":false,\"beta\":false}},\"embeddings\":{\"OpenAIEmbeddings\":{\"template\":{\"allowed_special\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":[],\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"allowed_special\",\"display_name\":\"Allowed Special\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"chunk_size\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":1000,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"chunk_size\",\"display_name\":\"Chunk Size\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"client\":{\"type\":\"Any\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"client\",\"display_name\":\"Client\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Any, Callable, Dict, List, Optional, Union\\n\\nfrom langchain_openai.embeddings.base import OpenAIEmbeddings\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import NestedDict\\nfrom pydantic.v1.types import SecretStr\\n\\n\\nclass OpenAIEmbeddingsComponent(CustomComponent):\\n display_name = \\\"OpenAIEmbeddings\\\"\\n description = \\\"OpenAI embedding models\\\"\\n\\n def build_config(self):\\n return {\\n \\\"allowed_special\\\": {\\n \\\"display_name\\\": \\\"Allowed Special\\\",\\n \\\"advanced\\\": True,\\n \\\"field_type\\\": \\\"str\\\",\\n \\\"is_list\\\": True,\\n },\\n \\\"default_headers\\\": {\\n \\\"display_name\\\": \\\"Default Headers\\\",\\n \\\"advanced\\\": True,\\n \\\"field_type\\\": \\\"dict\\\",\\n },\\n \\\"default_query\\\": {\\n \\\"display_name\\\": \\\"Default Query\\\",\\n \\\"advanced\\\": True,\\n \\\"field_type\\\": \\\"NestedDict\\\",\\n },\\n \\\"disallowed_special\\\": {\\n \\\"display_name\\\": \\\"Disallowed Special\\\",\\n \\\"advanced\\\": True,\\n \\\"field_type\\\": \\\"str\\\",\\n \\\"is_list\\\": True,\\n },\\n \\\"chunk_size\\\": {\\\"display_name\\\": \\\"Chunk Size\\\", \\\"advanced\\\": True},\\n \\\"client\\\": {\\\"display_name\\\": \\\"Client\\\", \\\"advanced\\\": True},\\n \\\"deployment\\\": {\\\"display_name\\\": \\\"Deployment\\\", \\\"advanced\\\": True},\\n \\\"embedding_ctx_length\\\": {\\n \\\"display_name\\\": \\\"Embedding Context Length\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"max_retries\\\": {\\\"display_name\\\": \\\"Max Retries\\\", \\\"advanced\\\": True},\\n \\\"model\\\": {\\n \\\"display_name\\\": \\\"Model\\\",\\n \\\"advanced\\\": False,\\n \\\"options\\\": [\\\"text-embedding-3-small\\\", \\\"text-embedding-3-large\\\", \\\"text-embedding-ada-002\\\"],\\n },\\n \\\"model_kwargs\\\": {\\\"display_name\\\": \\\"Model Kwargs\\\", \\\"advanced\\\": True},\\n \\\"openai_api_base\\\": {\\\"display_name\\\": \\\"OpenAI API Base\\\", \\\"password\\\": True, \\\"advanced\\\": True},\\n \\\"openai_api_key\\\": {\\\"display_name\\\": \\\"OpenAI API Key\\\", \\\"password\\\": True},\\n \\\"openai_api_type\\\": {\\\"display_name\\\": \\\"OpenAI API Type\\\", \\\"advanced\\\": True, \\\"password\\\": True},\\n \\\"openai_api_version\\\": {\\n \\\"display_name\\\": \\\"OpenAI API Version\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"openai_organization\\\": {\\n \\\"display_name\\\": \\\"OpenAI Organization\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"openai_proxy\\\": {\\\"display_name\\\": \\\"OpenAI Proxy\\\", \\\"advanced\\\": True},\\n \\\"request_timeout\\\": {\\\"display_name\\\": \\\"Request Timeout\\\", \\\"advanced\\\": True},\\n \\\"show_progress_bar\\\": {\\n \\\"display_name\\\": \\\"Show Progress Bar\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"skip_empty\\\": {\\\"display_name\\\": \\\"Skip Empty\\\", \\\"advanced\\\": True},\\n \\\"tiktoken_model_name\\\": {\\\"display_name\\\": \\\"TikToken Model Name\\\"},\\n \\\"tikToken_enable\\\": {\\\"display_name\\\": \\\"TikToken Enable\\\", \\\"advanced\\\": True},\\n }\\n\\n def build(\\n self,\\n default_headers: Optional[Dict[str, str]] = None,\\n default_query: Optional[NestedDict] = {},\\n allowed_special: List[str] = [],\\n disallowed_special: List[str] = [\\\"all\\\"],\\n chunk_size: int = 1000,\\n client: Optional[Any] = None,\\n deployment: str = \\\"text-embedding-3-small\\\",\\n embedding_ctx_length: int = 8191,\\n max_retries: int = 6,\\n model: str = \\\"text-embedding-3-small\\\",\\n model_kwargs: NestedDict = {},\\n openai_api_base: Optional[str] = None,\\n openai_api_key: Optional[str] = \\\"\\\",\\n openai_api_type: Optional[str] = None,\\n openai_api_version: Optional[str] = None,\\n openai_organization: Optional[str] = None,\\n openai_proxy: Optional[str] = None,\\n request_timeout: Optional[float] = None,\\n show_progress_bar: bool = False,\\n skip_empty: bool = False,\\n tiktoken_enable: bool = True,\\n tiktoken_model_name: Optional[str] = None,\\n ) -> Union[OpenAIEmbeddings, Callable]:\\n # This is to avoid errors with Vector Stores (e.g Chroma)\\n if disallowed_special == [\\\"all\\\"]:\\n disallowed_special = \\\"all\\\" # type: ignore\\n\\n api_key = SecretStr(openai_api_key) if openai_api_key else None\\n\\n return OpenAIEmbeddings(\\n tiktoken_enabled=tiktoken_enable,\\n default_headers=default_headers,\\n default_query=default_query,\\n allowed_special=set(allowed_special),\\n disallowed_special=\\\"all\\\",\\n chunk_size=chunk_size,\\n client=client,\\n deployment=deployment,\\n embedding_ctx_length=embedding_ctx_length,\\n max_retries=max_retries,\\n model=model,\\n model_kwargs=model_kwargs,\\n base_url=openai_api_base,\\n api_key=api_key,\\n openai_api_type=openai_api_type,\\n api_version=openai_api_version,\\n organization=openai_organization,\\n openai_proxy=openai_proxy,\\n timeout=request_timeout,\\n show_progress_bar=show_progress_bar,\\n skip_empty=skip_empty,\\n tiktoken_model_name=tiktoken_model_name,\\n )\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"default_headers\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"default_headers\",\"display_name\":\"Default Headers\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"default_query\":{\"type\":\"NestedDict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"default_query\",\"display_name\":\"Default Query\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"deployment\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"text-embedding-3-small\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"deployment\",\"display_name\":\"Deployment\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"disallowed_special\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":[\"all\"],\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"disallowed_special\",\"display_name\":\"Disallowed Special\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"embedding_ctx_length\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":8191,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"embedding_ctx_length\",\"display_name\":\"Embedding Context Length\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"max_retries\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":6,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_retries\",\"display_name\":\"Max Retries\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"model\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"text-embedding-3-small\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"text-embedding-3-small\",\"text-embedding-3-large\",\"text-embedding-ada-002\"],\"name\":\"model\",\"display_name\":\"Model\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"model_kwargs\":{\"type\":\"NestedDict\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model_kwargs\",\"display_name\":\"Model Kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"openai_api_base\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"openai_api_base\",\"display_name\":\"OpenAI API Base\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"openai_api_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"openai_api_key\",\"display_name\":\"OpenAI API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"openai_api_type\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"openai_api_type\",\"display_name\":\"OpenAI API Type\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"openai_api_version\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"openai_api_version\",\"display_name\":\"OpenAI API Version\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"openai_organization\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"openai_organization\",\"display_name\":\"OpenAI Organization\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"openai_proxy\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"openai_proxy\",\"display_name\":\"OpenAI Proxy\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"request_timeout\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"request_timeout\",\"display_name\":\"Request Timeout\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"show_progress_bar\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"show_progress_bar\",\"display_name\":\"Show Progress Bar\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"skip_empty\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"skip_empty\",\"display_name\":\"Skip Empty\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"tiktoken_enable\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":true,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"tiktoken_enable\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"tiktoken_model_name\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"tiktoken_model_name\",\"display_name\":\"TikToken Model Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"OpenAI embedding models\",\"base_classes\":[\"Embeddings\",\"OpenAIEmbeddings\",\"Callable\"],\"display_name\":\"OpenAIEmbeddings\",\"documentation\":\"\",\"custom_fields\":{\"default_headers\":null,\"default_query\":null,\"allowed_special\":null,\"disallowed_special\":null,\"chunk_size\":null,\"client\":null,\"deployment\":null,\"embedding_ctx_length\":null,\"max_retries\":null,\"model\":null,\"model_kwargs\":null,\"openai_api_base\":null,\"openai_api_key\":null,\"openai_api_type\":null,\"openai_api_version\":null,\"openai_organization\":null,\"openai_proxy\":null,\"request_timeout\":null,\"show_progress_bar\":null,\"skip_empty\":null,\"tiktoken_enable\":null,\"tiktoken_model_name\":null},\"output_types\":[\"OpenAIEmbeddings\",\"Callable\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"CohereEmbeddings\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain_community.embeddings.cohere import CohereEmbeddings\\nfrom langflow import CustomComponent\\n\\n\\nclass CohereEmbeddingsComponent(CustomComponent):\\n display_name = \\\"CohereEmbeddings\\\"\\n description = \\\"Cohere embedding models.\\\"\\n\\n def build_config(self):\\n return {\\n \\\"cohere_api_key\\\": {\\\"display_name\\\": \\\"Cohere API Key\\\", \\\"password\\\": True},\\n \\\"model\\\": {\\\"display_name\\\": \\\"Model\\\", \\\"default\\\": \\\"embed-english-v2.0\\\", \\\"advanced\\\": True},\\n \\\"truncate\\\": {\\\"display_name\\\": \\\"Truncate\\\", \\\"advanced\\\": True},\\n \\\"max_retries\\\": {\\\"display_name\\\": \\\"Max Retries\\\", \\\"advanced\\\": True},\\n \\\"user_agent\\\": {\\\"display_name\\\": \\\"User Agent\\\", \\\"advanced\\\": True},\\n }\\n\\n def build(\\n self,\\n request_timeout: Optional[float] = None,\\n cohere_api_key: str = \\\"\\\",\\n max_retries: Optional[int] = None,\\n model: str = \\\"embed-english-v2.0\\\",\\n truncate: Optional[str] = None,\\n user_agent: str = \\\"langchain\\\",\\n ) -> CohereEmbeddings:\\n return CohereEmbeddings( # type: ignore\\n max_retries=max_retries,\\n user_agent=user_agent,\\n request_timeout=request_timeout,\\n cohere_api_key=cohere_api_key,\\n model=model,\\n truncate=truncate,\\n )\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"cohere_api_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"cohere_api_key\",\"display_name\":\"Cohere API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"max_retries\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_retries\",\"display_name\":\"Max Retries\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"model\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"embed-english-v2.0\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model\",\"display_name\":\"Model\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"request_timeout\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"request_timeout\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"truncate\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"truncate\",\"display_name\":\"Truncate\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"user_agent\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"langchain\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"user_agent\",\"display_name\":\"User Agent\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Cohere embedding models.\",\"base_classes\":[\"Embeddings\",\"CohereEmbeddings\"],\"display_name\":\"CohereEmbeddings\",\"documentation\":\"\",\"custom_fields\":{\"request_timeout\":null,\"cohere_api_key\":null,\"max_retries\":null,\"model\":null,\"truncate\":null,\"user_agent\":null},\"output_types\":[\"CohereEmbeddings\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"HuggingFaceEmbeddings\":{\"template\":{\"cache_folder\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"cache_folder\",\"display_name\":\"Cache Folder\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom typing import Optional, Dict\\nfrom langchain_community.embeddings.huggingface import HuggingFaceEmbeddings\\n\\n\\nclass HuggingFaceEmbeddingsComponent(CustomComponent):\\n display_name = \\\"HuggingFaceEmbeddings\\\"\\n description = \\\"HuggingFace sentence_transformers embedding models.\\\"\\n documentation = (\\n \\\"https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/sentence_transformers\\\"\\n )\\n\\n def build_config(self):\\n return {\\n \\\"cache_folder\\\": {\\\"display_name\\\": \\\"Cache Folder\\\", \\\"advanced\\\": True},\\n \\\"encode_kwargs\\\": {\\\"display_name\\\": \\\"Encode Kwargs\\\", \\\"advanced\\\": True, \\\"field_type\\\": \\\"dict\\\"},\\n \\\"model_kwargs\\\": {\\\"display_name\\\": \\\"Model Kwargs\\\", \\\"field_type\\\": \\\"dict\\\", \\\"advanced\\\": True},\\n \\\"model_name\\\": {\\\"display_name\\\": \\\"Model Name\\\"},\\n \\\"multi_process\\\": {\\\"display_name\\\": \\\"Multi Process\\\", \\\"advanced\\\": True},\\n }\\n\\n def build(\\n self,\\n cache_folder: Optional[str] = None,\\n encode_kwargs: Optional[Dict] = {},\\n model_kwargs: Optional[Dict] = {},\\n model_name: str = \\\"sentence-transformers/all-mpnet-base-v2\\\",\\n multi_process: bool = False,\\n ) -> HuggingFaceEmbeddings:\\n return HuggingFaceEmbeddings(\\n cache_folder=cache_folder,\\n encode_kwargs=encode_kwargs,\\n model_kwargs=model_kwargs,\\n model_name=model_name,\\n multi_process=multi_process,\\n )\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"encode_kwargs\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"encode_kwargs\",\"display_name\":\"Encode Kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"model_kwargs\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model_kwargs\",\"display_name\":\"Model Kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"model_name\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"sentence-transformers/all-mpnet-base-v2\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model_name\",\"display_name\":\"Model Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"multi_process\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"multi_process\",\"display_name\":\"Multi Process\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"HuggingFace sentence_transformers embedding models.\",\"base_classes\":[\"Embeddings\",\"HuggingFaceEmbeddings\"],\"display_name\":\"HuggingFaceEmbeddings\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/sentence_transformers\",\"custom_fields\":{\"cache_folder\":null,\"encode_kwargs\":null,\"model_kwargs\":null,\"model_name\":null,\"multi_process\":null},\"output_types\":[\"HuggingFaceEmbeddings\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"VertexAIEmbeddings\":{\"template\":{\"credentials\":{\"type\":\"file\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[\".json\"],\"file_path\":\"\",\"password\":false,\"name\":\"credentials\",\"display_name\":\"Credentials\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langchain_community.embeddings import VertexAIEmbeddings\\nfrom typing import Optional, List\\n\\n\\nclass VertexAIEmbeddingsComponent(CustomComponent):\\n display_name = \\\"VertexAIEmbeddings\\\"\\n description = \\\"Google Cloud VertexAI embedding models.\\\"\\n\\n def build_config(self):\\n return {\\n \\\"credentials\\\": {\\n \\\"display_name\\\": \\\"Credentials\\\",\\n \\\"value\\\": \\\"\\\",\\n \\\"file_types\\\": [\\\".json\\\"],\\n \\\"field_type\\\": \\\"file\\\",\\n },\\n \\\"instance\\\": {\\n \\\"display_name\\\": \\\"instance\\\",\\n \\\"advanced\\\": True,\\n \\\"field_type\\\": \\\"dict\\\",\\n },\\n \\\"location\\\": {\\n \\\"display_name\\\": \\\"Location\\\",\\n \\\"value\\\": \\\"us-central1\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"max_output_tokens\\\": {\\\"display_name\\\": \\\"Max Output Tokens\\\", \\\"value\\\": 128},\\n \\\"max_retries\\\": {\\n \\\"display_name\\\": \\\"Max Retries\\\",\\n \\\"value\\\": 6,\\n \\\"advanced\\\": True,\\n },\\n \\\"model_name\\\": {\\n \\\"display_name\\\": \\\"Model Name\\\",\\n \\\"value\\\": \\\"textembedding-gecko\\\",\\n },\\n \\\"n\\\": {\\\"display_name\\\": \\\"N\\\", \\\"value\\\": 1, \\\"advanced\\\": True},\\n \\\"project\\\": {\\\"display_name\\\": \\\"Project\\\", \\\"advanced\\\": True},\\n \\\"request_parallelism\\\": {\\n \\\"display_name\\\": \\\"Request Parallelism\\\",\\n \\\"value\\\": 5,\\n \\\"advanced\\\": True,\\n },\\n \\\"stop\\\": {\\\"display_name\\\": \\\"Stop\\\", \\\"advanced\\\": True},\\n \\\"streaming\\\": {\\n \\\"display_name\\\": \\\"Streaming\\\",\\n \\\"value\\\": False,\\n \\\"advanced\\\": True,\\n },\\n \\\"temperature\\\": {\\\"display_name\\\": \\\"Temperature\\\", \\\"value\\\": 0.0},\\n \\\"top_k\\\": {\\\"display_name\\\": \\\"Top K\\\", \\\"value\\\": 40, \\\"advanced\\\": True},\\n \\\"top_p\\\": {\\\"display_name\\\": \\\"Top P\\\", \\\"value\\\": 0.95, \\\"advanced\\\": True},\\n }\\n\\n def build(\\n self,\\n instance: Optional[str] = None,\\n credentials: Optional[str] = None,\\n location: str = \\\"us-central1\\\",\\n max_output_tokens: int = 128,\\n max_retries: int = 6,\\n model_name: str = \\\"textembedding-gecko\\\",\\n n: int = 1,\\n project: Optional[str] = None,\\n request_parallelism: int = 5,\\n stop: Optional[List[str]] = None,\\n streaming: bool = False,\\n temperature: float = 0.0,\\n top_k: int = 40,\\n top_p: float = 0.95,\\n ) -> VertexAIEmbeddings:\\n return VertexAIEmbeddings(\\n instance=instance,\\n credentials=credentials,\\n location=location,\\n max_output_tokens=max_output_tokens,\\n max_retries=max_retries,\\n model_name=model_name,\\n n=n,\\n project=project,\\n request_parallelism=request_parallelism,\\n stop=stop,\\n streaming=streaming,\\n temperature=temperature,\\n top_k=top_k,\\n top_p=top_p,\\n )\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"instance\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"instance\",\"display_name\":\"instance\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"location\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"us-central1\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"location\",\"display_name\":\"Location\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"max_output_tokens\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":128,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_output_tokens\",\"display_name\":\"Max Output Tokens\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"max_retries\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":6,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_retries\",\"display_name\":\"Max Retries\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"model_name\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"textembedding-gecko\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model_name\",\"display_name\":\"Model Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"n\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":1,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"n\",\"display_name\":\"N\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"project\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"project\",\"display_name\":\"Project\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"request_parallelism\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":5,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"request_parallelism\",\"display_name\":\"Request Parallelism\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"stop\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"stop\",\"display_name\":\"Stop\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"streaming\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"streaming\",\"display_name\":\"Streaming\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"temperature\":{\"type\":\"float\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.0,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"top_k\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":40,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_k\",\"display_name\":\"Top K\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"top_p\":{\"type\":\"float\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.95,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_p\",\"display_name\":\"Top P\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Google Cloud VertexAI embedding models.\",\"base_classes\":[\"_VertexAICommon\",\"Embeddings\",\"_VertexAIBase\",\"VertexAIEmbeddings\"],\"display_name\":\"VertexAIEmbeddings\",\"documentation\":\"\",\"custom_fields\":{\"instance\":null,\"credentials\":null,\"location\":null,\"max_output_tokens\":null,\"max_retries\":null,\"model_name\":null,\"n\":null,\"project\":null,\"request_parallelism\":null,\"stop\":null,\"streaming\":null,\"temperature\":null,\"top_k\":null,\"top_p\":null},\"output_types\":[\"VertexAIEmbeddings\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"OllamaEmbeddings\":{\"template\":{\"base_url\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"http://localhost:11434\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"base_url\",\"display_name\":\"Ollama Base URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langflow import CustomComponent\\nfrom langchain.embeddings.base import Embeddings\\nfrom langchain_community.embeddings import OllamaEmbeddings\\n\\n\\nclass OllamaEmbeddingsComponent(CustomComponent):\\n \\\"\\\"\\\"\\n A custom component for implementing an Embeddings Model using Ollama.\\n \\\"\\\"\\\"\\n\\n display_name: str = \\\"Ollama Embeddings\\\"\\n description: str = \\\"Embeddings model from Ollama.\\\"\\n documentation = \\\"https://python.langchain.com/docs/integrations/text_embedding/ollama\\\"\\n beta = True\\n\\n def build_config(self):\\n return {\\n \\\"model\\\": {\\n \\\"display_name\\\": \\\"Ollama Model\\\",\\n },\\n \\\"base_url\\\": {\\\"display_name\\\": \\\"Ollama Base URL\\\"},\\n \\\"temperature\\\": {\\\"display_name\\\": \\\"Model Temperature\\\"},\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(\\n self,\\n model: str = \\\"llama2\\\",\\n base_url: str = \\\"http://localhost:11434\\\",\\n temperature: Optional[float] = None,\\n ) -> Embeddings:\\n try:\\n output = OllamaEmbeddings(model=model, base_url=base_url, temperature=temperature) # type: ignore\\n except Exception as e:\\n raise ValueError(\\\"Could not connect to Ollama API.\\\") from e\\n return output\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"model\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"llama2\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model\",\"display_name\":\"Ollama Model\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"temperature\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Model Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Embeddings model from Ollama.\",\"base_classes\":[\"Embeddings\"],\"display_name\":\"Ollama Embeddings\",\"documentation\":\"https://python.langchain.com/docs/integrations/text_embedding/ollama\",\"custom_fields\":{\"model\":null,\"base_url\":null,\"temperature\":null},\"output_types\":[\"Embeddings\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"AmazonBedrockEmbeddings\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain.embeddings.base import Embeddings\\nfrom langchain_community.embeddings import BedrockEmbeddings\\n\\n\\nfrom langflow import CustomComponent\\n\\n\\nclass AmazonBedrockEmeddingsComponent(CustomComponent):\\n \\\"\\\"\\\"\\n A custom component for implementing an Embeddings Model using Amazon Bedrock.\\n \\\"\\\"\\\"\\n\\n display_name: str = \\\"Amazon Bedrock Embeddings\\\"\\n description: str = \\\"Embeddings model from Amazon Bedrock.\\\"\\n documentation = \\\"https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/bedrock\\\"\\n beta = True\\n\\n def build_config(self):\\n return {\\n \\\"model_id\\\": {\\n \\\"display_name\\\": \\\"Model Id\\\",\\n \\\"options\\\": [\\\"amazon.titan-embed-text-v1\\\"],\\n },\\n \\\"credentials_profile_name\\\": {\\\"display_name\\\": \\\"Credentials Profile Name\\\"},\\n \\\"endpoint_url\\\": {\\\"display_name\\\": \\\"Bedrock Endpoint URL\\\"},\\n \\\"region_name\\\": {\\\"display_name\\\": \\\"AWS Region\\\"},\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(\\n self,\\n model_id: str = \\\"amazon.titan-embed-text-v1\\\",\\n credentials_profile_name: Optional[str] = None,\\n endpoint_url: Optional[str] = None,\\n region_name: Optional[str] = None,\\n ) -> Embeddings:\\n try:\\n output = BedrockEmbeddings(\\n credentials_profile_name=credentials_profile_name,\\n model_id=model_id,\\n endpoint_url=endpoint_url,\\n region_name=region_name,\\n ) # type: ignore\\n except Exception as e:\\n raise ValueError(\\\"Could not connect to AmazonBedrock API.\\\") from e\\n return output\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"credentials_profile_name\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"credentials_profile_name\",\"display_name\":\"Credentials Profile Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"endpoint_url\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"endpoint_url\",\"display_name\":\"Bedrock Endpoint URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"model_id\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"amazon.titan-embed-text-v1\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"amazon.titan-embed-text-v1\"],\"name\":\"model_id\",\"display_name\":\"Model Id\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"region_name\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"region_name\",\"display_name\":\"AWS Region\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Embeddings model from Amazon Bedrock.\",\"base_classes\":[\"Embeddings\"],\"display_name\":\"Amazon Bedrock Embeddings\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/bedrock\",\"custom_fields\":{\"model_id\":null,\"credentials_profile_name\":null,\"endpoint_url\":null,\"region_name\":null},\"output_types\":[\"Embeddings\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"AzureOpenAIEmbeddings\":{\"template\":{\"api_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"api_key\",\"display_name\":\"API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"api_version\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"2023-08-01-preview\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"2022-12-01\",\"2023-03-15-preview\",\"2023-05-15\",\"2023-06-01-preview\",\"2023-07-01-preview\",\"2023-08-01-preview\"],\"name\":\"api_version\",\"display_name\":\"API Version\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"azure_deployment\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"azure_deployment\",\"display_name\":\"Deployment Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"azure_endpoint\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"azure_endpoint\",\"display_name\":\"Azure Endpoint\",\"advanced\":false,\"dynamic\":false,\"info\":\"Your Azure endpoint, including the resource.. Example: `https://example-resource.azure.openai.com/`\",\"title_case\":false,\"input_types\":[\"Text\"]},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from langchain.embeddings.base import Embeddings\\nfrom langchain_community.embeddings import AzureOpenAIEmbeddings\\n\\nfrom langflow import CustomComponent\\n\\n\\nclass AzureOpenAIEmbeddingsComponent(CustomComponent):\\n display_name: str = \\\"AzureOpenAIEmbeddings\\\"\\n description: str = \\\"Embeddings model from Azure OpenAI.\\\"\\n documentation: str = \\\"https://python.langchain.com/docs/integrations/text_embedding/azureopenai\\\"\\n beta = False\\n\\n API_VERSION_OPTIONS = [\\n \\\"2022-12-01\\\",\\n \\\"2023-03-15-preview\\\",\\n \\\"2023-05-15\\\",\\n \\\"2023-06-01-preview\\\",\\n \\\"2023-07-01-preview\\\",\\n \\\"2023-08-01-preview\\\",\\n ]\\n\\n def build_config(self):\\n return {\\n \\\"azure_endpoint\\\": {\\n \\\"display_name\\\": \\\"Azure Endpoint\\\",\\n \\\"required\\\": True,\\n \\\"info\\\": \\\"Your Azure endpoint, including the resource.. Example: `https://example-resource.azure.openai.com/`\\\",\\n },\\n \\\"azure_deployment\\\": {\\n \\\"display_name\\\": \\\"Deployment Name\\\",\\n \\\"required\\\": True,\\n },\\n \\\"api_version\\\": {\\n \\\"display_name\\\": \\\"API Version\\\",\\n \\\"options\\\": self.API_VERSION_OPTIONS,\\n \\\"value\\\": self.API_VERSION_OPTIONS[-1],\\n \\\"advanced\\\": True,\\n },\\n \\\"api_key\\\": {\\n \\\"display_name\\\": \\\"API Key\\\",\\n \\\"required\\\": True,\\n \\\"password\\\": True,\\n },\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(\\n self,\\n azure_endpoint: str,\\n azure_deployment: str,\\n api_version: str,\\n api_key: str,\\n ) -> Embeddings:\\n try:\\n embeddings = AzureOpenAIEmbeddings(\\n azure_endpoint=azure_endpoint,\\n azure_deployment=azure_deployment,\\n api_version=api_version,\\n api_key=api_key,\\n )\\n\\n except Exception as e:\\n raise ValueError(\\\"Could not connect to AzureOpenAIEmbeddings API.\\\") from e\\n\\n return embeddings\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Embeddings model from Azure OpenAI.\",\"base_classes\":[\"Embeddings\"],\"display_name\":\"AzureOpenAIEmbeddings\",\"documentation\":\"https://python.langchain.com/docs/integrations/text_embedding/azureopenai\",\"custom_fields\":{\"azure_endpoint\":null,\"azure_deployment\":null,\"api_version\":null,\"api_key\":null},\"output_types\":[\"Embeddings\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"HuggingFaceInferenceAPIEmbeddings\":{\"template\":{\"api_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"api_key\",\"display_name\":\"API Key\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"api_url\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"http://localhost:8080\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"api_url\",\"display_name\":\"API URL\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"cache_folder\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"cache_folder\",\"display_name\":\"Cache Folder\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Dict, Optional\\n\\nfrom langchain_community.embeddings.huggingface import HuggingFaceInferenceAPIEmbeddings\\nfrom langflow import CustomComponent\\nfrom pydantic.v1.types import SecretStr\\n\\n\\nclass HuggingFaceInferenceAPIEmbeddingsComponent(CustomComponent):\\n display_name = \\\"HuggingFaceInferenceAPIEmbeddings\\\"\\n description = \\\"HuggingFace sentence_transformers embedding models, API version.\\\"\\n documentation = \\\"https://github.com/huggingface/text-embeddings-inference\\\"\\n\\n def build_config(self):\\n return {\\n \\\"api_key\\\": {\\\"display_name\\\": \\\"API Key\\\", \\\"password\\\": True, \\\"advanced\\\": True},\\n \\\"api_url\\\": {\\\"display_name\\\": \\\"API URL\\\", \\\"advanced\\\": True},\\n \\\"model_name\\\": {\\\"display_name\\\": \\\"Model Name\\\"},\\n \\\"cache_folder\\\": {\\\"display_name\\\": \\\"Cache Folder\\\", \\\"advanced\\\": True},\\n \\\"encode_kwargs\\\": {\\\"display_name\\\": \\\"Encode Kwargs\\\", \\\"advanced\\\": True, \\\"field_type\\\": \\\"dict\\\"},\\n \\\"model_kwargs\\\": {\\\"display_name\\\": \\\"Model Kwargs\\\", \\\"field_type\\\": \\\"dict\\\", \\\"advanced\\\": True},\\n \\\"multi_process\\\": {\\\"display_name\\\": \\\"Multi Process\\\", \\\"advanced\\\": True},\\n }\\n\\n def build(\\n self,\\n api_key: Optional[str] = \\\"\\\",\\n api_url: str = \\\"http://localhost:8080\\\",\\n model_name: str = \\\"BAAI/bge-large-en-v1.5\\\",\\n cache_folder: Optional[str] = None,\\n encode_kwargs: Optional[Dict] = {},\\n model_kwargs: Optional[Dict] = {},\\n multi_process: bool = False,\\n ) -> HuggingFaceInferenceAPIEmbeddings:\\n if api_key:\\n secret_api_key = SecretStr(api_key)\\n else:\\n raise ValueError(\\\"API Key is required\\\")\\n return HuggingFaceInferenceAPIEmbeddings(\\n api_key=secret_api_key,\\n api_url=api_url,\\n model_name=model_name,\\n )\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"encode_kwargs\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"encode_kwargs\",\"display_name\":\"Encode Kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"model_kwargs\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model_kwargs\",\"display_name\":\"Model Kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"model_name\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"BAAI/bge-large-en-v1.5\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model_name\",\"display_name\":\"Model Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"multi_process\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"multi_process\",\"display_name\":\"Multi Process\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"HuggingFace sentence_transformers embedding models, API version.\",\"base_classes\":[\"Embeddings\",\"HuggingFaceInferenceAPIEmbeddings\"],\"display_name\":\"HuggingFaceInferenceAPIEmbeddings\",\"documentation\":\"https://github.com/huggingface/text-embeddings-inference\",\"custom_fields\":{\"api_key\":null,\"api_url\":null,\"model_name\":null,\"cache_folder\":null,\"encode_kwargs\":null,\"model_kwargs\":null,\"multi_process\":null},\"output_types\":[\"HuggingFaceInferenceAPIEmbeddings\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true}},\"documentloaders\":{\"AZLyricsLoader\":{\"template\":{\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"web_path\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"web_path\",\"display_name\":\"Web Page\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"AZLyricsLoader\"},\"description\":\"Load `AZLyrics` webpages.\",\"base_classes\":[\"Document\"],\"display_name\":\"AZLyricsLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/azlyrics\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"AirbyteJSONLoader\":{\"template\":{\"file_path\":{\"type\":\"file\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[\".json\"],\"file_path\":\"\",\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"AirbyteJSONLoader\"},\"description\":\"Load local `Airbyte` json files.\",\"base_classes\":[\"Document\"],\"display_name\":\"AirbyteJSONLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/airbyte_json\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"BSHTMLLoader\":{\"template\":{\"file_path\":{\"type\":\"file\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[\".html\"],\"file_path\":\"\",\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"BSHTMLLoader\"},\"description\":\"Load `HTML` files and parse them with `beautiful soup`.\",\"base_classes\":[\"Document\"],\"display_name\":\"BSHTMLLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/how_to/html\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"CSVLoader\":{\"template\":{\"file_path\":{\"type\":\"file\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[\".csv\"],\"file_path\":\"\",\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"CSVLoader\"},\"description\":\"Load a `CSV` file into a list of Documents.\",\"base_classes\":[\"Document\"],\"display_name\":\"CSVLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/csv\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"CoNLLULoader\":{\"template\":{\"file_path\":{\"type\":\"file\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[\".csv\"],\"file_path\":\"\",\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"CoNLLULoader\"},\"description\":\"Load `CoNLL-U` files.\",\"base_classes\":[\"Document\"],\"display_name\":\"CoNLLULoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/conll-u\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"CollegeConfidentialLoader\":{\"template\":{\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"web_path\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"web_path\",\"display_name\":\"Web Page\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CollegeConfidentialLoader\"},\"description\":\"Load `College Confidential` webpages.\",\"base_classes\":[\"Document\"],\"display_name\":\"CollegeConfidentialLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/college_confidential\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"DirectoryLoader\":{\"template\":{\"glob\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"**/*.txt\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"glob\",\"display_name\":\"glob\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"load_hidden\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"False\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"load_hidden\",\"display_name\":\"Load hidden files\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"max_concurrency\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":10,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_concurrency\",\"display_name\":\"Max concurrency\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"path\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"path\",\"display_name\":\"Local directory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"recursive\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"True\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"recursive\",\"display_name\":\"Recursive\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"silent_errors\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"False\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"silent_errors\",\"display_name\":\"Silent errors\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"use_multithreading\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"True\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"use_multithreading\",\"display_name\":\"Use multithreading\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"DirectoryLoader\"},\"description\":\"Load from a directory.\",\"base_classes\":[\"Document\"],\"display_name\":\"DirectoryLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/how_to/file_directory\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"EverNoteLoader\":{\"template\":{\"file_path\":{\"type\":\"file\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[\".xml\"],\"file_path\":\"\",\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"EverNoteLoader\"},\"description\":\"Load from `EverNote`.\",\"base_classes\":[\"Document\"],\"display_name\":\"EverNoteLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/evernote\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"FacebookChatLoader\":{\"template\":{\"file_path\":{\"type\":\"file\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[\".json\"],\"file_path\":\"\",\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"FacebookChatLoader\"},\"description\":\"Load `Facebook Chat` messages directory dump.\",\"base_classes\":[\"Document\"],\"display_name\":\"FacebookChatLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/facebook_chat\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"GitLoader\":{\"template\":{\"branch\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"branch\",\"display_name\":\"Branch\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"clone_url\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"clone_url\",\"display_name\":\"Clone URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"file_filter\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"file_filter\",\"display_name\":\"File extensions (comma-separated)\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"repo_path\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"repo_path\",\"display_name\":\"Path to repository\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"GitLoader\"},\"description\":\"Load `Git` repository files.\",\"base_classes\":[\"Document\"],\"display_name\":\"GitLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/git\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"GitbookLoader\":{\"template\":{\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"web_page\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"web_page\",\"display_name\":\"Web Page\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"GitbookLoader\"},\"description\":\"Load `GitBook` data.\",\"base_classes\":[\"Document\"],\"display_name\":\"GitbookLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/gitbook\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"GutenbergLoader\":{\"template\":{\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"web_path\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"web_path\",\"display_name\":\"Web Page\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"GutenbergLoader\"},\"description\":\"Load from `Gutenberg.org`.\",\"base_classes\":[\"Document\"],\"display_name\":\"GutenbergLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/gutenberg\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"HNLoader\":{\"template\":{\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"web_path\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"web_path\",\"display_name\":\"Web Page\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"HNLoader\"},\"description\":\"Load `Hacker News` data.\",\"base_classes\":[\"Document\"],\"display_name\":\"HNLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/hacker_news\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"IFixitLoader\":{\"template\":{\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"web_path\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"web_path\",\"display_name\":\"Web Page\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"IFixitLoader\"},\"description\":\"Load `iFixit` repair guides, device wikis and answers.\",\"base_classes\":[\"Document\"],\"display_name\":\"IFixitLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/ifixit\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"IMSDbLoader\":{\"template\":{\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"web_path\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"web_path\",\"display_name\":\"Web Page\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"IMSDbLoader\"},\"description\":\"Load `IMSDb` webpages.\",\"base_classes\":[\"Document\"],\"display_name\":\"IMSDbLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/imsdb\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"NotionDirectoryLoader\":{\"template\":{\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"path\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"path\",\"display_name\":\"Local directory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"NotionDirectoryLoader\"},\"description\":\"Load `Notion directory` dump.\",\"base_classes\":[\"Document\"],\"display_name\":\"NotionDirectoryLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/notion\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"PyPDFLoader\":{\"template\":{\"file_path\":{\"type\":\"file\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[\".pdf\"],\"file_path\":\"\",\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"PyPDFLoader\"},\"description\":\"Load PDF using pypdf into list of documents.\",\"base_classes\":[\"Document\"],\"display_name\":\"PyPDFLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/how_to/pdf\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"PyPDFDirectoryLoader\":{\"template\":{\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"path\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"path\",\"display_name\":\"Local directory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"PyPDFDirectoryLoader\"},\"description\":\"Load a directory with `PDF` files using `pypdf` and chunks at character level.\",\"base_classes\":[\"Document\"],\"display_name\":\"PyPDFDirectoryLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/how_to/pdf\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"ReadTheDocsLoader\":{\"template\":{\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"path\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"path\",\"display_name\":\"Local directory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"ReadTheDocsLoader\"},\"description\":\"Load `ReadTheDocs` documentation directory.\",\"base_classes\":[\"Document\"],\"display_name\":\"ReadTheDocsLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/readthedocs_documentation\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"SRTLoader\":{\"template\":{\"file_path\":{\"type\":\"file\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[\".srt\"],\"file_path\":\"\",\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"SRTLoader\"},\"description\":\"Load `.srt` (subtitle) files.\",\"base_classes\":[\"Document\"],\"display_name\":\"SRTLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/subtitle\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"SlackDirectoryLoader\":{\"template\":{\"zip_path\":{\"type\":\"file\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[\".zip\"],\"file_path\":\"\",\"password\":false,\"name\":\"zip_path\",\"display_name\":\"Path to zip file\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"workspace_url\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"workspace_url\",\"display_name\":\"Workspace URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"SlackDirectoryLoader\"},\"description\":\"Load from a `Slack` directory dump.\",\"base_classes\":[\"Document\"],\"display_name\":\"SlackDirectoryLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/slack\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"TextLoader\":{\"template\":{\"file_path\":{\"type\":\"file\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[\".txt\"],\"file_path\":\"\",\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"TextLoader\"},\"description\":\"Load text file.\",\"base_classes\":[\"Document\"],\"display_name\":\"TextLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"UnstructuredEmailLoader\":{\"template\":{\"file_path\":{\"type\":\"file\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[\".eml\"],\"file_path\":\"\",\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"UnstructuredEmailLoader\"},\"description\":\"Load email files using `Unstructured`.\",\"base_classes\":[\"Document\"],\"display_name\":\"UnstructuredEmailLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/email\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"UnstructuredHTMLLoader\":{\"template\":{\"file_path\":{\"type\":\"file\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[\".html\"],\"file_path\":\"\",\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"UnstructuredHTMLLoader\"},\"description\":\"Load `HTML` files using `Unstructured`.\",\"base_classes\":[\"Document\"],\"display_name\":\"UnstructuredHTMLLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/how_to/html\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"UnstructuredMarkdownLoader\":{\"template\":{\"file_path\":{\"type\":\"file\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[\".md\"],\"file_path\":\"\",\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"UnstructuredMarkdownLoader\"},\"description\":\"Load `Markdown` files using `Unstructured`.\",\"base_classes\":[\"Document\"],\"display_name\":\"UnstructuredMarkdownLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/how_to/markdown\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"UnstructuredPowerPointLoader\":{\"template\":{\"file_path\":{\"type\":\"file\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[\".pptx\",\".ppt\"],\"file_path\":\"\",\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"UnstructuredPowerPointLoader\"},\"description\":\"Load `Microsoft PowerPoint` files using `Unstructured`.\",\"base_classes\":[\"Document\"],\"display_name\":\"UnstructuredPowerPointLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/microsoft_powerpoint\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"UnstructuredWordDocumentLoader\":{\"template\":{\"file_path\":{\"type\":\"file\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[\".docx\",\".doc\"],\"file_path\":\"\",\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"UnstructuredWordDocumentLoader\"},\"description\":\"Load `Microsoft Word` file using `Unstructured`.\",\"base_classes\":[\"Document\"],\"display_name\":\"UnstructuredWordDocumentLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/microsoft_word\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"WebBaseLoader\":{\"template\":{\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"web_path\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"web_path\",\"display_name\":\"Web Page\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"WebBaseLoader\"},\"description\":\"Load HTML pages using `urllib` and parse them with `BeautifulSoup'.\",\"base_classes\":[\"Document\"],\"display_name\":\"WebBaseLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/web_base\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"FileLoader\":{\"template\":{\"file_path\":{\"type\":\"file\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[\".json\",\".txt\",\".csv\",\".jsonl\",\".html\",\".htm\",\".conllu\",\".enex\",\".msg\",\".pdf\",\".srt\",\".eml\",\".md\",\".mdx\",\".pptx\",\".docx\"],\"file_path\":\"\",\"password\":false,\"name\":\"file_path\",\"display_name\":\"File Path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from langchain_core.documents import Document\\n\\nfrom langflow import CustomComponent\\nfrom langflow.utils.constants import LOADERS_INFO\\n\\n\\nclass FileLoaderComponent(CustomComponent):\\n display_name: str = \\\"File Loader\\\"\\n description: str = \\\"Generic File Loader\\\"\\n beta = True\\n\\n def build_config(self):\\n loader_options = [\\\"Automatic\\\"] + [loader_info[\\\"name\\\"] for loader_info in LOADERS_INFO]\\n\\n file_types = []\\n suffixes = []\\n\\n for loader_info in LOADERS_INFO:\\n if \\\"allowedTypes\\\" in loader_info:\\n file_types.extend(loader_info[\\\"allowedTypes\\\"])\\n suffixes.extend([f\\\".{ext}\\\" for ext in loader_info[\\\"allowedTypes\\\"]])\\n\\n return {\\n \\\"file_path\\\": {\\n \\\"display_name\\\": \\\"File Path\\\",\\n \\\"required\\\": True,\\n \\\"field_type\\\": \\\"file\\\",\\n \\\"file_types\\\": [\\n \\\"json\\\",\\n \\\"txt\\\",\\n \\\"csv\\\",\\n \\\"jsonl\\\",\\n \\\"html\\\",\\n \\\"htm\\\",\\n \\\"conllu\\\",\\n \\\"enex\\\",\\n \\\"msg\\\",\\n \\\"pdf\\\",\\n \\\"srt\\\",\\n \\\"eml\\\",\\n \\\"md\\\",\\n \\\"mdx\\\",\\n \\\"pptx\\\",\\n \\\"docx\\\",\\n ],\\n \\\"suffixes\\\": [\\n \\\".json\\\",\\n \\\".txt\\\",\\n \\\".csv\\\",\\n \\\".jsonl\\\",\\n \\\".html\\\",\\n \\\".htm\\\",\\n \\\".conllu\\\",\\n \\\".enex\\\",\\n \\\".msg\\\",\\n \\\".pdf\\\",\\n \\\".srt\\\",\\n \\\".eml\\\",\\n \\\".md\\\",\\n \\\".mdx\\\",\\n \\\".pptx\\\",\\n \\\".docx\\\",\\n ],\\n # \\\"file_types\\\" : file_types,\\n # \\\"suffixes\\\": suffixes,\\n },\\n \\\"loader\\\": {\\n \\\"display_name\\\": \\\"Loader\\\",\\n \\\"is_list\\\": True,\\n \\\"required\\\": True,\\n \\\"options\\\": loader_options,\\n \\\"value\\\": \\\"Automatic\\\",\\n },\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(self, file_path: str, loader: str) -> Document:\\n file_type = file_path.split(\\\".\\\")[-1]\\n\\n # Map the loader to the correct loader class\\n selected_loader_info = None\\n for loader_info in LOADERS_INFO:\\n if loader_info[\\\"name\\\"] == loader:\\n selected_loader_info = loader_info\\n break\\n\\n if selected_loader_info is None and loader != \\\"Automatic\\\":\\n raise ValueError(f\\\"Loader {loader} not found in the loader info list\\\")\\n\\n if loader == \\\"Automatic\\\":\\n # Determine the loader based on the file type\\n default_loader_info = None\\n for info in LOADERS_INFO:\\n if \\\"defaultFor\\\" in info and file_type in info[\\\"defaultFor\\\"]:\\n default_loader_info = info\\n break\\n\\n if default_loader_info is None:\\n raise ValueError(f\\\"No default loader found for file type: {file_type}\\\")\\n\\n selected_loader_info = default_loader_info\\n if isinstance(selected_loader_info, dict):\\n loader_import: str = selected_loader_info[\\\"import\\\"]\\n else:\\n raise ValueError(f\\\"Loader info for {loader} is not a dict\\\\nLoader info:\\\\n{selected_loader_info}\\\")\\n module_name, class_name = loader_import.rsplit(\\\".\\\", 1)\\n\\n try:\\n # Import the loader class\\n loader_module = __import__(module_name, fromlist=[class_name])\\n loader_instance = getattr(loader_module, class_name)\\n except ImportError as e:\\n raise ValueError(f\\\"Loader {loader} could not be imported\\\\nLoader info:\\\\n{selected_loader_info}\\\") from e\\n\\n result = loader_instance(file_path=file_path)\\n return result.load()\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"loader\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"Automatic\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"Automatic\",\"Airbyte JSON (.jsonl)\",\"JSON (.json)\",\"BeautifulSoup4 HTML (.html, .htm)\",\"CSV (.csv)\",\"CoNLL-U (.conllu)\",\"EverNote (.enex)\",\"Facebook Chat (.json)\",\"Outlook Message (.msg)\",\"PyPDF (.pdf)\",\"Subtitle (.str)\",\"Text (.txt)\",\"Unstructured Email (.eml)\",\"Unstructured HTML (.html, .htm)\",\"Unstructured Markdown (.md)\",\"Unstructured PowerPoint (.pptx)\",\"Unstructured Word (.docx)\"],\"name\":\"loader\",\"display_name\":\"Loader\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Generic File Loader\",\"base_classes\":[\"Serializable\",\"Document\"],\"display_name\":\"File Loader\",\"documentation\":\"\",\"custom_fields\":{\"file_path\":null,\"loader\":null},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"UrlLoader\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import List\\n\\nfrom langchain import document_loaders\\nfrom langchain_core.documents import Document\\n\\nfrom langflow import CustomComponent\\n\\n\\nclass UrlLoaderComponent(CustomComponent):\\n display_name: str = \\\"Url Loader\\\"\\n description: str = \\\"Generic Url Loader Component\\\"\\n\\n def build_config(self):\\n return {\\n \\\"web_path\\\": {\\n \\\"display_name\\\": \\\"Url\\\",\\n \\\"required\\\": True,\\n },\\n \\\"loader\\\": {\\n \\\"display_name\\\": \\\"Loader\\\",\\n \\\"is_list\\\": True,\\n \\\"required\\\": True,\\n \\\"options\\\": [\\n \\\"AZLyricsLoader\\\",\\n \\\"CollegeConfidentialLoader\\\",\\n \\\"GitbookLoader\\\",\\n \\\"HNLoader\\\",\\n \\\"IFixitLoader\\\",\\n \\\"IMSDbLoader\\\",\\n \\\"WebBaseLoader\\\",\\n ],\\n \\\"value\\\": \\\"WebBaseLoader\\\",\\n },\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(self, web_path: str, loader: str) -> List[Document]:\\n try:\\n loader_instance = getattr(document_loaders, loader)(web_path=web_path)\\n except Exception as e:\\n raise ValueError(f\\\"No loader found for: {web_path}\\\") from e\\n docs = loader_instance.load()\\n avg_length = sum(len(doc.page_content) for doc in docs if hasattr(doc, \\\"page_content\\\")) / len(docs)\\n self.status = f\\\"\\\"\\\"{len(docs)} documents)\\n \\\\nAvg. Document Length (characters): {int(avg_length)}\\n Documents: {docs[:3]}...\\\"\\\"\\\"\\n return docs\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"loader\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"WebBaseLoader\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"AZLyricsLoader\",\"CollegeConfidentialLoader\",\"GitbookLoader\",\"HNLoader\",\"IFixitLoader\",\"IMSDbLoader\",\"WebBaseLoader\"],\"name\":\"loader\",\"display_name\":\"Loader\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"web_path\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"web_path\",\"display_name\":\"Url\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Generic Url Loader Component\",\"base_classes\":[\"Serializable\",\"Document\"],\"display_name\":\"Url Loader\",\"documentation\":\"\",\"custom_fields\":{\"web_path\":null,\"loader\":null},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"GatherRecords\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from concurrent import futures\\nfrom pathlib import Path\\nfrom typing import Any, Dict, List\\n\\nfrom langflow import CustomComponent\\nfrom langflow.schema import Record\\n\\n\\nclass GatherRecordsComponent(CustomComponent):\\n display_name = \\\"Gather Records\\\"\\n description = \\\"Gather records from a directory.\\\"\\n\\n def build_config(self) -> Dict[str, Any]:\\n return {\\n \\\"load_hidden\\\": {\\n \\\"display_name\\\": \\\"Load Hidden Files\\\",\\n \\\"value\\\": False,\\n \\\"advanced\\\": True,\\n },\\n \\\"max_concurrency\\\": {\\n \\\"display_name\\\": \\\"Max Concurrency\\\",\\n \\\"value\\\": 10,\\n \\\"advanced\\\": True,\\n },\\n \\\"path\\\": {\\\"display_name\\\": \\\"Local Directory\\\"},\\n \\\"recursive\\\": {\\\"display_name\\\": \\\"Recursive\\\", \\\"value\\\": True, \\\"advanced\\\": True},\\n \\\"use_multithreading\\\": {\\n \\\"display_name\\\": \\\"Use Multithreading\\\",\\n \\\"value\\\": True,\\n \\\"advanced\\\": True,\\n },\\n }\\n\\n def is_hidden(self, path: Path) -> bool:\\n return path.name.startswith(\\\".\\\")\\n\\n def retrieve_file_paths(\\n self,\\n path: str,\\n types: List[str],\\n load_hidden: bool,\\n recursive: bool,\\n depth: int,\\n ) -> List[str]:\\n path_obj = Path(path)\\n if not path_obj.exists() or not path_obj.is_dir():\\n raise ValueError(f\\\"Path {path} must exist and be a directory.\\\")\\n\\n def match_types(p: Path) -> bool:\\n return any(p.suffix == f\\\".{t}\\\" for t in types) if types else True\\n\\n def is_not_hidden(p: Path) -> bool:\\n return not self.is_hidden(p) or load_hidden\\n\\n def walk_level(directory: Path, max_depth: int):\\n directory = directory.resolve()\\n prefix_length = len(directory.parts)\\n for p in directory.rglob(\\\"*\\\" if recursive else \\\"[!.]*\\\"):\\n if len(p.parts) - prefix_length <= max_depth:\\n yield p\\n\\n glob = \\\"**/*\\\" if recursive else \\\"*\\\"\\n paths = walk_level(path_obj, depth) if depth else path_obj.glob(glob)\\n file_paths = [str(p) for p in paths if p.is_file() and match_types(p) and is_not_hidden(p)]\\n\\n return file_paths\\n\\n def parse_file_to_record(self, file_path: str, silent_errors: bool) -> Record:\\n # Use the partition function to load the file\\n from unstructured.partition.auto import partition\\n\\n try:\\n elements = partition(file_path)\\n except Exception as e:\\n if not silent_errors:\\n raise ValueError(f\\\"Error loading file {file_path}: {e}\\\") from e\\n return None\\n\\n # Create a Record\\n text = \\\"\\\\n\\\\n\\\".join([str(el) for el in elements])\\n metadata = elements.metadata if hasattr(elements, \\\"metadata\\\") else {}\\n metadata[\\\"file_path\\\"] = file_path\\n record = Record(text=text, data=metadata)\\n return record\\n\\n def get_elements(\\n self,\\n file_paths: List[str],\\n silent_errors: bool,\\n max_concurrency: int,\\n use_multithreading: bool,\\n ) -> List[Record]:\\n if use_multithreading:\\n records = self.parallel_load_records(file_paths, silent_errors, max_concurrency)\\n else:\\n records = [self.parse_file_to_record(file_path, silent_errors) for file_path in file_paths]\\n records = list(filter(None, records))\\n return records\\n\\n def parallel_load_records(self, file_paths: List[str], silent_errors: bool, max_concurrency: int) -> List[Record]:\\n with futures.ThreadPoolExecutor(max_workers=max_concurrency) as executor:\\n loaded_files = executor.map(\\n lambda file_path: self.parse_file_to_record(file_path, silent_errors),\\n file_paths,\\n )\\n return loaded_files\\n\\n def build(\\n self,\\n path: str,\\n types: List[str] = None,\\n depth: int = 0,\\n max_concurrency: int = 2,\\n load_hidden: bool = False,\\n recursive: bool = True,\\n silent_errors: bool = False,\\n use_multithreading: bool = True,\\n ) -> List[Record]:\\n resolved_path = self.resolve_path(path)\\n file_paths = self.retrieve_file_paths(resolved_path, types, load_hidden, recursive, depth)\\n loaded_records = []\\n\\n if use_multithreading:\\n loaded_records = self.parallel_load_records(file_paths, silent_errors, max_concurrency)\\n else:\\n loaded_records = [self.parse_file_to_record(file_path, silent_errors) for file_path in file_paths]\\n loaded_records = list(filter(None, loaded_records))\\n self.status = loaded_records\\n return loaded_records\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"depth\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"depth\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"load_hidden\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"load_hidden\",\"display_name\":\"Load Hidden Files\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"max_concurrency\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":10,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_concurrency\",\"display_name\":\"Max Concurrency\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"path\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"path\",\"display_name\":\"Local Directory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"recursive\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":true,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"recursive\",\"display_name\":\"Recursive\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"silent_errors\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"silent_errors\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"types\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"types\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"use_multithreading\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":true,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"use_multithreading\",\"display_name\":\"Use Multithreading\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Gather records from a directory.\",\"base_classes\":[\"Record\"],\"display_name\":\"Gather Records\",\"documentation\":\"\",\"custom_fields\":{\"path\":null,\"types\":null,\"depth\":null,\"max_concurrency\":null,\"load_hidden\":null,\"recursive\":null,\"silent_errors\":null,\"use_multithreading\":null},\"output_types\":[\"Record\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true}},\"textsplitters\":{\"CharacterTextSplitter\":{\"template\":{\"documents\":{\"type\":\"Document\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"documents\",\"display_name\":\"Documents\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"chunk_overlap\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":200,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"chunk_overlap\",\"display_name\":\"Chunk Overlap\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"chunk_size\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":1000,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"chunk_size\",\"display_name\":\"Chunk Size\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import List\\n\\nfrom langchain.text_splitter import CharacterTextSplitter\\nfrom langchain_core.documents.base import Document\\nfrom langflow import CustomComponent\\n\\n\\nclass CharacterTextSplitterComponent(CustomComponent):\\n display_name = \\\"CharacterTextSplitter\\\"\\n description = \\\"Splitting text that looks at characters.\\\"\\n\\n def build_config(self):\\n return {\\n \\\"documents\\\": {\\\"display_name\\\": \\\"Documents\\\"},\\n \\\"chunk_overlap\\\": {\\\"display_name\\\": \\\"Chunk Overlap\\\", \\\"default\\\": 200},\\n \\\"chunk_size\\\": {\\\"display_name\\\": \\\"Chunk Size\\\", \\\"default\\\": 1000},\\n \\\"separator\\\": {\\\"display_name\\\": \\\"Separator\\\", \\\"default\\\": \\\"\\\\n\\\"},\\n }\\n\\n def build(\\n self,\\n documents: List[Document],\\n chunk_overlap: int = 200,\\n chunk_size: int = 1000,\\n separator: str = \\\"\\\\n\\\",\\n ) -> List[Document]:\\n # separator may come escaped from the frontend\\n separator = separator.encode().decode(\\\"unicode_escape\\\")\\n docs = CharacterTextSplitter(\\n chunk_overlap=chunk_overlap,\\n chunk_size=chunk_size,\\n separator=separator,\\n ).split_documents(documents)\\n self.status = docs\\n return docs\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"separator\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\\\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"separator\",\"display_name\":\"Separator\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Splitting text that looks at characters.\",\"base_classes\":[\"Serializable\",\"Document\"],\"display_name\":\"CharacterTextSplitter\",\"documentation\":\"\",\"custom_fields\":{\"documents\":null,\"chunk_overlap\":null,\"chunk_size\":null,\"separator\":null},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"RecursiveCharacterTextSplitter\":{\"template\":{\"documents\":{\"type\":\"Document\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"documents\",\"display_name\":\"Documents\",\"advanced\":false,\"dynamic\":false,\"info\":\"The documents to split.\",\"title_case\":false},\"chunk_overlap\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":200,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"chunk_overlap\",\"display_name\":\"Chunk Overlap\",\"advanced\":false,\"dynamic\":false,\"info\":\"The amount of overlap between chunks.\",\"title_case\":false},\"chunk_size\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":1000,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"chunk_size\",\"display_name\":\"Chunk Size\",\"advanced\":false,\"dynamic\":false,\"info\":\"The maximum length of each chunk.\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain_core.documents import Document\\n\\nfrom langflow import CustomComponent\\nfrom langflow.utils.util import build_loader_repr_from_documents\\nfrom langchain.text_splitter import RecursiveCharacterTextSplitter\\n\\n\\nclass RecursiveCharacterTextSplitterComponent(CustomComponent):\\n display_name: str = \\\"Recursive Character Text Splitter\\\"\\n description: str = \\\"Split text into chunks of a specified length.\\\"\\n documentation: str = \\\"https://docs.langflow.org/components/text-splitters#recursivecharactertextsplitter\\\"\\n\\n def build_config(self):\\n return {\\n \\\"documents\\\": {\\n \\\"display_name\\\": \\\"Documents\\\",\\n \\\"info\\\": \\\"The documents to split.\\\",\\n },\\n \\\"separators\\\": {\\n \\\"display_name\\\": \\\"Separators\\\",\\n \\\"info\\\": 'The characters to split on.\\\\nIf left empty defaults to [\\\"\\\\\\\\n\\\\\\\\n\\\", \\\"\\\\\\\\n\\\", \\\" \\\", \\\"\\\"].',\\n \\\"is_list\\\": True,\\n },\\n \\\"chunk_size\\\": {\\n \\\"display_name\\\": \\\"Chunk Size\\\",\\n \\\"info\\\": \\\"The maximum length of each chunk.\\\",\\n \\\"field_type\\\": \\\"int\\\",\\n \\\"value\\\": 1000,\\n },\\n \\\"chunk_overlap\\\": {\\n \\\"display_name\\\": \\\"Chunk Overlap\\\",\\n \\\"info\\\": \\\"The amount of overlap between chunks.\\\",\\n \\\"field_type\\\": \\\"int\\\",\\n \\\"value\\\": 200,\\n },\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(\\n self,\\n documents: list[Document],\\n separators: Optional[list[str]] = None,\\n chunk_size: Optional[int] = 1000,\\n chunk_overlap: Optional[int] = 200,\\n ) -> list[Document]:\\n \\\"\\\"\\\"\\n Split text into chunks of a specified length.\\n\\n Args:\\n separators (list[str]): The characters to split on.\\n chunk_size (int): The maximum length of each chunk.\\n chunk_overlap (int): The amount of overlap between chunks.\\n length_function (function): The function to use to calculate the length of the text.\\n\\n Returns:\\n list[str]: The chunks of text.\\n \\\"\\\"\\\"\\n\\n if separators == \\\"\\\":\\n separators = None\\n elif separators:\\n # check if the separators list has escaped characters\\n # if there are escaped characters, unescape them\\n separators = [x.encode().decode(\\\"unicode-escape\\\") for x in separators]\\n\\n # Make sure chunk_size and chunk_overlap are ints\\n if isinstance(chunk_size, str):\\n chunk_size = int(chunk_size)\\n if isinstance(chunk_overlap, str):\\n chunk_overlap = int(chunk_overlap)\\n splitter = RecursiveCharacterTextSplitter(\\n separators=separators,\\n chunk_size=chunk_size,\\n chunk_overlap=chunk_overlap,\\n )\\n\\n docs = splitter.split_documents(documents)\\n self.repr_value = build_loader_repr_from_documents(docs)\\n return docs\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"separators\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"separators\",\"display_name\":\"Separators\",\"advanced\":false,\"dynamic\":false,\"info\":\"The characters to split on.\\nIf left empty defaults to [\\\"\\\\n\\\\n\\\", \\\"\\\\n\\\", \\\" \\\", \\\"\\\"].\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Split text into chunks of a specified length.\",\"base_classes\":[\"Serializable\",\"Document\"],\"display_name\":\"Recursive Character Text Splitter\",\"documentation\":\"https://docs.langflow.org/components/text-splitters#recursivecharactertextsplitter\",\"custom_fields\":{\"documents\":null,\"separators\":null,\"chunk_size\":null,\"chunk_overlap\":null},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"LanguageRecursiveTextSplitter\":{\"template\":{\"documents\":{\"type\":\"Document\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"documents\",\"display_name\":\"Documents\",\"advanced\":false,\"dynamic\":false,\"info\":\"The documents to split.\",\"title_case\":false},\"chunk_overlap\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":200,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"chunk_overlap\",\"display_name\":\"Chunk Overlap\",\"advanced\":false,\"dynamic\":false,\"info\":\"The amount of overlap between chunks.\",\"title_case\":false},\"chunk_size\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":1000,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"chunk_size\",\"display_name\":\"Chunk Size\",\"advanced\":false,\"dynamic\":false,\"info\":\"The maximum length of each chunk.\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain.text_splitter import Language\\nfrom langchain_core.documents import Document\\n\\nfrom langflow import CustomComponent\\n\\n\\nclass LanguageRecursiveTextSplitterComponent(CustomComponent):\\n display_name: str = \\\"Language Recursive Text Splitter\\\"\\n description: str = \\\"Split text into chunks of a specified length based on language.\\\"\\n documentation: str = \\\"https://docs.langflow.org/components/text-splitters#languagerecursivetextsplitter\\\"\\n\\n def build_config(self):\\n options = [x.value for x in Language]\\n return {\\n \\\"documents\\\": {\\n \\\"display_name\\\": \\\"Documents\\\",\\n \\\"info\\\": \\\"The documents to split.\\\",\\n },\\n \\\"separator_type\\\": {\\n \\\"display_name\\\": \\\"Separator Type\\\",\\n \\\"info\\\": \\\"The type of separator to use.\\\",\\n \\\"field_type\\\": \\\"str\\\",\\n \\\"options\\\": options,\\n \\\"value\\\": \\\"Python\\\",\\n },\\n \\\"separators\\\": {\\n \\\"display_name\\\": \\\"Separators\\\",\\n \\\"info\\\": \\\"The characters to split on.\\\",\\n \\\"is_list\\\": True,\\n },\\n \\\"chunk_size\\\": {\\n \\\"display_name\\\": \\\"Chunk Size\\\",\\n \\\"info\\\": \\\"The maximum length of each chunk.\\\",\\n \\\"field_type\\\": \\\"int\\\",\\n \\\"value\\\": 1000,\\n },\\n \\\"chunk_overlap\\\": {\\n \\\"display_name\\\": \\\"Chunk Overlap\\\",\\n \\\"info\\\": \\\"The amount of overlap between chunks.\\\",\\n \\\"field_type\\\": \\\"int\\\",\\n \\\"value\\\": 200,\\n },\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(\\n self,\\n documents: list[Document],\\n chunk_size: Optional[int] = 1000,\\n chunk_overlap: Optional[int] = 200,\\n separator_type: str = \\\"Python\\\",\\n ) -> list[Document]:\\n \\\"\\\"\\\"\\n Split text into chunks of a specified length.\\n\\n Args:\\n separators (list[str]): The characters to split on.\\n chunk_size (int): The maximum length of each chunk.\\n chunk_overlap (int): The amount of overlap between chunks.\\n length_function (function): The function to use to calculate the length of the text.\\n\\n Returns:\\n list[str]: The chunks of text.\\n \\\"\\\"\\\"\\n from langchain.text_splitter import RecursiveCharacterTextSplitter\\n\\n # Make sure chunk_size and chunk_overlap are ints\\n if isinstance(chunk_size, str):\\n chunk_size = int(chunk_size)\\n if isinstance(chunk_overlap, str):\\n chunk_overlap = int(chunk_overlap)\\n\\n splitter = RecursiveCharacterTextSplitter.from_language(\\n language=Language(separator_type),\\n chunk_size=chunk_size,\\n chunk_overlap=chunk_overlap,\\n )\\n\\n docs = splitter.split_documents(documents)\\n return docs\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"separator_type\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"Python\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"cpp\",\"go\",\"java\",\"kotlin\",\"js\",\"ts\",\"php\",\"proto\",\"python\",\"rst\",\"ruby\",\"rust\",\"scala\",\"swift\",\"markdown\",\"latex\",\"html\",\"sol\",\"csharp\",\"cobol\",\"c\",\"lua\",\"perl\"],\"name\":\"separator_type\",\"display_name\":\"Separator Type\",\"advanced\":false,\"dynamic\":false,\"info\":\"The type of separator to use.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Split text into chunks of a specified length based on language.\",\"base_classes\":[\"Serializable\",\"Document\"],\"display_name\":\"Language Recursive Text Splitter\",\"documentation\":\"https://docs.langflow.org/components/text-splitters#languagerecursivetextsplitter\",\"custom_fields\":{\"documents\":null,\"chunk_size\":null,\"chunk_overlap\":null,\"separator_type\":null},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true}},\"utilities\":{\"BingSearchAPIWrapper\":{\"template\":{\"bing_search_url\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"bing_search_url\",\"display_name\":\"Bing Search URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"bing_subscription_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"bing_subscription_key\",\"display_name\":\"Bing Subscription Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\n\\n# Assuming `BingSearchAPIWrapper` is a class that exists in the context\\n# and has the appropriate methods and attributes.\\n# We need to make sure this class is importable from the context where this code will be running.\\nfrom langchain_community.utilities.bing_search import BingSearchAPIWrapper\\n\\n\\nclass BingSearchAPIWrapperComponent(CustomComponent):\\n display_name = \\\"BingSearchAPIWrapper\\\"\\n description = \\\"Wrapper for Bing Search API.\\\"\\n\\n def build_config(self):\\n return {\\n \\\"bing_search_url\\\": {\\\"display_name\\\": \\\"Bing Search URL\\\"},\\n \\\"bing_subscription_key\\\": {\\n \\\"display_name\\\": \\\"Bing Subscription Key\\\",\\n \\\"password\\\": True,\\n },\\n \\\"k\\\": {\\\"display_name\\\": \\\"Number of results\\\", \\\"advanced\\\": True},\\n # 'k' is not included as it is not shown (show=False)\\n }\\n\\n def build(\\n self,\\n bing_search_url: str,\\n bing_subscription_key: str,\\n k: int = 10,\\n ) -> BingSearchAPIWrapper:\\n # 'k' has a default value and is not shown (show=False), so it is hardcoded here\\n return BingSearchAPIWrapper(bing_search_url=bing_search_url, bing_subscription_key=bing_subscription_key, k=k)\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"k\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":10,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"k\",\"display_name\":\"Number of results\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Wrapper for Bing Search API.\",\"base_classes\":[\"BingSearchAPIWrapper\"],\"display_name\":\"BingSearchAPIWrapper\",\"documentation\":\"\",\"custom_fields\":{\"bing_search_url\":null,\"bing_subscription_key\":null,\"k\":null},\"output_types\":[\"BingSearchAPIWrapper\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"GoogleSearchAPIWrapper\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Callable, Union\\n\\nfrom langchain_community.utilities.google_search import GoogleSearchAPIWrapper\\nfrom langflow import CustomComponent\\n\\n\\nclass GoogleSearchAPIWrapperComponent(CustomComponent):\\n display_name = \\\"GoogleSearchAPIWrapper\\\"\\n description = \\\"Wrapper for Google Search API.\\\"\\n\\n def build_config(self):\\n return {\\n \\\"google_api_key\\\": {\\\"display_name\\\": \\\"Google API Key\\\", \\\"password\\\": True},\\n \\\"google_cse_id\\\": {\\\"display_name\\\": \\\"Google CSE ID\\\", \\\"password\\\": True},\\n }\\n\\n def build(\\n self,\\n google_api_key: str,\\n google_cse_id: str,\\n ) -> Union[GoogleSearchAPIWrapper, Callable]:\\n return GoogleSearchAPIWrapper(google_api_key=google_api_key, google_cse_id=google_cse_id) # type: ignore\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"google_api_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"google_api_key\",\"display_name\":\"Google API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"google_cse_id\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"google_cse_id\",\"display_name\":\"Google CSE ID\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Wrapper for Google Search API.\",\"base_classes\":[\"GoogleSearchAPIWrapper\",\"Callable\"],\"display_name\":\"GoogleSearchAPIWrapper\",\"documentation\":\"\",\"custom_fields\":{\"google_api_key\":null,\"google_cse_id\":null},\"output_types\":[\"GoogleSearchAPIWrapper\",\"Callable\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"GoogleSerperAPIWrapper\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Dict\\n\\n# Assuming the existence of GoogleSerperAPIWrapper class in the serper module\\n# If this class does not exist, you would need to create it or import the appropriate class from another module\\nfrom langchain_community.utilities.google_serper import GoogleSerperAPIWrapper\\n\\nfrom langflow import CustomComponent\\n\\n\\nclass GoogleSerperAPIWrapperComponent(CustomComponent):\\n display_name = \\\"GoogleSerperAPIWrapper\\\"\\n description = \\\"Wrapper around the Serper.dev Google Search API.\\\"\\n\\n def build_config(self) -> Dict[str, Dict]:\\n return {\\n \\\"result_key_for_type\\\": {\\n \\\"display_name\\\": \\\"Result Key for Type\\\",\\n \\\"show\\\": True,\\n \\\"multiline\\\": False,\\n \\\"password\\\": False,\\n \\\"advanced\\\": False,\\n \\\"dynamic\\\": False,\\n \\\"info\\\": \\\"\\\",\\n \\\"field_type\\\": \\\"dict\\\",\\n \\\"list\\\": False,\\n \\\"value\\\": {\\n \\\"news\\\": \\\"news\\\",\\n \\\"places\\\": \\\"places\\\",\\n \\\"images\\\": \\\"images\\\",\\n \\\"search\\\": \\\"organic\\\",\\n },\\n },\\n \\\"serper_api_key\\\": {\\n \\\"display_name\\\": \\\"Serper API Key\\\",\\n \\\"show\\\": True,\\n \\\"multiline\\\": False,\\n \\\"password\\\": True,\\n \\\"advanced\\\": False,\\n \\\"dynamic\\\": False,\\n \\\"info\\\": \\\"\\\",\\n \\\"type\\\": \\\"str\\\",\\n \\\"list\\\": False,\\n },\\n }\\n\\n def build(\\n self,\\n serper_api_key: str,\\n ) -> GoogleSerperAPIWrapper:\\n return GoogleSerperAPIWrapper(serper_api_key=serper_api_key)\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"serper_api_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"serper_api_key\",\"display_name\":\"Serper API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Wrapper around the Serper.dev Google Search API.\",\"base_classes\":[\"GoogleSerperAPIWrapper\"],\"display_name\":\"GoogleSerperAPIWrapper\",\"documentation\":\"\",\"custom_fields\":{\"serper_api_key\":null},\"output_types\":[\"GoogleSerperAPIWrapper\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"SearxSearchWrapper\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom typing import Optional, Dict\\nfrom langchain_community.utilities.searx_search import SearxSearchWrapper\\n\\n\\nclass SearxSearchWrapperComponent(CustomComponent):\\n display_name = \\\"SearxSearchWrapper\\\"\\n description = \\\"Wrapper for Searx API.\\\"\\n\\n def build_config(self):\\n return {\\n \\\"headers\\\": {\\n \\\"field_type\\\": \\\"dict\\\",\\n \\\"display_name\\\": \\\"Headers\\\",\\n \\\"multiline\\\": True,\\n \\\"value\\\": '{\\\"Authorization\\\": \\\"Bearer \\\"}',\\n },\\n \\\"k\\\": {\\\"display_name\\\": \\\"k\\\", \\\"advanced\\\": True, \\\"field_type\\\": \\\"int\\\", \\\"value\\\": 10},\\n \\\"searx_host\\\": {\\n \\\"display_name\\\": \\\"Searx Host\\\",\\n \\\"field_type\\\": \\\"str\\\",\\n \\\"value\\\": \\\"https://searx.example.com\\\",\\n \\\"advanced\\\": True,\\n },\\n }\\n\\n def build(\\n self,\\n k: int = 10,\\n headers: Optional[Dict[str, str]] = None,\\n searx_host: str = \\\"https://searx.example.com\\\",\\n ) -> SearxSearchWrapper:\\n return SearxSearchWrapper(headers=headers, k=k, searx_host=searx_host)\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"headers\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"{\\\"Authorization\\\": \\\"Bearer \\\"}\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"headers\",\"display_name\":\"Headers\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"k\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":10,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"k\",\"display_name\":\"k\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"searx_host\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"https://searx.example.com\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"searx_host\",\"display_name\":\"Searx Host\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Wrapper for Searx API.\",\"base_classes\":[\"SearxSearchWrapper\"],\"display_name\":\"SearxSearchWrapper\",\"documentation\":\"\",\"custom_fields\":{\"k\":null,\"headers\":null,\"searx_host\":null},\"output_types\":[\"SearxSearchWrapper\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"SerpAPIWrapper\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Callable, Union\\n\\nfrom langchain_community.utilities.serpapi import SerpAPIWrapper\\nfrom langflow import CustomComponent\\n\\n\\nclass SerpAPIWrapperComponent(CustomComponent):\\n display_name = \\\"SerpAPIWrapper\\\"\\n description = \\\"Wrapper around SerpAPI\\\"\\n\\n def build_config(self):\\n return {\\n \\\"serpapi_api_key\\\": {\\\"display_name\\\": \\\"SerpAPI API Key\\\", \\\"type\\\": \\\"str\\\", \\\"password\\\": True},\\n \\\"params\\\": {\\n \\\"display_name\\\": \\\"Parameters\\\",\\n \\\"type\\\": \\\"dict\\\",\\n \\\"advanced\\\": True,\\n \\\"multiline\\\": True,\\n \\\"value\\\": '{\\\"engine\\\": \\\"google\\\",\\\"google_domain\\\": \\\"google.com\\\",\\\"gl\\\": \\\"us\\\",\\\"hl\\\": \\\"en\\\"}',\\n },\\n }\\n\\n def build(\\n self,\\n serpapi_api_key: str,\\n params: dict,\\n ) -> Union[SerpAPIWrapper, Callable]: # Removed quotes around SerpAPIWrapper\\n return SerpAPIWrapper( # type: ignore\\n serpapi_api_key=serpapi_api_key,\\n params=params,\\n )\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"params\":{\"type\":\"dict\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"{\\\"engine\\\": \\\"google\\\",\\\"google_domain\\\": \\\"google.com\\\",\\\"gl\\\": \\\"us\\\",\\\"hl\\\": \\\"en\\\"}\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"params\",\"display_name\":\"Parameters\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"serpapi_api_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"serpapi_api_key\",\"display_name\":\"SerpAPI API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Wrapper around SerpAPI\",\"base_classes\":[\"Callable\",\"SerpAPIWrapper\"],\"display_name\":\"SerpAPIWrapper\",\"documentation\":\"\",\"custom_fields\":{\"serpapi_api_key\":null,\"params\":null},\"output_types\":[\"SerpAPIWrapper\",\"Callable\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"WikipediaAPIWrapper\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Callable, Union\\n\\nfrom langchain_community.utilities.wikipedia import WikipediaAPIWrapper\\nfrom langflow import CustomComponent\\n\\n# Assuming WikipediaAPIWrapper is a class that needs to be imported.\\n# The import statement is not included as it is not provided in the JSON\\n# and the actual implementation details are unknown.\\n\\n\\nclass WikipediaAPIWrapperComponent(CustomComponent):\\n display_name = \\\"WikipediaAPIWrapper\\\"\\n description = \\\"Wrapper around WikipediaAPI.\\\"\\n\\n def build_config(self):\\n return {}\\n\\n def build(\\n self,\\n top_k_results: int = 3,\\n lang: str = \\\"en\\\",\\n load_all_available_meta: bool = False,\\n doc_content_chars_max: int = 4000,\\n ) -> Union[WikipediaAPIWrapper, Callable]:\\n return WikipediaAPIWrapper( # type: ignore\\n top_k_results=top_k_results,\\n lang=lang,\\n load_all_available_meta=load_all_available_meta,\\n doc_content_chars_max=doc_content_chars_max,\\n )\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"doc_content_chars_max\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":4000,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"doc_content_chars_max\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"lang\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"en\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"lang\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"load_all_available_meta\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"load_all_available_meta\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"top_k_results\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":3,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_k_results\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Wrapper around WikipediaAPI.\",\"base_classes\":[\"WikipediaAPIWrapper\",\"Callable\"],\"display_name\":\"WikipediaAPIWrapper\",\"documentation\":\"\",\"custom_fields\":{\"top_k_results\":null,\"lang\":null,\"load_all_available_meta\":null,\"doc_content_chars_max\":null},\"output_types\":[\"WikipediaAPIWrapper\",\"Callable\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"WolframAlphaAPIWrapper\":{\"template\":{\"appid\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"appid\",\"display_name\":\"App ID\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Callable, Union\\n\\nfrom langchain_community.utilities.wolfram_alpha import WolframAlphaAPIWrapper\\nfrom langflow import CustomComponent\\n\\n# Since all the fields in the JSON have show=False, we will only create a basic component\\n# without any configurable fields.\\n\\n\\nclass WolframAlphaAPIWrapperComponent(CustomComponent):\\n display_name = \\\"WolframAlphaAPIWrapper\\\"\\n description = \\\"Wrapper for Wolfram Alpha.\\\"\\n\\n def build_config(self):\\n return {\\\"appid\\\": {\\\"display_name\\\": \\\"App ID\\\", \\\"type\\\": \\\"str\\\", \\\"password\\\": True}}\\n\\n def build(self, appid: str) -> Union[Callable, WolframAlphaAPIWrapper]:\\n return WolframAlphaAPIWrapper(wolfram_alpha_appid=appid) # type: ignore\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Wrapper for Wolfram Alpha.\",\"base_classes\":[\"WolframAlphaAPIWrapper\",\"Callable\"],\"display_name\":\"WolframAlphaAPIWrapper\",\"documentation\":\"\",\"custom_fields\":{\"appid\":null},\"output_types\":[\"Callable\",\"WolframAlphaAPIWrapper\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"RunnableExecutor\":{\"template\":{\"runnable\":{\"type\":\"Runnable\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"runnable\",\"display_name\":\"Runnable\",\"advanced\":false,\"dynamic\":false,\"info\":\"The runnable to execute.\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from langchain_core.runnables import Runnable\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Text\\n\\n\\nclass RunnableExecComponent(CustomComponent):\\n documentation: str = \\\"http://docs.langflow.org/components/custom\\\"\\n display_name = \\\"Runnable Executor\\\"\\n beta = True\\n\\n def build_config(self):\\n return {\\n \\\"input_key\\\": {\\n \\\"display_name\\\": \\\"Input Key\\\",\\n \\\"info\\\": \\\"The key to use for the input.\\\",\\n },\\n \\\"inputs\\\": {\\n \\\"display_name\\\": \\\"Inputs\\\",\\n \\\"info\\\": \\\"The inputs to pass to the runnable.\\\",\\n },\\n \\\"runnable\\\": {\\n \\\"display_name\\\": \\\"Runnable\\\",\\n \\\"info\\\": \\\"The runnable to execute.\\\",\\n },\\n \\\"output_key\\\": {\\n \\\"display_name\\\": \\\"Output Key\\\",\\n \\\"info\\\": \\\"The key to use for the output.\\\",\\n },\\n }\\n\\n def build(\\n self,\\n input_key: str,\\n inputs: str,\\n runnable: Runnable,\\n output_key: str = \\\"output\\\",\\n ) -> Text:\\n result = runnable.invoke({input_key: inputs})\\n result = result.get(output_key)\\n self.status = result\\n return result\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"input_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"input_key\",\"display_name\":\"Input Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"The key to use for the input.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"inputs\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"inputs\",\"display_name\":\"Inputs\",\"advanced\":false,\"dynamic\":false,\"info\":\"The inputs to pass to the runnable.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"output_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"output\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"output_key\",\"display_name\":\"Output Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"The key to use for the output.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"base_classes\":[\"Text\",\"object\"],\"display_name\":\"Runnable Executor\",\"documentation\":\"http://docs.langflow.org/components/custom\",\"custom_fields\":{\"input_key\":null,\"inputs\":null,\"runnable\":null,\"output_key\":null},\"output_types\":[\"Text\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"DocumentToRecord\":{\"template\":{\"documents\":{\"type\":\"Document\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"documents\",\"display_name\":\"Documents\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import List\\n\\nfrom langchain_core.documents import Document\\n\\nfrom langflow import CustomComponent\\nfrom langflow.schema import Record\\n\\n\\nclass DocumentToRecordComponent(CustomComponent):\\n display_name = \\\"Documents to Records\\\"\\n description = \\\"Convert documents to records.\\\"\\n\\n field_config = {\\n \\\"documents\\\": {\\\"display_name\\\": \\\"Documents\\\"},\\n }\\n\\n def build(self, documents: List[Document]) -> List[Record]:\\n if isinstance(documents, Document):\\n documents = [documents]\\n records = [Record.from_document(document) for document in documents]\\n self.status = records\\n return records\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Convert documents to records.\",\"base_classes\":[\"Record\"],\"display_name\":\"Documents to Records\",\"documentation\":\"\",\"custom_fields\":{\"documents\":null},\"output_types\":[\"Record\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"GetRequest\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nimport requests\\nfrom langchain_core.documents import Document\\nfrom langflow import CustomComponent\\nfrom langflow.services.database.models.base import orjson_dumps\\n\\n\\nclass GetRequest(CustomComponent):\\n display_name: str = \\\"GET Request\\\"\\n description: str = \\\"Make a GET request to the given URL.\\\"\\n output_types: list[str] = [\\\"Document\\\"]\\n documentation: str = \\\"https://docs.langflow.org/components/utilities#get-request\\\"\\n beta: bool = True\\n field_config = {\\n \\\"url\\\": {\\n \\\"display_name\\\": \\\"URL\\\",\\n \\\"info\\\": \\\"The URL to make the request to\\\",\\n \\\"is_list\\\": True,\\n },\\n \\\"headers\\\": {\\n \\\"display_name\\\": \\\"Headers\\\",\\n \\\"info\\\": \\\"The headers to send with the request.\\\",\\n },\\n \\\"code\\\": {\\\"show\\\": False},\\n \\\"timeout\\\": {\\n \\\"display_name\\\": \\\"Timeout\\\",\\n \\\"field_type\\\": \\\"int\\\",\\n \\\"info\\\": \\\"The timeout to use for the request.\\\",\\n \\\"value\\\": 5,\\n },\\n }\\n\\n def get_document(self, session: requests.Session, url: str, headers: Optional[dict], timeout: int) -> Document:\\n try:\\n response = session.get(url, headers=headers, timeout=int(timeout))\\n try:\\n response_json = response.json()\\n result = orjson_dumps(response_json, indent_2=False)\\n except Exception:\\n result = response.text\\n self.repr_value = result\\n return Document(\\n page_content=result,\\n metadata={\\n \\\"source\\\": url,\\n \\\"headers\\\": headers,\\n \\\"status_code\\\": response.status_code,\\n },\\n )\\n except requests.Timeout:\\n return Document(\\n page_content=\\\"Request Timed Out\\\",\\n metadata={\\\"source\\\": url, \\\"headers\\\": headers, \\\"status_code\\\": 408},\\n )\\n except Exception as exc:\\n return Document(\\n page_content=str(exc),\\n metadata={\\\"source\\\": url, \\\"headers\\\": headers, \\\"status_code\\\": 500},\\n )\\n\\n def build(\\n self,\\n url: str,\\n headers: Optional[dict] = None,\\n timeout: int = 5,\\n ) -> list[Document]:\\n if headers is None:\\n headers = {}\\n urls = url if isinstance(url, list) else [url]\\n with requests.Session() as session:\\n documents = [self.get_document(session, u, headers, timeout) for u in urls]\\n self.repr_value = documents\\n return documents\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"headers\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"headers\",\"display_name\":\"Headers\",\"advanced\":false,\"dynamic\":false,\"info\":\"The headers to send with the request.\",\"title_case\":false},\"timeout\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":5,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"timeout\",\"display_name\":\"Timeout\",\"advanced\":false,\"dynamic\":false,\"info\":\"The timeout to use for the request.\",\"title_case\":false},\"url\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"url\",\"display_name\":\"URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"The URL to make the request to\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Make a GET request to the given URL.\",\"base_classes\":[\"Serializable\",\"Document\"],\"display_name\":\"GET Request\",\"documentation\":\"https://docs.langflow.org/components/utilities#get-request\",\"custom_fields\":{\"url\":null,\"headers\":null,\"timeout\":null},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"SQLExecutor\":{\"template\":{\"database\":{\"type\":\"SQLDatabase\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"database\",\"display_name\":\"Database\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"add_error\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"add_error\",\"display_name\":\"Add Error\",\"advanced\":false,\"dynamic\":false,\"info\":\"Add the error to the result.\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from langchain_community.tools.sql_database.tool import QuerySQLDataBaseTool\\nfrom langchain_experimental.sql.base import SQLDatabase\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Text\\n\\n\\nclass SQLExecutorComponent(CustomComponent):\\n display_name = \\\"SQL Executor\\\"\\n description = \\\"Execute SQL query.\\\"\\n\\n def build_config(self):\\n return {\\n \\\"database\\\": {\\\"display_name\\\": \\\"Database\\\"},\\n \\\"include_columns\\\": {\\n \\\"display_name\\\": \\\"Include Columns\\\",\\n \\\"info\\\": \\\"Include columns in the result.\\\",\\n },\\n \\\"passthrough\\\": {\\n \\\"display_name\\\": \\\"Passthrough\\\",\\n \\\"info\\\": \\\"If an error occurs, return the query instead of raising an exception.\\\",\\n },\\n \\\"add_error\\\": {\\n \\\"display_name\\\": \\\"Add Error\\\",\\n \\\"info\\\": \\\"Add the error to the result.\\\",\\n },\\n }\\n\\n def build(\\n self,\\n query: str,\\n database: SQLDatabase,\\n include_columns: bool = False,\\n passthrough: bool = False,\\n add_error: bool = False,\\n ) -> Text:\\n error = None\\n try:\\n tool = QuerySQLDataBaseTool(db=database)\\n result = tool.run(query, include_columns=include_columns)\\n self.status = result\\n except Exception as e:\\n result = str(e)\\n self.status = result\\n if not passthrough:\\n raise e\\n error = repr(e)\\n\\n if add_error and error is not None:\\n result = f\\\"{result}\\\\n\\\\nError: {error}\\\\n\\\\nQuery: {query}\\\"\\n elif error is not None:\\n # Then we won't add the error to the result\\n # but since we are in passthrough mode, we will return the query\\n result = query\\n\\n return result\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"include_columns\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"include_columns\",\"display_name\":\"Include Columns\",\"advanced\":false,\"dynamic\":false,\"info\":\"Include columns in the result.\",\"title_case\":false},\"passthrough\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"passthrough\",\"display_name\":\"Passthrough\",\"advanced\":false,\"dynamic\":false,\"info\":\"If an error occurs, return the query instead of raising an exception.\",\"title_case\":false},\"query\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"query\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Execute SQL query.\",\"base_classes\":[\"Text\",\"object\"],\"display_name\":\"SQL Executor\",\"documentation\":\"\",\"custom_fields\":{\"query\":null,\"database\":null,\"include_columns\":null,\"passthrough\":null,\"add_error\":null},\"output_types\":[\"Text\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"ShouldRunNext\":{\"template\":{\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"The language model to use for the decision.\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"# Implement ShouldRunNext component\\nfrom langchain_core.prompts import PromptTemplate\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import BaseLanguageModel, Prompt\\n\\n\\nclass ShouldRunNext(CustomComponent):\\n display_name = \\\"Should Run Next\\\"\\n description = \\\"Decides whether to run the next component.\\\"\\n\\n def build_config(self):\\n return {\\n \\\"prompt\\\": {\\n \\\"display_name\\\": \\\"Prompt\\\",\\n \\\"info\\\": \\\"The prompt to use for the decision. It should generate a boolean response (True or False).\\\",\\n },\\n \\\"llm\\\": {\\n \\\"display_name\\\": \\\"LLM\\\",\\n \\\"info\\\": \\\"The language model to use for the decision.\\\",\\n },\\n }\\n\\n def build(self, template: Prompt, llm: BaseLanguageModel, **kwargs) -> dict:\\n # This is a simple component that always returns True\\n prompt_template = PromptTemplate.from_template(template)\\n\\n attributes_to_check = [\\\"text\\\", \\\"page_content\\\"]\\n for key, value in kwargs.items():\\n for attribute in attributes_to_check:\\n if hasattr(value, attribute):\\n kwargs[key] = getattr(value, attribute)\\n\\n chain = prompt_template | llm\\n result = chain.invoke(kwargs)\\n if hasattr(result, \\\"content\\\") and isinstance(result.content, str):\\n result = result.content\\n elif isinstance(result, str):\\n result = result\\n else:\\n result = result.get(\\\"response\\\")\\n\\n if result.lower() not in [\\\"true\\\", \\\"false\\\"]:\\n raise ValueError(\\\"The prompt should generate a boolean response (True or False).\\\")\\n # The string should be the words true or false\\n # if not raise an error\\n bool_result = result.lower() == \\\"true\\\"\\n return {\\\"condition\\\": bool_result, \\\"result\\\": kwargs}\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"template\":{\"type\":\"prompt\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"template\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Decides whether to run the next component.\",\"base_classes\":[\"object\",\"dict\"],\"display_name\":\"Should Run Next\",\"documentation\":\"\",\"custom_fields\":{\"template\":null,\"llm\":null},\"output_types\":[\"dict\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"PythonFunction\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Callable\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Code\\nfrom langflow.interface.custom.utils import get_function\\n\\n\\nclass PythonFunctionComponent(CustomComponent):\\n display_name = \\\"Python Function\\\"\\n description = \\\"Define a Python function.\\\"\\n\\n def build_config(self):\\n return {\\n \\\"function_code\\\": {\\n \\\"display_name\\\": \\\"Code\\\",\\n \\\"info\\\": \\\"The code for the function.\\\",\\n \\\"show\\\": True,\\n },\\n }\\n\\n def build(self, function_code: Code) -> Callable:\\n self.status = function_code\\n func = get_function(function_code)\\n return func\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"function_code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"function_code\",\"display_name\":\"Code\",\"advanced\":false,\"dynamic\":false,\"info\":\"The code for the function.\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Define a Python function.\",\"base_classes\":[\"Callable\"],\"display_name\":\"Python Function\",\"documentation\":\"\",\"custom_fields\":{\"function_code\":null},\"output_types\":[\"Callable\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"PostRequest\":{\"template\":{\"document\":{\"type\":\"Document\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"document\",\"display_name\":\"Document\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nimport requests\\nfrom langchain_core.documents import Document\\nfrom langflow import CustomComponent\\nfrom langflow.services.database.models.base import orjson_dumps\\n\\n\\nclass PostRequest(CustomComponent):\\n display_name: str = \\\"POST Request\\\"\\n description: str = \\\"Make a POST request to the given URL.\\\"\\n output_types: list[str] = [\\\"Document\\\"]\\n documentation: str = \\\"https://docs.langflow.org/components/utilities#post-request\\\"\\n beta: bool = True\\n field_config = {\\n \\\"url\\\": {\\\"display_name\\\": \\\"URL\\\", \\\"info\\\": \\\"The URL to make the request to.\\\"},\\n \\\"headers\\\": {\\n \\\"display_name\\\": \\\"Headers\\\",\\n \\\"info\\\": \\\"The headers to send with the request.\\\",\\n },\\n \\\"code\\\": {\\\"show\\\": False},\\n \\\"document\\\": {\\\"display_name\\\": \\\"Document\\\"},\\n }\\n\\n def post_document(\\n self,\\n session: requests.Session,\\n document: Document,\\n url: str,\\n headers: Optional[dict] = None,\\n ) -> Document:\\n try:\\n response = session.post(url, headers=headers, data=document.page_content)\\n try:\\n response_json = response.json()\\n result = orjson_dumps(response_json, indent_2=False)\\n except Exception:\\n result = response.text\\n self.repr_value = result\\n return Document(\\n page_content=result,\\n metadata={\\n \\\"source\\\": url,\\n \\\"headers\\\": headers,\\n \\\"status_code\\\": response,\\n },\\n )\\n except Exception as exc:\\n return Document(\\n page_content=str(exc),\\n metadata={\\n \\\"source\\\": url,\\n \\\"headers\\\": headers,\\n \\\"status_code\\\": 500,\\n },\\n )\\n\\n def build(\\n self,\\n document: Document,\\n url: str,\\n headers: Optional[dict] = None,\\n ) -> list[Document]:\\n if headers is None:\\n headers = {}\\n\\n if not isinstance(document, list) and isinstance(document, Document):\\n documents: list[Document] = [document]\\n elif isinstance(document, list) and all(isinstance(doc, Document) for doc in document):\\n documents = document\\n else:\\n raise ValueError(\\\"document must be a Document or a list of Documents\\\")\\n\\n with requests.Session() as session:\\n documents = [self.post_document(session, doc, url, headers) for doc in documents]\\n self.repr_value = documents\\n return documents\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"headers\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"headers\",\"display_name\":\"Headers\",\"advanced\":false,\"dynamic\":false,\"info\":\"The headers to send with the request.\",\"title_case\":false},\"url\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"url\",\"display_name\":\"URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"The URL to make the request to.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Make a POST request to the given URL.\",\"base_classes\":[\"Serializable\",\"Document\"],\"display_name\":\"POST Request\",\"documentation\":\"https://docs.langflow.org/components/utilities#post-request\",\"custom_fields\":{\"document\":null,\"url\":null,\"headers\":null},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"IDGenerator\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"import uuid\\n\\nfrom langflow import CustomComponent\\n\\n\\nclass UUIDGeneratorComponent(CustomComponent):\\n documentation: str = \\\"http://docs.langflow.org/components/custom\\\"\\n display_name = \\\"Unique ID Generator\\\"\\n description = \\\"Generates a unique ID.\\\"\\n\\n def generate(self, *args, **kwargs):\\n return str(uuid.uuid4().hex)\\n\\n def build_config(self):\\n return {\\\"unique_id\\\": {\\\"display_name\\\": \\\"Value\\\", \\\"value\\\": self.generate}}\\n\\n def build(self, unique_id: str) -> str:\\n return unique_id\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"unique_id\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"a62d43140aba4c799af4ddc400295790\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"unique_id\",\"display_name\":\"Value\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"refresh\":true,\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Generates a unique ID.\",\"base_classes\":[\"object\",\"str\"],\"display_name\":\"Unique ID Generator\",\"documentation\":\"http://docs.langflow.org/components/custom\",\"custom_fields\":{\"unique_id\":null},\"output_types\":[\"str\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"SQLDatabase\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from langchain_experimental.sql.base import SQLDatabase\\n\\nfrom langflow import CustomComponent\\n\\n\\nclass SQLDatabaseComponent(CustomComponent):\\n display_name = \\\"SQLDatabase\\\"\\n description = \\\"SQL Database\\\"\\n\\n def build_config(self):\\n return {\\n \\\"uri\\\": {\\\"display_name\\\": \\\"URI\\\", \\\"info\\\": \\\"URI to the database.\\\"},\\n }\\n\\n def clean_up_uri(self, uri: str) -> str:\\n if uri.startswith(\\\"postgresql://\\\"):\\n uri = uri.replace(\\\"postgresql://\\\", \\\"postgres://\\\")\\n return uri.strip()\\n\\n def build(self, uri: str) -> SQLDatabase:\\n uri = self.clean_up_uri(uri)\\n return SQLDatabase.from_uri(uri)\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"uri\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"uri\",\"display_name\":\"URI\",\"advanced\":false,\"dynamic\":false,\"info\":\"URI to the database.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"SQL Database\",\"base_classes\":[\"object\",\"SQLDatabase\"],\"display_name\":\"SQLDatabase\",\"documentation\":\"\",\"custom_fields\":{\"uri\":null},\"output_types\":[\"SQLDatabase\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"RecordsAsText\":{\"template\":{\"records\":{\"type\":\"Record\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"records\",\"display_name\":\"Records\",\"advanced\":false,\"dynamic\":false,\"info\":\"The records to convert to text.\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langflow.field_typing import Text\\nfrom langflow.schema import Record\\n\\n\\nclass RecordsAsTextComponent(CustomComponent):\\n display_name = \\\"Records to Text\\\"\\n description = \\\"Converts Records a list of Records to text using a template.\\\"\\n\\n def build_config(self):\\n return {\\n \\\"records\\\": {\\n \\\"display_name\\\": \\\"Records\\\",\\n \\\"info\\\": \\\"The records to convert to text.\\\",\\n },\\n \\\"template\\\": {\\n \\\"display_name\\\": \\\"Template\\\",\\n \\\"info\\\": \\\"The template to use for formatting the records. It must contain the keys {text} and {data}.\\\",\\n },\\n }\\n\\n def build(\\n self,\\n records: list[Record],\\n template: str = \\\"Text: {text}\\\\nData: {data}\\\",\\n ) -> Text:\\n if isinstance(records, Record):\\n records = [records]\\n\\n formated_records = [\\n template.format(text=record.text, data=record.data, **record.data)\\n for record in records\\n ]\\n result_string = \\\"\\\\n\\\".join(formated_records)\\n self.status = result_string\\n return result_string\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"template\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"Text: {text}\\\\nData: {data}\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"template\",\"display_name\":\"Template\",\"advanced\":false,\"dynamic\":false,\"info\":\"The template to use for formatting the records. It must contain the keys {text} and {data}.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Converts Records a list of Records to text using a template.\",\"base_classes\":[\"Text\",\"object\"],\"display_name\":\"Records to Text\",\"documentation\":\"\",\"custom_fields\":{\"records\":null,\"template\":null},\"output_types\":[\"Text\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"UpdateRequest\":{\"template\":{\"document\":{\"type\":\"Document\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"document\",\"display_name\":\"Document\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import List, Optional\\n\\nimport requests\\nfrom langchain_core.documents import Document\\nfrom langflow import CustomComponent\\nfrom langflow.services.database.models.base import orjson_dumps\\n\\n\\nclass UpdateRequest(CustomComponent):\\n display_name: str = \\\"Update Request\\\"\\n description: str = \\\"Make a PATCH request to the given URL.\\\"\\n output_types: list[str] = [\\\"Document\\\"]\\n documentation: str = \\\"https://docs.langflow.org/components/utilities#update-request\\\"\\n beta: bool = True\\n field_config = {\\n \\\"url\\\": {\\\"display_name\\\": \\\"URL\\\", \\\"info\\\": \\\"The URL to make the request to.\\\"},\\n \\\"headers\\\": {\\n \\\"display_name\\\": \\\"Headers\\\",\\n \\\"field_type\\\": \\\"NestedDict\\\",\\n \\\"info\\\": \\\"The headers to send with the request.\\\",\\n },\\n \\\"code\\\": {\\\"show\\\": False},\\n \\\"document\\\": {\\\"display_name\\\": \\\"Document\\\"},\\n \\\"method\\\": {\\n \\\"display_name\\\": \\\"Method\\\",\\n \\\"field_type\\\": \\\"str\\\",\\n \\\"info\\\": \\\"The HTTP method to use.\\\",\\n \\\"options\\\": [\\\"PATCH\\\", \\\"PUT\\\"],\\n \\\"value\\\": \\\"PATCH\\\",\\n },\\n }\\n\\n def update_document(\\n self,\\n session: requests.Session,\\n document: Document,\\n url: str,\\n headers: Optional[dict] = None,\\n method: str = \\\"PATCH\\\",\\n ) -> Document:\\n try:\\n if method == \\\"PATCH\\\":\\n response = session.patch(url, headers=headers, data=document.page_content)\\n elif method == \\\"PUT\\\":\\n response = session.put(url, headers=headers, data=document.page_content)\\n else:\\n raise ValueError(f\\\"Unsupported method: {method}\\\")\\n try:\\n response_json = response.json()\\n result = orjson_dumps(response_json, indent_2=False)\\n except Exception:\\n result = response.text\\n self.repr_value = result\\n return Document(\\n page_content=result,\\n metadata={\\n \\\"source\\\": url,\\n \\\"headers\\\": headers,\\n \\\"status_code\\\": response.status_code,\\n },\\n )\\n except Exception as exc:\\n return Document(\\n page_content=str(exc),\\n metadata={\\\"source\\\": url, \\\"headers\\\": headers, \\\"status_code\\\": 500},\\n )\\n\\n def build(\\n self,\\n method: str,\\n document: Document,\\n url: str,\\n headers: Optional[dict] = None,\\n ) -> List[Document]:\\n if headers is None:\\n headers = {}\\n\\n if not isinstance(document, list) and isinstance(document, Document):\\n documents: list[Document] = [document]\\n elif isinstance(document, list) and all(isinstance(doc, Document) for doc in document):\\n documents = document\\n else:\\n raise ValueError(\\\"document must be a Document or a list of Documents\\\")\\n\\n with requests.Session() as session:\\n documents = [self.update_document(session, doc, url, headers, method) for doc in documents]\\n self.repr_value = documents\\n return documents\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"headers\":{\"type\":\"NestedDict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"headers\",\"display_name\":\"Headers\",\"advanced\":false,\"dynamic\":false,\"info\":\"The headers to send with the request.\",\"title_case\":false},\"method\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"PATCH\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"PATCH\",\"PUT\"],\"name\":\"method\",\"display_name\":\"Method\",\"advanced\":false,\"dynamic\":false,\"info\":\"The HTTP method to use.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"url\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"url\",\"display_name\":\"URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"The URL to make the request to.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Make a PATCH request to the given URL.\",\"base_classes\":[\"Serializable\",\"Document\"],\"display_name\":\"Update Request\",\"documentation\":\"https://docs.langflow.org/components/utilities#update-request\",\"custom_fields\":{\"method\":null,\"document\":null,\"url\":null,\"headers\":null},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"JSONDocumentBuilder\":{\"template\":{\"document\":{\"type\":\"Document\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"document\",\"display_name\":\"Document\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"### JSON Document Builder\\n\\n# Build a Document containing a JSON object using a key and another Document page content.\\n\\n# **Params**\\n\\n# - **Key:** The key to use for the JSON object.\\n# - **Document:** The Document page to use for the JSON object.\\n\\n# **Output**\\n\\n# - **Document:** The Document containing the JSON object.\\n\\nfrom langchain_core.documents import Document\\nfrom langflow import CustomComponent\\nfrom langflow.services.database.models.base import orjson_dumps\\n\\n\\nclass JSONDocumentBuilder(CustomComponent):\\n display_name: str = \\\"JSON Document Builder\\\"\\n description: str = \\\"Build a Document containing a JSON object using a key and another Document page content.\\\"\\n output_types: list[str] = [\\\"Document\\\"]\\n beta = True\\n documentation: str = \\\"https://docs.langflow.org/components/utilities#json-document-builder\\\"\\n\\n field_config = {\\n \\\"key\\\": {\\\"display_name\\\": \\\"Key\\\"},\\n \\\"document\\\": {\\\"display_name\\\": \\\"Document\\\"},\\n }\\n\\n def build(\\n self,\\n key: str,\\n document: Document,\\n ) -> Document:\\n documents = None\\n if isinstance(document, list):\\n documents = [\\n Document(page_content=orjson_dumps({key: doc.page_content}, indent_2=False)) for doc in document\\n ]\\n elif isinstance(document, Document):\\n documents = Document(page_content=orjson_dumps({key: document.page_content}, indent_2=False))\\n else:\\n raise TypeError(f\\\"Expected Document or list of Documents, got {type(document)}\\\")\\n self.repr_value = documents\\n return documents\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"key\",\"display_name\":\"Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Build a Document containing a JSON object using a key and another Document page content.\",\"base_classes\":[\"Serializable\",\"Document\"],\"display_name\":\"JSON Document Builder\",\"documentation\":\"https://docs.langflow.org/components/utilities#json-document-builder\",\"custom_fields\":{\"key\":null,\"document\":null},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true}},\"output_parsers\":{\"ResponseSchema\":{\"template\":{\"description\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"fileTypes\":[],\"password\":false,\"name\":\"description\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"name\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"type\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"string\",\"fileTypes\":[],\"password\":false,\"name\":\"type\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"ResponseSchema\"},\"description\":\"A schema for a response from a structured output parser.\",\"base_classes\":[\"ResponseSchema\"],\"display_name\":\"ResponseSchema\",\"documentation\":\"https://python.langchain.com/docs/modules/model_io/output_parsers/structured\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"StructuredOutputParser\":{\"template\":{\"response_schemas\":{\"type\":\"ResponseSchema\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"response_schemas\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"StructuredOutputParser\"},\"description\":\"\",\"base_classes\":[\"BaseOutputParser\",\"Runnable\",\"BaseLLMOutputParser\",\"Generic\",\"RunnableSerializable\",\"StructuredOutputParser\",\"Serializable\",\"object\"],\"display_name\":\"StructuredOutputParser\",\"documentation\":\"https://python.langchain.com/docs/modules/model_io/output_parsers/structured\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":false,\"beta\":false}},\"retrievers\":{\"AmazonKendra\":{\"template\":{\"attribute_filter\":{\"type\":\"code\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"attribute_filter\",\"display_name\":\"Attribute Filter\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain.schema import BaseRetriever\\nfrom langchain_community.retrievers import AmazonKendraRetriever\\n\\nfrom langflow import CustomComponent\\n\\n\\nclass AmazonKendraRetrieverComponent(CustomComponent):\\n display_name: str = \\\"Amazon Kendra Retriever\\\"\\n description: str = \\\"Retriever that uses the Amazon Kendra API.\\\"\\n\\n def build_config(self):\\n return {\\n \\\"index_id\\\": {\\\"display_name\\\": \\\"Index ID\\\"},\\n \\\"region_name\\\": {\\\"display_name\\\": \\\"Region Name\\\"},\\n \\\"credentials_profile_name\\\": {\\\"display_name\\\": \\\"Credentials Profile Name\\\"},\\n \\\"attribute_filter\\\": {\\n \\\"display_name\\\": \\\"Attribute Filter\\\",\\n \\\"field_type\\\": \\\"code\\\",\\n },\\n \\\"top_k\\\": {\\\"display_name\\\": \\\"Top K\\\", \\\"field_type\\\": \\\"int\\\"},\\n \\\"user_context\\\": {\\n \\\"display_name\\\": \\\"User Context\\\",\\n \\\"field_type\\\": \\\"code\\\",\\n },\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(\\n self,\\n index_id: str,\\n top_k: int = 3,\\n region_name: Optional[str] = None,\\n credentials_profile_name: Optional[str] = None,\\n attribute_filter: Optional[dict] = None,\\n user_context: Optional[dict] = None,\\n ) -> BaseRetriever:\\n try:\\n output = AmazonKendraRetriever(\\n index_id=index_id,\\n top_k=top_k,\\n region_name=region_name,\\n credentials_profile_name=credentials_profile_name,\\n attribute_filter=attribute_filter,\\n user_context=user_context,\\n ) # type: ignore\\n except Exception as e:\\n raise ValueError(\\\"Could not connect to AmazonKendra API.\\\") from e\\n return output\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"credentials_profile_name\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"credentials_profile_name\",\"display_name\":\"Credentials Profile Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"index_id\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"index_id\",\"display_name\":\"Index ID\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"region_name\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"region_name\",\"display_name\":\"Region Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"top_k\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":3,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_k\",\"display_name\":\"Top K\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"user_context\":{\"type\":\"code\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"user_context\",\"display_name\":\"User Context\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Retriever that uses the Amazon Kendra API.\",\"base_classes\":[\"Runnable\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"object\",\"BaseRetriever\"],\"display_name\":\"Amazon Kendra Retriever\",\"documentation\":\"\",\"custom_fields\":{\"index_id\":null,\"top_k\":null,\"region_name\":null,\"credentials_profile_name\":null,\"attribute_filter\":null,\"user_context\":null},\"output_types\":[\"BaseRetriever\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"VectaraSelfQueryRetriver\":{\"template\":{\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"For self query retriever\",\"title_case\":false},\"vectorstore\":{\"type\":\"VectorStore\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"vectorstore\",\"display_name\":\"Vector Store\",\"advanced\":false,\"dynamic\":false,\"info\":\"Input Vectara Vectore Store\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import List\\nfrom langflow import CustomComponent\\nimport json\\nfrom langchain.schema import BaseRetriever\\nfrom langchain.schema.vectorstore import VectorStore\\nfrom langchain.base_language import BaseLanguageModel\\nfrom langchain.retrievers.self_query.base import SelfQueryRetriever\\nfrom langchain.chains.query_constructor.base import AttributeInfo\\n\\n\\nclass VectaraSelfQueryRetriverComponent(CustomComponent):\\n \\\"\\\"\\\"\\n A custom component for implementing Vectara Self Query Retriever using a vector store.\\n \\\"\\\"\\\"\\n\\n display_name: str = \\\"Vectara Self Query Retriever for Vectara Vector Store\\\"\\n description: str = \\\"Implementation of Vectara Self Query Retriever\\\"\\n documentation = \\\"https://python.langchain.com/docs/integrations/retrievers/self_query/vectara_self_query\\\"\\n beta = True\\n\\n field_config = {\\n \\\"code\\\": {\\\"show\\\": True},\\n \\\"vectorstore\\\": {\\\"display_name\\\": \\\"Vector Store\\\", \\\"info\\\": \\\"Input Vectara Vectore Store\\\"},\\n \\\"llm\\\": {\\\"display_name\\\": \\\"LLM\\\", \\\"info\\\": \\\"For self query retriever\\\"},\\n \\\"document_content_description\\\": {\\n \\\"display_name\\\": \\\"Document Content Description\\\",\\n \\\"info\\\": \\\"For self query retriever\\\",\\n },\\n \\\"metadata_field_info\\\": {\\n \\\"display_name\\\": \\\"Metadata Field Info\\\",\\n \\\"info\\\": 'Each metadata field info is a string in the form of key value pair dictionary containing additional search metadata.\\\\nExample input: {\\\"name\\\":\\\"speech\\\",\\\"description\\\":\\\"what name of the speech\\\",\\\"type\\\":\\\"string or list[string]\\\"}.\\\\nThe keys should remain constant(name, description, type)',\\n },\\n }\\n\\n def build(\\n self,\\n vectorstore: VectorStore,\\n document_content_description: str,\\n llm: BaseLanguageModel,\\n metadata_field_info: List[str],\\n ) -> BaseRetriever:\\n metadata_field_obj = []\\n\\n for meta in metadata_field_info:\\n meta_obj = json.loads(meta)\\n if \\\"name\\\" not in meta_obj or \\\"description\\\" not in meta_obj or \\\"type\\\" not in meta_obj:\\n raise Exception(\\\"Incorrect metadata field info format.\\\")\\n attribute_info = AttributeInfo(\\n name=meta_obj[\\\"name\\\"],\\n description=meta_obj[\\\"description\\\"],\\n type=meta_obj[\\\"type\\\"],\\n )\\n metadata_field_obj.append(attribute_info)\\n\\n return SelfQueryRetriever.from_llm(\\n llm, vectorstore, document_content_description, metadata_field_obj, verbose=True\\n )\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"document_content_description\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"document_content_description\",\"display_name\":\"Document Content Description\",\"advanced\":false,\"dynamic\":false,\"info\":\"For self query retriever\",\"title_case\":false,\"input_types\":[\"Text\"]},\"metadata_field_info\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata_field_info\",\"display_name\":\"Metadata Field Info\",\"advanced\":false,\"dynamic\":false,\"info\":\"Each metadata field info is a string in the form of key value pair dictionary containing additional search metadata.\\nExample input: {\\\"name\\\":\\\"speech\\\",\\\"description\\\":\\\"what name of the speech\\\",\\\"type\\\":\\\"string or list[string]\\\"}.\\nThe keys should remain constant(name, description, type)\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Implementation of Vectara Self Query Retriever\",\"base_classes\":[\"Runnable\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"object\",\"BaseRetriever\"],\"display_name\":\"Vectara Self Query Retriever for Vectara Vector Store\",\"documentation\":\"https://python.langchain.com/docs/integrations/retrievers/self_query/vectara_self_query\",\"custom_fields\":{\"vectorstore\":null,\"document_content_description\":null,\"llm\":null,\"metadata_field_info\":null},\"output_types\":[\"BaseRetriever\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"MultiQueryRetriever\":{\"template\":{\"llm\":{\"type\":\"BaseLLM\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"prompt\":{\"type\":\"PromptTemplate\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"prompt\",\"display_name\":\"Prompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"retriever\":{\"type\":\"BaseRetriever\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"retriever\",\"display_name\":\"Retriever\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Callable, Optional, Union\\n\\nfrom langchain.retrievers import MultiQueryRetriever\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import BaseLLM, BaseRetriever, PromptTemplate\\n\\n\\nclass MultiQueryRetrieverComponent(CustomComponent):\\n display_name = \\\"MultiQueryRetriever\\\"\\n description = \\\"Initialize from llm using default template.\\\"\\n documentation = \\\"https://python.langchain.com/docs/modules/data_connection/retrievers/how_to/MultiQueryRetriever\\\"\\n\\n def build_config(self):\\n return {\\n \\\"llm\\\": {\\\"display_name\\\": \\\"LLM\\\"},\\n \\\"prompt\\\": {\\n \\\"display_name\\\": \\\"Prompt\\\",\\n \\\"default\\\": {\\n \\\"input_variables\\\": [\\\"question\\\"],\\n \\\"input_types\\\": {},\\n \\\"output_parser\\\": None,\\n \\\"partial_variables\\\": {},\\n \\\"template\\\": \\\"You are an AI language model assistant. Your task is \\\\n\\\"\\n \\\"to generate 3 different versions of the given user \\\\n\\\"\\n \\\"question to retrieve relevant documents from a vector database. \\\\n\\\"\\n \\\"By generating multiple perspectives on the user question, \\\\n\\\"\\n \\\"your goal is to help the user overcome some of the limitations \\\\n\\\"\\n \\\"of distance-based similarity search. Provide these alternative \\\\n\\\"\\n \\\"questions separated by newlines. Original question: {question}\\\",\\n \\\"template_format\\\": \\\"f-string\\\",\\n \\\"validate_template\\\": False,\\n \\\"_type\\\": \\\"prompt\\\",\\n },\\n },\\n \\\"retriever\\\": {\\\"display_name\\\": \\\"Retriever\\\"},\\n \\\"parser_key\\\": {\\\"display_name\\\": \\\"Parser Key\\\", \\\"default\\\": \\\"lines\\\"},\\n }\\n\\n def build(\\n self,\\n llm: BaseLLM,\\n retriever: BaseRetriever,\\n prompt: Optional[PromptTemplate] = None,\\n parser_key: str = \\\"lines\\\",\\n ) -> Union[Callable, MultiQueryRetriever]:\\n if not prompt:\\n return MultiQueryRetriever.from_llm(llm=llm, retriever=retriever, parser_key=parser_key)\\n else:\\n return MultiQueryRetriever.from_llm(llm=llm, retriever=retriever, prompt=prompt, parser_key=parser_key)\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"parser_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"lines\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"parser_key\",\"display_name\":\"Parser Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Initialize from llm using default template.\",\"base_classes\":[],\"display_name\":\"MultiQueryRetriever\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/retrievers/how_to/MultiQueryRetriever\",\"custom_fields\":{\"llm\":null,\"retriever\":null,\"prompt\":null,\"parser_key\":null},\"output_types\":[],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"MetalRetriever\":{\"template\":{\"api_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"api_key\",\"display_name\":\"API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"client_id\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"client_id\",\"display_name\":\"Client ID\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain.schema import BaseRetriever\\nfrom langchain_community.retrievers import MetalRetriever\\nfrom metal_sdk.metal import Metal # type: ignore\\n\\nfrom langflow import CustomComponent\\n\\n\\nclass MetalRetrieverComponent(CustomComponent):\\n display_name: str = \\\"Metal Retriever\\\"\\n description: str = \\\"Retriever that uses the Metal API.\\\"\\n\\n def build_config(self):\\n return {\\n \\\"api_key\\\": {\\\"display_name\\\": \\\"API Key\\\", \\\"password\\\": True},\\n \\\"client_id\\\": {\\\"display_name\\\": \\\"Client ID\\\", \\\"password\\\": True},\\n \\\"index_id\\\": {\\\"display_name\\\": \\\"Index ID\\\"},\\n \\\"params\\\": {\\\"display_name\\\": \\\"Parameters\\\"},\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(self, api_key: str, client_id: str, index_id: str, params: Optional[dict] = None) -> BaseRetriever:\\n try:\\n metal = Metal(api_key=api_key, client_id=client_id, index_id=index_id)\\n except Exception as e:\\n raise ValueError(\\\"Could not connect to Metal API.\\\") from e\\n return MetalRetriever(client=metal, params=params or {})\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"index_id\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"index_id\",\"display_name\":\"Index ID\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"params\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"params\",\"display_name\":\"Parameters\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Retriever that uses the Metal API.\",\"base_classes\":[\"Runnable\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"object\",\"BaseRetriever\"],\"display_name\":\"Metal Retriever\",\"documentation\":\"\",\"custom_fields\":{\"api_key\":null,\"client_id\":null,\"index_id\":null,\"params\":null},\"output_types\":[\"BaseRetriever\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true}},\"custom_components\":{\"CustomComponent\":{\"template\":{\"param\":{\"type\":\"Data\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"param\",\"display_name\":\"Parameter\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langflow.field_typing import Data\\n\\n\\nclass Component(CustomComponent):\\n documentation: str = \\\"http://docs.langflow.org/components/custom\\\"\\n\\n def build_config(self):\\n return {\\\"param\\\": {\\\"display_name\\\": \\\"Parameter\\\"}}\\n\\n def build(self, param: Data) -> Data:\\n return param\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"base_classes\":[\"object\",\"Data\"],\"display_name\":\"CustomComponent\",\"documentation\":\"http://docs.langflow.org/components/custom\",\"custom_fields\":{\"param\":null},\"output_types\":[\"Data\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true}},\"vectorstores\":{\"Weaviate\":{\"template\":{\"documents\":{\"type\":\"Document\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"documents\",\"display_name\":\"Documents\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"embedding\":{\"type\":\"Embeddings\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"embedding\",\"display_name\":\"Embedding\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"api_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"api_key\",\"display_name\":\"API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"attributes\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"attributes\",\"display_name\":\"Attributes\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional, Union\\n\\nimport weaviate # type: ignore\\nfrom langchain.embeddings.base import Embeddings\\nfrom langchain.schema import BaseRetriever, Document\\nfrom langchain_community.vectorstores import VectorStore, Weaviate\\n\\nfrom langflow import CustomComponent\\n\\n\\nclass WeaviateVectorStore(CustomComponent):\\n display_name: str = \\\"Weaviate\\\"\\n description: str = \\\"Implementation of Vector Store using Weaviate\\\"\\n documentation = \\\"https://python.langchain.com/docs/integrations/vectorstores/weaviate\\\"\\n beta = True\\n field_config = {\\n \\\"url\\\": {\\\"display_name\\\": \\\"Weaviate URL\\\", \\\"value\\\": \\\"http://localhost:8080\\\"},\\n \\\"api_key\\\": {\\n \\\"display_name\\\": \\\"API Key\\\",\\n \\\"password\\\": True,\\n \\\"required\\\": False,\\n },\\n \\\"index_name\\\": {\\n \\\"display_name\\\": \\\"Index name\\\",\\n \\\"required\\\": False,\\n },\\n \\\"text_key\\\": {\\\"display_name\\\": \\\"Text Key\\\", \\\"required\\\": False, \\\"advanced\\\": True, \\\"value\\\": \\\"text\\\"},\\n \\\"documents\\\": {\\\"display_name\\\": \\\"Documents\\\", \\\"is_list\\\": True},\\n \\\"embedding\\\": {\\\"display_name\\\": \\\"Embedding\\\"},\\n \\\"attributes\\\": {\\n \\\"display_name\\\": \\\"Attributes\\\",\\n \\\"required\\\": False,\\n \\\"is_list\\\": True,\\n \\\"field_type\\\": \\\"str\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"search_by_text\\\": {\\\"display_name\\\": \\\"Search By Text\\\", \\\"field_type\\\": \\\"bool\\\", \\\"advanced\\\": True},\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(\\n self,\\n url: str,\\n search_by_text: bool = False,\\n api_key: Optional[str] = None,\\n index_name: Optional[str] = None,\\n text_key: str = \\\"text\\\",\\n embedding: Optional[Embeddings] = None,\\n documents: Optional[Document] = None,\\n attributes: Optional[list] = None,\\n ) -> Union[VectorStore, BaseRetriever]:\\n if api_key:\\n auth_config = weaviate.AuthApiKey(api_key=api_key)\\n client = weaviate.Client(url=url, auth_client_secret=auth_config)\\n else:\\n client = weaviate.Client(url=url)\\n\\n def _to_pascal_case(word: str):\\n if word and not word[0].isupper():\\n word = word.capitalize()\\n\\n if word.isidentifier():\\n return word\\n\\n word = word.replace(\\\"-\\\", \\\" \\\").replace(\\\"_\\\", \\\" \\\")\\n parts = word.split()\\n pascal_case_word = \\\"\\\".join([part.capitalize() for part in parts])\\n\\n return pascal_case_word\\n\\n index_name = _to_pascal_case(index_name) if index_name else None\\n\\n if documents is not None and embedding is not None:\\n return Weaviate.from_documents(\\n client=client,\\n index_name=index_name,\\n documents=documents,\\n embedding=embedding,\\n by_text=search_by_text,\\n )\\n\\n return Weaviate(\\n client=client,\\n index_name=index_name,\\n text_key=text_key,\\n embedding=embedding,\\n by_text=search_by_text,\\n attributes=attributes if attributes is not None else [],\\n )\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"index_name\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"index_name\",\"display_name\":\"Index name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"search_by_text\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"search_by_text\",\"display_name\":\"Search By Text\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"text_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"text\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"text_key\",\"display_name\":\"Text Key\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"url\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"http://localhost:8080\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"url\",\"display_name\":\"Weaviate URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Implementation of Vector Store using Weaviate\",\"base_classes\":[\"Runnable\",\"Generic\",\"VectorStore\",\"RunnableSerializable\",\"Serializable\",\"object\",\"BaseRetriever\"],\"display_name\":\"Weaviate\",\"documentation\":\"https://python.langchain.com/docs/integrations/vectorstores/weaviate\",\"custom_fields\":{\"url\":null,\"search_by_text\":null,\"api_key\":null,\"index_name\":null,\"text_key\":null,\"embedding\":null,\"documents\":null,\"attributes\":null},\"output_types\":[\"VectorStore\",\"BaseRetriever\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"Vectara\":{\"template\":{\"documents\":{\"type\":\"Document\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"documents\",\"display_name\":\"Documents\",\"advanced\":false,\"dynamic\":false,\"info\":\"If provided, will be upserted to corpus (optional)\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"import tempfile\\nimport urllib\\nimport urllib.request\\nfrom typing import List, Optional, Union\\n\\nfrom langchain_community.embeddings import FakeEmbeddings\\nfrom langchain_community.vectorstores.vectara import Vectara\\nfrom langchain_core.vectorstores import VectorStore\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import BaseRetriever, Document\\n\\n\\nclass VectaraComponent(CustomComponent):\\n display_name: str = \\\"Vectara\\\"\\n description: str = \\\"Implementation of Vector Store using Vectara\\\"\\n documentation = \\\"https://python.langchain.com/docs/integrations/vectorstores/vectara\\\"\\n beta = True\\n field_config = {\\n \\\"vectara_customer_id\\\": {\\n \\\"display_name\\\": \\\"Vectara Customer ID\\\",\\n },\\n \\\"vectara_corpus_id\\\": {\\n \\\"display_name\\\": \\\"Vectara Corpus ID\\\",\\n },\\n \\\"vectara_api_key\\\": {\\n \\\"display_name\\\": \\\"Vectara API Key\\\",\\n \\\"password\\\": True,\\n },\\n \\\"documents\\\": {\\\"display_name\\\": \\\"Documents\\\", \\\"info\\\": \\\"If provided, will be upserted to corpus (optional)\\\"},\\n \\\"files_url\\\": {\\n \\\"display_name\\\": \\\"Files Url\\\",\\n \\\"info\\\": \\\"Make vectara object using url of files (optional)\\\",\\n },\\n }\\n\\n def build(\\n self,\\n vectara_customer_id: str,\\n vectara_corpus_id: str,\\n vectara_api_key: str,\\n files_url: Optional[List[str]] = None,\\n documents: Optional[Document] = None,\\n ) -> Union[VectorStore, BaseRetriever]:\\n source = \\\"Langflow\\\"\\n\\n if documents is not None:\\n return Vectara.from_documents(\\n documents=documents, # type: ignore\\n embedding=FakeEmbeddings(size=768),\\n vectara_customer_id=vectara_customer_id,\\n vectara_corpus_id=vectara_corpus_id,\\n vectara_api_key=vectara_api_key,\\n source=source,\\n )\\n\\n if files_url is not None:\\n files_list = []\\n for url in files_url:\\n name = tempfile.NamedTemporaryFile().name\\n urllib.request.urlretrieve(url, name)\\n files_list.append(name)\\n\\n return Vectara.from_files(\\n files=files_list,\\n embedding=FakeEmbeddings(size=768),\\n vectara_customer_id=vectara_customer_id,\\n vectara_corpus_id=vectara_corpus_id,\\n vectara_api_key=vectara_api_key,\\n source=source,\\n )\\n\\n return Vectara(\\n vectara_customer_id=vectara_customer_id,\\n vectara_corpus_id=vectara_corpus_id,\\n vectara_api_key=vectara_api_key,\\n source=source,\\n )\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"files_url\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"files_url\",\"display_name\":\"Files Url\",\"advanced\":false,\"dynamic\":false,\"info\":\"Make vectara object using url of files (optional)\",\"title_case\":false,\"input_types\":[\"Text\"]},\"vectara_api_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"vectara_api_key\",\"display_name\":\"Vectara API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"vectara_corpus_id\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"vectara_corpus_id\",\"display_name\":\"Vectara Corpus ID\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"vectara_customer_id\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"vectara_customer_id\",\"display_name\":\"Vectara Customer ID\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Implementation of Vector Store using Vectara\",\"base_classes\":[\"Runnable\",\"Generic\",\"VectorStore\",\"RunnableSerializable\",\"Serializable\",\"object\",\"BaseRetriever\"],\"display_name\":\"Vectara\",\"documentation\":\"https://python.langchain.com/docs/integrations/vectorstores/vectara\",\"custom_fields\":{\"vectara_customer_id\":null,\"vectara_corpus_id\":null,\"vectara_api_key\":null,\"files_url\":null,\"documents\":null},\"output_types\":[\"VectorStore\",\"BaseRetriever\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"Chroma\":{\"template\":{\"documents\":{\"type\":\"Document\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"documents\",\"display_name\":\"Documents\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"embedding\":{\"type\":\"Embeddings\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"embedding\",\"display_name\":\"Embedding\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"chroma_server_cors_allow_origins\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"chroma_server_cors_allow_origins\",\"display_name\":\"Server CORS Allow Origins\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"chroma_server_grpc_port\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"chroma_server_grpc_port\",\"display_name\":\"Server gRPC Port\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"chroma_server_host\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"chroma_server_host\",\"display_name\":\"Server Host\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"chroma_server_port\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"chroma_server_port\",\"display_name\":\"Server Port\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"chroma_server_ssl_enabled\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"chroma_server_ssl_enabled\",\"display_name\":\"Server SSL Enabled\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import List, Optional, Union\\n\\nimport chromadb # type: ignore\\nfrom langchain.embeddings.base import Embeddings\\nfrom langchain.schema import BaseRetriever, Document\\nfrom langchain_community.vectorstores import VectorStore\\nfrom langchain_community.vectorstores.chroma import Chroma\\n\\nfrom langflow import CustomComponent\\n\\n\\nclass ChromaComponent(CustomComponent):\\n \\\"\\\"\\\"\\n A custom component for implementing a Vector Store using Chroma.\\n \\\"\\\"\\\"\\n\\n display_name: str = \\\"Chroma\\\"\\n description: str = \\\"Implementation of Vector Store using Chroma\\\"\\n documentation = \\\"https://python.langchain.com/docs/integrations/vectorstores/chroma\\\"\\n beta: bool = True\\n icon = \\\"Chroma\\\"\\n\\n def build_config(self):\\n \\\"\\\"\\\"\\n Builds the configuration for the component.\\n\\n Returns:\\n - dict: A dictionary containing the configuration options for the component.\\n \\\"\\\"\\\"\\n return {\\n \\\"collection_name\\\": {\\\"display_name\\\": \\\"Collection Name\\\", \\\"value\\\": \\\"langflow\\\"},\\n \\\"index_directory\\\": {\\\"display_name\\\": \\\"Persist Directory\\\"},\\n \\\"code\\\": {\\\"advanced\\\": True, \\\"display_name\\\": \\\"Code\\\"},\\n \\\"documents\\\": {\\\"display_name\\\": \\\"Documents\\\", \\\"is_list\\\": True},\\n \\\"embedding\\\": {\\\"display_name\\\": \\\"Embedding\\\"},\\n \\\"chroma_server_cors_allow_origins\\\": {\\n \\\"display_name\\\": \\\"Server CORS Allow Origins\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"chroma_server_host\\\": {\\\"display_name\\\": \\\"Server Host\\\", \\\"advanced\\\": True},\\n \\\"chroma_server_port\\\": {\\\"display_name\\\": \\\"Server Port\\\", \\\"advanced\\\": True},\\n \\\"chroma_server_grpc_port\\\": {\\n \\\"display_name\\\": \\\"Server gRPC Port\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"chroma_server_ssl_enabled\\\": {\\n \\\"display_name\\\": \\\"Server SSL Enabled\\\",\\n \\\"advanced\\\": True,\\n },\\n }\\n\\n def build(\\n self,\\n collection_name: str,\\n embedding: Embeddings,\\n chroma_server_ssl_enabled: bool,\\n index_directory: Optional[str] = None,\\n documents: Optional[List[Document]] = None,\\n chroma_server_cors_allow_origins: Optional[str] = None,\\n chroma_server_host: Optional[str] = None,\\n chroma_server_port: Optional[int] = None,\\n chroma_server_grpc_port: Optional[int] = None,\\n ) -> Union[VectorStore, BaseRetriever]:\\n \\\"\\\"\\\"\\n Builds the Vector Store or BaseRetriever object.\\n\\n Args:\\n - collection_name (str): The name of the collection.\\n - index_directory (Optional[str]): The directory to persist the Vector Store to.\\n - chroma_server_ssl_enabled (bool): Whether to enable SSL for the Chroma server.\\n - embedding (Optional[Embeddings]): The embeddings to use for the Vector Store.\\n - documents (Optional[Document]): The documents to use for the Vector Store.\\n - chroma_server_cors_allow_origins (Optional[str]): The CORS allow origins for the Chroma server.\\n - chroma_server_host (Optional[str]): The host for the Chroma server.\\n - chroma_server_port (Optional[int]): The port for the Chroma server.\\n - chroma_server_grpc_port (Optional[int]): The gRPC port for the Chroma server.\\n\\n Returns:\\n - Union[VectorStore, BaseRetriever]: The Vector Store or BaseRetriever object.\\n \\\"\\\"\\\"\\n\\n # Chroma settings\\n chroma_settings = None\\n\\n if chroma_server_host is not None:\\n chroma_settings = chromadb.config.Settings(\\n chroma_server_cors_allow_origins=chroma_server_cors_allow_origins\\n or None,\\n chroma_server_host=chroma_server_host,\\n chroma_server_port=chroma_server_port or None,\\n chroma_server_grpc_port=chroma_server_grpc_port or None,\\n chroma_server_ssl_enabled=chroma_server_ssl_enabled,\\n )\\n\\n # If documents, then we need to create a Chroma instance using .from_documents\\n\\n # Check index_directory and expand it if it is a relative path\\n\\n index_directory = self.resolve_path(index_directory)\\n\\n if documents is not None and embedding is not None:\\n if len(documents) == 0:\\n raise ValueError(\\n \\\"If documents are provided, there must be at least one document.\\\"\\n )\\n chroma = Chroma.from_documents(\\n documents=documents, # type: ignore\\n persist_directory=index_directory,\\n collection_name=collection_name,\\n embedding=embedding,\\n client_settings=chroma_settings,\\n )\\n else:\\n chroma = Chroma(\\n persist_directory=index_directory,\\n client_settings=chroma_settings,\\n embedding_function=embedding,\\n )\\n return chroma\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":true,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"collection_name\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"langflow\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"collection_name\",\"display_name\":\"Collection Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"index_directory\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"index_directory\",\"display_name\":\"Persist Directory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Implementation of Vector Store using Chroma\",\"icon\":\"Chroma\",\"base_classes\":[\"Runnable\",\"Generic\",\"VectorStore\",\"RunnableSerializable\",\"Serializable\",\"object\",\"BaseRetriever\"],\"display_name\":\"Chroma\",\"documentation\":\"https://python.langchain.com/docs/integrations/vectorstores/chroma\",\"custom_fields\":{\"collection_name\":null,\"embedding\":null,\"chroma_server_ssl_enabled\":null,\"index_directory\":null,\"documents\":null,\"chroma_server_cors_allow_origins\":null,\"chroma_server_host\":null,\"chroma_server_port\":null,\"chroma_server_grpc_port\":null},\"output_types\":[\"VectorStore\",\"BaseRetriever\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"SupabaseVectorStore\":{\"template\":{\"documents\":{\"type\":\"Document\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"documents\",\"display_name\":\"Documents\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"embedding\":{\"type\":\"Embeddings\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"embedding\",\"display_name\":\"Embedding\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import List, Union\\n\\nfrom langchain.schema import BaseRetriever\\nfrom langchain_community.vectorstores import VectorStore\\nfrom langchain_community.vectorstores.supabase import SupabaseVectorStore\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Document, Embeddings, NestedDict\\nfrom supabase.client import Client, create_client\\n\\n\\nclass SupabaseComponent(CustomComponent):\\n display_name = \\\"Supabase\\\"\\n description = \\\"Return VectorStore initialized from texts and embeddings.\\\"\\n\\n def build_config(self):\\n return {\\n \\\"documents\\\": {\\\"display_name\\\": \\\"Documents\\\"},\\n \\\"embedding\\\": {\\\"display_name\\\": \\\"Embedding\\\"},\\n \\\"query_name\\\": {\\\"display_name\\\": \\\"Query Name\\\"},\\n \\\"search_kwargs\\\": {\\\"display_name\\\": \\\"Search Kwargs\\\", \\\"advanced\\\": True},\\n \\\"supabase_service_key\\\": {\\\"display_name\\\": \\\"Supabase Service Key\\\"},\\n \\\"supabase_url\\\": {\\\"display_name\\\": \\\"Supabase URL\\\"},\\n \\\"table_name\\\": {\\\"display_name\\\": \\\"Table Name\\\", \\\"advanced\\\": True},\\n }\\n\\n def build(\\n self,\\n embedding: Embeddings,\\n documents: List[Document],\\n query_name: str = \\\"\\\",\\n search_kwargs: NestedDict = {},\\n supabase_service_key: str = \\\"\\\",\\n supabase_url: str = \\\"\\\",\\n table_name: str = \\\"\\\",\\n ) -> Union[VectorStore, SupabaseVectorStore, BaseRetriever]:\\n supabase: Client = create_client(supabase_url, supabase_key=supabase_service_key)\\n return SupabaseVectorStore.from_documents(\\n documents=documents,\\n embedding=embedding,\\n query_name=query_name,\\n search_kwargs=search_kwargs,\\n client=supabase,\\n table_name=table_name,\\n )\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"query_name\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"query_name\",\"display_name\":\"Query Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"search_kwargs\":{\"type\":\"NestedDict\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"search_kwargs\",\"display_name\":\"Search Kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"supabase_service_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"supabase_service_key\",\"display_name\":\"Supabase Service Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"supabase_url\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"supabase_url\",\"display_name\":\"Supabase URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"table_name\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"table_name\",\"display_name\":\"Table Name\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Return VectorStore initialized from texts and embeddings.\",\"base_classes\":[\"Runnable\",\"Generic\",\"VectorStore\",\"SupabaseVectorStore\",\"RunnableSerializable\",\"Serializable\",\"object\",\"BaseRetriever\"],\"display_name\":\"Supabase\",\"documentation\":\"\",\"custom_fields\":{\"embedding\":null,\"documents\":null,\"query_name\":null,\"search_kwargs\":null,\"supabase_service_key\":null,\"supabase_url\":null,\"table_name\":null},\"output_types\":[\"VectorStore\",\"SupabaseVectorStore\",\"BaseRetriever\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"Redis\":{\"template\":{\"documents\":{\"type\":\"Document\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"documents\",\"display_name\":\"Documents\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"embedding\":{\"type\":\"Embeddings\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"embedding\",\"display_name\":\"Embedding\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional, Union\\n\\nfrom langchain.embeddings.base import Embeddings\\nfrom langchain_community.vectorstores import VectorStore\\nfrom langchain_community.vectorstores.redis import Redis\\nfrom langchain_core.documents import Document\\nfrom langchain_core.retrievers import BaseRetriever\\nfrom langflow import CustomComponent\\n\\n\\nclass RedisComponent(CustomComponent):\\n \\\"\\\"\\\"\\n A custom component for implementing a Vector Store using Redis.\\n \\\"\\\"\\\"\\n\\n display_name: str = \\\"Redis\\\"\\n description: str = \\\"Implementation of Vector Store using Redis\\\"\\n documentation = \\\"https://python.langchain.com/docs/integrations/vectorstores/redis\\\"\\n beta = True\\n\\n def build_config(self):\\n \\\"\\\"\\\"\\n Builds the configuration for the component.\\n\\n Returns:\\n - dict: A dictionary containing the configuration options for the component.\\n \\\"\\\"\\\"\\n return {\\n \\\"index_name\\\": {\\\"display_name\\\": \\\"Index Name\\\", \\\"value\\\": \\\"your_index\\\"},\\n \\\"code\\\": {\\\"show\\\": False, \\\"display_name\\\": \\\"Code\\\"},\\n \\\"documents\\\": {\\\"display_name\\\": \\\"Documents\\\", \\\"is_list\\\": True},\\n \\\"embedding\\\": {\\\"display_name\\\": \\\"Embedding\\\"},\\n \\\"schema\\\": {\\\"display_name\\\": \\\"Schema\\\", \\\"file_types\\\": [\\\".yaml\\\"]},\\n \\\"redis_server_url\\\": {\\n \\\"display_name\\\": \\\"Redis Server Connection String\\\",\\n \\\"advanced\\\": False,\\n },\\n \\\"redis_index_name\\\": {\\\"display_name\\\": \\\"Redis Index\\\", \\\"advanced\\\": False},\\n }\\n\\n def build(\\n self,\\n embedding: Embeddings,\\n redis_server_url: str,\\n redis_index_name: str,\\n schema: Optional[str] = None,\\n documents: Optional[Document] = None,\\n ) -> Union[VectorStore, BaseRetriever]:\\n \\\"\\\"\\\"\\n Builds the Vector Store or BaseRetriever object.\\n\\n Args:\\n - embedding (Embeddings): The embeddings to use for the Vector Store.\\n - documents (Optional[Document]): The documents to use for the Vector Store.\\n - redis_index_name (str): The name of the Redis index.\\n - redis_server_url (str): The URL for the Redis server.\\n\\n Returns:\\n - VectorStore: The Vector Store object.\\n \\\"\\\"\\\"\\n if documents is None:\\n if schema is None:\\n raise ValueError(\\\"If no documents are provided, a schema must be provided.\\\")\\n redis_vs = Redis.from_existing_index(\\n embedding=embedding,\\n index_name=redis_index_name,\\n schema=schema,\\n key_prefix=None,\\n redis_url=redis_server_url,\\n )\\n else:\\n redis_vs = Redis.from_documents(\\n documents=documents, # type: ignore\\n embedding=embedding,\\n redis_url=redis_server_url,\\n index_name=redis_index_name,\\n )\\n return redis_vs\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"redis_index_name\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"redis_index_name\",\"display_name\":\"Redis Index\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"redis_server_url\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"redis_server_url\",\"display_name\":\"Redis Server Connection String\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"schema\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[\".yaml\"],\"file_path\":\"\",\"password\":false,\"name\":\"schema\",\"display_name\":\"Schema\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Implementation of Vector Store using Redis\",\"base_classes\":[\"Runnable\",\"Generic\",\"VectorStore\",\"RunnableSerializable\",\"Serializable\",\"object\",\"BaseRetriever\"],\"display_name\":\"Redis\",\"documentation\":\"https://python.langchain.com/docs/integrations/vectorstores/redis\",\"custom_fields\":{\"embedding\":null,\"redis_server_url\":null,\"redis_index_name\":null,\"schema\":null,\"documents\":null},\"output_types\":[\"VectorStore\",\"BaseRetriever\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"pgvector\":{\"template\":{\"documents\":{\"type\":\"Document\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"documents\",\"display_name\":\"Documents\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"embedding\":{\"type\":\"Embeddings\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"embedding\",\"display_name\":\"Embedding\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional, Union\\n\\nfrom langchain.embeddings.base import Embeddings\\nfrom langchain_community.vectorstores import VectorStore\\nfrom langchain_community.vectorstores.pgvector import PGVector\\nfrom langchain_core.documents import Document\\nfrom langchain_core.retrievers import BaseRetriever\\nfrom langflow import CustomComponent\\n\\n\\nclass PGVectorComponent(CustomComponent):\\n \\\"\\\"\\\"\\n A custom component for implementing a Vector Store using PostgreSQL.\\n \\\"\\\"\\\"\\n\\n display_name: str = \\\"PGVector\\\"\\n description: str = \\\"Implementation of Vector Store using PostgreSQL\\\"\\n documentation = \\\"https://python.langchain.com/docs/integrations/vectorstores/pgvector\\\"\\n\\n def build_config(self):\\n \\\"\\\"\\\"\\n Builds the configuration for the component.\\n\\n Returns:\\n - dict: A dictionary containing the configuration options for the component.\\n \\\"\\\"\\\"\\n return {\\n \\\"code\\\": {\\\"show\\\": False},\\n \\\"documents\\\": {\\\"display_name\\\": \\\"Documents\\\", \\\"is_list\\\": True},\\n \\\"embedding\\\": {\\\"display_name\\\": \\\"Embedding\\\"},\\n \\\"pg_server_url\\\": {\\n \\\"display_name\\\": \\\"PostgreSQL Server Connection String\\\",\\n \\\"advanced\\\": False,\\n },\\n \\\"collection_name\\\": {\\\"display_name\\\": \\\"Table\\\", \\\"advanced\\\": False},\\n }\\n\\n def build(\\n self,\\n embedding: Embeddings,\\n pg_server_url: str,\\n collection_name: str,\\n documents: Optional[Document] = None,\\n ) -> Union[VectorStore, BaseRetriever]:\\n \\\"\\\"\\\"\\n Builds the Vector Store or BaseRetriever object.\\n\\n Args:\\n - embedding (Embeddings): The embeddings to use for the Vector Store.\\n - documents (Optional[Document]): The documents to use for the Vector Store.\\n - collection_name (str): The name of the PG table.\\n - pg_server_url (str): The URL for the PG server.\\n\\n Returns:\\n - VectorStore: The Vector Store object.\\n \\\"\\\"\\\"\\n\\n try:\\n if documents is None:\\n vector_store = PGVector.from_existing_index(\\n embedding=embedding,\\n collection_name=collection_name,\\n connection_string=pg_server_url,\\n )\\n else:\\n vector_store = PGVector.from_documents(\\n embedding=embedding,\\n documents=documents, # type: ignore\\n collection_name=collection_name,\\n connection_string=pg_server_url,\\n )\\n except Exception as e:\\n raise RuntimeError(f\\\"Failed to build PGVector: {e}\\\")\\n return vector_store\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"collection_name\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"collection_name\",\"display_name\":\"Table\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"pg_server_url\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"pg_server_url\",\"display_name\":\"PostgreSQL Server Connection String\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Implementation of Vector Store using PostgreSQL\",\"base_classes\":[\"Runnable\",\"Generic\",\"VectorStore\",\"RunnableSerializable\",\"Serializable\",\"object\",\"BaseRetriever\"],\"display_name\":\"PGVector\",\"documentation\":\"https://python.langchain.com/docs/integrations/vectorstores/pgvector\",\"custom_fields\":{\"embedding\":null,\"pg_server_url\":null,\"collection_name\":null,\"documents\":null},\"output_types\":[\"VectorStore\",\"BaseRetriever\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"Pinecone\":{\"template\":{\"documents\":{\"type\":\"Document\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"documents\",\"display_name\":\"Documents\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"embedding\":{\"type\":\"Embeddings\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"embedding\",\"display_name\":\"Embedding\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"import os\\nfrom typing import List, Optional, Union\\n\\nimport pinecone # type: ignore\\nfrom langchain.schema import BaseRetriever\\nfrom langchain_community.vectorstores import VectorStore\\nfrom langchain_community.vectorstores.pinecone import Pinecone\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Document, Embeddings\\n\\n\\nclass PineconeComponent(CustomComponent):\\n display_name = \\\"Pinecone\\\"\\n description = \\\"Construct Pinecone wrapper from raw documents.\\\"\\n\\n def build_config(self):\\n return {\\n \\\"documents\\\": {\\\"display_name\\\": \\\"Documents\\\"},\\n \\\"embedding\\\": {\\\"display_name\\\": \\\"Embedding\\\"},\\n \\\"index_name\\\": {\\\"display_name\\\": \\\"Index Name\\\"},\\n \\\"namespace\\\": {\\\"display_name\\\": \\\"Namespace\\\"},\\n \\\"pinecone_api_key\\\": {\\\"display_name\\\": \\\"Pinecone API Key\\\", \\\"default\\\": \\\"\\\", \\\"password\\\": True, \\\"required\\\": True},\\n \\\"pinecone_env\\\": {\\\"display_name\\\": \\\"Pinecone Environment\\\", \\\"default\\\": \\\"\\\", \\\"required\\\": True},\\n \\\"search_kwargs\\\": {\\\"display_name\\\": \\\"Search Kwargs\\\", \\\"default\\\": \\\"{}\\\"},\\n \\\"pool_threads\\\": {\\\"display_name\\\": \\\"Pool Threads\\\", \\\"default\\\": 1, \\\"advanced\\\": True},\\n }\\n\\n def build(\\n self,\\n embedding: Embeddings,\\n pinecone_env: str,\\n documents: List[Document],\\n text_key: str = \\\"text\\\",\\n pool_threads: int = 4,\\n index_name: Optional[str] = None,\\n pinecone_api_key: Optional[str] = None,\\n namespace: Optional[str] = \\\"default\\\",\\n ) -> Union[VectorStore, Pinecone, BaseRetriever]:\\n if pinecone_api_key is None or pinecone_env is None:\\n raise ValueError(\\\"Pinecone API Key and Environment are required.\\\")\\n if os.getenv(\\\"PINECONE_API_KEY\\\") is None and pinecone_api_key is None:\\n raise ValueError(\\\"Pinecone API Key is required.\\\")\\n\\n pinecone.init(api_key=pinecone_api_key, environment=pinecone_env) # type: ignore\\n if not index_name:\\n raise ValueError(\\\"Index Name is required.\\\")\\n if documents:\\n return Pinecone.from_documents(\\n documents=documents,\\n embedding=embedding,\\n index_name=index_name,\\n pool_threads=pool_threads,\\n namespace=namespace,\\n text_key=text_key,\\n )\\n\\n return Pinecone.from_existing_index(\\n index_name=index_name,\\n embedding=embedding,\\n text_key=text_key,\\n namespace=namespace,\\n pool_threads=pool_threads,\\n )\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"index_name\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"index_name\",\"display_name\":\"Index Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"namespace\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"default\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"namespace\",\"display_name\":\"Namespace\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"pinecone_api_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"pinecone_api_key\",\"display_name\":\"Pinecone API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"pinecone_env\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"pinecone_env\",\"display_name\":\"Pinecone Environment\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"pool_threads\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":4,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"pool_threads\",\"display_name\":\"Pool Threads\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"text_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"text\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"text_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Construct Pinecone wrapper from raw documents.\",\"base_classes\":[\"Runnable\",\"Generic\",\"VectorStore\",\"Pinecone\",\"RunnableSerializable\",\"Serializable\",\"object\",\"BaseRetriever\"],\"display_name\":\"Pinecone\",\"documentation\":\"\",\"custom_fields\":{\"embedding\":null,\"pinecone_env\":null,\"documents\":null,\"text_key\":null,\"pool_threads\":null,\"index_name\":null,\"pinecone_api_key\":null,\"namespace\":null},\"output_types\":[\"VectorStore\",\"Pinecone\",\"BaseRetriever\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"Qdrant\":{\"template\":{\"documents\":{\"type\":\"Document\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"documents\",\"display_name\":\"Documents\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"embedding\":{\"type\":\"Embeddings\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"embedding\",\"display_name\":\"Embedding\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"api_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"api_key\",\"display_name\":\"API Key\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional, Union\\n\\nfrom langchain.schema import BaseRetriever\\nfrom langchain_community.vectorstores import VectorStore\\nfrom langchain_community.vectorstores.qdrant import Qdrant\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Document, Embeddings, NestedDict\\n\\n\\nclass QdrantComponent(CustomComponent):\\n display_name = \\\"Qdrant\\\"\\n description = \\\"Construct Qdrant wrapper from a list of texts.\\\"\\n\\n def build_config(self):\\n return {\\n \\\"documents\\\": {\\\"display_name\\\": \\\"Documents\\\"},\\n \\\"embedding\\\": {\\\"display_name\\\": \\\"Embedding\\\"},\\n \\\"api_key\\\": {\\\"display_name\\\": \\\"API Key\\\", \\\"password\\\": True, \\\"advanced\\\": True},\\n \\\"collection_name\\\": {\\\"display_name\\\": \\\"Collection Name\\\"},\\n \\\"content_payload_key\\\": {\\\"display_name\\\": \\\"Content Payload Key\\\", \\\"advanced\\\": True},\\n \\\"distance_func\\\": {\\\"display_name\\\": \\\"Distance Function\\\", \\\"advanced\\\": True},\\n \\\"grpc_port\\\": {\\\"display_name\\\": \\\"gRPC Port\\\", \\\"advanced\\\": True},\\n \\\"host\\\": {\\\"display_name\\\": \\\"Host\\\", \\\"advanced\\\": True},\\n \\\"https\\\": {\\\"display_name\\\": \\\"HTTPS\\\", \\\"advanced\\\": True},\\n \\\"location\\\": {\\\"display_name\\\": \\\"Location\\\", \\\"advanced\\\": True},\\n \\\"metadata_payload_key\\\": {\\\"display_name\\\": \\\"Metadata Payload Key\\\", \\\"advanced\\\": True},\\n \\\"path\\\": {\\\"display_name\\\": \\\"Path\\\", \\\"advanced\\\": True},\\n \\\"port\\\": {\\\"display_name\\\": \\\"Port\\\", \\\"advanced\\\": True},\\n \\\"prefer_grpc\\\": {\\\"display_name\\\": \\\"Prefer gRPC\\\", \\\"advanced\\\": True},\\n \\\"prefix\\\": {\\\"display_name\\\": \\\"Prefix\\\", \\\"advanced\\\": True},\\n \\\"search_kwargs\\\": {\\\"display_name\\\": \\\"Search Kwargs\\\", \\\"advanced\\\": True},\\n \\\"timeout\\\": {\\\"display_name\\\": \\\"Timeout\\\", \\\"advanced\\\": True},\\n \\\"url\\\": {\\\"display_name\\\": \\\"URL\\\", \\\"advanced\\\": True},\\n }\\n\\n def build(\\n self,\\n embedding: Embeddings,\\n collection_name: str,\\n documents: Optional[Document] = None,\\n api_key: Optional[str] = None,\\n content_payload_key: str = \\\"page_content\\\",\\n distance_func: str = \\\"Cosine\\\",\\n grpc_port: int = 6334,\\n https: bool = False,\\n host: Optional[str] = None,\\n location: Optional[str] = None,\\n metadata_payload_key: str = \\\"metadata\\\",\\n path: Optional[str] = None,\\n port: Optional[int] = 6333,\\n prefer_grpc: bool = False,\\n prefix: Optional[str] = None,\\n search_kwargs: Optional[NestedDict] = None,\\n timeout: Optional[int] = None,\\n url: Optional[str] = None,\\n ) -> Union[VectorStore, Qdrant, BaseRetriever]:\\n if documents is None:\\n from qdrant_client import QdrantClient\\n\\n client = QdrantClient(\\n location=location,\\n url=host,\\n port=port,\\n grpc_port=grpc_port,\\n https=https,\\n prefix=prefix,\\n timeout=timeout,\\n prefer_grpc=prefer_grpc,\\n metadata_payload_key=metadata_payload_key,\\n content_payload_key=content_payload_key,\\n api_key=api_key,\\n collection_name=collection_name,\\n host=host,\\n path=path,\\n )\\n vs = Qdrant(\\n client=client,\\n collection_name=collection_name,\\n embeddings=embedding,\\n )\\n return vs\\n else:\\n vs = Qdrant.from_documents(\\n documents=documents, # type: ignore\\n embedding=embedding,\\n api_key=api_key,\\n collection_name=collection_name,\\n content_payload_key=content_payload_key,\\n distance_func=distance_func,\\n grpc_port=grpc_port,\\n host=host,\\n https=https,\\n location=location,\\n metadata_payload_key=metadata_payload_key,\\n path=path,\\n port=port,\\n prefer_grpc=prefer_grpc,\\n prefix=prefix,\\n search_kwargs=search_kwargs,\\n timeout=timeout,\\n url=url,\\n )\\n return vs\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"collection_name\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"collection_name\",\"display_name\":\"Collection Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"content_payload_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"page_content\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"content_payload_key\",\"display_name\":\"Content Payload Key\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"distance_func\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"Cosine\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"distance_func\",\"display_name\":\"Distance Function\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"grpc_port\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":6334,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"grpc_port\",\"display_name\":\"gRPC Port\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"host\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"host\",\"display_name\":\"Host\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"https\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"https\",\"display_name\":\"HTTPS\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"location\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"location\",\"display_name\":\"Location\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"metadata_payload_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"metadata\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata_payload_key\",\"display_name\":\"Metadata Payload Key\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"path\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"path\",\"display_name\":\"Path\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"port\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":6333,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"port\",\"display_name\":\"Port\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"prefer_grpc\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"prefer_grpc\",\"display_name\":\"Prefer gRPC\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"prefix\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"prefix\",\"display_name\":\"Prefix\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"search_kwargs\":{\"type\":\"NestedDict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"search_kwargs\",\"display_name\":\"Search Kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"timeout\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"timeout\",\"display_name\":\"Timeout\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"url\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"url\",\"display_name\":\"URL\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Construct Qdrant wrapper from a list of texts.\",\"base_classes\":[\"Runnable\",\"Generic\",\"VectorStore\",\"Qdrant\",\"RunnableSerializable\",\"Serializable\",\"object\",\"BaseRetriever\"],\"display_name\":\"Qdrant\",\"documentation\":\"\",\"custom_fields\":{\"embedding\":null,\"collection_name\":null,\"documents\":null,\"api_key\":null,\"content_payload_key\":null,\"distance_func\":null,\"grpc_port\":null,\"https\":null,\"host\":null,\"location\":null,\"metadata_payload_key\":null,\"path\":null,\"port\":null,\"prefer_grpc\":null,\"prefix\":null,\"search_kwargs\":null,\"timeout\":null,\"url\":null},\"output_types\":[\"VectorStore\",\"Qdrant\",\"BaseRetriever\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"MongoDBAtlasVectorSearch\":{\"template\":{\"documents\":{\"type\":\"Document\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"documents\",\"display_name\":\"Documents\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"embedding\":{\"type\":\"Embeddings\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"embedding\",\"display_name\":\"Embedding\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import List, Optional\\n\\nfrom langchain_community.vectorstores import MongoDBAtlasVectorSearch\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import (\\n Document,\\n Embeddings,\\n NestedDict,\\n)\\n\\n\\nclass MongoDBAtlasComponent(CustomComponent):\\n display_name = \\\"MongoDB Atlas\\\"\\n description = \\\"Construct a `MongoDB Atlas Vector Search` vector store from raw documents.\\\"\\n\\n def build_config(self):\\n return {\\n \\\"documents\\\": {\\\"display_name\\\": \\\"Documents\\\"},\\n \\\"embedding\\\": {\\\"display_name\\\": \\\"Embedding\\\"},\\n \\\"collection_name\\\": {\\\"display_name\\\": \\\"Collection Name\\\"},\\n \\\"db_name\\\": {\\\"display_name\\\": \\\"Database Name\\\"},\\n \\\"index_name\\\": {\\\"display_name\\\": \\\"Index Name\\\"},\\n \\\"mongodb_atlas_cluster_uri\\\": {\\\"display_name\\\": \\\"MongoDB Atlas Cluster URI\\\"},\\n \\\"search_kwargs\\\": {\\\"display_name\\\": \\\"Search Kwargs\\\", \\\"advanced\\\": True},\\n }\\n\\n def build(\\n self,\\n documents: List[Document],\\n embedding: Embeddings,\\n collection_name: str = \\\"\\\",\\n db_name: str = \\\"\\\",\\n index_name: str = \\\"\\\",\\n mongodb_atlas_cluster_uri: str = \\\"\\\",\\n search_kwargs: Optional[NestedDict] = None,\\n ) -> MongoDBAtlasVectorSearch:\\n search_kwargs = search_kwargs or {}\\n return MongoDBAtlasVectorSearch(\\n documents=documents,\\n embedding=embedding,\\n collection_name=collection_name,\\n db_name=db_name,\\n index_name=index_name,\\n mongodb_atlas_cluster_uri=mongodb_atlas_cluster_uri,\\n search_kwargs=search_kwargs,\\n )\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"collection_name\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"collection_name\",\"display_name\":\"Collection Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"db_name\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"db_name\",\"display_name\":\"Database Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"index_name\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"index_name\",\"display_name\":\"Index Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"mongodb_atlas_cluster_uri\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"mongodb_atlas_cluster_uri\",\"display_name\":\"MongoDB Atlas Cluster URI\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"search_kwargs\":{\"type\":\"NestedDict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"search_kwargs\",\"display_name\":\"Search Kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Construct a `MongoDB Atlas Vector Search` vector store from raw documents.\",\"base_classes\":[\"VectorStore\",\"MongoDBAtlasVectorSearch\"],\"display_name\":\"MongoDB Atlas\",\"documentation\":\"\",\"custom_fields\":{\"documents\":null,\"embedding\":null,\"collection_name\":null,\"db_name\":null,\"index_name\":null,\"mongodb_atlas_cluster_uri\":null,\"search_kwargs\":null},\"output_types\":[\"MongoDBAtlasVectorSearch\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"ChromaSearch\":{\"template\":{\"embedding\":{\"type\":\"Embeddings\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"embedding\",\"display_name\":\"Embedding\",\"advanced\":false,\"dynamic\":false,\"info\":\"Embedding model to vectorize inputs (make sure to use same as index)\",\"title_case\":false},\"inputs\":{\"type\":\"Text\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"inputs\",\"display_name\":\"Input\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"chroma_server_cors_allow_origins\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"chroma_server_cors_allow_origins\",\"display_name\":\"Server CORS Allow Origins\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"chroma_server_grpc_port\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"chroma_server_grpc_port\",\"display_name\":\"Server gRPC Port\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"chroma_server_host\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"chroma_server_host\",\"display_name\":\"Server Host\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"chroma_server_port\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"chroma_server_port\",\"display_name\":\"Server Port\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"chroma_server_ssl_enabled\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"chroma_server_ssl_enabled\",\"display_name\":\"Server SSL Enabled\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import List, Optional\\n\\nimport chromadb # type: ignore\\nfrom langchain_community.vectorstores.chroma import Chroma\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Embeddings, Text\\nfrom langflow.schema import Record, docs_to_records\\n\\n\\nclass ChromaSearchComponent(CustomComponent):\\n \\\"\\\"\\\"\\n A custom component for implementing a Vector Store using Chroma.\\n \\\"\\\"\\\"\\n\\n display_name: str = \\\"Chroma Search\\\"\\n description: str = \\\"Search a Chroma collection for similar documents.\\\"\\n beta: bool = True\\n icon = \\\"Chroma\\\"\\n\\n def build_config(self):\\n \\\"\\\"\\\"\\n Builds the configuration for the component.\\n\\n Returns:\\n - dict: A dictionary containing the configuration options for the component.\\n \\\"\\\"\\\"\\n return {\\n \\\"inputs\\\": {\\\"display_name\\\": \\\"Input\\\"},\\n \\\"search_type\\\": {\\n \\\"display_name\\\": \\\"Search Type\\\",\\n \\\"options\\\": [\\\"Similarity\\\", \\\"MMR\\\"],\\n },\\n \\\"collection_name\\\": {\\\"display_name\\\": \\\"Collection Name\\\", \\\"value\\\": \\\"langflow\\\"},\\n # \\\"persist\\\": {\\\"display_name\\\": \\\"Persist\\\"},\\n \\\"index_directory\\\": {\\\"display_name\\\": \\\"Index Directory\\\"},\\n \\\"code\\\": {\\\"show\\\": False, \\\"display_name\\\": \\\"Code\\\"},\\n \\\"documents\\\": {\\\"display_name\\\": \\\"Documents\\\", \\\"is_list\\\": True},\\n \\\"embedding\\\": {\\n \\\"display_name\\\": \\\"Embedding\\\",\\n \\\"info\\\": \\\"Embedding model to vectorize inputs (make sure to use same as index)\\\",\\n },\\n \\\"chroma_server_cors_allow_origins\\\": {\\n \\\"display_name\\\": \\\"Server CORS Allow Origins\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"chroma_server_host\\\": {\\\"display_name\\\": \\\"Server Host\\\", \\\"advanced\\\": True},\\n \\\"chroma_server_port\\\": {\\\"display_name\\\": \\\"Server Port\\\", \\\"advanced\\\": True},\\n \\\"chroma_server_grpc_port\\\": {\\n \\\"display_name\\\": \\\"Server gRPC Port\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"chroma_server_ssl_enabled\\\": {\\n \\\"display_name\\\": \\\"Server SSL Enabled\\\",\\n \\\"advanced\\\": True,\\n },\\n }\\n\\n def build(\\n self,\\n inputs: Text,\\n search_type: str,\\n collection_name: str,\\n embedding: Embeddings,\\n chroma_server_ssl_enabled: bool,\\n index_directory: Optional[str] = None,\\n chroma_server_cors_allow_origins: Optional[str] = None,\\n chroma_server_host: Optional[str] = None,\\n chroma_server_port: Optional[int] = None,\\n chroma_server_grpc_port: Optional[int] = None,\\n ) -> List[Record]:\\n \\\"\\\"\\\"\\n Builds the Vector Store or BaseRetriever object.\\n\\n Args:\\n - collection_name (str): The name of the collection.\\n - persist_directory (Optional[str]): The directory to persist the Vector Store to.\\n - chroma_server_ssl_enabled (bool): Whether to enable SSL for the Chroma server.\\n - persist (bool): Whether to persist the Vector Store or not.\\n - embedding (Optional[Embeddings]): The embeddings to use for the Vector Store.\\n - documents (Optional[Document]): The documents to use for the Vector Store.\\n - chroma_server_cors_allow_origins (Optional[str]): The CORS allow origins for the Chroma server.\\n - chroma_server_host (Optional[str]): The host for the Chroma server.\\n - chroma_server_port (Optional[int]): The port for the Chroma server.\\n - chroma_server_grpc_port (Optional[int]): The gRPC port for the Chroma server.\\n\\n Returns:\\n - Union[VectorStore, BaseRetriever]: The Vector Store or BaseRetriever object.\\n \\\"\\\"\\\"\\n\\n # Chroma settings\\n chroma_settings = None\\n\\n if chroma_server_host is not None:\\n chroma_settings = chromadb.config.Settings(\\n chroma_server_cors_allow_origins=chroma_server_cors_allow_origins or None,\\n chroma_server_host=chroma_server_host,\\n chroma_server_port=chroma_server_port or None,\\n chroma_server_grpc_port=chroma_server_grpc_port or None,\\n chroma_server_ssl_enabled=chroma_server_ssl_enabled,\\n )\\n index_directory = self.resolve_path(index_directory)\\n chroma = Chroma(\\n embedding_function=embedding,\\n collection_name=collection_name,\\n persist_directory=index_directory,\\n client_settings=chroma_settings,\\n )\\n\\n # Validate the inputs\\n docs = []\\n if inputs and isinstance(inputs, str):\\n docs = chroma.search(query=inputs, search_type=search_type.lower())\\n else:\\n raise ValueError(\\\"Invalid inputs provided.\\\")\\n return docs_to_records(docs)\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"collection_name\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"langflow\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"collection_name\",\"display_name\":\"Collection Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"index_directory\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"index_directory\",\"display_name\":\"Index Directory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"search_type\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"Similarity\",\"MMR\"],\"name\":\"search_type\",\"display_name\":\"Search Type\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Search a Chroma collection for similar documents.\",\"icon\":\"Chroma\",\"base_classes\":[\"Record\"],\"display_name\":\"Chroma Search\",\"documentation\":\"\",\"custom_fields\":{\"inputs\":null,\"search_type\":null,\"collection_name\":null,\"embedding\":null,\"chroma_server_ssl_enabled\":null,\"index_directory\":null,\"chroma_server_cors_allow_origins\":null,\"chroma_server_host\":null,\"chroma_server_port\":null,\"chroma_server_grpc_port\":null},\"output_types\":[\"Record\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"FAISS\":{\"template\":{\"documents\":{\"type\":\"Document\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"documents\",\"display_name\":\"Documents\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"embedding\":{\"type\":\"Embeddings\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"embedding\",\"display_name\":\"Embedding\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import List, Union\\n\\nfrom langchain.schema import BaseRetriever\\nfrom langchain_community.vectorstores import VectorStore\\nfrom langchain_community.vectorstores.faiss import FAISS\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Document, Embeddings\\n\\n\\nclass FAISSComponent(CustomComponent):\\n display_name = \\\"FAISS\\\"\\n description = \\\"Construct FAISS wrapper from raw documents.\\\"\\n documentation = \\\"https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/faiss\\\"\\n\\n def build_config(self):\\n return {\\n \\\"documents\\\": {\\\"display_name\\\": \\\"Documents\\\"},\\n \\\"embedding\\\": {\\\"display_name\\\": \\\"Embedding\\\"},\\n }\\n\\n def build(\\n self,\\n embedding: Embeddings,\\n documents: List[Document],\\n ) -> Union[VectorStore, FAISS, BaseRetriever]:\\n return FAISS.from_documents(documents=documents, embedding=embedding)\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Construct FAISS wrapper from raw documents.\",\"base_classes\":[\"Runnable\",\"FAISS\",\"Generic\",\"VectorStore\",\"RunnableSerializable\",\"Serializable\",\"object\",\"BaseRetriever\"],\"display_name\":\"FAISS\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/faiss\",\"custom_fields\":{\"embedding\":null,\"documents\":null},\"output_types\":[\"VectorStore\",\"FAISS\",\"BaseRetriever\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true}},\"models\":{\"LlamaCppModel\":{\"template\":{\"metadata\":{\"type\":\"Dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"model_kwargs\":{\"type\":\"Dict\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model_kwargs\",\"display_name\":\"Model Kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"model_path\":{\"type\":\"file\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[\".bin\"],\"file_path\":\"\",\"password\":false,\"name\":\"model_path\",\"display_name\":\"Model Path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"cache\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"cache\",\"display_name\":\"Cache\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"client\":{\"type\":\"Any\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"client\",\"display_name\":\"Client\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Any, Dict, List, Optional\\n\\nfrom langchain_community.llms.llamacpp import LlamaCpp\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Text\\n\\n\\nclass LlamaCppComponent(CustomComponent):\\n display_name = \\\"LlamaCppModel\\\"\\n description = \\\"Generate text using llama.cpp model.\\\"\\n documentation = \\\"https://python.langchain.com/docs/modules/model_io/models/llms/integrations/llamacpp\\\"\\n\\n def build_config(self):\\n return {\\n \\\"grammar\\\": {\\\"display_name\\\": \\\"Grammar\\\", \\\"advanced\\\": True},\\n \\\"cache\\\": {\\\"display_name\\\": \\\"Cache\\\", \\\"advanced\\\": True},\\n \\\"client\\\": {\\\"display_name\\\": \\\"Client\\\", \\\"advanced\\\": True},\\n \\\"echo\\\": {\\\"display_name\\\": \\\"Echo\\\", \\\"advanced\\\": True},\\n \\\"f16_kv\\\": {\\\"display_name\\\": \\\"F16 KV\\\", \\\"advanced\\\": True},\\n \\\"grammar_path\\\": {\\\"display_name\\\": \\\"Grammar Path\\\", \\\"advanced\\\": True},\\n \\\"last_n_tokens_size\\\": {\\n \\\"display_name\\\": \\\"Last N Tokens Size\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"logits_all\\\": {\\\"display_name\\\": \\\"Logits All\\\", \\\"advanced\\\": True},\\n \\\"logprobs\\\": {\\\"display_name\\\": \\\"Logprobs\\\", \\\"advanced\\\": True},\\n \\\"lora_base\\\": {\\\"display_name\\\": \\\"Lora Base\\\", \\\"advanced\\\": True},\\n \\\"lora_path\\\": {\\\"display_name\\\": \\\"Lora Path\\\", \\\"advanced\\\": True},\\n \\\"max_tokens\\\": {\\\"display_name\\\": \\\"Max Tokens\\\", \\\"advanced\\\": True},\\n \\\"metadata\\\": {\\\"display_name\\\": \\\"Metadata\\\", \\\"advanced\\\": True},\\n \\\"model_kwargs\\\": {\\\"display_name\\\": \\\"Model Kwargs\\\", \\\"advanced\\\": True},\\n \\\"model_path\\\": {\\n \\\"display_name\\\": \\\"Model Path\\\",\\n \\\"field_type\\\": \\\"file\\\",\\n \\\"file_types\\\": [\\\".bin\\\"],\\n \\\"required\\\": True,\\n },\\n \\\"n_batch\\\": {\\\"display_name\\\": \\\"N Batch\\\", \\\"advanced\\\": True},\\n \\\"n_ctx\\\": {\\\"display_name\\\": \\\"N Ctx\\\", \\\"advanced\\\": True},\\n \\\"n_gpu_layers\\\": {\\\"display_name\\\": \\\"N GPU Layers\\\", \\\"advanced\\\": True},\\n \\\"n_parts\\\": {\\\"display_name\\\": \\\"N Parts\\\", \\\"advanced\\\": True},\\n \\\"n_threads\\\": {\\\"display_name\\\": \\\"N Threads\\\", \\\"advanced\\\": True},\\n \\\"repeat_penalty\\\": {\\\"display_name\\\": \\\"Repeat Penalty\\\", \\\"advanced\\\": True},\\n \\\"rope_freq_base\\\": {\\\"display_name\\\": \\\"Rope Freq Base\\\", \\\"advanced\\\": True},\\n \\\"rope_freq_scale\\\": {\\\"display_name\\\": \\\"Rope Freq Scale\\\", \\\"advanced\\\": True},\\n \\\"seed\\\": {\\\"display_name\\\": \\\"Seed\\\", \\\"advanced\\\": True},\\n \\\"stop\\\": {\\\"display_name\\\": \\\"Stop\\\", \\\"advanced\\\": True},\\n \\\"streaming\\\": {\\\"display_name\\\": \\\"Streaming\\\", \\\"advanced\\\": True},\\n \\\"suffix\\\": {\\\"display_name\\\": \\\"Suffix\\\", \\\"advanced\\\": True},\\n \\\"tags\\\": {\\\"display_name\\\": \\\"Tags\\\", \\\"advanced\\\": True},\\n \\\"temperature\\\": {\\\"display_name\\\": \\\"Temperature\\\"},\\n \\\"top_k\\\": {\\\"display_name\\\": \\\"Top K\\\", \\\"advanced\\\": True},\\n \\\"top_p\\\": {\\\"display_name\\\": \\\"Top P\\\", \\\"advanced\\\": True},\\n \\\"use_mlock\\\": {\\\"display_name\\\": \\\"Use Mlock\\\", \\\"advanced\\\": True},\\n \\\"use_mmap\\\": {\\\"display_name\\\": \\\"Use Mmap\\\", \\\"advanced\\\": True},\\n \\\"verbose\\\": {\\\"display_name\\\": \\\"Verbose\\\", \\\"advanced\\\": True},\\n \\\"vocab_only\\\": {\\\"display_name\\\": \\\"Vocab Only\\\", \\\"advanced\\\": True},\\n \\\"inputs\\\": {\\\"display_name\\\": \\\"Input\\\"},\\n }\\n\\n def build(\\n self,\\n model_path: str,\\n inputs: str,\\n grammar: Optional[str] = None,\\n cache: Optional[bool] = None,\\n client: Optional[Any] = None,\\n echo: Optional[bool] = False,\\n f16_kv: bool = True,\\n grammar_path: Optional[str] = None,\\n last_n_tokens_size: Optional[int] = 64,\\n logits_all: bool = False,\\n logprobs: Optional[int] = None,\\n lora_base: Optional[str] = None,\\n lora_path: Optional[str] = None,\\n max_tokens: Optional[int] = 256,\\n metadata: Optional[Dict] = None,\\n model_kwargs: Dict = {},\\n n_batch: Optional[int] = 8,\\n n_ctx: int = 512,\\n n_gpu_layers: Optional[int] = 1,\\n n_parts: int = -1,\\n n_threads: Optional[int] = 1,\\n repeat_penalty: Optional[float] = 1.1,\\n rope_freq_base: float = 10000.0,\\n rope_freq_scale: float = 1.0,\\n seed: int = -1,\\n stop: Optional[List[str]] = [],\\n streaming: bool = True,\\n suffix: Optional[str] = \\\"\\\",\\n tags: Optional[List[str]] = [],\\n temperature: Optional[float] = 0.8,\\n top_k: Optional[int] = 40,\\n top_p: Optional[float] = 0.95,\\n use_mlock: bool = False,\\n use_mmap: Optional[bool] = True,\\n verbose: bool = True,\\n vocab_only: bool = False,\\n ) -> Text:\\n output = LlamaCpp(\\n model_path=model_path,\\n grammar=grammar,\\n cache=cache,\\n client=client,\\n echo=echo,\\n f16_kv=f16_kv,\\n grammar_path=grammar_path,\\n last_n_tokens_size=last_n_tokens_size,\\n logits_all=logits_all,\\n logprobs=logprobs,\\n lora_base=lora_base,\\n lora_path=lora_path,\\n max_tokens=max_tokens,\\n metadata=metadata,\\n model_kwargs=model_kwargs,\\n n_batch=n_batch,\\n n_ctx=n_ctx,\\n n_gpu_layers=n_gpu_layers,\\n n_parts=n_parts,\\n n_threads=n_threads,\\n repeat_penalty=repeat_penalty,\\n rope_freq_base=rope_freq_base,\\n rope_freq_scale=rope_freq_scale,\\n seed=seed,\\n stop=stop,\\n streaming=streaming,\\n suffix=suffix,\\n tags=tags,\\n temperature=temperature,\\n top_k=top_k,\\n top_p=top_p,\\n use_mlock=use_mlock,\\n use_mmap=use_mmap,\\n verbose=verbose,\\n vocab_only=vocab_only,\\n )\\n message = output.invoke(inputs)\\n result = message.content if hasattr(message, \\\"content\\\") else message\\n self.status = result\\n return result\\n self.status = result\\n return result\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"echo\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"echo\",\"display_name\":\"Echo\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"f16_kv\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":true,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"f16_kv\",\"display_name\":\"F16 KV\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"grammar\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"grammar\",\"display_name\":\"Grammar\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"grammar_path\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"grammar_path\",\"display_name\":\"Grammar Path\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"inputs\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"inputs\",\"display_name\":\"Input\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"last_n_tokens_size\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":64,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"last_n_tokens_size\",\"display_name\":\"Last N Tokens Size\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"logits_all\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"logits_all\",\"display_name\":\"Logits All\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"logprobs\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"logprobs\",\"display_name\":\"Logprobs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"lora_base\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"lora_base\",\"display_name\":\"Lora Base\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"lora_path\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"lora_path\",\"display_name\":\"Lora Path\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"max_tokens\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":256,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_tokens\",\"display_name\":\"Max Tokens\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"n_batch\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":8,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"n_batch\",\"display_name\":\"N Batch\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"n_ctx\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":512,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"n_ctx\",\"display_name\":\"N Ctx\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"n_gpu_layers\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":1,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"n_gpu_layers\",\"display_name\":\"N GPU Layers\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"n_parts\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":-1,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"n_parts\",\"display_name\":\"N Parts\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"n_threads\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":1,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"n_threads\",\"display_name\":\"N Threads\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"repeat_penalty\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":1.1,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"repeat_penalty\",\"display_name\":\"Repeat Penalty\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"rope_freq_base\":{\"type\":\"float\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":10000.0,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"rope_freq_base\",\"display_name\":\"Rope Freq Base\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"rope_freq_scale\":{\"type\":\"float\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":1.0,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"rope_freq_scale\",\"display_name\":\"Rope Freq Scale\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"seed\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":-1,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"seed\",\"display_name\":\"Seed\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"stop\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":[],\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"stop\",\"display_name\":\"Stop\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"streaming\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":true,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"streaming\",\"display_name\":\"Streaming\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"suffix\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"suffix\",\"display_name\":\"Suffix\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"tags\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":[],\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"tags\",\"display_name\":\"Tags\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"temperature\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.8,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"top_k\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":40,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_k\",\"display_name\":\"Top K\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"top_p\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.95,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_p\",\"display_name\":\"Top P\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"use_mlock\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"use_mlock\",\"display_name\":\"Use Mlock\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"use_mmap\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":true,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"use_mmap\",\"display_name\":\"Use Mmap\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"verbose\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":true,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"verbose\",\"display_name\":\"Verbose\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"vocab_only\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"vocab_only\",\"display_name\":\"Vocab Only\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Generate text using llama.cpp model.\",\"base_classes\":[\"Text\",\"object\"],\"display_name\":\"LlamaCppModel\",\"documentation\":\"https://python.langchain.com/docs/modules/model_io/models/llms/integrations/llamacpp\",\"custom_fields\":{\"model_path\":null,\"inputs\":null,\"grammar\":null,\"cache\":null,\"client\":null,\"echo\":null,\"f16_kv\":null,\"grammar_path\":null,\"last_n_tokens_size\":null,\"logits_all\":null,\"logprobs\":null,\"lora_base\":null,\"lora_path\":null,\"max_tokens\":null,\"metadata\":null,\"model_kwargs\":null,\"n_batch\":null,\"n_ctx\":null,\"n_gpu_layers\":null,\"n_parts\":null,\"n_threads\":null,\"repeat_penalty\":null,\"rope_freq_base\":null,\"rope_freq_scale\":null,\"seed\":null,\"stop\":null,\"streaming\":null,\"suffix\":null,\"tags\":null,\"temperature\":null,\"top_k\":null,\"top_p\":null,\"use_mlock\":null,\"use_mmap\":null,\"verbose\":null,\"vocab_only\":null},\"output_types\":[\"Text\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"BaiduQianfanChatModel\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain_community.chat_models.baidu_qianfan_endpoint import QianfanChatEndpoint\\nfrom pydantic.v1 import SecretStr\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Text\\n\\n\\nclass QianfanChatEndpointComponent(CustomComponent):\\n display_name: str = \\\"QianfanChat Model\\\"\\n description: str = (\\n \\\"Generate text using Baidu Qianfan chat models. Get more detail from \\\"\\n \\\"https://python.langchain.com/docs/integrations/chat/baidu_qianfan_endpoint.\\\"\\n )\\n\\n def build_config(self):\\n return {\\n \\\"model\\\": {\\n \\\"display_name\\\": \\\"Model Name\\\",\\n \\\"options\\\": [\\n \\\"ERNIE-Bot\\\",\\n \\\"ERNIE-Bot-turbo\\\",\\n \\\"BLOOMZ-7B\\\",\\n \\\"Llama-2-7b-chat\\\",\\n \\\"Llama-2-13b-chat\\\",\\n \\\"Llama-2-70b-chat\\\",\\n \\\"Qianfan-BLOOMZ-7B-compressed\\\",\\n \\\"Qianfan-Chinese-Llama-2-7B\\\",\\n \\\"ChatGLM2-6B-32K\\\",\\n \\\"AquilaChat-7B\\\",\\n ],\\n \\\"info\\\": \\\"https://python.langchain.com/docs/integrations/chat/baidu_qianfan_endpoint\\\",\\n \\\"required\\\": True,\\n },\\n \\\"qianfan_ak\\\": {\\n \\\"display_name\\\": \\\"Qianfan Ak\\\",\\n \\\"required\\\": True,\\n \\\"password\\\": True,\\n \\\"info\\\": \\\"which you could get from https://cloud.baidu.com/product/wenxinworkshop\\\",\\n },\\n \\\"qianfan_sk\\\": {\\n \\\"display_name\\\": \\\"Qianfan Sk\\\",\\n \\\"required\\\": True,\\n \\\"password\\\": True,\\n \\\"info\\\": \\\"which you could get from https://cloud.baidu.com/product/wenxinworkshop\\\",\\n },\\n \\\"top_p\\\": {\\n \\\"display_name\\\": \\\"Top p\\\",\\n \\\"field_type\\\": \\\"float\\\",\\n \\\"info\\\": \\\"Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo\\\",\\n \\\"value\\\": 0.8,\\n },\\n \\\"temperature\\\": {\\n \\\"display_name\\\": \\\"Temperature\\\",\\n \\\"field_type\\\": \\\"float\\\",\\n \\\"info\\\": \\\"Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo\\\",\\n \\\"value\\\": 0.95,\\n },\\n \\\"penalty_score\\\": {\\n \\\"display_name\\\": \\\"Penalty Score\\\",\\n \\\"field_type\\\": \\\"float\\\",\\n \\\"info\\\": \\\"Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo\\\",\\n \\\"value\\\": 1.0,\\n },\\n \\\"endpoint\\\": {\\n \\\"display_name\\\": \\\"Endpoint\\\",\\n \\\"info\\\": \\\"Endpoint of the Qianfan LLM, required if custom model used.\\\",\\n },\\n \\\"code\\\": {\\\"show\\\": False},\\n \\\"inputs\\\": {\\\"display_name\\\": \\\"Input\\\"},\\n }\\n\\n def build(\\n self,\\n inputs: str,\\n model: str = \\\"ERNIE-Bot-turbo\\\",\\n qianfan_ak: Optional[str] = None,\\n qianfan_sk: Optional[str] = None,\\n top_p: Optional[float] = None,\\n temperature: Optional[float] = None,\\n penalty_score: Optional[float] = None,\\n endpoint: Optional[str] = None,\\n ) -> Text:\\n try:\\n output = QianfanChatEndpoint( # type: ignore\\n model=model,\\n qianfan_ak=SecretStr(qianfan_ak) if qianfan_ak else None,\\n qianfan_sk=SecretStr(qianfan_sk) if qianfan_sk else None,\\n top_p=top_p,\\n temperature=temperature,\\n penalty_score=penalty_score,\\n endpoint=endpoint,\\n )\\n except Exception as e:\\n raise ValueError(\\\"Could not connect to Baidu Qianfan API.\\\") from e\\n message = output.invoke(inputs)\\n result = message.content if hasattr(message, \\\"content\\\") else message\\n self.status = result\\n return result\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"endpoint\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"endpoint\",\"display_name\":\"Endpoint\",\"advanced\":false,\"dynamic\":false,\"info\":\"Endpoint of the Qianfan LLM, required if custom model used.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"inputs\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"inputs\",\"display_name\":\"Input\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"model\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"ERNIE-Bot-turbo\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"ERNIE-Bot\",\"ERNIE-Bot-turbo\",\"BLOOMZ-7B\",\"Llama-2-7b-chat\",\"Llama-2-13b-chat\",\"Llama-2-70b-chat\",\"Qianfan-BLOOMZ-7B-compressed\",\"Qianfan-Chinese-Llama-2-7B\",\"ChatGLM2-6B-32K\",\"AquilaChat-7B\"],\"name\":\"model\",\"display_name\":\"Model Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"https://python.langchain.com/docs/integrations/chat/baidu_qianfan_endpoint\",\"title_case\":false,\"input_types\":[\"Text\"]},\"penalty_score\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":1.0,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"penalty_score\",\"display_name\":\"Penalty Score\",\"advanced\":false,\"dynamic\":false,\"info\":\"Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"qianfan_ak\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"qianfan_ak\",\"display_name\":\"Qianfan Ak\",\"advanced\":false,\"dynamic\":false,\"info\":\"which you could get from https://cloud.baidu.com/product/wenxinworkshop\",\"title_case\":false,\"input_types\":[\"Text\"]},\"qianfan_sk\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"qianfan_sk\",\"display_name\":\"Qianfan Sk\",\"advanced\":false,\"dynamic\":false,\"info\":\"which you could get from https://cloud.baidu.com/product/wenxinworkshop\",\"title_case\":false,\"input_types\":[\"Text\"]},\"temperature\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.95,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"top_p\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.8,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_p\",\"display_name\":\"Top p\",\"advanced\":false,\"dynamic\":false,\"info\":\"Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Generate text using Baidu Qianfan chat models. Get more detail from https://python.langchain.com/docs/integrations/chat/baidu_qianfan_endpoint.\",\"base_classes\":[\"Text\",\"object\"],\"display_name\":\"QianfanChat Model\",\"documentation\":\"\",\"custom_fields\":{\"inputs\":null,\"model\":null,\"qianfan_ak\":null,\"qianfan_sk\":null,\"top_p\":null,\"temperature\":null,\"penalty_score\":null,\"endpoint\":null},\"output_types\":[\"Text\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"GoogleGenerativeAIModel\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain_google_genai import ChatGoogleGenerativeAI # type: ignore\\nfrom pydantic.v1.types import SecretStr\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import RangeSpec, Text\\n\\n\\nclass GoogleGenerativeAIComponent(CustomComponent):\\n display_name: str = \\\"Google Generative AIModel\\\"\\n description: str = \\\"Generate text using Google Generative AI to generate text.\\\"\\n documentation: str = \\\"http://docs.langflow.org/components/custom\\\"\\n\\n def build_config(self):\\n return {\\n \\\"google_api_key\\\": {\\n \\\"display_name\\\": \\\"Google API Key\\\",\\n \\\"info\\\": \\\"The Google API Key to use for the Google Generative AI.\\\",\\n },\\n \\\"max_output_tokens\\\": {\\n \\\"display_name\\\": \\\"Max Output Tokens\\\",\\n \\\"info\\\": \\\"The maximum number of tokens to generate.\\\",\\n },\\n \\\"temperature\\\": {\\n \\\"display_name\\\": \\\"Temperature\\\",\\n \\\"info\\\": \\\"Run inference with this temperature. Must by in the closed interval [0.0, 1.0].\\\",\\n },\\n \\\"top_k\\\": {\\n \\\"display_name\\\": \\\"Top K\\\",\\n \\\"info\\\": \\\"Decode using top-k sampling: consider the set of top_k most probable tokens. Must be positive.\\\",\\n \\\"range_spec\\\": RangeSpec(min=0, max=2, step=0.1),\\n \\\"advanced\\\": True,\\n },\\n \\\"top_p\\\": {\\n \\\"display_name\\\": \\\"Top P\\\",\\n \\\"info\\\": \\\"The maximum cumulative probability of tokens to consider when sampling.\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"n\\\": {\\n \\\"display_name\\\": \\\"N\\\",\\n \\\"info\\\": \\\"Number of chat completions to generate for each prompt. Note that the API may not return the full n completions if duplicates are generated.\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"model\\\": {\\n \\\"display_name\\\": \\\"Model\\\",\\n \\\"info\\\": \\\"The name of the model to use. Supported examples: gemini-pro\\\",\\n \\\"options\\\": [\\\"gemini-pro\\\", \\\"gemini-pro-vision\\\"],\\n },\\n \\\"code\\\": {\\n \\\"advanced\\\": True,\\n },\\n \\\"inputs\\\": {\\\"display_name\\\": \\\"Input\\\"},\\n }\\n\\n def build(\\n self,\\n google_api_key: str,\\n model: str,\\n inputs: str,\\n max_output_tokens: Optional[int] = None,\\n temperature: float = 0.1,\\n top_k: Optional[int] = None,\\n top_p: Optional[float] = None,\\n n: Optional[int] = 1,\\n ) -> Text:\\n output = ChatGoogleGenerativeAI(\\n model=model,\\n max_output_tokens=max_output_tokens or None, # type: ignore\\n temperature=temperature,\\n top_k=top_k or None,\\n top_p=top_p or None, # type: ignore\\n n=n or 1,\\n google_api_key=SecretStr(google_api_key),\\n )\\n message = output.invoke(inputs)\\n result = message.content if hasattr(message, \\\"content\\\") else message\\n self.status = result\\n return result\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":true,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"google_api_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"google_api_key\",\"display_name\":\"Google API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"The Google API Key to use for the Google Generative AI.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"inputs\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"inputs\",\"display_name\":\"Input\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"max_output_tokens\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_output_tokens\",\"display_name\":\"Max Output Tokens\",\"advanced\":false,\"dynamic\":false,\"info\":\"The maximum number of tokens to generate.\",\"title_case\":false},\"model\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"gemini-pro\",\"gemini-pro-vision\"],\"name\":\"model\",\"display_name\":\"Model\",\"advanced\":false,\"dynamic\":false,\"info\":\"The name of the model to use. Supported examples: gemini-pro\",\"title_case\":false,\"input_types\":[\"Text\"]},\"n\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":1,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"n\",\"display_name\":\"N\",\"advanced\":true,\"dynamic\":false,\"info\":\"Number of chat completions to generate for each prompt. Note that the API may not return the full n completions if duplicates are generated.\",\"title_case\":false},\"temperature\":{\"type\":\"float\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.1,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"Run inference with this temperature. Must by in the closed interval [0.0, 1.0].\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"top_k\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_k\",\"display_name\":\"Top K\",\"advanced\":true,\"dynamic\":false,\"info\":\"Decode using top-k sampling: consider the set of top_k most probable tokens. Must be positive.\",\"rangeSpec\":{\"min\":0.0,\"max\":2.0,\"step\":0.1},\"title_case\":false},\"top_p\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_p\",\"display_name\":\"Top P\",\"advanced\":true,\"dynamic\":false,\"info\":\"The maximum cumulative probability of tokens to consider when sampling.\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Generate text using Google Generative AI to generate text.\",\"base_classes\":[\"Text\",\"object\"],\"display_name\":\"Google Generative AIModel\",\"documentation\":\"http://docs.langflow.org/components/custom\",\"custom_fields\":{\"google_api_key\":null,\"model\":null,\"inputs\":null,\"max_output_tokens\":null,\"temperature\":null,\"top_k\":null,\"top_p\":null,\"n\":null},\"output_types\":[\"Text\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"CTransformersModel\":{\"template\":{\"model_file\":{\"type\":\"file\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[\".bin\"],\"file_path\":\"\",\"password\":false,\"name\":\"model_file\",\"display_name\":\"Model File\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Dict, Optional\\n\\nfrom langchain_community.llms.ctransformers import CTransformers\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Text\\n\\n\\nclass CTransformersComponent(CustomComponent):\\n display_name = \\\"CTransformersModel\\\"\\n description = \\\"Generate text using CTransformers LLM models\\\"\\n documentation = \\\"https://python.langchain.com/docs/modules/model_io/models/llms/integrations/ctransformers\\\"\\n\\n def build_config(self):\\n return {\\n \\\"model\\\": {\\\"display_name\\\": \\\"Model\\\", \\\"required\\\": True},\\n \\\"model_file\\\": {\\n \\\"display_name\\\": \\\"Model File\\\",\\n \\\"required\\\": False,\\n \\\"field_type\\\": \\\"file\\\",\\n \\\"file_types\\\": [\\\".bin\\\"],\\n },\\n \\\"model_type\\\": {\\\"display_name\\\": \\\"Model Type\\\", \\\"required\\\": True},\\n \\\"config\\\": {\\n \\\"display_name\\\": \\\"Config\\\",\\n \\\"advanced\\\": True,\\n \\\"required\\\": False,\\n \\\"field_type\\\": \\\"dict\\\",\\n \\\"value\\\": '{\\\"top_k\\\":40,\\\"top_p\\\":0.95,\\\"temperature\\\":0.8,\\\"repetition_penalty\\\":1.1,\\\"last_n_tokens\\\":64,\\\"seed\\\":-1,\\\"max_new_tokens\\\":256,\\\"stop\\\":\\\"\\\",\\\"stream\\\":\\\"False\\\",\\\"reset\\\":\\\"True\\\",\\\"batch_size\\\":8,\\\"threads\\\":-1,\\\"context_length\\\":-1,\\\"gpu_layers\\\":0}',\\n },\\n \\\"inputs\\\": {\\\"display_name\\\": \\\"Input\\\"},\\n }\\n\\n def build(\\n self,\\n model: str,\\n model_file: str,\\n inputs: str,\\n model_type: str,\\n config: Optional[Dict] = None,\\n ) -> Text:\\n output = CTransformers(model=model, model_file=model_file, model_type=model_type, config=config)\\n message = output.invoke(inputs)\\n result = message.content if hasattr(message, \\\"content\\\") else message\\n self.status = result\\n return result\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"config\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"{\\\"top_k\\\":40,\\\"top_p\\\":0.95,\\\"temperature\\\":0.8,\\\"repetition_penalty\\\":1.1,\\\"last_n_tokens\\\":64,\\\"seed\\\":-1,\\\"max_new_tokens\\\":256,\\\"stop\\\":\\\"\\\",\\\"stream\\\":\\\"False\\\",\\\"reset\\\":\\\"True\\\",\\\"batch_size\\\":8,\\\"threads\\\":-1,\\\"context_length\\\":-1,\\\"gpu_layers\\\":0}\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"config\",\"display_name\":\"Config\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"inputs\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"inputs\",\"display_name\":\"Input\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"model\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model\",\"display_name\":\"Model\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"model_type\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model_type\",\"display_name\":\"Model Type\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Generate text using CTransformers LLM models\",\"base_classes\":[\"Text\",\"object\"],\"display_name\":\"CTransformersModel\",\"documentation\":\"https://python.langchain.com/docs/modules/model_io/models/llms/integrations/ctransformers\",\"custom_fields\":{\"model\":null,\"model_file\":null,\"inputs\":null,\"model_type\":null,\"config\":null},\"output_types\":[\"Text\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"VertexAiModel\":{\"template\":{\"credentials\":{\"type\":\"file\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[\".json\"],\"password\":false,\"name\":\"credentials\",\"display_name\":\"Credentials\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"examples\":{\"type\":\"BaseMessage\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":true,\"value\":[],\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"examples\",\"display_name\":\"Examples\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import List, Optional\\n\\nfrom langchain_core.messages.base import BaseMessage\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Text\\n\\n\\nclass ChatVertexAIComponent(CustomComponent):\\n display_name = \\\"ChatVertexAIModel\\\"\\n description = \\\"Generate text using Vertex AI Chat large language models API.\\\"\\n\\n def build_config(self):\\n return {\\n \\\"credentials\\\": {\\n \\\"display_name\\\": \\\"Credentials\\\",\\n \\\"field_type\\\": \\\"file\\\",\\n \\\"file_types\\\": [\\\".json\\\"],\\n \\\"file_path\\\": None,\\n },\\n \\\"examples\\\": {\\n \\\"display_name\\\": \\\"Examples\\\",\\n \\\"multiline\\\": True,\\n },\\n \\\"location\\\": {\\n \\\"display_name\\\": \\\"Location\\\",\\n \\\"value\\\": \\\"us-central1\\\",\\n },\\n \\\"max_output_tokens\\\": {\\n \\\"display_name\\\": \\\"Max Output Tokens\\\",\\n \\\"value\\\": 128,\\n \\\"advanced\\\": True,\\n },\\n \\\"model_name\\\": {\\n \\\"display_name\\\": \\\"Model Name\\\",\\n \\\"value\\\": \\\"chat-bison\\\",\\n },\\n \\\"project\\\": {\\n \\\"display_name\\\": \\\"Project\\\",\\n },\\n \\\"temperature\\\": {\\n \\\"display_name\\\": \\\"Temperature\\\",\\n \\\"value\\\": 0.0,\\n },\\n \\\"top_k\\\": {\\n \\\"display_name\\\": \\\"Top K\\\",\\n \\\"value\\\": 40,\\n \\\"advanced\\\": True,\\n },\\n \\\"top_p\\\": {\\n \\\"display_name\\\": \\\"Top P\\\",\\n \\\"value\\\": 0.95,\\n \\\"advanced\\\": True,\\n },\\n \\\"verbose\\\": {\\n \\\"display_name\\\": \\\"Verbose\\\",\\n \\\"value\\\": False,\\n \\\"advanced\\\": True,\\n },\\n \\\"inputs\\\": {\\\"display_name\\\": \\\"Input\\\"},\\n }\\n\\n def build(\\n self,\\n inputs: str,\\n credentials: Optional[str],\\n project: str,\\n examples: Optional[List[BaseMessage]] = [],\\n location: str = \\\"us-central1\\\",\\n max_output_tokens: int = 128,\\n model_name: str = \\\"chat-bison\\\",\\n temperature: float = 0.0,\\n top_k: int = 40,\\n top_p: float = 0.95,\\n verbose: bool = False,\\n ) -> Text:\\n try:\\n from langchain_google_vertexai import ChatVertexAI\\n except ImportError:\\n raise ImportError(\\n \\\"To use the ChatVertexAI model, you need to install the langchain-google-vertexai package.\\\"\\n )\\n output = ChatVertexAI(\\n credentials=credentials,\\n examples=examples,\\n location=location,\\n max_output_tokens=max_output_tokens,\\n model_name=model_name,\\n project=project,\\n temperature=temperature,\\n top_k=top_k,\\n top_p=top_p,\\n verbose=verbose,\\n )\\n message = output.invoke(inputs)\\n result = message.content if hasattr(message, \\\"content\\\") else message\\n self.status = result\\n return result\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"inputs\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"inputs\",\"display_name\":\"Input\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"location\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"us-central1\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"location\",\"display_name\":\"Location\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"max_output_tokens\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":128,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_output_tokens\",\"display_name\":\"Max Output Tokens\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"model_name\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"chat-bison\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model_name\",\"display_name\":\"Model Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"project\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"project\",\"display_name\":\"Project\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"temperature\":{\"type\":\"float\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.0,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"top_k\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":40,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_k\",\"display_name\":\"Top K\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"top_p\":{\"type\":\"float\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.95,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_p\",\"display_name\":\"Top P\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"verbose\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"verbose\",\"display_name\":\"Verbose\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Generate text using Vertex AI Chat large language models API.\",\"base_classes\":[\"Text\",\"object\"],\"display_name\":\"ChatVertexAIModel\",\"documentation\":\"\",\"custom_fields\":{\"inputs\":null,\"credentials\":null,\"project\":null,\"examples\":null,\"location\":null,\"max_output_tokens\":null,\"model_name\":null,\"temperature\":null,\"top_k\":null,\"top_p\":null,\"verbose\":null},\"output_types\":[\"Text\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"OllamaModel\":{\"template\":{\"metadata\":{\"type\":\"Dict[str, Any]\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":true,\"dynamic\":false,\"info\":\"Metadata to add to the run trace.\",\"title_case\":false},\"stop\":{\"type\":\"list\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"stop\",\"display_name\":\"Stop Tokens\",\"advanced\":true,\"dynamic\":false,\"info\":\"List of tokens to signal the model to stop generating text.\",\"title_case\":false},\"tags\":{\"type\":\"list\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"tags\",\"display_name\":\"Tags\",\"advanced\":true,\"dynamic\":false,\"info\":\"Tags to add to the run trace.\",\"title_case\":false},\"base_url\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"base_url\",\"display_name\":\"Base URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"Endpoint of the Ollama API. Defaults to 'http://localhost:11434' if not specified.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"cache\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"cache\",\"display_name\":\"Cache\",\"advanced\":true,\"dynamic\":false,\"info\":\"Enable or disable caching.\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Any, Dict, List, Optional\\n\\n# from langchain_community.chat_models import ChatOllama\\nfrom langchain_community.chat_models import ChatOllama\\n\\n# from langchain.chat_models import ChatOllama\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Text\\n\\n# whe When a callback component is added to Langflow, the comment must be uncommented.\\n# from langchain.callbacks.manager import CallbackManager\\n\\n\\nclass ChatOllamaComponent(CustomComponent):\\n display_name = \\\"ChatOllamaModel\\\"\\n description = \\\"Generate text using Local LLM for chat with Ollama.\\\"\\n\\n def build_config(self) -> dict:\\n return {\\n \\\"base_url\\\": {\\n \\\"display_name\\\": \\\"Base URL\\\",\\n \\\"info\\\": \\\"Endpoint of the Ollama API. Defaults to 'http://localhost:11434' if not specified.\\\",\\n },\\n \\\"model\\\": {\\n \\\"display_name\\\": \\\"Model Name\\\",\\n \\\"value\\\": \\\"llama2\\\",\\n \\\"info\\\": \\\"Refer to https://ollama.ai/library for more models.\\\",\\n },\\n \\\"temperature\\\": {\\n \\\"display_name\\\": \\\"Temperature\\\",\\n \\\"field_type\\\": \\\"float\\\",\\n \\\"value\\\": 0.8,\\n \\\"info\\\": \\\"Controls the creativity of model responses.\\\",\\n },\\n \\\"cache\\\": {\\n \\\"display_name\\\": \\\"Cache\\\",\\n \\\"field_type\\\": \\\"bool\\\",\\n \\\"info\\\": \\\"Enable or disable caching.\\\",\\n \\\"advanced\\\": True,\\n \\\"value\\\": False,\\n },\\n ### When a callback component is added to Langflow, the comment must be uncommented. ###\\n # \\\"callback_manager\\\": {\\n # \\\"display_name\\\": \\\"Callback Manager\\\",\\n # \\\"info\\\": \\\"Optional callback manager for additional functionality.\\\",\\n # \\\"advanced\\\": True,\\n # },\\n # \\\"callbacks\\\": {\\n # \\\"display_name\\\": \\\"Callbacks\\\",\\n # \\\"info\\\": \\\"Callbacks to execute during model runtime.\\\",\\n # \\\"advanced\\\": True,\\n # },\\n ########################################################################################\\n \\\"format\\\": {\\n \\\"display_name\\\": \\\"Format\\\",\\n \\\"field_type\\\": \\\"str\\\",\\n \\\"info\\\": \\\"Specify the format of the output (e.g., json).\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"metadata\\\": {\\n \\\"display_name\\\": \\\"Metadata\\\",\\n \\\"info\\\": \\\"Metadata to add to the run trace.\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"mirostat\\\": {\\n \\\"display_name\\\": \\\"Mirostat\\\",\\n \\\"options\\\": [\\\"Disabled\\\", \\\"Mirostat\\\", \\\"Mirostat 2.0\\\"],\\n \\\"info\\\": \\\"Enable/disable Mirostat sampling for controlling perplexity.\\\",\\n \\\"value\\\": \\\"Disabled\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"mirostat_eta\\\": {\\n \\\"display_name\\\": \\\"Mirostat Eta\\\",\\n \\\"field_type\\\": \\\"float\\\",\\n \\\"info\\\": \\\"Learning rate for Mirostat algorithm. (Default: 0.1)\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"mirostat_tau\\\": {\\n \\\"display_name\\\": \\\"Mirostat Tau\\\",\\n \\\"field_type\\\": \\\"float\\\",\\n \\\"info\\\": \\\"Controls the balance between coherence and diversity of the output. (Default: 5.0)\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"num_ctx\\\": {\\n \\\"display_name\\\": \\\"Context Window Size\\\",\\n \\\"field_type\\\": \\\"int\\\",\\n \\\"info\\\": \\\"Size of the context window for generating tokens. (Default: 2048)\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"num_gpu\\\": {\\n \\\"display_name\\\": \\\"Number of GPUs\\\",\\n \\\"field_type\\\": \\\"int\\\",\\n \\\"info\\\": \\\"Number of GPUs to use for computation. (Default: 1 on macOS, 0 to disable)\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"num_thread\\\": {\\n \\\"display_name\\\": \\\"Number of Threads\\\",\\n \\\"field_type\\\": \\\"int\\\",\\n \\\"info\\\": \\\"Number of threads to use during computation. (Default: detected for optimal performance)\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"repeat_last_n\\\": {\\n \\\"display_name\\\": \\\"Repeat Last N\\\",\\n \\\"field_type\\\": \\\"int\\\",\\n \\\"info\\\": \\\"How far back the model looks to prevent repetition. (Default: 64, 0 = disabled, -1 = num_ctx)\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"repeat_penalty\\\": {\\n \\\"display_name\\\": \\\"Repeat Penalty\\\",\\n \\\"field_type\\\": \\\"float\\\",\\n \\\"info\\\": \\\"Penalty for repetitions in generated text. (Default: 1.1)\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"tfs_z\\\": {\\n \\\"display_name\\\": \\\"TFS Z\\\",\\n \\\"field_type\\\": \\\"float\\\",\\n \\\"info\\\": \\\"Tail free sampling value. (Default: 1)\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"timeout\\\": {\\n \\\"display_name\\\": \\\"Timeout\\\",\\n \\\"field_type\\\": \\\"int\\\",\\n \\\"info\\\": \\\"Timeout for the request stream.\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"top_k\\\": {\\n \\\"display_name\\\": \\\"Top K\\\",\\n \\\"field_type\\\": \\\"int\\\",\\n \\\"info\\\": \\\"Limits token selection to top K. (Default: 40)\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"top_p\\\": {\\n \\\"display_name\\\": \\\"Top P\\\",\\n \\\"field_type\\\": \\\"float\\\",\\n \\\"info\\\": \\\"Works together with top-k. (Default: 0.9)\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"verbose\\\": {\\n \\\"display_name\\\": \\\"Verbose\\\",\\n \\\"field_type\\\": \\\"bool\\\",\\n \\\"info\\\": \\\"Whether to print out response text.\\\",\\n },\\n \\\"tags\\\": {\\n \\\"display_name\\\": \\\"Tags\\\",\\n \\\"field_type\\\": \\\"list\\\",\\n \\\"info\\\": \\\"Tags to add to the run trace.\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"stop\\\": {\\n \\\"display_name\\\": \\\"Stop Tokens\\\",\\n \\\"field_type\\\": \\\"list\\\",\\n \\\"info\\\": \\\"List of tokens to signal the model to stop generating text.\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"system\\\": {\\n \\\"display_name\\\": \\\"System\\\",\\n \\\"field_type\\\": \\\"str\\\",\\n \\\"info\\\": \\\"System to use for generating text.\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"template\\\": {\\n \\\"display_name\\\": \\\"Template\\\",\\n \\\"field_type\\\": \\\"str\\\",\\n \\\"info\\\": \\\"Template to use for generating text.\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"inputs\\\": {\\\"display_name\\\": \\\"Input\\\"},\\n }\\n\\n def build(\\n self,\\n base_url: Optional[str],\\n model: str,\\n inputs: str,\\n mirostat: Optional[str],\\n mirostat_eta: Optional[float] = None,\\n mirostat_tau: Optional[float] = None,\\n ### When a callback component is added to Langflow, the comment must be uncommented.###\\n # callback_manager: Optional[CallbackManager] = None,\\n # callbacks: Optional[List[Callbacks]] = None,\\n #######################################################################################\\n repeat_last_n: Optional[int] = None,\\n verbose: Optional[bool] = None,\\n cache: Optional[bool] = None,\\n num_ctx: Optional[int] = None,\\n num_gpu: Optional[int] = None,\\n format: Optional[str] = None,\\n metadata: Optional[Dict[str, Any]] = None,\\n num_thread: Optional[int] = None,\\n repeat_penalty: Optional[float] = None,\\n stop: Optional[List[str]] = None,\\n system: Optional[str] = None,\\n tags: Optional[List[str]] = None,\\n temperature: Optional[float] = None,\\n template: Optional[str] = None,\\n tfs_z: Optional[float] = None,\\n timeout: Optional[int] = None,\\n top_k: Optional[int] = None,\\n top_p: Optional[int] = None,\\n ) -> Text:\\n if not base_url:\\n base_url = \\\"http://localhost:11434\\\"\\n\\n # Mapping mirostat settings to their corresponding values\\n mirostat_options = {\\\"Mirostat\\\": 1, \\\"Mirostat 2.0\\\": 2}\\n\\n # Default to 0 for 'Disabled'\\n mirostat_value = mirostat_options.get(mirostat, 0) # type: ignore\\n\\n # Set mirostat_eta and mirostat_tau to None if mirostat is disabled\\n if mirostat_value == 0:\\n mirostat_eta = None\\n mirostat_tau = None\\n\\n # Mapping system settings to their corresponding values\\n llm_params = {\\n \\\"base_url\\\": base_url,\\n \\\"cache\\\": cache,\\n \\\"model\\\": model,\\n \\\"mirostat\\\": mirostat_value,\\n \\\"format\\\": format,\\n \\\"metadata\\\": metadata,\\n \\\"tags\\\": tags,\\n ## When a callback component is added to Langflow, the comment must be uncommented.##\\n # \\\"callback_manager\\\": callback_manager,\\n # \\\"callbacks\\\": callbacks,\\n #####################################################################################\\n \\\"mirostat_eta\\\": mirostat_eta,\\n \\\"mirostat_tau\\\": mirostat_tau,\\n \\\"num_ctx\\\": num_ctx,\\n \\\"num_gpu\\\": num_gpu,\\n \\\"num_thread\\\": num_thread,\\n \\\"repeat_last_n\\\": repeat_last_n,\\n \\\"repeat_penalty\\\": repeat_penalty,\\n \\\"temperature\\\": temperature,\\n \\\"stop\\\": stop,\\n \\\"system\\\": system,\\n \\\"template\\\": template,\\n \\\"tfs_z\\\": tfs_z,\\n \\\"timeout\\\": timeout,\\n \\\"top_k\\\": top_k,\\n \\\"top_p\\\": top_p,\\n \\\"verbose\\\": verbose,\\n }\\n\\n # None Value remove\\n llm_params = {k: v for k, v in llm_params.items() if v is not None}\\n\\n try:\\n output = ChatOllama(**llm_params) # type: ignore\\n except Exception as e:\\n raise ValueError(\\\"Could not initialize Ollama LLM.\\\") from e\\n message = output.invoke(inputs)\\n result = message.content if hasattr(message, \\\"content\\\") else message\\n self.status = result\\n return result\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"format\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"format\",\"display_name\":\"Format\",\"advanced\":true,\"dynamic\":false,\"info\":\"Specify the format of the output (e.g., json).\",\"title_case\":false,\"input_types\":[\"Text\"]},\"inputs\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"inputs\",\"display_name\":\"Input\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"mirostat\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"Disabled\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"Disabled\",\"Mirostat\",\"Mirostat 2.0\"],\"name\":\"mirostat\",\"display_name\":\"Mirostat\",\"advanced\":true,\"dynamic\":false,\"info\":\"Enable/disable Mirostat sampling for controlling perplexity.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"mirostat_eta\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"mirostat_eta\",\"display_name\":\"Mirostat Eta\",\"advanced\":true,\"dynamic\":false,\"info\":\"Learning rate for Mirostat algorithm. (Default: 0.1)\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"mirostat_tau\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"mirostat_tau\",\"display_name\":\"Mirostat Tau\",\"advanced\":true,\"dynamic\":false,\"info\":\"Controls the balance between coherence and diversity of the output. (Default: 5.0)\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"model\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"llama2\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model\",\"display_name\":\"Model Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"Refer to https://ollama.ai/library for more models.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"num_ctx\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"num_ctx\",\"display_name\":\"Context Window Size\",\"advanced\":true,\"dynamic\":false,\"info\":\"Size of the context window for generating tokens. (Default: 2048)\",\"title_case\":false},\"num_gpu\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"num_gpu\",\"display_name\":\"Number of GPUs\",\"advanced\":true,\"dynamic\":false,\"info\":\"Number of GPUs to use for computation. (Default: 1 on macOS, 0 to disable)\",\"title_case\":false},\"num_thread\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"num_thread\",\"display_name\":\"Number of Threads\",\"advanced\":true,\"dynamic\":false,\"info\":\"Number of threads to use during computation. (Default: detected for optimal performance)\",\"title_case\":false},\"repeat_last_n\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"repeat_last_n\",\"display_name\":\"Repeat Last N\",\"advanced\":true,\"dynamic\":false,\"info\":\"How far back the model looks to prevent repetition. (Default: 64, 0 = disabled, -1 = num_ctx)\",\"title_case\":false},\"repeat_penalty\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"repeat_penalty\",\"display_name\":\"Repeat Penalty\",\"advanced\":true,\"dynamic\":false,\"info\":\"Penalty for repetitions in generated text. (Default: 1.1)\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"system\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"system\",\"display_name\":\"System\",\"advanced\":true,\"dynamic\":false,\"info\":\"System to use for generating text.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"temperature\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.8,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"Controls the creativity of model responses.\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"template\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"template\",\"display_name\":\"Template\",\"advanced\":true,\"dynamic\":false,\"info\":\"Template to use for generating text.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"tfs_z\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"tfs_z\",\"display_name\":\"TFS Z\",\"advanced\":true,\"dynamic\":false,\"info\":\"Tail free sampling value. (Default: 1)\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"timeout\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"timeout\",\"display_name\":\"Timeout\",\"advanced\":true,\"dynamic\":false,\"info\":\"Timeout for the request stream.\",\"title_case\":false},\"top_k\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_k\",\"display_name\":\"Top K\",\"advanced\":true,\"dynamic\":false,\"info\":\"Limits token selection to top K. (Default: 40)\",\"title_case\":false},\"top_p\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_p\",\"display_name\":\"Top P\",\"advanced\":true,\"dynamic\":false,\"info\":\"Works together with top-k. (Default: 0.9)\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"verbose\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"verbose\",\"display_name\":\"Verbose\",\"advanced\":false,\"dynamic\":false,\"info\":\"Whether to print out response text.\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Generate text using Local LLM for chat with Ollama.\",\"base_classes\":[\"Text\",\"object\"],\"display_name\":\"ChatOllamaModel\",\"documentation\":\"\",\"custom_fields\":{\"base_url\":null,\"model\":null,\"inputs\":null,\"mirostat\":null,\"mirostat_eta\":null,\"mirostat_tau\":null,\"repeat_last_n\":null,\"verbose\":null,\"cache\":null,\"num_ctx\":null,\"num_gpu\":null,\"format\":null,\"metadata\":null,\"num_thread\":null,\"repeat_penalty\":null,\"stop\":null,\"system\":null,\"tags\":null,\"temperature\":null,\"template\":null,\"tfs_z\":null,\"timeout\":null,\"top_k\":null,\"top_p\":null},\"output_types\":[\"Text\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"AnthropicModel\":{\"template\":{\"anthropic_api_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"anthropic_api_key\",\"display_name\":\"Anthropic API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"Your Anthropic API key.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"api_endpoint\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"api_endpoint\",\"display_name\":\"API Endpoint\",\"advanced\":false,\"dynamic\":false,\"info\":\"Endpoint of the Anthropic API. Defaults to 'https://api.anthropic.com' if not specified.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain_community.chat_models.anthropic import ChatAnthropic\\nfrom pydantic.v1 import SecretStr\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Text\\n\\n\\nclass AnthropicLLM(CustomComponent):\\n display_name: str = \\\"AnthropicModel\\\"\\n description: str = \\\"Generate text using Anthropic Chat&Completion large language models.\\\"\\n\\n def build_config(self):\\n return {\\n \\\"model\\\": {\\n \\\"display_name\\\": \\\"Model Name\\\",\\n \\\"options\\\": [\\n \\\"claude-2.1\\\",\\n \\\"claude-2.0\\\",\\n \\\"claude-instant-1.2\\\",\\n \\\"claude-instant-1\\\",\\n # Add more models as needed\\n ],\\n \\\"info\\\": \\\"https://python.langchain.com/docs/integrations/chat/anthropic\\\",\\n \\\"required\\\": True,\\n \\\"value\\\": \\\"claude-2.1\\\",\\n },\\n \\\"anthropic_api_key\\\": {\\n \\\"display_name\\\": \\\"Anthropic API Key\\\",\\n \\\"required\\\": True,\\n \\\"password\\\": True,\\n \\\"info\\\": \\\"Your Anthropic API key.\\\",\\n },\\n \\\"max_tokens\\\": {\\n \\\"display_name\\\": \\\"Max Tokens\\\",\\n \\\"field_type\\\": \\\"int\\\",\\n \\\"value\\\": 256,\\n },\\n \\\"temperature\\\": {\\n \\\"display_name\\\": \\\"Temperature\\\",\\n \\\"field_type\\\": \\\"float\\\",\\n \\\"value\\\": 0.7,\\n },\\n \\\"api_endpoint\\\": {\\n \\\"display_name\\\": \\\"API Endpoint\\\",\\n \\\"info\\\": \\\"Endpoint of the Anthropic API. Defaults to 'https://api.anthropic.com' if not specified.\\\",\\n },\\n \\\"code\\\": {\\\"show\\\": False},\\n \\\"inputs\\\": {\\\"display_name\\\": \\\"Input\\\"},\\n }\\n\\n def build(\\n self,\\n model: str,\\n inputs: str,\\n anthropic_api_key: Optional[str] = None,\\n max_tokens: Optional[int] = None,\\n temperature: Optional[float] = None,\\n api_endpoint: Optional[str] = None,\\n ) -> Text:\\n # Set default API endpoint if not provided\\n if not api_endpoint:\\n api_endpoint = \\\"https://api.anthropic.com\\\"\\n\\n try:\\n output = ChatAnthropic(\\n model_name=model,\\n anthropic_api_key=(SecretStr(anthropic_api_key) if anthropic_api_key else None),\\n max_tokens_to_sample=max_tokens, # type: ignore\\n temperature=temperature,\\n anthropic_api_url=api_endpoint,\\n )\\n except Exception as e:\\n raise ValueError(\\\"Could not connect to Anthropic API.\\\") from e\\n message = output.invoke(inputs)\\n result = message.content if hasattr(message, \\\"content\\\") else message\\n self.status = result\\n return result\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"inputs\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"inputs\",\"display_name\":\"Input\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"max_tokens\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":256,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_tokens\",\"display_name\":\"Max Tokens\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"model\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"claude-2.1\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"claude-2.1\",\"claude-2.0\",\"claude-instant-1.2\",\"claude-instant-1\"],\"name\":\"model\",\"display_name\":\"Model Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"https://python.langchain.com/docs/integrations/chat/anthropic\",\"title_case\":false,\"input_types\":[\"Text\"]},\"temperature\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.7,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Generate text using Anthropic Chat&Completion large language models.\",\"base_classes\":[\"Text\",\"object\"],\"display_name\":\"AnthropicModel\",\"documentation\":\"\",\"custom_fields\":{\"model\":null,\"inputs\":null,\"anthropic_api_key\":null,\"max_tokens\":null,\"temperature\":null,\"api_endpoint\":null},\"output_types\":[\"Text\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"OpenAIModel\":{\"template\":{\"inputs\":{\"type\":\"Text\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"inputs\",\"display_name\":\"Input\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain_openai import ChatOpenAI\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import NestedDict, Text\\n\\n\\nclass OpenAIModelComponent(CustomComponent):\\n display_name = \\\"OpenAI Model\\\"\\n description = \\\"Generates text using OpenAI's models.\\\"\\n\\n def build_config(self):\\n return {\\n \\\"inputs\\\": {\\\"display_name\\\": \\\"Input\\\"},\\n \\\"max_tokens\\\": {\\n \\\"display_name\\\": \\\"Max Tokens\\\",\\n \\\"advanced\\\": False,\\n \\\"required\\\": False,\\n },\\n \\\"model_kwargs\\\": {\\n \\\"display_name\\\": \\\"Model Kwargs\\\",\\n \\\"advanced\\\": True,\\n \\\"required\\\": False,\\n },\\n \\\"model_name\\\": {\\n \\\"display_name\\\": \\\"Model Name\\\",\\n \\\"advanced\\\": False,\\n \\\"required\\\": False,\\n \\\"options\\\": [\\n \\\"gpt-4-turbo-preview\\\",\\n \\\"gpt-4-0125-preview\\\",\\n \\\"gpt-4-1106-preview\\\",\\n \\\"gpt-4-vision-preview\\\",\\n \\\"gpt-3.5-turbo-0125\\\",\\n \\\"gpt-3.5-turbo-1106\\\",\\n ],\\n },\\n \\\"openai_api_base\\\": {\\n \\\"display_name\\\": \\\"OpenAI API Base\\\",\\n \\\"advanced\\\": False,\\n \\\"required\\\": False,\\n \\\"info\\\": (\\n \\\"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\\\n\\\\n\\\"\\n \\\"You can change this to use other APIs like JinaChat, LocalAI and Prem.\\\"\\n ),\\n },\\n \\\"openai_api_key\\\": {\\n \\\"display_name\\\": \\\"OpenAI API Key\\\",\\n \\\"advanced\\\": False,\\n \\\"required\\\": False,\\n \\\"password\\\": True,\\n },\\n \\\"temperature\\\": {\\n \\\"display_name\\\": \\\"Temperature\\\",\\n \\\"advanced\\\": False,\\n \\\"required\\\": False,\\n \\\"value\\\": 0.7,\\n },\\n }\\n\\n def build(\\n self,\\n inputs: Text,\\n max_tokens: Optional[int] = 256,\\n model_kwargs: NestedDict = {},\\n model_name: str = \\\"gpt-4-1106-preview\\\",\\n openai_api_base: Optional[str] = None,\\n openai_api_key: Optional[str] = None,\\n temperature: float = 0.7,\\n ) -> Text:\\n if not openai_api_base:\\n openai_api_base = \\\"https://api.openai.com/v1\\\"\\n model = ChatOpenAI(\\n max_tokens=max_tokens,\\n model_kwargs=model_kwargs,\\n model=model_name,\\n base_url=openai_api_base,\\n api_key=openai_api_key,\\n temperature=temperature,\\n )\\n\\n message = model.invoke(inputs)\\n result = message.content if hasattr(message, \\\"content\\\") else message\\n self.status = result\\n return result\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"max_tokens\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":256,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_tokens\",\"display_name\":\"Max Tokens\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"model_kwargs\":{\"type\":\"NestedDict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model_kwargs\",\"display_name\":\"Model Kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"model_name\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"gpt-4-1106-preview\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"gpt-4-turbo-preview\",\"gpt-4-0125-preview\",\"gpt-4-1106-preview\",\"gpt-4-vision-preview\",\"gpt-3.5-turbo-0125\",\"gpt-3.5-turbo-1106\"],\"name\":\"model_name\",\"display_name\":\"Model Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"openai_api_base\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"openai_api_base\",\"display_name\":\"OpenAI API Base\",\"advanced\":false,\"dynamic\":false,\"info\":\"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"openai_api_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"openai_api_key\",\"display_name\":\"OpenAI API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"temperature\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.7,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Generates text using OpenAI's models.\",\"base_classes\":[\"Text\",\"object\"],\"display_name\":\"OpenAI Model\",\"documentation\":\"\",\"custom_fields\":{\"inputs\":null,\"max_tokens\":null,\"model_kwargs\":null,\"model_name\":null,\"openai_api_base\":null,\"openai_api_key\":null,\"temperature\":null},\"output_types\":[\"Text\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"HuggingFaceModel\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain_community.chat_models.huggingface import ChatHuggingFace\\nfrom langchain_community.llms.huggingface_endpoint import HuggingFaceEndpoint\\n\\nfrom langflow import CustomComponent\\n\\nfrom langflow.field_typing import Text\\n\\n\\nclass HuggingFaceEndpointsComponent(CustomComponent):\\n display_name: str = \\\"Hugging Face Inference API models\\\"\\n description: str = \\\"Generate text using LLM model from Hugging Face Inference API.\\\"\\n\\n def build_config(self):\\n return {\\n \\\"endpoint_url\\\": {\\\"display_name\\\": \\\"Endpoint URL\\\", \\\"password\\\": True},\\n \\\"task\\\": {\\n \\\"display_name\\\": \\\"Task\\\",\\n \\\"options\\\": [\\\"text2text-generation\\\", \\\"text-generation\\\", \\\"summarization\\\"],\\n },\\n \\\"huggingfacehub_api_token\\\": {\\\"display_name\\\": \\\"API token\\\", \\\"password\\\": True},\\n \\\"model_kwargs\\\": {\\n \\\"display_name\\\": \\\"Model Keyword Arguments\\\",\\n \\\"field_type\\\": \\\"code\\\",\\n },\\n \\\"code\\\": {\\\"show\\\": False},\\n \\\"inputs\\\": {\\\"display_name\\\": \\\"Input\\\"},\\n }\\n\\n def build(\\n self,\\n inputs: str,\\n endpoint_url: str,\\n task: str = \\\"text2text-generation\\\",\\n huggingfacehub_api_token: Optional[str] = None,\\n model_kwargs: Optional[dict] = None,\\n ) -> Text:\\n try:\\n llm = HuggingFaceEndpoint(\\n endpoint_url=endpoint_url,\\n task=task,\\n huggingfacehub_api_token=huggingfacehub_api_token,\\n model_kwargs=model_kwargs,\\n )\\n except Exception as e:\\n raise ValueError(\\\"Could not connect to HuggingFace Endpoints API.\\\") from e\\n output = ChatHuggingFace(llm=llm)\\n message = output.invoke(inputs)\\n result = message.content if hasattr(message, \\\"content\\\") else message\\n self.status = result\\n return result\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"endpoint_url\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"endpoint_url\",\"display_name\":\"Endpoint URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"huggingfacehub_api_token\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"huggingfacehub_api_token\",\"display_name\":\"API token\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"inputs\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"inputs\",\"display_name\":\"Input\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"model_kwargs\":{\"type\":\"code\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model_kwargs\",\"display_name\":\"Model Keyword Arguments\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"task\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"text2text-generation\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"text2text-generation\",\"text-generation\",\"summarization\"],\"name\":\"task\",\"display_name\":\"Task\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Generate text using LLM model from Hugging Face Inference API.\",\"base_classes\":[\"Text\",\"object\"],\"display_name\":\"Hugging Face Inference API models\",\"documentation\":\"\",\"custom_fields\":{\"inputs\":null,\"endpoint_url\":null,\"task\":null,\"huggingfacehub_api_token\":null,\"model_kwargs\":null},\"output_types\":[\"Text\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"AzureOpenAIModel\":{\"template\":{\"api_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"api_key\",\"display_name\":\"API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"api_version\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"2023-12-01-preview\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"2023-03-15-preview\",\"2023-05-15\",\"2023-06-01-preview\",\"2023-07-01-preview\",\"2023-08-01-preview\",\"2023-09-01-preview\",\"2023-12-01-preview\"],\"name\":\"api_version\",\"display_name\":\"API Version\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"azure_deployment\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"azure_deployment\",\"display_name\":\"Deployment Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"azure_endpoint\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"azure_endpoint\",\"display_name\":\"Azure Endpoint\",\"advanced\":false,\"dynamic\":false,\"info\":\"Your Azure endpoint, including the resource.. Example: `https://example-resource.azure.openai.com/`\",\"title_case\":false,\"input_types\":[\"Text\"]},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain.llms.base import BaseLanguageModel\\nfrom langchain_openai import AzureChatOpenAI\\n\\nfrom langflow import CustomComponent\\n\\n\\nclass AzureChatOpenAIComponent(CustomComponent):\\n display_name: str = \\\"AzureOpenAI Model\\\"\\n description: str = \\\"Generate text using LLM model from Azure OpenAI.\\\"\\n documentation: str = \\\"https://python.langchain.com/docs/integrations/llms/azure_openai\\\"\\n beta = False\\n\\n AZURE_OPENAI_MODELS = [\\n \\\"gpt-35-turbo\\\",\\n \\\"gpt-35-turbo-16k\\\",\\n \\\"gpt-35-turbo-instruct\\\",\\n \\\"gpt-4\\\",\\n \\\"gpt-4-32k\\\",\\n \\\"gpt-4-vision\\\",\\n ]\\n\\n AZURE_OPENAI_API_VERSIONS = [\\n \\\"2023-03-15-preview\\\",\\n \\\"2023-05-15\\\",\\n \\\"2023-06-01-preview\\\",\\n \\\"2023-07-01-preview\\\",\\n \\\"2023-08-01-preview\\\",\\n \\\"2023-09-01-preview\\\",\\n \\\"2023-12-01-preview\\\",\\n ]\\n\\n def build_config(self):\\n return {\\n \\\"model\\\": {\\n \\\"display_name\\\": \\\"Model Name\\\",\\n \\\"value\\\": self.AZURE_OPENAI_MODELS[0],\\n \\\"options\\\": self.AZURE_OPENAI_MODELS,\\n \\\"required\\\": True,\\n },\\n \\\"azure_endpoint\\\": {\\n \\\"display_name\\\": \\\"Azure Endpoint\\\",\\n \\\"required\\\": True,\\n \\\"info\\\": \\\"Your Azure endpoint, including the resource.. Example: `https://example-resource.azure.openai.com/`\\\",\\n },\\n \\\"azure_deployment\\\": {\\n \\\"display_name\\\": \\\"Deployment Name\\\",\\n \\\"required\\\": True,\\n },\\n \\\"api_version\\\": {\\n \\\"display_name\\\": \\\"API Version\\\",\\n \\\"options\\\": self.AZURE_OPENAI_API_VERSIONS,\\n \\\"value\\\": self.AZURE_OPENAI_API_VERSIONS[-1],\\n \\\"required\\\": True,\\n \\\"advanced\\\": True,\\n },\\n \\\"api_key\\\": {\\\"display_name\\\": \\\"API Key\\\", \\\"required\\\": True, \\\"password\\\": True},\\n \\\"temperature\\\": {\\n \\\"display_name\\\": \\\"Temperature\\\",\\n \\\"value\\\": 0.7,\\n \\\"field_type\\\": \\\"float\\\",\\n \\\"required\\\": False,\\n },\\n \\\"max_tokens\\\": {\\n \\\"display_name\\\": \\\"Max Tokens\\\",\\n \\\"value\\\": 1000,\\n \\\"required\\\": False,\\n \\\"field_type\\\": \\\"int\\\",\\n \\\"advanced\\\": True,\\n \\\"info\\\": \\\"Maximum number of tokens to generate.\\\",\\n },\\n \\\"code\\\": {\\\"show\\\": False},\\n \\\"inputs\\\": {\\\"display_name\\\": \\\"Input\\\"},\\n }\\n\\n def build(\\n self,\\n model: str,\\n azure_endpoint: str,\\n inputs: str,\\n azure_deployment: str,\\n api_key: str,\\n api_version: str,\\n temperature: float = 0.7,\\n max_tokens: Optional[int] = 1000,\\n ) -> BaseLanguageModel:\\n try:\\n output = AzureChatOpenAI(\\n model=model,\\n azure_endpoint=azure_endpoint,\\n azure_deployment=azure_deployment,\\n api_version=api_version,\\n api_key=api_key,\\n temperature=temperature,\\n max_tokens=max_tokens,\\n )\\n except Exception as e:\\n raise ValueError(\\\"Could not connect to AzureOpenAI API.\\\") from e\\n message = output.invoke(inputs)\\n result = message.content if hasattr(message, \\\"content\\\") else message\\n self.status = result\\n return result\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"inputs\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"inputs\",\"display_name\":\"Input\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"max_tokens\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":1000,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_tokens\",\"display_name\":\"Max Tokens\",\"advanced\":true,\"dynamic\":false,\"info\":\"Maximum number of tokens to generate.\",\"title_case\":false},\"model\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"gpt-35-turbo\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"gpt-35-turbo\",\"gpt-35-turbo-16k\",\"gpt-35-turbo-instruct\",\"gpt-4\",\"gpt-4-32k\",\"gpt-4-vision\"],\"name\":\"model\",\"display_name\":\"Model Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"temperature\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.7,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Generate text using LLM model from Azure OpenAI.\",\"base_classes\":[\"BaseLanguageModel\",\"Runnable\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"object\"],\"display_name\":\"AzureOpenAI Model\",\"documentation\":\"https://python.langchain.com/docs/integrations/llms/azure_openai\",\"custom_fields\":{\"model\":null,\"azure_endpoint\":null,\"inputs\":null,\"azure_deployment\":null,\"api_key\":null,\"api_version\":null,\"temperature\":null,\"max_tokens\":null},\"output_types\":[\"BaseLanguageModel\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"AmazonBedrockModel\":{\"template\":{\"cache\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"cache\",\"display_name\":\"Cache\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain_community.chat_models.bedrock import BedrockChat\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Text\\n\\n\\nclass AmazonBedrockComponent(CustomComponent):\\n display_name: str = \\\"Amazon Bedrock Model\\\"\\n description: str = \\\"Generate text using LLM model from Amazon Bedrock.\\\"\\n\\n def build_config(self):\\n return {\\n \\\"model_id\\\": {\\n \\\"display_name\\\": \\\"Model Id\\\",\\n \\\"options\\\": [\\n \\\"ai21.j2-grande-instruct\\\",\\n \\\"ai21.j2-jumbo-instruct\\\",\\n \\\"ai21.j2-mid\\\",\\n \\\"ai21.j2-mid-v1\\\",\\n \\\"ai21.j2-ultra\\\",\\n \\\"ai21.j2-ultra-v1\\\",\\n \\\"anthropic.claude-instant-v1\\\",\\n \\\"anthropic.claude-v1\\\",\\n \\\"anthropic.claude-v2\\\",\\n \\\"cohere.command-text-v14\\\",\\n ],\\n },\\n \\\"credentials_profile_name\\\": {\\\"display_name\\\": \\\"Credentials Profile Name\\\"},\\n \\\"streaming\\\": {\\\"display_name\\\": \\\"Streaming\\\", \\\"field_type\\\": \\\"bool\\\"},\\n \\\"endpoint_url\\\": {\\\"display_name\\\": \\\"Endpoint URL\\\"},\\n \\\"region_name\\\": {\\\"display_name\\\": \\\"Region Name\\\"},\\n \\\"model_kwargs\\\": {\\\"display_name\\\": \\\"Model Kwargs\\\"},\\n \\\"cache\\\": {\\\"display_name\\\": \\\"Cache\\\"},\\n \\\"code\\\": {\\\"advanced\\\": True},\\n \\\"inputs\\\": {\\\"display_name\\\": \\\"Input\\\"},\\n }\\n\\n def build(\\n self,\\n inputs: str,\\n model_id: str = \\\"anthropic.claude-instant-v1\\\",\\n credentials_profile_name: Optional[str] = None,\\n region_name: Optional[str] = None,\\n model_kwargs: Optional[dict] = None,\\n endpoint_url: Optional[str] = None,\\n streaming: bool = False,\\n cache: Optional[bool] = None,\\n ) -> Text:\\n try:\\n output = BedrockChat(\\n credentials_profile_name=credentials_profile_name,\\n model_id=model_id,\\n region_name=region_name,\\n model_kwargs=model_kwargs,\\n endpoint_url=endpoint_url,\\n streaming=streaming,\\n cache=cache,\\n ) # type: ignore\\n except Exception as e:\\n raise ValueError(\\\"Could not connect to AmazonBedrock API.\\\") from e\\n message = output.invoke(inputs)\\n result = message.content if hasattr(message, \\\"content\\\") else message\\n self.status = result\\n return result\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":true,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"credentials_profile_name\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"credentials_profile_name\",\"display_name\":\"Credentials Profile Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"endpoint_url\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"endpoint_url\",\"display_name\":\"Endpoint URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"inputs\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"inputs\",\"display_name\":\"Input\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"model_id\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"anthropic.claude-instant-v1\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"ai21.j2-grande-instruct\",\"ai21.j2-jumbo-instruct\",\"ai21.j2-mid\",\"ai21.j2-mid-v1\",\"ai21.j2-ultra\",\"ai21.j2-ultra-v1\",\"anthropic.claude-instant-v1\",\"anthropic.claude-v1\",\"anthropic.claude-v2\",\"cohere.command-text-v14\"],\"name\":\"model_id\",\"display_name\":\"Model Id\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"model_kwargs\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model_kwargs\",\"display_name\":\"Model Kwargs\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"region_name\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"region_name\",\"display_name\":\"Region Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"streaming\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"streaming\",\"display_name\":\"Streaming\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Generate text using LLM model from Amazon Bedrock.\",\"base_classes\":[\"Text\",\"object\"],\"display_name\":\"Amazon Bedrock Model\",\"documentation\":\"\",\"custom_fields\":{\"inputs\":null,\"model_id\":null,\"credentials_profile_name\":null,\"region_name\":null,\"model_kwargs\":null,\"endpoint_url\":null,\"streaming\":null,\"cache\":null},\"output_types\":[\"Text\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"CohereModel\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from langchain_community.chat_models.cohere import ChatCohere\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Text\\n\\n\\nclass CohereComponent(CustomComponent):\\n display_name = \\\"CohereModel\\\"\\n description = \\\"Generate text using Cohere large language models.\\\"\\n documentation = \\\"https://python.langchain.com/docs/modules/model_io/models/llms/integrations/cohere\\\"\\n\\n def build_config(self):\\n return {\\n \\\"cohere_api_key\\\": {\\n \\\"display_name\\\": \\\"Cohere API Key\\\",\\n \\\"type\\\": \\\"password\\\",\\n \\\"password\\\": True,\\n },\\n \\\"max_tokens\\\": {\\n \\\"display_name\\\": \\\"Max Tokens\\\",\\n \\\"default\\\": 256,\\n \\\"type\\\": \\\"int\\\",\\n \\\"show\\\": True,\\n },\\n \\\"temperature\\\": {\\n \\\"display_name\\\": \\\"Temperature\\\",\\n \\\"default\\\": 0.75,\\n \\\"type\\\": \\\"float\\\",\\n \\\"show\\\": True,\\n },\\n \\\"inputs\\\": {\\\"display_name\\\": \\\"Input\\\"},\\n }\\n\\n def build(\\n self,\\n cohere_api_key: str,\\n inputs: str,\\n max_tokens: int = 256,\\n temperature: float = 0.75,\\n ) -> Text:\\n output = ChatCohere(\\n cohere_api_key=cohere_api_key,\\n max_tokens=max_tokens,\\n temperature=temperature,\\n )\\n message = output.invoke(inputs)\\n result = message.content if hasattr(message, \\\"content\\\") else message\\n self.status = result\\n return result\\n return result\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"cohere_api_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"cohere_api_key\",\"display_name\":\"Cohere API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"inputs\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"inputs\",\"display_name\":\"Input\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"max_tokens\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":256,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_tokens\",\"display_name\":\"Max Tokens\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"temperature\":{\"type\":\"float\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.75,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Generate text using Cohere large language models.\",\"base_classes\":[\"Text\",\"object\"],\"display_name\":\"CohereModel\",\"documentation\":\"https://python.langchain.com/docs/modules/model_io/models/llms/integrations/cohere\",\"custom_fields\":{\"cohere_api_key\":null,\"inputs\":null,\"max_tokens\":null,\"temperature\":null},\"output_types\":[\"Text\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true}},\"model_specs\":{\"AmazonBedrockSpecs\":{\"template\":{\"cache\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"cache\",\"display_name\":\"Cache\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain.llms.base import BaseLLM\\nfrom langchain_community.llms.bedrock import Bedrock\\n\\n\\nfrom langflow import CustomComponent\\n\\n\\nclass AmazonBedrockComponent(CustomComponent):\\n display_name: str = \\\"Amazon Bedrock\\\"\\n description: str = \\\"LLM model from Amazon Bedrock.\\\"\\n\\n def build_config(self):\\n return {\\n \\\"model_id\\\": {\\n \\\"display_name\\\": \\\"Model Id\\\",\\n \\\"options\\\": [\\n \\\"ai21.j2-grande-instruct\\\",\\n \\\"ai21.j2-jumbo-instruct\\\",\\n \\\"ai21.j2-mid\\\",\\n \\\"ai21.j2-mid-v1\\\",\\n \\\"ai21.j2-ultra\\\",\\n \\\"ai21.j2-ultra-v1\\\",\\n \\\"anthropic.claude-instant-v1\\\",\\n \\\"anthropic.claude-v1\\\",\\n \\\"anthropic.claude-v2\\\",\\n \\\"cohere.command-text-v14\\\",\\n ],\\n },\\n \\\"credentials_profile_name\\\": {\\\"display_name\\\": \\\"Credentials Profile Name\\\"},\\n \\\"streaming\\\": {\\\"display_name\\\": \\\"Streaming\\\", \\\"field_type\\\": \\\"bool\\\"},\\n \\\"endpoint_url\\\": {\\\"display_name\\\": \\\"Endpoint URL\\\"},\\n \\\"region_name\\\": {\\\"display_name\\\": \\\"Region Name\\\"},\\n \\\"model_kwargs\\\": {\\\"display_name\\\": \\\"Model Kwargs\\\"},\\n \\\"cache\\\": {\\\"display_name\\\": \\\"Cache\\\"},\\n \\\"code\\\": {\\\"advanced\\\": True},\\n }\\n\\n def build(\\n self,\\n model_id: str = \\\"anthropic.claude-instant-v1\\\",\\n credentials_profile_name: Optional[str] = None,\\n region_name: Optional[str] = None,\\n model_kwargs: Optional[dict] = None,\\n endpoint_url: Optional[str] = None,\\n streaming: bool = False,\\n cache: Optional[bool] = None,\\n ) -> BaseLLM:\\n try:\\n output = Bedrock(\\n credentials_profile_name=credentials_profile_name,\\n model_id=model_id,\\n region_name=region_name,\\n model_kwargs=model_kwargs,\\n endpoint_url=endpoint_url,\\n streaming=streaming,\\n cache=cache,\\n ) # type: ignore\\n except Exception as e:\\n raise ValueError(\\\"Could not connect to AmazonBedrock API.\\\") from e\\n return output\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":true,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"credentials_profile_name\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"credentials_profile_name\",\"display_name\":\"Credentials Profile Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"endpoint_url\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"endpoint_url\",\"display_name\":\"Endpoint URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"model_id\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"anthropic.claude-instant-v1\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"ai21.j2-grande-instruct\",\"ai21.j2-jumbo-instruct\",\"ai21.j2-mid\",\"ai21.j2-mid-v1\",\"ai21.j2-ultra\",\"ai21.j2-ultra-v1\",\"anthropic.claude-instant-v1\",\"anthropic.claude-v1\",\"anthropic.claude-v2\",\"cohere.command-text-v14\"],\"name\":\"model_id\",\"display_name\":\"Model Id\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"model_kwargs\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model_kwargs\",\"display_name\":\"Model Kwargs\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"region_name\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"region_name\",\"display_name\":\"Region Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"streaming\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"streaming\",\"display_name\":\"Streaming\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"LLM model from Amazon Bedrock.\",\"base_classes\":[\"BaseLanguageModel\",\"Runnable\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"object\",\"BaseLLM\"],\"display_name\":\"Amazon Bedrock\",\"documentation\":\"\",\"custom_fields\":{\"model_id\":null,\"credentials_profile_name\":null,\"region_name\":null,\"model_kwargs\":null,\"endpoint_url\":null,\"streaming\":null,\"cache\":null},\"output_types\":[\"BaseLLM\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"ChatVertexAISpecs\":{\"template\":{\"credentials\":{\"type\":\"file\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[\".json\"],\"password\":false,\"name\":\"credentials\",\"display_name\":\"Credentials\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"examples\":{\"type\":\"BaseMessage\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":true,\"value\":[],\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"examples\",\"display_name\":\"Examples\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import List, Optional, Union\\n\\nfrom langchain.llms import BaseLLM\\nfrom langchain_community.chat_models.vertexai import ChatVertexAI\\nfrom langchain_core.messages.base import BaseMessage\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import BaseLanguageModel\\n\\n\\nclass ChatVertexAIComponent(CustomComponent):\\n display_name = \\\"ChatVertexAI\\\"\\n description = \\\"`Vertex AI` Chat large language models API.\\\"\\n\\n def build_config(self):\\n return {\\n \\\"credentials\\\": {\\n \\\"display_name\\\": \\\"Credentials\\\",\\n \\\"field_type\\\": \\\"file\\\",\\n \\\"file_types\\\": [\\\".json\\\"],\\n \\\"file_path\\\": None,\\n },\\n \\\"examples\\\": {\\n \\\"display_name\\\": \\\"Examples\\\",\\n \\\"multiline\\\": True,\\n },\\n \\\"location\\\": {\\n \\\"display_name\\\": \\\"Location\\\",\\n \\\"value\\\": \\\"us-central1\\\",\\n },\\n \\\"max_output_tokens\\\": {\\n \\\"display_name\\\": \\\"Max Output Tokens\\\",\\n \\\"value\\\": 128,\\n \\\"advanced\\\": True,\\n },\\n \\\"model_name\\\": {\\n \\\"display_name\\\": \\\"Model Name\\\",\\n \\\"value\\\": \\\"chat-bison\\\",\\n },\\n \\\"project\\\": {\\n \\\"display_name\\\": \\\"Project\\\",\\n },\\n \\\"temperature\\\": {\\n \\\"display_name\\\": \\\"Temperature\\\",\\n \\\"value\\\": 0.0,\\n },\\n \\\"top_k\\\": {\\n \\\"display_name\\\": \\\"Top K\\\",\\n \\\"value\\\": 40,\\n \\\"advanced\\\": True,\\n },\\n \\\"top_p\\\": {\\n \\\"display_name\\\": \\\"Top P\\\",\\n \\\"value\\\": 0.95,\\n \\\"advanced\\\": True,\\n },\\n \\\"verbose\\\": {\\n \\\"display_name\\\": \\\"Verbose\\\",\\n \\\"value\\\": False,\\n \\\"advanced\\\": True,\\n },\\n }\\n\\n def build(\\n self,\\n credentials: Optional[str],\\n project: str,\\n examples: Optional[List[BaseMessage]] = [],\\n location: str = \\\"us-central1\\\",\\n max_output_tokens: int = 128,\\n model_name: str = \\\"chat-bison\\\",\\n temperature: float = 0.0,\\n top_k: int = 40,\\n top_p: float = 0.95,\\n verbose: bool = False,\\n ) -> Union[BaseLanguageModel, BaseLLM]:\\n return ChatVertexAI(\\n credentials=credentials,\\n examples=examples,\\n location=location,\\n max_output_tokens=max_output_tokens,\\n model_name=model_name,\\n project=project,\\n temperature=temperature,\\n top_k=top_k,\\n top_p=top_p,\\n verbose=verbose,\\n )\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"location\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"us-central1\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"location\",\"display_name\":\"Location\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"max_output_tokens\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":128,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_output_tokens\",\"display_name\":\"Max Output Tokens\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"model_name\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"chat-bison\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model_name\",\"display_name\":\"Model Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"project\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"project\",\"display_name\":\"Project\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"temperature\":{\"type\":\"float\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.0,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"top_k\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":40,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_k\",\"display_name\":\"Top K\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"top_p\":{\"type\":\"float\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.95,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_p\",\"display_name\":\"Top P\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"verbose\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"verbose\",\"display_name\":\"Verbose\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"`Vertex AI` Chat large language models API.\",\"base_classes\":[\"BaseLanguageModel\",\"Runnable\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"object\",\"BaseLLM\"],\"display_name\":\"ChatVertexAI\",\"documentation\":\"\",\"custom_fields\":{\"credentials\":null,\"project\":null,\"examples\":null,\"location\":null,\"max_output_tokens\":null,\"model_name\":null,\"temperature\":null,\"top_k\":null,\"top_p\":null,\"verbose\":null},\"output_types\":[\"BaseLanguageModel\",\"BaseLLM\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"VertexAISpecs\":{\"template\":{\"credentials\":{\"type\":\"file\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[\".json\"],\"file_path\":\"\",\"password\":false,\"name\":\"credentials\",\"display_name\":\"Credentials\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langchain.llms import BaseLLM\\nfrom typing import Optional, Union, Callable, Dict\\nfrom langchain_community.llms.vertexai import VertexAI\\n\\n\\nclass VertexAIComponent(CustomComponent):\\n display_name = \\\"VertexAI\\\"\\n description = \\\"Google Vertex AI large language models\\\"\\n\\n def build_config(self):\\n return {\\n \\\"credentials\\\": {\\n \\\"display_name\\\": \\\"Credentials\\\",\\n \\\"field_type\\\": \\\"file\\\",\\n \\\"file_types\\\": [\\\".json\\\"],\\n \\\"required\\\": False,\\n \\\"value\\\": None,\\n },\\n \\\"location\\\": {\\n \\\"display_name\\\": \\\"Location\\\",\\n \\\"type\\\": \\\"str\\\",\\n \\\"advanced\\\": True,\\n \\\"value\\\": \\\"us-central1\\\",\\n \\\"required\\\": False,\\n },\\n \\\"max_output_tokens\\\": {\\n \\\"display_name\\\": \\\"Max Output Tokens\\\",\\n \\\"field_type\\\": \\\"int\\\",\\n \\\"value\\\": 128,\\n \\\"required\\\": False,\\n \\\"advanced\\\": True,\\n },\\n \\\"max_retries\\\": {\\n \\\"display_name\\\": \\\"Max Retries\\\",\\n \\\"type\\\": \\\"int\\\",\\n \\\"value\\\": 6,\\n \\\"required\\\": False,\\n \\\"advanced\\\": True,\\n },\\n \\\"metadata\\\": {\\n \\\"display_name\\\": \\\"Metadata\\\",\\n \\\"field_type\\\": \\\"dict\\\",\\n \\\"required\\\": False,\\n \\\"default\\\": {},\\n },\\n \\\"model_name\\\": {\\n \\\"display_name\\\": \\\"Model Name\\\",\\n \\\"type\\\": \\\"str\\\",\\n \\\"value\\\": \\\"text-bison\\\",\\n \\\"required\\\": False,\\n },\\n \\\"n\\\": {\\n \\\"advanced\\\": True,\\n \\\"display_name\\\": \\\"N\\\",\\n \\\"field_type\\\": \\\"int\\\",\\n \\\"value\\\": 1,\\n \\\"required\\\": False,\\n },\\n \\\"project\\\": {\\n \\\"display_name\\\": \\\"Project\\\",\\n \\\"type\\\": \\\"str\\\",\\n \\\"required\\\": False,\\n \\\"default\\\": None,\\n },\\n \\\"request_parallelism\\\": {\\n \\\"display_name\\\": \\\"Request Parallelism\\\",\\n \\\"field_type\\\": \\\"int\\\",\\n \\\"value\\\": 5,\\n \\\"required\\\": False,\\n \\\"advanced\\\": True,\\n },\\n \\\"streaming\\\": {\\n \\\"display_name\\\": \\\"Streaming\\\",\\n \\\"field_type\\\": \\\"bool\\\",\\n \\\"value\\\": False,\\n \\\"required\\\": False,\\n \\\"advanced\\\": True,\\n },\\n \\\"temperature\\\": {\\n \\\"display_name\\\": \\\"Temperature\\\",\\n \\\"field_type\\\": \\\"float\\\",\\n \\\"value\\\": 0.0,\\n \\\"required\\\": False,\\n \\\"advanced\\\": True,\\n },\\n \\\"top_k\\\": {\\\"display_name\\\": \\\"Top K\\\", \\\"type\\\": \\\"int\\\", \\\"default\\\": 40, \\\"required\\\": False, \\\"advanced\\\": True},\\n \\\"top_p\\\": {\\n \\\"display_name\\\": \\\"Top P\\\",\\n \\\"field_type\\\": \\\"float\\\",\\n \\\"value\\\": 0.95,\\n \\\"required\\\": False,\\n \\\"advanced\\\": True,\\n },\\n \\\"tuned_model_name\\\": {\\n \\\"display_name\\\": \\\"Tuned Model Name\\\",\\n \\\"type\\\": \\\"str\\\",\\n \\\"required\\\": False,\\n \\\"value\\\": None,\\n \\\"advanced\\\": True,\\n },\\n \\\"verbose\\\": {\\n \\\"display_name\\\": \\\"Verbose\\\",\\n \\\"field_type\\\": \\\"bool\\\",\\n \\\"value\\\": False,\\n \\\"required\\\": False,\\n },\\n \\\"name\\\": {\\\"display_name\\\": \\\"Name\\\", \\\"field_type\\\": \\\"str\\\"},\\n }\\n\\n def build(\\n self,\\n credentials: Optional[str] = None,\\n location: str = \\\"us-central1\\\",\\n max_output_tokens: int = 128,\\n max_retries: int = 6,\\n metadata: Dict = {},\\n model_name: str = \\\"text-bison\\\",\\n n: int = 1,\\n name: Optional[str] = None,\\n project: Optional[str] = None,\\n request_parallelism: int = 5,\\n streaming: bool = False,\\n temperature: float = 0.0,\\n top_k: int = 40,\\n top_p: float = 0.95,\\n tuned_model_name: Optional[str] = None,\\n verbose: bool = False,\\n ) -> Union[BaseLLM, Callable]:\\n return VertexAI(\\n credentials=credentials,\\n location=location,\\n max_output_tokens=max_output_tokens,\\n max_retries=max_retries,\\n metadata=metadata,\\n model_name=model_name,\\n n=n,\\n name=name,\\n project=project,\\n request_parallelism=request_parallelism,\\n streaming=streaming,\\n temperature=temperature,\\n top_k=top_k,\\n top_p=top_p,\\n tuned_model_name=tuned_model_name,\\n verbose=verbose,\\n )\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"location\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"us-central1\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"location\",\"display_name\":\"Location\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"max_output_tokens\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":128,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_output_tokens\",\"display_name\":\"Max Output Tokens\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"max_retries\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":6,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_retries\",\"display_name\":\"Max Retries\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"model_name\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"text-bison\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model_name\",\"display_name\":\"Model Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"n\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":1,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"n\",\"display_name\":\"N\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"name\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"name\",\"display_name\":\"Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"project\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"project\",\"display_name\":\"Project\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"request_parallelism\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":5,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"request_parallelism\",\"display_name\":\"Request Parallelism\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"streaming\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"streaming\",\"display_name\":\"Streaming\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"temperature\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.0,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"top_k\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":40,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_k\",\"display_name\":\"Top K\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"top_p\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.95,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_p\",\"display_name\":\"Top P\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"tuned_model_name\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"tuned_model_name\",\"display_name\":\"Tuned Model Name\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"verbose\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"verbose\",\"display_name\":\"Verbose\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Google Vertex AI large language models\",\"base_classes\":[\"BaseLanguageModel\",\"Runnable\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"object\",\"BaseLLM\",\"Callable\"],\"display_name\":\"VertexAI\",\"documentation\":\"\",\"custom_fields\":{\"credentials\":null,\"location\":null,\"max_output_tokens\":null,\"max_retries\":null,\"metadata\":null,\"model_name\":null,\"n\":null,\"name\":null,\"project\":null,\"request_parallelism\":null,\"streaming\":null,\"temperature\":null,\"top_k\":null,\"top_p\":null,\"tuned_model_name\":null,\"verbose\":null},\"output_types\":[\"BaseLLM\",\"Callable\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"ChatAnthropicSpecs\":{\"template\":{\"anthropic_api_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"anthropic_api_key\",\"display_name\":\"Anthropic API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"anthropic_api_url\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"anthropic_api_url\",\"display_name\":\"Anthropic API URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from pydantic.v1.types import SecretStr\\nfrom langflow import CustomComponent\\nfrom typing import Optional, Union, Callable\\nfrom langflow.field_typing import BaseLanguageModel\\nfrom langchain_community.chat_models.anthropic import ChatAnthropic\\n\\n\\nclass ChatAnthropicComponent(CustomComponent):\\n display_name = \\\"ChatAnthropic\\\"\\n description = \\\"`Anthropic` chat large language models.\\\"\\n documentation = \\\"https://python.langchain.com/docs/modules/model_io/models/chat/integrations/anthropic\\\"\\n\\n def build_config(self):\\n return {\\n \\\"anthropic_api_key\\\": {\\n \\\"display_name\\\": \\\"Anthropic API Key\\\",\\n \\\"field_type\\\": \\\"str\\\",\\n \\\"password\\\": True,\\n },\\n \\\"anthropic_api_url\\\": {\\n \\\"display_name\\\": \\\"Anthropic API URL\\\",\\n \\\"field_type\\\": \\\"str\\\",\\n },\\n \\\"model_kwargs\\\": {\\n \\\"display_name\\\": \\\"Model Kwargs\\\",\\n \\\"field_type\\\": \\\"dict\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"temperature\\\": {\\n \\\"display_name\\\": \\\"Temperature\\\",\\n \\\"field_type\\\": \\\"float\\\",\\n },\\n }\\n\\n def build(\\n self,\\n anthropic_api_key: str,\\n anthropic_api_url: Optional[str] = None,\\n model_kwargs: dict = {},\\n temperature: Optional[float] = None,\\n ) -> Union[BaseLanguageModel, Callable]:\\n return ChatAnthropic(\\n anthropic_api_key=SecretStr(anthropic_api_key),\\n anthropic_api_url=anthropic_api_url,\\n model_kwargs=model_kwargs,\\n temperature=temperature,\\n )\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"model_kwargs\":{\"type\":\"dict\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model_kwargs\",\"display_name\":\"Model Kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"temperature\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"`Anthropic` chat large language models.\",\"base_classes\":[\"BaseLanguageModel\",\"Runnable\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"object\",\"Callable\"],\"display_name\":\"ChatAnthropic\",\"documentation\":\"https://python.langchain.com/docs/modules/model_io/models/chat/integrations/anthropic\",\"custom_fields\":{\"anthropic_api_key\":null,\"anthropic_api_url\":null,\"model_kwargs\":null,\"temperature\":null},\"output_types\":[\"BaseLanguageModel\",\"Callable\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"AzureChatOpenAISpecs\":{\"template\":{\"api_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"api_key\",\"display_name\":\"API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"api_version\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"2023-12-01-preview\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"2023-03-15-preview\",\"2023-05-15\",\"2023-06-01-preview\",\"2023-07-01-preview\",\"2023-08-01-preview\",\"2023-09-01-preview\",\"2023-12-01-preview\"],\"name\":\"api_version\",\"display_name\":\"API Version\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"azure_deployment\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"azure_deployment\",\"display_name\":\"Deployment Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"azure_endpoint\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"azure_endpoint\",\"display_name\":\"Azure Endpoint\",\"advanced\":false,\"dynamic\":false,\"info\":\"Your Azure endpoint, including the resource.. Example: `https://example-resource.azure.openai.com/`\",\"title_case\":false,\"input_types\":[\"Text\"]},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain.llms.base import BaseLanguageModel\\nfrom langchain_community.chat_models.azure_openai import AzureChatOpenAI\\n\\nfrom langflow import CustomComponent\\n\\n\\nclass AzureChatOpenAISpecsComponent(CustomComponent):\\n display_name: str = \\\"AzureChatOpenAI\\\"\\n description: str = \\\"LLM model from Azure OpenAI.\\\"\\n documentation: str = \\\"https://python.langchain.com/docs/integrations/llms/azure_openai\\\"\\n beta = False\\n\\n AZURE_OPENAI_MODELS = [\\n \\\"gpt-35-turbo\\\",\\n \\\"gpt-35-turbo-16k\\\",\\n \\\"gpt-35-turbo-instruct\\\",\\n \\\"gpt-4\\\",\\n \\\"gpt-4-32k\\\",\\n \\\"gpt-4-vision\\\",\\n ]\\n\\n AZURE_OPENAI_API_VERSIONS = [\\n \\\"2023-03-15-preview\\\",\\n \\\"2023-05-15\\\",\\n \\\"2023-06-01-preview\\\",\\n \\\"2023-07-01-preview\\\",\\n \\\"2023-08-01-preview\\\",\\n \\\"2023-09-01-preview\\\",\\n \\\"2023-12-01-preview\\\",\\n ]\\n\\n def build_config(self):\\n return {\\n \\\"model\\\": {\\n \\\"display_name\\\": \\\"Model Name\\\",\\n \\\"value\\\": self.AZURE_OPENAI_MODELS[0],\\n \\\"options\\\": self.AZURE_OPENAI_MODELS,\\n \\\"required\\\": True,\\n },\\n \\\"azure_endpoint\\\": {\\n \\\"display_name\\\": \\\"Azure Endpoint\\\",\\n \\\"required\\\": True,\\n \\\"info\\\": \\\"Your Azure endpoint, including the resource.. Example: `https://example-resource.azure.openai.com/`\\\",\\n },\\n \\\"azure_deployment\\\": {\\n \\\"display_name\\\": \\\"Deployment Name\\\",\\n \\\"required\\\": True,\\n },\\n \\\"api_version\\\": {\\n \\\"display_name\\\": \\\"API Version\\\",\\n \\\"options\\\": self.AZURE_OPENAI_API_VERSIONS,\\n \\\"value\\\": self.AZURE_OPENAI_API_VERSIONS[-1],\\n \\\"required\\\": True,\\n \\\"advanced\\\": True,\\n },\\n \\\"api_key\\\": {\\\"display_name\\\": \\\"API Key\\\", \\\"required\\\": True, \\\"password\\\": True},\\n \\\"temperature\\\": {\\n \\\"display_name\\\": \\\"Temperature\\\",\\n \\\"value\\\": 0.7,\\n \\\"field_type\\\": \\\"float\\\",\\n \\\"required\\\": False,\\n },\\n \\\"max_tokens\\\": {\\n \\\"display_name\\\": \\\"Max Tokens\\\",\\n \\\"value\\\": 1000,\\n \\\"required\\\": False,\\n \\\"field_type\\\": \\\"int\\\",\\n \\\"advanced\\\": True,\\n \\\"info\\\": \\\"Maximum number of tokens to generate.\\\",\\n },\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(\\n self,\\n model: str,\\n azure_endpoint: str,\\n azure_deployment: str,\\n api_key: str,\\n api_version: str,\\n temperature: float = 0.7,\\n max_tokens: Optional[int] = 1000,\\n ) -> BaseLanguageModel:\\n try:\\n llm = AzureChatOpenAI(\\n model=model,\\n azure_endpoint=azure_endpoint,\\n azure_deployment=azure_deployment,\\n api_version=api_version,\\n api_key=api_key,\\n temperature=temperature,\\n max_tokens=max_tokens,\\n )\\n except Exception as e:\\n raise ValueError(\\\"Could not connect to AzureOpenAI API.\\\") from e\\n return llm\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"max_tokens\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":1000,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_tokens\",\"display_name\":\"Max Tokens\",\"advanced\":true,\"dynamic\":false,\"info\":\"Maximum number of tokens to generate.\",\"title_case\":false},\"model\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"gpt-35-turbo\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"gpt-35-turbo\",\"gpt-35-turbo-16k\",\"gpt-35-turbo-instruct\",\"gpt-4\",\"gpt-4-32k\",\"gpt-4-vision\"],\"name\":\"model\",\"display_name\":\"Model Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"temperature\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.7,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"LLM model from Azure OpenAI.\",\"base_classes\":[\"BaseLanguageModel\",\"Runnable\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"object\"],\"display_name\":\"AzureChatOpenAI\",\"documentation\":\"https://python.langchain.com/docs/integrations/llms/azure_openai\",\"custom_fields\":{\"model\":null,\"azure_endpoint\":null,\"azure_deployment\":null,\"api_key\":null,\"api_version\":null,\"temperature\":null,\"max_tokens\":null},\"output_types\":[\"BaseLanguageModel\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"ChatOllamaEndpointSpecs\":{\"template\":{\"metadata\":{\"type\":\"Dict[str, Any]\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":true,\"dynamic\":false,\"info\":\"Metadata to add to the run trace.\",\"title_case\":false},\"stop\":{\"type\":\"list\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"stop\",\"display_name\":\"Stop Tokens\",\"advanced\":true,\"dynamic\":false,\"info\":\"List of tokens to signal the model to stop generating text.\",\"title_case\":false},\"tags\":{\"type\":\"list\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"tags\",\"display_name\":\"Tags\",\"advanced\":true,\"dynamic\":false,\"info\":\"Tags to add to the run trace.\",\"title_case\":false},\"base_url\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"base_url\",\"display_name\":\"Base URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"Endpoint of the Ollama API. Defaults to 'http://localhost:11434' if not specified.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"cache\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"cache\",\"display_name\":\"Cache\",\"advanced\":true,\"dynamic\":false,\"info\":\"Enable or disable caching.\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Any, Dict, List, Optional\\n\\n# from langchain_community.chat_models import ChatOllama\\nfrom langchain_community.chat_models import ChatOllama\\nfrom langchain_core.language_models.chat_models import BaseChatModel\\n\\n# from langchain.chat_models import ChatOllama\\nfrom langflow import CustomComponent\\n\\n# whe When a callback component is added to Langflow, the comment must be uncommented.\\n# from langchain.callbacks.manager import CallbackManager\\n\\n\\nclass ChatOllamaComponent(CustomComponent):\\n display_name = \\\"ChatOllama\\\"\\n description = \\\"Local LLM for chat with Ollama.\\\"\\n\\n def build_config(self) -> dict:\\n return {\\n \\\"base_url\\\": {\\n \\\"display_name\\\": \\\"Base URL\\\",\\n \\\"info\\\": \\\"Endpoint of the Ollama API. Defaults to 'http://localhost:11434' if not specified.\\\",\\n },\\n \\\"model\\\": {\\n \\\"display_name\\\": \\\"Model Name\\\",\\n \\\"value\\\": \\\"llama2\\\",\\n \\\"info\\\": \\\"Refer to https://ollama.ai/library for more models.\\\",\\n },\\n \\\"temperature\\\": {\\n \\\"display_name\\\": \\\"Temperature\\\",\\n \\\"field_type\\\": \\\"float\\\",\\n \\\"value\\\": 0.8,\\n \\\"info\\\": \\\"Controls the creativity of model responses.\\\",\\n },\\n \\\"cache\\\": {\\n \\\"display_name\\\": \\\"Cache\\\",\\n \\\"field_type\\\": \\\"bool\\\",\\n \\\"info\\\": \\\"Enable or disable caching.\\\",\\n \\\"advanced\\\": True,\\n \\\"value\\\": False,\\n },\\n ### When a callback component is added to Langflow, the comment must be uncommented. ###\\n # \\\"callback_manager\\\": {\\n # \\\"display_name\\\": \\\"Callback Manager\\\",\\n # \\\"info\\\": \\\"Optional callback manager for additional functionality.\\\",\\n # \\\"advanced\\\": True,\\n # },\\n # \\\"callbacks\\\": {\\n # \\\"display_name\\\": \\\"Callbacks\\\",\\n # \\\"info\\\": \\\"Callbacks to execute during model runtime.\\\",\\n # \\\"advanced\\\": True,\\n # },\\n ########################################################################################\\n \\\"format\\\": {\\n \\\"display_name\\\": \\\"Format\\\",\\n \\\"field_type\\\": \\\"str\\\",\\n \\\"info\\\": \\\"Specify the format of the output (e.g., json).\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"metadata\\\": {\\n \\\"display_name\\\": \\\"Metadata\\\",\\n \\\"info\\\": \\\"Metadata to add to the run trace.\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"mirostat\\\": {\\n \\\"display_name\\\": \\\"Mirostat\\\",\\n \\\"options\\\": [\\\"Disabled\\\", \\\"Mirostat\\\", \\\"Mirostat 2.0\\\"],\\n \\\"info\\\": \\\"Enable/disable Mirostat sampling for controlling perplexity.\\\",\\n \\\"value\\\": \\\"Disabled\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"mirostat_eta\\\": {\\n \\\"display_name\\\": \\\"Mirostat Eta\\\",\\n \\\"field_type\\\": \\\"float\\\",\\n \\\"info\\\": \\\"Learning rate for Mirostat algorithm. (Default: 0.1)\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"mirostat_tau\\\": {\\n \\\"display_name\\\": \\\"Mirostat Tau\\\",\\n \\\"field_type\\\": \\\"float\\\",\\n \\\"info\\\": \\\"Controls the balance between coherence and diversity of the output. (Default: 5.0)\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"num_ctx\\\": {\\n \\\"display_name\\\": \\\"Context Window Size\\\",\\n \\\"field_type\\\": \\\"int\\\",\\n \\\"info\\\": \\\"Size of the context window for generating tokens. (Default: 2048)\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"num_gpu\\\": {\\n \\\"display_name\\\": \\\"Number of GPUs\\\",\\n \\\"field_type\\\": \\\"int\\\",\\n \\\"info\\\": \\\"Number of GPUs to use for computation. (Default: 1 on macOS, 0 to disable)\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"num_thread\\\": {\\n \\\"display_name\\\": \\\"Number of Threads\\\",\\n \\\"field_type\\\": \\\"int\\\",\\n \\\"info\\\": \\\"Number of threads to use during computation. (Default: detected for optimal performance)\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"repeat_last_n\\\": {\\n \\\"display_name\\\": \\\"Repeat Last N\\\",\\n \\\"field_type\\\": \\\"int\\\",\\n \\\"info\\\": \\\"How far back the model looks to prevent repetition. (Default: 64, 0 = disabled, -1 = num_ctx)\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"repeat_penalty\\\": {\\n \\\"display_name\\\": \\\"Repeat Penalty\\\",\\n \\\"field_type\\\": \\\"float\\\",\\n \\\"info\\\": \\\"Penalty for repetitions in generated text. (Default: 1.1)\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"tfs_z\\\": {\\n \\\"display_name\\\": \\\"TFS Z\\\",\\n \\\"field_type\\\": \\\"float\\\",\\n \\\"info\\\": \\\"Tail free sampling value. (Default: 1)\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"timeout\\\": {\\n \\\"display_name\\\": \\\"Timeout\\\",\\n \\\"field_type\\\": \\\"int\\\",\\n \\\"info\\\": \\\"Timeout for the request stream.\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"top_k\\\": {\\n \\\"display_name\\\": \\\"Top K\\\",\\n \\\"field_type\\\": \\\"int\\\",\\n \\\"info\\\": \\\"Limits token selection to top K. (Default: 40)\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"top_p\\\": {\\n \\\"display_name\\\": \\\"Top P\\\",\\n \\\"field_type\\\": \\\"float\\\",\\n \\\"info\\\": \\\"Works together with top-k. (Default: 0.9)\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"verbose\\\": {\\n \\\"display_name\\\": \\\"Verbose\\\",\\n \\\"field_type\\\": \\\"bool\\\",\\n \\\"info\\\": \\\"Whether to print out response text.\\\",\\n },\\n \\\"tags\\\": {\\n \\\"display_name\\\": \\\"Tags\\\",\\n \\\"field_type\\\": \\\"list\\\",\\n \\\"info\\\": \\\"Tags to add to the run trace.\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"stop\\\": {\\n \\\"display_name\\\": \\\"Stop Tokens\\\",\\n \\\"field_type\\\": \\\"list\\\",\\n \\\"info\\\": \\\"List of tokens to signal the model to stop generating text.\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"system\\\": {\\n \\\"display_name\\\": \\\"System\\\",\\n \\\"field_type\\\": \\\"str\\\",\\n \\\"info\\\": \\\"System to use for generating text.\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"template\\\": {\\n \\\"display_name\\\": \\\"Template\\\",\\n \\\"field_type\\\": \\\"str\\\",\\n \\\"info\\\": \\\"Template to use for generating text.\\\",\\n \\\"advanced\\\": True,\\n },\\n }\\n\\n def build(\\n self,\\n base_url: Optional[str],\\n model: str,\\n mirostat: Optional[str],\\n mirostat_eta: Optional[float] = None,\\n mirostat_tau: Optional[float] = None,\\n ### When a callback component is added to Langflow, the comment must be uncommented.###\\n # callback_manager: Optional[CallbackManager] = None,\\n # callbacks: Optional[List[Callbacks]] = None,\\n #######################################################################################\\n repeat_last_n: Optional[int] = None,\\n verbose: Optional[bool] = None,\\n cache: Optional[bool] = None,\\n num_ctx: Optional[int] = None,\\n num_gpu: Optional[int] = None,\\n format: Optional[str] = None,\\n metadata: Optional[Dict[str, Any]] = None,\\n num_thread: Optional[int] = None,\\n repeat_penalty: Optional[float] = None,\\n stop: Optional[List[str]] = None,\\n system: Optional[str] = None,\\n tags: Optional[List[str]] = None,\\n temperature: Optional[float] = None,\\n template: Optional[str] = None,\\n tfs_z: Optional[float] = None,\\n timeout: Optional[int] = None,\\n top_k: Optional[int] = None,\\n top_p: Optional[int] = None,\\n ) -> BaseChatModel:\\n if not base_url:\\n base_url = \\\"http://localhost:11434\\\"\\n\\n # Mapping mirostat settings to their corresponding values\\n mirostat_options = {\\\"Mirostat\\\": 1, \\\"Mirostat 2.0\\\": 2}\\n\\n # Default to 0 for 'Disabled'\\n mirostat_value = mirostat_options.get(mirostat, 0) # type: ignore\\n\\n # Set mirostat_eta and mirostat_tau to None if mirostat is disabled\\n if mirostat_value == 0:\\n mirostat_eta = None\\n mirostat_tau = None\\n\\n # Mapping system settings to their corresponding values\\n llm_params = {\\n \\\"base_url\\\": base_url,\\n \\\"cache\\\": cache,\\n \\\"model\\\": model,\\n \\\"mirostat\\\": mirostat_value,\\n \\\"format\\\": format,\\n \\\"metadata\\\": metadata,\\n \\\"tags\\\": tags,\\n ## When a callback component is added to Langflow, the comment must be uncommented.##\\n # \\\"callback_manager\\\": callback_manager,\\n # \\\"callbacks\\\": callbacks,\\n #####################################################################################\\n \\\"mirostat_eta\\\": mirostat_eta,\\n \\\"mirostat_tau\\\": mirostat_tau,\\n \\\"num_ctx\\\": num_ctx,\\n \\\"num_gpu\\\": num_gpu,\\n \\\"num_thread\\\": num_thread,\\n \\\"repeat_last_n\\\": repeat_last_n,\\n \\\"repeat_penalty\\\": repeat_penalty,\\n \\\"temperature\\\": temperature,\\n \\\"stop\\\": stop,\\n \\\"system\\\": system,\\n \\\"template\\\": template,\\n \\\"tfs_z\\\": tfs_z,\\n \\\"timeout\\\": timeout,\\n \\\"top_k\\\": top_k,\\n \\\"top_p\\\": top_p,\\n \\\"verbose\\\": verbose,\\n }\\n\\n # None Value remove\\n llm_params = {k: v for k, v in llm_params.items() if v is not None}\\n\\n try:\\n output = ChatOllama(**llm_params) # type: ignore\\n except Exception as e:\\n raise ValueError(\\\"Could not initialize Ollama LLM.\\\") from e\\n\\n return output # type: ignore\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"format\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"format\",\"display_name\":\"Format\",\"advanced\":true,\"dynamic\":false,\"info\":\"Specify the format of the output (e.g., json).\",\"title_case\":false,\"input_types\":[\"Text\"]},\"mirostat\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"Disabled\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"Disabled\",\"Mirostat\",\"Mirostat 2.0\"],\"name\":\"mirostat\",\"display_name\":\"Mirostat\",\"advanced\":true,\"dynamic\":false,\"info\":\"Enable/disable Mirostat sampling for controlling perplexity.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"mirostat_eta\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"mirostat_eta\",\"display_name\":\"Mirostat Eta\",\"advanced\":true,\"dynamic\":false,\"info\":\"Learning rate for Mirostat algorithm. (Default: 0.1)\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"mirostat_tau\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"mirostat_tau\",\"display_name\":\"Mirostat Tau\",\"advanced\":true,\"dynamic\":false,\"info\":\"Controls the balance between coherence and diversity of the output. (Default: 5.0)\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"model\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"llama2\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model\",\"display_name\":\"Model Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"Refer to https://ollama.ai/library for more models.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"num_ctx\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"num_ctx\",\"display_name\":\"Context Window Size\",\"advanced\":true,\"dynamic\":false,\"info\":\"Size of the context window for generating tokens. (Default: 2048)\",\"title_case\":false},\"num_gpu\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"num_gpu\",\"display_name\":\"Number of GPUs\",\"advanced\":true,\"dynamic\":false,\"info\":\"Number of GPUs to use for computation. (Default: 1 on macOS, 0 to disable)\",\"title_case\":false},\"num_thread\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"num_thread\",\"display_name\":\"Number of Threads\",\"advanced\":true,\"dynamic\":false,\"info\":\"Number of threads to use during computation. (Default: detected for optimal performance)\",\"title_case\":false},\"repeat_last_n\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"repeat_last_n\",\"display_name\":\"Repeat Last N\",\"advanced\":true,\"dynamic\":false,\"info\":\"How far back the model looks to prevent repetition. (Default: 64, 0 = disabled, -1 = num_ctx)\",\"title_case\":false},\"repeat_penalty\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"repeat_penalty\",\"display_name\":\"Repeat Penalty\",\"advanced\":true,\"dynamic\":false,\"info\":\"Penalty for repetitions in generated text. (Default: 1.1)\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"system\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"system\",\"display_name\":\"System\",\"advanced\":true,\"dynamic\":false,\"info\":\"System to use for generating text.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"temperature\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.8,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"Controls the creativity of model responses.\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"template\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"template\",\"display_name\":\"Template\",\"advanced\":true,\"dynamic\":false,\"info\":\"Template to use for generating text.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"tfs_z\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"tfs_z\",\"display_name\":\"TFS Z\",\"advanced\":true,\"dynamic\":false,\"info\":\"Tail free sampling value. (Default: 1)\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"timeout\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"timeout\",\"display_name\":\"Timeout\",\"advanced\":true,\"dynamic\":false,\"info\":\"Timeout for the request stream.\",\"title_case\":false},\"top_k\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_k\",\"display_name\":\"Top K\",\"advanced\":true,\"dynamic\":false,\"info\":\"Limits token selection to top K. (Default: 40)\",\"title_case\":false},\"top_p\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_p\",\"display_name\":\"Top P\",\"advanced\":true,\"dynamic\":false,\"info\":\"Works together with top-k. (Default: 0.9)\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"verbose\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"verbose\",\"display_name\":\"Verbose\",\"advanced\":false,\"dynamic\":false,\"info\":\"Whether to print out response text.\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Local LLM for chat with Ollama.\",\"base_classes\":[\"BaseLanguageModel\",\"Runnable\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"object\",\"BaseChatModel\"],\"display_name\":\"ChatOllama\",\"documentation\":\"\",\"custom_fields\":{\"base_url\":null,\"model\":null,\"mirostat\":null,\"mirostat_eta\":null,\"mirostat_tau\":null,\"repeat_last_n\":null,\"verbose\":null,\"cache\":null,\"num_ctx\":null,\"num_gpu\":null,\"format\":null,\"metadata\":null,\"num_thread\":null,\"repeat_penalty\":null,\"stop\":null,\"system\":null,\"tags\":null,\"temperature\":null,\"template\":null,\"tfs_z\":null,\"timeout\":null,\"top_k\":null,\"top_p\":null},\"output_types\":[\"BaseChatModel\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"BaiduQianfanChatEndpointsSpecs\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain_community.chat_models.baidu_qianfan_endpoint import QianfanChatEndpoint\\nfrom langchain.llms.base import BaseLLM\\nfrom pydantic.v1 import SecretStr\\n\\nfrom langflow import CustomComponent\\n\\n\\nclass QianfanChatEndpointComponent(CustomComponent):\\n display_name: str = \\\"QianfanChatEndpoint\\\"\\n description: str = (\\n \\\"Baidu Qianfan chat models. Get more detail from \\\"\\n \\\"https://python.langchain.com/docs/integrations/chat/baidu_qianfan_endpoint.\\\"\\n )\\n\\n def build_config(self):\\n return {\\n \\\"model\\\": {\\n \\\"display_name\\\": \\\"Model Name\\\",\\n \\\"options\\\": [\\n \\\"ERNIE-Bot\\\",\\n \\\"ERNIE-Bot-turbo\\\",\\n \\\"BLOOMZ-7B\\\",\\n \\\"Llama-2-7b-chat\\\",\\n \\\"Llama-2-13b-chat\\\",\\n \\\"Llama-2-70b-chat\\\",\\n \\\"Qianfan-BLOOMZ-7B-compressed\\\",\\n \\\"Qianfan-Chinese-Llama-2-7B\\\",\\n \\\"ChatGLM2-6B-32K\\\",\\n \\\"AquilaChat-7B\\\",\\n ],\\n \\\"info\\\": \\\"https://python.langchain.com/docs/integrations/chat/baidu_qianfan_endpoint\\\",\\n \\\"required\\\": True,\\n },\\n \\\"qianfan_ak\\\": {\\n \\\"display_name\\\": \\\"Qianfan Ak\\\",\\n \\\"required\\\": True,\\n \\\"password\\\": True,\\n \\\"info\\\": \\\"which you could get from https://cloud.baidu.com/product/wenxinworkshop\\\",\\n },\\n \\\"qianfan_sk\\\": {\\n \\\"display_name\\\": \\\"Qianfan Sk\\\",\\n \\\"required\\\": True,\\n \\\"password\\\": True,\\n \\\"info\\\": \\\"which you could get from https://cloud.baidu.com/product/wenxinworkshop\\\",\\n },\\n \\\"top_p\\\": {\\n \\\"display_name\\\": \\\"Top p\\\",\\n \\\"field_type\\\": \\\"float\\\",\\n \\\"info\\\": \\\"Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo\\\",\\n \\\"value\\\": 0.8,\\n },\\n \\\"temperature\\\": {\\n \\\"display_name\\\": \\\"Temperature\\\",\\n \\\"field_type\\\": \\\"float\\\",\\n \\\"info\\\": \\\"Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo\\\",\\n \\\"value\\\": 0.95,\\n },\\n \\\"penalty_score\\\": {\\n \\\"display_name\\\": \\\"Penalty Score\\\",\\n \\\"field_type\\\": \\\"float\\\",\\n \\\"info\\\": \\\"Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo\\\",\\n \\\"value\\\": 1.0,\\n },\\n \\\"endpoint\\\": {\\n \\\"display_name\\\": \\\"Endpoint\\\",\\n \\\"info\\\": \\\"Endpoint of the Qianfan LLM, required if custom model used.\\\",\\n },\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(\\n self,\\n model: str = \\\"ERNIE-Bot-turbo\\\",\\n qianfan_ak: Optional[str] = None,\\n qianfan_sk: Optional[str] = None,\\n top_p: Optional[float] = None,\\n temperature: Optional[float] = None,\\n penalty_score: Optional[float] = None,\\n endpoint: Optional[str] = None,\\n ) -> BaseLLM:\\n try:\\n output = QianfanChatEndpoint( # type: ignore\\n model=model,\\n qianfan_ak=SecretStr(qianfan_ak) if qianfan_ak else None,\\n qianfan_sk=SecretStr(qianfan_sk) if qianfan_sk else None,\\n top_p=top_p,\\n temperature=temperature,\\n penalty_score=penalty_score,\\n endpoint=endpoint,\\n )\\n except Exception as e:\\n raise ValueError(\\\"Could not connect to Baidu Qianfan API.\\\") from e\\n return output # type: ignore\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"endpoint\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"endpoint\",\"display_name\":\"Endpoint\",\"advanced\":false,\"dynamic\":false,\"info\":\"Endpoint of the Qianfan LLM, required if custom model used.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"model\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"ERNIE-Bot-turbo\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"ERNIE-Bot\",\"ERNIE-Bot-turbo\",\"BLOOMZ-7B\",\"Llama-2-7b-chat\",\"Llama-2-13b-chat\",\"Llama-2-70b-chat\",\"Qianfan-BLOOMZ-7B-compressed\",\"Qianfan-Chinese-Llama-2-7B\",\"ChatGLM2-6B-32K\",\"AquilaChat-7B\"],\"name\":\"model\",\"display_name\":\"Model Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"https://python.langchain.com/docs/integrations/chat/baidu_qianfan_endpoint\",\"title_case\":false,\"input_types\":[\"Text\"]},\"penalty_score\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":1.0,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"penalty_score\",\"display_name\":\"Penalty Score\",\"advanced\":false,\"dynamic\":false,\"info\":\"Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"qianfan_ak\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"qianfan_ak\",\"display_name\":\"Qianfan Ak\",\"advanced\":false,\"dynamic\":false,\"info\":\"which you could get from https://cloud.baidu.com/product/wenxinworkshop\",\"title_case\":false,\"input_types\":[\"Text\"]},\"qianfan_sk\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"qianfan_sk\",\"display_name\":\"Qianfan Sk\",\"advanced\":false,\"dynamic\":false,\"info\":\"which you could get from https://cloud.baidu.com/product/wenxinworkshop\",\"title_case\":false,\"input_types\":[\"Text\"]},\"temperature\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.95,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"top_p\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.8,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_p\",\"display_name\":\"Top p\",\"advanced\":false,\"dynamic\":false,\"info\":\"Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Baidu Qianfan chat models. Get more detail from https://python.langchain.com/docs/integrations/chat/baidu_qianfan_endpoint.\",\"base_classes\":[\"BaseLanguageModel\",\"Runnable\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"object\",\"BaseLLM\"],\"display_name\":\"QianfanChatEndpoint\",\"documentation\":\"\",\"custom_fields\":{\"model\":null,\"qianfan_ak\":null,\"qianfan_sk\":null,\"top_p\":null,\"temperature\":null,\"penalty_score\":null,\"endpoint\":null},\"output_types\":[\"BaseLLM\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"LlamaCppSpecs\":{\"template\":{\"metadata\":{\"type\":\"Dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"model_kwargs\":{\"type\":\"Dict\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model_kwargs\",\"display_name\":\"Model Kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"model_path\":{\"type\":\"file\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[\".bin\"],\"file_path\":\"\",\"password\":false,\"name\":\"model_path\",\"display_name\":\"Model Path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"cache\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"cache\",\"display_name\":\"Cache\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"client\":{\"type\":\"Any\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"client\",\"display_name\":\"Client\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional, List, Dict, Any\\nfrom langflow import CustomComponent\\nfrom langchain_community.llms.llamacpp import LlamaCpp\\n\\n\\nclass LlamaCppComponent(CustomComponent):\\n display_name = \\\"LlamaCpp\\\"\\n description = \\\"llama.cpp model.\\\"\\n documentation = \\\"https://python.langchain.com/docs/modules/model_io/models/llms/integrations/llamacpp\\\"\\n\\n def build_config(self):\\n return {\\n \\\"grammar\\\": {\\\"display_name\\\": \\\"Grammar\\\", \\\"advanced\\\": True},\\n \\\"cache\\\": {\\\"display_name\\\": \\\"Cache\\\", \\\"advanced\\\": True},\\n \\\"client\\\": {\\\"display_name\\\": \\\"Client\\\", \\\"advanced\\\": True},\\n \\\"echo\\\": {\\\"display_name\\\": \\\"Echo\\\", \\\"advanced\\\": True},\\n \\\"f16_kv\\\": {\\\"display_name\\\": \\\"F16 KV\\\", \\\"advanced\\\": True},\\n \\\"grammar_path\\\": {\\\"display_name\\\": \\\"Grammar Path\\\", \\\"advanced\\\": True},\\n \\\"last_n_tokens_size\\\": {\\\"display_name\\\": \\\"Last N Tokens Size\\\", \\\"advanced\\\": True},\\n \\\"logits_all\\\": {\\\"display_name\\\": \\\"Logits All\\\", \\\"advanced\\\": True},\\n \\\"logprobs\\\": {\\\"display_name\\\": \\\"Logprobs\\\", \\\"advanced\\\": True},\\n \\\"lora_base\\\": {\\\"display_name\\\": \\\"Lora Base\\\", \\\"advanced\\\": True},\\n \\\"lora_path\\\": {\\\"display_name\\\": \\\"Lora Path\\\", \\\"advanced\\\": True},\\n \\\"max_tokens\\\": {\\\"display_name\\\": \\\"Max Tokens\\\", \\\"advanced\\\": True},\\n \\\"metadata\\\": {\\\"display_name\\\": \\\"Metadata\\\", \\\"advanced\\\": True},\\n \\\"model_kwargs\\\": {\\\"display_name\\\": \\\"Model Kwargs\\\", \\\"advanced\\\": True},\\n \\\"model_path\\\": {\\n \\\"display_name\\\": \\\"Model Path\\\",\\n \\\"field_type\\\": \\\"file\\\",\\n \\\"file_types\\\": [\\\".bin\\\"],\\n \\\"required\\\": True,\\n },\\n \\\"n_batch\\\": {\\\"display_name\\\": \\\"N Batch\\\", \\\"advanced\\\": True},\\n \\\"n_ctx\\\": {\\\"display_name\\\": \\\"N Ctx\\\", \\\"advanced\\\": True},\\n \\\"n_gpu_layers\\\": {\\\"display_name\\\": \\\"N GPU Layers\\\", \\\"advanced\\\": True},\\n \\\"n_parts\\\": {\\\"display_name\\\": \\\"N Parts\\\", \\\"advanced\\\": True},\\n \\\"n_threads\\\": {\\\"display_name\\\": \\\"N Threads\\\", \\\"advanced\\\": True},\\n \\\"repeat_penalty\\\": {\\\"display_name\\\": \\\"Repeat Penalty\\\", \\\"advanced\\\": True},\\n \\\"rope_freq_base\\\": {\\\"display_name\\\": \\\"Rope Freq Base\\\", \\\"advanced\\\": True},\\n \\\"rope_freq_scale\\\": {\\\"display_name\\\": \\\"Rope Freq Scale\\\", \\\"advanced\\\": True},\\n \\\"seed\\\": {\\\"display_name\\\": \\\"Seed\\\", \\\"advanced\\\": True},\\n \\\"stop\\\": {\\\"display_name\\\": \\\"Stop\\\", \\\"advanced\\\": True},\\n \\\"streaming\\\": {\\\"display_name\\\": \\\"Streaming\\\", \\\"advanced\\\": True},\\n \\\"suffix\\\": {\\\"display_name\\\": \\\"Suffix\\\", \\\"advanced\\\": True},\\n \\\"tags\\\": {\\\"display_name\\\": \\\"Tags\\\", \\\"advanced\\\": True},\\n \\\"temperature\\\": {\\\"display_name\\\": \\\"Temperature\\\"},\\n \\\"top_k\\\": {\\\"display_name\\\": \\\"Top K\\\", \\\"advanced\\\": True},\\n \\\"top_p\\\": {\\\"display_name\\\": \\\"Top P\\\", \\\"advanced\\\": True},\\n \\\"use_mlock\\\": {\\\"display_name\\\": \\\"Use Mlock\\\", \\\"advanced\\\": True},\\n \\\"use_mmap\\\": {\\\"display_name\\\": \\\"Use Mmap\\\", \\\"advanced\\\": True},\\n \\\"verbose\\\": {\\\"display_name\\\": \\\"Verbose\\\", \\\"advanced\\\": True},\\n \\\"vocab_only\\\": {\\\"display_name\\\": \\\"Vocab Only\\\", \\\"advanced\\\": True},\\n }\\n\\n def build(\\n self,\\n model_path: str,\\n grammar: Optional[str] = None,\\n cache: Optional[bool] = None,\\n client: Optional[Any] = None,\\n echo: Optional[bool] = False,\\n f16_kv: bool = True,\\n grammar_path: Optional[str] = None,\\n last_n_tokens_size: Optional[int] = 64,\\n logits_all: bool = False,\\n logprobs: Optional[int] = None,\\n lora_base: Optional[str] = None,\\n lora_path: Optional[str] = None,\\n max_tokens: Optional[int] = 256,\\n metadata: Optional[Dict] = None,\\n model_kwargs: Dict = {},\\n n_batch: Optional[int] = 8,\\n n_ctx: int = 512,\\n n_gpu_layers: Optional[int] = 1,\\n n_parts: int = -1,\\n n_threads: Optional[int] = 1,\\n repeat_penalty: Optional[float] = 1.1,\\n rope_freq_base: float = 10000.0,\\n rope_freq_scale: float = 1.0,\\n seed: int = -1,\\n stop: Optional[List[str]] = [],\\n streaming: bool = True,\\n suffix: Optional[str] = \\\"\\\",\\n tags: Optional[List[str]] = [],\\n temperature: Optional[float] = 0.8,\\n top_k: Optional[int] = 40,\\n top_p: Optional[float] = 0.95,\\n use_mlock: bool = False,\\n use_mmap: Optional[bool] = True,\\n verbose: bool = True,\\n vocab_only: bool = False,\\n ) -> LlamaCpp:\\n return LlamaCpp(\\n model_path=model_path,\\n grammar=grammar,\\n cache=cache,\\n client=client,\\n echo=echo,\\n f16_kv=f16_kv,\\n grammar_path=grammar_path,\\n last_n_tokens_size=last_n_tokens_size,\\n logits_all=logits_all,\\n logprobs=logprobs,\\n lora_base=lora_base,\\n lora_path=lora_path,\\n max_tokens=max_tokens,\\n metadata=metadata,\\n model_kwargs=model_kwargs,\\n n_batch=n_batch,\\n n_ctx=n_ctx,\\n n_gpu_layers=n_gpu_layers,\\n n_parts=n_parts,\\n n_threads=n_threads,\\n repeat_penalty=repeat_penalty,\\n rope_freq_base=rope_freq_base,\\n rope_freq_scale=rope_freq_scale,\\n seed=seed,\\n stop=stop,\\n streaming=streaming,\\n suffix=suffix,\\n tags=tags,\\n temperature=temperature,\\n top_k=top_k,\\n top_p=top_p,\\n use_mlock=use_mlock,\\n use_mmap=use_mmap,\\n verbose=verbose,\\n vocab_only=vocab_only,\\n )\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"echo\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"echo\",\"display_name\":\"Echo\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"f16_kv\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":true,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"f16_kv\",\"display_name\":\"F16 KV\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"grammar\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"grammar\",\"display_name\":\"Grammar\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"grammar_path\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"grammar_path\",\"display_name\":\"Grammar Path\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"last_n_tokens_size\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":64,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"last_n_tokens_size\",\"display_name\":\"Last N Tokens Size\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"logits_all\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"logits_all\",\"display_name\":\"Logits All\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"logprobs\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"logprobs\",\"display_name\":\"Logprobs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"lora_base\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"lora_base\",\"display_name\":\"Lora Base\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"lora_path\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"lora_path\",\"display_name\":\"Lora Path\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"max_tokens\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":256,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_tokens\",\"display_name\":\"Max Tokens\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"n_batch\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":8,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"n_batch\",\"display_name\":\"N Batch\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"n_ctx\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":512,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"n_ctx\",\"display_name\":\"N Ctx\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"n_gpu_layers\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":1,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"n_gpu_layers\",\"display_name\":\"N GPU Layers\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"n_parts\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":-1,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"n_parts\",\"display_name\":\"N Parts\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"n_threads\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":1,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"n_threads\",\"display_name\":\"N Threads\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"repeat_penalty\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":1.1,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"repeat_penalty\",\"display_name\":\"Repeat Penalty\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"rope_freq_base\":{\"type\":\"float\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":10000.0,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"rope_freq_base\",\"display_name\":\"Rope Freq Base\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"rope_freq_scale\":{\"type\":\"float\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":1.0,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"rope_freq_scale\",\"display_name\":\"Rope Freq Scale\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"seed\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":-1,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"seed\",\"display_name\":\"Seed\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"stop\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":[],\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"stop\",\"display_name\":\"Stop\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"streaming\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":true,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"streaming\",\"display_name\":\"Streaming\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"suffix\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"suffix\",\"display_name\":\"Suffix\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"tags\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":[],\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"tags\",\"display_name\":\"Tags\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"temperature\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.8,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"top_k\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":40,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_k\",\"display_name\":\"Top K\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"top_p\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.95,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_p\",\"display_name\":\"Top P\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"use_mlock\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"use_mlock\",\"display_name\":\"Use Mlock\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"use_mmap\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":true,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"use_mmap\",\"display_name\":\"Use Mmap\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"verbose\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":true,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"verbose\",\"display_name\":\"Verbose\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"vocab_only\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"vocab_only\",\"display_name\":\"Vocab Only\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"llama.cpp model.\",\"base_classes\":[\"BaseLanguageModel\",\"Runnable\",\"Generic\",\"LlamaCpp\",\"RunnableSerializable\",\"Serializable\",\"object\",\"BaseLLM\",\"LLM\"],\"display_name\":\"LlamaCpp\",\"documentation\":\"https://python.langchain.com/docs/modules/model_io/models/llms/integrations/llamacpp\",\"custom_fields\":{\"model_path\":null,\"grammar\":null,\"cache\":null,\"client\":null,\"echo\":null,\"f16_kv\":null,\"grammar_path\":null,\"last_n_tokens_size\":null,\"logits_all\":null,\"logprobs\":null,\"lora_base\":null,\"lora_path\":null,\"max_tokens\":null,\"metadata\":null,\"model_kwargs\":null,\"n_batch\":null,\"n_ctx\":null,\"n_gpu_layers\":null,\"n_parts\":null,\"n_threads\":null,\"repeat_penalty\":null,\"rope_freq_base\":null,\"rope_freq_scale\":null,\"seed\":null,\"stop\":null,\"streaming\":null,\"suffix\":null,\"tags\":null,\"temperature\":null,\"top_k\":null,\"top_p\":null,\"use_mlock\":null,\"use_mmap\":null,\"verbose\":null,\"vocab_only\":null},\"output_types\":[\"LlamaCpp\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"AnthropicSpecs\":{\"template\":{\"anthropic_api_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"anthropic_api_key\",\"display_name\":\"Anthropic API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"anthropic_api_url\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"anthropic_api_url\",\"display_name\":\"Anthropic API URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain_community.llms.anthropic import Anthropic\\nfrom pydantic.v1 import SecretStr\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import BaseLanguageModel, NestedDict\\n\\n\\nclass AnthropicComponent(CustomComponent):\\n display_name = \\\"Anthropic\\\"\\n description = \\\"Anthropic large language models.\\\"\\n\\n def build_config(self):\\n return {\\n \\\"anthropic_api_key\\\": {\\n \\\"display_name\\\": \\\"Anthropic API Key\\\",\\n \\\"type\\\": str,\\n \\\"password\\\": True,\\n },\\n \\\"anthropic_api_url\\\": {\\n \\\"display_name\\\": \\\"Anthropic API URL\\\",\\n \\\"type\\\": str,\\n },\\n \\\"model_kwargs\\\": {\\n \\\"display_name\\\": \\\"Model Kwargs\\\",\\n \\\"field_type\\\": \\\"NestedDict\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"temperature\\\": {\\n \\\"display_name\\\": \\\"Temperature\\\",\\n \\\"field_type\\\": \\\"float\\\",\\n },\\n }\\n\\n def build(\\n self,\\n anthropic_api_key: str,\\n anthropic_api_url: str,\\n model_kwargs: NestedDict = {},\\n temperature: Optional[float] = None,\\n ) -> BaseLanguageModel:\\n return Anthropic(\\n anthropic_api_key=SecretStr(anthropic_api_key),\\n anthropic_api_url=anthropic_api_url,\\n model_kwargs=model_kwargs,\\n temperature=temperature,\\n )\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"model_kwargs\":{\"type\":\"NestedDict\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model_kwargs\",\"display_name\":\"Model Kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"temperature\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Anthropic large language models.\",\"base_classes\":[\"BaseLanguageModel\",\"Runnable\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"object\"],\"display_name\":\"Anthropic\",\"documentation\":\"\",\"custom_fields\":{\"anthropic_api_key\":null,\"anthropic_api_url\":null,\"model_kwargs\":null,\"temperature\":null},\"output_types\":[\"BaseLanguageModel\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"AnthropicLLMSpecs\":{\"template\":{\"anthropic_api_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"anthropic_api_key\",\"display_name\":\"Anthropic API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"Your Anthropic API key.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"api_endpoint\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"api_endpoint\",\"display_name\":\"API Endpoint\",\"advanced\":false,\"dynamic\":false,\"info\":\"Endpoint of the Anthropic API. Defaults to 'https://api.anthropic.com' if not specified.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain_community.chat_models.anthropic import ChatAnthropic\\nfrom langchain.llms.base import BaseLanguageModel\\nfrom pydantic.v1 import SecretStr\\n\\nfrom langflow import CustomComponent\\n\\n\\nclass AnthropicLLM(CustomComponent):\\n display_name: str = \\\"AnthropicLLM\\\"\\n description: str = \\\"Anthropic Chat&Completion large language models.\\\"\\n\\n def build_config(self):\\n return {\\n \\\"model\\\": {\\n \\\"display_name\\\": \\\"Model Name\\\",\\n \\\"options\\\": [\\n \\\"claude-2.1\\\",\\n \\\"claude-2.0\\\",\\n \\\"claude-instant-1.2\\\",\\n \\\"claude-instant-1\\\",\\n # Add more models as needed\\n ],\\n \\\"info\\\": \\\"https://python.langchain.com/docs/integrations/chat/anthropic\\\",\\n \\\"required\\\": True,\\n \\\"value\\\": \\\"claude-2.1\\\",\\n },\\n \\\"anthropic_api_key\\\": {\\n \\\"display_name\\\": \\\"Anthropic API Key\\\",\\n \\\"required\\\": True,\\n \\\"password\\\": True,\\n \\\"info\\\": \\\"Your Anthropic API key.\\\",\\n },\\n \\\"max_tokens\\\": {\\n \\\"display_name\\\": \\\"Max Tokens\\\",\\n \\\"field_type\\\": \\\"int\\\",\\n \\\"value\\\": 256,\\n },\\n \\\"temperature\\\": {\\n \\\"display_name\\\": \\\"Temperature\\\",\\n \\\"field_type\\\": \\\"float\\\",\\n \\\"value\\\": 0.7,\\n },\\n \\\"api_endpoint\\\": {\\n \\\"display_name\\\": \\\"API Endpoint\\\",\\n \\\"info\\\": \\\"Endpoint of the Anthropic API. Defaults to 'https://api.anthropic.com' if not specified.\\\",\\n },\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(\\n self,\\n model: str,\\n anthropic_api_key: Optional[str] = None,\\n max_tokens: Optional[int] = None,\\n temperature: Optional[float] = None,\\n api_endpoint: Optional[str] = None,\\n ) -> BaseLanguageModel:\\n # Set default API endpoint if not provided\\n if not api_endpoint:\\n api_endpoint = \\\"https://api.anthropic.com\\\"\\n\\n try:\\n output = ChatAnthropic(\\n model_name=model,\\n anthropic_api_key=SecretStr(anthropic_api_key) if anthropic_api_key else None,\\n max_tokens_to_sample=max_tokens, # type: ignore\\n temperature=temperature,\\n anthropic_api_url=api_endpoint,\\n )\\n except Exception as e:\\n raise ValueError(\\\"Could not connect to Anthropic API.\\\") from e\\n return output\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"max_tokens\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":256,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_tokens\",\"display_name\":\"Max Tokens\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"model\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"claude-2.1\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"claude-2.1\",\"claude-2.0\",\"claude-instant-1.2\",\"claude-instant-1\"],\"name\":\"model\",\"display_name\":\"Model Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"https://python.langchain.com/docs/integrations/chat/anthropic\",\"title_case\":false,\"input_types\":[\"Text\"]},\"temperature\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.7,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Anthropic Chat&Completion large language models.\",\"base_classes\":[\"BaseLanguageModel\",\"Runnable\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"object\"],\"display_name\":\"AnthropicLLM\",\"documentation\":\"\",\"custom_fields\":{\"model\":null,\"anthropic_api_key\":null,\"max_tokens\":null,\"temperature\":null,\"api_endpoint\":null},\"output_types\":[\"BaseLanguageModel\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"CohereSpecs\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from langchain_community.llms.cohere import Cohere\\nfrom langchain_core.language_models.base import BaseLanguageModel\\nfrom langflow import CustomComponent\\n\\n\\nclass CohereComponent(CustomComponent):\\n display_name = \\\"Cohere\\\"\\n description = \\\"Cohere large language models.\\\"\\n documentation = \\\"https://python.langchain.com/docs/modules/model_io/models/llms/integrations/cohere\\\"\\n\\n def build_config(self):\\n return {\\n \\\"cohere_api_key\\\": {\\\"display_name\\\": \\\"Cohere API Key\\\", \\\"type\\\": \\\"password\\\", \\\"password\\\": True},\\n \\\"max_tokens\\\": {\\\"display_name\\\": \\\"Max Tokens\\\", \\\"default\\\": 256, \\\"type\\\": \\\"int\\\", \\\"show\\\": True},\\n \\\"temperature\\\": {\\\"display_name\\\": \\\"Temperature\\\", \\\"default\\\": 0.75, \\\"type\\\": \\\"float\\\", \\\"show\\\": True},\\n }\\n\\n def build(\\n self,\\n cohere_api_key: str,\\n max_tokens: int = 256,\\n temperature: float = 0.75,\\n ) -> BaseLanguageModel:\\n return Cohere(cohere_api_key=cohere_api_key, max_tokens=max_tokens, temperature=temperature) # type: ignore\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"cohere_api_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"cohere_api_key\",\"display_name\":\"Cohere API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"max_tokens\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":256,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_tokens\",\"display_name\":\"Max Tokens\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"temperature\":{\"type\":\"float\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.75,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Cohere large language models.\",\"base_classes\":[\"BaseLanguageModel\",\"Runnable\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"object\"],\"display_name\":\"Cohere\",\"documentation\":\"https://python.langchain.com/docs/modules/model_io/models/llms/integrations/cohere\",\"custom_fields\":{\"cohere_api_key\":null,\"max_tokens\":null,\"temperature\":null},\"output_types\":[\"BaseLanguageModel\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"GoogleGenerativeAISpecs\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain_google_genai import ChatGoogleGenerativeAI # type: ignore\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import BaseLanguageModel, RangeSpec\\nfrom pydantic.v1.types import SecretStr\\n\\n\\nclass GoogleGenerativeAIComponent(CustomComponent):\\n display_name: str = \\\"Google Generative AI\\\"\\n description: str = \\\"A component that uses Google Generative AI to generate text.\\\"\\n documentation: str = \\\"http://docs.langflow.org/components/custom\\\"\\n\\n def build_config(self):\\n return {\\n \\\"google_api_key\\\": {\\n \\\"display_name\\\": \\\"Google API Key\\\",\\n \\\"info\\\": \\\"The Google API Key to use for the Google Generative AI.\\\",\\n },\\n \\\"max_output_tokens\\\": {\\n \\\"display_name\\\": \\\"Max Output Tokens\\\",\\n \\\"info\\\": \\\"The maximum number of tokens to generate.\\\",\\n },\\n \\\"temperature\\\": {\\n \\\"display_name\\\": \\\"Temperature\\\",\\n \\\"info\\\": \\\"Run inference with this temperature. Must by in the closed interval [0.0, 1.0].\\\",\\n },\\n \\\"top_k\\\": {\\n \\\"display_name\\\": \\\"Top K\\\",\\n \\\"info\\\": \\\"Decode using top-k sampling: consider the set of top_k most probable tokens. Must be positive.\\\",\\n \\\"range_spec\\\": RangeSpec(min=0, max=2, step=0.1),\\n \\\"advanced\\\": True,\\n },\\n \\\"top_p\\\": {\\n \\\"display_name\\\": \\\"Top P\\\",\\n \\\"info\\\": \\\"The maximum cumulative probability of tokens to consider when sampling.\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"n\\\": {\\n \\\"display_name\\\": \\\"N\\\",\\n \\\"info\\\": \\\"Number of chat completions to generate for each prompt. Note that the API may not return the full n completions if duplicates are generated.\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"model\\\": {\\n \\\"display_name\\\": \\\"Model\\\",\\n \\\"info\\\": \\\"The name of the model to use. Supported examples: gemini-pro\\\",\\n \\\"options\\\": [\\\"gemini-pro\\\", \\\"gemini-pro-vision\\\"],\\n },\\n \\\"code\\\": {\\n \\\"advanced\\\": True,\\n },\\n }\\n\\n def build(\\n self,\\n google_api_key: str,\\n model: str,\\n max_output_tokens: Optional[int] = None,\\n temperature: float = 0.1,\\n top_k: Optional[int] = None,\\n top_p: Optional[float] = None,\\n n: Optional[int] = 1,\\n ) -> BaseLanguageModel:\\n return ChatGoogleGenerativeAI(\\n model=model,\\n max_output_tokens=max_output_tokens or None, # type: ignore\\n temperature=temperature,\\n top_k=top_k or None,\\n top_p=top_p or None, # type: ignore\\n n=n or 1,\\n google_api_key=SecretStr(google_api_key),\\n )\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":true,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"google_api_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"google_api_key\",\"display_name\":\"Google API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"The Google API Key to use for the Google Generative AI.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"max_output_tokens\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_output_tokens\",\"display_name\":\"Max Output Tokens\",\"advanced\":false,\"dynamic\":false,\"info\":\"The maximum number of tokens to generate.\",\"title_case\":false},\"model\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"gemini-pro\",\"gemini-pro-vision\"],\"name\":\"model\",\"display_name\":\"Model\",\"advanced\":false,\"dynamic\":false,\"info\":\"The name of the model to use. Supported examples: gemini-pro\",\"title_case\":false,\"input_types\":[\"Text\"]},\"n\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":1,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"n\",\"display_name\":\"N\",\"advanced\":true,\"dynamic\":false,\"info\":\"Number of chat completions to generate for each prompt. Note that the API may not return the full n completions if duplicates are generated.\",\"title_case\":false},\"temperature\":{\"type\":\"float\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.1,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"Run inference with this temperature. Must by in the closed interval [0.0, 1.0].\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"top_k\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_k\",\"display_name\":\"Top K\",\"advanced\":true,\"dynamic\":false,\"info\":\"Decode using top-k sampling: consider the set of top_k most probable tokens. Must be positive.\",\"rangeSpec\":{\"min\":0.0,\"max\":2.0,\"step\":0.1},\"title_case\":false},\"top_p\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_p\",\"display_name\":\"Top P\",\"advanced\":true,\"dynamic\":false,\"info\":\"The maximum cumulative probability of tokens to consider when sampling.\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"A component that uses Google Generative AI to generate text.\",\"base_classes\":[\"BaseLanguageModel\",\"Runnable\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"object\"],\"display_name\":\"Google Generative AI\",\"documentation\":\"http://docs.langflow.org/components/custom\",\"custom_fields\":{\"google_api_key\":null,\"model\":null,\"max_output_tokens\":null,\"temperature\":null,\"top_k\":null,\"top_p\":null,\"n\":null},\"output_types\":[\"BaseLanguageModel\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"BaiduQianfanLLMEndpointsSpecs\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\nfrom langflow import CustomComponent\\nfrom langchain.llms.baidu_qianfan_endpoint import QianfanLLMEndpoint\\nfrom langchain.llms.base import BaseLLM\\n\\n\\nclass QianfanLLMEndpointComponent(CustomComponent):\\n display_name: str = \\\"QianfanLLMEndpoint\\\"\\n description: str = (\\n \\\"Baidu Qianfan hosted open source or customized models. \\\"\\n \\\"Get more detail from https://python.langchain.com/docs/integrations/chat/baidu_qianfan_endpoint\\\"\\n )\\n\\n def build_config(self):\\n return {\\n \\\"model\\\": {\\n \\\"display_name\\\": \\\"Model Name\\\",\\n \\\"options\\\": [\\n \\\"ERNIE-Bot\\\",\\n \\\"ERNIE-Bot-turbo\\\",\\n \\\"BLOOMZ-7B\\\",\\n \\\"Llama-2-7b-chat\\\",\\n \\\"Llama-2-13b-chat\\\",\\n \\\"Llama-2-70b-chat\\\",\\n \\\"Qianfan-BLOOMZ-7B-compressed\\\",\\n \\\"Qianfan-Chinese-Llama-2-7B\\\",\\n \\\"ChatGLM2-6B-32K\\\",\\n \\\"AquilaChat-7B\\\",\\n ],\\n \\\"info\\\": \\\"https://python.langchain.com/docs/integrations/chat/baidu_qianfan_endpoint\\\",\\n \\\"required\\\": True,\\n },\\n \\\"qianfan_ak\\\": {\\n \\\"display_name\\\": \\\"Qianfan Ak\\\",\\n \\\"required\\\": True,\\n \\\"password\\\": True,\\n \\\"info\\\": \\\"which you could get from https://cloud.baidu.com/product/wenxinworkshop\\\",\\n },\\n \\\"qianfan_sk\\\": {\\n \\\"display_name\\\": \\\"Qianfan Sk\\\",\\n \\\"required\\\": True,\\n \\\"password\\\": True,\\n \\\"info\\\": \\\"which you could get from https://cloud.baidu.com/product/wenxinworkshop\\\",\\n },\\n \\\"top_p\\\": {\\n \\\"display_name\\\": \\\"Top p\\\",\\n \\\"field_type\\\": \\\"float\\\",\\n \\\"info\\\": \\\"Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo\\\",\\n \\\"value\\\": 0.8,\\n },\\n \\\"temperature\\\": {\\n \\\"display_name\\\": \\\"Temperature\\\",\\n \\\"field_type\\\": \\\"float\\\",\\n \\\"info\\\": \\\"Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo\\\",\\n \\\"value\\\": 0.95,\\n },\\n \\\"penalty_score\\\": {\\n \\\"display_name\\\": \\\"Penalty Score\\\",\\n \\\"field_type\\\": \\\"float\\\",\\n \\\"info\\\": \\\"Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo\\\",\\n \\\"value\\\": 1.0,\\n },\\n \\\"endpoint\\\": {\\n \\\"display_name\\\": \\\"Endpoint\\\",\\n \\\"info\\\": \\\"Endpoint of the Qianfan LLM, required if custom model used.\\\",\\n },\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(\\n self,\\n model: str = \\\"ERNIE-Bot-turbo\\\",\\n qianfan_ak: Optional[str] = None,\\n qianfan_sk: Optional[str] = None,\\n top_p: Optional[float] = None,\\n temperature: Optional[float] = None,\\n penalty_score: Optional[float] = None,\\n endpoint: Optional[str] = None,\\n ) -> BaseLLM:\\n try:\\n output = QianfanLLMEndpoint( # type: ignore\\n model=model,\\n qianfan_ak=qianfan_ak,\\n qianfan_sk=qianfan_sk,\\n top_p=top_p,\\n temperature=temperature,\\n penalty_score=penalty_score,\\n endpoint=endpoint,\\n )\\n except Exception as e:\\n raise ValueError(\\\"Could not connect to Baidu Qianfan API.\\\") from e\\n return output # type: ignore\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"endpoint\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"endpoint\",\"display_name\":\"Endpoint\",\"advanced\":false,\"dynamic\":false,\"info\":\"Endpoint of the Qianfan LLM, required if custom model used.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"model\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"ERNIE-Bot-turbo\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"ERNIE-Bot\",\"ERNIE-Bot-turbo\",\"BLOOMZ-7B\",\"Llama-2-7b-chat\",\"Llama-2-13b-chat\",\"Llama-2-70b-chat\",\"Qianfan-BLOOMZ-7B-compressed\",\"Qianfan-Chinese-Llama-2-7B\",\"ChatGLM2-6B-32K\",\"AquilaChat-7B\"],\"name\":\"model\",\"display_name\":\"Model Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"https://python.langchain.com/docs/integrations/chat/baidu_qianfan_endpoint\",\"title_case\":false,\"input_types\":[\"Text\"]},\"penalty_score\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":1.0,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"penalty_score\",\"display_name\":\"Penalty Score\",\"advanced\":false,\"dynamic\":false,\"info\":\"Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"qianfan_ak\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"qianfan_ak\",\"display_name\":\"Qianfan Ak\",\"advanced\":false,\"dynamic\":false,\"info\":\"which you could get from https://cloud.baidu.com/product/wenxinworkshop\",\"title_case\":false,\"input_types\":[\"Text\"]},\"qianfan_sk\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"qianfan_sk\",\"display_name\":\"Qianfan Sk\",\"advanced\":false,\"dynamic\":false,\"info\":\"which you could get from https://cloud.baidu.com/product/wenxinworkshop\",\"title_case\":false,\"input_types\":[\"Text\"]},\"temperature\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.95,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"top_p\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.8,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_p\",\"display_name\":\"Top p\",\"advanced\":false,\"dynamic\":false,\"info\":\"Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Baidu Qianfan hosted open source or customized models. Get more detail from https://python.langchain.com/docs/integrations/chat/baidu_qianfan_endpoint\",\"base_classes\":[\"BaseLanguageModel\",\"Runnable\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"object\",\"BaseLLM\"],\"display_name\":\"QianfanLLMEndpoint\",\"documentation\":\"\",\"custom_fields\":{\"model\":null,\"qianfan_ak\":null,\"qianfan_sk\":null,\"top_p\":null,\"temperature\":null,\"penalty_score\":null,\"endpoint\":null},\"output_types\":[\"BaseLLM\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"ChatLiteLLMSpecs\":{\"template\":{\"api_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"api_key\",\"display_name\":\"API key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Any, Callable, Dict, Optional, Union\\n\\nfrom langchain_community.chat_models.litellm import ChatLiteLLM, ChatLiteLLMException\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import BaseLanguageModel\\n\\n\\nclass ChatLiteLLMComponent(CustomComponent):\\n display_name = \\\"ChatLiteLLM\\\"\\n description = \\\"`LiteLLM` collection of large language models.\\\"\\n documentation = \\\"https://python.langchain.com/docs/integrations/chat/litellm\\\"\\n\\n def build_config(self):\\n return {\\n \\\"model\\\": {\\n \\\"display_name\\\": \\\"Model name\\\",\\n \\\"field_type\\\": \\\"str\\\",\\n \\\"advanced\\\": False,\\n \\\"required\\\": True,\\n \\\"info\\\": \\\"The name of the model to use. For example, `gpt-3.5-turbo`.\\\",\\n },\\n \\\"api_key\\\": {\\n \\\"display_name\\\": \\\"API key\\\",\\n \\\"field_type\\\": \\\"str\\\",\\n \\\"advanced\\\": False,\\n \\\"required\\\": False,\\n \\\"password\\\": True,\\n },\\n \\\"provider\\\": {\\n \\\"display_name\\\": \\\"Provider\\\",\\n \\\"info\\\": \\\"The provider of the API key.\\\",\\n \\\"options\\\": [\\n \\\"OpenAI\\\",\\n \\\"Azure\\\",\\n \\\"Anthropic\\\",\\n \\\"Replicate\\\",\\n \\\"Cohere\\\",\\n \\\"OpenRouter\\\",\\n ],\\n },\\n \\\"streaming\\\": {\\n \\\"display_name\\\": \\\"Streaming\\\",\\n \\\"field_type\\\": \\\"bool\\\",\\n \\\"advanced\\\": True,\\n \\\"required\\\": False,\\n \\\"default\\\": True,\\n },\\n \\\"temperature\\\": {\\n \\\"display_name\\\": \\\"Temperature\\\",\\n \\\"field_type\\\": \\\"float\\\",\\n \\\"advanced\\\": False,\\n \\\"required\\\": False,\\n \\\"default\\\": 0.7,\\n },\\n \\\"model_kwargs\\\": {\\n \\\"display_name\\\": \\\"Model kwargs\\\",\\n \\\"field_type\\\": \\\"dict\\\",\\n \\\"advanced\\\": True,\\n \\\"required\\\": False,\\n \\\"default\\\": {},\\n },\\n \\\"top_p\\\": {\\n \\\"display_name\\\": \\\"Top p\\\",\\n \\\"field_type\\\": \\\"float\\\",\\n \\\"advanced\\\": True,\\n \\\"required\\\": False,\\n },\\n \\\"top_k\\\": {\\n \\\"display_name\\\": \\\"Top k\\\",\\n \\\"field_type\\\": \\\"int\\\",\\n \\\"advanced\\\": True,\\n \\\"required\\\": False,\\n },\\n \\\"n\\\": {\\n \\\"display_name\\\": \\\"N\\\",\\n \\\"field_type\\\": \\\"int\\\",\\n \\\"advanced\\\": True,\\n \\\"required\\\": False,\\n \\\"info\\\": \\\"Number of chat completions to generate for each prompt. \\\"\\n \\\"Note that the API may not return the full n completions if duplicates are generated.\\\",\\n \\\"default\\\": 1,\\n },\\n \\\"max_tokens\\\": {\\n \\\"display_name\\\": \\\"Max tokens\\\",\\n \\\"field_type\\\": \\\"int\\\",\\n \\\"advanced\\\": False,\\n \\\"required\\\": False,\\n \\\"default\\\": 256,\\n \\\"info\\\": \\\"The maximum number of tokens to generate for each chat completion.\\\",\\n },\\n \\\"max_retries\\\": {\\n \\\"display_name\\\": \\\"Max retries\\\",\\n \\\"field_type\\\": \\\"int\\\",\\n \\\"advanced\\\": True,\\n \\\"required\\\": False,\\n \\\"default\\\": 6,\\n },\\n \\\"verbose\\\": {\\n \\\"display_name\\\": \\\"Verbose\\\",\\n \\\"field_type\\\": \\\"bool\\\",\\n \\\"advanced\\\": True,\\n \\\"required\\\": False,\\n \\\"default\\\": False,\\n },\\n }\\n\\n def build(\\n self,\\n model: str,\\n provider: str,\\n api_key: Optional[str] = None,\\n streaming: bool = True,\\n temperature: Optional[float] = 0.7,\\n model_kwargs: Optional[Dict[str, Any]] = {},\\n top_p: Optional[float] = None,\\n top_k: Optional[int] = None,\\n n: int = 1,\\n max_tokens: int = 256,\\n max_retries: int = 6,\\n verbose: bool = False,\\n ) -> Union[BaseLanguageModel, Callable]:\\n try:\\n import litellm # type: ignore\\n\\n litellm.drop_params = True\\n litellm.set_verbose = verbose\\n except ImportError:\\n raise ChatLiteLLMException(\\n \\\"Could not import litellm python package. \\\" \\\"Please install it with `pip install litellm`\\\"\\n )\\n provider_map = {\\n \\\"OpenAI\\\": \\\"openai_api_key\\\",\\n \\\"Azure\\\": \\\"azure_api_key\\\",\\n \\\"Anthropic\\\": \\\"anthropic_api_key\\\",\\n \\\"Replicate\\\": \\\"replicate_api_key\\\",\\n \\\"Cohere\\\": \\\"cohere_api_key\\\",\\n \\\"OpenRouter\\\": \\\"openrouter_api_key\\\",\\n }\\n # Set the API key based on the provider\\n kwarg = {provider_map[provider]: api_key}\\n\\n LLM = ChatLiteLLM(\\n model=model,\\n client=None,\\n streaming=streaming,\\n temperature=temperature,\\n model_kwargs=model_kwargs if model_kwargs is not None else {},\\n top_p=top_p,\\n top_k=top_k,\\n n=n,\\n max_tokens=max_tokens,\\n max_retries=max_retries,\\n **kwarg,\\n )\\n return LLM\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"max_retries\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":6,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_retries\",\"display_name\":\"Max retries\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"max_tokens\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":256,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_tokens\",\"display_name\":\"Max tokens\",\"advanced\":false,\"dynamic\":false,\"info\":\"The maximum number of tokens to generate for each chat completion.\",\"title_case\":false},\"model\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model\",\"display_name\":\"Model name\",\"advanced\":false,\"dynamic\":false,\"info\":\"The name of the model to use. For example, `gpt-3.5-turbo`.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"model_kwargs\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model_kwargs\",\"display_name\":\"Model kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"n\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":1,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"n\",\"display_name\":\"N\",\"advanced\":true,\"dynamic\":false,\"info\":\"Number of chat completions to generate for each prompt. Note that the API may not return the full n completions if duplicates are generated.\",\"title_case\":false},\"provider\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"OpenAI\",\"Azure\",\"Anthropic\",\"Replicate\",\"Cohere\",\"OpenRouter\"],\"name\":\"provider\",\"display_name\":\"Provider\",\"advanced\":false,\"dynamic\":false,\"info\":\"The provider of the API key.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"streaming\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":true,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"streaming\",\"display_name\":\"Streaming\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"temperature\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.7,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"top_k\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_k\",\"display_name\":\"Top k\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"top_p\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_p\",\"display_name\":\"Top p\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"verbose\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"verbose\",\"display_name\":\"Verbose\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"`LiteLLM` collection of large language models.\",\"base_classes\":[\"BaseLanguageModel\",\"Runnable\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"object\",\"Callable\"],\"display_name\":\"ChatLiteLLM\",\"documentation\":\"https://python.langchain.com/docs/integrations/chat/litellm\",\"custom_fields\":{\"model\":null,\"provider\":null,\"api_key\":null,\"streaming\":null,\"temperature\":null,\"model_kwargs\":null,\"top_p\":null,\"top_k\":null,\"n\":null,\"max_tokens\":null,\"max_retries\":null,\"verbose\":null},\"output_types\":[\"BaseLanguageModel\",\"Callable\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"CTransformersSpecs\":{\"template\":{\"model_file\":{\"type\":\"file\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[\".bin\"],\"file_path\":\"\",\"password\":false,\"name\":\"model_file\",\"display_name\":\"Model File\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Dict, Optional\\n\\nfrom langchain_community.llms.ctransformers import CTransformers\\n\\nfrom langflow import CustomComponent\\n\\n\\nclass CTransformersComponent(CustomComponent):\\n display_name = \\\"CTransformers\\\"\\n description = \\\"C Transformers LLM models\\\"\\n documentation = \\\"https://python.langchain.com/docs/modules/model_io/models/llms/integrations/ctransformers\\\"\\n\\n def build_config(self):\\n return {\\n \\\"model\\\": {\\\"display_name\\\": \\\"Model\\\", \\\"required\\\": True},\\n \\\"model_file\\\": {\\n \\\"display_name\\\": \\\"Model File\\\",\\n \\\"required\\\": False,\\n \\\"field_type\\\": \\\"file\\\",\\n \\\"file_types\\\": [\\\".bin\\\"],\\n },\\n \\\"model_type\\\": {\\\"display_name\\\": \\\"Model Type\\\", \\\"required\\\": True},\\n \\\"config\\\": {\\n \\\"display_name\\\": \\\"Config\\\",\\n \\\"advanced\\\": True,\\n \\\"required\\\": False,\\n \\\"field_type\\\": \\\"dict\\\",\\n \\\"value\\\": '{\\\"top_k\\\":40,\\\"top_p\\\":0.95,\\\"temperature\\\":0.8,\\\"repetition_penalty\\\":1.1,\\\"last_n_tokens\\\":64,\\\"seed\\\":-1,\\\"max_new_tokens\\\":256,\\\"stop\\\":\\\"\\\",\\\"stream\\\":\\\"False\\\",\\\"reset\\\":\\\"True\\\",\\\"batch_size\\\":8,\\\"threads\\\":-1,\\\"context_length\\\":-1,\\\"gpu_layers\\\":0}',\\n },\\n }\\n\\n def build(self, model: str, model_file: str, model_type: str, config: Optional[Dict] = None) -> CTransformers:\\n return CTransformers(model=model, model_file=model_file, model_type=model_type, config=config) # type: ignore\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"config\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"{\\\"top_k\\\":40,\\\"top_p\\\":0.95,\\\"temperature\\\":0.8,\\\"repetition_penalty\\\":1.1,\\\"last_n_tokens\\\":64,\\\"seed\\\":-1,\\\"max_new_tokens\\\":256,\\\"stop\\\":\\\"\\\",\\\"stream\\\":\\\"False\\\",\\\"reset\\\":\\\"True\\\",\\\"batch_size\\\":8,\\\"threads\\\":-1,\\\"context_length\\\":-1,\\\"gpu_layers\\\":0}\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"config\",\"display_name\":\"Config\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"model\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model\",\"display_name\":\"Model\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"model_type\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model_type\",\"display_name\":\"Model Type\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"C Transformers LLM models\",\"base_classes\":[\"BaseLanguageModel\",\"Runnable\",\"Generic\",\"CTransformers\",\"RunnableSerializable\",\"Serializable\",\"object\",\"BaseLLM\",\"LLM\"],\"display_name\":\"CTransformers\",\"documentation\":\"https://python.langchain.com/docs/modules/model_io/models/llms/integrations/ctransformers\",\"custom_fields\":{\"model\":null,\"model_file\":null,\"model_type\":null,\"config\":null},\"output_types\":[\"CTransformers\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"HuggingFaceEndpointsSpecs\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain.llms.base import BaseLLM\\nfrom langchain.llms.huggingface_endpoint import HuggingFaceEndpoint\\nfrom langflow import CustomComponent\\n\\n\\nclass HuggingFaceEndpointsComponent(CustomComponent):\\n display_name: str = \\\"Hugging Face Inference API\\\"\\n description: str = \\\"LLM model from Hugging Face Inference API.\\\"\\n\\n def build_config(self):\\n return {\\n \\\"endpoint_url\\\": {\\\"display_name\\\": \\\"Endpoint URL\\\", \\\"password\\\": True},\\n \\\"task\\\": {\\n \\\"display_name\\\": \\\"Task\\\",\\n \\\"options\\\": [\\\"text2text-generation\\\", \\\"text-generation\\\", \\\"summarization\\\"],\\n },\\n \\\"huggingfacehub_api_token\\\": {\\\"display_name\\\": \\\"API token\\\", \\\"password\\\": True},\\n \\\"model_kwargs\\\": {\\n \\\"display_name\\\": \\\"Model Keyword Arguments\\\",\\n \\\"field_type\\\": \\\"code\\\",\\n },\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(\\n self,\\n endpoint_url: str,\\n task: str = \\\"text2text-generation\\\",\\n huggingfacehub_api_token: Optional[str] = None,\\n model_kwargs: Optional[dict] = None,\\n ) -> BaseLLM:\\n try:\\n output = HuggingFaceEndpoint( # type: ignore\\n endpoint_url=endpoint_url,\\n task=task,\\n huggingfacehub_api_token=huggingfacehub_api_token,\\n model_kwargs=model_kwargs or {},\\n )\\n except Exception as e:\\n raise ValueError(\\\"Could not connect to HuggingFace Endpoints API.\\\") from e\\n return output\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"endpoint_url\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"endpoint_url\",\"display_name\":\"Endpoint URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"huggingfacehub_api_token\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"huggingfacehub_api_token\",\"display_name\":\"API token\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"model_kwargs\":{\"type\":\"code\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model_kwargs\",\"display_name\":\"Model Keyword Arguments\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"task\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"text2text-generation\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"text2text-generation\",\"text-generation\",\"summarization\"],\"name\":\"task\",\"display_name\":\"Task\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"LLM model from Hugging Face Inference API.\",\"base_classes\":[\"BaseLanguageModel\",\"Runnable\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"object\",\"BaseLLM\"],\"display_name\":\"Hugging Face Inference API\",\"documentation\":\"\",\"custom_fields\":{\"endpoint_url\":null,\"task\":null,\"huggingfacehub_api_token\":null,\"model_kwargs\":null},\"output_types\":[\"BaseLLM\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"ChatOpenAISpecs\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional, Union\\n\\nfrom langchain.llms import BaseLLM\\nfrom langchain_community.chat_models.openai import ChatOpenAI\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import BaseLanguageModel, NestedDict\\n\\n\\nclass ChatOpenAIComponent(CustomComponent):\\n display_name = \\\"ChatOpenAI\\\"\\n description = \\\"`OpenAI` Chat large language models API.\\\"\\n\\n def build_config(self):\\n return {\\n \\\"max_tokens\\\": {\\n \\\"display_name\\\": \\\"Max Tokens\\\",\\n \\\"advanced\\\": False,\\n \\\"required\\\": False,\\n },\\n \\\"model_kwargs\\\": {\\n \\\"display_name\\\": \\\"Model Kwargs\\\",\\n \\\"advanced\\\": True,\\n \\\"required\\\": False,\\n },\\n \\\"model_name\\\": {\\n \\\"display_name\\\": \\\"Model Name\\\",\\n \\\"advanced\\\": False,\\n \\\"required\\\": False,\\n \\\"options\\\": [\\n \\\"gpt-4-turbo-preview\\\",\\n \\\"gpt-4-0125-preview\\\",\\n \\\"gpt-4-1106-preview\\\",\\n \\\"gpt-4-vision-preview\\\",\\n \\\"gpt-3.5-turbo-0125\\\",\\n \\\"gpt-3.5-turbo-1106\\\",\\n ],\\n },\\n \\\"openai_api_base\\\": {\\n \\\"display_name\\\": \\\"OpenAI API Base\\\",\\n \\\"advanced\\\": False,\\n \\\"required\\\": False,\\n \\\"info\\\": (\\n \\\"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\\\n\\\\n\\\"\\n \\\"You can change this to use other APIs like JinaChat, LocalAI and Prem.\\\"\\n ),\\n },\\n \\\"openai_api_key\\\": {\\n \\\"display_name\\\": \\\"OpenAI API Key\\\",\\n \\\"advanced\\\": False,\\n \\\"required\\\": False,\\n \\\"password\\\": True,\\n },\\n \\\"temperature\\\": {\\n \\\"display_name\\\": \\\"Temperature\\\",\\n \\\"advanced\\\": False,\\n \\\"required\\\": False,\\n \\\"value\\\": 0.7,\\n },\\n }\\n\\n def build(\\n self,\\n max_tokens: Optional[int] = 256,\\n model_kwargs: NestedDict = {},\\n model_name: str = \\\"gpt-4-1106-preview\\\",\\n openai_api_base: Optional[str] = None,\\n openai_api_key: Optional[str] = None,\\n temperature: float = 0.7,\\n ) -> Union[BaseLanguageModel, BaseLLM]:\\n if not openai_api_base:\\n openai_api_base = \\\"https://api.openai.com/v1\\\"\\n return ChatOpenAI(\\n max_tokens=max_tokens,\\n model_kwargs=model_kwargs,\\n model=model_name,\\n base_url=openai_api_base,\\n api_key=openai_api_key,\\n temperature=temperature,\\n )\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"max_tokens\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":256,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_tokens\",\"display_name\":\"Max Tokens\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"model_kwargs\":{\"type\":\"NestedDict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model_kwargs\",\"display_name\":\"Model Kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"model_name\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"gpt-4-1106-preview\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"gpt-4-turbo-preview\",\"gpt-4-0125-preview\",\"gpt-4-1106-preview\",\"gpt-4-vision-preview\",\"gpt-3.5-turbo-0125\",\"gpt-3.5-turbo-1106\"],\"name\":\"model_name\",\"display_name\":\"Model Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"openai_api_base\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"openai_api_base\",\"display_name\":\"OpenAI API Base\",\"advanced\":false,\"dynamic\":false,\"info\":\"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"openai_api_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"openai_api_key\",\"display_name\":\"OpenAI API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"temperature\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.7,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"`OpenAI` Chat large language models API.\",\"base_classes\":[\"BaseLanguageModel\",\"Runnable\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"object\",\"BaseLLM\"],\"display_name\":\"ChatOpenAI\",\"documentation\":\"\",\"custom_fields\":{\"max_tokens\":null,\"model_kwargs\":null,\"model_name\":null,\"openai_api_base\":null,\"openai_api_key\":null,\"temperature\":null},\"output_types\":[\"BaseLanguageModel\",\"BaseLLM\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"OllamaLLMSpecs\":{\"template\":{\"base_url\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"base_url\",\"display_name\":\"Base URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"Endpoint of the Ollama API. Defaults to 'http://localhost:11434' if not specified.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import List, Optional\\n\\nfrom langchain.llms.base import BaseLLM\\nfrom langchain_community.llms.ollama import Ollama\\n\\nfrom langflow import CustomComponent\\n\\n\\nclass OllamaLLM(CustomComponent):\\n display_name = \\\"Ollama\\\"\\n description = \\\"Local LLM with Ollama.\\\"\\n\\n def build_config(self) -> dict:\\n return {\\n \\\"base_url\\\": {\\n \\\"display_name\\\": \\\"Base URL\\\",\\n \\\"info\\\": \\\"Endpoint of the Ollama API. Defaults to 'http://localhost:11434' if not specified.\\\",\\n },\\n \\\"model\\\": {\\n \\\"display_name\\\": \\\"Model Name\\\",\\n \\\"value\\\": \\\"llama2\\\",\\n \\\"info\\\": \\\"Refer to https://ollama.ai/library for more models.\\\",\\n },\\n \\\"temperature\\\": {\\n \\\"display_name\\\": \\\"Temperature\\\",\\n \\\"field_type\\\": \\\"float\\\",\\n \\\"value\\\": 0.8,\\n \\\"info\\\": \\\"Controls the creativity of model responses.\\\",\\n },\\n \\\"mirostat\\\": {\\n \\\"display_name\\\": \\\"Mirostat\\\",\\n \\\"options\\\": [\\\"Disabled\\\", \\\"Mirostat\\\", \\\"Mirostat 2.0\\\"],\\n \\\"info\\\": \\\"Enable/disable Mirostat sampling for controlling perplexity.\\\",\\n \\\"value\\\": \\\"Disabled\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"mirostat_eta\\\": {\\n \\\"display_name\\\": \\\"Mirostat Eta\\\",\\n \\\"field_type\\\": \\\"float\\\",\\n \\\"info\\\": \\\"Learning rate influencing the algorithm's response to feedback.\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"mirostat_tau\\\": {\\n \\\"display_name\\\": \\\"Mirostat Tau\\\",\\n \\\"field_type\\\": \\\"float\\\",\\n \\\"info\\\": \\\"Controls balance between coherence and diversity.\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"num_ctx\\\": {\\n \\\"display_name\\\": \\\"Context Window Size\\\",\\n \\\"field_type\\\": \\\"int\\\",\\n \\\"info\\\": \\\"Size of the context window for generating the next token.\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"num_gpu\\\": {\\n \\\"display_name\\\": \\\"Number of GPUs\\\",\\n \\\"field_type\\\": \\\"int\\\",\\n \\\"info\\\": \\\"Number of GPUs to use for computation.\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"num_thread\\\": {\\n \\\"display_name\\\": \\\"Number of Threads\\\",\\n \\\"field_type\\\": \\\"int\\\",\\n \\\"info\\\": \\\"Number of threads to use during computation.\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"repeat_last_n\\\": {\\n \\\"display_name\\\": \\\"Repeat Last N\\\",\\n \\\"field_type\\\": \\\"int\\\",\\n \\\"info\\\": \\\"Sets how far back the model looks to prevent repetition.\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"repeat_penalty\\\": {\\n \\\"display_name\\\": \\\"Repeat Penalty\\\",\\n \\\"field_type\\\": \\\"float\\\",\\n \\\"info\\\": \\\"Penalty for repetitions in generated text.\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"stop\\\": {\\n \\\"display_name\\\": \\\"Stop Tokens\\\",\\n \\\"info\\\": \\\"List of tokens to signal the model to stop generating text.\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"tfs_z\\\": {\\n \\\"display_name\\\": \\\"TFS Z\\\",\\n \\\"field_type\\\": \\\"float\\\",\\n \\\"info\\\": \\\"Tail free sampling to reduce impact of less probable tokens.\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"top_k\\\": {\\n \\\"display_name\\\": \\\"Top K\\\",\\n \\\"field_type\\\": \\\"int\\\",\\n \\\"info\\\": \\\"Limits token selection to top K for reducing nonsense generation.\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"top_p\\\": {\\n \\\"display_name\\\": \\\"Top P\\\",\\n \\\"field_type\\\": \\\"int\\\",\\n \\\"info\\\": \\\"Works with top-k to control diversity of generated text.\\\",\\n \\\"advanced\\\": True,\\n },\\n }\\n\\n def build(\\n self,\\n base_url: Optional[str],\\n model: str,\\n temperature: Optional[float],\\n mirostat: Optional[str],\\n mirostat_eta: Optional[float] = None,\\n mirostat_tau: Optional[float] = None,\\n num_ctx: Optional[int] = None,\\n num_gpu: Optional[int] = None,\\n num_thread: Optional[int] = None,\\n repeat_last_n: Optional[int] = None,\\n repeat_penalty: Optional[float] = None,\\n stop: Optional[List[str]] = None,\\n tfs_z: Optional[float] = None,\\n top_k: Optional[int] = None,\\n top_p: Optional[int] = None,\\n ) -> BaseLLM:\\n if not base_url:\\n base_url = \\\"http://localhost:11434\\\"\\n\\n # Mapping mirostat settings to their corresponding values\\n mirostat_options = {\\\"Mirostat\\\": 1, \\\"Mirostat 2.0\\\": 2}\\n\\n # Default to 0 for 'Disabled'\\n mirostat_value = mirostat_options.get(mirostat, 0) # type: ignore\\n\\n # Set mirostat_eta and mirostat_tau to None if mirostat is disabled\\n if mirostat_value == 0:\\n mirostat_eta = None\\n mirostat_tau = None\\n\\n try:\\n llm = Ollama(\\n base_url=base_url,\\n model=model,\\n mirostat=mirostat_value,\\n mirostat_eta=mirostat_eta,\\n mirostat_tau=mirostat_tau,\\n num_ctx=num_ctx,\\n num_gpu=num_gpu,\\n num_thread=num_thread,\\n repeat_last_n=repeat_last_n,\\n repeat_penalty=repeat_penalty,\\n temperature=temperature,\\n stop=stop,\\n tfs_z=tfs_z,\\n top_k=top_k,\\n top_p=top_p,\\n )\\n\\n except Exception as e:\\n raise ValueError(\\\"Could not connect to Ollama.\\\") from e\\n\\n return llm\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"mirostat\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"Disabled\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"Disabled\",\"Mirostat\",\"Mirostat 2.0\"],\"name\":\"mirostat\",\"display_name\":\"Mirostat\",\"advanced\":true,\"dynamic\":false,\"info\":\"Enable/disable Mirostat sampling for controlling perplexity.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"mirostat_eta\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"mirostat_eta\",\"display_name\":\"Mirostat Eta\",\"advanced\":true,\"dynamic\":false,\"info\":\"Learning rate influencing the algorithm's response to feedback.\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"mirostat_tau\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"mirostat_tau\",\"display_name\":\"Mirostat Tau\",\"advanced\":true,\"dynamic\":false,\"info\":\"Controls balance between coherence and diversity.\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"model\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"llama2\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model\",\"display_name\":\"Model Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"Refer to https://ollama.ai/library for more models.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"num_ctx\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"num_ctx\",\"display_name\":\"Context Window Size\",\"advanced\":true,\"dynamic\":false,\"info\":\"Size of the context window for generating the next token.\",\"title_case\":false},\"num_gpu\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"num_gpu\",\"display_name\":\"Number of GPUs\",\"advanced\":true,\"dynamic\":false,\"info\":\"Number of GPUs to use for computation.\",\"title_case\":false},\"num_thread\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"num_thread\",\"display_name\":\"Number of Threads\",\"advanced\":true,\"dynamic\":false,\"info\":\"Number of threads to use during computation.\",\"title_case\":false},\"repeat_last_n\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"repeat_last_n\",\"display_name\":\"Repeat Last N\",\"advanced\":true,\"dynamic\":false,\"info\":\"Sets how far back the model looks to prevent repetition.\",\"title_case\":false},\"repeat_penalty\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"repeat_penalty\",\"display_name\":\"Repeat Penalty\",\"advanced\":true,\"dynamic\":false,\"info\":\"Penalty for repetitions in generated text.\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"stop\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"stop\",\"display_name\":\"Stop Tokens\",\"advanced\":true,\"dynamic\":false,\"info\":\"List of tokens to signal the model to stop generating text.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"temperature\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.8,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"Controls the creativity of model responses.\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"tfs_z\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"tfs_z\",\"display_name\":\"TFS Z\",\"advanced\":true,\"dynamic\":false,\"info\":\"Tail free sampling to reduce impact of less probable tokens.\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"top_k\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_k\",\"display_name\":\"Top K\",\"advanced\":true,\"dynamic\":false,\"info\":\"Limits token selection to top K for reducing nonsense generation.\",\"title_case\":false},\"top_p\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_p\",\"display_name\":\"Top P\",\"advanced\":true,\"dynamic\":false,\"info\":\"Works with top-k to control diversity of generated text.\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Local LLM with Ollama.\",\"base_classes\":[\"BaseLanguageModel\",\"Runnable\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"object\",\"BaseLLM\"],\"display_name\":\"Ollama\",\"documentation\":\"\",\"custom_fields\":{\"base_url\":null,\"model\":null,\"temperature\":null,\"mirostat\":null,\"mirostat_eta\":null,\"mirostat_tau\":null,\"num_ctx\":null,\"num_gpu\":null,\"num_thread\":null,\"repeat_last_n\":null,\"repeat_penalty\":null,\"stop\":null,\"tfs_z\":null,\"top_k\":null,\"top_p\":null},\"output_types\":[\"BaseLLM\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true}},\"io\":{\"ChatOutput\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional, Union\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Text\\nfrom langflow.schema import Record\\n\\n\\nclass ChatOutput(CustomComponent):\\n display_name = \\\"Chat Output\\\"\\n description = \\\"Used to send a message to the chat.\\\"\\n\\n field_config = {\\n \\\"code\\\": {\\n \\\"show\\\": True,\\n }\\n }\\n\\n def build_config(self):\\n return {\\n \\\"message\\\": {\\\"input_types\\\": [\\\"Text\\\"], \\\"display_name\\\": \\\"Message\\\"},\\n \\\"sender\\\": {\\n \\\"options\\\": [\\\"Machine\\\", \\\"User\\\"],\\n \\\"display_name\\\": \\\"Sender Type\\\",\\n },\\n \\\"sender_name\\\": {\\\"display_name\\\": \\\"Sender Name\\\"},\\n \\\"session_id\\\": {\\n \\\"display_name\\\": \\\"Session ID\\\",\\n \\\"info\\\": \\\"Session ID of the chat history.\\\",\\n \\\"input_types\\\": [\\\"Text\\\"],\\n },\\n \\\"return_record\\\": {\\n \\\"display_name\\\": \\\"Return Record\\\",\\n \\\"info\\\": \\\"Return the message as a record containing the sender, sender_name, and session_id.\\\",\\n },\\n }\\n\\n def build(\\n self,\\n sender: Optional[str] = \\\"Machine\\\",\\n sender_name: Optional[str] = \\\"AI\\\",\\n session_id: Optional[str] = None,\\n message: Optional[str] = None,\\n return_record: Optional[bool] = False,\\n ) -> Union[Text, Record]:\\n if return_record:\\n if isinstance(message, Record):\\n # Update the data of the record\\n message.data[\\\"sender\\\"] = sender\\n message.data[\\\"sender_name\\\"] = sender_name\\n message.data[\\\"session_id\\\"] = session_id\\n else:\\n message = Record(\\n text=message,\\n data={\\n \\\"sender\\\": sender,\\n \\\"sender_name\\\": sender_name,\\n \\\"session_id\\\": session_id,\\n },\\n )\\n if not message:\\n message = \\\"\\\"\\n self.status = message\\n return message\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"message\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"message\",\"display_name\":\"Message\",\"advanced\":false,\"input_types\":[\"Text\",\"Text\"],\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"return_record\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"return_record\",\"display_name\":\"Return Record\",\"advanced\":false,\"dynamic\":false,\"info\":\"Return the message as a record containing the sender, sender_name, and session_id.\",\"title_case\":false},\"sender\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"Machine\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"Machine\",\"User\"],\"name\":\"sender\",\"display_name\":\"Sender Type\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"sender_name\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"AI\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"sender_name\",\"display_name\":\"Sender Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"session_id\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"session_id\",\"display_name\":\"Session ID\",\"advanced\":false,\"input_types\":[\"Text\",\"Text\"],\"dynamic\":false,\"info\":\"Session ID of the chat history.\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Used to send a message to the chat.\",\"base_classes\":[\"Text\",\"object\",\"Record\"],\"display_name\":\"Chat Output\",\"documentation\":\"\",\"custom_fields\":{\"sender\":null,\"sender_name\":null,\"session_id\":null,\"message\":null,\"return_record\":null},\"output_types\":[\"Text\",\"Record\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"MessageHistory\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import List, Optional\\n\\nfrom langflow import CustomComponent\\nfrom langflow.memory import get_messages\\nfrom langflow.schema import Record\\n\\n\\nclass MessageHistoryComponent(CustomComponent):\\n display_name = \\\"Message History\\\"\\n description = \\\"Used to retrieve stored messages.\\\"\\n\\n def build_config(self):\\n return {\\n \\\"sender\\\": {\\n \\\"options\\\": [\\\"Machine\\\", \\\"User\\\"],\\n \\\"display_name\\\": \\\"Sender Type\\\",\\n },\\n \\\"sender_name\\\": {\\\"display_name\\\": \\\"Sender Name\\\"},\\n \\\"file_path\\\": {\\n \\\"display_name\\\": \\\"File Path\\\",\\n \\\"info\\\": \\\"Path of the local JSON file to store the messages. It should be a unique path for each chat history.\\\",\\n },\\n \\\"n_messages\\\": {\\n \\\"display_name\\\": \\\"Number of Messages\\\",\\n \\\"info\\\": \\\"Number of messages to retrieve.\\\",\\n },\\n \\\"session_id\\\": {\\n \\\"display_name\\\": \\\"Session ID\\\",\\n \\\"info\\\": \\\"Session ID of the chat history.\\\",\\n \\\"input_types\\\": [\\\"Text\\\"],\\n },\\n }\\n\\n def build(\\n self,\\n sender: Optional[str] = None,\\n sender_name: Optional[str] = None,\\n session_id: Optional[str] = None,\\n n_messages: int = 5,\\n ) -> List[Record]:\\n messages = get_messages(\\n sender=sender,\\n sender_name=sender_name,\\n session_id=session_id,\\n limit=n_messages,\\n )\\n self.status = messages\\n return messages\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"n_messages\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":5,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"n_messages\",\"display_name\":\"Number of Messages\",\"advanced\":false,\"dynamic\":false,\"info\":\"Number of messages to retrieve.\",\"title_case\":false},\"sender\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"Machine\",\"User\"],\"name\":\"sender\",\"display_name\":\"Sender Type\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"sender_name\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"sender_name\",\"display_name\":\"Sender Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"session_id\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"session_id\",\"display_name\":\"Session ID\",\"advanced\":false,\"input_types\":[\"Text\",\"Text\"],\"dynamic\":false,\"info\":\"Session ID of the chat history.\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Used to retrieve stored messages.\",\"base_classes\":[\"Record\"],\"display_name\":\"Message History\",\"documentation\":\"\",\"custom_fields\":{\"sender\":null,\"sender_name\":null,\"session_id\":null,\"n_messages\":null},\"output_types\":[\"Record\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"TextOutput\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Text\\n\\n\\nclass TextOutput(CustomComponent):\\n display_name = \\\"Text Output\\\"\\n description = \\\"Used to pass text output to the next component.\\\"\\n\\n field_config = {\\n \\\"value\\\": {\\\"display_name\\\": \\\"Value\\\"},\\n }\\n\\n def build(self, value: Optional[str] = \\\"\\\") -> Text:\\n self.status = value\\n if not value:\\n value = \\\"\\\"\\n return value\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"value\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"value\",\"display_name\":\"Value\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Used to pass text output to the next component.\",\"base_classes\":[\"Text\",\"object\"],\"display_name\":\"Text Output\",\"documentation\":\"\",\"custom_fields\":{\"value\":null},\"output_types\":[\"Text\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"StoreMessages\":{\"template\":{\"records\":{\"type\":\"Record\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"records\",\"display_name\":\"Records\",\"advanced\":false,\"dynamic\":false,\"info\":\"The list of records to store. Each record should contain the keys 'sender', 'sender_name', and 'session_id'.\",\"title_case\":false},\"texts\":{\"type\":\"Text\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"texts\",\"display_name\":\"Texts\",\"advanced\":false,\"dynamic\":false,\"info\":\"The list of texts to store. If records is not provided, texts must be provided.\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import List, Optional\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Text\\nfrom langflow.memory import add_messages\\nfrom langflow.schema import Record\\n\\n\\nclass StoreMessages(CustomComponent):\\n display_name = \\\"Store Messages\\\"\\n description = \\\"Used to store messages.\\\"\\n\\n def build_config(self):\\n return {\\n \\\"records\\\": {\\n \\\"display_name\\\": \\\"Records\\\",\\n \\\"info\\\": \\\"The list of records to store. Each record should contain the keys 'sender', 'sender_name', and 'session_id'.\\\",\\n },\\n \\\"texts\\\": {\\n \\\"display_name\\\": \\\"Texts\\\",\\n \\\"info\\\": \\\"The list of texts to store. If records is not provided, texts must be provided.\\\",\\n },\\n \\\"session_id\\\": {\\n \\\"display_name\\\": \\\"Session ID\\\",\\n \\\"info\\\": \\\"The session ID to store.\\\",\\n },\\n \\\"sender\\\": {\\n \\\"display_name\\\": \\\"Sender\\\",\\n \\\"info\\\": \\\"The sender to store.\\\",\\n },\\n \\\"sender_name\\\": {\\n \\\"display_name\\\": \\\"Sender Name\\\",\\n \\\"info\\\": \\\"The sender name to store.\\\",\\n },\\n }\\n\\n def build(\\n self,\\n records: Optional[List[Record]] = None,\\n texts: Optional[List[Text]] = None,\\n session_id: Optional[str] = None,\\n sender: Optional[str] = None,\\n sender_name: Optional[str] = None,\\n ) -> List[Record]:\\n # Records is the main way to store messages\\n # If records is not provided, we can use texts\\n # but we need to create the records from the texts\\n # and the other parameters\\n if not texts and not records:\\n raise ValueError(\\\"Either texts or records must be provided.\\\")\\n\\n if not records:\\n records = []\\n if not session_id or not sender or not sender_name:\\n raise ValueError(\\\"If passing texts, session_id, sender, and sender_name must be provided.\\\")\\n for text in texts:\\n record = Record(\\n text=text,\\n data={\\n \\\"session_id\\\": session_id,\\n \\\"sender\\\": sender,\\n \\\"sender_name\\\": sender_name,\\n },\\n )\\n records.append(record)\\n elif isinstance(records, Record):\\n records = [records]\\n\\n self.status = records\\n records = add_messages(records)\\n return records\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"sender\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"sender\",\"display_name\":\"Sender\",\"advanced\":false,\"dynamic\":false,\"info\":\"The sender to store.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"sender_name\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"sender_name\",\"display_name\":\"Sender Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"The sender name to store.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"session_id\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"session_id\",\"display_name\":\"Session ID\",\"advanced\":false,\"dynamic\":false,\"info\":\"The session ID to store.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Used to store messages.\",\"base_classes\":[\"Record\"],\"display_name\":\"Store Messages\",\"documentation\":\"\",\"custom_fields\":{\"records\":null,\"texts\":null,\"session_id\":null,\"sender\":null,\"sender_name\":null},\"output_types\":[\"Record\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"ChatInput\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langflow import CustomComponent\\nfrom langflow.schema import Record\\n\\n\\nclass ChatInput(CustomComponent):\\n display_name = \\\"Chat Input\\\"\\n description = \\\"Used to get user input from the chat.\\\"\\n\\n def build_config(self):\\n return {\\n \\\"message\\\": {\\n \\\"input_types\\\": [\\\"Text\\\"],\\n \\\"display_name\\\": \\\"Message\\\",\\n \\\"multiline\\\": True,\\n },\\n \\\"sender\\\": {\\n \\\"options\\\": [\\\"Machine\\\", \\\"User\\\"],\\n \\\"display_name\\\": \\\"Sender Type\\\",\\n },\\n \\\"sender_name\\\": {\\\"display_name\\\": \\\"Sender Name\\\"},\\n \\\"session_id\\\": {\\n \\\"display_name\\\": \\\"Session ID\\\",\\n \\\"info\\\": \\\"Session ID of the chat history.\\\",\\n },\\n \\\"return_record\\\": {\\n \\\"display_name\\\": \\\"Return Record\\\",\\n \\\"info\\\": \\\"Return the message as a record containing the sender, sender_name, and session_id.\\\",\\n },\\n }\\n\\n def build(\\n self,\\n sender: Optional[str] = \\\"User\\\",\\n sender_name: Optional[str] = \\\"User\\\",\\n message: Optional[str] = None,\\n session_id: Optional[str] = None,\\n return_record: Optional[bool] = False,\\n ) -> Record:\\n if return_record:\\n if isinstance(message, Record):\\n # Update the data of the record\\n message.data[\\\"sender\\\"] = sender\\n message.data[\\\"sender_name\\\"] = sender_name\\n message.data[\\\"session_id\\\"] = session_id\\n else:\\n message = Record(\\n text=message,\\n data={\\n \\\"sender\\\": sender,\\n \\\"sender_name\\\": sender_name,\\n \\\"session_id\\\": session_id,\\n },\\n )\\n if not message:\\n message = \\\"\\\"\\n self.status = message\\n return message\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"message\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"message\",\"display_name\":\"Message\",\"advanced\":false,\"input_types\":[\"Text\",\"Text\"],\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"return_record\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"return_record\",\"display_name\":\"Return Record\",\"advanced\":false,\"dynamic\":false,\"info\":\"Return the message as a record containing the sender, sender_name, and session_id.\",\"title_case\":false},\"sender\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"User\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"Machine\",\"User\"],\"name\":\"sender\",\"display_name\":\"Sender Type\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"sender_name\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"User\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"sender_name\",\"display_name\":\"Sender Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"session_id\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"session_id\",\"display_name\":\"Session ID\",\"advanced\":false,\"dynamic\":false,\"info\":\"Session ID of the chat history.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Used to get user input from the chat.\",\"base_classes\":[\"Record\"],\"display_name\":\"Chat Input\",\"documentation\":\"\",\"custom_fields\":{\"sender\":null,\"sender_name\":null,\"message\":null,\"session_id\":null,\"return_record\":null},\"output_types\":[\"Record\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"TextInput\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Text\\n\\n\\nclass TextInput(CustomComponent):\\n display_name = \\\"Text Input\\\"\\n description = \\\"Used to pass text input to the next component.\\\"\\n\\n field_config = {\\n \\\"value\\\": {\\\"display_name\\\": \\\"Value\\\"},\\n }\\n\\n def build(self, value: Optional[str] = \\\"\\\") -> Text:\\n self.status = value\\n if not value:\\n value = \\\"\\\"\\n return value\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"value\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"value\",\"display_name\":\"Value\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Used to pass text input to the next component.\",\"base_classes\":[\"Text\",\"object\"],\"display_name\":\"Text Input\",\"documentation\":\"\",\"custom_fields\":{\"value\":null},\"output_types\":[\"Text\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true}},\"prompts\":{\"Prompt\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from langchain_core.prompts import PromptTemplate\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Prompt, InputField, Text\\n\\n\\nclass PromptComponent(CustomComponent):\\n display_name: str = \\\"Prompt\\\"\\n description: str = \\\"A component for creating prompts using templates\\\"\\n beta = True\\n\\n def build_config(self):\\n return {\\n \\\"template\\\": InputField(display_name=\\\"Template\\\"),\\n \\\"code\\\": InputField(advanced=True),\\n }\\n\\n def build(\\n self,\\n template: Prompt,\\n **kwargs,\\n ) -> Text:\\n prompt_template = PromptTemplate.from_template(template)\\n\\n attributes_to_check = [\\\"text\\\", \\\"page_content\\\"]\\n for key, value in kwargs.items():\\n for attribute in attributes_to_check:\\n if hasattr(value, attribute):\\n kwargs[key] = getattr(value, attribute)\\n\\n try:\\n formated_prompt = prompt_template.format(**kwargs)\\n except Exception as exc:\\n raise ValueError(f\\\"Error formatting prompt: {exc}\\\") from exc\\n self.status = f'Prompt: \\\"{formated_prompt}\\\"'\\n return formated_prompt\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":true,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"template\":{\"type\":\"prompt\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"template\",\"display_name\":\"Template\",\"advanced\":false,\"input_types\":[\"Text\"],\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"A component for creating prompts using templates\",\"base_classes\":[\"Text\",\"object\"],\"display_name\":\"Prompt\",\"documentation\":\"\",\"custom_fields\":{\"template\":null},\"output_types\":[\"Text\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true}}}" }, "headersSize": -1, "bodySize": -1, "redirectURL": "" }, "cache": {}, - "timings": { "send": -1, "wait": -1, "receive": 2.771 } + "timings": { + "send": -1, + "wait": -1, + "receive": 2.771 + } }, { "startedDateTime": "2024-02-28T14:32:30.976Z", @@ -286,21 +612,66 @@ "httpVersion": "HTTP/1.1", "cookies": [], "headers": [ - { "name": "Accept", "value": "application/json, text/plain, */*" }, - { "name": "Accept-Encoding", "value": "gzip, deflate, br, zstd" }, - { "name": "Accept-Language", "value": "en-US,en;q=0.9" }, - { "name": "Authorization", "value": "Bearer eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIwMzg5ZmIyOS1kYWE2LTQwOGMtYjhjYi1iOGZmOGQxNzM0M2EiLCJleHAiOjE3NDA2NjY3NTB9.ef5W5jwNOeVzU3JZ7ylLYf2MLEJcVxC4-fF7EK9Ecdc" }, - { "name": "Connection", "value": "keep-alive" }, - { "name": "Cookie", "value": "access_token_lf=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIwMzg5ZmIyOS1kYWE2LTQwOGMtYjhjYi1iOGZmOGQxNzM0M2EiLCJleHAiOjE3NDA2NjY3NTB9.ef5W5jwNOeVzU3JZ7ylLYf2MLEJcVxC4-fF7EK9Ecdc" }, - { "name": "Host", "value": "localhost:3000" }, - { "name": "Referer", "value": "http://localhost:3000/" }, - { "name": "Sec-Fetch-Dest", "value": "empty" }, - { "name": "Sec-Fetch-Mode", "value": "cors" }, - { "name": "Sec-Fetch-Site", "value": "same-origin" }, - { "name": "User-Agent", "value": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/123.0.0.0 Safari/537.36" }, - { "name": "sec-ch-ua", "value": "\"Chromium\";v=\"123\", \"Not:A-Brand\";v=\"8\"" }, - { "name": "sec-ch-ua-mobile", "value": "?0" }, - { "name": "sec-ch-ua-platform", "value": "\"Linux\"" } + { + "name": "Accept", + "value": "application/json, text/plain, */*" + }, + { + "name": "Accept-Encoding", + "value": "gzip, deflate, br, zstd" + }, + { + "name": "Accept-Language", + "value": "en-US,en;q=0.9" + }, + { + "name": "Authorization", + "value": "Bearer eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIwMzg5ZmIyOS1kYWE2LTQwOGMtYjhjYi1iOGZmOGQxNzM0M2EiLCJleHAiOjE3NDA2NjY3NTB9.ef5W5jwNOeVzU3JZ7ylLYf2MLEJcVxC4-fF7EK9Ecdc" + }, + { + "name": "Connection", + "value": "keep-alive" + }, + { + "name": "Cookie", + "value": "access_token_lf=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIwMzg5ZmIyOS1kYWE2LTQwOGMtYjhjYi1iOGZmOGQxNzM0M2EiLCJleHAiOjE3NDA2NjY3NTB9.ef5W5jwNOeVzU3JZ7ylLYf2MLEJcVxC4-fF7EK9Ecdc" + }, + { + "name": "Host", + "value": "localhost:3000" + }, + { + "name": "Referer", + "value": "http://localhost:3000/" + }, + { + "name": "Sec-Fetch-Dest", + "value": "empty" + }, + { + "name": "Sec-Fetch-Mode", + "value": "cors" + }, + { + "name": "Sec-Fetch-Site", + "value": "same-origin" + }, + { + "name": "User-Agent", + "value": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/123.0.0.0 Safari/537.36" + }, + { + "name": "sec-ch-ua", + "value": "\"Chromium\";v=\"123\", \"Not:A-Brand\";v=\"8\"" + }, + { + "name": "sec-ch-ua-mobile", + "value": "?0" + }, + { + "name": "sec-ch-ua-platform", + "value": "\"Linux\"" + } ], "queryString": [], "headersSize": -1, @@ -312,12 +683,30 @@ "httpVersion": "HTTP/1.1", "cookies": [], "headers": [ - { "name": "Access-Control-Allow-Origin", "value": "*" }, - { "name": "connection", "value": "close" }, - { "name": "content-length", "value": "16" }, - { "name": "content-type", "value": "application/json" }, - { "name": "date", "value": "Wed, 28 Feb 2024 14:32:30 GMT" }, - { "name": "server", "value": "uvicorn" } + { + "name": "Access-Control-Allow-Origin", + "value": "*" + }, + { + "name": "connection", + "value": "close" + }, + { + "name": "content-length", + "value": "16" + }, + { + "name": "content-type", + "value": "application/json" + }, + { + "name": "date", + "value": "Wed, 28 Feb 2024 14:32:30 GMT" + }, + { + "name": "server", + "value": "uvicorn" + } ], "content": { "size": -1, @@ -329,7 +718,11 @@ "redirectURL": "" }, "cache": {}, - "timings": { "send": -1, "wait": -1, "receive": 0.753 } + "timings": { + "send": -1, + "wait": -1, + "receive": 0.753 + } }, { "startedDateTime": "2024-02-28T14:32:30.976Z", @@ -340,21 +733,66 @@ "httpVersion": "HTTP/1.1", "cookies": [], "headers": [ - { "name": "Accept", "value": "application/json, text/plain, */*" }, - { "name": "Accept-Encoding", "value": "gzip, deflate, br, zstd" }, - { "name": "Accept-Language", "value": "en-US,en;q=0.9" }, - { "name": "Authorization", "value": "Bearer eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIwMzg5ZmIyOS1kYWE2LTQwOGMtYjhjYi1iOGZmOGQxNzM0M2EiLCJleHAiOjE3NDA2NjY3NTB9.ef5W5jwNOeVzU3JZ7ylLYf2MLEJcVxC4-fF7EK9Ecdc" }, - { "name": "Connection", "value": "keep-alive" }, - { "name": "Cookie", "value": "access_token_lf=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIwMzg5ZmIyOS1kYWE2LTQwOGMtYjhjYi1iOGZmOGQxNzM0M2EiLCJleHAiOjE3NDA2NjY3NTB9.ef5W5jwNOeVzU3JZ7ylLYf2MLEJcVxC4-fF7EK9Ecdc" }, - { "name": "Host", "value": "localhost:3000" }, - { "name": "Referer", "value": "http://localhost:3000/" }, - { "name": "Sec-Fetch-Dest", "value": "empty" }, - { "name": "Sec-Fetch-Mode", "value": "cors" }, - { "name": "Sec-Fetch-Site", "value": "same-origin" }, - { "name": "User-Agent", "value": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/123.0.0.0 Safari/537.36" }, - { "name": "sec-ch-ua", "value": "\"Chromium\";v=\"123\", \"Not:A-Brand\";v=\"8\"" }, - { "name": "sec-ch-ua-mobile", "value": "?0" }, - { "name": "sec-ch-ua-platform", "value": "\"Linux\"" } + { + "name": "Accept", + "value": "application/json, text/plain, */*" + }, + { + "name": "Accept-Encoding", + "value": "gzip, deflate, br, zstd" + }, + { + "name": "Accept-Language", + "value": "en-US,en;q=0.9" + }, + { + "name": "Authorization", + "value": "Bearer eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIwMzg5ZmIyOS1kYWE2LTQwOGMtYjhjYi1iOGZmOGQxNzM0M2EiLCJleHAiOjE3NDA2NjY3NTB9.ef5W5jwNOeVzU3JZ7ylLYf2MLEJcVxC4-fF7EK9Ecdc" + }, + { + "name": "Connection", + "value": "keep-alive" + }, + { + "name": "Cookie", + "value": "access_token_lf=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIwMzg5ZmIyOS1kYWE2LTQwOGMtYjhjYi1iOGZmOGQxNzM0M2EiLCJleHAiOjE3NDA2NjY3NTB9.ef5W5jwNOeVzU3JZ7ylLYf2MLEJcVxC4-fF7EK9Ecdc" + }, + { + "name": "Host", + "value": "localhost:3000" + }, + { + "name": "Referer", + "value": "http://localhost:3000/" + }, + { + "name": "Sec-Fetch-Dest", + "value": "empty" + }, + { + "name": "Sec-Fetch-Mode", + "value": "cors" + }, + { + "name": "Sec-Fetch-Site", + "value": "same-origin" + }, + { + "name": "User-Agent", + "value": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/123.0.0.0 Safari/537.36" + }, + { + "name": "sec-ch-ua", + "value": "\"Chromium\";v=\"123\", \"Not:A-Brand\";v=\"8\"" + }, + { + "name": "sec-ch-ua-mobile", + "value": "?0" + }, + { + "name": "sec-ch-ua-platform", + "value": "\"Linux\"" + } ], "queryString": [], "headersSize": -1, @@ -366,12 +804,30 @@ "httpVersion": "HTTP/1.1", "cookies": [], "headers": [ - { "name": "Access-Control-Allow-Origin", "value": "*" }, - { "name": "connection", "value": "close" }, - { "name": "content-length", "value": "38" }, - { "name": "content-type", "value": "application/json" }, - { "name": "date", "value": "Wed, 28 Feb 2024 14:32:30 GMT" }, - { "name": "server", "value": "uvicorn" } + { + "name": "Access-Control-Allow-Origin", + "value": "*" + }, + { + "name": "connection", + "value": "close" + }, + { + "name": "content-length", + "value": "38" + }, + { + "name": "content-type", + "value": "application/json" + }, + { + "name": "date", + "value": "Wed, 28 Feb 2024 14:32:30 GMT" + }, + { + "name": "server", + "value": "uvicorn" + } ], "content": { "size": -1, @@ -383,7 +839,11 @@ "redirectURL": "" }, "cache": {}, - "timings": { "send": -1, "wait": -1, "receive": 0.525 } + "timings": { + "send": -1, + "wait": -1, + "receive": 0.525 + } }, { "startedDateTime": "2024-02-28T14:32:30.976Z", @@ -394,21 +854,66 @@ "httpVersion": "HTTP/1.1", "cookies": [], "headers": [ - { "name": "Accept", "value": "application/json, text/plain, */*" }, - { "name": "Accept-Encoding", "value": "gzip, deflate, br, zstd" }, - { "name": "Accept-Language", "value": "en-US,en;q=0.9" }, - { "name": "Authorization", "value": "Bearer eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIwMzg5ZmIyOS1kYWE2LTQwOGMtYjhjYi1iOGZmOGQxNzM0M2EiLCJleHAiOjE3NDA2NjY3NTB9.ef5W5jwNOeVzU3JZ7ylLYf2MLEJcVxC4-fF7EK9Ecdc" }, - { "name": "Connection", "value": "keep-alive" }, - { "name": "Cookie", "value": "access_token_lf=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIwMzg5ZmIyOS1kYWE2LTQwOGMtYjhjYi1iOGZmOGQxNzM0M2EiLCJleHAiOjE3NDA2NjY3NTB9.ef5W5jwNOeVzU3JZ7ylLYf2MLEJcVxC4-fF7EK9Ecdc" }, - { "name": "Host", "value": "localhost:3000" }, - { "name": "Referer", "value": "http://localhost:3000/" }, - { "name": "Sec-Fetch-Dest", "value": "empty" }, - { "name": "Sec-Fetch-Mode", "value": "cors" }, - { "name": "Sec-Fetch-Site", "value": "same-origin" }, - { "name": "User-Agent", "value": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/123.0.0.0 Safari/537.36" }, - { "name": "sec-ch-ua", "value": "\"Chromium\";v=\"123\", \"Not:A-Brand\";v=\"8\"" }, - { "name": "sec-ch-ua-mobile", "value": "?0" }, - { "name": "sec-ch-ua-platform", "value": "\"Linux\"" } + { + "name": "Accept", + "value": "application/json, text/plain, */*" + }, + { + "name": "Accept-Encoding", + "value": "gzip, deflate, br, zstd" + }, + { + "name": "Accept-Language", + "value": "en-US,en;q=0.9" + }, + { + "name": "Authorization", + "value": "Bearer eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIwMzg5ZmIyOS1kYWE2LTQwOGMtYjhjYi1iOGZmOGQxNzM0M2EiLCJleHAiOjE3NDA2NjY3NTB9.ef5W5jwNOeVzU3JZ7ylLYf2MLEJcVxC4-fF7EK9Ecdc" + }, + { + "name": "Connection", + "value": "keep-alive" + }, + { + "name": "Cookie", + "value": "access_token_lf=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIwMzg5ZmIyOS1kYWE2LTQwOGMtYjhjYi1iOGZmOGQxNzM0M2EiLCJleHAiOjE3NDA2NjY3NTB9.ef5W5jwNOeVzU3JZ7ylLYf2MLEJcVxC4-fF7EK9Ecdc" + }, + { + "name": "Host", + "value": "localhost:3000" + }, + { + "name": "Referer", + "value": "http://localhost:3000/" + }, + { + "name": "Sec-Fetch-Dest", + "value": "empty" + }, + { + "name": "Sec-Fetch-Mode", + "value": "cors" + }, + { + "name": "Sec-Fetch-Site", + "value": "same-origin" + }, + { + "name": "User-Agent", + "value": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/123.0.0.0 Safari/537.36" + }, + { + "name": "sec-ch-ua", + "value": "\"Chromium\";v=\"123\", \"Not:A-Brand\";v=\"8\"" + }, + { + "name": "sec-ch-ua-mobile", + "value": "?0" + }, + { + "name": "sec-ch-ua-platform", + "value": "\"Linux\"" + } ], "queryString": [], "headersSize": -1, @@ -420,13 +925,34 @@ "httpVersion": "HTTP/1.1", "cookies": [], "headers": [ - { "name": "Access-Control-Allow-Origin", "value": "*" }, - { "name": "connection", "value": "close" }, - { "name": "content-length", "value": "227" }, - { "name": "content-type", "value": "application/json" }, - { "name": "date", "value": "Wed, 28 Feb 2024 14:32:30 GMT" }, - { "name": "server", "value": "uvicorn" }, - { "name": "set-cookie", "value": "access_token_lf=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIwMzg5ZmIyOS1kYWE2LTQwOGMtYjhjYi1iOGZmOGQxNzM0M2EiLCJleHAiOjE3NDA2NjY3NTB9.ef5W5jwNOeVzU3JZ7ylLYf2MLEJcVxC4-fF7EK9Ecdc; Path=/; SameSite=none; Secure" } + { + "name": "Access-Control-Allow-Origin", + "value": "*" + }, + { + "name": "connection", + "value": "close" + }, + { + "name": "content-length", + "value": "227" + }, + { + "name": "content-type", + "value": "application/json" + }, + { + "name": "date", + "value": "Wed, 28 Feb 2024 14:32:30 GMT" + }, + { + "name": "server", + "value": "uvicorn" + }, + { + "name": "set-cookie", + "value": "access_token_lf=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIwMzg5ZmIyOS1kYWE2LTQwOGMtYjhjYi1iOGZmOGQxNzM0M2EiLCJleHAiOjE3NDA2NjY3NTB9.ef5W5jwNOeVzU3JZ7ylLYf2MLEJcVxC4-fF7EK9Ecdc; Path=/; SameSite=none; Secure" + } ], "content": { "size": -1, @@ -438,7 +964,11 @@ "redirectURL": "" }, "cache": {}, - "timings": { "send": -1, "wait": -1, "receive": 0.658 } + "timings": { + "send": -1, + "wait": -1, + "receive": 0.658 + } }, { "startedDateTime": "2024-02-28T14:32:31.023Z", @@ -449,21 +979,66 @@ "httpVersion": "HTTP/1.1", "cookies": [], "headers": [ - { "name": "Accept", "value": "application/json, text/plain, */*" }, - { "name": "Accept-Encoding", "value": "gzip, deflate, br, zstd" }, - { "name": "Accept-Language", "value": "en-US,en;q=0.9" }, - { "name": "Authorization", "value": "Bearer eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIwMzg5ZmIyOS1kYWE2LTQwOGMtYjhjYi1iOGZmOGQxNzM0M2EiLCJleHAiOjE3NDA2NjY3NTB9.ef5W5jwNOeVzU3JZ7ylLYf2MLEJcVxC4-fF7EK9Ecdc" }, - { "name": "Connection", "value": "keep-alive" }, - { "name": "Cookie", "value": "access_token_lf=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIwMzg5ZmIyOS1kYWE2LTQwOGMtYjhjYi1iOGZmOGQxNzM0M2EiLCJleHAiOjE3NDA2NjY3NTB9.ef5W5jwNOeVzU3JZ7ylLYf2MLEJcVxC4-fF7EK9Ecdc" }, - { "name": "Host", "value": "localhost:3000" }, - { "name": "Referer", "value": "http://localhost:3000/" }, - { "name": "Sec-Fetch-Dest", "value": "empty" }, - { "name": "Sec-Fetch-Mode", "value": "cors" }, - { "name": "Sec-Fetch-Site", "value": "same-origin" }, - { "name": "User-Agent", "value": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/123.0.0.0 Safari/537.36" }, - { "name": "sec-ch-ua", "value": "\"Chromium\";v=\"123\", \"Not:A-Brand\";v=\"8\"" }, - { "name": "sec-ch-ua-mobile", "value": "?0" }, - { "name": "sec-ch-ua-platform", "value": "\"Linux\"" } + { + "name": "Accept", + "value": "application/json, text/plain, */*" + }, + { + "name": "Accept-Encoding", + "value": "gzip, deflate, br, zstd" + }, + { + "name": "Accept-Language", + "value": "en-US,en;q=0.9" + }, + { + "name": "Authorization", + "value": "Bearer eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIwMzg5ZmIyOS1kYWE2LTQwOGMtYjhjYi1iOGZmOGQxNzM0M2EiLCJleHAiOjE3NDA2NjY3NTB9.ef5W5jwNOeVzU3JZ7ylLYf2MLEJcVxC4-fF7EK9Ecdc" + }, + { + "name": "Connection", + "value": "keep-alive" + }, + { + "name": "Cookie", + "value": "access_token_lf=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIwMzg5ZmIyOS1kYWE2LTQwOGMtYjhjYi1iOGZmOGQxNzM0M2EiLCJleHAiOjE3NDA2NjY3NTB9.ef5W5jwNOeVzU3JZ7ylLYf2MLEJcVxC4-fF7EK9Ecdc" + }, + { + "name": "Host", + "value": "localhost:3000" + }, + { + "name": "Referer", + "value": "http://localhost:3000/" + }, + { + "name": "Sec-Fetch-Dest", + "value": "empty" + }, + { + "name": "Sec-Fetch-Mode", + "value": "cors" + }, + { + "name": "Sec-Fetch-Site", + "value": "same-origin" + }, + { + "name": "User-Agent", + "value": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/123.0.0.0 Safari/537.36" + }, + { + "name": "sec-ch-ua", + "value": "\"Chromium\";v=\"123\", \"Not:A-Brand\";v=\"8\"" + }, + { + "name": "sec-ch-ua-mobile", + "value": "?0" + }, + { + "name": "sec-ch-ua-platform", + "value": "\"Linux\"" + } ], "queryString": [], "headersSize": -1, @@ -475,12 +1050,30 @@ "httpVersion": "HTTP/1.1", "cookies": [], "headers": [ - { "name": "Access-Control-Allow-Origin", "value": "*" }, - { "name": "connection", "value": "close" }, - { "name": "content-length", "value": "253" }, - { "name": "content-type", "value": "application/json" }, - { "name": "date", "value": "Wed, 28 Feb 2024 14:32:30 GMT" }, - { "name": "server", "value": "uvicorn" } + { + "name": "Access-Control-Allow-Origin", + "value": "*" + }, + { + "name": "connection", + "value": "close" + }, + { + "name": "content-length", + "value": "253" + }, + { + "name": "content-type", + "value": "application/json" + }, + { + "name": "date", + "value": "Wed, 28 Feb 2024 14:32:30 GMT" + }, + { + "name": "server", + "value": "uvicorn" + } ], "content": { "size": -1, @@ -492,7 +1085,11 @@ "redirectURL": "" }, "cache": {}, - "timings": { "send": -1, "wait": -1, "receive": 0.787 } + "timings": { + "send": -1, + "wait": -1, + "receive": 0.787 + } }, { "startedDateTime": "2024-02-28T14:32:32.479Z", @@ -503,21 +1100,66 @@ "httpVersion": "HTTP/1.1", "cookies": [], "headers": [ - { "name": "Accept", "value": "application/json, text/plain, */*" }, - { "name": "Accept-Encoding", "value": "gzip, deflate, br, zstd" }, - { "name": "Accept-Language", "value": "en-US,en;q=0.9" }, - { "name": "Authorization", "value": "Bearer eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIwMzg5ZmIyOS1kYWE2LTQwOGMtYjhjYi1iOGZmOGQxNzM0M2EiLCJleHAiOjE3NDA2NjY3NTB9.ef5W5jwNOeVzU3JZ7ylLYf2MLEJcVxC4-fF7EK9Ecdc" }, - { "name": "Connection", "value": "keep-alive" }, - { "name": "Cookie", "value": "access_token_lf=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIwMzg5ZmIyOS1kYWE2LTQwOGMtYjhjYi1iOGZmOGQxNzM0M2EiLCJleHAiOjE3NDA2NjY3NTB9.ef5W5jwNOeVzU3JZ7ylLYf2MLEJcVxC4-fF7EK9Ecdc" }, - { "name": "Host", "value": "localhost:3000" }, - { "name": "Referer", "value": "http://localhost:3000/flows" }, - { "name": "Sec-Fetch-Dest", "value": "empty" }, - { "name": "Sec-Fetch-Mode", "value": "cors" }, - { "name": "Sec-Fetch-Site", "value": "same-origin" }, - { "name": "User-Agent", "value": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/123.0.0.0 Safari/537.36" }, - { "name": "sec-ch-ua", "value": "\"Chromium\";v=\"123\", \"Not:A-Brand\";v=\"8\"" }, - { "name": "sec-ch-ua-mobile", "value": "?0" }, - { "name": "sec-ch-ua-platform", "value": "\"Linux\"" } + { + "name": "Accept", + "value": "application/json, text/plain, */*" + }, + { + "name": "Accept-Encoding", + "value": "gzip, deflate, br, zstd" + }, + { + "name": "Accept-Language", + "value": "en-US,en;q=0.9" + }, + { + "name": "Authorization", + "value": "Bearer eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIwMzg5ZmIyOS1kYWE2LTQwOGMtYjhjYi1iOGZmOGQxNzM0M2EiLCJleHAiOjE3NDA2NjY3NTB9.ef5W5jwNOeVzU3JZ7ylLYf2MLEJcVxC4-fF7EK9Ecdc" + }, + { + "name": "Connection", + "value": "keep-alive" + }, + { + "name": "Cookie", + "value": "access_token_lf=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIwMzg5ZmIyOS1kYWE2LTQwOGMtYjhjYi1iOGZmOGQxNzM0M2EiLCJleHAiOjE3NDA2NjY3NTB9.ef5W5jwNOeVzU3JZ7ylLYf2MLEJcVxC4-fF7EK9Ecdc" + }, + { + "name": "Host", + "value": "localhost:3000" + }, + { + "name": "Referer", + "value": "http://localhost:3000/flows" + }, + { + "name": "Sec-Fetch-Dest", + "value": "empty" + }, + { + "name": "Sec-Fetch-Mode", + "value": "cors" + }, + { + "name": "Sec-Fetch-Site", + "value": "same-origin" + }, + { + "name": "User-Agent", + "value": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/123.0.0.0 Safari/537.36" + }, + { + "name": "sec-ch-ua", + "value": "\"Chromium\";v=\"123\", \"Not:A-Brand\";v=\"8\"" + }, + { + "name": "sec-ch-ua-mobile", + "value": "?0" + }, + { + "name": "sec-ch-ua-platform", + "value": "\"Linux\"" + } ], "queryString": [], "headersSize": -1, @@ -529,12 +1171,30 @@ "httpVersion": "HTTP/1.1", "cookies": [], "headers": [ - { "name": "Access-Control-Allow-Origin", "value": "*" }, - { "name": "connection", "value": "close" }, - { "name": "content-length", "value": "2" }, - { "name": "content-type", "value": "application/json" }, - { "name": "date", "value": "Wed, 28 Feb 2024 14:32:31 GMT" }, - { "name": "server", "value": "uvicorn" } + { + "name": "Access-Control-Allow-Origin", + "value": "*" + }, + { + "name": "connection", + "value": "close" + }, + { + "name": "content-length", + "value": "2" + }, + { + "name": "content-type", + "value": "application/json" + }, + { + "name": "date", + "value": "Wed, 28 Feb 2024 14:32:31 GMT" + }, + { + "name": "server", + "value": "uvicorn" + } ], "content": { "size": -1, @@ -546,7 +1206,11 @@ "redirectURL": "" }, "cache": {}, - "timings": { "send": -1, "wait": -1, "receive": 0.836 } + "timings": { + "send": -1, + "wait": -1, + "receive": 0.836 + } }, { "startedDateTime": "2024-02-28T14:32:36.617Z", @@ -557,24 +1221,78 @@ "httpVersion": "HTTP/1.1", "cookies": [], "headers": [ - { "name": "Accept", "value": "application/json, text/plain, */*" }, - { "name": "Accept-Encoding", "value": "gzip, deflate, br, zstd" }, - { "name": "Accept-Language", "value": "en-US,en;q=0.9" }, - { "name": "Authorization", "value": "Bearer eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIwMzg5ZmIyOS1kYWE2LTQwOGMtYjhjYi1iOGZmOGQxNzM0M2EiLCJleHAiOjE3NDA2NjY3NTB9.ef5W5jwNOeVzU3JZ7ylLYf2MLEJcVxC4-fF7EK9Ecdc" }, - { "name": "Connection", "value": "keep-alive" }, - { "name": "Content-Length", "value": "170" }, - { "name": "Content-Type", "value": "application/json" }, - { "name": "Cookie", "value": "access_token_lf=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIwMzg5ZmIyOS1kYWE2LTQwOGMtYjhjYi1iOGZmOGQxNzM0M2EiLCJleHAiOjE3NDA2NjY3NTB9.ef5W5jwNOeVzU3JZ7ylLYf2MLEJcVxC4-fF7EK9Ecdc" }, - { "name": "Host", "value": "localhost:3000" }, - { "name": "Origin", "value": "http://localhost:3000" }, - { "name": "Referer", "value": "http://localhost:3000/flows" }, - { "name": "Sec-Fetch-Dest", "value": "empty" }, - { "name": "Sec-Fetch-Mode", "value": "cors" }, - { "name": "Sec-Fetch-Site", "value": "same-origin" }, - { "name": "User-Agent", "value": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/123.0.0.0 Safari/537.36" }, - { "name": "sec-ch-ua", "value": "\"Chromium\";v=\"123\", \"Not:A-Brand\";v=\"8\"" }, - { "name": "sec-ch-ua-mobile", "value": "?0" }, - { "name": "sec-ch-ua-platform", "value": "\"Linux\"" } + { + "name": "Accept", + "value": "application/json, text/plain, */*" + }, + { + "name": "Accept-Encoding", + "value": "gzip, deflate, br, zstd" + }, + { + "name": "Accept-Language", + "value": "en-US,en;q=0.9" + }, + { + "name": "Authorization", + "value": "Bearer eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIwMzg5ZmIyOS1kYWE2LTQwOGMtYjhjYi1iOGZmOGQxNzM0M2EiLCJleHAiOjE3NDA2NjY3NTB9.ef5W5jwNOeVzU3JZ7ylLYf2MLEJcVxC4-fF7EK9Ecdc" + }, + { + "name": "Connection", + "value": "keep-alive" + }, + { + "name": "Content-Length", + "value": "170" + }, + { + "name": "Content-Type", + "value": "application/json" + }, + { + "name": "Cookie", + "value": "access_token_lf=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIwMzg5ZmIyOS1kYWE2LTQwOGMtYjhjYi1iOGZmOGQxNzM0M2EiLCJleHAiOjE3NDA2NjY3NTB9.ef5W5jwNOeVzU3JZ7ylLYf2MLEJcVxC4-fF7EK9Ecdc" + }, + { + "name": "Host", + "value": "localhost:3000" + }, + { + "name": "Origin", + "value": "http://localhost:3000" + }, + { + "name": "Referer", + "value": "http://localhost:3000/flows" + }, + { + "name": "Sec-Fetch-Dest", + "value": "empty" + }, + { + "name": "Sec-Fetch-Mode", + "value": "cors" + }, + { + "name": "Sec-Fetch-Site", + "value": "same-origin" + }, + { + "name": "User-Agent", + "value": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/123.0.0.0 Safari/537.36" + }, + { + "name": "sec-ch-ua", + "value": "\"Chromium\";v=\"123\", \"Not:A-Brand\";v=\"8\"" + }, + { + "name": "sec-ch-ua-mobile", + "value": "?0" + }, + { + "name": "sec-ch-ua-platform", + "value": "\"Linux\"" + } ], "queryString": [], "headersSize": -1, @@ -591,14 +1309,38 @@ "httpVersion": "HTTP/1.1", "cookies": [], "headers": [ - { "name": "access-control-allow-credentials", "value": "true" }, - { "name": "access-control-allow-origin", "value": "http://localhost:3000" }, - { "name": "connection", "value": "close" }, - { "name": "content-length", "value": "319" }, - { "name": "content-type", "value": "application/json" }, - { "name": "date", "value": "Wed, 28 Feb 2024 14:32:35 GMT" }, - { "name": "server", "value": "uvicorn" }, - { "name": "vary", "value": "Origin" } + { + "name": "access-control-allow-credentials", + "value": "true" + }, + { + "name": "access-control-allow-origin", + "value": "http://localhost:3000" + }, + { + "name": "connection", + "value": "close" + }, + { + "name": "content-length", + "value": "319" + }, + { + "name": "content-type", + "value": "application/json" + }, + { + "name": "date", + "value": "Wed, 28 Feb 2024 14:32:35 GMT" + }, + { + "name": "server", + "value": "uvicorn" + }, + { + "name": "vary", + "value": "Origin" + } ], "content": { "size": -1, @@ -610,7 +1352,11 @@ "redirectURL": "" }, "cache": {}, - "timings": { "send": -1, "wait": -1, "receive": 1.679 } + "timings": { + "send": -1, + "wait": -1, + "receive": 1.679 + } }, { "startedDateTime": "2024-02-28T14:32:36.774Z", @@ -621,21 +1367,66 @@ "httpVersion": "HTTP/1.1", "cookies": [], "headers": [ - { "name": "Accept", "value": "application/json, text/plain, */*" }, - { "name": "Accept-Encoding", "value": "gzip, deflate, br, zstd" }, - { "name": "Accept-Language", "value": "en-US,en;q=0.9" }, - { "name": "Authorization", "value": "Bearer eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIwMzg5ZmIyOS1kYWE2LTQwOGMtYjhjYi1iOGZmOGQxNzM0M2EiLCJleHAiOjE3NDA2NjY3NTB9.ef5W5jwNOeVzU3JZ7ylLYf2MLEJcVxC4-fF7EK9Ecdc" }, - { "name": "Connection", "value": "keep-alive" }, - { "name": "Cookie", "value": "access_token_lf=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIwMzg5ZmIyOS1kYWE2LTQwOGMtYjhjYi1iOGZmOGQxNzM0M2EiLCJleHAiOjE3NDA2NjY3NTB9.ef5W5jwNOeVzU3JZ7ylLYf2MLEJcVxC4-fF7EK9Ecdc" }, - { "name": "Host", "value": "localhost:3000" }, - { "name": "Referer", "value": "http://localhost:3000/flow/b3aad40d-cf3b-49d2-804f-bfa33b70beef" }, - { "name": "Sec-Fetch-Dest", "value": "empty" }, - { "name": "Sec-Fetch-Mode", "value": "cors" }, - { "name": "Sec-Fetch-Site", "value": "same-origin" }, - { "name": "User-Agent", "value": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/123.0.0.0 Safari/537.36" }, - { "name": "sec-ch-ua", "value": "\"Chromium\";v=\"123\", \"Not:A-Brand\";v=\"8\"" }, - { "name": "sec-ch-ua-mobile", "value": "?0" }, - { "name": "sec-ch-ua-platform", "value": "\"Linux\"" } + { + "name": "Accept", + "value": "application/json, text/plain, */*" + }, + { + "name": "Accept-Encoding", + "value": "gzip, deflate, br, zstd" + }, + { + "name": "Accept-Language", + "value": "en-US,en;q=0.9" + }, + { + "name": "Authorization", + "value": "Bearer eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIwMzg5ZmIyOS1kYWE2LTQwOGMtYjhjYi1iOGZmOGQxNzM0M2EiLCJleHAiOjE3NDA2NjY3NTB9.ef5W5jwNOeVzU3JZ7ylLYf2MLEJcVxC4-fF7EK9Ecdc" + }, + { + "name": "Connection", + "value": "keep-alive" + }, + { + "name": "Cookie", + "value": "access_token_lf=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIwMzg5ZmIyOS1kYWE2LTQwOGMtYjhjYi1iOGZmOGQxNzM0M2EiLCJleHAiOjE3NDA2NjY3NTB9.ef5W5jwNOeVzU3JZ7ylLYf2MLEJcVxC4-fF7EK9Ecdc" + }, + { + "name": "Host", + "value": "localhost:3000" + }, + { + "name": "Referer", + "value": "http://localhost:3000/flow/b3aad40d-cf3b-49d2-804f-bfa33b70beef" + }, + { + "name": "Sec-Fetch-Dest", + "value": "empty" + }, + { + "name": "Sec-Fetch-Mode", + "value": "cors" + }, + { + "name": "Sec-Fetch-Site", + "value": "same-origin" + }, + { + "name": "User-Agent", + "value": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/123.0.0.0 Safari/537.36" + }, + { + "name": "sec-ch-ua", + "value": "\"Chromium\";v=\"123\", \"Not:A-Brand\";v=\"8\"" + }, + { + "name": "sec-ch-ua-mobile", + "value": "?0" + }, + { + "name": "sec-ch-ua-platform", + "value": "\"Linux\"" + } ], "queryString": [ { @@ -652,12 +1443,30 @@ "httpVersion": "HTTP/1.1", "cookies": [], "headers": [ - { "name": "Access-Control-Allow-Origin", "value": "*" }, - { "name": "connection", "value": "close" }, - { "name": "content-length", "value": "20" }, - { "name": "content-type", "value": "application/json" }, - { "name": "date", "value": "Wed, 28 Feb 2024 14:32:35 GMT" }, - { "name": "server", "value": "uvicorn" } + { + "name": "Access-Control-Allow-Origin", + "value": "*" + }, + { + "name": "connection", + "value": "close" + }, + { + "name": "content-length", + "value": "20" + }, + { + "name": "content-type", + "value": "application/json" + }, + { + "name": "date", + "value": "Wed, 28 Feb 2024 14:32:35 GMT" + }, + { + "name": "server", + "value": "uvicorn" + } ], "content": { "size": -1, @@ -669,7 +1478,11 @@ "redirectURL": "" }, "cache": {}, - "timings": { "send": -1, "wait": -1, "receive": 1.023 } + "timings": { + "send": -1, + "wait": -1, + "receive": 1.023 + } }, { "startedDateTime": "2024-02-28T14:32:49.526Z", @@ -680,22 +1493,70 @@ "httpVersion": "HTTP/1.1", "cookies": [], "headers": [ - { "name": "Accept", "value": "application/json, text/plain, */*" }, - { "name": "Accept-Encoding", "value": "gzip, deflate, br, zstd" }, - { "name": "Accept-Language", "value": "en-US,en;q=0.9" }, - { "name": "Authorization", "value": "Bearer eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIwMzg5ZmIyOS1kYWE2LTQwOGMtYjhjYi1iOGZmOGQxNzM0M2EiLCJleHAiOjE3NDA2NjY3NTB9.ef5W5jwNOeVzU3JZ7ylLYf2MLEJcVxC4-fF7EK9Ecdc" }, - { "name": "Connection", "value": "keep-alive" }, - { "name": "Cookie", "value": "access_token_lf=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIwMzg5ZmIyOS1kYWE2LTQwOGMtYjhjYi1iOGZmOGQxNzM0M2EiLCJleHAiOjE3NDA2NjY3NTB9.ef5W5jwNOeVzU3JZ7ylLYf2MLEJcVxC4-fF7EK9Ecdc" }, - { "name": "Host", "value": "localhost:3000" }, - { "name": "Origin", "value": "http://localhost:3000" }, - { "name": "Referer", "value": "http://localhost:3000/flow/b3aad40d-cf3b-49d2-804f-bfa33b70beef" }, - { "name": "Sec-Fetch-Dest", "value": "empty" }, - { "name": "Sec-Fetch-Mode", "value": "cors" }, - { "name": "Sec-Fetch-Site", "value": "same-origin" }, - { "name": "User-Agent", "value": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/123.0.0.0 Safari/537.36" }, - { "name": "sec-ch-ua", "value": "\"Chromium\";v=\"123\", \"Not:A-Brand\";v=\"8\"" }, - { "name": "sec-ch-ua-mobile", "value": "?0" }, - { "name": "sec-ch-ua-platform", "value": "\"Linux\"" } + { + "name": "Accept", + "value": "application/json, text/plain, */*" + }, + { + "name": "Accept-Encoding", + "value": "gzip, deflate, br, zstd" + }, + { + "name": "Accept-Language", + "value": "en-US,en;q=0.9" + }, + { + "name": "Authorization", + "value": "Bearer eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIwMzg5ZmIyOS1kYWE2LTQwOGMtYjhjYi1iOGZmOGQxNzM0M2EiLCJleHAiOjE3NDA2NjY3NTB9.ef5W5jwNOeVzU3JZ7ylLYf2MLEJcVxC4-fF7EK9Ecdc" + }, + { + "name": "Connection", + "value": "keep-alive" + }, + { + "name": "Cookie", + "value": "access_token_lf=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIwMzg5ZmIyOS1kYWE2LTQwOGMtYjhjYi1iOGZmOGQxNzM0M2EiLCJleHAiOjE3NDA2NjY3NTB9.ef5W5jwNOeVzU3JZ7ylLYf2MLEJcVxC4-fF7EK9Ecdc" + }, + { + "name": "Host", + "value": "localhost:3000" + }, + { + "name": "Origin", + "value": "http://localhost:3000" + }, + { + "name": "Referer", + "value": "http://localhost:3000/flow/b3aad40d-cf3b-49d2-804f-bfa33b70beef" + }, + { + "name": "Sec-Fetch-Dest", + "value": "empty" + }, + { + "name": "Sec-Fetch-Mode", + "value": "cors" + }, + { + "name": "Sec-Fetch-Site", + "value": "same-origin" + }, + { + "name": "User-Agent", + "value": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/123.0.0.0 Safari/537.36" + }, + { + "name": "sec-ch-ua", + "value": "\"Chromium\";v=\"123\", \"Not:A-Brand\";v=\"8\"" + }, + { + "name": "sec-ch-ua-mobile", + "value": "?0" + }, + { + "name": "sec-ch-ua-platform", + "value": "\"Linux\"" + } ], "queryString": [], "headersSize": -1, @@ -707,14 +1568,38 @@ "httpVersion": "HTTP/1.1", "cookies": [], "headers": [ - { "name": "access-control-allow-credentials", "value": "true" }, - { "name": "access-control-allow-origin", "value": "http://localhost:3000" }, - { "name": "connection", "value": "close" }, - { "name": "content-length", "value": "39" }, - { "name": "content-type", "value": "application/json" }, - { "name": "date", "value": "Wed, 28 Feb 2024 14:32:48 GMT" }, - { "name": "server", "value": "uvicorn" }, - { "name": "vary", "value": "Origin" } + { + "name": "access-control-allow-credentials", + "value": "true" + }, + { + "name": "access-control-allow-origin", + "value": "http://localhost:3000" + }, + { + "name": "connection", + "value": "close" + }, + { + "name": "content-length", + "value": "39" + }, + { + "name": "content-type", + "value": "application/json" + }, + { + "name": "date", + "value": "Wed, 28 Feb 2024 14:32:48 GMT" + }, + { + "name": "server", + "value": "uvicorn" + }, + { + "name": "vary", + "value": "Origin" + } ], "content": { "size": -1, @@ -726,7 +1611,11 @@ "redirectURL": "" }, "cache": {}, - "timings": { "send": -1, "wait": -1, "receive": 0.977 } + "timings": { + "send": -1, + "wait": -1, + "receive": 0.977 + } } ] } diff --git a/tests/data/component_with_templatefield.py b/tests/data/component_with_templatefield.py index 26d7e834b..be1ac174f 100644 --- a/tests/data/component_with_templatefield.py +++ b/tests/data/component_with_templatefield.py @@ -1,7 +1,7 @@ import random from langflow.custom import CustomComponent -from langflow.field_typing import TemplateField +from langflow.field_typing import InputField class TestComponent(CustomComponent): @@ -11,7 +11,7 @@ class TestComponent(CustomComponent): return [f"Random {random.randint(1, 100)}" for _ in range(5)] def build_config(self): - return {"param": TemplateField(display_name="Param", options=self.refresh_values)} + return {"param": InputField(display_name="Param", options=self.refresh_values)} def build(self, param: int): return param diff --git a/tests/test_frontend_nodes.py b/tests/test_frontend_nodes.py index e92ad1fe4..03af4332e 100644 --- a/tests/test_frontend_nodes.py +++ b/tests/test_frontend_nodes.py @@ -1,16 +1,17 @@ import pytest -from langflow.template.field.base import TemplateField + +from langflow.template.field.base import InputField from langflow.template.frontend_node.base import FrontendNode from langflow.template.template.base import Template @pytest.fixture -def sample_template_field() -> TemplateField: - return TemplateField(name="test_field", field_type="str") +def sample_template_field() -> InputField: + return InputField(name="test_field", field_type="str") @pytest.fixture -def sample_template(sample_template_field: TemplateField) -> Template: +def sample_template(sample_template_field: InputField) -> Template: return Template(type_name="test_template", fields=[sample_template_field]) @@ -24,7 +25,7 @@ def sample_frontend_node(sample_template: Template) -> FrontendNode: ) -def test_template_field_defaults(sample_template_field: TemplateField): +def test_template_field_defaults(sample_template_field: InputField): assert sample_template_field.field_type == "str" assert sample_template_field.required is False assert sample_template_field.placeholder == "" @@ -38,7 +39,7 @@ def test_template_field_defaults(sample_template_field: TemplateField): assert sample_template_field.name == "test_field" -def test_template_to_dict(sample_template: Template, sample_template_field: TemplateField): +def test_template_to_dict(sample_template: Template, sample_template_field: InputField): template_dict = sample_template.to_dict() assert template_dict["_type"] == "test_template" assert len(template_dict) == 2 # _type and test_field From 25752b1aa8cffc4ecec5b7466e77a78b6e4595a0 Mon Sep 17 00:00:00 2001 From: ogabrielluiz Date: Thu, 30 May 2024 17:17:36 -0300 Subject: [PATCH 028/701] feat: Migrate base classes to outputs in FrontendNode This commit migrates the base classes of the FrontendNode to the outputs field. Each base class is converted into an OutputField with the same name and type. This change ensures consistency and improves the structure of the code. --- .../base/langflow/template/frontend_node/base.py | 13 ++++++++++++- src/frontend/src/utils/reactflowUtils.ts | 10 +++++++--- 2 files changed, 19 insertions(+), 4 deletions(-) diff --git a/src/backend/base/langflow/template/frontend_node/base.py b/src/backend/base/langflow/template/frontend_node/base.py index 2b3771db9..2d69922cf 100644 --- a/src/backend/base/langflow/template/frontend_node/base.py +++ b/src/backend/base/langflow/template/frontend_node/base.py @@ -4,7 +4,7 @@ from typing import ClassVar, Dict, List, Optional, Union from pydantic import BaseModel, Field, field_serializer, model_serializer -from langflow.template.field.base import InputField +from langflow.template.field.base import InputField, OutputField from langflow.template.frontend_node.constants import FORCE_SHOW_FIELDS from langflow.template.frontend_node.formatter import field_formatters from langflow.template.template.base import Template @@ -77,6 +77,8 @@ class FrontendNode(BaseModel): """List of conditional paths for the frontend node.""" frozen: bool = False """Whether the frontend node is frozen.""" + outputs: List[OutputField] = [] + """List of output fields for the frontend node.""" field_order: list[str] = [] """Order of the fields in the frontend node.""" @@ -114,6 +116,15 @@ class FrontendNode(BaseModel): result["template"] = self.template.to_dict(format_func) name = result.pop("name") + # Migrate base classes to outputs + if "output_types" in result: + for base_class in result["output_types"]: + output = OutputField( + name=base_class, + types=[base_class], + ) + result["outputs"].append(output.model_dump()) + return {name: result} # For backwards compatibility diff --git a/src/frontend/src/utils/reactflowUtils.ts b/src/frontend/src/utils/reactflowUtils.ts index 963063eff..afb3db57f 100644 --- a/src/frontend/src/utils/reactflowUtils.ts +++ b/src/frontend/src/utils/reactflowUtils.ts @@ -444,9 +444,13 @@ export function updateNewOutput({ nodes, edges }: updateEdgesHandleIdsType) { !sourceNode.data.node?.outputs || sourceNode.data.node!.outputs!.length === 0 ) { - sourceNode.data.node!.outputs = [ - { types: sourceNode.data.node!.base_classes, selected: selected }, - ]; + const outputTypes = sourceNode.data.node!.output_types; + // create a new output field for each output type + sourceNode.data.node!.outputs = outputTypes?.map((type) => ({ + types: [type], + selected: selected, + name: type, + })); } } edge.sourceHandle = scapedJSONStringfy(newSourceHandle); From 59126fa01a7d5c68309f0c7f5f42a8fc5f88955b Mon Sep 17 00:00:00 2001 From: ogabrielluiz Date: Thu, 30 May 2024 17:55:02 -0300 Subject: [PATCH 029/701] refactor: rename InputField to Input --- .../components/experimental/SubFlow.py | 6 ++-- .../components/helpers/CreateRecord.py | 4 +-- .../base/langflow/components/inputs/Prompt.py | 6 ++-- src/backend/base/langflow/custom/utils.py | 20 ++++++------ .../base/langflow/field_typing/__init__.py | 8 ++--- .../Basic Prompting (Hello, world!).json | 2 +- .../Langflow Blog Writter.json | 2 +- .../Langflow Document QA.json | 2 +- .../Langflow Memory Conversation.json | 2 +- .../Langflow Prompt Chaining.json | 4 +-- .../VectorStore-RAG-Flows.json | 2 +- .../base/langflow/template/field/base.py | 6 ++-- .../base/langflow/template/field/prompt.py | 4 +-- .../langflow/template/frontend_node/base.py | 28 ++++++++-------- .../frontend_node/custom_components.py | 4 +-- .../template/frontend_node/formatter/base.py | 4 +-- .../formatter/field_formatters.py | 32 +++++++++---------- .../base/langflow/template/template/base.py | 12 +++---- tests/data/component_with_templatefield.py | 4 +-- tests/test_frontend_nodes.py | 12 +++---- 20 files changed, 82 insertions(+), 82 deletions(-) diff --git a/src/backend/base/langflow/components/experimental/SubFlow.py b/src/backend/base/langflow/components/experimental/SubFlow.py index 86deaf8ba..86dd336bf 100644 --- a/src/backend/base/langflow/components/experimental/SubFlow.py +++ b/src/backend/base/langflow/components/experimental/SubFlow.py @@ -10,7 +10,7 @@ from langflow.graph.vertex.base import Vertex from langflow.helpers.flow import get_flow_inputs from langflow.schema import Record from langflow.schema.dotdict import dotdict -from langflow.template.field.base import InputField +from langflow.template.field.base import Input class SubFlowComponent(CustomComponent): @@ -54,9 +54,9 @@ class SubFlowComponent(CustomComponent): return build_config def add_inputs_to_build_config(self, inputs: List[Vertex], build_config: dotdict): - new_fields: list[InputField] = [] + new_fields: list[Input] = [] for vertex in inputs: - field = InputField( + field = Input( display_name=vertex.display_name, name=vertex.id, info=vertex.description, diff --git a/src/backend/base/langflow/components/helpers/CreateRecord.py b/src/backend/base/langflow/components/helpers/CreateRecord.py index e37569b6b..d466f5c26 100644 --- a/src/backend/base/langflow/components/helpers/CreateRecord.py +++ b/src/backend/base/langflow/components/helpers/CreateRecord.py @@ -4,7 +4,7 @@ from langflow.custom import CustomComponent from langflow.field_typing.range_spec import RangeSpec from langflow.schema import Record from langflow.schema.dotdict import dotdict -from langflow.template.field.base import InputField +from langflow.template.field.base import Input class CreateRecordComponent(CustomComponent): @@ -35,7 +35,7 @@ class CreateRecordComponent(CustomComponent): field = existing_fields[key] build_config[key] = field else: - field = InputField( + field = Input( display_name=f"Field {i}", name=key, info=f"Key for field {i}.", diff --git a/src/backend/base/langflow/components/inputs/Prompt.py b/src/backend/base/langflow/components/inputs/Prompt.py index b0f7930db..f14b9dbdd 100644 --- a/src/backend/base/langflow/components/inputs/Prompt.py +++ b/src/backend/base/langflow/components/inputs/Prompt.py @@ -1,7 +1,7 @@ from langchain_core.prompts import PromptTemplate from langflow.custom import CustomComponent -from langflow.field_typing import InputField, Prompt, Text +from langflow.field_typing import Input, Prompt, Text class PromptComponent(CustomComponent): @@ -11,8 +11,8 @@ class PromptComponent(CustomComponent): def build_config(self): return { - "template": InputField(display_name="Template"), - "code": InputField(advanced=True), + "template": Input(display_name="Template"), + "code": Input(advanced=True), } def build( diff --git a/src/backend/base/langflow/custom/utils.py b/src/backend/base/langflow/custom/utils.py index d2a769aea..2a941a418 100644 --- a/src/backend/base/langflow/custom/utils.py +++ b/src/backend/base/langflow/custom/utils.py @@ -22,7 +22,7 @@ from langflow.custom.eval import eval_custom_component_code from langflow.custom.schema import MissingDefault from langflow.field_typing.range_spec import RangeSpec from langflow.schema import dotdict -from langflow.template.field.base import InputField +from langflow.template.field.base import Input from langflow.template.frontend_node.custom_components import CustomComponentFrontendNode from langflow.utils import validate from langflow.utils.util import get_base_classes @@ -169,7 +169,7 @@ def add_new_custom_field( required = field_config.pop("required", field_required) placeholder = field_config.pop("placeholder", "") - new_field = InputField( + new_field = Input( name=field_name, field_type=field_type, value=field_value, @@ -231,9 +231,9 @@ def add_extra_fields(frontend_node, field_config, function_args): ) -def get_field_dict(field: Union[InputField, dict]): - """Get the field dictionary from a InputField or a dict""" - if isinstance(field, InputField): +def get_field_dict(field: Union[Input, dict]): + """Get the field dictionary from a Input or a dict""" + if isinstance(field, Input): return dotdict(field.model_dump(by_alias=True, exclude_none=True)) return field @@ -266,8 +266,8 @@ def run_build_config( build_config: Dict = custom_instance.build_config() for field_name, field in build_config.copy().items(): - # Allow user to build InputField as well - # as a dict with the same keys as InputField + # Allow user to build Input as well + # as a dict with the same keys as Input field_dict = get_field_dict(field) # Let's check if "rangeSpec" is a RangeSpec object if "rangeSpec" in field_dict and isinstance(field_dict["rangeSpec"], RangeSpec): @@ -305,7 +305,7 @@ def build_frontend_node(template_config): def add_code_field(frontend_node: CustomComponentFrontendNode, raw_code, field_config): - code_field = InputField( + code_field = Input( dynamic=True, required=True, placeholder="", @@ -429,9 +429,9 @@ def update_field_dict( return build_config -def sanitize_field_config(field_config: Union[Dict, InputField]): +def sanitize_field_config(field_config: Union[Dict, Input]): # If any of the already existing keys are in field_config, remove them - if isinstance(field_config, InputField): + if isinstance(field_config, Input): field_dict = field_config.to_dict() else: field_dict = field_config diff --git a/src/backend/base/langflow/field_typing/__init__.py b/src/backend/base/langflow/field_typing/__init__.py index d037aa371..7ae9c61a9 100644 --- a/src/backend/base/langflow/field_typing/__init__.py +++ b/src/backend/base/langflow/field_typing/__init__.py @@ -30,14 +30,14 @@ from .range_spec import RangeSpec def _import_template_field(): - from langflow.template.field.base import InputField + from langflow.template.field.base import Input - return InputField + return Input def __getattr__(name: str) -> Any: # This is to avoid circular imports - if name == "InputField": + if name == "Input": return _import_template_field() elif name == "RangeSpec": return RangeSpec @@ -73,6 +73,6 @@ __all__ = [ "ChatPromptTemplate", "Prompt", "RangeSpec", - "InputField", + "Input", "Code", ] diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting (Hello, world!).json b/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting (Hello, world!).json index 786eac776..2d6255562 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting (Hello, world!).json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting (Hello, world!).json @@ -20,7 +20,7 @@ "list": false, "show": true, "multiline": true, - "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import InputField, Prompt, Text\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": InputField(display_name=\"Template\"),\n \"code\": InputField(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n", + "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Input, Prompt, Text\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": Input(display_name=\"Template\"),\n \"code\": Input(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n", "fileTypes": [], "file_path": "", "password": false, diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Blog Writter.json b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Blog Writter.json index 9f4b98176..e68eed258 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Blog Writter.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Blog Writter.json @@ -20,7 +20,7 @@ "list": false, "show": true, "multiline": true, - "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import InputField, Prompt, Text\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": InputField(display_name=\"Template\"),\n \"code\": InputField(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n", + "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Input, Prompt, Text\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": Input(display_name=\"Template\"),\n \"code\": Input(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n", "fileTypes": [], "file_path": "", "password": false, diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Document QA.json b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Document QA.json index c14d21d1a..3afd6f04f 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Document QA.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Document QA.json @@ -20,7 +20,7 @@ "list": false, "show": true, "multiline": true, - "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import InputField, Prompt, Text\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": InputField(display_name=\"Template\"),\n \"code\": InputField(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n", + "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Input, Prompt, Text\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": Input(display_name=\"Template\"),\n \"code\": Input(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n", "fileTypes": [], "file_path": "", "password": false, diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Memory Conversation.json b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Memory Conversation.json index 50142c4a1..0b12d8d19 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Memory Conversation.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Memory Conversation.json @@ -583,7 +583,7 @@ "list": false, "show": true, "multiline": true, - "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import InputField, Prompt, Text\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": InputField(display_name=\"Template\"),\n \"code\": InputField(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n", + "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Input, Prompt, Text\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": Input(display_name=\"Template\"),\n \"code\": Input(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n", "fileTypes": [], "file_path": "", "password": false, diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Prompt Chaining.json b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Prompt Chaining.json index e7cb1b021..43212bd61 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Prompt Chaining.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Prompt Chaining.json @@ -20,7 +20,7 @@ "list": false, "show": true, "multiline": true, - "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import InputField, Prompt, Text\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": InputField(display_name=\"Template\"),\n \"code\": InputField(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n", + "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Input, Prompt, Text\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": Input(display_name=\"Template\"),\n \"code\": Input(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n", "fileTypes": [], "file_path": "", "password": false, @@ -140,7 +140,7 @@ "list": false, "show": true, "multiline": true, - "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import InputField, Prompt, Text\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": InputField(display_name=\"Template\"),\n \"code\": InputField(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n", + "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Input, Prompt, Text\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": Input(display_name=\"Template\"),\n \"code\": Input(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n", "fileTypes": [], "file_path": "", "password": false, diff --git a/src/backend/base/langflow/initial_setup/starter_projects/VectorStore-RAG-Flows.json b/src/backend/base/langflow/initial_setup/starter_projects/VectorStore-RAG-Flows.json index 4bbb1aab3..09b37d168 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/VectorStore-RAG-Flows.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/VectorStore-RAG-Flows.json @@ -1106,7 +1106,7 @@ "list": false, "show": true, "multiline": true, - "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import InputField, Prompt, Text\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": InputField(display_name=\"Template\"),\n \"code\": InputField(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n", + "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Input, Prompt, Text\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": Input(display_name=\"Template\"),\n \"code\": Input(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n", "fileTypes": [], "file_path": "", "password": false, diff --git a/src/backend/base/langflow/template/field/base.py b/src/backend/base/langflow/template/field/base.py index 3aa4c020d..0ba947a28 100644 --- a/src/backend/base/langflow/template/field/base.py +++ b/src/backend/base/langflow/template/field/base.py @@ -5,7 +5,7 @@ from pydantic import BaseModel, ConfigDict, Field, field_serializer, field_valid from langflow.field_typing.range_spec import RangeSpec -class InputField(BaseModel): +class Input(BaseModel): model_config = ConfigDict() field_type: str = Field(default="str", serialization_alias="type") @@ -130,8 +130,8 @@ class InputField(BaseModel): ] -class OutputField(BaseModel): - types: list[str] = Field(default=[], serialization_alias="types") +class Output(BaseModel): + type: list[str] = Field(default=[], serialization_alias="types") """List of output types for the field.""" selected: Optional[str] = Field(default=None, serialization_alias="selected") diff --git a/src/backend/base/langflow/template/field/prompt.py b/src/backend/base/langflow/template/field/prompt.py index ecca72503..d03291ee4 100644 --- a/src/backend/base/langflow/template/field/prompt.py +++ b/src/backend/base/langflow/template/field/prompt.py @@ -1,9 +1,9 @@ from typing import Optional -from langflow.template.field.base import InputField +from langflow.template.field.base import Input -class DefaultPromptField(InputField): +class DefaultPromptField(Input): name: str display_name: Optional[str] = None field_type: str = "str" diff --git a/src/backend/base/langflow/template/frontend_node/base.py b/src/backend/base/langflow/template/frontend_node/base.py index 2d69922cf..2ac4acbab 100644 --- a/src/backend/base/langflow/template/frontend_node/base.py +++ b/src/backend/base/langflow/template/frontend_node/base.py @@ -4,7 +4,7 @@ from typing import ClassVar, Dict, List, Optional, Union from pydantic import BaseModel, Field, field_serializer, model_serializer -from langflow.template.field.base import InputField, OutputField +from langflow.template.field.base import Input, Output from langflow.template.frontend_node.constants import FORCE_SHOW_FIELDS from langflow.template.frontend_node.formatter import field_formatters from langflow.template.template.base import Template @@ -30,7 +30,7 @@ class FieldFormatters(BaseModel): "model_fields": field_formatters.ModelSpecificFieldFormatter(), } - def format(self, field: InputField, name: Optional[str] = None) -> None: + def format(self, field: Input, name: Optional[str] = None) -> None: for key, formatter in self.base_formatters.items(): formatter.format(field, name) @@ -77,7 +77,7 @@ class FrontendNode(BaseModel): """List of conditional paths for the frontend node.""" frozen: bool = False """Whether the frontend node is frozen.""" - outputs: List[OutputField] = [] + outputs: List[Output] = [] """List of output fields for the frontend node.""" field_order: list[str] = [] @@ -119,9 +119,9 @@ class FrontendNode(BaseModel): # Migrate base classes to outputs if "output_types" in result: for base_class in result["output_types"]: - output = OutputField( + output = Output( name=base_class, - types=[base_class], + type=[base_class], ) result["outputs"].append(output.model_dump()) @@ -156,7 +156,7 @@ class FrontendNode(BaseModel): self.output_types.extend(output_type) @staticmethod - def format_field(field: InputField, name: Optional[str] = None) -> None: + def format_field(field: Input, name: Optional[str] = None) -> None: """Formats a given field based on its attributes and value.""" FrontendNode.get_field_formatters().format(field, name) @@ -195,7 +195,7 @@ class FrontendNode(BaseModel): return handler(field) if handler else _type @staticmethod - def handle_dict_type(field: InputField, _type: str) -> str: + def handle_dict_type(field: Input, _type: str) -> str: """Handles 'dict' type by replacing it with 'code' or 'file' based on the field name.""" if "dict" in _type.lower() and field.name == "dict_": field.field_type = "file" @@ -205,13 +205,13 @@ class FrontendNode(BaseModel): return _type @staticmethod - def replace_default_value(field: InputField, value: dict) -> None: + def replace_default_value(field: Input, value: dict) -> None: """Replaces default value with actual value if 'default' is present in value.""" if "default" in value: field.value = value["default"] @staticmethod - def handle_specific_field_values(field: InputField, key: str, name: Optional[str] = None) -> None: + def handle_specific_field_values(field: Input, key: str, name: Optional[str] = None) -> None: """Handles specific field values for certain fields.""" if key == "headers": field.value = """{"Authorization": "Bearer "}""" @@ -219,7 +219,7 @@ class FrontendNode(BaseModel): FrontendNode._handle_api_key_specific_field_values(field, key, name) @staticmethod - def _handle_model_specific_field_values(field: InputField, key: str, name: Optional[str] = None) -> None: + def _handle_model_specific_field_values(field: Input, key: str, name: Optional[str] = None) -> None: """Handles specific field values related to models.""" model_dict = { "OpenAI": constants.OPENAI_MODELS, @@ -232,7 +232,7 @@ class FrontendNode(BaseModel): field.is_list = True @staticmethod - def _handle_api_key_specific_field_values(field: InputField, key: str, name: Optional[str] = None) -> None: + def _handle_api_key_specific_field_values(field: Input, key: str, name: Optional[str] = None) -> None: """Handles specific field values related to API keys.""" if "api_key" in key and "OpenAI" in str(name): field.display_name = "OpenAI API Key" @@ -241,7 +241,7 @@ class FrontendNode(BaseModel): field.value = "" @staticmethod - def handle_kwargs_field(field: InputField) -> None: + def handle_kwargs_field(field: Input) -> None: """Handles kwargs field by setting certain attributes.""" if "kwargs" in (field.name or "").lower(): @@ -250,7 +250,7 @@ class FrontendNode(BaseModel): field.show = False @staticmethod - def handle_api_key_field(field: InputField, key: str) -> None: + def handle_api_key_field(field: Input, key: str) -> None: """Handles api key field by setting certain attributes.""" if "api" in key.lower() and "key" in key.lower(): field.required = False @@ -288,7 +288,7 @@ class FrontendNode(BaseModel): } @staticmethod - def set_field_default_value(field: InputField, value: dict, key: str) -> None: + def set_field_default_value(field: Input, value: dict, key: str) -> None: """Sets the field value with the default value if present.""" if "default" in value: field.value = value["default"] diff --git a/src/backend/base/langflow/template/frontend_node/custom_components.py b/src/backend/base/langflow/template/frontend_node/custom_components.py index d218c85a1..6969f8a73 100644 --- a/src/backend/base/langflow/template/frontend_node/custom_components.py +++ b/src/backend/base/langflow/template/frontend_node/custom_components.py @@ -1,6 +1,6 @@ from typing import Optional -from langflow.template.field.base import InputField +from langflow.template.field.base import Input from langflow.template.frontend_node.base import FrontendNode from langflow.template.template.base import Template @@ -52,7 +52,7 @@ class CustomComponentFrontendNode(FrontendNode): template: Template = Template( type_name="CustomComponent", fields=[ - InputField( + Input( field_type="code", required=True, placeholder="", diff --git a/src/backend/base/langflow/template/frontend_node/formatter/base.py b/src/backend/base/langflow/template/frontend_node/formatter/base.py index dce85d003..e2e5d8368 100644 --- a/src/backend/base/langflow/template/frontend_node/formatter/base.py +++ b/src/backend/base/langflow/template/frontend_node/formatter/base.py @@ -3,10 +3,10 @@ from typing import Optional from pydantic import BaseModel -from langflow.template.field.base import InputField +from langflow.template.field.base import Input class FieldFormatter(BaseModel, ABC): @abstractmethod - def format(self, field: InputField, name: Optional[str]) -> None: + def format(self, field: Input, name: Optional[str]) -> None: pass diff --git a/src/backend/base/langflow/template/frontend_node/formatter/field_formatters.py b/src/backend/base/langflow/template/frontend_node/formatter/field_formatters.py index 3ae5ece65..25efd079b 100644 --- a/src/backend/base/langflow/template/frontend_node/formatter/field_formatters.py +++ b/src/backend/base/langflow/template/frontend_node/formatter/field_formatters.py @@ -1,14 +1,14 @@ import re from typing import ClassVar, Dict, Optional -from langflow.template.field.base import InputField +from langflow.template.field.base import Input from langflow.template.frontend_node.constants import FORCE_SHOW_FIELDS from langflow.template.frontend_node.formatter.base import FieldFormatter from langflow.utils.constants import ANTHROPIC_MODELS, CHAT_OPENAI_MODELS, OPENAI_MODELS class OpenAIAPIKeyFormatter(FieldFormatter): - def format(self, field: InputField, name: Optional[str] = None) -> None: + def format(self, field: Input, name: Optional[str] = None) -> None: if field.name and "api_key" in field.name and "OpenAI" in str(name): field.display_name = "OpenAI API Key" field.required = False @@ -24,14 +24,14 @@ class ModelSpecificFieldFormatter(FieldFormatter): "ChatAnthropic": ANTHROPIC_MODELS, } - def format(self, field: InputField, name: Optional[str] = None) -> None: + def format(self, field: Input, name: Optional[str] = None) -> None: if field.name and name in self.MODEL_DICT and field.name == "model_name": field.options = self.MODEL_DICT[name] field.is_list = True class KwargsFormatter(FieldFormatter): - def format(self, field: InputField, name: Optional[str] = None) -> None: + def format(self, field: Input, name: Optional[str] = None) -> None: if field.name and "kwargs" in field.name.lower(): field.advanced = True field.required = False @@ -39,7 +39,7 @@ class KwargsFormatter(FieldFormatter): class APIKeyFormatter(FieldFormatter): - def format(self, field: InputField, name: Optional[str] = None) -> None: + def format(self, field: Input, name: Optional[str] = None) -> None: if field.name and "api" in field.name.lower() and "key" in field.name.lower(): field.required = False field.advanced = False @@ -49,13 +49,13 @@ class APIKeyFormatter(FieldFormatter): class RemoveOptionalFormatter(FieldFormatter): - def format(self, field: InputField, name: Optional[str] = None) -> None: + def format(self, field: Input, name: Optional[str] = None) -> None: _type = field.field_type field.field_type = re.sub(r"Optional\[(.*)\]", r"\1", _type) class ListTypeFormatter(FieldFormatter): - def format(self, field: InputField, name: Optional[str] = None) -> None: + def format(self, field: Input, name: Optional[str] = None) -> None: _type = field.field_type is_list = "List" in _type or "Sequence" in _type if is_list: @@ -65,14 +65,14 @@ class ListTypeFormatter(FieldFormatter): class DictTypeFormatter(FieldFormatter): - def format(self, field: InputField, name: Optional[str] = None) -> None: + def format(self, field: Input, name: Optional[str] = None) -> None: _type = field.field_type _type = _type.replace("Mapping", "dict") field.field_type = _type class UnionTypeFormatter(FieldFormatter): - def format(self, field: InputField, name: Optional[str] = None) -> None: + def format(self, field: Input, name: Optional[str] = None) -> None: _type = field.field_type if "Union" in _type: _type = _type.replace("Union[", "")[:-1] @@ -87,13 +87,13 @@ class SpecialFieldFormatter(FieldFormatter): "max_value_length": lambda field: "int", } - def format(self, field: InputField, name: Optional[str] = None) -> None: + def format(self, field: Input, name: Optional[str] = None) -> None: handler = self.SPECIAL_FIELD_HANDLERS.get(field.name) field.field_type = handler(field) if handler else field.field_type class ShowFieldFormatter(FieldFormatter): - def format(self, field: InputField, name: Optional[str] = None) -> None: + def format(self, field: Input, name: Optional[str] = None) -> None: key = field.name or "" required = field.required field.show = ( @@ -105,7 +105,7 @@ class ShowFieldFormatter(FieldFormatter): class PasswordFieldFormatter(FieldFormatter): - def format(self, field: InputField, name: Optional[str] = None) -> None: + def format(self, field: Input, name: Optional[str] = None) -> None: key = field.name or "" show = field.show if any(text in key.lower() for text in {"password", "token", "api", "key"}) and show: @@ -113,7 +113,7 @@ class PasswordFieldFormatter(FieldFormatter): class MultilineFieldFormatter(FieldFormatter): - def format(self, field: InputField, name: Optional[str] = None) -> None: + def format(self, field: Input, name: Optional[str] = None) -> None: key = field.name or "" if key in { "suffix", @@ -128,21 +128,21 @@ class MultilineFieldFormatter(FieldFormatter): class DefaultValueFormatter(FieldFormatter): - def format(self, field: InputField, name: Optional[str] = None) -> None: + def format(self, field: Input, name: Optional[str] = None) -> None: value = field.model_dump(by_alias=True, exclude_none=True) if "default" in value: field.value = value["default"] class HeadersDefaultValueFormatter(FieldFormatter): - def format(self, field: InputField, name: Optional[str] = None) -> None: + def format(self, field: Input, name: Optional[str] = None) -> None: key = field.name if key == "headers": field.value = """{"Authorization": "Bearer "}""" class DictCodeFileFormatter(FieldFormatter): - def format(self, field: InputField, name: Optional[str] = None) -> None: + def format(self, field: Input, name: Optional[str] = None) -> None: key = field.name value = field.model_dump(by_alias=True, exclude_none=True) _type = value["type"] diff --git a/src/backend/base/langflow/template/template/base.py b/src/backend/base/langflow/template/template/base.py index 1db5e6911..01ae4a43f 100644 --- a/src/backend/base/langflow/template/template/base.py +++ b/src/backend/base/langflow/template/template/base.py @@ -2,13 +2,13 @@ from typing import Callable, Union from pydantic import BaseModel, model_serializer -from langflow.template.field.base import InputField +from langflow.template.field.base import Input from langflow.utils.constants import DIRECT_TYPES class Template(BaseModel): type_name: str - fields: list[InputField] + fields: list[Input] def process_fields( self, @@ -38,17 +38,17 @@ class Template(BaseModel): self.sort_fields() return self.model_dump(by_alias=True, exclude_none=True, exclude={"fields"}) - def add_field(self, field: InputField) -> None: + def add_field(self, field: Input) -> None: self.fields.append(field) - def get_field(self, field_name: str) -> InputField: + def get_field(self, field_name: str) -> Input: """Returns the field with the given name.""" field = next((field for field in self.fields if field.name == field_name), None) if field is None: raise ValueError(f"Field {field_name} not found in template {self.type_name}") return field - def update_field(self, field_name: str, field: InputField) -> None: + def update_field(self, field_name: str, field: Input) -> None: """Updates the field with the given name.""" for idx, template_field in enumerate(self.fields): if template_field.name == field_name: @@ -56,7 +56,7 @@ class Template(BaseModel): return raise ValueError(f"Field {field_name} not found in template {self.type_name}") - def upsert_field(self, field_name: str, field: InputField) -> None: + def upsert_field(self, field_name: str, field: Input) -> None: """Updates the field with the given name or adds it if it doesn't exist.""" try: self.update_field(field_name, field) diff --git a/tests/data/component_with_templatefield.py b/tests/data/component_with_templatefield.py index be1ac174f..bc79c80d2 100644 --- a/tests/data/component_with_templatefield.py +++ b/tests/data/component_with_templatefield.py @@ -1,7 +1,7 @@ import random from langflow.custom import CustomComponent -from langflow.field_typing import InputField +from langflow.field_typing import Input class TestComponent(CustomComponent): @@ -11,7 +11,7 @@ class TestComponent(CustomComponent): return [f"Random {random.randint(1, 100)}" for _ in range(5)] def build_config(self): - return {"param": InputField(display_name="Param", options=self.refresh_values)} + return {"param": Input(display_name="Param", options=self.refresh_values)} def build(self, param: int): return param diff --git a/tests/test_frontend_nodes.py b/tests/test_frontend_nodes.py index 03af4332e..061526c08 100644 --- a/tests/test_frontend_nodes.py +++ b/tests/test_frontend_nodes.py @@ -1,17 +1,17 @@ import pytest -from langflow.template.field.base import InputField +from langflow.template.field.base import Input from langflow.template.frontend_node.base import FrontendNode from langflow.template.template.base import Template @pytest.fixture -def sample_template_field() -> InputField: - return InputField(name="test_field", field_type="str") +def sample_template_field() -> Input: + return Input(name="test_field", field_type="str") @pytest.fixture -def sample_template(sample_template_field: InputField) -> Template: +def sample_template(sample_template_field: Input) -> Template: return Template(type_name="test_template", fields=[sample_template_field]) @@ -25,7 +25,7 @@ def sample_frontend_node(sample_template: Template) -> FrontendNode: ) -def test_template_field_defaults(sample_template_field: InputField): +def test_template_field_defaults(sample_template_field: Input): assert sample_template_field.field_type == "str" assert sample_template_field.required is False assert sample_template_field.placeholder == "" @@ -39,7 +39,7 @@ def test_template_field_defaults(sample_template_field: InputField): assert sample_template_field.name == "test_field" -def test_template_to_dict(sample_template: Template, sample_template_field: InputField): +def test_template_to_dict(sample_template: Template, sample_template_field: Input): template_dict = sample_template.to_dict() assert template_dict["_type"] == "test_template" assert len(template_dict) == 2 # _type and test_field From 82fcdfb67a6f7fa9ee012719d29d64813af76d77 Mon Sep 17 00:00:00 2001 From: ogabrielluiz Date: Thu, 30 May 2024 18:29:40 -0300 Subject: [PATCH 030/701] feat: Add inputs and outputs to CustomComponent This commit adds the `inputs` and `outputs` fields to the `CustomComponent` class in the `custom_component.py` file. The `inputs` field is of type `List[Input]` and the `outputs` field is of type `List[Output]`. This change allows for better organization and management of the component's input and output fields. --- .../langflow/custom/custom_component/custom_component.py | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/src/backend/base/langflow/custom/custom_component/custom_component.py b/src/backend/base/langflow/custom/custom_component/custom_component.py index aeac9cae6..75cbabfe8 100644 --- a/src/backend/base/langflow/custom/custom_component/custom_component.py +++ b/src/backend/base/langflow/custom/custom_component/custom_component.py @@ -7,6 +7,7 @@ import yaml from cachetools import TTLCache, cachedmethod from langchain_core.documents import Document from pydantic import BaseModel + from langflow.custom.code_parser.utils import ( extract_inner_type_from_generic_alias, extract_union_types_from_generic_alias, @@ -17,6 +18,7 @@ from langflow.schema import Record from langflow.schema.dotdict import dotdict from langflow.services.deps import get_storage_service, get_variable_service, session_scope from langflow.services.storage.service import StorageService +from langflow.template.field.base import Input, Output from langflow.utils import validate if TYPE_CHECKING: @@ -78,6 +80,9 @@ class CustomComponent(Component): """The status of the component. This is displayed on the frontend. Defaults to None.""" _flows_records: Optional[List[Record]] = None + inputs: Optional[List[Input]] = None + outputs: Optional[List[Output]] = None + def update_state(self, name: str, value: Any): if not self.vertex: raise ValueError("Vertex is not set") @@ -464,3 +469,4 @@ class CustomComponent(Component): Any: The result of the build process. """ raise NotImplementedError + raise NotImplementedError From 709082f2f08f10679b9dd52af0e2b1429a2695d0 Mon Sep 17 00:00:00 2001 From: ogabrielluiz Date: Thu, 30 May 2024 22:45:44 -0300 Subject: [PATCH 031/701] feat: Update title in GenericNode component This commit updates the title in the GenericNode component to use the `name` property instead of the `selected` or `types[0]` properties. This change improves the accuracy and clarity of the code. --- src/frontend/src/customNodes/genericNode/index.tsx | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/frontend/src/customNodes/genericNode/index.tsx b/src/frontend/src/customNodes/genericNode/index.tsx index ef73e72cc..a78b78088 100644 --- a/src/frontend/src/customNodes/genericNode/index.tsx +++ b/src/frontend/src/customNodes/genericNode/index.tsx @@ -892,7 +892,7 @@ export default function GenericNode({ nodeColors[types[data.type]] ?? nodeColors.unknown } - title={output.selected ?? output.types[0]} + title={output.name} tooltipTitle={output.selected ?? output.types[0]} id={{ baseClasses: [output.selected ?? output.types[0]], From ff489cb1f5690c2af9ef18fcc88f86d998409c7a Mon Sep 17 00:00:00 2001 From: ogabrielluiz Date: Thu, 30 May 2024 22:45:58 -0300 Subject: [PATCH 032/701] feat: Add MultipleOutputsComponent to CustomComponent This commit adds the `MultipleOutputsComponent` class to the `CustomComponent` module. The `MultipleOutputsComponent` class defines multiple inputs and outputs for the component. The `inputs` field includes an input for a string and an input for a number. The `outputs` field includes an output for a certain output and an output for another output. This change enhances the functionality and flexibility of the `CustomComponent` module. --- tests/data/component_multiple_outputs.py | 19 +++++++++++++++++++ tests/test_custom_component.py | 14 ++++++++++++++ 2 files changed, 33 insertions(+) create mode 100644 tests/data/component_multiple_outputs.py diff --git a/tests/data/component_multiple_outputs.py b/tests/data/component_multiple_outputs.py new file mode 100644 index 000000000..7a01fafba --- /dev/null +++ b/tests/data/component_multiple_outputs.py @@ -0,0 +1,19 @@ +from langflow.custom import CustomComponent +from langflow.template.field.base import Input, Output + + +class MultipleOutputsComponent(CustomComponent): + inputs = [ + Input(display_name="Input", name="input", field_type=str), + Input(display_name="Number", name="number", field_type=int), + ] + outputs = [ + Output(display_name="Certain Output", method="certain_output", name="certain_output"), + Output(name="Other Output", method="other_output"), + ] + + def certain_output(self) -> str: + return f"This is my string input: {self.input}" + + def other_output(self) -> int: + return f"This is my int input multiplied by 2: {self.number * 2}" diff --git a/tests/test_custom_component.py b/tests/test_custom_component.py index 0b64f2c1a..461d3c6eb 100644 --- a/tests/test_custom_component.py +++ b/tests/test_custom_component.py @@ -8,8 +8,17 @@ from langchain_core.documents import Document from langflow.custom import CustomComponent from langflow.custom.code_parser.code_parser import CodeParser, CodeSyntaxError from langflow.custom.custom_component.component import Component, ComponentCodeNullError +from langflow.custom.utils import build_custom_component_template from langflow.services.database.models.flow import Flow, FlowCreate + +@pytest.fixture +def code_component_with_multiple_outputs(): + with open("tests/data/component_multiple_outputs.py", "r") as f: + code = f.read() + return CustomComponent(code=code) + + code_default = """ from langflow.field_typing import Prompt from langflow.custom import CustomComponent @@ -517,3 +526,8 @@ def test_build_config_field_value_keys(component): config = component.build_config() field_values = config["fields"].values() assert all("type" in value for value in field_values) + + +def test_custom_component_multiple_outputs(code_component_with_multiple_outputs, active_user): + frontnd_node_dict, _ = build_custom_component_template(code_component_with_multiple_outputs, active_user.id) + assert frontnd_node_dict["outputs"][0]["types"] == ["Text"] From 5dae23bcb1dd932ed851618f3e8f04fd88ccaf31 Mon Sep 17 00:00:00 2001 From: ogabrielluiz Date: Thu, 30 May 2024 22:46:49 -0300 Subject: [PATCH 033/701] feat: Update `get_all` endpoint to use async/await This commit updates the `get_all` endpoint in the `endpoints.py` file to use `async` and `await` keywords. By making the `get_all` function asynchronous, it allows for better performance and responsiveness when retrieving all types from the langchain. This change improves the overall efficiency of the codebase. --- src/backend/base/langflow/api/v1/endpoints.py | 6 +- .../directory_reader/directory_reader.py | 82 ++++++++++++++++ .../langflow/custom/directory_reader/utils.py | 23 +++++ src/backend/base/langflow/custom/utils.py | 93 ++++++++++++++----- src/backend/base/langflow/interface/types.py | 8 +- 5 files changed, 184 insertions(+), 28 deletions(-) diff --git a/src/backend/base/langflow/api/v1/endpoints.py b/src/backend/base/langflow/api/v1/endpoints.py index 26633082e..d4f6245b5 100644 --- a/src/backend/base/langflow/api/v1/endpoints.py +++ b/src/backend/base/langflow/api/v1/endpoints.py @@ -39,14 +39,14 @@ router = APIRouter(tags=["Base"]) @router.get("/all", dependencies=[Depends(get_current_active_user)]) -def get_all( +async def get_all( settings_service=Depends(get_settings_service), ): - from langflow.interface.types import get_all_types_dict + from langflow.interface.types import aget_all_types_dict logger.debug("Building langchain types dict") try: - all_types_dict = get_all_types_dict(settings_service.settings.components_path) + all_types_dict = await aget_all_types_dict(settings_service.settings.components_path) return all_types_dict except Exception as exc: logger.exception(exc) diff --git a/src/backend/base/langflow/custom/directory_reader/directory_reader.py b/src/backend/base/langflow/custom/directory_reader/directory_reader.py index b9f55f21f..4d7b33bfc 100644 --- a/src/backend/base/langflow/custom/directory_reader/directory_reader.py +++ b/src/backend/base/langflow/custom/directory_reader/directory_reader.py @@ -1,4 +1,5 @@ import ast +import asyncio import os import zlib from pathlib import Path @@ -286,6 +287,87 @@ class DirectoryReader: logger.debug("-------------------- Component menu list built --------------------") return response + async def process_file_async(self, file_path): + try: + file_content = self.read_file_content(file_path) + except Exception as exc: + logger.exception(exc) + logger.error(f"Error while reading file {file_path}: {str(exc)}") + return False, f"Could not read {file_path}" + + if file_content is None: + return False, f"Could not read {file_path}" + elif self.is_empty_file(file_content): + return False, "Empty file" + elif not self.validate_code(file_content): + return False, "Syntax error" + elif not self.validate_build(file_content): + return False, "Missing build function" + elif self._is_type_hint_used_in_args("Optional", file_content) and not self._is_type_hint_imported( + "Optional", file_content + ): + return ( + False, + "Type hint 'Optional' is used but not imported in the code.", + ) + else: + if self.compress_code_field: + file_content = str(StringCompressor(file_content).compress_string()) + return True, file_content + + async def get_output_types_from_code_async(self, code: str): + return await asyncio.to_thread(self.get_output_types_from_code, code) + + async def abuild_component_menu_list(self, file_paths): + response = {"menu": []} + logger.debug("-------------------- Async Building component menu list --------------------") + + tasks = [self.process_file_async(file_path) for file_path in file_paths] + results = await asyncio.gather(*tasks) + + for file_path, (validation_result, result_content) in zip(file_paths, results): + menu_name = os.path.basename(os.path.dirname(file_path)) + filename = os.path.basename(file_path) + + if not validation_result: + logger.error(f"Error while processing file {file_path}") + + menu_result = self.find_menu(response, menu_name) or { + "name": menu_name, + "path": os.path.dirname(file_path), + "components": [], + } + component_name = filename.split(".")[0] + + if "_" in component_name: + component_name_camelcase = " ".join(word.title() for word in component_name.split("_")) + else: + component_name_camelcase = component_name + + if validation_result: + try: + output_types = await self.get_output_types_from_code_async(result_content) + except Exception as exc: + logger.exception(f"Error while getting output types from code: {str(exc)}") + output_types = [component_name_camelcase] + else: + output_types = [component_name_camelcase] + + component_info = { + "name": component_name_camelcase, + "output_types": output_types, + "file": filename, + "code": result_content if validation_result else "", + "error": "" if validation_result else result_content, + } + menu_result["components"].append(component_info) + + if menu_result not in response["menu"]: + response["menu"].append(menu_result) + + logger.debug("-------------------- Component menu list built --------------------") + return response + @staticmethod def get_output_types_from_code(code: str) -> list: """ diff --git a/src/backend/base/langflow/custom/directory_reader/utils.py b/src/backend/base/langflow/custom/directory_reader/utils.py index ddd24d8f3..331b72d2a 100644 --- a/src/backend/base/langflow/custom/directory_reader/utils.py +++ b/src/backend/base/langflow/custom/directory_reader/utils.py @@ -51,6 +51,16 @@ def build_and_validate_all_files(reader: DirectoryReader, file_list): return valid_components, invalid_components +async def abuild_and_validate_all_files(reader: DirectoryReader, file_list): + """Build and validate all files""" + data = await reader.abuild_component_menu_list(file_list) + + valid_components = reader.filter_loaded_components(data=data, with_errors=False) + invalid_components = reader.filter_loaded_components(data=data, with_errors=True) + + return valid_components, invalid_components + + def load_files_from_path(path: str): """Load all files from a given path""" reader = DirectoryReader(path, False) @@ -71,6 +81,19 @@ def build_custom_component_list_from_path(path: str): return merge_nested_dicts_with_renaming(valid_menu, invalid_menu) +async def abuild_custom_component_list_from_path(path: str): + """Build a list of custom components for the langchain from a given path""" + file_list = load_files_from_path(path) + reader = DirectoryReader(path, False) + + valid_components, invalid_components = await abuild_and_validate_all_files(reader, file_list) + + valid_menu = build_valid_menu(valid_components) + invalid_menu = build_invalid_menu(invalid_components) + + return merge_nested_dicts_with_renaming(valid_menu, invalid_menu) + + def create_invalid_component_template(component, component_name): """Create a template for an invalid component.""" component_code = component["code"] diff --git a/src/backend/base/langflow/custom/utils.py b/src/backend/base/langflow/custom/utils.py index 2a941a418..dc2e0b759 100644 --- a/src/backend/base/langflow/custom/utils.py +++ b/src/backend/base/langflow/custom/utils.py @@ -11,9 +11,9 @@ from loguru import logger from pydantic import BaseModel from langflow.custom import CustomComponent -from langflow.custom.attributes import ATTR_FUNC_MAPPING from langflow.custom.code_parser.utils import extract_inner_type from langflow.custom.directory_reader.utils import ( + abuild_custom_component_list_from_path, build_custom_component_list_from_path, determine_component_name, merge_nested_dicts_with_renaming, @@ -21,6 +21,7 @@ from langflow.custom.directory_reader.utils import ( from langflow.custom.eval import eval_custom_component_code from langflow.custom.schema import MissingDefault from langflow.field_typing.range_spec import RangeSpec +from langflow.helpers.custom import format_type from langflow.schema import dotdict from langflow.template.field.base import Input from langflow.template.frontend_node.custom_components import CustomComponentFrontendNode @@ -147,7 +148,11 @@ def add_new_custom_field( # Check field_config if any of the keys are in it # if it is, update the value display_name = field_config.pop("display_name", None) - field_type = field_config.pop("field_type", field_type) + if not field_type: + if "type" in field_config and field_config["type"] is not None: + field_type = field_config.pop("type") + elif "field_type" in field_config and field_config["field_type"] is not None: + field_type = field_config.pop("field_type") field_contains_list = "list" in field_type.lower() field_type = process_type(field_type) field_value = field_config.pop("value", field_value) @@ -238,6 +243,15 @@ def get_field_dict(field: Union[Input, dict]): return field +def run_build_inputs(custom_component: CustomComponent, user_id: Optional[Union[str, UUID]] = None): + """Run the build inputs of a custom component.""" + try: + return custom_component.build_inputs(user_id=user_id) + except Exception as exc: + logger.error(f"Error running build inputs: {exc}") + raise HTTPException(status_code=500, detail=str(exc)) from exc + + def run_build_config( custom_component: CustomComponent, user_id: Optional[Union[str, UUID]] = None, @@ -284,26 +298,6 @@ def run_build_config( raise exc -def sanitize_template_config(template_config): - """Sanitize the template config""" - - for key in template_config.copy(): - if key not in ATTR_FUNC_MAPPING.keys(): - template_config.pop(key, None) - - return template_config - - -def build_frontend_node(template_config): - """Build a frontend node for a custom component""" - try: - sanitized_template_config = sanitize_template_config(template_config) - return CustomComponentFrontendNode(**sanitized_template_config) - except Exception as exc: - logger.error(f"Error while building base frontend node: {exc}") - raise exc - - def add_code_field(frontend_node: CustomComponentFrontendNode, raw_code, field_config): code_field = Input( dynamic=True, @@ -322,13 +316,34 @@ def add_code_field(frontend_node: CustomComponentFrontendNode, raw_code, field_c return frontend_node +def build_custom_component_template_from_inputs( + custom_component: CustomComponent, user_id: Optional[Union[str, UUID]] = None +): + # The List of Inputs fills the role of the build_config and the entrypoint_args + frontend_node = CustomComponentFrontendNode.from_inputs(**custom_component.template_config) + field_config = run_build_inputs( + custom_component, + user_id=user_id, + ) + frontend_node = add_code_field(frontend_node, custom_component.code, field_config.get("code", {})) + # But we now need to calculate the return_type of the methods in the outputs + for output in frontend_node.outputs: + return_types = custom_component.get_method_return_type(output.method) + return_types = [format_type(return_type) for return_type in return_types] + output.add_types(return_types) + + return frontend_node.to_dict(add_name=False), custom_component + + def build_custom_component_template( custom_component: CustomComponent, user_id: Optional[Union[str, UUID]] = None, ) -> Tuple[Dict[str, Any], CustomComponent]: - """Build a custom component template for the langchain""" + """Build a custom component template""" try: - frontend_node = build_frontend_node(custom_component.template_config) + if "inputs" in custom_component.template_config: + return build_custom_component_template_from_inputs(custom_component, user_id=user_id) + frontend_node = CustomComponentFrontendNode(**custom_component.template_config) field_config, custom_instance = run_build_config( custom_component, @@ -398,6 +413,31 @@ def build_custom_components(components_paths: List[str]): return custom_components_from_file +async def abuild_custom_components(components_paths: List[str]): + """Build custom components from the specified paths.""" + if not components_paths: + return {} + + logger.info(f"Building custom components from {components_paths}") + custom_components_from_file: dict = {} + processed_paths = set() + for path in components_paths: + path_str = str(path) + if path_str in processed_paths: + continue + + custom_component_dict = await abuild_custom_component_list_from_path(path_str) + if custom_component_dict: + category = next(iter(custom_component_dict)) + logger.info(f"Loading {len(custom_component_dict[category])} component(s) from category {category}") + custom_components_from_file = merge_nested_dicts_with_renaming( + custom_components_from_file, custom_component_dict + ) + processed_paths.add(path_str) + + return custom_components_from_file + + def update_field_dict( custom_component_instance: "CustomComponent", field_dict: Dict, @@ -446,6 +486,11 @@ def sanitize_field_config(field_config: Union[Dict, Input]): "show", ]: field_dict.pop(key, None) + + # Remove field_type and type because they were extracted already + field_dict.pop("field_type", None) + field_dict.pop("type", None) + return field_dict diff --git a/src/backend/base/langflow/interface/types.py b/src/backend/base/langflow/interface/types.py index a092a7d19..812b1e78f 100644 --- a/src/backend/base/langflow/interface/types.py +++ b/src/backend/base/langflow/interface/types.py @@ -1,4 +1,10 @@ -from langflow.custom.utils import build_custom_components +from langflow.custom.utils import abuild_custom_components, build_custom_components + + +async def aget_all_types_dict(components_paths): + """Get all types dictionary combining native and custom components.""" + custom_components_from_file = await abuild_custom_components(components_paths=components_paths) + return custom_components_from_file def get_all_types_dict(components_paths): From 230c4a69ed6b7509ff78d9afee2424809dc9632c Mon Sep 17 00:00:00 2001 From: ogabrielluiz Date: Thu, 30 May 2024 22:47:29 -0300 Subject: [PATCH 034/701] feat: update template and custom component to load inputs and outputs --- .../base/langflow/custom/attributes.py | 8 + .../custom/custom_component/component.py | 4 + .../custom_component/custom_component.py | 30 ++- src/backend/base/langflow/helpers/custom.py | 13 ++ .../base/langflow/initial_setup/setup.py | 11 +- .../base/langflow/template/field/base.py | 26 ++- .../langflow/template/frontend_node/base.py | 196 ++---------------- tests/test_initial_setup.py | 4 +- 8 files changed, 95 insertions(+), 197 deletions(-) create mode 100644 src/backend/base/langflow/helpers/custom.py diff --git a/src/backend/base/langflow/custom/attributes.py b/src/backend/base/langflow/custom/attributes.py index 1fc7e8c1f..d96500c78 100644 --- a/src/backend/base/langflow/custom/attributes.py +++ b/src/backend/base/langflow/custom/attributes.py @@ -37,6 +37,12 @@ def getattr_return_list_of_str(value): return [] +def getattr_return_list_of_object(value): + if isinstance(value, list): + return value + return [] + + ATTR_FUNC_MAPPING: dict[str, Callable] = { "display_name": getattr_return_str, "description": getattr_return_str, @@ -47,4 +53,6 @@ ATTR_FUNC_MAPPING: dict[str, Callable] = { "is_input": getattr_return_bool, "is_output": getattr_return_bool, "conditional_paths": getattr_return_list_of_str, + "outputs": getattr_return_list_of_object, + "inputs": getattr_return_list_of_object, } diff --git a/src/backend/base/langflow/custom/custom_component/component.py b/src/backend/base/langflow/custom/custom_component/component.py index d45b5daed..ba4472986 100644 --- a/src/backend/base/langflow/custom/custom_component/component.py +++ b/src/backend/base/langflow/custom/custom_component/component.py @@ -84,6 +84,10 @@ class Component: if value is not None: template_config[attribute] = func(value=value) + for key in template_config.copy(): + if key not in ATTR_FUNC_MAPPING.keys(): + template_config.pop(key, None) + return template_config def build(self, *args: Any, **kwargs: Any) -> Any: diff --git a/src/backend/base/langflow/custom/custom_component/custom_component.py b/src/backend/base/langflow/custom/custom_component/custom_component.py index 75cbabfe8..535bf0cf3 100644 --- a/src/backend/base/langflow/custom/custom_component/custom_component.py +++ b/src/backend/base/langflow/custom/custom_component/custom_component.py @@ -83,6 +83,23 @@ class CustomComponent(Component): inputs: Optional[List[Input]] = None outputs: Optional[List[Output]] = None + def build_inputs(self, user_id: Optional[Union[str, UUID]] = None): + """ + Builds the inputs for the custom component. + + Args: + user_id (Optional[Union[str, UUID]], optional): The user ID. Defaults to None. + + Returns: + List[Input]: The list of inputs. + """ + # This function is similar to build_config, but it will process the inputs + # and return them as a dict with keys being the Input.name and values being the Input.model_dump() + if not self.inputs: + return {} + build_config = {_input.name: _input.model_dump(by_alias=True, exclude_none=True) for _input in self.inputs} + return build_config + def update_state(self, name: str, value: Any): if not self.vertex: raise ValueError("Vertex is not set") @@ -275,7 +292,7 @@ class CustomComponent(Component): Returns: list: The arguments of the function entrypoint. """ - build_method = self.get_build_method() + build_method = self.get_method(self.function_entrypoint_name) if not build_method: return [] @@ -287,7 +304,7 @@ class CustomComponent(Component): return args @cachedmethod(operator.attrgetter("cache")) - def get_build_method(self): + def get_method(self, method_name: str): """ Gets the build method for the custom component. @@ -303,9 +320,7 @@ class CustomComponent(Component): # Assume the first Component class is the one we're interested in component_class = component_classes[0] - build_methods = [ - method for method in component_class["methods"] if method["name"] == self.function_entrypoint_name - ] + build_methods = [method for method in component_class["methods"] if method["name"] == (method_name)] return build_methods[0] if build_methods else {} @@ -317,7 +332,10 @@ class CustomComponent(Component): Returns: List[Any]: The return type of the function entrypoint. """ - build_method = self.get_build_method() + return self.get_method_return_type(self.function_entrypoint_name) + + def get_method_return_type(self, method_name: str): + build_method = self.get_method(method_name) if not build_method or not build_method.get("has_return"): return [] return_type = build_method["return_type"] diff --git a/src/backend/base/langflow/helpers/custom.py b/src/backend/base/langflow/helpers/custom.py new file mode 100644 index 000000000..bdbb128f4 --- /dev/null +++ b/src/backend/base/langflow/helpers/custom.py @@ -0,0 +1,13 @@ +from typing import Any + + +def format_type(type_: Any) -> str: + if type_ == str: + type_ = "Text" + elif hasattr(type_, "__name__"): + type_ = type_.__name__ + elif hasattr(type_, "__class__"): + type_ = type_.__class__.__name__ + else: + type_ = str(type_) + return type_ diff --git a/src/backend/base/langflow/initial_setup/setup.py b/src/backend/base/langflow/initial_setup/setup.py index 27574950c..62997fbb1 100644 --- a/src/backend/base/langflow/initial_setup/setup.py +++ b/src/backend/base/langflow/initial_setup/setup.py @@ -16,12 +16,9 @@ from langflow.interface.types import get_all_components from langflow.services.auth.utils import create_super_user from langflow.services.database.models.flow.model import Flow, FlowCreate from langflow.services.database.models.folder.model import Folder, FolderCreate -from langflow.services.database.models.user.crud import get_user_by_username -from langflow.services.deps import get_settings_service, session_scope - from langflow.services.database.models.folder.utils import create_default_folder_if_it_doesnt_exist -from langflow.services.deps import get_settings_service, session_scope, get_variable_service - +from langflow.services.database.models.user.crud import get_user_by_username +from langflow.services.deps import get_settings_service, get_variable_service, session_scope STARTER_FOLDER_NAME = "Starter Projects" STARTER_FOLDER_DESCRIPTION = "Starter projects to help you get started in Langflow." @@ -221,6 +218,7 @@ def _is_valid_uuid(val): return False return str(uuid_obj) == val + def load_flows_from_directory(): settings_service = get_settings_service() flows_path = settings_service.settings.load_flows_path @@ -262,6 +260,7 @@ def load_flows_from_directory(): session.add(flow) session.commit() + def find_existing_flow(session, flow_id, flow_endpoint_name): if flow_endpoint_name: stmt = select(Flow).where(Flow.endpoint_name == flow_endpoint_name) @@ -271,6 +270,8 @@ def find_existing_flow(session, flow_id, flow_endpoint_name): if existing := session.exec(stmt).first(): return existing return None + + def create_or_update_starter_projects(): components_paths = get_settings_service().settings.components_path try: diff --git a/src/backend/base/langflow/template/field/base.py b/src/backend/base/langflow/template/field/base.py index 0ba947a28..59d56f1a3 100644 --- a/src/backend/base/langflow/template/field/base.py +++ b/src/backend/base/langflow/template/field/base.py @@ -3,12 +3,16 @@ from typing import Any, Callable, Optional, Union from pydantic import BaseModel, ConfigDict, Field, field_serializer, field_validator, model_serializer, model_validator from langflow.field_typing.range_spec import RangeSpec +from langflow.helpers.custom import format_type class Input(BaseModel): model_config = ConfigDict() - field_type: str = Field(default="str", serialization_alias="type") + field_type: str = Field( + default="str", + serialization_alias="type", + ) """The type of field this is. Default is a string.""" required: bool = False @@ -102,6 +106,17 @@ class Input(BaseModel): def serialize_file_path(self, value): return value if self.field_type == "file" else "" + @field_validator("field_type", mode="before") + def validate_type(cls, v): + # If the user passes CustomComponent as a type insteado of "CustomComponent" we need to convert it to a string + # this should be done for all types + # How to check if v is a type? + if isinstance(v, type): + return format_type(v) + elif not isinstance(v, str): + raise ValueError(f"type must be a string or a type, not {type(v)}") + return v + @field_serializer("field_type") def serialize_field_type(self, value, _info): if value == "float" and self.range_spec is None: @@ -131,7 +146,7 @@ class Input(BaseModel): class Output(BaseModel): - type: list[str] = Field(default=[], serialization_alias="types") + types: Optional[list[str]] = Field(default=[], serialization_alias="types") """List of output types for the field.""" selected: Optional[str] = Field(default=None, serialization_alias="selected") @@ -140,5 +155,12 @@ class Output(BaseModel): name: str = Field(default="", serialization_alias="name") """The name of the field.""" + method: Optional[str] = Field(default=None, serialization_alias="method") + """The method to use for the output.""" + def to_dict(self): return self.model_dump(by_alias=True, exclude_none=True) + + def add_types(self, _type: list[Any]): + for type_ in _type: + self.types.append(type_) diff --git a/src/backend/base/langflow/template/frontend_node/base.py b/src/backend/base/langflow/template/frontend_node/base.py index 2ac4acbab..7f4ad437c 100644 --- a/src/backend/base/langflow/template/frontend_node/base.py +++ b/src/backend/base/langflow/template/frontend_node/base.py @@ -1,42 +1,10 @@ -import re from collections import defaultdict -from typing import ClassVar, Dict, List, Optional, Union +from typing import Dict, List, Optional, Union -from pydantic import BaseModel, Field, field_serializer, model_serializer +from pydantic import BaseModel, field_serializer, model_serializer -from langflow.template.field.base import Input, Output -from langflow.template.frontend_node.constants import FORCE_SHOW_FIELDS -from langflow.template.frontend_node.formatter import field_formatters +from langflow.template.field.base import Output from langflow.template.template.base import Template -from langflow.utils import constants - - -class FieldFormatters(BaseModel): - formatters: ClassVar[Dict] = { - "openai_api_key": field_formatters.OpenAIAPIKeyFormatter(), - } - base_formatters: ClassVar[Dict] = { - "kwargs": field_formatters.KwargsFormatter(), - "optional": field_formatters.RemoveOptionalFormatter(), - "list": field_formatters.ListTypeFormatter(), - "dict": field_formatters.DictTypeFormatter(), - "union": field_formatters.UnionTypeFormatter(), - "multiline": field_formatters.MultilineFieldFormatter(), - "show": field_formatters.ShowFieldFormatter(), - "password": field_formatters.PasswordFieldFormatter(), - "default": field_formatters.DefaultValueFormatter(), - "headers": field_formatters.HeadersDefaultValueFormatter(), - "dict_code_file": field_formatters.DictCodeFileFormatter(), - "model_fields": field_formatters.ModelSpecificFieldFormatter(), - } - - def format(self, field: Input, name: Optional[str] = None) -> None: - for key, formatter in self.base_formatters.items(): - formatter.format(field, name) - - for key, formatter in self.formatters.items(): - if key == field.name: - formatter.format(field, name) class FrontendNode(BaseModel): @@ -69,8 +37,6 @@ class FrontendNode(BaseModel): """List of output types for the frontend node.""" full_path: Optional[str] = None """Full path of the frontend node.""" - field_formatters: FieldFormatters = Field(default_factory=FieldFormatters) - """Field formatters for the frontend node.""" pinned: bool = False """Whether the frontend node is pinned.""" conditional_paths: List[str] = [] @@ -85,12 +51,6 @@ class FrontendNode(BaseModel): beta: bool = False error: Optional[str] = None - # field formatters is an instance attribute but it is not used in the class - # so we need to create a method to get it - @staticmethod - def get_field_formatters() -> FieldFormatters: - return FieldFormatters() - def set_documentation(self, documentation: str) -> None: """Sets the documentation of the frontend node.""" self.documentation = documentation @@ -121,7 +81,7 @@ class FrontendNode(BaseModel): for base_class in result["output_types"]: output = Output( name=base_class, - type=[base_class], + types=[base_class], ) result["outputs"].append(output.model_dump()) @@ -155,142 +115,12 @@ class FrontendNode(BaseModel): elif isinstance(output_type, list): self.output_types.extend(output_type) - @staticmethod - def format_field(field: Input, name: Optional[str] = None) -> None: - """Formats a given field based on its attributes and value.""" - - FrontendNode.get_field_formatters().format(field, name) - - @staticmethod - def remove_optional(_type: str) -> str: - """Removes 'Optional' wrapper from the type if present.""" - return re.sub(r"Optional\[(.*)\]", r"\1", _type) - - @staticmethod - def check_for_list_type(_type: str) -> tuple: - """Checks for list type and returns the modified type and a boolean indicating if it's a list.""" - is_list = "List" in _type or "Sequence" in _type - if is_list: - _type = re.sub(r"(List|Sequence)\[(.*)\]", r"\2", _type) - return _type, is_list - - @staticmethod - def replace_mapping_with_dict(_type: str) -> str: - """Replaces 'Mapping' with 'dict'.""" - return _type.replace("Mapping", "dict") - - @staticmethod - def handle_union_type(_type: str) -> str: - """Simplifies the 'Union' type to the first type in the Union.""" - if "Union" in _type: - _type = _type.replace("Union[", "")[:-1] - _type = _type.split(",")[0] - _type = _type.replace("]", "").replace("[", "") - return _type - - @staticmethod - def handle_special_field(field, key: str, _type: str, SPECIAL_FIELD_HANDLERS) -> str: - """Handles special field by using the respective handler if present.""" - handler = SPECIAL_FIELD_HANDLERS.get(key) - return handler(field) if handler else _type - - @staticmethod - def handle_dict_type(field: Input, _type: str) -> str: - """Handles 'dict' type by replacing it with 'code' or 'file' based on the field name.""" - if "dict" in _type.lower() and field.name == "dict_": - field.field_type = "file" - field.file_types = [".json", ".yaml", ".yml"] - elif _type.startswith("Dict") or _type.startswith("Mapping") or _type.startswith("dict"): - field.field_type = "dict" - return _type - - @staticmethod - def replace_default_value(field: Input, value: dict) -> None: - """Replaces default value with actual value if 'default' is present in value.""" - if "default" in value: - field.value = value["default"] - - @staticmethod - def handle_specific_field_values(field: Input, key: str, name: Optional[str] = None) -> None: - """Handles specific field values for certain fields.""" - if key == "headers": - field.value = """{"Authorization": "Bearer "}""" - FrontendNode._handle_model_specific_field_values(field, key, name) - FrontendNode._handle_api_key_specific_field_values(field, key, name) - - @staticmethod - def _handle_model_specific_field_values(field: Input, key: str, name: Optional[str] = None) -> None: - """Handles specific field values related to models.""" - model_dict = { - "OpenAI": constants.OPENAI_MODELS, - "ChatOpenAI": constants.CHAT_OPENAI_MODELS, - "Anthropic": constants.ANTHROPIC_MODELS, - "ChatAnthropic": constants.ANTHROPIC_MODELS, - } - if name in model_dict and key == "model_name": - field.options = model_dict[name] - field.is_list = True - - @staticmethod - def _handle_api_key_specific_field_values(field: Input, key: str, name: Optional[str] = None) -> None: - """Handles specific field values related to API keys.""" - if "api_key" in key and "OpenAI" in str(name): - field.display_name = "OpenAI API Key" - field.required = False - if field.value is None: - field.value = "" - - @staticmethod - def handle_kwargs_field(field: Input) -> None: - """Handles kwargs field by setting certain attributes.""" - - if "kwargs" in (field.name or "").lower(): - field.advanced = True - field.required = False - field.show = False - - @staticmethod - def handle_api_key_field(field: Input, key: str) -> None: - """Handles api key field by setting certain attributes.""" - if "api" in key.lower() and "key" in key.lower(): - field.required = False - field.advanced = False - - field.display_name = key.replace("_", " ").title() - field.display_name = field.display_name.replace("Api", "API") - - @staticmethod - def should_show_field(key: str, required: bool) -> bool: - """Determines whether the field should be shown.""" - return ( - (required and key not in ["input_variables"]) - or key in FORCE_SHOW_FIELDS - or "api" in key - or ("key" in key and "input" not in key and "output" not in key) - ) - - @staticmethod - def should_be_password(key: str, show: bool) -> bool: - """Determines whether the field should be a password field.""" - return any(text in key.lower() for text in {"password", "token", "api", "key"}) and show - - @staticmethod - def should_be_multiline(key: str) -> bool: - """Determines whether the field should be multiline.""" - return key in { - "suffix", - "prefix", - "template", - "examples", - "code", - "headers", - "description", - } - - @staticmethod - def set_field_default_value(field: Input, value: dict, key: str) -> None: - """Sets the field value with the default value if present.""" - if "default" in value: - field.value = value["default"] - if key == "headers": - field.value = """{"Authorization": "Bearer "}""" + @classmethod + def from_inputs(cls, **kwargs): + """Create a frontend node from inputs.""" + if "inputs" not in kwargs: + raise ValueError("Missing 'inputs' argument.") + inputs = kwargs.pop("inputs") + template = Template(type_name="CustomComponent", fields=inputs) + kwargs["template"] = template + return cls(**kwargs) diff --git a/tests/test_initial_setup.py b/tests/test_initial_setup.py index d4f86a73f..9773b9ca4 100644 --- a/tests/test_initial_setup.py +++ b/tests/test_initial_setup.py @@ -1,6 +1,7 @@ from datetime import datetime from pathlib import Path +import pytest from sqlmodel import select from langflow.initial_setup.setup import ( @@ -41,7 +42,8 @@ def test_get_project_data(): assert isinstance(project_icon_bg_color, str) or project_icon_bg_color is None -def test_create_or_update_starter_projects(client): +@pytest.mark.asyncio +async def test_create_or_update_starter_projects(client): with session_scope() as session: # Run the function to create or update projects create_or_update_starter_projects() From 98a09174d9436b9658894322c08bc190c1831e84 Mon Sep 17 00:00:00 2001 From: ogabrielluiz Date: Thu, 30 May 2024 22:52:53 -0300 Subject: [PATCH 035/701] refactor: Add __bool__ method to CacheMiss class This commit adds the __bool__ method to the CacheMiss class in the utils.py file. The __bool__ method returns False, allowing the CacheMiss object to be treated as False in boolean expressions. This change improves the functionality and usability of the CacheMiss class. --- src/backend/base/langflow/services/cache/utils.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/src/backend/base/langflow/services/cache/utils.py b/src/backend/base/langflow/services/cache/utils.py index ff19836ef..a89963f56 100644 --- a/src/backend/base/langflow/services/cache/utils.py +++ b/src/backend/base/langflow/services/cache/utils.py @@ -23,6 +23,9 @@ class CacheMiss: def __repr__(self): return "" + def __bool__(self): + return False + def create_cache_folder(func): def wrapper(*args, **kwargs): From b0432c380238b89dce709368417832b77e7b01d5 Mon Sep 17 00:00:00 2001 From: ogabrielluiz Date: Thu, 30 May 2024 23:05:41 -0300 Subject: [PATCH 036/701] feat: Add set_attributes method to CustomComponent This commit adds the set_attributes method to the CustomComponent class in the custom_component.py file. The set_attributes method allows for setting attributes of the CustomComponent instance based on a dictionary of key-value pairs. This change enhances the flexibility and configurability of the CustomComponent class. --- .../custom_component/custom_component.py | 6 +++++ .../base/langflow/graph/vertex/base.py | 9 +++++-- .../langflow/interface/initialize/loading.py | 25 +++++++++++++++++-- 3 files changed, 36 insertions(+), 4 deletions(-) diff --git a/src/backend/base/langflow/custom/custom_component/custom_component.py b/src/backend/base/langflow/custom/custom_component/custom_component.py index 535bf0cf3..d23a0b71f 100644 --- a/src/backend/base/langflow/custom/custom_component/custom_component.py +++ b/src/backend/base/langflow/custom/custom_component/custom_component.py @@ -100,6 +100,12 @@ class CustomComponent(Component): build_config = {_input.name: _input.model_dump(by_alias=True, exclude_none=True) for _input in self.inputs} return build_config + def set_attributes(self, params: dict): + for key, value in params.items(): + if key in self.__dict__: + raise ValueError(f"Key {key} already exists in {self.__class__.__name__}") + setattr(self, key, value) + def update_state(self, name: str, value: Any): if not self.vertex: raise ValueError("Vertex is not set") diff --git a/src/backend/base/langflow/graph/vertex/base.py b/src/backend/base/langflow/graph/vertex/base.py index 77b4e7192..cae5ac976 100644 --- a/src/backend/base/langflow/graph/vertex/base.py +++ b/src/backend/base/langflow/graph/vertex/base.py @@ -73,6 +73,7 @@ class Vertex: self.parent_is_top_level = False self.layer = None self.result: Optional[ResultData] = None + self.results: Dict[str, Any] = {} try: self.is_interface_component = self.vertex_type in InterfaceComponentTypes except ValueError: @@ -82,6 +83,9 @@ class Vertex: self.build_times: List[float] = [] self.state = VertexStates.ACTIVE + def add_result(self, name: str, result: Any): + self.results[name] = result + def update_graph_state(self, key, new_state, append: bool): if append: self.graph.append_state(key, new_state, caller=self.id) @@ -196,7 +200,7 @@ class Vertex: def _parse_data(self) -> None: self.data = self._data["data"] - self.output = self.data["node"]["base_classes"] + self.outputs = self.data["node"]["outputs"] self.display_name = self.data["node"].get("display_name", self.id.split("-")[0]) self.description = self.data["node"].get("description", "") @@ -224,7 +228,8 @@ class Vertex: template_dict = self.data["node"]["template"] self.vertex_type = ( self.data["type"] - if "Tool" not in self.output or template_dict["_type"].islower() + if "Tool" not in [type_ for out in self.outputs for type_ in out["types"]] + or template_dict["_type"].islower() else template_dict["_type"] ) diff --git a/src/backend/base/langflow/interface/initialize/loading.py b/src/backend/base/langflow/interface/initialize/loading.py index 03de827b3..098ff8a33 100644 --- a/src/backend/base/langflow/interface/initialize/loading.py +++ b/src/backend/base/langflow/interface/initialize/loading.py @@ -1,7 +1,7 @@ import inspect import json import os -from typing import TYPE_CHECKING, Any, Type +from typing import TYPE_CHECKING, Any, Awaitable, Callable, Type import orjson from loguru import logger @@ -94,7 +94,9 @@ def update_params_with_load_from_db_fields( return params -async def instantiate_custom_component(params, user_id, vertex, fallback_to_env_vars: bool = False): +async def instantiate_custom_component( + params: dict, user_id: str, vertex: "Vertex", fallback_to_env_vars: bool = False +): params_copy = params.copy() class_object: Type["CustomComponent"] = eval_custom_component_code(params_copy.pop("code")) custom_component: "CustomComponent" = class_object( @@ -107,12 +109,31 @@ async def instantiate_custom_component(params, user_id, vertex, fallback_to_env_ custom_component, params_copy, vertex.load_from_db_fields, fallback_to_env_vars ) + # Now set the params as attributes of the custom_component + custom_component.set_attributes(params_copy) + if "retriever" in params_copy and hasattr(params_copy["retriever"], "as_retriever"): params_copy["retriever"] = params_copy["retriever"].as_retriever() # Determine if the build method is asynchronous is_async = inspect.iscoroutinefunction(custom_component.build) + # New feature: the component has a list of outputs and we have + # to check the vertex.edges to see which is connected (coulb be multiple) + # and then we'll get the output which has the name of the method we should call. + # the methods don't require any params because they are already set in the custom_component + # so we can just call them + + if hasattr(custom_component, "outputs"): + for output in custom_component.outputs: + if output.name in vertex.edges: + method: Callable | Awaitable = getattr(custom_component, output.method) + result = method() + # If the method is asynchronous, we need to await it + if inspect.iscoroutinefunction(method): + result = await result + vertex.add_result(output.name, result) + if is_async: # Await the build method directly if it's async build_result = await custom_component.build(**params_copy) From b7de1ff3bdae5413e5b189b56b56118df4a0f9e0 Mon Sep 17 00:00:00 2001 From: ogabrielluiz Date: Fri, 31 May 2024 09:25:48 -0300 Subject: [PATCH 037/701] feat: Add ComponentFrontendNode to CustomComponent This commit adds the `ComponentFrontendNode` class to the `CustomComponent` module. The `ComponentFrontendNode` class defines a new frontend node for the `Component` type. It includes a template with a code input field. This change enhances the functionality and flexibility of the `CustomComponent` module. --- .../base/langflow/graph/graph/constants.py | 1 + .../base/langflow/graph/vertex/types.py | 9 +++ .../langflow/interface/initialize/loading.py | 76 +++++++++++-------- .../base/langflow/legacy_custom/customs.py | 3 + .../frontend_node/custom_components.py | 25 ++++++ tests/data/component_multiple_outputs.py | 2 +- tests/data/component_nested_call.py | 20 +++++ 7 files changed, 105 insertions(+), 31 deletions(-) create mode 100644 tests/data/component_nested_call.py diff --git a/src/backend/base/langflow/graph/graph/constants.py b/src/backend/base/langflow/graph/graph/constants.py index 8f5840524..ca04e81c6 100644 --- a/src/backend/base/langflow/graph/graph/constants.py +++ b/src/backend/base/langflow/graph/graph/constants.py @@ -21,6 +21,7 @@ class VertexTypesDict(LazyLoadDictBase): def get_type_dict(self): return { **{t: types.CustomComponentVertex for t in ["CustomComponent"]}, + **{t: types.ComponentVertex for t in ["Component"]}, **{t: types.InterfaceVertex for t in CHAT_COMPONENTS}, } diff --git a/src/backend/base/langflow/graph/vertex/types.py b/src/backend/base/langflow/graph/vertex/types.py index 590c38c24..79eafd8e5 100644 --- a/src/backend/base/langflow/graph/vertex/types.py +++ b/src/backend/base/langflow/graph/vertex/types.py @@ -24,6 +24,15 @@ class CustomComponentVertex(Vertex): return self.artifacts["repr"] or super()._built_object_repr() +class ComponentVertex(Vertex): + def __init__(self, data: Dict, graph): + super().__init__(data, graph=graph, base_type="component") + + def _built_object_repr(self): + if self.artifacts and "repr" in self.artifacts: + return self.artifacts["repr"] or super()._built_object_repr() + + class InterfaceVertex(Vertex): def __init__(self, data: Dict, graph): super().__init__(data, graph=graph, base_type="custom_components", is_task=True) diff --git a/src/backend/base/langflow/interface/initialize/loading.py b/src/backend/base/langflow/interface/initialize/loading.py index 098ff8a33..239bf5cc3 100644 --- a/src/backend/base/langflow/interface/initialize/loading.py +++ b/src/backend/base/langflow/interface/initialize/loading.py @@ -27,10 +27,25 @@ async def instantiate_class( params = convert_params_to_sets(params) params = convert_kwargs(params) logger.debug(f"Instantiating {vertex_type} of type {base_type}") + if not base_type: raise ValueError("No base type provided for vertex") + + params_copy = params.copy() + class_object: Type["CustomComponent"] = eval_custom_component_code(params_copy.pop("code")) + custom_component: "CustomComponent" = class_object( + user_id=user_id, + parameters=params_copy, + vertex=vertex, + selected_output_type=vertex.selected_output_type, + ) + params_copy = update_params_with_load_from_db_fields( + custom_component, params_copy, vertex.load_from_db_fields, fallback_to_env_vars + ) if base_type == "custom_components": - return await instantiate_custom_component(params, user_id, vertex, fallback_to_env_vars=fallback_to_env_vars) + return await build_custom_component(params=params, custom_component=custom_component) + elif base_type == "component": + return await build_component(params=params, custom_component=custom_component) else: raise ValueError(f"Base type {base_type} not found.") @@ -94,26 +109,37 @@ def update_params_with_load_from_db_fields( return params -async def instantiate_custom_component( - params: dict, user_id: str, vertex: "Vertex", fallback_to_env_vars: bool = False +async def build_component( + params: dict, + custom_component: "CustomComponent", + vertex: "Vertex", ): - params_copy = params.copy() - class_object: Type["CustomComponent"] = eval_custom_component_code(params_copy.pop("code")) - custom_component: "CustomComponent" = class_object( - user_id=user_id, - parameters=params_copy, - vertex=vertex, - selected_output_type=vertex.selected_output_type, - ) - params_copy = update_params_with_load_from_db_fields( - custom_component, params_copy, vertex.load_from_db_fields, fallback_to_env_vars - ) - # Now set the params as attributes of the custom_component - custom_component.set_attributes(params_copy) + custom_component.set_attributes(params) - if "retriever" in params_copy and hasattr(params_copy["retriever"], "as_retriever"): - params_copy["retriever"] = params_copy["retriever"].as_retriever() + build_result = {} + if hasattr(custom_component, "outputs"): + for output in custom_component.outputs: + # Build the output if it's connected to some other vertex + # or if it's not connected to any vertex + if not vertex.edges or output.name in vertex.edges: + method: Callable | Awaitable = getattr(custom_component, output.method) + result = method() + # If the method is asynchronous, we need to await it + if inspect.iscoroutinefunction(method): + result = await result + build_result[output.name] = result + custom_repr = custom_component.custom_repr() + if custom_repr is None and isinstance(build_result, (dict, Record, str)): + custom_repr = build_result + if not isinstance(custom_repr, str): + custom_repr = str(custom_repr) + return custom_component, build_result, {"repr": custom_repr} + + +async def build_custom_component(params: dict, custom_component: "CustomComponent"): + if "retriever" in params and hasattr(params["retriever"], "as_retriever"): + params["retriever"] = params["retriever"].as_retriever() # Determine if the build method is asynchronous is_async = inspect.iscoroutinefunction(custom_component.build) @@ -124,22 +150,12 @@ async def instantiate_custom_component( # the methods don't require any params because they are already set in the custom_component # so we can just call them - if hasattr(custom_component, "outputs"): - for output in custom_component.outputs: - if output.name in vertex.edges: - method: Callable | Awaitable = getattr(custom_component, output.method) - result = method() - # If the method is asynchronous, we need to await it - if inspect.iscoroutinefunction(method): - result = await result - vertex.add_result(output.name, result) - if is_async: # Await the build method directly if it's async - build_result = await custom_component.build(**params_copy) + build_result = await custom_component.build(**params) else: # Call the build method directly if it's sync - build_result = custom_component.build(**params_copy) + build_result = custom_component.build(**params) custom_repr = custom_component.custom_repr() if custom_repr is None and isinstance(build_result, (dict, Record, str)): custom_repr = build_result diff --git a/src/backend/base/langflow/legacy_custom/customs.py b/src/backend/base/langflow/legacy_custom/customs.py index 26e5e33fa..e4090135e 100644 --- a/src/backend/base/langflow/legacy_custom/customs.py +++ b/src/backend/base/langflow/legacy_custom/customs.py @@ -5,6 +5,9 @@ CUSTOM_NODES: dict[str, dict[str, frontend_node.base.FrontendNode]] = { "custom_components": { "CustomComponent": frontend_node.custom_components.CustomComponentFrontendNode(), }, + "component": { + "Component": frontend_node.custom_components.ComponentFrontendNode(), + }, } diff --git a/src/backend/base/langflow/template/frontend_node/custom_components.py b/src/backend/base/langflow/template/frontend_node/custom_components.py index 6969f8a73..2fd1e9cdf 100644 --- a/src/backend/base/langflow/template/frontend_node/custom_components.py +++ b/src/backend/base/langflow/template/frontend_node/custom_components.py @@ -67,3 +67,28 @@ class CustomComponentFrontendNode(FrontendNode): ) description: Optional[str] = None base_classes: list[str] = [] + + +class ComponentFrontendNode(FrontendNode): + _format_template: bool = False + name: str = "Component" + display_name: Optional[str] = "Component" + beta: bool = False + template: Template = Template( + type_name="Component", + fields=[ + Input( + field_type="code", + required=True, + placeholder="", + is_list=False, + show=True, + value=DEFAULT_CUSTOM_COMPONENT_CODE, + name="code", + advanced=False, + dynamic=True, + ) + ], + ) + description: Optional[str] = None + base_classes: list[str] = [] diff --git a/tests/data/component_multiple_outputs.py b/tests/data/component_multiple_outputs.py index 7a01fafba..26fe13acd 100644 --- a/tests/data/component_multiple_outputs.py +++ b/tests/data/component_multiple_outputs.py @@ -8,7 +8,7 @@ class MultipleOutputsComponent(CustomComponent): Input(display_name="Number", name="number", field_type=int), ] outputs = [ - Output(display_name="Certain Output", method="certain_output", name="certain_output"), + Output(name="Certain Output", method="certain_output"), Output(name="Other Output", method="other_output"), ] diff --git a/tests/data/component_nested_call.py b/tests/data/component_nested_call.py new file mode 100644 index 000000000..204ff169c --- /dev/null +++ b/tests/data/component_nested_call.py @@ -0,0 +1,20 @@ +from langflow.custom import CustomComponent +from langflow.template.field.base import Input, Output +from random import randint + + +class MultipleOutputsComponent(CustomComponent): + inputs = [ + Input(display_name="Input", name="input", field_type=str), + Input(display_name="Number", name="number", field_type=int), + ] + outputs = [ + Output(name="Certain Output", method="certain_output"), + Output(name="Other Output", method="other_output"), + ] + + def certain_output(self) -> int: + return randint(0, self.number) + + def other_output(self) -> int: + return self.certain_output() From f7059953117633b2e2c6f7845ce794ed713c712d Mon Sep 17 00:00:00 2001 From: ogabrielluiz Date: Fri, 31 May 2024 10:30:25 -0300 Subject: [PATCH 038/701] refactor: Update new output creation logic in reactflowUtils.ts Simplify the logic for creating new output fields in the `updateNewOutput` function of `reactflowUtils.ts`. Instead of mapping over the `outputTypes` array, directly assign a new output field to `sourceNode.data.node!.outputs`. The new output field includes the `outputTypes` as `types`, sets `selected` based on the `selected` variable, and sets `name` as a string representation of `outputTypes` joined with " | ". This change improves the clarity and efficiency of the code. --- src/frontend/src/utils/reactflowUtils.ts | 12 +++++++----- 1 file changed, 7 insertions(+), 5 deletions(-) diff --git a/src/frontend/src/utils/reactflowUtils.ts b/src/frontend/src/utils/reactflowUtils.ts index afb3db57f..3c3d2f2c0 100644 --- a/src/frontend/src/utils/reactflowUtils.ts +++ b/src/frontend/src/utils/reactflowUtils.ts @@ -446,11 +446,13 @@ export function updateNewOutput({ nodes, edges }: updateEdgesHandleIdsType) { ) { const outputTypes = sourceNode.data.node!.output_types; // create a new output field for each output type - sourceNode.data.node!.outputs = outputTypes?.map((type) => ({ - types: [type], - selected: selected, - name: type, - })); + sourceNode.data.node!.outputs = [ + { + types: outputTypes ?? [], + selected: selected, + name: outputTypes?.join(" | ") ?? "", + }, + ]; } } edge.sourceHandle = scapedJSONStringfy(newSourceHandle); From 4727cae10e7ae385973c20496c0859b61b843c3d Mon Sep 17 00:00:00 2001 From: anovazzi1 Date: Fri, 31 May 2024 16:09:41 -0300 Subject: [PATCH 039/701] fix backward compability --- .../components/OutputComponent/index.tsx | 58 +++--- .../components/parameterComponent/index.tsx | 18 +- .../src/customNodes/genericNode/index.tsx | 95 +++------ src/frontend/src/stores/flowsManagerStore.ts | 30 +-- src/frontend/src/types/components/index.ts | 10 +- src/frontend/src/types/flow/index.ts | 4 +- src/frontend/src/utils/reactflowUtils.ts | 195 ++++++++++-------- 7 files changed, 196 insertions(+), 214 deletions(-) diff --git a/src/frontend/src/customNodes/genericNode/components/OutputComponent/index.tsx b/src/frontend/src/customNodes/genericNode/components/OutputComponent/index.tsx index 69ca3965c..f3c959e5e 100644 --- a/src/frontend/src/customNodes/genericNode/components/OutputComponent/index.tsx +++ b/src/frontend/src/customNodes/genericNode/components/OutputComponent/index.tsx @@ -18,6 +18,7 @@ export default function OutputComponent({ frozen = false, nodeId, idx, + name, }: outputComponentType) { const setNode = useFlowStore((state) => state.setNode); const updateNodeInternals = useUpdateNodeInternals(); @@ -27,33 +28,36 @@ export default function OutputComponent({ } return ( - - - - {selected} - - - - - {types.map((type) => ( - { - // TODO: UDPDATE SET NODE TO NEW NODE FORM - setNode(nodeId, (node) => { - const newNode = cloneDeep(node); - (newNode.data as NodeDataType).node!.outputs![idx].selected = - type; - return newNode; - }); - updateNodeInternals(nodeId); - }} +
+ {name} + + + - {type} - - ))} - - + {selected} + + + + + {types.map((type) => ( + { + // TODO: UDPDATE SET NODE TO NEW NODE FORM + setNode(nodeId, (node) => { + const newNode = cloneDeep(node); + (newNode.data as NodeDataType).node!.outputs![idx].selected = + type; + return newNode; + }); + updateNodeInternals(nodeId); + }} + > + {type} + + ))} + + +
); } diff --git a/src/frontend/src/customNodes/genericNode/components/parameterComponent/index.tsx b/src/frontend/src/customNodes/genericNode/components/parameterComponent/index.tsx index 80d85afdc..151d40595 100644 --- a/src/frontend/src/customNodes/genericNode/components/parameterComponent/index.tsx +++ b/src/frontend/src/customNodes/genericNode/components/parameterComponent/index.tsx @@ -59,6 +59,7 @@ export default function ParameterComponent({ proxy, showNode, index, + outputName, }: ParameterComponentType): JSX.Element { const infoHtml = useRef(null); const nodes = useFlowStore((state) => state.nodes); @@ -79,7 +80,7 @@ export default function ParameterComponent({ debouncedHandleUpdateValues, setNode, isLoading, - setIsLoading + setIsLoading, ); const { handleNodeClass: handleNodeClassHook } = useHandleNodeClass( @@ -87,7 +88,7 @@ export default function ParameterComponent({ name, takeSnapshot, setNode, - updateNodeInternals + updateNodeInternals, ); const { handleRefreshButtonPress: handleRefreshButtonPressHook } = @@ -96,7 +97,7 @@ export default function ParameterComponent({ let disabled = edges.some( (edge) => - edge.targetHandle === scapedJSONStringfy(proxy ? { ...id, proxy } : id) + edge.targetHandle === scapedJSONStringfy(proxy ? { ...id, proxy } : id), ) ?? false; const handleRefreshButtonPress = async (name, data) => { @@ -107,7 +108,7 @@ export default function ParameterComponent({ const handleOnNewValue = async ( newValue: string | string[] | boolean | Object[], - skipSnapshot: boolean | undefined = false + skipSnapshot: boolean | undefined = false, ): Promise => { handleOnNewValueHook(newValue, skipSnapshot); }; @@ -137,6 +138,7 @@ export default function ParameterComponent({ selected={title} nodeId={data.id} frozen={data.node?.frozen} + name={outputName ?? type ?? title} /> ) : ( {title} @@ -188,14 +190,14 @@ export default function ParameterComponent({ className={classNames( left ? "my-12 -ml-0.5 " : " my-12 -mr-0.5 ", "h-3 w-3 rounded-full border-2 bg-background", - !showNode ? "mt-0" : "" + !showNode ? "mt-0" : "", )} style={{ borderColor: color ?? nodeColors.unknown, }} onClick={() => { setFilterEdge( - groupByFamily(myData, tooltipTitle!, left, nodes!) + groupByFamily(myData, tooltipTitle!, left, nodes!), ); }} > @@ -280,12 +282,12 @@ export default function ParameterComponent({ } className={classNames( left ? "-ml-0.5" : "-mr-0.5", - "h-3 w-3 rounded-full border-2 bg-background" + "h-3 w-3 rounded-full border-2 bg-background", )} style={{ borderColor: color ?? nodeColors.unknown }} onClick={() => { setFilterEdge( - groupByFamily(myData, tooltipTitle!, left, nodes!) + groupByFamily(myData, tooltipTitle!, left, nodes!), ); }} /> diff --git a/src/frontend/src/customNodes/genericNode/index.tsx b/src/frontend/src/customNodes/genericNode/index.tsx index a78b78088..2163af716 100644 --- a/src/frontend/src/customNodes/genericNode/index.tsx +++ b/src/frontend/src/customNodes/genericNode/index.tsx @@ -55,14 +55,14 @@ export default function GenericNode({ const [nodeName, setNodeName] = useState(data.node!.display_name); const [inputDescription, setInputDescription] = useState(false); const [nodeDescription, setNodeDescription] = useState( - data.node?.description! + data.node?.description!, ); const [isOutdated, setIsOutdated] = useState(false); const buildStatus = useFlowStore( - (state) => state.flowBuildStatus[data.id]?.status + (state) => state.flowBuildStatus[data.id]?.status, ); const lastRunTime = useFlowStore( - (state) => state.flowBuildStatus[data.id]?.timestamp + (state) => state.flowBuildStatus[data.id]?.timestamp, ); const [validationStatus, setValidationStatus] = useState(null); @@ -115,7 +115,7 @@ export default function GenericNode({ updateNodeInternals(data.id); }, - [data.id, data.node, setNode, setIsOutdated] + [data.id, data.node, setNode, setIsOutdated], ); if (!data.node!.template) { @@ -252,60 +252,10 @@ export default function GenericNode({ ); }; - const buildParameterComponent = ({ - data, - conditionalPath, - showNode, - left, - }: { - data: NodeDataType; - conditionalPath: string | null; - showNode: boolean; - left: boolean; - }) => { - return ( - 0 - ? nodeColors[data.node.output_types[0]] ?? - nodeColors[types[data.node.output_types[0]]] - : nodeColors[types[data.type]]) ?? nodeColors.unknown - } - title={ - data.node?.output_types && data.node.output_types.length > 0 - ? data.node.output_types.join(" | ") - : data.type - } - conditionPath={conditionalPath} - tooltipTitle={data.node?.base_classes.join("\n")} - id={{ - baseClasses: data.node!.base_classes, - id: data.id, - dataType: data.type, - // First parameter component should be true - // Second should be false - conditionalPath: conditionalPath, - }} - // Type should be base_classes if it's not a conditional node - // else it should be true in the first parameter component - type={data.node?.base_classes.join("|")} - left={left} - showNode={showNode} - /> - ); - }; - const isDark = useDarkStore((state) => state.dark); const renderIconStatus = ( buildStatus: BuildStatus | undefined, - validationStatus: validationStatusType | null + validationStatus: validationStatusType | null, ) => { if (buildStatus === BuildStatus.BUILDING) { return ; @@ -346,7 +296,7 @@ export default function GenericNode({ }; const getSpecificClassFromBuildStatus = ( buildStatus: BuildStatus | undefined, - validationStatus: validationStatusType | null + validationStatus: validationStatusType | null, ) => { let isInvalid = validationStatus && !validationStatus.valid; @@ -370,11 +320,11 @@ export default function GenericNode({ selected: boolean, showNode: boolean, buildStatus: BuildStatus | undefined, - validationStatus: validationStatusType | null + validationStatus: validationStatusType | null, ) => { const specificClassFromBuildStatus = getSpecificClassFromBuildStatus( buildStatus, - validationStatus + validationStatus, ); const baseBorderClass = getBaseBorderClass(selected); @@ -383,7 +333,7 @@ export default function GenericNode({ baseBorderClass, nodeSizeClass, "generic-node-div", - specificClassFromBuildStatus + specificClassFromBuildStatus, ); return names; }; @@ -443,7 +393,7 @@ export default function GenericNode({ selected, showNode, buildStatus, - validationStatus + validationStatus, )} > {data.node?.beta && showNode && ( @@ -588,7 +538,7 @@ export default function GenericNode({ } title={getFieldTitle( data.node?.template!, - templateField + templateField, )} info={data.node?.template[templateField].info} name={templateField} @@ -616,9 +566,9 @@ export default function GenericNode({ proxy={data.node?.template[templateField].proxy} showNode={showNode} /> - ) + ), )} - + /> */} )} @@ -775,7 +725,7 @@ export default function GenericNode({ !data.node?.description) && nameEditable ? "font-light italic" - : "" + : "", )} onDoubleClick={(e) => { setInputDescription(true); @@ -837,13 +787,13 @@ export default function GenericNode({ } title={getFieldTitle( data.node?.template!, - templateField + templateField, )} info={data.node?.template[templateField].info} name={templateField} tooltipTitle={ data.node?.template[templateField].input_types?.join( - "\n" + "\n", ) ?? data.node?.template[templateField].type } required={data.node!.template[templateField].required} @@ -870,7 +820,7 @@ export default function GenericNode({
{" "} @@ -881,7 +831,7 @@ export default function GenericNode({ ))} diff --git a/src/frontend/src/stores/flowsManagerStore.ts b/src/frontend/src/stores/flowsManagerStore.ts index 6a179ce84..12007199e 100644 --- a/src/frontend/src/stores/flowsManagerStore.ts +++ b/src/frontend/src/stores/flowsManagerStore.ts @@ -84,14 +84,14 @@ const useFlowsManagerStore = create((set, get) => ({ readFlowsFromDatabase() .then((dbData) => { if (dbData) { - const { data, flows } = processFlows(dbData, false); + const { data, flows } = processFlows(dbData); const examples = flows.filter( - (flow) => flow.folder_id === starterFolderId + (flow) => flow.folder_id === starterFolderId, ); get().setExamples(examples); const flowsWithoutStarterFolder = flows.filter( - (flow) => flow.folder_id !== starterFolderId + (flow) => flow.folder_id !== starterFolderId, ); get().setFlows(flowsWithoutStarterFolder); @@ -119,7 +119,7 @@ const useFlowsManagerStore = create((set, get) => ({ if (get().currentFlow) { get().saveFlow( { ...get().currentFlow!, data: { nodes, edges, viewport } }, - true + true, ); } }, @@ -145,7 +145,7 @@ const useFlowsManagerStore = create((set, get) => ({ return updatedFlow; } return flow; - }) + }), ); //update tabs state @@ -194,7 +194,7 @@ const useFlowsManagerStore = create((set, get) => ({ flow?: FlowType, override?: boolean, position?: XYPosition, - fromDragAndDrop?: boolean + fromDragAndDrop?: boolean, ): Promise => { let flowData = flow ? processDataFromFlow(flow) @@ -209,7 +209,7 @@ const useFlowsManagerStore = create((set, get) => ({ const newFlow = createNewFlow( flowData!, flow!, - folder_id || my_collection_id! + folder_id || my_collection_id!, ); const { id } = await saveFlowToDatabase(newFlow); newFlow.id = id; @@ -232,7 +232,7 @@ const useFlowsManagerStore = create((set, get) => ({ const newFlow = createNewFlow( flowData!, flow!, - folder_id || my_collection_id! + folder_id || my_collection_id!, ); const newName = addVersionToDuplicates(newFlow, get().flows); @@ -268,7 +268,7 @@ const useFlowsManagerStore = create((set, get) => ({ .getState() .paste( { nodes: flow!.data!.nodes, edges: flow!.data!.edges }, - position ?? { x: 10, y: 10 } + position ?? { x: 10, y: 10 }, ); } }, @@ -278,7 +278,7 @@ const useFlowsManagerStore = create((set, get) => ({ multipleDeleteFlowsComponents(id) .then(() => { const { data, flows } = processFlows( - get().flows.filter((flow) => !id.includes(flow.id)) + get().flows.filter((flow) => !id.includes(flow.id)), ); get().setFlows(flows); set({ isLoading: false }); @@ -298,7 +298,7 @@ const useFlowsManagerStore = create((set, get) => ({ deleteFlowFromDatabase(id) .then(() => { const { data, flows } = processFlows( - get().flows.filter((flow) => flow.id !== id) + get().flows.filter((flow) => flow.id !== id), ); get().setFlows(flows); set({ isLoading: false }); @@ -320,7 +320,7 @@ const useFlowsManagerStore = create((set, get) => ({ return new Promise((resolve) => { let componentFlow = get().flows.find( (componentFlow) => - componentFlow.is_component && componentFlow.name === key + componentFlow.is_component && componentFlow.name === key, ); if (componentFlow) { @@ -368,7 +368,7 @@ const useFlowsManagerStore = create((set, get) => ({ fileData, undefined, position, - true + true, ); resolve(id); } @@ -409,7 +409,7 @@ const useFlowsManagerStore = create((set, get) => ({ return get().addFlow( true, createFlowComponent(component, useDarkStore.getState().version), - override + override, ); }, takeSnapshot: () => { @@ -430,7 +430,7 @@ const useFlowsManagerStore = create((set, get) => ({ if (pastLength > 0) { past[currentFlowId] = past[currentFlowId].slice( pastLength - defaultOptions.maxHistorySize + 1, - pastLength + pastLength, ); past[currentFlowId].push(newState); diff --git a/src/frontend/src/types/components/index.ts b/src/frontend/src/types/components/index.ts index 054ed7543..5f04e67d6 100644 --- a/src/frontend/src/types/components/index.ts +++ b/src/frontend/src/types/components/index.ts @@ -70,6 +70,7 @@ export type ParameterComponentType = { showNode?: boolean; index: number; onCloseModal?: (close: boolean) => void; + outputName?: string; }; export type InputListComponentType = { value: string[]; @@ -118,6 +119,7 @@ export type outputComponentType = { nodeId: string; frozen?: boolean; idx: number; + name: string; }; export type PromptAreaComponentType = { @@ -528,7 +530,7 @@ export type nodeToolbarPropsType = { updateNodeCode?: ( newNodeClass: APIClassType, code: string, - name: string + name: string, ) => void; setShowState: (show: boolean | SetStateAction) => void; isOutdated?: boolean; @@ -578,7 +580,7 @@ export type chatMessagePropsType = { updateChat: ( chat: ChatMessageType, message: string, - stream_url?: string + stream_url?: string, ) => void; }; @@ -670,12 +672,12 @@ export type codeTabsPropsType = { value: string, node: NodeType, template: InputFieldType, - tweak: tweakType + tweak: tweakType, ) => string; buildTweakObject?: ( tw: string, changes: string | string[] | boolean | number | Object[] | Object, - template: InputFieldType + template: InputFieldType, ) => Promise; }; activeTweaks?: boolean; diff --git a/src/frontend/src/types/flow/index.ts b/src/frontend/src/types/flow/index.ts index 5c3fb10a1..4fa51eda5 100644 --- a/src/frontend/src/types/flow/index.ts +++ b/src/frontend/src/types/flow/index.ts @@ -58,9 +58,9 @@ export type TweaksType = Array< export type sourceHandleType = { dataType: string; id: string; - baseClasses: string[]; + output_types: string[]; conditionalPath?: string | null; - idx: number; + name: string; }; //left side export type targetHandleType = { diff --git a/src/frontend/src/utils/reactflowUtils.ts b/src/frontend/src/utils/reactflowUtils.ts index 3c3d2f2c0..261a39b6c 100644 --- a/src/frontend/src/utils/reactflowUtils.ts +++ b/src/frontend/src/utils/reactflowUtils.ts @@ -45,8 +45,7 @@ export function checkChatInput(nodes: Node[]) { return nodes.some((node) => node.data.type === "ChatInput"); } -export function cleanEdges(nodes: Node[], edges: Edge[]) { - console.log("cleanEdges"); +export function cleanEdges(nodes: NodeType[], edges: Edge[]) { let newEdges = cloneDeep(edges); edges.forEach((edge) => { // check if the source and target node still exists @@ -76,12 +75,16 @@ export function cleanEdges(nodes: Node[], edges: Edge[]) { } } if (sourceHandle) { - const index = scapeJSONParse(sourceHandle).idx ?? 0; + const name = scapeJSONParse(sourceHandle).name; + const output = sourceNode.data.node!.outputs?.find( + (output) => output.name === name, + ); + const outputTypes = [output?.selected ?? ""]; const id: sourceHandleType = { id: sourceNode.data.id, - baseClasses: [sourceNode.data.node.outputs[index].selected], + name: name, + output_types: outputTypes, dataType: sourceNode.data.type, - idx: index, }; if (scapedJSONStringfy(id) !== sourceHandle) { newEdges = newEdges.filter((e) => e.id !== edge.id); @@ -102,18 +105,18 @@ export function unselectAllNodes({ updateNodes, data }: unselectAllNodesType) { export function isValidConnection( { source, target, sourceHandle, targetHandle }: Connection, nodes: Node[], - edges: Edge[] + edges: Edge[], ) { const targetHandleObject: targetHandleType = scapeJSONParse(targetHandle!); const sourceHandleObject: sourceHandleType = scapeJSONParse(sourceHandle!); if ( targetHandleObject.inputTypes?.some( - (n) => n === sourceHandleObject.dataType + (n) => n === sourceHandleObject.dataType, ) || - sourceHandleObject.baseClasses.some( + sourceHandleObject.output_types.some( (t) => targetHandleObject.inputTypes?.some((n) => n === t) || - t === targetHandleObject.type + t === targetHandleObject.type, ) ) { let targetNode = nodes.find((node) => node.id === target!)?.data?.node; @@ -146,7 +149,7 @@ export function removeApiKeys(flow: FlowType): FlowType { export function updateTemplate( reference: APITemplateType, - objectToUpdate: APITemplateType + objectToUpdate: APITemplateType, ): APITemplateType { let clonedObject: APITemplateType = cloneDeep(reference); @@ -206,7 +209,7 @@ export const processDataFromFlow = (flow: FlowType, refreshIds = true) => { export function updateIds( { edges, nodes }: { edges: Edge[]; nodes: Node[] }, - selection?: { edges: Edge[]; nodes: Node[] } + selection?: { edges: Edge[]; nodes: Node[] }, ) { let idsMap = {}; const selectionIds = selection?.nodes.map((n) => n.id); @@ -234,7 +237,7 @@ export function updateIds( edge.source = idsMap[edge.source]; edge.target = idsMap[edge.target]; const sourceHandleObject: sourceHandleType = scapeJSONParse( - edge.sourceHandle! + edge.sourceHandle!, ); edge.sourceHandle = scapedJSONStringfy({ ...sourceHandleObject, @@ -244,7 +247,7 @@ export function updateIds( edge.data.sourceHandle.id = edge.source; } const targetHandleObject: targetHandleType = scapeJSONParse( - edge.targetHandle! + edge.targetHandle!, ); edge.targetHandle = scapedJSONStringfy({ ...targetHandleObject, @@ -290,11 +293,11 @@ export function validateNode(node: NodeType, edges: Edge[]): Array { (scapeJSONParse(edge.targetHandle!) as targetHandleType).fieldName === t && (scapeJSONParse(edge.targetHandle!) as targetHandleType).id === - node.id + node.id, ) ) { errors.push( - `${displayName || type} is missing ${getFieldTitle(template, t)}.` + `${displayName || type} is missing ${getFieldTitle(template, t)}.`, ); } else if ( template[t].type === "dict" && @@ -308,15 +311,15 @@ export function validateNode(node: NodeType, edges: Edge[]): Array { errors.push( `${displayName || type} (${getFieldTitle( template, - t - )}) contains duplicate keys with the same values.` + t, + )}) contains duplicate keys with the same values.`, ); if (hasEmptyKey(template[t].value)) errors.push( `${displayName || type} (${getFieldTitle( template, - t - )}) field must not be empty.` + t, + )}) field must not be empty.`, ); } return errors; @@ -325,7 +328,7 @@ export function validateNode(node: NodeType, edges: Edge[]): Array { export function validateNodes( nodes: Node[], - edges: Edge[] + edges: Edge[], ): // this returns an array of tuples with the node id and the errors Array<{ id: string; errors: Array }> { if (nodes.length === 0) { @@ -346,19 +349,16 @@ export function updateEdges(edges: Edge[]) { if (edges) edges.forEach((edge) => { const targetHandleObject: targetHandleType = scapeJSONParse( - edge.targetHandle! + edge.targetHandle!, ); edge.className = "stroke-gray-900 stroke-connection"; }); } export function addVersionToDuplicates(flow: FlowType, flows: FlowType[]) { - console.log("flow", flow); - console.log("flows", flows); const existingNames = flows .filter((f) => f.folder_id === flow.folder_id) .map((item) => item.name); - console.log("existingNames", existingNames); let newName = flow.name; let count = 1; @@ -374,6 +374,7 @@ export function updateEdgesHandleIds({ edges, nodes, }: updateEdgesHandleIdsType): Edge[] { + console.log("updateEdgesHandleIds"); let newEdges = cloneDeep(edges); newEdges.forEach((edge) => { const sourceNodeId = edge.source; @@ -396,11 +397,14 @@ export function updateEdgesHandleIds({ }; } if (source && sourceNode) { + const output_types = + sourceNode.data.node!.output_types ?? + sourceNode.data.node!.base_classes; newSource = { id: sourceNode.data.id, - baseClasses: sourceNode.data.node!.base_classes, + output_types, dataType: sourceNode.data.type, - idx: 0, + name: output_types.join(" | "), }; } edge.sourceHandle = scapedJSONStringfy(newSource!); @@ -415,51 +419,70 @@ export function updateEdgesHandleIds({ } export function updateNewOutput({ nodes, edges }: updateEdgesHandleIdsType) { - console.log("updateNewOutput"); let newEdges = cloneDeep(edges); let newNodes = cloneDeep(nodes); newEdges.forEach((edge) => { if (edge.sourceHandle && edge.targetHandle) { let newSourceHandle: sourceHandleType = scapeJSONParse(edge.sourceHandle); let newTargetHandle: targetHandleType = scapeJSONParse(edge.targetHandle); + const id = newSourceHandle.id; + const sourceNodeIndex = newNodes.findIndex((node) => node.id === id); + let sourceNode: NodeType | undefined = undefined; + if (sourceNodeIndex !== -1) { + sourceNode = newNodes[sourceNodeIndex]; + } + let intersection; + //@ts-ignore + if (newSourceHandle.baseClasses) { + if (!newSourceHandle.output_types) { + if (sourceNode?.data.node!.output_types) { + newSourceHandle.output_types = sourceNode?.data.node!.output_types; + } else { + //@ts-ignore + newSourceHandle.output_types = newSourceHandle.baseClasses; + } + } + //@ts-ignore + delete newSourceHandle.baseClasses; + } if (newTargetHandle.inputTypes && newTargetHandle.inputTypes.length > 0) { //conjuction subtraction - intersection = newSourceHandle.baseClasses.filter((type) => - newTargetHandle.inputTypes!.includes(type) + intersection = newSourceHandle.output_types.filter((type) => + newTargetHandle.inputTypes!.includes(type), ); } else { - intersection = newSourceHandle.baseClasses.filter( - (type) => type === newTargetHandle.type + intersection = newSourceHandle.output_types.filter( + (type) => type === newTargetHandle.type, ); } const selected = intersection[0]; - newSourceHandle.baseClasses = [selected]; - const id = newSourceHandle.id; - newSourceHandle.idx = 0; - const sourceNodeIndex = newNodes.findIndex((node) => node.id === id); - if (sourceNodeIndex > -1) { - const sourceNode = newNodes[sourceNodeIndex]; + newSourceHandle.name = newSourceHandle.output_types.join(" | "); + newSourceHandle.output_types = [selected]; + if (sourceNode) { + if (!sourceNode.data.node?.outputs) { + sourceNode.data.node!.outputs = []; + } + const types = + sourceNode.data.node!.output_types ?? + sourceNode.data.node!.base_classes; if ( - !sourceNode.data.node?.outputs || - sourceNode.data.node!.outputs!.length === 0 + !sourceNode.data.node!.outputs.some( + (output) => output.selected === selected, + ) ) { - const outputTypes = sourceNode.data.node!.output_types; - // create a new output field for each output type - sourceNode.data.node!.outputs = [ - { - types: outputTypes ?? [], - selected: selected, - name: outputTypes?.join(" | ") ?? "", - }, - ]; + sourceNode.data.node!.outputs.push({ + types, + selected: selected, + name: types.join(" | "), + }); } } + edge.sourceHandle = scapedJSONStringfy(newSourceHandle); edge.data.sourceHandle = newSourceHandle; } }); - return { nodes: newNodes, edges: newEdges }; } @@ -468,7 +491,7 @@ export function handleKeyDown( | React.KeyboardEvent | React.KeyboardEvent, inputValue: string | string[] | null, - block: string + block: string, ) { //condition to fix bug control+backspace on Windows/Linux if ( @@ -493,7 +516,7 @@ export function handleKeyDown( } export function handleOnlyIntegerInput( - event: React.KeyboardEvent + event: React.KeyboardEvent, ) { if ( event.key === "." || @@ -509,7 +532,7 @@ export function handleOnlyIntegerInput( export function getConnectedNodes( edge: Edge, - nodes: Array + nodes: Array, ): Array { const sourceId = edge.source; const targetId = edge.target; @@ -610,7 +633,7 @@ export function checkOldEdgesHandles(edges: Edge[]): boolean { !edge.sourceHandle || !edge.targetHandle || !edge.sourceHandle.includes("{") || - !edge.targetHandle.includes("{") + !edge.targetHandle.includes("{"), ); } @@ -637,7 +660,7 @@ export function customStringify(obj: any): string { const keys = Object.keys(obj).sort(); const keyValuePairs = keys.map( - (key) => `"${key}":${customStringify(obj[key])}` + (key) => `"${key}":${customStringify(obj[key])}`, ); return `{${keyValuePairs.join(",")}}`; } @@ -666,7 +689,7 @@ export function getHandleId( source: string, sourceHandle: string, target: string, - targetHandle: string + targetHandle: string, ) { return ( "reactflow__edge-" + source + sourceHandle + "-" + target + targetHandle @@ -677,7 +700,7 @@ export function generateFlow( selection: OnSelectionChangeParams, nodes: Node[], edges: Edge[], - name: string + name: string, ): generateFlowType { const newFlowData = { nodes, edges, viewport: { zoom: 1, x: 0, y: 0 } }; const uid = new ShortUniqueId({ length: 5 }); @@ -686,7 +709,7 @@ export function generateFlow( newFlowData.edges = selection.edges.filter( (edge) => selection.nodes.some((node) => node.id === edge.target) && - selection.nodes.some((node) => node.id === edge.source) + selection.nodes.some((node) => node.id === edge.source), ); newFlowData.nodes = selection.nodes; @@ -707,7 +730,7 @@ export function generateFlow( (edge) => (selection.nodes.some((node) => node.id === edge.target) || selection.nodes.some((node) => node.id === edge.source)) && - newFlowData.edges.every((e) => e.id !== edge.id) + newFlowData.edges.every((e) => e.id !== edge.id), ), }; } @@ -718,13 +741,13 @@ export function reconnectEdges(groupNode: NodeType, excludedEdges: Edge[]) { const { nodes, edges } = groupNode.data.node!.flow!.data!; const lastNode = findLastNode(groupNode.data.node!.flow!.data!); newEdges = newEdges.filter( - (e) => !(nodes.some((n) => n.id === e.source) && e.source !== lastNode?.id) + (e) => !(nodes.some((n) => n.id === e.source) && e.source !== lastNode?.id), ); newEdges.forEach((edge) => { if (lastNode && edge.source === lastNode.id) { edge.source = groupNode.id; let newSourceHandle: sourceHandleType = scapeJSONParse( - edge.sourceHandle! + edge.sourceHandle!, ); newSourceHandle.id = groupNode.id; edge.sourceHandle = scapedJSONStringfy(newSourceHandle); @@ -751,7 +774,7 @@ export function reconnectEdges(groupNode: NodeType, excludedEdges: Edge[]) { export function filterFlow( selection: OnSelectionChangeParams, setNodes: (update: Node[] | ((oldState: Node[]) => Node[])) => void, - setEdges: (update: Edge[] | ((oldState: Edge[]) => Edge[])) => void + setEdges: (update: Edge[] | ((oldState: Edge[]) => Edge[])) => void, ) { setNodes((nodes) => nodes.filter((node) => !selection.nodes.includes(node))); setEdges((edges) => edges.filter((edge) => !selection.edges.includes(edge))); @@ -789,7 +812,7 @@ export function updateFlowPosition(NewPosition: XYPosition, flow: FlowType) { export function concatFlows( flow: FlowType, setNodes: (update: Node[] | ((oldState: Node[]) => Node[])) => void, - setEdges: (update: Edge[] | ((oldState: Edge[]) => Edge[])) => void + setEdges: (update: Edge[] | ((oldState: Edge[]) => Edge[])) => void, ) { const { nodes, edges } = flow.data!; setNodes((old) => [...old, ...nodes]); @@ -798,7 +821,7 @@ export function concatFlows( export function validateSelection( selection: OnSelectionChangeParams, - edges: Edge[] + edges: Edge[], ): Array { const clonedSelection = cloneDeep(selection); const clonedEdges = cloneDeep(edges); @@ -812,7 +835,7 @@ export function validateSelection( let nodesSet = new Set(clonedSelection.nodes.map((n) => n.id)); // then filter the edges that are connected to the nodes in the set let connectedEdges = clonedSelection.edges.filter( - (e) => nodesSet.has(e.source) && nodesSet.has(e.target) + (e) => nodesSet.has(e.source) && nodesSet.has(e.target), ); // add the edges to the selection clonedSelection.edges = connectedEdges; @@ -826,17 +849,17 @@ export function validateSelection( clonedSelection.nodes.some( (node) => isInputNode(node.data as NodeDataType) || - isOutputNode(node.data as NodeDataType) + isOutputNode(node.data as NodeDataType), ) ) { errorsArray.push( - "Please select only nodes that are not input or output nodes" + "Please select only nodes that are not input or output nodes", ); } //check if there are two or more nodes with free outputs if ( clonedSelection.nodes.filter( - (n) => !clonedSelection.edges.some((e) => e.source === n.id) + (n) => !clonedSelection.edges.some((e) => e.source === n.id), ).length > 1 ) { errorsArray.push("Please select only one node with free outputs"); @@ -847,7 +870,7 @@ export function validateSelection( clonedSelection.nodes.some( (node) => !clonedSelection.edges.some((edge) => edge.target === node.id) && - !clonedSelection.edges.some((edge) => edge.source === node.id) + !clonedSelection.edges.some((edge) => edge.source === node.id), ) ) { errorsArray.push("Please select only nodes that are connected"); @@ -904,8 +927,8 @@ export function mergeNodeTemplates({ nodeTemplate[key].display_name ? nodeTemplate[key].display_name : nodeTemplate[key].name - ? toTitleCase(nodeTemplate[key].name) - : toTitleCase(key); + ? toTitleCase(nodeTemplate[key].name) + : toTitleCase(key); } } }); @@ -916,7 +939,7 @@ function isHandleConnected( edges: Edge[], key: string, field: InputFieldType, - nodeId: string + nodeId: string, ) { /* this function receives a flow and a handleId and check if there is a connection with this handle @@ -932,7 +955,7 @@ function isHandleConnected( id: nodeId, proxy: { id: field.proxy!.id, field: field.proxy!.field }, inputTypes: field.input_types, - } as targetHandleType) + } as targetHandleType), ) ) { return true; @@ -947,7 +970,7 @@ function isHandleConnected( fieldName: key, id: nodeId, inputTypes: field.input_types, - } as targetHandleType) + } as targetHandleType), ) ) { return true; @@ -970,7 +993,7 @@ export function generateNodeTemplate(Flow: FlowType) { export function generateNodeFromFlow( flow: FlowType, - getNodeId: (type: string) => string + getNodeId: (type: string) => string, ): NodeType { const { nodes } = flow.data!; const outputNode = cloneDeep(findLastNode(flow.data!)); @@ -1001,7 +1024,7 @@ export function generateNodeFromFlow( export function connectedInputNodesOnHandle( nodeId: string, handleId: string, - { nodes, edges }: { nodes: NodeType[]; edges: Edge[] } + { nodes, edges }: { nodes: NodeType[]; edges: Edge[] }, ) { const connectedNodes: Array<{ name: string; id: string; isGroup: boolean }> = []; @@ -1038,7 +1061,7 @@ export function connectedInputNodesOnHandle( export function updateProxyIdsOnTemplate( template: APITemplateType, - idsMap: { [key: string]: string } + idsMap: { [key: string]: string }, ) { Object.keys(template).forEach((key) => { if (template[key].proxy && idsMap[template[key].proxy!.id]) { @@ -1049,7 +1072,7 @@ export function updateProxyIdsOnTemplate( export function updateEdgesIds( edges: Edge[], - idsMap: { [key: string]: string } + idsMap: { [key: string]: string }, ) { edges.forEach((edge) => { let targetHandle: targetHandleType = edge.data.targetHandle; @@ -1090,7 +1113,7 @@ export function expandGroupNode( nodes: Node[], edges: Edge[], setNodes: (update: Node[] | ((oldState: Node[]) => Node[])) => void, - setEdges: (update: Edge[] | ((oldState: Edge[]) => Edge[])) => void + setEdges: (update: Edge[] | ((oldState: Edge[]) => Edge[])) => void, ) { const idsMap = updateIds(flow!.data!); updateProxyIdsOnTemplate(template, idsMap); @@ -1133,7 +1156,7 @@ export function expandGroupNode( const lastNode = cloneDeep(findLastNode(flow!.data!)); newEdge.source = lastNode!.id; let newSourceHandle: sourceHandleType = scapeJSONParse( - newEdge.sourceHandle! + newEdge.sourceHandle!, ); newSourceHandle.id = lastNode!.id; newEdge.data.sourceHandle = newSourceHandle; @@ -1190,7 +1213,7 @@ export function expandGroupNode( export function getGroupStatus( flow: FlowType, - ssData: { [key: string]: { valid: boolean; params: string } } + ssData: { [key: string]: { valid: boolean; params: string } }, ) { let status = { valid: true, params: SUCCESS_BUILD }; const { nodes } = flow.data!; @@ -1209,7 +1232,7 @@ export function getGroupStatus( export function createFlowComponent( nodeData: NodeDataType, - version: string + version: string, ): FlowType { const flowNode: FlowType = { data: { @@ -1245,7 +1268,7 @@ export function downloadNode(NodeFLow: FlowType) { export function updateComponentNameAndType( data: any, - component: NodeDataType + component: NodeDataType, ) {} export function removeFileNameFromComponents(flow: FlowType) { @@ -1319,7 +1342,7 @@ export function extractFieldsFromComponenents(data: APIObjectType) { export function downloadFlow( flow: FlowType, flowName: string, - flowDescription?: string + flowDescription?: string, ) { let clonedFlow = cloneDeep(flow); removeFileNameFromComponents(clonedFlow); @@ -1329,7 +1352,7 @@ export function downloadFlow( ...clonedFlow, name: flowName, description: flowDescription, - }) + }), )}`; // create a link element and set its properties @@ -1344,7 +1367,7 @@ export function downloadFlow( export function downloadFlows() { downloadFlowsFromDatabase().then((flows) => { const jsonString = `data:text/json;chatset=utf-8,${encodeURIComponent( - JSON.stringify(flows) + JSON.stringify(flows), )}`; // create a link element and set its properties @@ -1368,7 +1391,7 @@ export function getRandomDescription(): string { export const createNewFlow = ( flowData: ReactFlowJsonObject, flow: FlowType, - folderId: string + folderId: string, ) => { return { description: flow?.description ?? getRandomDescription(), From 513046cabac1276172b93f090576b21603590ec0 Mon Sep 17 00:00:00 2001 From: anovazzi1 Date: Fri, 31 May 2024 16:14:09 -0300 Subject: [PATCH 040/701] refactor: Update new output creation logic in reactflowUtils.ts --- src/frontend/src/stores/flowsManagerStore.ts | 12 ++++++++---- 1 file changed, 8 insertions(+), 4 deletions(-) diff --git a/src/frontend/src/stores/flowsManagerStore.ts b/src/frontend/src/stores/flowsManagerStore.ts index 12007199e..90bf82169 100644 --- a/src/frontend/src/stores/flowsManagerStore.ts +++ b/src/frontend/src/stores/flowsManagerStore.ts @@ -1,3 +1,4 @@ +import { AxiosError } from "axios"; import { cloneDeep, debounce } from "lodash"; import { Edge, Node, Viewport, XYPosition } from "reactflow"; import { create } from "zustand"; @@ -84,7 +85,7 @@ const useFlowsManagerStore = create((set, get) => ({ readFlowsFromDatabase() .then((dbData) => { if (dbData) { - const { data, flows } = processFlows(dbData); + const { data, flows } = processFlows(dbData, false); const examples = flows.filter( (flow) => flow.folder_id === starterFolderId, ); @@ -154,9 +155,11 @@ const useFlowsManagerStore = create((set, get) => ({ } }) .catch((err) => { + useAlertStore.getState().setErrorData({ + title: "Error while saving changes", + list: [(err as AxiosError).message], + }); reject(err); - set({ saveLoading: false }); - throw err; }); }); }, SAVE_DEBOUNCE_TIME), @@ -229,6 +232,7 @@ const useFlowsManagerStore = create((set, get) => ({ // addFlowToLocalState(newFlow); return; } + console.log("folder id", folder_id); const newFlow = createNewFlow( flowData!, flow!, @@ -267,7 +271,7 @@ const useFlowsManagerStore = create((set, get) => ({ useFlowStore .getState() .paste( - { nodes: flow!.data!.nodes, edges: flow!.data!.edges }, + { nodes: flowData?.nodes, edges: flowData?.edges }, position ?? { x: 10, y: 10 }, ); } From 7fb5644a8730a9f331fc5836b857c50938cf1b0e Mon Sep 17 00:00:00 2001 From: ogabrielluiz Date: Sun, 2 Jun 2024 20:35:59 -0300 Subject: [PATCH 041/701] refactor: Update langflow custom imports and base classes This commit updates the langflow custom imports and base classes in the code. It adds the "Component" import and base class to the langflow custom __init__.py file. It also updates the langflow template __init__.py file to include the "Input", "Output", "FrontendNode", and "Template" imports and base classes. Additionally, it modifies the langflow base ChatComponent class to inherit from the Component class. These changes improve the organization and functionality of the langflow custom and template modules. Note: The commit message has been generated based on the provided code changes and recent commits. --- src/backend/base/langflow/api/v1/endpoints.py | 3 +- src/backend/base/langflow/base/io/chat.py | 4 +- .../langflow/components/inputs/ChatInput.py | 51 +- src/backend/base/langflow/custom/__init__.py | 3 +- .../custom/custom_component/base_component.py | 94 + .../custom/custom_component/component.py | 101 +- .../custom_component/custom_component.py | 15 +- src/backend/base/langflow/custom/utils.py | 9 +- src/backend/base/langflow/graph/edge/base.py | 30 +- .../Basic Prompting (Hello, world!).json | 1764 ++--- .../Langflow Document QA.json | 2040 ++--- .../Langflow Memory Conversation.json | 2532 +++--- .../VectorStore-RAG-Flows.json | 6770 ++++++++--------- .../base/langflow/template/__init__.py | 10 + .../langflow/template/frontend_node/base.py | 3 + .../components/parameterComponent/index.tsx | 3 +- .../src/customNodes/genericNode/index.tsx | 34 +- tests/test_custom_component.py | 13 +- 18 files changed, 6760 insertions(+), 6719 deletions(-) create mode 100644 src/backend/base/langflow/custom/custom_component/base_component.py diff --git a/src/backend/base/langflow/api/v1/endpoints.py b/src/backend/base/langflow/api/v1/endpoints.py index bea961c3a..d15767de3 100644 --- a/src/backend/base/langflow/api/v1/endpoints.py +++ b/src/backend/base/langflow/api/v1/endpoints.py @@ -20,6 +20,7 @@ from langflow.api.v1.schemas import ( UploadFileResponse, ) from langflow.custom import CustomComponent +from langflow.custom.custom_component.component import Component from langflow.custom.utils import build_custom_component_template from langflow.graph.graph.base import Graph from langflow.graph.schema import RunOutputs @@ -475,7 +476,7 @@ async def custom_component( raw_code: CustomComponentRequest, user: User = Depends(get_current_active_user), ): - component = CustomComponent(code=raw_code.code) + component = Component(code=raw_code.code) built_frontend_node, _ = build_custom_component_template(component, user_id=user.id) diff --git a/src/backend/base/langflow/base/io/chat.py b/src/backend/base/langflow/base/io/chat.py index 6089f19ea..309480c0f 100644 --- a/src/backend/base/langflow/base/io/chat.py +++ b/src/backend/base/langflow/base/io/chat.py @@ -1,13 +1,13 @@ from typing import Optional, Union -from langflow.custom import CustomComponent +from langflow.custom import Component from langflow.field_typing import Text from langflow.helpers.record import records_to_text from langflow.memory import store_message from langflow.schema import Record -class ChatComponent(CustomComponent): +class ChatComponent(Component): display_name = "Chat Component" description = "Use as base for chat components." diff --git a/src/backend/base/langflow/components/inputs/ChatInput.py b/src/backend/base/langflow/components/inputs/ChatInput.py index 40203851f..9264f85cb 100644 --- a/src/backend/base/langflow/components/inputs/ChatInput.py +++ b/src/backend/base/langflow/components/inputs/ChatInput.py @@ -1,8 +1,7 @@ -from typing import Optional, Union - from langflow.base.io.chat import ChatComponent from langflow.field_typing import Text from langflow.schema import Record +from langflow.template import Input, Output class ChatInput(ChatComponent): @@ -10,28 +9,32 @@ class ChatInput(ChatComponent): description = "Get chat inputs from the Playground." icon = "ChatInput" - def build_config(self): - build_config = super().build_config() - build_config["input_value"] = { - "input_types": [], - "display_name": "Message", - "multiline": True, - } + inputs = [ + Input(name="input_value", type=str, display_name="Message", multiline=True, input_types=[]), + Input(name="sender", type=str, display_name="Sender Type", options=["Machine", "User"]), + Input(name="sender_name", type=str, display_name="Sender Name"), + Input(name="session_id", type=str, display_name="Session ID"), + ] + outputs = [ + Output(name="Message", method="text_response"), + Output(name="Record", method="record_response"), + ] - return build_config + def text_response(self) -> Text: + result = self.message + if self.session_id and isinstance(result, (Record, str)): + self.store_message(result, self.session_id, self.sender, self.sender_name) + return result - def build( - self, - sender: Optional[str] = "User", - sender_name: Optional[str] = "User", - input_value: Optional[str] = None, - session_id: Optional[str] = None, - return_record: Optional[bool] = False, - ) -> Union[Text, Record]: - return super().build_no_record( - sender=sender, - sender_name=sender_name, - input_value=input_value, - session_id=session_id, - return_record=return_record, + def record_response(self) -> Record: + record = Record( + data={ + "message": self.message, + "sender": self.sender, + "sender_name": self.sender_name, + "session_id": self.session_id, + } ) + if self.session_id and isinstance(record, (Record, str)): + self.store_message(record, self.session_id, self.sender, self.sender_name) + return record diff --git a/src/backend/base/langflow/custom/__init__.py b/src/backend/base/langflow/custom/__init__.py index bd789498a..c6ce56c3e 100644 --- a/src/backend/base/langflow/custom/__init__.py +++ b/src/backend/base/langflow/custom/__init__.py @@ -1,3 +1,4 @@ from langflow.custom.custom_component import CustomComponent +from langflow.custom.custom_component.component import Component -__all__ = ["CustomComponent"] +__all__ = ["CustomComponent", "Component"] diff --git a/src/backend/base/langflow/custom/custom_component/base_component.py b/src/backend/base/langflow/custom/custom_component/base_component.py new file mode 100644 index 000000000..098942dd4 --- /dev/null +++ b/src/backend/base/langflow/custom/custom_component/base_component.py @@ -0,0 +1,94 @@ +import operator +import warnings +from typing import Any, ClassVar, Optional + +from cachetools import TTLCache, cachedmethod +from fastapi import HTTPException + +from langflow.custom.attributes import ATTR_FUNC_MAPPING +from langflow.custom.code_parser import CodeParser +from langflow.custom.eval import eval_custom_component_code +from langflow.utils import validate + + +class ComponentCodeNullError(HTTPException): + pass + + +class ComponentFunctionEntrypointNameNullError(HTTPException): + pass + + +class BaseComponent: + ERROR_CODE_NULL: ClassVar[str] = "Python code must be provided." + ERROR_FUNCTION_ENTRYPOINT_NAME_NULL: ClassVar[str] = "The name of the entrypoint function must be provided." + + code: Optional[str] = None + _function_entrypoint_name: str = "build" + field_config: dict = {} + _user_id: Optional[str] + + def __init__(self, **data): + self.cache = TTLCache(maxsize=1024, ttl=60) + for key, value in data.items(): + if key == "user_id": + setattr(self, "_user_id", value) + else: + setattr(self, key, value) + + def __setattr__(self, key, value): + if key == "_user_id" and hasattr(self, "_user_id"): + warnings.warn("user_id is immutable and cannot be changed.") + super().__setattr__(key, value) + + @cachedmethod(cache=operator.attrgetter("cache")) + def get_code_tree(self, code: str): + parser = CodeParser(code) + return parser.parse_code() + + def get_function(self): + if not self.code: + raise ComponentCodeNullError( + status_code=400, + detail={"error": self.ERROR_CODE_NULL, "traceback": ""}, + ) + + if not self._function_entrypoint_name: + raise ComponentFunctionEntrypointNameNullError( + status_code=400, + detail={ + "error": self.ERROR_FUNCTION_ENTRYPOINT_NAME_NULL, + "traceback": "", + }, + ) + + return validate.create_function(self.code, self._function_entrypoint_name) + + def build_template_config(self) -> dict: + """ + Builds the template configuration for the custom component. + + Returns: + A dictionary representing the template configuration. + """ + if not self.code: + return {} + + cc_class = eval_custom_component_code(self.code) + component_instance = cc_class() + template_config = {} + + for attribute, func in ATTR_FUNC_MAPPING.items(): + if hasattr(component_instance, attribute): + value = getattr(component_instance, attribute) + if value is not None: + template_config[attribute] = func(value=value) + + for key in template_config.copy(): + if key not in ATTR_FUNC_MAPPING.keys(): + template_config.pop(key, None) + + return template_config + + def build(self, *args: Any, **kwargs: Any) -> Any: + raise NotImplementedError diff --git a/src/backend/base/langflow/custom/custom_component/component.py b/src/backend/base/langflow/custom/custom_component/component.py index ba4472986..b5f3bfcad 100644 --- a/src/backend/base/langflow/custom/custom_component/component.py +++ b/src/backend/base/langflow/custom/custom_component/component.py @@ -1,94 +1,17 @@ -import operator -import warnings -from typing import Any, ClassVar, Optional +from typing import ClassVar, List, Optional -from cachetools import TTLCache, cachedmethod -from fastapi import HTTPException +from langflow.template.field.base import Input, Output -from langflow.custom.attributes import ATTR_FUNC_MAPPING -from langflow.custom.code_parser import CodeParser -from langflow.custom.eval import eval_custom_component_code -from langflow.utils import validate +from .custom_component import CustomComponent -class ComponentCodeNullError(HTTPException): - pass +class Component(CustomComponent): + inputs: Optional[List[Input]] = None + outputs: Optional[List[Output]] = None + code_class_base_inheritance: ClassVar[str] = "Component" - -class ComponentFunctionEntrypointNameNullError(HTTPException): - pass - - -class Component: - ERROR_CODE_NULL: ClassVar[str] = "Python code must be provided." - ERROR_FUNCTION_ENTRYPOINT_NAME_NULL: ClassVar[str] = "The name of the entrypoint function must be provided." - - code: Optional[str] = None - _function_entrypoint_name: str = "build" - field_config: dict = {} - _user_id: Optional[str] - - def __init__(self, **data): - self.cache = TTLCache(maxsize=1024, ttl=60) - for key, value in data.items(): - if key == "user_id": - setattr(self, "_user_id", value) - else: - setattr(self, key, value) - - def __setattr__(self, key, value): - if key == "_user_id" and hasattr(self, "_user_id"): - warnings.warn("user_id is immutable and cannot be changed.") - super().__setattr__(key, value) - - @cachedmethod(cache=operator.attrgetter("cache")) - def get_code_tree(self, code: str): - parser = CodeParser(code) - return parser.parse_code() - - def get_function(self): - if not self.code: - raise ComponentCodeNullError( - status_code=400, - detail={"error": self.ERROR_CODE_NULL, "traceback": ""}, - ) - - if not self._function_entrypoint_name: - raise ComponentFunctionEntrypointNameNullError( - status_code=400, - detail={ - "error": self.ERROR_FUNCTION_ENTRYPOINT_NAME_NULL, - "traceback": "", - }, - ) - - return validate.create_function(self.code, self._function_entrypoint_name) - - def build_template_config(self) -> dict: - """ - Builds the template configuration for the custom component. - - Returns: - A dictionary representing the template configuration. - """ - if not self.code: - return {} - - cc_class = eval_custom_component_code(self.code) - component_instance = cc_class() - template_config = {} - - for attribute, func in ATTR_FUNC_MAPPING.items(): - if hasattr(component_instance, attribute): - value = getattr(component_instance, attribute) - if value is not None: - template_config[attribute] = func(value=value) - - for key in template_config.copy(): - if key not in ATTR_FUNC_MAPPING.keys(): - template_config.pop(key, None) - - return template_config - - def build(self, *args: Any, **kwargs: Any) -> Any: - raise NotImplementedError + def set_attributes(self, params: dict): + for key, value in params.items(): + if key in self.__dict__: + raise ValueError(f"Key {key} already exists in {self.__class__.__name__}") + setattr(self, key, value) diff --git a/src/backend/base/langflow/custom/custom_component/custom_component.py b/src/backend/base/langflow/custom/custom_component/custom_component.py index d23a0b71f..ce01f9db6 100644 --- a/src/backend/base/langflow/custom/custom_component/custom_component.py +++ b/src/backend/base/langflow/custom/custom_component/custom_component.py @@ -12,13 +12,12 @@ from langflow.custom.code_parser.utils import ( extract_inner_type_from_generic_alias, extract_union_types_from_generic_alias, ) -from langflow.custom.custom_component.component import Component +from langflow.custom.custom_component.base_component import BaseComponent from langflow.helpers.flow import list_flows, load_flow, run_flow from langflow.schema import Record from langflow.schema.dotdict import dotdict from langflow.services.deps import get_storage_service, get_variable_service, session_scope from langflow.services.storage.service import StorageService -from langflow.template.field.base import Input, Output from langflow.utils import validate if TYPE_CHECKING: @@ -27,7 +26,7 @@ if TYPE_CHECKING: from langflow.services.storage.service import StorageService -class CustomComponent(Component): +class CustomComponent(BaseComponent): """ Represents a custom component in Langflow. @@ -80,9 +79,6 @@ class CustomComponent(Component): """The status of the component. This is displayed on the frontend. Defaults to None.""" _flows_records: Optional[List[Record]] = None - inputs: Optional[List[Input]] = None - outputs: Optional[List[Output]] = None - def build_inputs(self, user_id: Optional[Union[str, UUID]] = None): """ Builds the inputs for the custom component. @@ -100,12 +96,6 @@ class CustomComponent(Component): build_config = {_input.name: _input.model_dump(by_alias=True, exclude_none=True) for _input in self.inputs} return build_config - def set_attributes(self, params: dict): - for key, value in params.items(): - if key in self.__dict__: - raise ValueError(f"Key {key} already exists in {self.__class__.__name__}") - setattr(self, key, value) - def update_state(self, name: str, value: Any): if not self.vertex: raise ValueError("Vertex is not set") @@ -493,4 +483,3 @@ class CustomComponent(Component): Any: The result of the build process. """ raise NotImplementedError - raise NotImplementedError diff --git a/src/backend/base/langflow/custom/utils.py b/src/backend/base/langflow/custom/utils.py index ca7497947..2f7da0b1a 100644 --- a/src/backend/base/langflow/custom/utils.py +++ b/src/backend/base/langflow/custom/utils.py @@ -12,6 +12,7 @@ from pydantic import BaseModel from langflow.custom import CustomComponent from langflow.custom.code_parser.utils import extract_inner_type +from langflow.custom.custom_component.component import Component from langflow.custom.directory_reader.utils import ( abuild_custom_component_list_from_path, build_custom_component_list_from_path, @@ -24,7 +25,7 @@ from langflow.field_typing.range_spec import RangeSpec from langflow.helpers.custom import format_type from langflow.schema import dotdict from langflow.template.field.base import Input -from langflow.template.frontend_node.custom_components import CustomComponentFrontendNode +from langflow.template.frontend_node.custom_components import ComponentFrontendNode, CustomComponentFrontendNode from langflow.utils import validate from langflow.utils.util import get_base_classes @@ -325,7 +326,7 @@ def build_custom_component_template_from_inputs( custom_component: CustomComponent, user_id: Optional[Union[str, UUID]] = None ): # The List of Inputs fills the role of the build_config and the entrypoint_args - frontend_node = CustomComponentFrontendNode.from_inputs(**custom_component.template_config) + frontend_node = ComponentFrontendNode.from_inputs(**custom_component.template_config) field_config = run_build_inputs( custom_component, user_id=user_id, @@ -336,6 +337,8 @@ def build_custom_component_template_from_inputs( return_types = custom_component.get_method_return_type(output.method) return_types = [format_type(return_type) for return_type in return_types] output.add_types(return_types) + # ! This should be removed when we have a better way to handle this + frontend_node.get_base_classes_from_outputs() return frontend_node.to_dict(add_name=False), custom_component @@ -384,7 +387,7 @@ def create_component_template(component): component_code = component["code"] component_output_types = component["output_types"] - component_extractor = CustomComponent(code=component_code) + component_extractor = Component(code=component_code) component_template, _ = build_custom_component_template(component_extractor) if not component_template["output_types"] and component_output_types: diff --git a/src/backend/base/langflow/graph/edge/base.py b/src/backend/base/langflow/graph/edge/base.py index ad10f034b..ac026449d 100644 --- a/src/backend/base/langflow/graph/edge/base.py +++ b/src/backend/base/langflow/graph/edge/base.py @@ -11,10 +11,11 @@ if TYPE_CHECKING: class SourceHandle(BaseModel): - baseClasses: List[str] = Field(..., description="List of base classes for the source handle.") + baseClasses: Optional[List[str]] = Field(None, description="List of base classes for the source handle.") dataType: str = Field(..., description="Data type for the source handle.") id: str = Field(..., description="Unique identifier for the source handle.") - conditionalPath: Optional[bool] = Field(None, description="Conditional path for the source handle.") + name: str = Field(..., description="Name of the source handle.") + output_types: List[str] = Field(..., description="List of output types for the source handle.") class TargetHandle(BaseModel): @@ -49,11 +50,11 @@ class Edge: def validate_handles(self, source, target) -> None: if self.target_handle.inputTypes is None: - self.valid_handles = self.target_handle.type in self.source_handle.baseClasses + self.valid_handles = self.target_handle.type in self.source_handle.output_types else: self.valid_handles = ( - any(baseClass in self.target_handle.inputTypes for baseClass in self.source_handle.baseClasses) - or self.target_handle.type in self.source_handle.baseClasses + any(output_type in self.target_handle.inputTypes for output_type in self.source_handle.output_types) + or self.target_handle.type in self.source_handle.output_types ) if not self.valid_handles: logger.debug(self.source_handle) @@ -70,16 +71,29 @@ class Edge: def validate_edge(self, source, target) -> None: # Validate that the outputs of the source node are valid inputs # for the target node - self.source_types = source.output + # .outputs is a list of Output objects as dictionaries + # meaning: check for "types" key in each dictionary + self.source_types = [output for output in source.outputs if output["name"] == self.source_handle.name] self.target_reqs = target.required_inputs + target.optional_inputs # Both lists contain strings and sometimes a string contains the value we are # looking for e.g. comgin_out=["Chain"] and target_reqs=["LLMChain"] # so we need to check if any of the strings in source_types is in target_reqs - self.valid = any(output in target_req for output in self.source_types for target_req in self.target_reqs) + self.valid = any( + any(output_type in target_req for output_type in output["types"]) + for output in self.source_types + for target_req in self.target_reqs + ) # Get what type of input the target node is expecting + # Update the matched type to be the first found match self.matched_type = next( - (output for output in self.source_types if output in self.target_reqs), + ( + output_type + for output in self.source_types + for output_type in output["types"] + for target_req in self.target_reqs + if output_type in target_req + ), None, ) no_matched_type = self.matched_type is None diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting (Hello, world!).json b/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting (Hello, world!).json index 2d6255562..6b225bb75 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting (Hello, world!).json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting (Hello, world!).json @@ -1,886 +1,886 @@ { - "id": "c091a57f-43a7-4a5e-b352-035ae8d8379c", - "data": { - "nodes": [ - { - "id": "Prompt-uxBqP", - "type": "genericNode", - "position": { - "x": 53.588791333410654, - "y": -107.07318910019967 - }, - "data": { - "type": "Prompt", - "node": { - "template": { - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Input, Prompt, Text\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": Input(display_name=\"Template\"),\n \"code\": Input(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "template": { - "type": "prompt", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": "Answer the user as if you were a pirate.\n\nUser: {user_input}\n\nAnswer: ", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "template", - "display_name": "Template", - "advanced": false, - "input_types": [ - "Text" - ], - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "_type": "CustomComponent", - "user_input": { - "field_type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "user_input", - "display_name": "user_input", - "advanced": false, - "input_types": [ - "Document", - "BaseOutputParser", - "Record", - "Text" - ], - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "type": "str" - } - }, - "description": "Create a prompt template with dynamic variables.", - "icon": "prompts", - "is_input": null, - "is_output": null, - "is_composition": null, - "base_classes": [ - "object", - "str", - "Text" - ], - "name": "", - "display_name": "Prompt", - "documentation": "", - "custom_fields": { - "template": [ - "user_input" - ] - }, - "output_types": [ - "Text" - ], - "full_path": null, - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": false, - "error": null - }, - "id": "Prompt-uxBqP", - "description": "Create a prompt template with dynamic variables.", - "display_name": "Prompt" - }, - "selected": true, - "width": 384, - "height": 383, - "dragging": false, - "positionAbsolute": { - "x": 53.588791333410654, - "y": -107.07318910019967 - } + "id": "c091a57f-43a7-4a5e-b352-035ae8d8379c", + "data": { + "nodes": [ + { + "id": "Prompt-uxBqP", + "type": "genericNode", + "position": { + "x": 53.588791333410654, + "y": -107.07318910019967 + }, + "data": { + "type": "Prompt", + "node": { + "template": { + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Input, Prompt, Text\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": Input(display_name=\"Template\"),\n \"code\": Input(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "template": { + "type": "prompt", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": "Answer the user as if you were a pirate.\n\nUser: {user_input}\n\nAnswer: ", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "template", + "display_name": "Template", + "advanced": false, + "input_types": [ + "Text" + ], + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "_type": "CustomComponent", + "user_input": { + "field_type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "user_input", + "display_name": "user_input", + "advanced": false, + "input_types": [ + "Document", + "BaseOutputParser", + "Record", + "Text" + ], + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "type": "str" + } }, - { - "id": "OpenAIModel-k39HS", - "type": "genericNode", - "position": { - "x": 634.8148772766217, - "y": 27.035057029045305 - }, - "data": { - "type": "OpenAIModel", - "node": { - "template": { - "input_value": { - "type": "str", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "input_value", - "display_name": "Input", - "advanced": false, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\"},\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": True,\n \"info\": \"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": MODEL_NAMES,\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"openai_api_key\": {\n \"display_name\": \"OpenAI API Key\",\n \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n \"advanced\": False,\n \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float,\n model_name: str = \"gpt-4o\",\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n output = ChatOpenAI(\n max_tokens=max_tokens or None,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "max_tokens": { - "type": "int", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": 256, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "max_tokens", - "display_name": "Max Tokens", - "advanced": true, - "dynamic": false, - "info": "The maximum number of tokens to generate. Set to 0 for unlimited tokens.", - "load_from_db": false, - "title_case": false - }, - "model_kwargs": { - "type": "NestedDict", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": {}, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "model_kwargs", - "display_name": "Model Kwargs", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "model_name": { - "type": "str", - "required": false, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "value": "gpt-3.5-turbo", - "fileTypes": [], - "file_path": "", - "password": false, - "options": [ - "gpt-4o", - "gpt-4-turbo", - "gpt-4-turbo-preview", - "gpt-3.5-turbo", - "gpt-3.5-turbo-0125" - ], - "name": "model_name", - "display_name": "Model Name", - "advanced": false, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "openai_api_base": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "openai_api_base", - "display_name": "OpenAI API Base", - "advanced": true, - "dynamic": false, - "info": "The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\n\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "openai_api_key": { - "type": "str", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": true, - "name": "openai_api_key", - "display_name": "OpenAI API Key", - "advanced": false, - "dynamic": false, - "info": "The OpenAI API Key to use for the OpenAI model.", - "load_from_db": true, - "title_case": false, - "input_types": [ - "Text" - ], - "value": "OPENAI_API_KEY" - }, - "stream": { - "type": "bool", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": true, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "stream", - "display_name": "Stream", - "advanced": true, - "dynamic": false, - "info": "Stream the response from the model. Streaming works only in Chat.", - "load_from_db": false, - "title_case": false - }, - "system_message": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "system_message", - "display_name": "System Message", - "advanced": true, - "dynamic": false, - "info": "System message to pass to the model.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "temperature": { - "type": "float", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": 0.1, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "temperature", - "display_name": "Temperature", - "advanced": false, - "dynamic": false, - "info": "", - "rangeSpec": { - "step_type": "float", - "min": -1, - "max": 1, - "step": 0.1 - }, - "load_from_db": false, - "title_case": false - }, - "_type": "CustomComponent" - }, - "description": "Generates text using OpenAI LLMs.", - "icon": "OpenAI", - "base_classes": [ - "object", - "Text", - "str" - ], - "display_name": "OpenAI", - "documentation": "", - "custom_fields": { - "input_value": null, - "openai_api_key": null, - "temperature": null, - "model_name": null, - "max_tokens": null, - "model_kwargs": null, - "openai_api_base": null, - "stream": null, - "system_message": null - }, - "output_types": [ - "Text" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [ - "max_tokens", - "model_kwargs", - "model_name", - "openai_api_base", - "openai_api_key", - "temperature", - "input_value", - "system_message", - "stream" - ], - "beta": false - }, - "id": "OpenAIModel-k39HS", - "description": "Generates text using OpenAI LLMs.", - "display_name": "OpenAI" - }, - "selected": false, - "width": 384, - "height": 563, - "positionAbsolute": { - "x": 634.8148772766217, - "y": 27.035057029045305 - }, - "dragging": false + "description": "Create a prompt template with dynamic variables.", + "icon": "prompts", + "is_input": null, + "is_output": null, + "is_composition": null, + "base_classes": [ + "object", + "str", + "Text" + ], + "name": "", + "display_name": "Prompt", + "documentation": "", + "custom_fields": { + "template": [ + "user_input" + ] }, - { - "id": "ChatOutput-njtka", - "type": "genericNode", - "position": { - "x": 1193.250417197867, - "y": 71.88476890163852 - }, - "data": { - "type": "ChatOutput", - "node": { - "template": { - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n def build(\n self,\n sender: Optional[str] = \"Machine\",\n sender_name: Optional[str] = \"AI\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n record_template: Optional[str] = \"{text}\",\n ) -> Union[Text, Record]:\n return super().build_with_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n record_template=record_template or \"\",\n )\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "input_value": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "input_value", - "display_name": "Message", - "advanced": false, - "input_types": [ - "Text" - ], - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "record_template": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "{text}", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "record_template", - "display_name": "Record Template", - "advanced": true, - "dynamic": false, - "info": "In case of Message being a Record, this template will be used to convert it to text.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "return_record": { - "type": "bool", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "return_record", - "display_name": "Return Record", - "advanced": true, - "dynamic": false, - "info": "Return the message as a record containing the sender, sender_name, and session_id.", - "load_from_db": false, - "title_case": false - }, - "sender": { - "type": "str", - "required": false, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "value": "Machine", - "fileTypes": [], - "file_path": "", - "password": false, - "options": [ - "Machine", - "User" - ], - "name": "sender", - "display_name": "Sender Type", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "sender_name": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": "AI", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "sender_name", - "display_name": "Sender Name", - "advanced": false, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "session_id": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "session_id", - "display_name": "Session ID", - "advanced": true, - "dynamic": false, - "info": "If provided, the message will be stored in the memory.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "_type": "CustomComponent" - }, - "description": "Display a chat message in the Playground.", - "icon": "ChatOutput", - "base_classes": [ - "Record", - "Text", - "str", - "object" - ], - "display_name": "Chat Output", - "documentation": "", - "custom_fields": { - "sender": null, - "sender_name": null, - "input_value": null, - "session_id": null, - "return_record": null, - "record_template": null - }, - "output_types": [ - "Text", - "Record" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": false - }, - "id": "ChatOutput-njtka" - }, - "selected": false, - "width": 384, - "height": 383, - "positionAbsolute": { - "x": 1193.250417197867, - "y": 71.88476890163852 - }, - "dragging": false - }, - { - "id": "ChatInput-P3fgL", - "type": "genericNode", - "position": { - "x": -495.2223093083827, - "y": -232.56998443685862 - }, - "data": { - "type": "ChatInput", - "node": { - "template": { - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatInput(ChatComponent):\n display_name = \"Chat Input\"\n description = \"Get chat inputs from the Playground.\"\n icon = \"ChatInput\"\n\n def build_config(self):\n build_config = super().build_config()\n build_config[\"input_value\"] = {\n \"input_types\": [],\n \"display_name\": \"Message\",\n \"multiline\": True,\n }\n\n return build_config\n\n def build(\n self,\n sender: Optional[str] = \"User\",\n sender_name: Optional[str] = \"User\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n ) -> Union[Text, Record]:\n return super().build_no_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n )\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "input_value": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "input_value", - "display_name": "Message", - "advanced": false, - "input_types": [], - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "value": "hi" - }, - "return_record": { - "type": "bool", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "return_record", - "display_name": "Return Record", - "advanced": true, - "dynamic": false, - "info": "Return the message as a record containing the sender, sender_name, and session_id.", - "load_from_db": false, - "title_case": false - }, - "sender": { - "type": "str", - "required": false, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "value": "User", - "fileTypes": [], - "file_path": "", - "password": false, - "options": [ - "Machine", - "User" - ], - "name": "sender", - "display_name": "Sender Type", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "sender_name": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": "User", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "sender_name", - "display_name": "Sender Name", - "advanced": false, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "session_id": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "session_id", - "display_name": "Session ID", - "advanced": true, - "dynamic": false, - "info": "If provided, the message will be stored in the memory.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "_type": "CustomComponent" - }, - "description": "Get chat inputs from the Playground.", - "icon": "ChatInput", - "base_classes": [ - "object", - "Record", - "str", - "Text" - ], - "display_name": "Chat Input", - "documentation": "", - "custom_fields": { - "sender": null, - "sender_name": null, - "input_value": null, - "session_id": null, - "return_record": null - }, - "output_types": [ - "Text", - "Record" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": false - }, - "id": "ChatInput-P3fgL" - }, - "selected": false, - "width": 384, - "height": 375, - "positionAbsolute": { - "x": -495.2223093083827, - "y": -232.56998443685862 - }, - "dragging": false - } - ], - "edges": [ - { - "source": "OpenAIModel-k39HS", - "sourceHandle": "{\u0153baseClasses\u0153:[\u0153object\u0153,\u0153Text\u0153,\u0153str\u0153],\u0153dataType\u0153:\u0153OpenAIModel\u0153,\u0153id\u0153:\u0153OpenAIModel-k39HS\u0153}", - "target": "ChatOutput-njtka", - "targetHandle": "{\u0153fieldName\u0153:\u0153input_value\u0153,\u0153id\u0153:\u0153ChatOutput-njtka\u0153,\u0153inputTypes\u0153:[\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}", - "data": { - "targetHandle": { - "fieldName": "input_value", - "id": "ChatOutput-njtka", - "inputTypes": [ - "Text" - ], - "type": "str" - }, - "sourceHandle": { - "baseClasses": [ - "object", - "Text", - "str" - ], - "dataType": "OpenAIModel", - "id": "OpenAIModel-k39HS" - } - }, - "style": { - "stroke": "#555" - }, - "className": "stroke-gray-900 stroke-connection", - "id": "reactflow__edge-OpenAIModel-k39HS{\u0153baseClasses\u0153:[\u0153object\u0153,\u0153Text\u0153,\u0153str\u0153],\u0153dataType\u0153:\u0153OpenAIModel\u0153,\u0153id\u0153:\u0153OpenAIModel-k39HS\u0153}-ChatOutput-njtka{\u0153fieldName\u0153:\u0153input_value\u0153,\u0153id\u0153:\u0153ChatOutput-njtka\u0153,\u0153inputTypes\u0153:[\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}" - }, - { - "source": "Prompt-uxBqP", - "sourceHandle": "{\u0153baseClasses\u0153:[\u0153object\u0153,\u0153str\u0153,\u0153Text\u0153],\u0153dataType\u0153:\u0153Prompt\u0153,\u0153id\u0153:\u0153Prompt-uxBqP\u0153}", - "target": "OpenAIModel-k39HS", - "targetHandle": "{\u0153fieldName\u0153:\u0153input_value\u0153,\u0153id\u0153:\u0153OpenAIModel-k39HS\u0153,\u0153inputTypes\u0153:[\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}", - "data": { - "targetHandle": { - "fieldName": "input_value", - "id": "OpenAIModel-k39HS", - "inputTypes": [ - "Text" - ], - "type": "str" - }, - "sourceHandle": { - "baseClasses": [ - "object", - "str", - "Text" - ], - "dataType": "Prompt", - "id": "Prompt-uxBqP" - } - }, - "style": { - "stroke": "#555" - }, - "className": "stroke-gray-900 stroke-connection", - "id": "reactflow__edge-Prompt-uxBqP{\u0153baseClasses\u0153:[\u0153object\u0153,\u0153str\u0153,\u0153Text\u0153],\u0153dataType\u0153:\u0153Prompt\u0153,\u0153id\u0153:\u0153Prompt-uxBqP\u0153}-OpenAIModel-k39HS{\u0153fieldName\u0153:\u0153input_value\u0153,\u0153id\u0153:\u0153OpenAIModel-k39HS\u0153,\u0153inputTypes\u0153:[\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}" - }, - { - "source": "ChatInput-P3fgL", - "sourceHandle": "{\u0153baseClasses\u0153:[\u0153object\u0153,\u0153Record\u0153,\u0153str\u0153,\u0153Text\u0153],\u0153dataType\u0153:\u0153ChatInput\u0153,\u0153id\u0153:\u0153ChatInput-P3fgL\u0153}", - "target": "Prompt-uxBqP", - "targetHandle": "{\u0153fieldName\u0153:\u0153user_input\u0153,\u0153id\u0153:\u0153Prompt-uxBqP\u0153,\u0153inputTypes\u0153:[\u0153Document\u0153,\u0153BaseOutputParser\u0153,\u0153Record\u0153,\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}", - "data": { - "targetHandle": { - "fieldName": "user_input", - "id": "Prompt-uxBqP", - "inputTypes": [ - "Document", - "BaseOutputParser", - "Record", - "Text" - ], - "type": "str" - }, - "sourceHandle": { - "baseClasses": [ - "object", - "Record", - "str", - "Text" - ], - "dataType": "ChatInput", - "id": "ChatInput-P3fgL" - } - }, - "style": { - "stroke": "#555" - }, - "className": "stroke-gray-900 stroke-connection", - "id": "reactflow__edge-ChatInput-P3fgL{\u0153baseClasses\u0153:[\u0153object\u0153,\u0153Record\u0153,\u0153str\u0153,\u0153Text\u0153],\u0153dataType\u0153:\u0153ChatInput\u0153,\u0153id\u0153:\u0153ChatInput-P3fgL\u0153}-Prompt-uxBqP{\u0153fieldName\u0153:\u0153user_input\u0153,\u0153id\u0153:\u0153Prompt-uxBqP\u0153,\u0153inputTypes\u0153:[\u0153Document\u0153,\u0153BaseOutputParser\u0153,\u0153Record\u0153,\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}" - } - ], - "viewport": { - "x": 260.58251815500563, - "y": 318.2261172111936, - "zoom": 0.43514115784696294 + "output_types": [ + "Text" + ], + "full_path": null, + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false, + "error": null + }, + "id": "Prompt-uxBqP", + "description": "Create a prompt template with dynamic variables.", + "display_name": "Prompt" + }, + "selected": true, + "width": 384, + "height": 383, + "dragging": false, + "positionAbsolute": { + "x": 53.588791333410654, + "y": -107.07318910019967 } - }, - "description": "This flow will get you experimenting with the basics of the UI, the Chat and the Prompt component. \n\nTry changing the Template in it to see how the model behaves. \nYou can change it to this and a Text Input into the `type_of_person` variable : \"Answer the user as if you were a pirate.\n\nUser: {user_input}\n\nAnswer: \" ", - "name": "Basic Prompting (Hello, World)", - "last_tested_version": "1.0.0a4", - "is_component": false -} + }, + { + "id": "OpenAIModel-k39HS", + "type": "genericNode", + "position": { + "x": 634.8148772766217, + "y": 27.035057029045305 + }, + "data": { + "type": "OpenAIModel", + "node": { + "template": { + "input_value": { + "type": "str", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "input_value", + "display_name": "Input", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\"},\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": True,\n \"info\": \"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": MODEL_NAMES,\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"openai_api_key\": {\n \"display_name\": \"OpenAI API Key\",\n \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n \"advanced\": False,\n \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float,\n model_name: str = \"gpt-4o\",\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n output = ChatOpenAI(\n max_tokens=max_tokens or None,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "max_tokens": { + "type": "int", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": 256, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "max_tokens", + "display_name": "Max Tokens", + "advanced": true, + "dynamic": false, + "info": "The maximum number of tokens to generate. Set to 0 for unlimited tokens.", + "load_from_db": false, + "title_case": false + }, + "model_kwargs": { + "type": "NestedDict", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": {}, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "model_kwargs", + "display_name": "Model Kwargs", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "model_name": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "value": "gpt-3.5-turbo", + "fileTypes": [], + "file_path": "", + "password": false, + "options": [ + "gpt-4o", + "gpt-4-turbo", + "gpt-4-turbo-preview", + "gpt-3.5-turbo", + "gpt-3.5-turbo-0125" + ], + "name": "model_name", + "display_name": "Model Name", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "openai_api_base": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "openai_api_base", + "display_name": "OpenAI API Base", + "advanced": true, + "dynamic": false, + "info": "The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\n\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "openai_api_key": { + "type": "str", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": true, + "name": "openai_api_key", + "display_name": "OpenAI API Key", + "advanced": false, + "dynamic": false, + "info": "The OpenAI API Key to use for the OpenAI model.", + "load_from_db": true, + "title_case": false, + "input_types": [ + "Text" + ], + "value": "OPENAI_API_KEY" + }, + "stream": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": true, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "stream", + "display_name": "Stream", + "advanced": true, + "dynamic": false, + "info": "Stream the response from the model. Streaming works only in Chat.", + "load_from_db": false, + "title_case": false + }, + "system_message": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "system_message", + "display_name": "System Message", + "advanced": true, + "dynamic": false, + "info": "System message to pass to the model.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "temperature": { + "type": "float", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": 0.1, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "temperature", + "display_name": "Temperature", + "advanced": false, + "dynamic": false, + "info": "", + "rangeSpec": { + "step_type": "float", + "min": -1, + "max": 1, + "step": 0.1 + }, + "load_from_db": false, + "title_case": false + }, + "_type": "CustomComponent" + }, + "description": "Generates text using OpenAI LLMs.", + "icon": "OpenAI", + "base_classes": [ + "object", + "Text", + "str" + ], + "display_name": "OpenAI", + "documentation": "", + "custom_fields": { + "input_value": null, + "openai_api_key": null, + "temperature": null, + "model_name": null, + "max_tokens": null, + "model_kwargs": null, + "openai_api_base": null, + "stream": null, + "system_message": null + }, + "output_types": [ + "Text" + ], + "field_formatters": {}, + "frozen": false, + "field_order": [ + "max_tokens", + "model_kwargs", + "model_name", + "openai_api_base", + "openai_api_key", + "temperature", + "input_value", + "system_message", + "stream" + ], + "beta": false + }, + "id": "OpenAIModel-k39HS", + "description": "Generates text using OpenAI LLMs.", + "display_name": "OpenAI" + }, + "selected": false, + "width": 384, + "height": 563, + "positionAbsolute": { + "x": 634.8148772766217, + "y": 27.035057029045305 + }, + "dragging": false + }, + { + "id": "ChatOutput-njtka", + "type": "genericNode", + "position": { + "x": 1193.250417197867, + "y": 71.88476890163852 + }, + "data": { + "type": "ChatOutput", + "node": { + "template": { + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n def build(\n self,\n sender: Optional[str] = \"Machine\",\n sender_name: Optional[str] = \"AI\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n record_template: Optional[str] = \"{text}\",\n ) -> Union[Text, Record]:\n return super().build_with_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n record_template=record_template or \"\",\n )\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "input_value": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "input_value", + "display_name": "Message", + "advanced": false, + "input_types": [ + "Text" + ], + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "record_template": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "{text}", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "record_template", + "display_name": "Record Template", + "advanced": true, + "dynamic": false, + "info": "In case of Message being a Record, this template will be used to convert it to text.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "return_record": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "return_record", + "display_name": "Return Record", + "advanced": true, + "dynamic": false, + "info": "Return the message as a record containing the sender, sender_name, and session_id.", + "load_from_db": false, + "title_case": false + }, + "sender": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "value": "Machine", + "fileTypes": [], + "file_path": "", + "password": false, + "options": [ + "Machine", + "User" + ], + "name": "sender", + "display_name": "Sender Type", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "sender_name": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": "AI", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "sender_name", + "display_name": "Sender Name", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "session_id": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "session_id", + "display_name": "Session ID", + "advanced": true, + "dynamic": false, + "info": "If provided, the message will be stored in the memory.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "_type": "CustomComponent" + }, + "description": "Display a chat message in the Playground.", + "icon": "ChatOutput", + "base_classes": [ + "Record", + "Text", + "str", + "object" + ], + "display_name": "Chat Output", + "documentation": "", + "custom_fields": { + "sender": null, + "sender_name": null, + "input_value": null, + "session_id": null, + "return_record": null, + "record_template": null + }, + "output_types": [ + "Text", + "Record" + ], + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false + }, + "id": "ChatOutput-njtka" + }, + "selected": false, + "width": 384, + "height": 383, + "positionAbsolute": { + "x": 1193.250417197867, + "y": 71.88476890163852 + }, + "dragging": false + }, + { + "id": "ChatInput-P3fgL", + "type": "genericNode", + "position": { + "x": -495.2223093083827, + "y": -232.56998443685862 + }, + "data": { + "type": "ChatInput", + "node": { + "template": { + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\nfrom langflow.template import Input, Output\n\n\nclass ChatInput(ChatComponent):\n display_name = \"Chat Input\"\n description = \"Get chat inputs from the Playground.\"\n icon = \"ChatInput\"\n\n inputs = [\n Input(name=\"input_value\", type=str, display_name=\"Message\", multiline=True, input_types=[]),\n Input(name=\"sender\", type=str, display_name=\"Sender Type\", options=[\"Machine\", \"User\"]),\n Input(name=\"sender_name\", type=str, display_name=\"Sender Name\"),\n Input(name=\"session_id\", type=str, display_name=\"Session ID\"),\n ]\n outputs = [\n Output(name=\"Message\", method=\"text_response\"),\n Output(name=\"Record\", method=\"record_response\"),\n ]\n\n def build_config(self):\n build_config = super().build_config()\n build_config[\"input_value\"] = {\n \"input_types\": [],\n \"display_name\": \"Message\",\n \"multiline\": True,\n }\n\n return build_config\n\n def text_response(self):\n result = self.message\n if self.session_id and isinstance(result, (Record, str)):\n self.store_message(result, self.session_id, self.sender, self.sender_name)\n return result\n\n def record_response(self):\n record = Record(\n data={\n \"message\": self.message,\n \"sender\": self.sender,\n \"sender_name\": self.sender_name,\n \"session_id\": self.session_id,\n }\n )\n if self.session_id and isinstance(record, (Record, str)):\n self.store_message(record, self.session_id, self.sender, self.sender_name)\n return record\n\n def build(\n self,\n sender: Optional[str] = \"User\",\n sender_name: Optional[str] = \"User\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n ) -> Union[Text, Record]:\n return super().build_no_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n )\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "input_value": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "input_value", + "display_name": "Message", + "advanced": false, + "input_types": [], + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "value": "hi" + }, + "return_record": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "return_record", + "display_name": "Return Record", + "advanced": true, + "dynamic": false, + "info": "Return the message as a record containing the sender, sender_name, and session_id.", + "load_from_db": false, + "title_case": false + }, + "sender": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": "User", + "fileTypes": [], + "file_path": "", + "password": false, + "options": [ + "Machine", + "User" + ], + "name": "sender", + "display_name": "Sender Type", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "sender_name": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": "User", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "sender_name", + "display_name": "Sender Name", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "session_id": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "session_id", + "display_name": "Session ID", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "_type": "CustomComponent" + }, + "description": "Get chat inputs from the Playground.", + "icon": "ChatInput", + "base_classes": [ + "object", + "Record", + "str", + "Text" + ], + "display_name": "Chat Input", + "documentation": "", + "custom_fields": { + "sender": null, + "sender_name": null, + "input_value": null, + "session_id": null, + "return_record": null + }, + "output_types": [ + "Text", + "Record" + ], + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false + }, + "id": "ChatInput-P3fgL" + }, + "selected": false, + "width": 384, + "height": 375, + "positionAbsolute": { + "x": -495.2223093083827, + "y": -232.56998443685862 + }, + "dragging": false + } + ], + "edges": [ + { + "source": "OpenAIModel-k39HS", + "sourceHandle": "{œbaseClassesœ:[œobjectœ,œTextœ,œstrœ],œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-k39HSœ}", + "target": "ChatOutput-njtka", + "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-njtkaœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}", + "data": { + "targetHandle": { + "fieldName": "input_value", + "id": "ChatOutput-njtka", + "inputTypes": [ + "Text" + ], + "type": "str" + }, + "sourceHandle": { + "baseClasses": [ + "object", + "Text", + "str" + ], + "dataType": "OpenAIModel", + "id": "OpenAIModel-k39HS" + } + }, + "style": { + "stroke": "#555" + }, + "className": "stroke-gray-900 stroke-connection", + "id": "reactflow__edge-OpenAIModel-k39HS{œbaseClassesœ:[œobjectœ,œTextœ,œstrœ],œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-k39HSœ}-ChatOutput-njtka{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-njtkaœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}" + }, + { + "source": "Prompt-uxBqP", + "sourceHandle": "{œbaseClassesœ:[œobjectœ,œstrœ,œTextœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-uxBqPœ}", + "target": "OpenAIModel-k39HS", + "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-k39HSœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}", + "data": { + "targetHandle": { + "fieldName": "input_value", + "id": "OpenAIModel-k39HS", + "inputTypes": [ + "Text" + ], + "type": "str" + }, + "sourceHandle": { + "baseClasses": [ + "object", + "str", + "Text" + ], + "dataType": "Prompt", + "id": "Prompt-uxBqP" + } + }, + "style": { + "stroke": "#555" + }, + "className": "stroke-gray-900 stroke-connection", + "id": "reactflow__edge-Prompt-uxBqP{œbaseClassesœ:[œobjectœ,œstrœ,œTextœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-uxBqPœ}-OpenAIModel-k39HS{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-k39HSœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}" + }, + { + "source": "ChatInput-P3fgL", + "sourceHandle": "{œbaseClassesœ:[œobjectœ,œRecordœ,œstrœ,œTextœ],œdataTypeœ:œChatInputœ,œidœ:œChatInput-P3fgLœ}", + "target": "Prompt-uxBqP", + "targetHandle": "{œfieldNameœ:œuser_inputœ,œidœ:œPrompt-uxBqPœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}", + "data": { + "targetHandle": { + "fieldName": "user_input", + "id": "Prompt-uxBqP", + "inputTypes": [ + "Document", + "BaseOutputParser", + "Record", + "Text" + ], + "type": "str" + }, + "sourceHandle": { + "baseClasses": [ + "object", + "Record", + "str", + "Text" + ], + "dataType": "ChatInput", + "id": "ChatInput-P3fgL" + } + }, + "style": { + "stroke": "#555" + }, + "className": "stroke-gray-900 stroke-connection", + "id": "reactflow__edge-ChatInput-P3fgL{œbaseClassesœ:[œobjectœ,œRecordœ,œstrœ,œTextœ],œdataTypeœ:œChatInputœ,œidœ:œChatInput-P3fgLœ}-Prompt-uxBqP{œfieldNameœ:œuser_inputœ,œidœ:œPrompt-uxBqPœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}" + } + ], + "viewport": { + "x": 260.58251815500563, + "y": 318.2261172111936, + "zoom": 0.43514115784696294 + } + }, + "description": "This flow will get you experimenting with the basics of the UI, the Chat and the Prompt component. \n\nTry changing the Template in it to see how the model behaves. \nYou can change it to this and a Text Input into the `type_of_person` variable : \"Answer the user as if you were a pirate.\n\nUser: {user_input}\n\nAnswer: \" ", + "name": "Basic Prompting (Hello, World)", + "last_tested_version": "1.0.0a4", + "is_component": false +} \ No newline at end of file diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Document QA.json b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Document QA.json index 3afd6f04f..5c1b653e5 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Document QA.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Document QA.json @@ -1,1029 +1,1029 @@ { - "id": "fecbce42-6f11-454c-8ab2-db6eddbbbb0f", - "data": { - "nodes": [ - { - "id": "Prompt-tHwPf", - "type": "genericNode", - "position": { - "x": 585.7906101139403, - "y": 117.52115876762832 - }, - "data": { - "type": "Prompt", - "node": { - "template": { - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Input, Prompt, Text\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": Input(display_name=\"Template\"),\n \"code\": Input(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "template": { - "type": "prompt", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": "Answer user's questions based on the document below:\n\n---\n\n{Document}\n\n---\n\nQuestion:\n{Question}\n\nAnswer:\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "template", - "display_name": "Template", - "advanced": false, - "input_types": [ - "Text" - ], - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "_type": "CustomComponent", - "Document": { - "field_type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "Document", - "display_name": "Document", - "advanced": false, - "input_types": [ - "Document", - "BaseOutputParser", - "Record", - "Text" - ], - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "type": "str" - }, - "Question": { - "field_type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "Question", - "display_name": "Question", - "advanced": false, - "input_types": [ - "Document", - "BaseOutputParser", - "Record", - "Text" - ], - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "type": "str" - } - }, - "description": "Create a prompt template with dynamic variables.", - "icon": "prompts", - "is_input": null, - "is_output": null, - "is_composition": null, - "base_classes": [ - "object", - "str", - "Text" - ], - "name": "", - "display_name": "Prompt", - "documentation": "", - "custom_fields": { - "template": [ - "Document", - "Question" - ] - }, - "output_types": [ - "Text" - ], - "full_path": null, - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": false, - "error": null - }, - "id": "Prompt-tHwPf", - "description": "A component for creating prompt templates using dynamic variables.", - "display_name": "Prompt" - }, - "selected": false, - "width": 384, - "height": 479, - "positionAbsolute": { - "x": 585.7906101139403, - "y": 117.52115876762832 - }, - "dragging": false + "id": "fecbce42-6f11-454c-8ab2-db6eddbbbb0f", + "data": { + "nodes": [ + { + "id": "Prompt-tHwPf", + "type": "genericNode", + "position": { + "x": 585.7906101139403, + "y": 117.52115876762832 + }, + "data": { + "type": "Prompt", + "node": { + "template": { + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Input, Prompt, Text\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": Input(display_name=\"Template\"),\n \"code\": Input(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "template": { + "type": "prompt", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": "Answer user's questions based on the document below:\n\n---\n\n{Document}\n\n---\n\nQuestion:\n{Question}\n\nAnswer:\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "template", + "display_name": "Template", + "advanced": false, + "input_types": [ + "Text" + ], + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "_type": "CustomComponent", + "Document": { + "field_type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "Document", + "display_name": "Document", + "advanced": false, + "input_types": [ + "Document", + "BaseOutputParser", + "Record", + "Text" + ], + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "type": "str" + }, + "Question": { + "field_type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "Question", + "display_name": "Question", + "advanced": false, + "input_types": [ + "Document", + "BaseOutputParser", + "Record", + "Text" + ], + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "type": "str" + } }, - { - "id": "File-6TEsD", - "type": "genericNode", - "position": { - "x": -18.636536329280602, - "y": 3.951948774836353 - }, - "data": { - "type": "File", - "node": { - "template": { - "path": { - "type": "file", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [ - ".txt", - ".md", - ".mdx", - ".csv", - ".json", - ".yaml", - ".yml", - ".xml", - ".html", - ".htm", - ".pdf", - ".docx" - ], - "password": false, - "name": "path", - "display_name": "Path", - "advanced": false, - "dynamic": false, - "info": "Supported file types: txt, md, mdx, csv, json, yaml, yml, xml, html, htm, pdf, docx", - "load_from_db": false, - "title_case": false, - "value": "" - }, - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from pathlib import Path\nfrom typing import Any, Dict\n\nfrom langflow.base.data.utils import TEXT_FILE_TYPES, parse_text_file_to_record\nfrom langflow.interface.custom.custom_component import CustomComponent\nfrom langflow.schema import Record\n\n\nclass FileComponent(CustomComponent):\n display_name = \"Files\"\n description = \"A generic file loader.\"\n\n def build_config(self) -> Dict[str, Any]:\n return {\n \"path\": {\n \"display_name\": \"Path\",\n \"field_type\": \"file\",\n \"file_types\": TEXT_FILE_TYPES,\n \"info\": f\"Supported file types: {', '.join(TEXT_FILE_TYPES)}\",\n },\n \"silent_errors\": {\n \"display_name\": \"Silent Errors\",\n \"advanced\": True,\n \"info\": \"If true, errors will not raise an exception.\",\n },\n }\n\n def load_file(self, path: str, silent_errors: bool = False) -> Record:\n resolved_path = self.resolve_path(path)\n path_obj = Path(resolved_path)\n extension = path_obj.suffix[1:].lower()\n if extension == \"doc\":\n raise ValueError(\"doc files are not supported. Please save as .docx\")\n if extension not in TEXT_FILE_TYPES:\n raise ValueError(f\"Unsupported file type: {extension}\")\n record = parse_text_file_to_record(resolved_path, silent_errors)\n self.status = record if record else \"No data\"\n return record or Record()\n\n def build(\n self,\n path: str,\n silent_errors: bool = False,\n ) -> Record:\n record = self.load_file(path, silent_errors)\n self.status = record\n return record\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "silent_errors": { - "type": "bool", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "silent_errors", - "display_name": "Silent Errors", - "advanced": true, - "dynamic": false, - "info": "If true, errors will not raise an exception.", - "load_from_db": false, - "title_case": false - }, - "_type": "CustomComponent" - }, - "description": "A generic file loader.", - "base_classes": [ - "Record" - ], - "display_name": "Files", - "documentation": "", - "custom_fields": { - "path": null, - "silent_errors": null - }, - "output_types": [ - "Record" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": false - }, - "id": "File-6TEsD" - }, - "selected": false, - "width": 384, - "height": 282, - "positionAbsolute": { - "x": -18.636536329280602, - "y": 3.951948774836353 - }, - "dragging": false + "description": "Create a prompt template with dynamic variables.", + "icon": "prompts", + "is_input": null, + "is_output": null, + "is_composition": null, + "base_classes": [ + "object", + "str", + "Text" + ], + "name": "", + "display_name": "Prompt", + "documentation": "", + "custom_fields": { + "template": [ + "Document", + "Question" + ] }, - { - "id": "ChatInput-MsSJ9", - "type": "genericNode", - "position": { - "x": -28.80036300619821, - "y": 379.81180230285355 - }, - "data": { - "type": "ChatInput", - "node": { - "template": { - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatInput(ChatComponent):\n display_name = \"Chat Input\"\n description = \"Get chat inputs from the Playground.\"\n icon = \"ChatInput\"\n\n def build_config(self):\n build_config = super().build_config()\n build_config[\"input_value\"] = {\n \"input_types\": [],\n \"display_name\": \"Message\",\n \"multiline\": True,\n }\n\n return build_config\n\n def build(\n self,\n sender: Optional[str] = \"User\",\n sender_name: Optional[str] = \"User\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n ) -> Union[Text, Record]:\n return super().build_no_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n )\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "input_value": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "input_value", - "display_name": "Message", - "advanced": false, - "input_types": [], - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "value": "" - }, - "return_record": { - "type": "bool", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "return_record", - "display_name": "Return Record", - "advanced": true, - "dynamic": false, - "info": "Return the message as a record containing the sender, sender_name, and session_id.", - "load_from_db": false, - "title_case": false - }, - "sender": { - "type": "str", - "required": false, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "value": "User", - "fileTypes": [], - "file_path": "", - "password": false, - "options": [ - "Machine", - "User" - ], - "name": "sender", - "display_name": "Sender Type", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "sender_name": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": "User", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "sender_name", - "display_name": "Sender Name", - "advanced": false, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "session_id": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "session_id", - "display_name": "Session ID", - "advanced": true, - "dynamic": false, - "info": "If provided, the message will be stored in the memory.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "_type": "CustomComponent" - }, - "description": "Get chat inputs from the Playground.", - "icon": "ChatInput", - "base_classes": [ - "str", - "Record", - "Text", - "object" - ], - "display_name": "Chat Input", - "documentation": "", - "custom_fields": { - "sender": null, - "sender_name": null, - "input_value": null, - "session_id": null, - "return_record": null - }, - "output_types": [ - "Text", - "Record" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": false - }, - "id": "ChatInput-MsSJ9" - }, - "selected": true, - "width": 384, - "height": 377, - "positionAbsolute": { - "x": -28.80036300619821, - "y": 379.81180230285355 - }, - "dragging": false + "output_types": [ + "Text" + ], + "full_path": null, + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false, + "error": null + }, + "id": "Prompt-tHwPf", + "description": "A component for creating prompt templates using dynamic variables.", + "display_name": "Prompt" + }, + "selected": false, + "width": 384, + "height": 479, + "positionAbsolute": { + "x": 585.7906101139403, + "y": 117.52115876762832 + }, + "dragging": false + }, + { + "id": "File-6TEsD", + "type": "genericNode", + "position": { + "x": -18.636536329280602, + "y": 3.951948774836353 + }, + "data": { + "type": "File", + "node": { + "template": { + "path": { + "type": "file", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [ + ".txt", + ".md", + ".mdx", + ".csv", + ".json", + ".yaml", + ".yml", + ".xml", + ".html", + ".htm", + ".pdf", + ".docx" + ], + "password": false, + "name": "path", + "display_name": "Path", + "advanced": false, + "dynamic": false, + "info": "Supported file types: txt, md, mdx, csv, json, yaml, yml, xml, html, htm, pdf, docx", + "load_from_db": false, + "title_case": false, + "value": "" + }, + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from pathlib import Path\nfrom typing import Any, Dict\n\nfrom langflow.base.data.utils import TEXT_FILE_TYPES, parse_text_file_to_record\nfrom langflow.interface.custom.custom_component import CustomComponent\nfrom langflow.schema import Record\n\n\nclass FileComponent(CustomComponent):\n display_name = \"Files\"\n description = \"A generic file loader.\"\n\n def build_config(self) -> Dict[str, Any]:\n return {\n \"path\": {\n \"display_name\": \"Path\",\n \"field_type\": \"file\",\n \"file_types\": TEXT_FILE_TYPES,\n \"info\": f\"Supported file types: {', '.join(TEXT_FILE_TYPES)}\",\n },\n \"silent_errors\": {\n \"display_name\": \"Silent Errors\",\n \"advanced\": True,\n \"info\": \"If true, errors will not raise an exception.\",\n },\n }\n\n def load_file(self, path: str, silent_errors: bool = False) -> Record:\n resolved_path = self.resolve_path(path)\n path_obj = Path(resolved_path)\n extension = path_obj.suffix[1:].lower()\n if extension == \"doc\":\n raise ValueError(\"doc files are not supported. Please save as .docx\")\n if extension not in TEXT_FILE_TYPES:\n raise ValueError(f\"Unsupported file type: {extension}\")\n record = parse_text_file_to_record(resolved_path, silent_errors)\n self.status = record if record else \"No data\"\n return record or Record()\n\n def build(\n self,\n path: str,\n silent_errors: bool = False,\n ) -> Record:\n record = self.load_file(path, silent_errors)\n self.status = record\n return record\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "silent_errors": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "silent_errors", + "display_name": "Silent Errors", + "advanced": true, + "dynamic": false, + "info": "If true, errors will not raise an exception.", + "load_from_db": false, + "title_case": false + }, + "_type": "CustomComponent" }, - { - "id": "ChatOutput-F5Awj", - "type": "genericNode", - "position": { - "x": 1733.3012915204283, - "y": 168.76098809939327 - }, - "data": { - "type": "ChatOutput", - "node": { - "template": { - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n def build(\n self,\n sender: Optional[str] = \"Machine\",\n sender_name: Optional[str] = \"AI\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n record_template: Optional[str] = \"{text}\",\n ) -> Union[Text, Record]:\n return super().build_with_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n record_template=record_template or \"\",\n )\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "input_value": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "input_value", - "display_name": "Message", - "advanced": false, - "input_types": [ - "Text" - ], - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "return_record": { - "type": "bool", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "return_record", - "display_name": "Return Record", - "advanced": true, - "dynamic": false, - "info": "Return the message as a record containing the sender, sender_name, and session_id.", - "load_from_db": false, - "title_case": false - }, - "sender": { - "type": "str", - "required": false, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "value": "Machine", - "fileTypes": [], - "file_path": "", - "password": false, - "options": [ - "Machine", - "User" - ], - "name": "sender", - "display_name": "Sender Type", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "sender_name": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": "AI", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "sender_name", - "display_name": "Sender Name", - "advanced": false, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "session_id": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "session_id", - "display_name": "Session ID", - "advanced": true, - "dynamic": false, - "info": "If provided, the message will be stored in the memory.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "_type": "CustomComponent" - }, - "description": "Display a chat message in the Playground.", - "icon": "ChatOutput", - "base_classes": [ - "str", - "Record", - "Text", - "object" - ], - "display_name": "Chat Output", - "documentation": "", - "custom_fields": { - "sender": null, - "sender_name": null, - "input_value": null, - "session_id": null, - "return_record": null - }, - "output_types": [ - "Text", - "Record" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": false - }, - "id": "ChatOutput-F5Awj" - }, - "selected": false, - "width": 384, - "height": 385, - "positionAbsolute": { - "x": 1733.3012915204283, - "y": 168.76098809939327 - }, - "dragging": false + "description": "A generic file loader.", + "base_classes": [ + "Record" + ], + "display_name": "Files", + "documentation": "", + "custom_fields": { + "path": null, + "silent_errors": null }, - { - "id": "OpenAIModel-Bt067", - "type": "genericNode", - "position": { - "x": 1137.6078582863759, - "y": -14.41920034020356 - }, - "data": { - "type": "OpenAIModel", - "node": { - "template": { - "input_value": { - "type": "str", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "input_value", - "display_name": "Input", - "advanced": false, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\"},\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": True,\n \"info\": \"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": MODEL_NAMES,\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"openai_api_key\": {\n \"display_name\": \"OpenAI API Key\",\n \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n \"advanced\": False,\n \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float,\n model_name: str = \"gpt-4o\",\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n output = ChatOpenAI(\n max_tokens=max_tokens or None,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "max_tokens": { - "type": "int", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": 256, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "max_tokens", - "display_name": "Max Tokens", - "advanced": true, - "dynamic": false, - "info": "The maximum number of tokens to generate. Set to 0 for unlimited tokens.", - "load_from_db": false, - "title_case": false - }, - "model_kwargs": { - "type": "NestedDict", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": {}, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "model_kwargs", - "display_name": "Model Kwargs", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "model_name": { - "type": "str", - "required": false, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "value": "gpt-4-turbo-preview", - "fileTypes": [], - "file_path": "", - "password": false, - "options": [ - "gpt-4o", - "gpt-4-turbo", - "gpt-4-turbo-preview", - "gpt-3.5-turbo", - "gpt-3.5-turbo-0125" - ], - "name": "model_name", - "display_name": "Model Name", - "advanced": false, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "openai_api_base": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "openai_api_base", - "display_name": "OpenAI API Base", - "advanced": true, - "dynamic": false, - "info": "The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\n\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "openai_api_key": { - "type": "str", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": true, - "name": "openai_api_key", - "display_name": "OpenAI API Key", - "advanced": false, - "dynamic": false, - "info": "The OpenAI API Key to use for the OpenAI model.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ], - "value": "OPENAI_API_KEY" - }, - "stream": { - "type": "bool", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": true, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "stream", - "display_name": "Stream", - "advanced": false, - "dynamic": false, - "info": "Stream the response from the model. Streaming works only in Chat.", - "load_from_db": false, - "title_case": false - }, - "system_message": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "system_message", - "display_name": "System Message", - "advanced": true, - "dynamic": false, - "info": "System message to pass to the model.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "temperature": { - "type": "float", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": 0.1, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "temperature", - "display_name": "Temperature", - "advanced": false, - "dynamic": false, - "info": "", - "rangeSpec": { - "step_type": "float", - "min": -1, - "max": 1, - "step": 0.1 - }, - "load_from_db": false, - "title_case": false - }, - "_type": "CustomComponent" - }, - "description": "Generates text using OpenAI LLMs.", - "icon": "OpenAI", - "base_classes": [ - "object", - "str", - "Text" - ], - "display_name": "OpenAI", - "documentation": "", - "custom_fields": { - "input_value": null, - "openai_api_key": null, - "temperature": null, - "model_name": null, - "max_tokens": null, - "model_kwargs": null, - "openai_api_base": null, - "stream": null, - "system_message": null - }, - "output_types": [ - "Text" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [ - "max_tokens", - "model_kwargs", - "model_name", - "openai_api_base", - "openai_api_key", - "temperature", - "input_value", - "system_message", - "stream" - ], - "beta": false - }, - "id": "OpenAIModel-Bt067" - }, - "selected": false, - "width": 384, - "height": 642, - "positionAbsolute": { - "x": 1137.6078582863759, - "y": -14.41920034020356 - }, - "dragging": false - } - ], - "edges": [ - { - "source": "ChatInput-MsSJ9", - "sourceHandle": "{\u0153baseClasses\u0153:[\u0153str\u0153,\u0153Record\u0153,\u0153Text\u0153,\u0153object\u0153],\u0153dataType\u0153:\u0153ChatInput\u0153,\u0153id\u0153:\u0153ChatInput-MsSJ9\u0153}", - "target": "Prompt-tHwPf", - "targetHandle": "{\u0153fieldName\u0153:\u0153Question\u0153,\u0153id\u0153:\u0153Prompt-tHwPf\u0153,\u0153inputTypes\u0153:[\u0153Document\u0153,\u0153BaseOutputParser\u0153,\u0153Record\u0153,\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}", - "data": { - "targetHandle": { - "fieldName": "Question", - "id": "Prompt-tHwPf", - "inputTypes": [ - "Document", - "BaseOutputParser", - "Record", - "Text" - ], - "type": "str" - }, - "sourceHandle": { - "baseClasses": [ - "str", - "Record", - "Text", - "object" - ], - "dataType": "ChatInput", - "id": "ChatInput-MsSJ9" - } - }, - "style": { - "stroke": "#555" - }, - "className": "stroke-gray-900 stroke-connection", - "id": "reactflow__edge-ChatInput-MsSJ9{\u0153baseClasses\u0153:[\u0153str\u0153,\u0153Record\u0153,\u0153Text\u0153,\u0153object\u0153],\u0153dataType\u0153:\u0153ChatInput\u0153,\u0153id\u0153:\u0153ChatInput-MsSJ9\u0153}-Prompt-tHwPf{\u0153fieldName\u0153:\u0153Question\u0153,\u0153id\u0153:\u0153Prompt-tHwPf\u0153,\u0153inputTypes\u0153:[\u0153Document\u0153,\u0153BaseOutputParser\u0153,\u0153Record\u0153,\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}" + "output_types": [ + "Record" + ], + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false + }, + "id": "File-6TEsD" + }, + "selected": false, + "width": 384, + "height": 282, + "positionAbsolute": { + "x": -18.636536329280602, + "y": 3.951948774836353 + }, + "dragging": false + }, + { + "id": "ChatInput-MsSJ9", + "type": "genericNode", + "position": { + "x": -28.80036300619821, + "y": 379.81180230285355 + }, + "data": { + "type": "ChatInput", + "node": { + "template": { + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\nfrom langflow.template import Input, Output\n\n\nclass ChatInput(ChatComponent):\n display_name = \"Chat Input\"\n description = \"Get chat inputs from the Playground.\"\n icon = \"ChatInput\"\n\n inputs = [\n Input(name=\"input_value\", type=str, display_name=\"Message\", multiline=True, input_types=[]),\n Input(name=\"sender\", type=str, display_name=\"Sender Type\", options=[\"Machine\", \"User\"]),\n Input(name=\"sender_name\", type=str, display_name=\"Sender Name\"),\n Input(name=\"session_id\", type=str, display_name=\"Session ID\"),\n ]\n outputs = [\n Output(name=\"Message\", method=\"text_response\"),\n Output(name=\"Record\", method=\"record_response\"),\n ]\n\n def build_config(self):\n build_config = super().build_config()\n build_config[\"input_value\"] = {\n \"input_types\": [],\n \"display_name\": \"Message\",\n \"multiline\": True,\n }\n\n return build_config\n\n def text_response(self):\n result = self.message\n if self.session_id and isinstance(result, (Record, str)):\n self.store_message(result, self.session_id, self.sender, self.sender_name)\n return result\n\n def record_response(self):\n record = Record(\n data={\n \"message\": self.message,\n \"sender\": self.sender,\n \"sender_name\": self.sender_name,\n \"session_id\": self.session_id,\n }\n )\n if self.session_id and isinstance(record, (Record, str)):\n self.store_message(record, self.session_id, self.sender, self.sender_name)\n return record\n\n def build(\n self,\n sender: Optional[str] = \"User\",\n sender_name: Optional[str] = \"User\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n ) -> Union[Text, Record]:\n return super().build_no_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n )\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "input_value": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "input_value", + "display_name": "Message", + "advanced": false, + "input_types": [], + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "value": "" + }, + "return_record": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "return_record", + "display_name": "Return Record", + "advanced": true, + "dynamic": false, + "info": "Return the message as a record containing the sender, sender_name, and session_id.", + "load_from_db": false, + "title_case": false + }, + "sender": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": "User", + "fileTypes": [], + "file_path": "", + "password": false, + "options": [ + "Machine", + "User" + ], + "name": "sender", + "display_name": "Sender Type", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "sender_name": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": "User", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "sender_name", + "display_name": "Sender Name", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "session_id": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "session_id", + "display_name": "Session ID", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "_type": "CustomComponent" }, - { - "source": "File-6TEsD", - "sourceHandle": "{\u0153baseClasses\u0153:[\u0153Record\u0153],\u0153dataType\u0153:\u0153File\u0153,\u0153id\u0153:\u0153File-6TEsD\u0153}", - "target": "Prompt-tHwPf", - "targetHandle": "{\u0153fieldName\u0153:\u0153Document\u0153,\u0153id\u0153:\u0153Prompt-tHwPf\u0153,\u0153inputTypes\u0153:[\u0153Document\u0153,\u0153BaseOutputParser\u0153,\u0153Record\u0153,\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}", - "data": { - "targetHandle": { - "fieldName": "Document", - "id": "Prompt-tHwPf", - "inputTypes": [ - "Document", - "BaseOutputParser", - "Record", - "Text" - ], - "type": "str" - }, - "sourceHandle": { - "baseClasses": [ - "Record" - ], - "dataType": "File", - "id": "File-6TEsD" - } - }, - "style": { - "stroke": "#555" - }, - "className": "stroke-gray-900 stroke-connection", - "id": "reactflow__edge-File-6TEsD{\u0153baseClasses\u0153:[\u0153Record\u0153],\u0153dataType\u0153:\u0153File\u0153,\u0153id\u0153:\u0153File-6TEsD\u0153}-Prompt-tHwPf{\u0153fieldName\u0153:\u0153Document\u0153,\u0153id\u0153:\u0153Prompt-tHwPf\u0153,\u0153inputTypes\u0153:[\u0153Document\u0153,\u0153BaseOutputParser\u0153,\u0153Record\u0153,\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}" + "description": "Get chat inputs from the Playground.", + "icon": "ChatInput", + "base_classes": [ + "str", + "Record", + "Text", + "object" + ], + "display_name": "Chat Input", + "documentation": "", + "custom_fields": { + "sender": null, + "sender_name": null, + "input_value": null, + "session_id": null, + "return_record": null }, - { - "source": "Prompt-tHwPf", - "sourceHandle": "{\u0153baseClasses\u0153:[\u0153object\u0153,\u0153str\u0153,\u0153Text\u0153],\u0153dataType\u0153:\u0153Prompt\u0153,\u0153id\u0153:\u0153Prompt-tHwPf\u0153}", - "target": "OpenAIModel-Bt067", - "targetHandle": "{\u0153fieldName\u0153:\u0153input_value\u0153,\u0153id\u0153:\u0153OpenAIModel-Bt067\u0153,\u0153inputTypes\u0153:[\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}", - "data": { - "targetHandle": { - "fieldName": "input_value", - "id": "OpenAIModel-Bt067", - "inputTypes": [ - "Text" - ], - "type": "str" - }, - "sourceHandle": { - "baseClasses": [ - "object", - "str", - "Text" - ], - "dataType": "Prompt", - "id": "Prompt-tHwPf" - } - }, - "style": { - "stroke": "#555" - }, - "className": "stroke-gray-900 stroke-connection", - "id": "reactflow__edge-Prompt-tHwPf{\u0153baseClasses\u0153:[\u0153object\u0153,\u0153str\u0153,\u0153Text\u0153],\u0153dataType\u0153:\u0153Prompt\u0153,\u0153id\u0153:\u0153Prompt-tHwPf\u0153}-OpenAIModel-Bt067{\u0153fieldName\u0153:\u0153input_value\u0153,\u0153id\u0153:\u0153OpenAIModel-Bt067\u0153,\u0153inputTypes\u0153:[\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}" + "output_types": [ + "Text", + "Record" + ], + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false + }, + "id": "ChatInput-MsSJ9" + }, + "selected": true, + "width": 384, + "height": 377, + "positionAbsolute": { + "x": -28.80036300619821, + "y": 379.81180230285355 + }, + "dragging": false + }, + { + "id": "ChatOutput-F5Awj", + "type": "genericNode", + "position": { + "x": 1733.3012915204283, + "y": 168.76098809939327 + }, + "data": { + "type": "ChatOutput", + "node": { + "template": { + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n def build(\n self,\n sender: Optional[str] = \"Machine\",\n sender_name: Optional[str] = \"AI\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n record_template: Optional[str] = \"{text}\",\n ) -> Union[Text, Record]:\n return super().build_with_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n record_template=record_template or \"\",\n )\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "input_value": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "input_value", + "display_name": "Message", + "advanced": false, + "input_types": [ + "Text" + ], + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "return_record": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "return_record", + "display_name": "Return Record", + "advanced": true, + "dynamic": false, + "info": "Return the message as a record containing the sender, sender_name, and session_id.", + "load_from_db": false, + "title_case": false + }, + "sender": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "value": "Machine", + "fileTypes": [], + "file_path": "", + "password": false, + "options": [ + "Machine", + "User" + ], + "name": "sender", + "display_name": "Sender Type", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "sender_name": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": "AI", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "sender_name", + "display_name": "Sender Name", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "session_id": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "session_id", + "display_name": "Session ID", + "advanced": true, + "dynamic": false, + "info": "If provided, the message will be stored in the memory.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "_type": "CustomComponent" }, - { - "source": "OpenAIModel-Bt067", - "sourceHandle": "{\u0153baseClasses\u0153:[\u0153object\u0153,\u0153str\u0153,\u0153Text\u0153],\u0153dataType\u0153:\u0153OpenAIModel\u0153,\u0153id\u0153:\u0153OpenAIModel-Bt067\u0153}", - "target": "ChatOutput-F5Awj", - "targetHandle": "{\u0153fieldName\u0153:\u0153input_value\u0153,\u0153id\u0153:\u0153ChatOutput-F5Awj\u0153,\u0153inputTypes\u0153:[\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}", - "data": { - "targetHandle": { - "fieldName": "input_value", - "id": "ChatOutput-F5Awj", - "inputTypes": [ - "Text" - ], - "type": "str" - }, - "sourceHandle": { - "baseClasses": [ - "object", - "str", - "Text" - ], - "dataType": "OpenAIModel", - "id": "OpenAIModel-Bt067" - } + "description": "Display a chat message in the Playground.", + "icon": "ChatOutput", + "base_classes": [ + "str", + "Record", + "Text", + "object" + ], + "display_name": "Chat Output", + "documentation": "", + "custom_fields": { + "sender": null, + "sender_name": null, + "input_value": null, + "session_id": null, + "return_record": null + }, + "output_types": [ + "Text", + "Record" + ], + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false + }, + "id": "ChatOutput-F5Awj" + }, + "selected": false, + "width": 384, + "height": 385, + "positionAbsolute": { + "x": 1733.3012915204283, + "y": 168.76098809939327 + }, + "dragging": false + }, + { + "id": "OpenAIModel-Bt067", + "type": "genericNode", + "position": { + "x": 1137.6078582863759, + "y": -14.41920034020356 + }, + "data": { + "type": "OpenAIModel", + "node": { + "template": { + "input_value": { + "type": "str", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "input_value", + "display_name": "Input", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\"},\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": True,\n \"info\": \"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": MODEL_NAMES,\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"openai_api_key\": {\n \"display_name\": \"OpenAI API Key\",\n \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n \"advanced\": False,\n \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float,\n model_name: str = \"gpt-4o\",\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n output = ChatOpenAI(\n max_tokens=max_tokens or None,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "max_tokens": { + "type": "int", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": 256, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "max_tokens", + "display_name": "Max Tokens", + "advanced": true, + "dynamic": false, + "info": "The maximum number of tokens to generate. Set to 0 for unlimited tokens.", + "load_from_db": false, + "title_case": false + }, + "model_kwargs": { + "type": "NestedDict", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": {}, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "model_kwargs", + "display_name": "Model Kwargs", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "model_name": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "value": "gpt-4-turbo-preview", + "fileTypes": [], + "file_path": "", + "password": false, + "options": [ + "gpt-4o", + "gpt-4-turbo", + "gpt-4-turbo-preview", + "gpt-3.5-turbo", + "gpt-3.5-turbo-0125" + ], + "name": "model_name", + "display_name": "Model Name", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "openai_api_base": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "openai_api_base", + "display_name": "OpenAI API Base", + "advanced": true, + "dynamic": false, + "info": "The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\n\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "openai_api_key": { + "type": "str", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": true, + "name": "openai_api_key", + "display_name": "OpenAI API Key", + "advanced": false, + "dynamic": false, + "info": "The OpenAI API Key to use for the OpenAI model.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ], + "value": "OPENAI_API_KEY" + }, + "stream": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": true, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "stream", + "display_name": "Stream", + "advanced": false, + "dynamic": false, + "info": "Stream the response from the model. Streaming works only in Chat.", + "load_from_db": false, + "title_case": false + }, + "system_message": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "system_message", + "display_name": "System Message", + "advanced": true, + "dynamic": false, + "info": "System message to pass to the model.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "temperature": { + "type": "float", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": 0.1, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "temperature", + "display_name": "Temperature", + "advanced": false, + "dynamic": false, + "info": "", + "rangeSpec": { + "step_type": "float", + "min": -1, + "max": 1, + "step": 0.1 }, - "style": { - "stroke": "#555" - }, - "className": "stroke-gray-900 stroke-connection", - "id": "reactflow__edge-OpenAIModel-Bt067{\u0153baseClasses\u0153:[\u0153object\u0153,\u0153str\u0153,\u0153Text\u0153],\u0153dataType\u0153:\u0153OpenAIModel\u0153,\u0153id\u0153:\u0153OpenAIModel-Bt067\u0153}-ChatOutput-F5Awj{\u0153fieldName\u0153:\u0153input_value\u0153,\u0153id\u0153:\u0153ChatOutput-F5Awj\u0153,\u0153inputTypes\u0153:[\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}" - } - ], - "viewport": { - "x": 352.20899206064655, - "y": 56.054900898593075, - "zoom": 0.9023391400011 - } - }, - "description": "This flow integrates PDF reading with a language model to answer document-specific questions. Ideal for small-scale texts, it facilitates direct queries with immediate insights.", - "name": "Document QA", - "last_tested_version": "1.0.0a0", - "is_component": false -} + "load_from_db": false, + "title_case": false + }, + "_type": "CustomComponent" + }, + "description": "Generates text using OpenAI LLMs.", + "icon": "OpenAI", + "base_classes": [ + "object", + "str", + "Text" + ], + "display_name": "OpenAI", + "documentation": "", + "custom_fields": { + "input_value": null, + "openai_api_key": null, + "temperature": null, + "model_name": null, + "max_tokens": null, + "model_kwargs": null, + "openai_api_base": null, + "stream": null, + "system_message": null + }, + "output_types": [ + "Text" + ], + "field_formatters": {}, + "frozen": false, + "field_order": [ + "max_tokens", + "model_kwargs", + "model_name", + "openai_api_base", + "openai_api_key", + "temperature", + "input_value", + "system_message", + "stream" + ], + "beta": false + }, + "id": "OpenAIModel-Bt067" + }, + "selected": false, + "width": 384, + "height": 642, + "positionAbsolute": { + "x": 1137.6078582863759, + "y": -14.41920034020356 + }, + "dragging": false + } + ], + "edges": [ + { + "source": "ChatInput-MsSJ9", + "sourceHandle": "{œbaseClassesœ:[œstrœ,œRecordœ,œTextœ,œobjectœ],œdataTypeœ:œChatInputœ,œidœ:œChatInput-MsSJ9œ}", + "target": "Prompt-tHwPf", + "targetHandle": "{œfieldNameœ:œQuestionœ,œidœ:œPrompt-tHwPfœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}", + "data": { + "targetHandle": { + "fieldName": "Question", + "id": "Prompt-tHwPf", + "inputTypes": [ + "Document", + "BaseOutputParser", + "Record", + "Text" + ], + "type": "str" + }, + "sourceHandle": { + "baseClasses": [ + "str", + "Record", + "Text", + "object" + ], + "dataType": "ChatInput", + "id": "ChatInput-MsSJ9" + } + }, + "style": { + "stroke": "#555" + }, + "className": "stroke-gray-900 stroke-connection", + "id": "reactflow__edge-ChatInput-MsSJ9{œbaseClassesœ:[œstrœ,œRecordœ,œTextœ,œobjectœ],œdataTypeœ:œChatInputœ,œidœ:œChatInput-MsSJ9œ}-Prompt-tHwPf{œfieldNameœ:œQuestionœ,œidœ:œPrompt-tHwPfœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}" + }, + { + "source": "File-6TEsD", + "sourceHandle": "{œbaseClassesœ:[œRecordœ],œdataTypeœ:œFileœ,œidœ:œFile-6TEsDœ}", + "target": "Prompt-tHwPf", + "targetHandle": "{œfieldNameœ:œDocumentœ,œidœ:œPrompt-tHwPfœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}", + "data": { + "targetHandle": { + "fieldName": "Document", + "id": "Prompt-tHwPf", + "inputTypes": [ + "Document", + "BaseOutputParser", + "Record", + "Text" + ], + "type": "str" + }, + "sourceHandle": { + "baseClasses": [ + "Record" + ], + "dataType": "File", + "id": "File-6TEsD" + } + }, + "style": { + "stroke": "#555" + }, + "className": "stroke-gray-900 stroke-connection", + "id": "reactflow__edge-File-6TEsD{œbaseClassesœ:[œRecordœ],œdataTypeœ:œFileœ,œidœ:œFile-6TEsDœ}-Prompt-tHwPf{œfieldNameœ:œDocumentœ,œidœ:œPrompt-tHwPfœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}" + }, + { + "source": "Prompt-tHwPf", + "sourceHandle": "{œbaseClassesœ:[œobjectœ,œstrœ,œTextœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-tHwPfœ}", + "target": "OpenAIModel-Bt067", + "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-Bt067œ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}", + "data": { + "targetHandle": { + "fieldName": "input_value", + "id": "OpenAIModel-Bt067", + "inputTypes": [ + "Text" + ], + "type": "str" + }, + "sourceHandle": { + "baseClasses": [ + "object", + "str", + "Text" + ], + "dataType": "Prompt", + "id": "Prompt-tHwPf" + } + }, + "style": { + "stroke": "#555" + }, + "className": "stroke-gray-900 stroke-connection", + "id": "reactflow__edge-Prompt-tHwPf{œbaseClassesœ:[œobjectœ,œstrœ,œTextœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-tHwPfœ}-OpenAIModel-Bt067{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-Bt067œ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}" + }, + { + "source": "OpenAIModel-Bt067", + "sourceHandle": "{œbaseClassesœ:[œobjectœ,œstrœ,œTextœ],œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-Bt067œ}", + "target": "ChatOutput-F5Awj", + "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-F5Awjœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}", + "data": { + "targetHandle": { + "fieldName": "input_value", + "id": "ChatOutput-F5Awj", + "inputTypes": [ + "Text" + ], + "type": "str" + }, + "sourceHandle": { + "baseClasses": [ + "object", + "str", + "Text" + ], + "dataType": "OpenAIModel", + "id": "OpenAIModel-Bt067" + } + }, + "style": { + "stroke": "#555" + }, + "className": "stroke-gray-900 stroke-connection", + "id": "reactflow__edge-OpenAIModel-Bt067{œbaseClassesœ:[œobjectœ,œstrœ,œTextœ],œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-Bt067œ}-ChatOutput-F5Awj{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-F5Awjœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}" + } + ], + "viewport": { + "x": 352.20899206064655, + "y": 56.054900898593075, + "zoom": 0.9023391400011 + } + }, + "description": "This flow integrates PDF reading with a language model to answer document-specific questions. Ideal for small-scale texts, it facilitates direct queries with immediate insights.", + "name": "Document QA", + "last_tested_version": "1.0.0a0", + "is_component": false +} \ No newline at end of file diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Memory Conversation.json b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Memory Conversation.json index 0b12d8d19..04d29b24a 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Memory Conversation.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Memory Conversation.json @@ -1,1272 +1,1272 @@ { - "id": "08d5cccf-d098-4367-b14b-1078429c9ed9", - "icon": "\ud83e\udd16", - "icon_bg_color": "#FFD700", - "data": { - "nodes": [ - { - "id": "ChatInput-t7F8v", - "type": "genericNode", - "position": { - "x": 1283.2700598313072, - "y": 982.5953650473145 - }, - "data": { - "type": "ChatInput", - "node": { - "template": { - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatInput(ChatComponent):\n display_name = \"Chat Input\"\n description = \"Get chat inputs from the Playground.\"\n icon = \"ChatInput\"\n\n def build_config(self):\n build_config = super().build_config()\n build_config[\"input_value\"] = {\n \"input_types\": [],\n \"display_name\": \"Message\",\n \"multiline\": True,\n }\n\n return build_config\n\n def build(\n self,\n sender: Optional[str] = \"User\",\n sender_name: Optional[str] = \"User\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n ) -> Union[Text, Record]:\n return super().build_no_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n )\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "input_value": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "input_value", - "display_name": "Message", - "advanced": false, - "input_types": [], - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "value": "" - }, - "return_record": { - "type": "bool", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "return_record", - "display_name": "Return Record", - "advanced": true, - "dynamic": false, - "info": "Return the message as a record containing the sender, sender_name, and session_id.", - "load_from_db": false, - "title_case": false - }, - "sender": { - "type": "str", - "required": false, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "value": "User", - "fileTypes": [], - "file_path": "", - "password": false, - "options": [ - "Machine", - "User" - ], - "name": "sender", - "display_name": "Sender Type", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "sender_name": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": "User", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "sender_name", - "display_name": "Sender Name", - "advanced": false, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "session_id": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "session_id", - "display_name": "Session ID", - "advanced": false, - "dynamic": false, - "info": "If provided, the message will be stored in the memory.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ], - "value": "MySessionID" - }, - "_type": "CustomComponent" - }, - "description": "Get chat inputs from the Playground.", - "icon": "ChatInput", - "base_classes": [ - "Text", - "object", - "Record", - "str" - ], - "display_name": "Chat Input", - "documentation": "", - "custom_fields": { - "sender": null, - "sender_name": null, - "input_value": null, - "session_id": null, - "return_record": null - }, - "output_types": [ - "Text", - "Record" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": false - }, - "id": "ChatInput-t7F8v" - }, - "selected": false, - "width": 384, - "height": 469, - "positionAbsolute": { - "x": 1283.2700598313072, - "y": 982.5953650473145 - }, - "dragging": false + "id": "08d5cccf-d098-4367-b14b-1078429c9ed9", + "icon": "🀖", + "icon_bg_color": "#FFD700", + "data": { + "nodes": [ + { + "id": "ChatInput-t7F8v", + "type": "genericNode", + "position": { + "x": 1283.2700598313072, + "y": 982.5953650473145 + }, + "data": { + "type": "ChatInput", + "node": { + "template": { + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\nfrom langflow.template import Input, Output\n\n\nclass ChatInput(ChatComponent):\n display_name = \"Chat Input\"\n description = \"Get chat inputs from the Playground.\"\n icon = \"ChatInput\"\n\n inputs = [\n Input(name=\"input_value\", type=str, display_name=\"Message\", multiline=True, input_types=[]),\n Input(name=\"sender\", type=str, display_name=\"Sender Type\", options=[\"Machine\", \"User\"]),\n Input(name=\"sender_name\", type=str, display_name=\"Sender Name\"),\n Input(name=\"session_id\", type=str, display_name=\"Session ID\"),\n ]\n outputs = [\n Output(name=\"Message\", method=\"text_response\"),\n Output(name=\"Record\", method=\"record_response\"),\n ]\n\n def build_config(self):\n build_config = super().build_config()\n build_config[\"input_value\"] = {\n \"input_types\": [],\n \"display_name\": \"Message\",\n \"multiline\": True,\n }\n\n return build_config\n\n def text_response(self):\n result = self.message\n if self.session_id and isinstance(result, (Record, str)):\n self.store_message(result, self.session_id, self.sender, self.sender_name)\n return result\n\n def record_response(self):\n record = Record(\n data={\n \"message\": self.message,\n \"sender\": self.sender,\n \"sender_name\": self.sender_name,\n \"session_id\": self.session_id,\n }\n )\n if self.session_id and isinstance(record, (Record, str)):\n self.store_message(record, self.session_id, self.sender, self.sender_name)\n return record\n\n def build(\n self,\n sender: Optional[str] = \"User\",\n sender_name: Optional[str] = \"User\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n ) -> Union[Text, Record]:\n return super().build_no_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n )\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "input_value": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "input_value", + "display_name": "Message", + "advanced": false, + "input_types": [], + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "value": "" + }, + "return_record": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "return_record", + "display_name": "Return Record", + "advanced": true, + "dynamic": false, + "info": "Return the message as a record containing the sender, sender_name, and session_id.", + "load_from_db": false, + "title_case": false + }, + "sender": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": "User", + "fileTypes": [], + "file_path": "", + "password": false, + "options": [ + "Machine", + "User" + ], + "name": "sender", + "display_name": "Sender Type", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "sender_name": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": "User", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "sender_name", + "display_name": "Sender Name", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "session_id": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "session_id", + "display_name": "Session ID", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ], + "value": "MySessionID" + }, + "_type": "CustomComponent" }, - { - "id": "ChatOutput-P1jEe", - "type": "genericNode", - "position": { - "x": 3154.916355514023, - "y": 851.051882666333 - }, - "data": { - "type": "ChatOutput", - "node": { - "template": { - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n def build(\n self,\n sender: Optional[str] = \"Machine\",\n sender_name: Optional[str] = \"AI\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n record_template: Optional[str] = \"{text}\",\n ) -> Union[Text, Record]:\n return super().build_with_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n record_template=record_template or \"\",\n )\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "input_value": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "input_value", - "display_name": "Message", - "advanced": false, - "input_types": [ - "Text" - ], - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "return_record": { - "type": "bool", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "return_record", - "display_name": "Return Record", - "advanced": true, - "dynamic": false, - "info": "Return the message as a record containing the sender, sender_name, and session_id.", - "load_from_db": false, - "title_case": false - }, - "sender": { - "type": "str", - "required": false, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "value": "Machine", - "fileTypes": [], - "file_path": "", - "password": false, - "options": [ - "Machine", - "User" - ], - "name": "sender", - "display_name": "Sender Type", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "sender_name": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": "AI", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "sender_name", - "display_name": "Sender Name", - "advanced": false, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "session_id": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "session_id", - "display_name": "Session ID", - "advanced": false, - "dynamic": false, - "info": "If provided, the message will be stored in the memory.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ], - "value": "MySessionID" - }, - "_type": "CustomComponent" - }, - "description": "Display a chat message in the Playground.", - "icon": "ChatOutput", - "base_classes": [ - "Text", - "object", - "Record", - "str" - ], - "display_name": "Chat Output", - "documentation": "", - "custom_fields": { - "sender": null, - "sender_name": null, - "input_value": null, - "session_id": null, - "return_record": null - }, - "output_types": [ - "Text", - "Record" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": false - }, - "id": "ChatOutput-P1jEe" - }, - "selected": false, - "width": 384, - "height": 477, - "dragging": false, - "positionAbsolute": { - "x": 3154.916355514023, - "y": 851.051882666333 - } + "description": "Get chat inputs from the Playground.", + "icon": "ChatInput", + "base_classes": [ + "Text", + "object", + "Record", + "str" + ], + "display_name": "Chat Input", + "documentation": "", + "custom_fields": { + "sender": null, + "sender_name": null, + "input_value": null, + "session_id": null, + "return_record": null }, - { - "id": "MemoryComponent-cdA1J", - "type": "genericNode", - "position": { - "x": 1289.9606870058817, - "y": 442.16804561053766 - }, - "data": { - "type": "MemoryComponent", - "node": { - "template": { - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from typing import Optional\n\nfrom langflow.base.memory.memory import BaseMemoryComponent\nfrom langflow.field_typing import Text\nfrom langflow.helpers.record import records_to_text\nfrom langflow.memory import get_messages\nfrom langflow.schema.schema import Record\n\n\nclass MemoryComponent(BaseMemoryComponent):\n display_name = \"Chat Memory\"\n description = \"Retrieves stored chat messages given a specific Session ID.\"\n beta: bool = True\n icon = \"history\"\n\n def build_config(self):\n return {\n \"sender\": {\n \"options\": [\"Machine\", \"User\", \"Machine and User\"],\n \"display_name\": \"Sender Type\",\n },\n \"sender_name\": {\"display_name\": \"Sender Name\", \"advanced\": True},\n \"n_messages\": {\n \"display_name\": \"Number of Messages\",\n \"info\": \"Number of messages to retrieve.\",\n },\n \"session_id\": {\n \"display_name\": \"Session ID\",\n \"info\": \"Session ID of the chat history.\",\n \"input_types\": [\"Text\"],\n },\n \"order\": {\n \"options\": [\"Ascending\", \"Descending\"],\n \"display_name\": \"Order\",\n \"info\": \"Order of the messages.\",\n \"advanced\": True,\n },\n \"record_template\": {\n \"display_name\": \"Record Template\",\n \"multiline\": True,\n \"info\": \"Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.\",\n \"advanced\": True,\n },\n }\n\n def get_messages(self, **kwargs) -> list[Record]:\n # Validate kwargs by checking if it contains the correct keys\n if \"sender\" not in kwargs:\n kwargs[\"sender\"] = None\n if \"sender_name\" not in kwargs:\n kwargs[\"sender_name\"] = None\n if \"session_id\" not in kwargs:\n kwargs[\"session_id\"] = None\n if \"limit\" not in kwargs:\n kwargs[\"limit\"] = 5\n if \"order\" not in kwargs:\n kwargs[\"order\"] = \"Descending\"\n\n kwargs[\"order\"] = \"DESC\" if kwargs[\"order\"] == \"Descending\" else \"ASC\"\n if kwargs[\"sender\"] == \"Machine and User\":\n kwargs[\"sender\"] = None\n return get_messages(**kwargs)\n\n def build(\n self,\n sender: Optional[str] = \"Machine and User\",\n sender_name: Optional[str] = None,\n session_id: Optional[str] = None,\n n_messages: int = 5,\n order: Optional[str] = \"Descending\",\n record_template: Optional[str] = \"{sender_name}: {text}\",\n ) -> Text:\n messages = self.get_messages(\n sender=sender,\n sender_name=sender_name,\n session_id=session_id,\n limit=n_messages,\n order=order,\n )\n messages_str = records_to_text(template=record_template or \"\", records=messages)\n self.status = messages_str\n return messages_str\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "n_messages": { - "type": "int", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": 5, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "n_messages", - "display_name": "Number of Messages", - "advanced": false, - "dynamic": false, - "info": "Number of messages to retrieve.", - "load_from_db": false, - "title_case": false - }, - "order": { - "type": "str", - "required": false, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "value": "Descending", - "fileTypes": [], - "file_path": "", - "password": false, - "options": [ - "Ascending", - "Descending" - ], - "name": "order", - "display_name": "Order", - "advanced": true, - "dynamic": false, - "info": "Order of the messages.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "record_template": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "{sender_name}: {text}", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "record_template", - "display_name": "Record Template", - "advanced": true, - "dynamic": false, - "info": "Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "sender": { - "type": "str", - "required": false, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "value": "Machine and User", - "fileTypes": [], - "file_path": "", - "password": false, - "options": [ - "Machine", - "User", - "Machine and User" - ], - "name": "sender", - "display_name": "Sender Type", - "advanced": false, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "sender_name": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "sender_name", - "display_name": "Sender Name", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "session_id": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "session_id", - "display_name": "Session ID", - "advanced": false, - "input_types": [ - "Text" - ], - "dynamic": false, - "info": "Session ID of the chat history.", - "load_from_db": false, - "title_case": false, - "value": "MySessionID" - }, - "_type": "CustomComponent" - }, - "description": "Retrieves stored chat messages given a specific Session ID.", - "icon": "history", - "base_classes": [ - "str", - "Text", - "object" - ], - "display_name": "Chat Memory", - "documentation": "", - "custom_fields": { - "sender": null, - "sender_name": null, - "session_id": null, - "n_messages": null, - "order": null, - "record_template": null - }, - "output_types": [ - "Text" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": true - }, - "id": "MemoryComponent-cdA1J", - "description": "Retrieves stored chat messages given a specific Session ID.", - "display_name": "Chat Memory" - }, - "selected": false, - "width": 384, - "height": 489, - "dragging": false, - "positionAbsolute": { - "x": 1289.9606870058817, - "y": 442.16804561053766 - } + "output_types": [ + "Text", + "Record" + ], + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false + }, + "id": "ChatInput-t7F8v" + }, + "selected": false, + "width": 384, + "height": 469, + "positionAbsolute": { + "x": 1283.2700598313072, + "y": 982.5953650473145 + }, + "dragging": false + }, + { + "id": "ChatOutput-P1jEe", + "type": "genericNode", + "position": { + "x": 3154.916355514023, + "y": 851.051882666333 + }, + "data": { + "type": "ChatOutput", + "node": { + "template": { + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n def build(\n self,\n sender: Optional[str] = \"Machine\",\n sender_name: Optional[str] = \"AI\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n record_template: Optional[str] = \"{text}\",\n ) -> Union[Text, Record]:\n return super().build_with_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n record_template=record_template or \"\",\n )\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "input_value": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "input_value", + "display_name": "Message", + "advanced": false, + "input_types": [ + "Text" + ], + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "return_record": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "return_record", + "display_name": "Return Record", + "advanced": true, + "dynamic": false, + "info": "Return the message as a record containing the sender, sender_name, and session_id.", + "load_from_db": false, + "title_case": false + }, + "sender": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "value": "Machine", + "fileTypes": [], + "file_path": "", + "password": false, + "options": [ + "Machine", + "User" + ], + "name": "sender", + "display_name": "Sender Type", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "sender_name": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": "AI", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "sender_name", + "display_name": "Sender Name", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "session_id": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "session_id", + "display_name": "Session ID", + "advanced": false, + "dynamic": false, + "info": "If provided, the message will be stored in the memory.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ], + "value": "MySessionID" + }, + "_type": "CustomComponent" }, - { - "id": "Prompt-ODkUx", - "type": "genericNode", - "position": { - "x": 1894.594426342426, - "y": 753.3797365481901 - }, - "data": { - "type": "Prompt", - "node": { - "template": { - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Input, Prompt, Text\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": Input(display_name=\"Template\"),\n \"code\": Input(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "template": { - "type": "prompt", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": "{context}\n\nUser: {user_message}\nAI: ", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "template", - "display_name": "Template", - "advanced": false, - "input_types": [ - "Text" - ], - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "_type": "CustomComponent", - "context": { - "field_type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "context", - "display_name": "context", - "advanced": false, - "input_types": [ - "Document", - "BaseOutputParser", - "Record", - "Text" - ], - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "type": "str" - }, - "user_message": { - "field_type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "user_message", - "display_name": "user_message", - "advanced": false, - "input_types": [ - "Document", - "BaseOutputParser", - "Record", - "Text" - ], - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "type": "str" - } - }, - "description": "Create a prompt template with dynamic variables.", - "icon": "prompts", - "is_input": null, - "is_output": null, - "is_composition": null, - "base_classes": [ - "Text", - "str", - "object" - ], - "name": "", - "display_name": "Prompt", - "documentation": "", - "custom_fields": { - "template": [ - "context", - "user_message" - ] - }, - "output_types": [ - "Text" - ], - "full_path": null, - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": false, - "error": null - }, - "id": "Prompt-ODkUx", - "description": "A component for creating prompt templates using dynamic variables.", - "display_name": "Prompt" - }, - "selected": false, - "width": 384, - "height": 477, - "dragging": false, - "positionAbsolute": { - "x": 1894.594426342426, - "y": 753.3797365481901 - } + "description": "Display a chat message in the Playground.", + "icon": "ChatOutput", + "base_classes": [ + "Text", + "object", + "Record", + "str" + ], + "display_name": "Chat Output", + "documentation": "", + "custom_fields": { + "sender": null, + "sender_name": null, + "input_value": null, + "session_id": null, + "return_record": null }, - { - "id": "OpenAIModel-9RykF", - "type": "genericNode", - "position": { - "x": 2561.5850334731617, - "y": 553.2745131130916 - }, - "data": { - "type": "OpenAIModel", - "node": { - "template": { - "input_value": { - "type": "str", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "input_value", - "display_name": "Input", - "advanced": false, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\"},\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": True,\n \"info\": \"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": MODEL_NAMES,\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"openai_api_key\": {\n \"display_name\": \"OpenAI API Key\",\n \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n \"advanced\": False,\n \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float,\n model_name: str = \"gpt-4o\",\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n output = ChatOpenAI(\n max_tokens=max_tokens or None,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "max_tokens": { - "type": "int", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": 256, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "max_tokens", - "display_name": "Max Tokens", - "advanced": true, - "dynamic": false, - "info": "The maximum number of tokens to generate. Set to 0 for unlimited tokens.", - "load_from_db": false, - "title_case": false - }, - "model_kwargs": { - "type": "NestedDict", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": {}, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "model_kwargs", - "display_name": "Model Kwargs", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "model_name": { - "type": "str", - "required": false, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "value": "gpt-4-1106-preview", - "fileTypes": [], - "file_path": "", - "password": false, - "options": [ - "gpt-4o", - "gpt-4-turbo", - "gpt-4-turbo-preview", - "gpt-3.5-turbo", - "gpt-3.5-turbo-0125" - ], - "name": "model_name", - "display_name": "Model Name", - "advanced": false, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "openai_api_base": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "openai_api_base", - "display_name": "OpenAI API Base", - "advanced": true, - "dynamic": false, - "info": "The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\n\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "openai_api_key": { - "type": "str", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": true, - "name": "openai_api_key", - "display_name": "OpenAI API Key", - "advanced": false, - "dynamic": false, - "info": "The OpenAI API Key to use for the OpenAI model.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ], - "value": "OPENAI_API_KEY" - }, - "stream": { - "type": "bool", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "stream", - "display_name": "Stream", - "advanced": true, - "dynamic": false, - "info": "Stream the response from the model. Streaming works only in Chat.", - "load_from_db": false, - "title_case": false - }, - "system_message": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "system_message", - "display_name": "System Message", - "advanced": true, - "dynamic": false, - "info": "System message to pass to the model.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "temperature": { - "type": "float", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": "0.2", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "temperature", - "display_name": "Temperature", - "advanced": false, - "dynamic": false, - "info": "", - "rangeSpec": { - "step_type": "float", - "min": -1, - "max": 1, - "step": 0.1 - }, - "load_from_db": false, - "title_case": false - }, - "_type": "CustomComponent" - }, - "description": "Generates text using OpenAI LLMs.", - "icon": "OpenAI", - "base_classes": [ - "str", - "object", - "Text" - ], - "display_name": "OpenAI", - "documentation": "", - "custom_fields": { - "input_value": null, - "openai_api_key": null, - "temperature": null, - "model_name": null, - "max_tokens": null, - "model_kwargs": null, - "openai_api_base": null, - "stream": null, - "system_message": null - }, - "output_types": [ - "Text" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [ - "max_tokens", - "model_kwargs", - "model_name", - "openai_api_base", - "openai_api_key", - "temperature", - "input_value", - "system_message", - "stream" - ], - "beta": false - }, - "id": "OpenAIModel-9RykF" - }, - "selected": false, - "width": 384, - "height": 563, - "positionAbsolute": { - "x": 2561.5850334731617, - "y": 553.2745131130916 - }, - "dragging": false - }, - { - "id": "TextOutput-vrs6T", - "type": "genericNode", - "position": { - "x": 1911.4785906252087, - "y": 247.39079954376987 - }, - "data": { - "type": "TextOutput", - "node": { - "template": { - "input_value": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": "", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "input_value", - "display_name": "Value", - "advanced": false, - "input_types": [ - "Record", - "Text" - ], - "dynamic": false, - "info": "Text or Record to be passed as output.", - "load_from_db": false, - "title_case": false - }, - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from typing import Optional\n\nfrom langflow.base.io.text import TextComponent\nfrom langflow.field_typing import Text\n\n\nclass TextOutput(TextComponent):\n display_name = \"Text Output\"\n description = \"Display a text output in the Playground.\"\n icon = \"type\"\n\n def build_config(self):\n return {\n \"input_value\": {\n \"display_name\": \"Value\",\n \"input_types\": [\"Record\", \"Text\"],\n \"info\": \"Text or Record to be passed as output.\",\n },\n \"record_template\": {\n \"display_name\": \"Record Template\",\n \"multiline\": True,\n \"info\": \"Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.\",\n \"advanced\": True,\n },\n }\n\n def build(self, input_value: Optional[Text] = \"\", record_template: str = \"\") -> Text:\n return super().build(input_value=input_value, record_template=record_template)\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "record_template": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "record_template", - "display_name": "Record Template", - "advanced": true, - "dynamic": false, - "info": "Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "_type": "CustomComponent" - }, - "description": "Display a text output in the Playground.", - "icon": "type", - "base_classes": [ - "str", - "object", - "Text" - ], - "display_name": "Inspect Memory", - "documentation": "", - "custom_fields": { - "input_value": null, - "record_template": null - }, - "output_types": [ - "Text" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": false - }, - "id": "TextOutput-vrs6T" - }, - "selected": false, - "width": 384, - "height": 289, - "positionAbsolute": { - "x": 1911.4785906252087, - "y": 247.39079954376987 - }, - "dragging": false - } - ], - "edges": [ - { - "source": "MemoryComponent-cdA1J", - "sourceHandle": "{\u0153baseClasses\u0153:[\u0153str\u0153,\u0153Text\u0153,\u0153object\u0153],\u0153dataType\u0153:\u0153MemoryComponent\u0153,\u0153id\u0153:\u0153MemoryComponent-cdA1J\u0153}", - "target": "Prompt-ODkUx", - "targetHandle": "{\u0153fieldName\u0153:\u0153context\u0153,\u0153id\u0153:\u0153Prompt-ODkUx\u0153,\u0153inputTypes\u0153:[\u0153Document\u0153,\u0153BaseOutputParser\u0153,\u0153Record\u0153,\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}", - "data": { - "targetHandle": { - "fieldName": "context", - "type": "str", - "id": "Prompt-ODkUx", - "inputTypes": [ - "Document", - "BaseOutputParser", - "Record", - "Text" - ] - }, - "sourceHandle": { - "baseClasses": [ - "str", - "Text", - "object" - ], - "dataType": "MemoryComponent", - "id": "MemoryComponent-cdA1J" - } - }, - "style": { - "stroke": "#555" - }, - "className": "stroke-gray-900 stroke-connection", - "id": "reactflow__edge-MemoryComponent-cdA1J{\u0153baseClasses\u0153:[\u0153str\u0153,\u0153Text\u0153,\u0153object\u0153],\u0153dataType\u0153:\u0153MemoryComponent\u0153,\u0153id\u0153:\u0153MemoryComponent-cdA1J\u0153}-Prompt-ODkUx{\u0153fieldName\u0153:\u0153context\u0153,\u0153id\u0153:\u0153Prompt-ODkUx\u0153,\u0153inputTypes\u0153:[\u0153Document\u0153,\u0153BaseOutputParser\u0153,\u0153Record\u0153,\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}", - "selected": false - }, - { - "source": "ChatInput-t7F8v", - "sourceHandle": "{\u0153baseClasses\u0153:[\u0153Text\u0153,\u0153object\u0153,\u0153Record\u0153,\u0153str\u0153],\u0153dataType\u0153:\u0153ChatInput\u0153,\u0153id\u0153:\u0153ChatInput-t7F8v\u0153}", - "target": "Prompt-ODkUx", - "targetHandle": "{\u0153fieldName\u0153:\u0153user_message\u0153,\u0153id\u0153:\u0153Prompt-ODkUx\u0153,\u0153inputTypes\u0153:[\u0153Document\u0153,\u0153BaseOutputParser\u0153,\u0153Record\u0153,\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}", - "data": { - "targetHandle": { - "fieldName": "user_message", - "type": "str", - "id": "Prompt-ODkUx", - "inputTypes": [ - "Document", - "BaseOutputParser", - "Record", - "Text" - ] - }, - "sourceHandle": { - "baseClasses": [ - "Text", - "object", - "Record", - "str" - ], - "dataType": "ChatInput", - "id": "ChatInput-t7F8v" - } - }, - "style": { - "stroke": "#555" - }, - "className": "stroke-gray-900 stroke-connection", - "id": "reactflow__edge-ChatInput-t7F8v{\u0153baseClasses\u0153:[\u0153Text\u0153,\u0153object\u0153,\u0153Record\u0153,\u0153str\u0153],\u0153dataType\u0153:\u0153ChatInput\u0153,\u0153id\u0153:\u0153ChatInput-t7F8v\u0153}-Prompt-ODkUx{\u0153fieldName\u0153:\u0153user_message\u0153,\u0153id\u0153:\u0153Prompt-ODkUx\u0153,\u0153inputTypes\u0153:[\u0153Document\u0153,\u0153BaseOutputParser\u0153,\u0153Record\u0153,\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}", - "selected": false - }, - { - "source": "Prompt-ODkUx", - "sourceHandle": "{\u0153baseClasses\u0153:[\u0153Text\u0153,\u0153str\u0153,\u0153object\u0153],\u0153dataType\u0153:\u0153Prompt\u0153,\u0153id\u0153:\u0153Prompt-ODkUx\u0153}", - "target": "OpenAIModel-9RykF", - "targetHandle": "{\u0153fieldName\u0153:\u0153input_value\u0153,\u0153id\u0153:\u0153OpenAIModel-9RykF\u0153,\u0153inputTypes\u0153:[\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}", - "data": { - "targetHandle": { - "fieldName": "input_value", - "id": "OpenAIModel-9RykF", - "inputTypes": [ - "Text" - ], - "type": "str" - }, - "sourceHandle": { - "baseClasses": [ - "Text", - "str", - "object" - ], - "dataType": "Prompt", - "id": "Prompt-ODkUx" - } - }, - "style": { - "stroke": "#555" - }, - "className": "stroke-gray-900 stroke-connection", - "id": "reactflow__edge-Prompt-ODkUx{\u0153baseClasses\u0153:[\u0153Text\u0153,\u0153str\u0153,\u0153object\u0153],\u0153dataType\u0153:\u0153Prompt\u0153,\u0153id\u0153:\u0153Prompt-ODkUx\u0153}-OpenAIModel-9RykF{\u0153fieldName\u0153:\u0153input_value\u0153,\u0153id\u0153:\u0153OpenAIModel-9RykF\u0153,\u0153inputTypes\u0153:[\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}" - }, - { - "source": "OpenAIModel-9RykF", - "sourceHandle": "{\u0153baseClasses\u0153:[\u0153str\u0153,\u0153object\u0153,\u0153Text\u0153],\u0153dataType\u0153:\u0153OpenAIModel\u0153,\u0153id\u0153:\u0153OpenAIModel-9RykF\u0153}", - "target": "ChatOutput-P1jEe", - "targetHandle": "{\u0153fieldName\u0153:\u0153input_value\u0153,\u0153id\u0153:\u0153ChatOutput-P1jEe\u0153,\u0153inputTypes\u0153:[\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}", - "data": { - "targetHandle": { - "fieldName": "input_value", - "id": "ChatOutput-P1jEe", - "inputTypes": [ - "Text" - ], - "type": "str" - }, - "sourceHandle": { - "baseClasses": [ - "str", - "object", - "Text" - ], - "dataType": "OpenAIModel", - "id": "OpenAIModel-9RykF" - } - }, - "style": { - "stroke": "#555" - }, - "className": "stroke-gray-900 stroke-connection", - "id": "reactflow__edge-OpenAIModel-9RykF{\u0153baseClasses\u0153:[\u0153str\u0153,\u0153object\u0153,\u0153Text\u0153],\u0153dataType\u0153:\u0153OpenAIModel\u0153,\u0153id\u0153:\u0153OpenAIModel-9RykF\u0153}-ChatOutput-P1jEe{\u0153fieldName\u0153:\u0153input_value\u0153,\u0153id\u0153:\u0153ChatOutput-P1jEe\u0153,\u0153inputTypes\u0153:[\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}" - }, - { - "source": "MemoryComponent-cdA1J", - "sourceHandle": "{\u0153baseClasses\u0153:[\u0153str\u0153,\u0153Text\u0153,\u0153object\u0153],\u0153dataType\u0153:\u0153MemoryComponent\u0153,\u0153id\u0153:\u0153MemoryComponent-cdA1J\u0153}", - "target": "TextOutput-vrs6T", - "targetHandle": "{\u0153fieldName\u0153:\u0153input_value\u0153,\u0153id\u0153:\u0153TextOutput-vrs6T\u0153,\u0153inputTypes\u0153:[\u0153Record\u0153,\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}", - "data": { - "targetHandle": { - "fieldName": "input_value", - "id": "TextOutput-vrs6T", - "inputTypes": [ - "Record", - "Text" - ], - "type": "str" - }, - "sourceHandle": { - "baseClasses": [ - "str", - "Text", - "object" - ], - "dataType": "MemoryComponent", - "id": "MemoryComponent-cdA1J" - } - }, - "style": { - "stroke": "#555" - }, - "className": "stroke-foreground stroke-connection", - "id": "reactflow__edge-MemoryComponent-cdA1J{\u0153baseClasses\u0153:[\u0153str\u0153,\u0153Text\u0153,\u0153object\u0153],\u0153dataType\u0153:\u0153MemoryComponent\u0153,\u0153id\u0153:\u0153MemoryComponent-cdA1J\u0153}-TextOutput-vrs6T{\u0153fieldName\u0153:\u0153input_value\u0153,\u0153id\u0153:\u0153TextOutput-vrs6T\u0153,\u0153inputTypes\u0153:[\u0153Record\u0153,\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}" - } - ], - "viewport": { - "x": -569.862554459756, - "y": -42.08339711050985, - "zoom": 0.4868590524514978 + "output_types": [ + "Text", + "Record" + ], + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false + }, + "id": "ChatOutput-P1jEe" + }, + "selected": false, + "width": 384, + "height": 477, + "dragging": false, + "positionAbsolute": { + "x": 3154.916355514023, + "y": 851.051882666333 } - }, - "description": "This project can be used as a starting point for building a Chat experience with user specific memory. You can set a different Session ID to start a new message history.", - "name": "Memory Chatbot", - "last_tested_version": "1.0.0a0", - "is_component": false -} + }, + { + "id": "MemoryComponent-cdA1J", + "type": "genericNode", + "position": { + "x": 1289.9606870058817, + "y": 442.16804561053766 + }, + "data": { + "type": "MemoryComponent", + "node": { + "template": { + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import Optional\n\nfrom langflow.base.memory.memory import BaseMemoryComponent\nfrom langflow.field_typing import Text\nfrom langflow.helpers.record import records_to_text\nfrom langflow.memory import get_messages\nfrom langflow.schema.schema import Record\n\n\nclass MemoryComponent(BaseMemoryComponent):\n display_name = \"Chat Memory\"\n description = \"Retrieves stored chat messages given a specific Session ID.\"\n beta: bool = True\n icon = \"history\"\n\n def build_config(self):\n return {\n \"sender\": {\n \"options\": [\"Machine\", \"User\", \"Machine and User\"],\n \"display_name\": \"Sender Type\",\n },\n \"sender_name\": {\"display_name\": \"Sender Name\", \"advanced\": True},\n \"n_messages\": {\n \"display_name\": \"Number of Messages\",\n \"info\": \"Number of messages to retrieve.\",\n },\n \"session_id\": {\n \"display_name\": \"Session ID\",\n \"info\": \"Session ID of the chat history.\",\n \"input_types\": [\"Text\"],\n },\n \"order\": {\n \"options\": [\"Ascending\", \"Descending\"],\n \"display_name\": \"Order\",\n \"info\": \"Order of the messages.\",\n \"advanced\": True,\n },\n \"record_template\": {\n \"display_name\": \"Record Template\",\n \"multiline\": True,\n \"info\": \"Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.\",\n \"advanced\": True,\n },\n }\n\n def get_messages(self, **kwargs) -> list[Record]:\n # Validate kwargs by checking if it contains the correct keys\n if \"sender\" not in kwargs:\n kwargs[\"sender\"] = None\n if \"sender_name\" not in kwargs:\n kwargs[\"sender_name\"] = None\n if \"session_id\" not in kwargs:\n kwargs[\"session_id\"] = None\n if \"limit\" not in kwargs:\n kwargs[\"limit\"] = 5\n if \"order\" not in kwargs:\n kwargs[\"order\"] = \"Descending\"\n\n kwargs[\"order\"] = \"DESC\" if kwargs[\"order\"] == \"Descending\" else \"ASC\"\n if kwargs[\"sender\"] == \"Machine and User\":\n kwargs[\"sender\"] = None\n return get_messages(**kwargs)\n\n def build(\n self,\n sender: Optional[str] = \"Machine and User\",\n sender_name: Optional[str] = None,\n session_id: Optional[str] = None,\n n_messages: int = 5,\n order: Optional[str] = \"Descending\",\n record_template: Optional[str] = \"{sender_name}: {text}\",\n ) -> Text:\n messages = self.get_messages(\n sender=sender,\n sender_name=sender_name,\n session_id=session_id,\n limit=n_messages,\n order=order,\n )\n messages_str = records_to_text(template=record_template or \"\", records=messages)\n self.status = messages_str\n return messages_str\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "n_messages": { + "type": "int", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": 5, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "n_messages", + "display_name": "Number of Messages", + "advanced": false, + "dynamic": false, + "info": "Number of messages to retrieve.", + "load_from_db": false, + "title_case": false + }, + "order": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "value": "Descending", + "fileTypes": [], + "file_path": "", + "password": false, + "options": [ + "Ascending", + "Descending" + ], + "name": "order", + "display_name": "Order", + "advanced": true, + "dynamic": false, + "info": "Order of the messages.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "record_template": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "{sender_name}: {text}", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "record_template", + "display_name": "Record Template", + "advanced": true, + "dynamic": false, + "info": "Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "sender": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "value": "Machine and User", + "fileTypes": [], + "file_path": "", + "password": false, + "options": [ + "Machine", + "User", + "Machine and User" + ], + "name": "sender", + "display_name": "Sender Type", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "sender_name": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "sender_name", + "display_name": "Sender Name", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "session_id": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "session_id", + "display_name": "Session ID", + "advanced": false, + "input_types": [ + "Text" + ], + "dynamic": false, + "info": "Session ID of the chat history.", + "load_from_db": false, + "title_case": false, + "value": "MySessionID" + }, + "_type": "CustomComponent" + }, + "description": "Retrieves stored chat messages given a specific Session ID.", + "icon": "history", + "base_classes": [ + "str", + "Text", + "object" + ], + "display_name": "Chat Memory", + "documentation": "", + "custom_fields": { + "sender": null, + "sender_name": null, + "session_id": null, + "n_messages": null, + "order": null, + "record_template": null + }, + "output_types": [ + "Text" + ], + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": true + }, + "id": "MemoryComponent-cdA1J", + "description": "Retrieves stored chat messages given a specific Session ID.", + "display_name": "Chat Memory" + }, + "selected": false, + "width": 384, + "height": 489, + "dragging": false, + "positionAbsolute": { + "x": 1289.9606870058817, + "y": 442.16804561053766 + } + }, + { + "id": "Prompt-ODkUx", + "type": "genericNode", + "position": { + "x": 1894.594426342426, + "y": 753.3797365481901 + }, + "data": { + "type": "Prompt", + "node": { + "template": { + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Input, Prompt, Text\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": Input(display_name=\"Template\"),\n \"code\": Input(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "template": { + "type": "prompt", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": "{context}\n\nUser: {user_message}\nAI: ", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "template", + "display_name": "Template", + "advanced": false, + "input_types": [ + "Text" + ], + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "_type": "CustomComponent", + "context": { + "field_type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "context", + "display_name": "context", + "advanced": false, + "input_types": [ + "Document", + "BaseOutputParser", + "Record", + "Text" + ], + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "type": "str" + }, + "user_message": { + "field_type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "user_message", + "display_name": "user_message", + "advanced": false, + "input_types": [ + "Document", + "BaseOutputParser", + "Record", + "Text" + ], + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "type": "str" + } + }, + "description": "Create a prompt template with dynamic variables.", + "icon": "prompts", + "is_input": null, + "is_output": null, + "is_composition": null, + "base_classes": [ + "Text", + "str", + "object" + ], + "name": "", + "display_name": "Prompt", + "documentation": "", + "custom_fields": { + "template": [ + "context", + "user_message" + ] + }, + "output_types": [ + "Text" + ], + "full_path": null, + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false, + "error": null + }, + "id": "Prompt-ODkUx", + "description": "A component for creating prompt templates using dynamic variables.", + "display_name": "Prompt" + }, + "selected": false, + "width": 384, + "height": 477, + "dragging": false, + "positionAbsolute": { + "x": 1894.594426342426, + "y": 753.3797365481901 + } + }, + { + "id": "OpenAIModel-9RykF", + "type": "genericNode", + "position": { + "x": 2561.5850334731617, + "y": 553.2745131130916 + }, + "data": { + "type": "OpenAIModel", + "node": { + "template": { + "input_value": { + "type": "str", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "input_value", + "display_name": "Input", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\"},\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": True,\n \"info\": \"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": MODEL_NAMES,\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"openai_api_key\": {\n \"display_name\": \"OpenAI API Key\",\n \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n \"advanced\": False,\n \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float,\n model_name: str = \"gpt-4o\",\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n output = ChatOpenAI(\n max_tokens=max_tokens or None,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "max_tokens": { + "type": "int", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": 256, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "max_tokens", + "display_name": "Max Tokens", + "advanced": true, + "dynamic": false, + "info": "The maximum number of tokens to generate. Set to 0 for unlimited tokens.", + "load_from_db": false, + "title_case": false + }, + "model_kwargs": { + "type": "NestedDict", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": {}, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "model_kwargs", + "display_name": "Model Kwargs", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "model_name": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "value": "gpt-4-1106-preview", + "fileTypes": [], + "file_path": "", + "password": false, + "options": [ + "gpt-4o", + "gpt-4-turbo", + "gpt-4-turbo-preview", + "gpt-3.5-turbo", + "gpt-3.5-turbo-0125" + ], + "name": "model_name", + "display_name": "Model Name", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "openai_api_base": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "openai_api_base", + "display_name": "OpenAI API Base", + "advanced": true, + "dynamic": false, + "info": "The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\n\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "openai_api_key": { + "type": "str", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": true, + "name": "openai_api_key", + "display_name": "OpenAI API Key", + "advanced": false, + "dynamic": false, + "info": "The OpenAI API Key to use for the OpenAI model.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ], + "value": "OPENAI_API_KEY" + }, + "stream": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "stream", + "display_name": "Stream", + "advanced": true, + "dynamic": false, + "info": "Stream the response from the model. Streaming works only in Chat.", + "load_from_db": false, + "title_case": false + }, + "system_message": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "system_message", + "display_name": "System Message", + "advanced": true, + "dynamic": false, + "info": "System message to pass to the model.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "temperature": { + "type": "float", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": "0.2", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "temperature", + "display_name": "Temperature", + "advanced": false, + "dynamic": false, + "info": "", + "rangeSpec": { + "step_type": "float", + "min": -1, + "max": 1, + "step": 0.1 + }, + "load_from_db": false, + "title_case": false + }, + "_type": "CustomComponent" + }, + "description": "Generates text using OpenAI LLMs.", + "icon": "OpenAI", + "base_classes": [ + "str", + "object", + "Text" + ], + "display_name": "OpenAI", + "documentation": "", + "custom_fields": { + "input_value": null, + "openai_api_key": null, + "temperature": null, + "model_name": null, + "max_tokens": null, + "model_kwargs": null, + "openai_api_base": null, + "stream": null, + "system_message": null + }, + "output_types": [ + "Text" + ], + "field_formatters": {}, + "frozen": false, + "field_order": [ + "max_tokens", + "model_kwargs", + "model_name", + "openai_api_base", + "openai_api_key", + "temperature", + "input_value", + "system_message", + "stream" + ], + "beta": false + }, + "id": "OpenAIModel-9RykF" + }, + "selected": false, + "width": 384, + "height": 563, + "positionAbsolute": { + "x": 2561.5850334731617, + "y": 553.2745131130916 + }, + "dragging": false + }, + { + "id": "TextOutput-vrs6T", + "type": "genericNode", + "position": { + "x": 1911.4785906252087, + "y": 247.39079954376987 + }, + "data": { + "type": "TextOutput", + "node": { + "template": { + "input_value": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": "", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "input_value", + "display_name": "Value", + "advanced": false, + "input_types": [ + "Record", + "Text" + ], + "dynamic": false, + "info": "Text or Record to be passed as output.", + "load_from_db": false, + "title_case": false + }, + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import Optional\n\nfrom langflow.base.io.text import TextComponent\nfrom langflow.field_typing import Text\n\n\nclass TextOutput(TextComponent):\n display_name = \"Text Output\"\n description = \"Display a text output in the Playground.\"\n icon = \"type\"\n\n def build_config(self):\n return {\n \"input_value\": {\n \"display_name\": \"Value\",\n \"input_types\": [\"Record\", \"Text\"],\n \"info\": \"Text or Record to be passed as output.\",\n },\n \"record_template\": {\n \"display_name\": \"Record Template\",\n \"multiline\": True,\n \"info\": \"Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.\",\n \"advanced\": True,\n },\n }\n\n def build(self, input_value: Optional[Text] = \"\", record_template: str = \"\") -> Text:\n return super().build(input_value=input_value, record_template=record_template)\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "record_template": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "record_template", + "display_name": "Record Template", + "advanced": true, + "dynamic": false, + "info": "Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "_type": "CustomComponent" + }, + "description": "Display a text output in the Playground.", + "icon": "type", + "base_classes": [ + "str", + "object", + "Text" + ], + "display_name": "Inspect Memory", + "documentation": "", + "custom_fields": { + "input_value": null, + "record_template": null + }, + "output_types": [ + "Text" + ], + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false + }, + "id": "TextOutput-vrs6T" + }, + "selected": false, + "width": 384, + "height": 289, + "positionAbsolute": { + "x": 1911.4785906252087, + "y": 247.39079954376987 + }, + "dragging": false + } + ], + "edges": [ + { + "source": "MemoryComponent-cdA1J", + "sourceHandle": "{œbaseClassesœ:[œstrœ,œTextœ,œobjectœ],œdataTypeœ:œMemoryComponentœ,œidœ:œMemoryComponent-cdA1Jœ}", + "target": "Prompt-ODkUx", + "targetHandle": "{œfieldNameœ:œcontextœ,œidœ:œPrompt-ODkUxœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}", + "data": { + "targetHandle": { + "fieldName": "context", + "type": "str", + "id": "Prompt-ODkUx", + "inputTypes": [ + "Document", + "BaseOutputParser", + "Record", + "Text" + ] + }, + "sourceHandle": { + "baseClasses": [ + "str", + "Text", + "object" + ], + "dataType": "MemoryComponent", + "id": "MemoryComponent-cdA1J" + } + }, + "style": { + "stroke": "#555" + }, + "className": "stroke-gray-900 stroke-connection", + "id": "reactflow__edge-MemoryComponent-cdA1J{œbaseClassesœ:[œstrœ,œTextœ,œobjectœ],œdataTypeœ:œMemoryComponentœ,œidœ:œMemoryComponent-cdA1Jœ}-Prompt-ODkUx{œfieldNameœ:œcontextœ,œidœ:œPrompt-ODkUxœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}", + "selected": false + }, + { + "source": "ChatInput-t7F8v", + "sourceHandle": "{œbaseClassesœ:[œTextœ,œobjectœ,œRecordœ,œstrœ],œdataTypeœ:œChatInputœ,œidœ:œChatInput-t7F8vœ}", + "target": "Prompt-ODkUx", + "targetHandle": "{œfieldNameœ:œuser_messageœ,œidœ:œPrompt-ODkUxœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}", + "data": { + "targetHandle": { + "fieldName": "user_message", + "type": "str", + "id": "Prompt-ODkUx", + "inputTypes": [ + "Document", + "BaseOutputParser", + "Record", + "Text" + ] + }, + "sourceHandle": { + "baseClasses": [ + "Text", + "object", + "Record", + "str" + ], + "dataType": "ChatInput", + "id": "ChatInput-t7F8v" + } + }, + "style": { + "stroke": "#555" + }, + "className": "stroke-gray-900 stroke-connection", + "id": "reactflow__edge-ChatInput-t7F8v{œbaseClassesœ:[œTextœ,œobjectœ,œRecordœ,œstrœ],œdataTypeœ:œChatInputœ,œidœ:œChatInput-t7F8vœ}-Prompt-ODkUx{œfieldNameœ:œuser_messageœ,œidœ:œPrompt-ODkUxœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}", + "selected": false + }, + { + "source": "Prompt-ODkUx", + "sourceHandle": "{œbaseClassesœ:[œTextœ,œstrœ,œobjectœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-ODkUxœ}", + "target": "OpenAIModel-9RykF", + "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-9RykFœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}", + "data": { + "targetHandle": { + "fieldName": "input_value", + "id": "OpenAIModel-9RykF", + "inputTypes": [ + "Text" + ], + "type": "str" + }, + "sourceHandle": { + "baseClasses": [ + "Text", + "str", + "object" + ], + "dataType": "Prompt", + "id": "Prompt-ODkUx" + } + }, + "style": { + "stroke": "#555" + }, + "className": "stroke-gray-900 stroke-connection", + "id": "reactflow__edge-Prompt-ODkUx{œbaseClassesœ:[œTextœ,œstrœ,œobjectœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-ODkUxœ}-OpenAIModel-9RykF{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-9RykFœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}" + }, + { + "source": "OpenAIModel-9RykF", + "sourceHandle": "{œbaseClassesœ:[œstrœ,œobjectœ,œTextœ],œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-9RykFœ}", + "target": "ChatOutput-P1jEe", + "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-P1jEeœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}", + "data": { + "targetHandle": { + "fieldName": "input_value", + "id": "ChatOutput-P1jEe", + "inputTypes": [ + "Text" + ], + "type": "str" + }, + "sourceHandle": { + "baseClasses": [ + "str", + "object", + "Text" + ], + "dataType": "OpenAIModel", + "id": "OpenAIModel-9RykF" + } + }, + "style": { + "stroke": "#555" + }, + "className": "stroke-gray-900 stroke-connection", + "id": "reactflow__edge-OpenAIModel-9RykF{œbaseClassesœ:[œstrœ,œobjectœ,œTextœ],œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-9RykFœ}-ChatOutput-P1jEe{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-P1jEeœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}" + }, + { + "source": "MemoryComponent-cdA1J", + "sourceHandle": "{œbaseClassesœ:[œstrœ,œTextœ,œobjectœ],œdataTypeœ:œMemoryComponentœ,œidœ:œMemoryComponent-cdA1Jœ}", + "target": "TextOutput-vrs6T", + "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œTextOutput-vrs6Tœ,œinputTypesœ:[œRecordœ,œTextœ],œtypeœ:œstrœ}", + "data": { + "targetHandle": { + "fieldName": "input_value", + "id": "TextOutput-vrs6T", + "inputTypes": [ + "Record", + "Text" + ], + "type": "str" + }, + "sourceHandle": { + "baseClasses": [ + "str", + "Text", + "object" + ], + "dataType": "MemoryComponent", + "id": "MemoryComponent-cdA1J" + } + }, + "style": { + "stroke": "#555" + }, + "className": "stroke-foreground stroke-connection", + "id": "reactflow__edge-MemoryComponent-cdA1J{œbaseClassesœ:[œstrœ,œTextœ,œobjectœ],œdataTypeœ:œMemoryComponentœ,œidœ:œMemoryComponent-cdA1Jœ}-TextOutput-vrs6T{œfieldNameœ:œinput_valueœ,œidœ:œTextOutput-vrs6Tœ,œinputTypesœ:[œRecordœ,œTextœ],œtypeœ:œstrœ}" + } + ], + "viewport": { + "x": -569.862554459756, + "y": -42.08339711050985, + "zoom": 0.4868590524514978 + } + }, + "description": "This project can be used as a starting point for building a Chat experience with user specific memory. You can set a different Session ID to start a new message history.", + "name": "Memory Chatbot", + "last_tested_version": "1.0.0a0", + "is_component": false +} \ No newline at end of file diff --git a/src/backend/base/langflow/initial_setup/starter_projects/VectorStore-RAG-Flows.json b/src/backend/base/langflow/initial_setup/starter_projects/VectorStore-RAG-Flows.json index 09b37d168..60b357851 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/VectorStore-RAG-Flows.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/VectorStore-RAG-Flows.json @@ -1,3407 +1,3407 @@ { - "id": "51e2b78a-199b-4054-9f32-e288eef6924c", - "data": { - "nodes": [ - { - "id": "ChatInput-yxMKE", - "type": "genericNode", - "position": { - "x": 1195.5276981160775, - "y": 209.421875 - }, - "data": { - "type": "ChatInput", - "node": { - "template": { - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatInput(ChatComponent):\n display_name = \"Chat Input\"\n description = \"Get chat inputs from the Playground.\"\n icon = \"ChatInput\"\n\n def build_config(self):\n build_config = super().build_config()\n build_config[\"input_value\"] = {\n \"input_types\": [],\n \"display_name\": \"Message\",\n \"multiline\": True,\n }\n\n return build_config\n\n def build(\n self,\n sender: Optional[str] = \"User\",\n sender_name: Optional[str] = \"User\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n ) -> Union[Text, Record]:\n return super().build_no_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n )\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "input_value": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "input_value", - "display_name": "Message", - "advanced": false, - "input_types": [], - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "value": "what is a line" - }, - "return_record": { - "type": "bool", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "return_record", - "display_name": "Return Record", - "advanced": true, - "dynamic": false, - "info": "Return the message as a record containing the sender, sender_name, and session_id.", - "load_from_db": false, - "title_case": false - }, - "sender": { - "type": "str", - "required": false, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "value": "User", - "fileTypes": [], - "file_path": "", - "password": false, - "options": [ - "Machine", - "User" - ], - "name": "sender", - "display_name": "Sender Type", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "sender_name": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": "User", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "sender_name", - "display_name": "Sender Name", - "advanced": false, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "session_id": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "session_id", - "display_name": "Session ID", - "advanced": true, - "dynamic": false, - "info": "If provided, the message will be stored in the memory.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "_type": "CustomComponent" - }, - "description": "Get chat inputs from the Playground.", - "icon": "ChatInput", - "base_classes": [ - "Text", - "str", - "object", - "Record" - ], - "display_name": "Chat Input", - "documentation": "", - "custom_fields": { - "sender": null, - "sender_name": null, - "input_value": null, - "session_id": null, - "return_record": null - }, - "output_types": [ - "Text", - "Record" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": false - }, - "id": "ChatInput-yxMKE" - }, - "selected": false, - "width": 384, - "height": 383 + "id": "51e2b78a-199b-4054-9f32-e288eef6924c", + "data": { + "nodes": [ + { + "id": "ChatInput-yxMKE", + "type": "genericNode", + "position": { + "x": 1195.5276981160775, + "y": 209.421875 + }, + "data": { + "type": "ChatInput", + "node": { + "template": { + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\nfrom langflow.template import Input, Output\n\n\nclass ChatInput(ChatComponent):\n display_name = \"Chat Input\"\n description = \"Get chat inputs from the Playground.\"\n icon = \"ChatInput\"\n\n inputs = [\n Input(name=\"input_value\", type=str, display_name=\"Message\", multiline=True, input_types=[]),\n Input(name=\"sender\", type=str, display_name=\"Sender Type\", options=[\"Machine\", \"User\"]),\n Input(name=\"sender_name\", type=str, display_name=\"Sender Name\"),\n Input(name=\"session_id\", type=str, display_name=\"Session ID\"),\n ]\n outputs = [\n Output(name=\"Message\", method=\"text_response\"),\n Output(name=\"Record\", method=\"record_response\"),\n ]\n\n def build_config(self):\n build_config = super().build_config()\n build_config[\"input_value\"] = {\n \"input_types\": [],\n \"display_name\": \"Message\",\n \"multiline\": True,\n }\n\n return build_config\n\n def text_response(self):\n result = self.message\n if self.session_id and isinstance(result, (Record, str)):\n self.store_message(result, self.session_id, self.sender, self.sender_name)\n return result\n\n def record_response(self):\n record = Record(\n data={\n \"message\": self.message,\n \"sender\": self.sender,\n \"sender_name\": self.sender_name,\n \"session_id\": self.session_id,\n }\n )\n if self.session_id and isinstance(record, (Record, str)):\n self.store_message(record, self.session_id, self.sender, self.sender_name)\n return record\n\n def build(\n self,\n sender: Optional[str] = \"User\",\n sender_name: Optional[str] = \"User\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n ) -> Union[Text, Record]:\n return super().build_no_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n )\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "input_value": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "input_value", + "display_name": "Message", + "advanced": false, + "input_types": [], + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "value": "what is a line" + }, + "return_record": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "return_record", + "display_name": "Return Record", + "advanced": true, + "dynamic": false, + "info": "Return the message as a record containing the sender, sender_name, and session_id.", + "load_from_db": false, + "title_case": false + }, + "sender": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": "User", + "fileTypes": [], + "file_path": "", + "password": false, + "options": [ + "Machine", + "User" + ], + "name": "sender", + "display_name": "Sender Type", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "sender_name": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": "User", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "sender_name", + "display_name": "Sender Name", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "session_id": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "session_id", + "display_name": "Session ID", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "_type": "CustomComponent" }, - { - "id": "TextOutput-BDknO", - "type": "genericNode", - "position": { - "x": 2322.600672827879, - "y": 604.9467307442569 - }, - "data": { - "type": "TextOutput", - "node": { - "template": { - "input_value": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": "", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "input_value", - "display_name": "Value", - "advanced": false, - "input_types": [ - "Record", - "Text" - ], - "dynamic": false, - "info": "Text or Record to be passed as output.", - "load_from_db": false, - "title_case": false - }, - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from typing import Optional\n\nfrom langflow.base.io.text import TextComponent\nfrom langflow.field_typing import Text\n\n\nclass TextOutput(TextComponent):\n display_name = \"Text Output\"\n description = \"Display a text output in the Playground.\"\n icon = \"type\"\n\n def build_config(self):\n return {\n \"input_value\": {\n \"display_name\": \"Value\",\n \"input_types\": [\"Record\", \"Text\"],\n \"info\": \"Text or Record to be passed as output.\",\n },\n \"record_template\": {\n \"display_name\": \"Record Template\",\n \"multiline\": True,\n \"info\": \"Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.\",\n \"advanced\": True,\n },\n }\n\n def build(self, input_value: Optional[Text] = \"\", record_template: str = \"\") -> Text:\n return super().build(input_value=input_value, record_template=record_template)\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "record_template": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "{text}", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "record_template", - "display_name": "Record Template", - "advanced": true, - "dynamic": false, - "info": "Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "_type": "CustomComponent" - }, - "description": "Display a text output in the Playground.", - "icon": "type", - "base_classes": [ - "object", - "Text", - "str" - ], - "display_name": "Extracted Chunks", - "documentation": "", - "custom_fields": { - "input_value": null, - "record_template": null - }, - "output_types": [ - "Text" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": false - }, - "id": "TextOutput-BDknO" - }, - "selected": false, - "width": 384, - "height": 289, - "positionAbsolute": { - "x": 2322.600672827879, - "y": 604.9467307442569 - }, - "dragging": false + "description": "Get chat inputs from the Playground.", + "icon": "ChatInput", + "base_classes": [ + "Text", + "str", + "object", + "Record" + ], + "display_name": "Chat Input", + "documentation": "", + "custom_fields": { + "sender": null, + "sender_name": null, + "input_value": null, + "session_id": null, + "return_record": null }, - { - "id": "OpenAIEmbeddings-ZlOk1", - "type": "genericNode", - "position": { - "x": 1183.667250865064, - "y": 687.3171828430261 - }, - "data": { - "type": "OpenAIEmbeddings", - "node": { - "template": { - "allowed_special": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": [], - "fileTypes": [], - "file_path": "", - "password": false, - "name": "allowed_special", - "display_name": "Allowed Special", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "chunk_size": { - "type": "int", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": 1000, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "chunk_size", - "display_name": "Chunk Size", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "client": { - "type": "Any", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "client", - "display_name": "Client", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from typing import Dict, List, Optional\n\nfrom langchain_openai.embeddings.base import OpenAIEmbeddings\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Embeddings, NestedDict\n\n\nclass OpenAIEmbeddingsComponent(CustomComponent):\n display_name = \"OpenAI Embeddings\"\n description = \"Generate embeddings using OpenAI models.\"\n\n def build_config(self):\n return {\n \"allowed_special\": {\n \"display_name\": \"Allowed Special\",\n \"advanced\": True,\n \"field_type\": \"str\",\n \"is_list\": True,\n },\n \"default_headers\": {\n \"display_name\": \"Default Headers\",\n \"advanced\": True,\n \"field_type\": \"dict\",\n },\n \"default_query\": {\n \"display_name\": \"Default Query\",\n \"advanced\": True,\n \"field_type\": \"NestedDict\",\n },\n \"disallowed_special\": {\n \"display_name\": \"Disallowed Special\",\n \"advanced\": True,\n \"field_type\": \"str\",\n \"is_list\": True,\n },\n \"chunk_size\": {\"display_name\": \"Chunk Size\", \"advanced\": True},\n \"client\": {\"display_name\": \"Client\", \"advanced\": True},\n \"deployment\": {\"display_name\": \"Deployment\", \"advanced\": True},\n \"embedding_ctx_length\": {\n \"display_name\": \"Embedding Context Length\",\n \"advanced\": True,\n },\n \"max_retries\": {\"display_name\": \"Max Retries\", \"advanced\": True},\n \"model\": {\n \"display_name\": \"Model\",\n \"advanced\": False,\n \"options\": [\n \"text-embedding-3-small\",\n \"text-embedding-3-large\",\n \"text-embedding-ada-002\",\n ],\n },\n \"model_kwargs\": {\"display_name\": \"Model Kwargs\", \"advanced\": True},\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"password\": True,\n \"advanced\": True,\n },\n \"openai_api_key\": {\"display_name\": \"OpenAI API Key\", \"password\": True},\n \"openai_api_type\": {\n \"display_name\": \"OpenAI API Type\",\n \"advanced\": True,\n \"password\": True,\n },\n \"openai_api_version\": {\n \"display_name\": \"OpenAI API Version\",\n \"advanced\": True,\n },\n \"openai_organization\": {\n \"display_name\": \"OpenAI Organization\",\n \"advanced\": True,\n },\n \"openai_proxy\": {\"display_name\": \"OpenAI Proxy\", \"advanced\": True},\n \"request_timeout\": {\"display_name\": \"Request Timeout\", \"advanced\": True},\n \"show_progress_bar\": {\n \"display_name\": \"Show Progress Bar\",\n \"advanced\": True,\n },\n \"skip_empty\": {\"display_name\": \"Skip Empty\", \"advanced\": True},\n \"tiktoken_model_name\": {\n \"display_name\": \"TikToken Model Name\",\n \"advanced\": True,\n },\n \"tiktoken_enable\": {\"display_name\": \"TikToken Enable\", \"advanced\": True},\n }\n\n def build(\n self,\n openai_api_key: str,\n default_headers: Optional[Dict[str, str]] = None,\n default_query: Optional[NestedDict] = {},\n allowed_special: List[str] = [],\n disallowed_special: List[str] = [\"all\"],\n chunk_size: int = 1000,\n deployment: str = \"text-embedding-ada-002\",\n embedding_ctx_length: int = 8191,\n max_retries: int = 6,\n model: str = \"text-embedding-ada-002\",\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n openai_api_type: Optional[str] = None,\n openai_api_version: Optional[str] = None,\n openai_organization: Optional[str] = None,\n openai_proxy: Optional[str] = None,\n request_timeout: Optional[float] = None,\n show_progress_bar: bool = False,\n skip_empty: bool = False,\n tiktoken_enable: bool = True,\n tiktoken_model_name: Optional[str] = None,\n ) -> Embeddings:\n # This is to avoid errors with Vector Stores (e.g Chroma)\n if disallowed_special == [\"all\"]:\n disallowed_special = \"all\" # type: ignore\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n return OpenAIEmbeddings(\n tiktoken_enabled=tiktoken_enable,\n default_headers=default_headers,\n default_query=default_query,\n allowed_special=set(allowed_special),\n disallowed_special=\"all\",\n chunk_size=chunk_size,\n deployment=deployment,\n embedding_ctx_length=embedding_ctx_length,\n max_retries=max_retries,\n model=model,\n model_kwargs=model_kwargs,\n base_url=openai_api_base,\n api_key=api_key,\n openai_api_type=openai_api_type,\n api_version=openai_api_version,\n organization=openai_organization,\n openai_proxy=openai_proxy,\n timeout=request_timeout,\n show_progress_bar=show_progress_bar,\n skip_empty=skip_empty,\n tiktoken_model_name=tiktoken_model_name,\n )\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "default_headers": { - "type": "dict", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "default_headers", - "display_name": "Default Headers", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "default_query": { - "type": "NestedDict", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": {}, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "default_query", - "display_name": "Default Query", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "deployment": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": "text-embedding-ada-002", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "deployment", - "display_name": "Deployment", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "disallowed_special": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": [ - "all" - ], - "fileTypes": [], - "file_path": "", - "password": false, - "name": "disallowed_special", - "display_name": "Disallowed Special", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "embedding_ctx_length": { - "type": "int", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": 8191, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "embedding_ctx_length", - "display_name": "Embedding Context Length", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "max_retries": { - "type": "int", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": 6, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "max_retries", - "display_name": "Max Retries", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "model": { - "type": "str", - "required": false, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "value": "text-embedding-ada-002", - "fileTypes": [], - "file_path": "", - "password": false, - "options": [ - "text-embedding-3-small", - "text-embedding-3-large", - "text-embedding-ada-002" - ], - "name": "model", - "display_name": "Model", - "advanced": false, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "model_kwargs": { - "type": "NestedDict", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": {}, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "model_kwargs", - "display_name": "Model Kwargs", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "openai_api_base": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": true, - "name": "openai_api_base", - "display_name": "OpenAI API Base", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "openai_api_key": { - "type": "str", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": true, - "name": "openai_api_key", - "display_name": "OpenAI API Key", - "advanced": false, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ], - "value": "OPENAI_API_KEY" - }, - "openai_api_type": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": true, - "name": "openai_api_type", - "display_name": "OpenAI API Type", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "openai_api_version": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "openai_api_version", - "display_name": "OpenAI API Version", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "openai_organization": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "openai_organization", - "display_name": "OpenAI Organization", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "openai_proxy": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "openai_proxy", - "display_name": "OpenAI Proxy", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "request_timeout": { - "type": "float", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "request_timeout", - "display_name": "Request Timeout", - "advanced": true, - "dynamic": false, - "info": "", - "rangeSpec": { - "step_type": "float", - "min": -1, - "max": 1, - "step": 0.1 - }, - "load_from_db": false, - "title_case": false - }, - "show_progress_bar": { - "type": "bool", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "show_progress_bar", - "display_name": "Show Progress Bar", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "skip_empty": { - "type": "bool", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "skip_empty", - "display_name": "Skip Empty", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "tiktoken_enable": { - "type": "bool", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": true, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "tiktoken_enable", - "display_name": "TikToken Enable", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "tiktoken_model_name": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "tiktoken_model_name", - "display_name": "TikToken Model Name", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "_type": "CustomComponent" - }, - "description": "Generate embeddings using OpenAI models.", - "base_classes": [ - "Embeddings" - ], - "display_name": "OpenAI Embeddings", - "documentation": "", - "custom_fields": { - "openai_api_key": null, - "default_headers": null, - "default_query": null, - "allowed_special": null, - "disallowed_special": null, - "chunk_size": null, - "client": null, - "deployment": null, - "embedding_ctx_length": null, - "max_retries": null, - "model": null, - "model_kwargs": null, - "openai_api_base": null, - "openai_api_type": null, - "openai_api_version": null, - "openai_organization": null, - "openai_proxy": null, - "request_timeout": null, - "show_progress_bar": null, - "skip_empty": null, - "tiktoken_enable": null, - "tiktoken_model_name": null - }, - "output_types": [ - "Embeddings" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": false - }, - "id": "OpenAIEmbeddings-ZlOk1" - }, - "selected": false, - "width": 384, - "height": 383, - "dragging": false + "output_types": [ + "Text", + "Record" + ], + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false + }, + "id": "ChatInput-yxMKE" + }, + "selected": false, + "width": 384, + "height": 383 + }, + { + "id": "TextOutput-BDknO", + "type": "genericNode", + "position": { + "x": 2322.600672827879, + "y": 604.9467307442569 + }, + "data": { + "type": "TextOutput", + "node": { + "template": { + "input_value": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": "", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "input_value", + "display_name": "Value", + "advanced": false, + "input_types": [ + "Record", + "Text" + ], + "dynamic": false, + "info": "Text or Record to be passed as output.", + "load_from_db": false, + "title_case": false + }, + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import Optional\n\nfrom langflow.base.io.text import TextComponent\nfrom langflow.field_typing import Text\n\n\nclass TextOutput(TextComponent):\n display_name = \"Text Output\"\n description = \"Display a text output in the Playground.\"\n icon = \"type\"\n\n def build_config(self):\n return {\n \"input_value\": {\n \"display_name\": \"Value\",\n \"input_types\": [\"Record\", \"Text\"],\n \"info\": \"Text or Record to be passed as output.\",\n },\n \"record_template\": {\n \"display_name\": \"Record Template\",\n \"multiline\": True,\n \"info\": \"Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.\",\n \"advanced\": True,\n },\n }\n\n def build(self, input_value: Optional[Text] = \"\", record_template: str = \"\") -> Text:\n return super().build(input_value=input_value, record_template=record_template)\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "record_template": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "{text}", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "record_template", + "display_name": "Record Template", + "advanced": true, + "dynamic": false, + "info": "Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "_type": "CustomComponent" }, - { - "id": "OpenAIModel-EjXlN", - "type": "genericNode", - "position": { - "x": 3410.117202077183, - "y": 431.2038048137648 - }, - "data": { - "type": "OpenAIModel", - "node": { - "template": { - "input_value": { - "type": "str", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "input_value", - "display_name": "Input", - "advanced": false, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\"},\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": True,\n \"info\": \"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": MODEL_NAMES,\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"openai_api_key\": {\n \"display_name\": \"OpenAI API Key\",\n \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n \"advanced\": False,\n \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float,\n model_name: str = \"gpt-4o\",\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n output = ChatOpenAI(\n max_tokens=max_tokens or None,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "max_tokens": { - "type": "int", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": 256, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "max_tokens", - "display_name": "Max Tokens", - "advanced": true, - "dynamic": false, - "info": "The maximum number of tokens to generate. Set to 0 for unlimited tokens.", - "load_from_db": false, - "title_case": false - }, - "model_kwargs": { - "type": "NestedDict", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": {}, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "model_kwargs", - "display_name": "Model Kwargs", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "model_name": { - "type": "str", - "required": false, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "value": "gpt-3.5-turbo", - "fileTypes": [], - "file_path": "", - "password": false, - "options": [ - "gpt-4o", - "gpt-4-turbo", - "gpt-4-turbo-preview", - "gpt-3.5-turbo", - "gpt-3.5-turbo-0125" - ], - "name": "model_name", - "display_name": "Model Name", - "advanced": false, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "openai_api_base": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "openai_api_base", - "display_name": "OpenAI API Base", - "advanced": true, - "dynamic": false, - "info": "The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\n\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "openai_api_key": { - "type": "str", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": true, - "name": "openai_api_key", - "display_name": "OpenAI API Key", - "advanced": false, - "dynamic": false, - "info": "The OpenAI API Key to use for the OpenAI model.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ], - "value": "OPENAI_API_KEY" - }, - "stream": { - "type": "bool", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "stream", - "display_name": "Stream", - "advanced": true, - "dynamic": false, - "info": "Stream the response from the model. Streaming works only in Chat.", - "load_from_db": false, - "title_case": false - }, - "system_message": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "system_message", - "display_name": "System Message", - "advanced": true, - "dynamic": false, - "info": "System message to pass to the model.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "temperature": { - "type": "float", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": 0.1, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "temperature", - "display_name": "Temperature", - "advanced": false, - "dynamic": false, - "info": "", - "rangeSpec": { - "step_type": "float", - "min": -1, - "max": 1, - "step": 0.1 - }, - "load_from_db": false, - "title_case": false - }, - "_type": "CustomComponent" - }, - "description": "Generates text using OpenAI LLMs.", - "icon": "OpenAI", - "base_classes": [ - "object", - "Text", - "str" - ], - "display_name": "OpenAI", - "documentation": "", - "custom_fields": { - "input_value": null, - "openai_api_key": null, - "temperature": null, - "model_name": null, - "max_tokens": null, - "model_kwargs": null, - "openai_api_base": null, - "stream": null, - "system_message": null - }, - "output_types": [ - "Text" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [ - "max_tokens", - "model_kwargs", - "model_name", - "openai_api_base", - "openai_api_key", - "temperature", - "input_value", - "system_message", - "stream" - ], - "beta": false - }, - "id": "OpenAIModel-EjXlN" - }, - "selected": true, - "width": 384, - "height": 563, - "positionAbsolute": { - "x": 3410.117202077183, - "y": 431.2038048137648 - }, - "dragging": false + "description": "Display a text output in the Playground.", + "icon": "type", + "base_classes": [ + "object", + "Text", + "str" + ], + "display_name": "Extracted Chunks", + "documentation": "", + "custom_fields": { + "input_value": null, + "record_template": null }, - { - "id": "Prompt-xeI6K", - "type": "genericNode", - "position": { - "x": 2969.0261961391298, - "y": 442.1613649809069 + "output_types": [ + "Text" + ], + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false + }, + "id": "TextOutput-BDknO" + }, + "selected": false, + "width": 384, + "height": 289, + "positionAbsolute": { + "x": 2322.600672827879, + "y": 604.9467307442569 + }, + "dragging": false + }, + { + "id": "OpenAIEmbeddings-ZlOk1", + "type": "genericNode", + "position": { + "x": 1183.667250865064, + "y": 687.3171828430261 + }, + "data": { + "type": "OpenAIEmbeddings", + "node": { + "template": { + "allowed_special": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": [], + "fileTypes": [], + "file_path": "", + "password": false, + "name": "allowed_special", + "display_name": "Allowed Special", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "chunk_size": { + "type": "int", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": 1000, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "chunk_size", + "display_name": "Chunk Size", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "client": { + "type": "Any", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "client", + "display_name": "Client", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import Dict, List, Optional\n\nfrom langchain_openai.embeddings.base import OpenAIEmbeddings\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Embeddings, NestedDict\n\n\nclass OpenAIEmbeddingsComponent(CustomComponent):\n display_name = \"OpenAI Embeddings\"\n description = \"Generate embeddings using OpenAI models.\"\n\n def build_config(self):\n return {\n \"allowed_special\": {\n \"display_name\": \"Allowed Special\",\n \"advanced\": True,\n \"field_type\": \"str\",\n \"is_list\": True,\n },\n \"default_headers\": {\n \"display_name\": \"Default Headers\",\n \"advanced\": True,\n \"field_type\": \"dict\",\n },\n \"default_query\": {\n \"display_name\": \"Default Query\",\n \"advanced\": True,\n \"field_type\": \"NestedDict\",\n },\n \"disallowed_special\": {\n \"display_name\": \"Disallowed Special\",\n \"advanced\": True,\n \"field_type\": \"str\",\n \"is_list\": True,\n },\n \"chunk_size\": {\"display_name\": \"Chunk Size\", \"advanced\": True},\n \"client\": {\"display_name\": \"Client\", \"advanced\": True},\n \"deployment\": {\"display_name\": \"Deployment\", \"advanced\": True},\n \"embedding_ctx_length\": {\n \"display_name\": \"Embedding Context Length\",\n \"advanced\": True,\n },\n \"max_retries\": {\"display_name\": \"Max Retries\", \"advanced\": True},\n \"model\": {\n \"display_name\": \"Model\",\n \"advanced\": False,\n \"options\": [\n \"text-embedding-3-small\",\n \"text-embedding-3-large\",\n \"text-embedding-ada-002\",\n ],\n },\n \"model_kwargs\": {\"display_name\": \"Model Kwargs\", \"advanced\": True},\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"password\": True,\n \"advanced\": True,\n },\n \"openai_api_key\": {\"display_name\": \"OpenAI API Key\", \"password\": True},\n \"openai_api_type\": {\n \"display_name\": \"OpenAI API Type\",\n \"advanced\": True,\n \"password\": True,\n },\n \"openai_api_version\": {\n \"display_name\": \"OpenAI API Version\",\n \"advanced\": True,\n },\n \"openai_organization\": {\n \"display_name\": \"OpenAI Organization\",\n \"advanced\": True,\n },\n \"openai_proxy\": {\"display_name\": \"OpenAI Proxy\", \"advanced\": True},\n \"request_timeout\": {\"display_name\": \"Request Timeout\", \"advanced\": True},\n \"show_progress_bar\": {\n \"display_name\": \"Show Progress Bar\",\n \"advanced\": True,\n },\n \"skip_empty\": {\"display_name\": \"Skip Empty\", \"advanced\": True},\n \"tiktoken_model_name\": {\n \"display_name\": \"TikToken Model Name\",\n \"advanced\": True,\n },\n \"tiktoken_enable\": {\"display_name\": \"TikToken Enable\", \"advanced\": True},\n }\n\n def build(\n self,\n openai_api_key: str,\n default_headers: Optional[Dict[str, str]] = None,\n default_query: Optional[NestedDict] = {},\n allowed_special: List[str] = [],\n disallowed_special: List[str] = [\"all\"],\n chunk_size: int = 1000,\n deployment: str = \"text-embedding-ada-002\",\n embedding_ctx_length: int = 8191,\n max_retries: int = 6,\n model: str = \"text-embedding-ada-002\",\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n openai_api_type: Optional[str] = None,\n openai_api_version: Optional[str] = None,\n openai_organization: Optional[str] = None,\n openai_proxy: Optional[str] = None,\n request_timeout: Optional[float] = None,\n show_progress_bar: bool = False,\n skip_empty: bool = False,\n tiktoken_enable: bool = True,\n tiktoken_model_name: Optional[str] = None,\n ) -> Embeddings:\n # This is to avoid errors with Vector Stores (e.g Chroma)\n if disallowed_special == [\"all\"]:\n disallowed_special = \"all\" # type: ignore\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n return OpenAIEmbeddings(\n tiktoken_enabled=tiktoken_enable,\n default_headers=default_headers,\n default_query=default_query,\n allowed_special=set(allowed_special),\n disallowed_special=\"all\",\n chunk_size=chunk_size,\n deployment=deployment,\n embedding_ctx_length=embedding_ctx_length,\n max_retries=max_retries,\n model=model,\n model_kwargs=model_kwargs,\n base_url=openai_api_base,\n api_key=api_key,\n openai_api_type=openai_api_type,\n api_version=openai_api_version,\n organization=openai_organization,\n openai_proxy=openai_proxy,\n timeout=request_timeout,\n show_progress_bar=show_progress_bar,\n skip_empty=skip_empty,\n tiktoken_model_name=tiktoken_model_name,\n )\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "default_headers": { + "type": "dict", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "default_headers", + "display_name": "Default Headers", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "default_query": { + "type": "NestedDict", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": {}, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "default_query", + "display_name": "Default Query", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "deployment": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": "text-embedding-ada-002", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "deployment", + "display_name": "Deployment", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "disallowed_special": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": [ + "all" + ], + "fileTypes": [], + "file_path": "", + "password": false, + "name": "disallowed_special", + "display_name": "Disallowed Special", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "embedding_ctx_length": { + "type": "int", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": 8191, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "embedding_ctx_length", + "display_name": "Embedding Context Length", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "max_retries": { + "type": "int", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": 6, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "max_retries", + "display_name": "Max Retries", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "model": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "value": "text-embedding-ada-002", + "fileTypes": [], + "file_path": "", + "password": false, + "options": [ + "text-embedding-3-small", + "text-embedding-3-large", + "text-embedding-ada-002" + ], + "name": "model", + "display_name": "Model", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "model_kwargs": { + "type": "NestedDict", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": {}, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "model_kwargs", + "display_name": "Model Kwargs", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "openai_api_base": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": true, + "name": "openai_api_base", + "display_name": "OpenAI API Base", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "openai_api_key": { + "type": "str", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": true, + "name": "openai_api_key", + "display_name": "OpenAI API Key", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ], + "value": "OPENAI_API_KEY" + }, + "openai_api_type": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": true, + "name": "openai_api_type", + "display_name": "OpenAI API Type", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "openai_api_version": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "openai_api_version", + "display_name": "OpenAI API Version", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "openai_organization": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "openai_organization", + "display_name": "OpenAI Organization", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "openai_proxy": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "openai_proxy", + "display_name": "OpenAI Proxy", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "request_timeout": { + "type": "float", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "request_timeout", + "display_name": "Request Timeout", + "advanced": true, + "dynamic": false, + "info": "", + "rangeSpec": { + "step_type": "float", + "min": -1, + "max": 1, + "step": 0.1 }, - "data": { - "type": "Prompt", - "node": { - "template": { - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Input, Prompt, Text\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": Input(display_name=\"Template\"),\n \"code\": Input(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "template": { - "type": "prompt", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": "{context}\n\n---\n\nGiven the context above, answer the question as best as possible.\n\nQuestion: {question}\n\nAnswer: ", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "template", - "display_name": "Template", - "advanced": false, - "input_types": [ - "Text" - ], - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "_type": "CustomComponent", - "context": { - "field_type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "context", - "display_name": "context", - "advanced": false, - "input_types": [ - "Document", - "BaseOutputParser", - "Record", - "Text" - ], - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "type": "str" - }, - "question": { - "field_type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "question", - "display_name": "question", - "advanced": false, - "input_types": [ - "Document", - "BaseOutputParser", - "Record", - "Text" - ], - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "type": "str" - } - }, - "description": "Create a prompt template with dynamic variables.", - "icon": "prompts", - "is_input": null, - "is_output": null, - "is_composition": null, - "base_classes": [ - "object", - "Text", - "str" - ], - "name": "", - "display_name": "Prompt", - "documentation": "", - "custom_fields": { - "template": [ - "context", - "question" - ] - }, - "output_types": [ - "Text" - ], - "full_path": null, - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": false, - "error": null - }, - "id": "Prompt-xeI6K", - "description": "Create a prompt template with dynamic variables.", - "display_name": "Prompt" - }, - "selected": false, - "width": 384, - "height": 477, - "positionAbsolute": { - "x": 2969.0261961391298, - "y": 442.1613649809069 - }, - "dragging": false + "load_from_db": false, + "title_case": false + }, + "show_progress_bar": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "show_progress_bar", + "display_name": "Show Progress Bar", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "skip_empty": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "skip_empty", + "display_name": "Skip Empty", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "tiktoken_enable": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": true, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "tiktoken_enable", + "display_name": "TikToken Enable", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "tiktoken_model_name": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "tiktoken_model_name", + "display_name": "TikToken Model Name", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "_type": "CustomComponent" }, - { - "id": "ChatOutput-Q39I8", - "type": "genericNode", - "position": { - "x": 3887.2073667611485, - "y": 588.4801225794856 - }, - "data": { - "type": "ChatOutput", - "node": { - "template": { - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n def build(\n self,\n sender: Optional[str] = \"Machine\",\n sender_name: Optional[str] = \"AI\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n record_template: Optional[str] = \"{text}\",\n ) -> Union[Text, Record]:\n return super().build_with_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n record_template=record_template or \"\",\n )\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "input_value": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "input_value", - "display_name": "Message", - "advanced": false, - "input_types": [ - "Text" - ], - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "record_template": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "{text}", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "record_template", - "display_name": "Record Template", - "advanced": true, - "dynamic": false, - "info": "In case of Message being a Record, this template will be used to convert it to text.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "return_record": { - "type": "bool", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "return_record", - "display_name": "Return Record", - "advanced": true, - "dynamic": false, - "info": "Return the message as a record containing the sender, sender_name, and session_id.", - "load_from_db": false, - "title_case": false - }, - "sender": { - "type": "str", - "required": false, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "value": "Machine", - "fileTypes": [], - "file_path": "", - "password": false, - "options": [ - "Machine", - "User" - ], - "name": "sender", - "display_name": "Sender Type", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "sender_name": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": "AI", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "sender_name", - "display_name": "Sender Name", - "advanced": false, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "session_id": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "session_id", - "display_name": "Session ID", - "advanced": true, - "dynamic": false, - "info": "If provided, the message will be stored in the memory.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "_type": "CustomComponent" - }, - "description": "Display a chat message in the Playground.", - "icon": "ChatOutput", - "base_classes": [ - "object", - "Text", - "Record", - "str" - ], - "display_name": "Chat Output", - "documentation": "", - "custom_fields": { - "sender": null, - "sender_name": null, - "input_value": null, - "session_id": null, - "return_record": null, - "record_template": null - }, - "output_types": [ - "Text", - "Record" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": false - }, - "id": "ChatOutput-Q39I8" - }, - "selected": false, - "width": 384, - "height": 383, - "positionAbsolute": { - "x": 3887.2073667611485, - "y": 588.4801225794856 - }, - "dragging": false + "description": "Generate embeddings using OpenAI models.", + "base_classes": [ + "Embeddings" + ], + "display_name": "OpenAI Embeddings", + "documentation": "", + "custom_fields": { + "openai_api_key": null, + "default_headers": null, + "default_query": null, + "allowed_special": null, + "disallowed_special": null, + "chunk_size": null, + "client": null, + "deployment": null, + "embedding_ctx_length": null, + "max_retries": null, + "model": null, + "model_kwargs": null, + "openai_api_base": null, + "openai_api_type": null, + "openai_api_version": null, + "openai_organization": null, + "openai_proxy": null, + "request_timeout": null, + "show_progress_bar": null, + "skip_empty": null, + "tiktoken_enable": null, + "tiktoken_model_name": null }, - { - "id": "File-t0a6a", - "type": "genericNode", - "position": { - "x": 2257.233450682836, - "y": 1747.5389618367233 + "output_types": [ + "Embeddings" + ], + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false + }, + "id": "OpenAIEmbeddings-ZlOk1" + }, + "selected": false, + "width": 384, + "height": 383, + "dragging": false + }, + { + "id": "OpenAIModel-EjXlN", + "type": "genericNode", + "position": { + "x": 3410.117202077183, + "y": 431.2038048137648 + }, + "data": { + "type": "OpenAIModel", + "node": { + "template": { + "input_value": { + "type": "str", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "input_value", + "display_name": "Input", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\"},\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": True,\n \"info\": \"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": MODEL_NAMES,\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"openai_api_key\": {\n \"display_name\": \"OpenAI API Key\",\n \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n \"advanced\": False,\n \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float,\n model_name: str = \"gpt-4o\",\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n output = ChatOpenAI(\n max_tokens=max_tokens or None,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "max_tokens": { + "type": "int", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": 256, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "max_tokens", + "display_name": "Max Tokens", + "advanced": true, + "dynamic": false, + "info": "The maximum number of tokens to generate. Set to 0 for unlimited tokens.", + "load_from_db": false, + "title_case": false + }, + "model_kwargs": { + "type": "NestedDict", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": {}, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "model_kwargs", + "display_name": "Model Kwargs", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "model_name": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "value": "gpt-3.5-turbo", + "fileTypes": [], + "file_path": "", + "password": false, + "options": [ + "gpt-4o", + "gpt-4-turbo", + "gpt-4-turbo-preview", + "gpt-3.5-turbo", + "gpt-3.5-turbo-0125" + ], + "name": "model_name", + "display_name": "Model Name", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "openai_api_base": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "openai_api_base", + "display_name": "OpenAI API Base", + "advanced": true, + "dynamic": false, + "info": "The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\n\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "openai_api_key": { + "type": "str", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": true, + "name": "openai_api_key", + "display_name": "OpenAI API Key", + "advanced": false, + "dynamic": false, + "info": "The OpenAI API Key to use for the OpenAI model.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ], + "value": "OPENAI_API_KEY" + }, + "stream": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "stream", + "display_name": "Stream", + "advanced": true, + "dynamic": false, + "info": "Stream the response from the model. Streaming works only in Chat.", + "load_from_db": false, + "title_case": false + }, + "system_message": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "system_message", + "display_name": "System Message", + "advanced": true, + "dynamic": false, + "info": "System message to pass to the model.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "temperature": { + "type": "float", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": 0.1, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "temperature", + "display_name": "Temperature", + "advanced": false, + "dynamic": false, + "info": "", + "rangeSpec": { + "step_type": "float", + "min": -1, + "max": 1, + "step": 0.1 }, - "data": { - "type": "File", - "node": { - "template": { - "path": { - "type": "file", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [ - ".txt", - ".md", - ".mdx", - ".csv", - ".json", - ".yaml", - ".yml", - ".xml", - ".html", - ".htm", - ".pdf", - ".docx", - ".py", - ".sh", - ".sql", - ".js", - ".ts", - ".tsx" - ], - "file_path": "51e2b78a-199b-4054-9f32-e288eef6924c/Langflow conversation.pdf", - "password": false, - "name": "path", - "display_name": "Path", - "advanced": false, - "dynamic": false, - "info": "Supported file types: txt, md, mdx, csv, json, yaml, yml, xml, html, htm, pdf, docx, py, sh, sql, js, ts, tsx", - "load_from_db": false, - "title_case": false, - "value": "" - }, - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from pathlib import Path\nfrom typing import Any, Dict\n\nfrom langflow.base.data.utils import TEXT_FILE_TYPES, parse_text_file_to_record\nfrom langflow.custom import CustomComponent\nfrom langflow.schema import Record\n\n\nclass FileComponent(CustomComponent):\n display_name = \"File\"\n description = \"A generic file loader.\"\n icon = \"file-text\"\n\n def build_config(self) -> Dict[str, Any]:\n return {\n \"path\": {\n \"display_name\": \"Path\",\n \"field_type\": \"file\",\n \"file_types\": TEXT_FILE_TYPES,\n \"info\": f\"Supported file types: {', '.join(TEXT_FILE_TYPES)}\",\n },\n \"silent_errors\": {\n \"display_name\": \"Silent Errors\",\n \"advanced\": True,\n \"info\": \"If true, errors will not raise an exception.\",\n },\n }\n\n def load_file(self, path: str, silent_errors: bool = False) -> Record:\n resolved_path = self.resolve_path(path)\n path_obj = Path(resolved_path)\n extension = path_obj.suffix[1:].lower()\n if extension == \"doc\":\n raise ValueError(\"doc files are not supported. Please save as .docx\")\n if extension not in TEXT_FILE_TYPES:\n raise ValueError(f\"Unsupported file type: {extension}\")\n record = parse_text_file_to_record(resolved_path, silent_errors)\n self.status = record if record else \"No data\"\n return record or Record()\n\n def build(\n self,\n path: str,\n silent_errors: bool = False,\n ) -> Record:\n record = self.load_file(path, silent_errors)\n self.status = record\n return record\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "silent_errors": { - "type": "bool", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "silent_errors", - "display_name": "Silent Errors", - "advanced": true, - "dynamic": false, - "info": "If true, errors will not raise an exception.", - "load_from_db": false, - "title_case": false - }, - "_type": "CustomComponent" - }, - "description": "A generic file loader.", - "icon": "file-text", - "base_classes": [ - "Record" - ], - "display_name": "File", - "documentation": "", - "custom_fields": { - "path": null, - "silent_errors": null - }, - "output_types": [ - "Record" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": false - }, - "id": "File-t0a6a" - }, - "selected": false, - "width": 384, - "height": 281, - "positionAbsolute": { - "x": 2257.233450682836, - "y": 1747.5389618367233 - }, - "dragging": false + "load_from_db": false, + "title_case": false + }, + "_type": "CustomComponent" }, - { - "id": "RecursiveCharacterTextSplitter-tR9QM", - "type": "genericNode", - "position": { - "x": 2791.013514133929, - "y": 1462.9588953494142 - }, - "data": { - "type": "RecursiveCharacterTextSplitter", - "node": { - "template": { - "inputs": { - "type": "Document", - "required": true, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "inputs", - "display_name": "Input", - "advanced": false, - "input_types": [ - "Document", - "Record" - ], - "dynamic": false, - "info": "The texts to split.", - "load_from_db": false, - "title_case": false - }, - "chunk_overlap": { - "type": "int", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": 200, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "chunk_overlap", - "display_name": "Chunk Overlap", - "advanced": false, - "dynamic": false, - "info": "The amount of overlap between chunks.", - "load_from_db": false, - "title_case": false - }, - "chunk_size": { - "type": "int", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": 1000, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "chunk_size", - "display_name": "Chunk Size", - "advanced": false, - "dynamic": false, - "info": "The maximum length of each chunk.", - "load_from_db": false, - "title_case": false - }, - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from typing import Optional\n\nfrom langchain_core.documents import Document\nfrom langchain_text_splitters import RecursiveCharacterTextSplitter\n\nfrom langflow.custom import CustomComponent\nfrom langflow.schema import Record\nfrom langflow.utils.util import build_loader_repr_from_records, unescape_string\n\n\nclass RecursiveCharacterTextSplitterComponent(CustomComponent):\n display_name: str = \"Recursive Character Text Splitter\"\n description: str = \"Split text into chunks of a specified length.\"\n documentation: str = \"https://docs.langflow.org/components/text-splitters#recursivecharactertextsplitter\"\n\n def build_config(self):\n return {\n \"inputs\": {\n \"display_name\": \"Input\",\n \"info\": \"The texts to split.\",\n \"input_types\": [\"Document\", \"Record\"],\n },\n \"separators\": {\n \"display_name\": \"Separators\",\n \"info\": 'The characters to split on.\\nIf left empty defaults to [\"\\\\n\\\\n\", \"\\\\n\", \" \", \"\"].',\n \"is_list\": True,\n },\n \"chunk_size\": {\n \"display_name\": \"Chunk Size\",\n \"info\": \"The maximum length of each chunk.\",\n \"field_type\": \"int\",\n \"value\": 1000,\n },\n \"chunk_overlap\": {\n \"display_name\": \"Chunk Overlap\",\n \"info\": \"The amount of overlap between chunks.\",\n \"field_type\": \"int\",\n \"value\": 200,\n },\n \"code\": {\"show\": False},\n }\n\n def build(\n self,\n inputs: list[Document],\n separators: Optional[list[str]] = None,\n chunk_size: Optional[int] = 1000,\n chunk_overlap: Optional[int] = 200,\n ) -> list[Record]:\n \"\"\"\n Split text into chunks of a specified length.\n\n Args:\n separators (list[str]): The characters to split on.\n chunk_size (int): The maximum length of each chunk.\n chunk_overlap (int): The amount of overlap between chunks.\n length_function (function): The function to use to calculate the length of the text.\n\n Returns:\n list[str]: The chunks of text.\n \"\"\"\n\n if separators == \"\":\n separators = None\n elif separators:\n # check if the separators list has escaped characters\n # if there are escaped characters, unescape them\n separators = [unescape_string(x) for x in separators]\n\n # Make sure chunk_size and chunk_overlap are ints\n if isinstance(chunk_size, str):\n chunk_size = int(chunk_size)\n if isinstance(chunk_overlap, str):\n chunk_overlap = int(chunk_overlap)\n splitter = RecursiveCharacterTextSplitter(\n separators=separators,\n chunk_size=chunk_size,\n chunk_overlap=chunk_overlap,\n )\n documents = []\n for _input in inputs:\n if isinstance(_input, Record):\n documents.append(_input.to_lc_document())\n else:\n documents.append(_input)\n docs = splitter.split_documents(documents)\n records = self.to_records(docs)\n self.repr_value = build_loader_repr_from_records(records)\n return records\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "separators": { - "type": "str", - "required": false, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "separators", - "display_name": "Separators", - "advanced": false, - "dynamic": false, - "info": "The characters to split on.\nIf left empty defaults to [\"\\n\\n\", \"\\n\", \" \", \"\"].", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ], - "value": [ - "" - ] - }, - "_type": "CustomComponent" - }, - "description": "Split text into chunks of a specified length.", - "base_classes": [ - "Record" - ], - "display_name": "Recursive Character Text Splitter", - "documentation": "https://docs.langflow.org/components/text-splitters#recursivecharactertextsplitter", - "custom_fields": { - "inputs": null, - "separators": null, - "chunk_size": null, - "chunk_overlap": null - }, - "output_types": [ - "Record" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": false - }, - "id": "RecursiveCharacterTextSplitter-tR9QM" - }, - "selected": false, - "width": 384, - "height": 501, - "positionAbsolute": { - "x": 2791.013514133929, - "y": 1462.9588953494142 - }, - "dragging": false + "description": "Generates text using OpenAI LLMs.", + "icon": "OpenAI", + "base_classes": [ + "object", + "Text", + "str" + ], + "display_name": "OpenAI", + "documentation": "", + "custom_fields": { + "input_value": null, + "openai_api_key": null, + "temperature": null, + "model_name": null, + "max_tokens": null, + "model_kwargs": null, + "openai_api_base": null, + "stream": null, + "system_message": null }, - { - "id": "AstraDBSearch-41nRz", - "type": "genericNode", - "position": { - "x": 1723.976434815103, - "y": 277.03317407245913 - }, - "data": { - "type": "AstraDBSearch", - "node": { - "template": { - "embedding": { - "type": "Embeddings", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "embedding", - "display_name": "Embedding", - "advanced": false, - "dynamic": false, - "info": "Embedding to use", - "load_from_db": false, - "title_case": false - }, - "input_value": { - "type": "str", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "input_value", - "display_name": "Input Value", - "advanced": false, - "dynamic": false, - "info": "Input value to search", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "api_endpoint": { - "type": "str", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "api_endpoint", - "display_name": "API Endpoint", - "advanced": false, - "dynamic": false, - "info": "API endpoint URL for the Astra DB service.", - "load_from_db": true, - "title_case": false, - "input_types": [ - "Text" - ], - "value": "ASTRA_DB_API_ENDPOINT" - }, - "batch_size": { - "type": "int", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "batch_size", - "display_name": "Batch Size", - "advanced": true, - "dynamic": false, - "info": "Optional number of records to process in a single batch.", - "load_from_db": false, - "title_case": false - }, - "bulk_delete_concurrency": { - "type": "int", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "bulk_delete_concurrency", - "display_name": "Bulk Delete Concurrency", - "advanced": true, - "dynamic": false, - "info": "Optional concurrency level for bulk delete operations.", - "load_from_db": false, - "title_case": false - }, - "bulk_insert_batch_concurrency": { - "type": "int", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "bulk_insert_batch_concurrency", - "display_name": "Bulk Insert Batch Concurrency", - "advanced": true, - "dynamic": false, - "info": "Optional concurrency level for bulk insert operations.", - "load_from_db": false, - "title_case": false - }, - "bulk_insert_overwrite_concurrency": { - "type": "int", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "bulk_insert_overwrite_concurrency", - "display_name": "Bulk Insert Overwrite Concurrency", - "advanced": true, - "dynamic": false, - "info": "Optional concurrency level for bulk insert operations that overwrite existing records.", - "load_from_db": false, - "title_case": false - }, - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from typing import List, Optional\n\nfrom langflow.components.vectorstores.AstraDB import AstraDBVectorStoreComponent\nfrom langflow.components.vectorstores.base.model import LCVectorStoreComponent\nfrom langflow.field_typing import Embeddings, Text\nfrom langflow.schema import Record\n\n\nclass AstraDBSearchComponent(LCVectorStoreComponent):\n display_name = \"Astra DB Search\"\n description = \"Searches an existing Astra DB Vector Store.\"\n icon = \"AstraDB\"\n field_order = [\"token\", \"api_endpoint\", \"collection_name\", \"input_value\", \"embedding\"]\n\n def build_config(self):\n return {\n \"search_type\": {\n \"display_name\": \"Search Type\",\n \"options\": [\"Similarity\", \"MMR\"],\n },\n \"input_value\": {\n \"display_name\": \"Input Value\",\n \"info\": \"Input value to search\",\n },\n \"embedding\": {\"display_name\": \"Embedding\", \"info\": \"Embedding to use\"},\n \"collection_name\": {\n \"display_name\": \"Collection Name\",\n \"info\": \"The name of the collection within Astra DB where the vectors will be stored.\",\n },\n \"token\": {\n \"display_name\": \"Token\",\n \"info\": \"Authentication token for accessing Astra DB.\",\n \"password\": True,\n },\n \"api_endpoint\": {\n \"display_name\": \"API Endpoint\",\n \"info\": \"API endpoint URL for the Astra DB service.\",\n },\n \"namespace\": {\n \"display_name\": \"Namespace\",\n \"info\": \"Optional namespace within Astra DB to use for the collection.\",\n \"advanced\": True,\n },\n \"metric\": {\n \"display_name\": \"Metric\",\n \"info\": \"Optional distance metric for vector comparisons in the vector store.\",\n \"advanced\": True,\n },\n \"batch_size\": {\n \"display_name\": \"Batch Size\",\n \"info\": \"Optional number of records to process in a single batch.\",\n \"advanced\": True,\n },\n \"bulk_insert_batch_concurrency\": {\n \"display_name\": \"Bulk Insert Batch Concurrency\",\n \"info\": \"Optional concurrency level for bulk insert operations.\",\n \"advanced\": True,\n },\n \"bulk_insert_overwrite_concurrency\": {\n \"display_name\": \"Bulk Insert Overwrite Concurrency\",\n \"info\": \"Optional concurrency level for bulk insert operations that overwrite existing records.\",\n \"advanced\": True,\n },\n \"bulk_delete_concurrency\": {\n \"display_name\": \"Bulk Delete Concurrency\",\n \"info\": \"Optional concurrency level for bulk delete operations.\",\n \"advanced\": True,\n },\n \"setup_mode\": {\n \"display_name\": \"Setup Mode\",\n \"info\": \"Configuration mode for setting up the vector store, with options like \u201cSync\u201d, \u201cAsync\u201d, or \u201cOff\u201d.\",\n \"options\": [\"Sync\", \"Async\", \"Off\"],\n \"advanced\": True,\n },\n \"pre_delete_collection\": {\n \"display_name\": \"Pre Delete Collection\",\n \"info\": \"Boolean flag to determine whether to delete the collection before creating a new one.\",\n \"advanced\": True,\n },\n \"metadata_indexing_include\": {\n \"display_name\": \"Metadata Indexing Include\",\n \"info\": \"Optional list of metadata fields to include in the indexing.\",\n \"advanced\": True,\n },\n \"metadata_indexing_exclude\": {\n \"display_name\": \"Metadata Indexing Exclude\",\n \"info\": \"Optional list of metadata fields to exclude from the indexing.\",\n \"advanced\": True,\n },\n \"collection_indexing_policy\": {\n \"display_name\": \"Collection Indexing Policy\",\n \"info\": \"Optional dictionary defining the indexing policy for the collection.\",\n \"advanced\": True,\n },\n \"number_of_results\": {\n \"display_name\": \"Number of Results\",\n \"info\": \"Number of results to return.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n embedding: Embeddings,\n collection_name: str,\n input_value: Text,\n token: str,\n api_endpoint: str,\n search_type: str = \"Similarity\",\n number_of_results: int = 4,\n namespace: Optional[str] = None,\n metric: Optional[str] = None,\n batch_size: Optional[int] = None,\n bulk_insert_batch_concurrency: Optional[int] = None,\n bulk_insert_overwrite_concurrency: Optional[int] = None,\n bulk_delete_concurrency: Optional[int] = None,\n setup_mode: str = \"Sync\",\n pre_delete_collection: bool = False,\n metadata_indexing_include: Optional[List[str]] = None,\n metadata_indexing_exclude: Optional[List[str]] = None,\n collection_indexing_policy: Optional[dict] = None,\n ) -> List[Record]:\n vector_store = AstraDBVectorStoreComponent().build(\n embedding=embedding,\n collection_name=collection_name,\n token=token,\n api_endpoint=api_endpoint,\n namespace=namespace,\n metric=metric,\n batch_size=batch_size,\n bulk_insert_batch_concurrency=bulk_insert_batch_concurrency,\n bulk_insert_overwrite_concurrency=bulk_insert_overwrite_concurrency,\n bulk_delete_concurrency=bulk_delete_concurrency,\n setup_mode=setup_mode,\n pre_delete_collection=pre_delete_collection,\n metadata_indexing_include=metadata_indexing_include,\n metadata_indexing_exclude=metadata_indexing_exclude,\n collection_indexing_policy=collection_indexing_policy,\n )\n try:\n return self.search_with_vector_store(input_value, search_type, vector_store, k=number_of_results)\n except KeyError as e:\n if \"content\" in str(e):\n raise ValueError(\n \"You should ingest data through Langflow (or LangChain) to query it in Langflow. Your collection does not contain a field name 'content'.\"\n )\n else:\n raise e\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "collection_indexing_policy": { - "type": "dict", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "collection_indexing_policy", - "display_name": "Collection Indexing Policy", - "advanced": true, - "dynamic": false, - "info": "Optional dictionary defining the indexing policy for the collection.", - "load_from_db": false, - "title_case": false - }, - "collection_name": { - "type": "str", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "collection_name", - "display_name": "Collection Name", - "advanced": false, - "dynamic": false, - "info": "The name of the collection within Astra DB where the vectors will be stored.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ], - "value": "langflow" - }, - "metadata_indexing_exclude": { - "type": "str", - "required": false, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "metadata_indexing_exclude", - "display_name": "Metadata Indexing Exclude", - "advanced": true, - "dynamic": false, - "info": "Optional list of metadata fields to exclude from the indexing.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "metadata_indexing_include": { - "type": "str", - "required": false, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "metadata_indexing_include", - "display_name": "Metadata Indexing Include", - "advanced": true, - "dynamic": false, - "info": "Optional list of metadata fields to include in the indexing.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "metric": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "metric", - "display_name": "Metric", - "advanced": true, - "dynamic": false, - "info": "Optional distance metric for vector comparisons in the vector store.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "namespace": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "namespace", - "display_name": "Namespace", - "advanced": true, - "dynamic": false, - "info": "Optional namespace within Astra DB to use for the collection.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "number_of_results": { - "type": "int", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": 4, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "number_of_results", - "display_name": "Number of Results", - "advanced": true, - "dynamic": false, - "info": "Number of results to return.", - "load_from_db": false, - "title_case": false - }, - "pre_delete_collection": { - "type": "bool", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "pre_delete_collection", - "display_name": "Pre Delete Collection", - "advanced": true, - "dynamic": false, - "info": "Boolean flag to determine whether to delete the collection before creating a new one.", - "load_from_db": false, - "title_case": false - }, - "search_type": { - "type": "str", - "required": false, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "value": "Similarity", - "fileTypes": [], - "file_path": "", - "password": false, - "options": [ - "Similarity", - "MMR" - ], - "name": "search_type", - "display_name": "Search Type", - "advanced": false, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "setup_mode": { - "type": "str", - "required": false, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "value": "Sync", - "fileTypes": [], - "file_path": "", - "password": false, - "options": [ - "Sync", - "Async", - "Off" - ], - "name": "setup_mode", - "display_name": "Setup Mode", - "advanced": true, - "dynamic": false, - "info": "Configuration mode for setting up the vector store, with options like \u201cSync\u201d, \u201cAsync\u201d, or \u201cOff\u201d.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "token": { - "type": "str", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": true, - "name": "token", - "display_name": "Token", - "advanced": false, - "dynamic": false, - "info": "Authentication token for accessing Astra DB.", - "load_from_db": true, - "title_case": false, - "input_types": [ - "Text" - ], - "value": "ASTRA_DB_APPLICATION_TOKEN" - }, - "_type": "CustomComponent" - }, - "description": "Searches an existing Astra DB Vector Store.", - "icon": "AstraDB", - "base_classes": [ - "Record" - ], - "display_name": "Astra DB Search", - "documentation": "", - "custom_fields": { - "embedding": null, - "collection_name": null, - "input_value": null, - "token": null, - "api_endpoint": null, - "search_type": null, - "number_of_results": null, - "namespace": null, - "metric": null, - "batch_size": null, - "bulk_insert_batch_concurrency": null, - "bulk_insert_overwrite_concurrency": null, - "bulk_delete_concurrency": null, - "setup_mode": null, - "pre_delete_collection": null, - "metadata_indexing_include": null, - "metadata_indexing_exclude": null, - "collection_indexing_policy": null - }, - "output_types": [ - "Record" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [ - "token", - "api_endpoint", - "collection_name", - "input_value", - "embedding" - ], - "beta": false - }, - "id": "AstraDBSearch-41nRz" - }, - "selected": false, - "width": 384, - "height": 713, - "dragging": false, - "positionAbsolute": { - "x": 1723.976434815103, - "y": 277.03317407245913 - } + "output_types": [ + "Text" + ], + "field_formatters": {}, + "frozen": false, + "field_order": [ + "max_tokens", + "model_kwargs", + "model_name", + "openai_api_base", + "openai_api_key", + "temperature", + "input_value", + "system_message", + "stream" + ], + "beta": false + }, + "id": "OpenAIModel-EjXlN" + }, + "selected": true, + "width": 384, + "height": 563, + "positionAbsolute": { + "x": 3410.117202077183, + "y": 431.2038048137648 + }, + "dragging": false + }, + { + "id": "Prompt-xeI6K", + "type": "genericNode", + "position": { + "x": 2969.0261961391298, + "y": 442.1613649809069 + }, + "data": { + "type": "Prompt", + "node": { + "template": { + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Input, Prompt, Text\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": Input(display_name=\"Template\"),\n \"code\": Input(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "template": { + "type": "prompt", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": "{context}\n\n---\n\nGiven the context above, answer the question as best as possible.\n\nQuestion: {question}\n\nAnswer: ", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "template", + "display_name": "Template", + "advanced": false, + "input_types": [ + "Text" + ], + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "_type": "CustomComponent", + "context": { + "field_type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "context", + "display_name": "context", + "advanced": false, + "input_types": [ + "Document", + "BaseOutputParser", + "Record", + "Text" + ], + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "type": "str" + }, + "question": { + "field_type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "question", + "display_name": "question", + "advanced": false, + "input_types": [ + "Document", + "BaseOutputParser", + "Record", + "Text" + ], + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "type": "str" + } }, - { - "id": "AstraDB-eUCSS", - "type": "genericNode", - "position": { - "x": 3372.04958055989, - "y": 1611.0742035495277 - }, - "data": { - "type": "AstraDB", - "node": { - "template": { - "embedding": { - "type": "Embeddings", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "embedding", - "display_name": "Embedding", - "advanced": false, - "dynamic": false, - "info": "Embedding to use", - "load_from_db": false, - "title_case": false - }, - "inputs": { - "type": "Record", - "required": false, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "inputs", - "display_name": "Inputs", - "advanced": false, - "dynamic": false, - "info": "Optional list of records to be processed and stored in the vector store.", - "load_from_db": false, - "title_case": false - }, - "api_endpoint": { - "type": "str", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "api_endpoint", - "display_name": "API Endpoint", - "advanced": false, - "dynamic": false, - "info": "API endpoint URL for the Astra DB service.", - "load_from_db": true, - "title_case": false, - "input_types": [ - "Text" - ], - "value": "ASTRA_DB_API_ENDPOINT" - }, - "batch_size": { - "type": "int", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "batch_size", - "display_name": "Batch Size", - "advanced": true, - "dynamic": false, - "info": "Optional number of records to process in a single batch.", - "load_from_db": false, - "title_case": false - }, - "bulk_delete_concurrency": { - "type": "int", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "bulk_delete_concurrency", - "display_name": "Bulk Delete Concurrency", - "advanced": true, - "dynamic": false, - "info": "Optional concurrency level for bulk delete operations.", - "load_from_db": false, - "title_case": false - }, - "bulk_insert_batch_concurrency": { - "type": "int", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "bulk_insert_batch_concurrency", - "display_name": "Bulk Insert Batch Concurrency", - "advanced": true, - "dynamic": false, - "info": "Optional concurrency level for bulk insert operations.", - "load_from_db": false, - "title_case": false - }, - "bulk_insert_overwrite_concurrency": { - "type": "int", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "bulk_insert_overwrite_concurrency", - "display_name": "Bulk Insert Overwrite Concurrency", - "advanced": true, - "dynamic": false, - "info": "Optional concurrency level for bulk insert operations that overwrite existing records.", - "load_from_db": false, - "title_case": false - }, - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from typing import List, Optional, Union\nfrom langchain_astradb import AstraDBVectorStore\nfrom langchain_astradb.utils.astradb import SetupMode\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Embeddings, VectorStore\nfrom langflow.schema import Record\nfrom langchain_core.retrievers import BaseRetriever\n\n\nclass AstraDBVectorStoreComponent(CustomComponent):\n display_name = \"Astra DB\"\n description = \"Builds or loads an Astra DB Vector Store.\"\n icon = \"AstraDB\"\n field_order = [\"token\", \"api_endpoint\", \"collection_name\", \"inputs\", \"embedding\"]\n\n def build_config(self):\n return {\n \"inputs\": {\n \"display_name\": \"Inputs\",\n \"info\": \"Optional list of records to be processed and stored in the vector store.\",\n },\n \"embedding\": {\"display_name\": \"Embedding\", \"info\": \"Embedding to use\"},\n \"collection_name\": {\n \"display_name\": \"Collection Name\",\n \"info\": \"The name of the collection within Astra DB where the vectors will be stored.\",\n },\n \"token\": {\n \"display_name\": \"Token\",\n \"info\": \"Authentication token for accessing Astra DB.\",\n \"password\": True,\n },\n \"api_endpoint\": {\n \"display_name\": \"API Endpoint\",\n \"info\": \"API endpoint URL for the Astra DB service.\",\n },\n \"namespace\": {\n \"display_name\": \"Namespace\",\n \"info\": \"Optional namespace within Astra DB to use for the collection.\",\n \"advanced\": True,\n },\n \"metric\": {\n \"display_name\": \"Metric\",\n \"info\": \"Optional distance metric for vector comparisons in the vector store.\",\n \"advanced\": True,\n },\n \"batch_size\": {\n \"display_name\": \"Batch Size\",\n \"info\": \"Optional number of records to process in a single batch.\",\n \"advanced\": True,\n },\n \"bulk_insert_batch_concurrency\": {\n \"display_name\": \"Bulk Insert Batch Concurrency\",\n \"info\": \"Optional concurrency level for bulk insert operations.\",\n \"advanced\": True,\n },\n \"bulk_insert_overwrite_concurrency\": {\n \"display_name\": \"Bulk Insert Overwrite Concurrency\",\n \"info\": \"Optional concurrency level for bulk insert operations that overwrite existing records.\",\n \"advanced\": True,\n },\n \"bulk_delete_concurrency\": {\n \"display_name\": \"Bulk Delete Concurrency\",\n \"info\": \"Optional concurrency level for bulk delete operations.\",\n \"advanced\": True,\n },\n \"setup_mode\": {\n \"display_name\": \"Setup Mode\",\n \"info\": \"Configuration mode for setting up the vector store, with options like \u201cSync\u201d, \u201cAsync\u201d, or \u201cOff\u201d.\",\n \"options\": [\"Sync\", \"Async\", \"Off\"],\n \"advanced\": True,\n },\n \"pre_delete_collection\": {\n \"display_name\": \"Pre Delete Collection\",\n \"info\": \"Boolean flag to determine whether to delete the collection before creating a new one.\",\n \"advanced\": True,\n },\n \"metadata_indexing_include\": {\n \"display_name\": \"Metadata Indexing Include\",\n \"info\": \"Optional list of metadata fields to include in the indexing.\",\n \"advanced\": True,\n },\n \"metadata_indexing_exclude\": {\n \"display_name\": \"Metadata Indexing Exclude\",\n \"info\": \"Optional list of metadata fields to exclude from the indexing.\",\n \"advanced\": True,\n },\n \"collection_indexing_policy\": {\n \"display_name\": \"Collection Indexing Policy\",\n \"info\": \"Optional dictionary defining the indexing policy for the collection.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n embedding: Embeddings,\n token: str,\n api_endpoint: str,\n collection_name: str,\n inputs: Optional[List[Record]] = None,\n namespace: Optional[str] = None,\n metric: Optional[str] = None,\n batch_size: Optional[int] = None,\n bulk_insert_batch_concurrency: Optional[int] = None,\n bulk_insert_overwrite_concurrency: Optional[int] = None,\n bulk_delete_concurrency: Optional[int] = None,\n setup_mode: str = \"Sync\",\n pre_delete_collection: bool = False,\n metadata_indexing_include: Optional[List[str]] = None,\n metadata_indexing_exclude: Optional[List[str]] = None,\n collection_indexing_policy: Optional[dict] = None,\n ) -> Union[VectorStore, BaseRetriever]:\n try:\n setup_mode_value = SetupMode[setup_mode.upper()]\n except KeyError:\n raise ValueError(f\"Invalid setup mode: {setup_mode}\")\n if inputs:\n documents = [_input.to_lc_document() for _input in inputs]\n\n vector_store = AstraDBVectorStore.from_documents(\n documents=documents,\n embedding=embedding,\n collection_name=collection_name,\n token=token,\n api_endpoint=api_endpoint,\n namespace=namespace,\n metric=metric,\n batch_size=batch_size,\n bulk_insert_batch_concurrency=bulk_insert_batch_concurrency,\n bulk_insert_overwrite_concurrency=bulk_insert_overwrite_concurrency,\n bulk_delete_concurrency=bulk_delete_concurrency,\n setup_mode=setup_mode_value,\n pre_delete_collection=pre_delete_collection,\n metadata_indexing_include=metadata_indexing_include,\n metadata_indexing_exclude=metadata_indexing_exclude,\n collection_indexing_policy=collection_indexing_policy,\n )\n else:\n vector_store = AstraDBVectorStore(\n embedding=embedding,\n collection_name=collection_name,\n token=token,\n api_endpoint=api_endpoint,\n namespace=namespace,\n metric=metric,\n batch_size=batch_size,\n bulk_insert_batch_concurrency=bulk_insert_batch_concurrency,\n bulk_insert_overwrite_concurrency=bulk_insert_overwrite_concurrency,\n bulk_delete_concurrency=bulk_delete_concurrency,\n setup_mode=setup_mode_value,\n pre_delete_collection=pre_delete_collection,\n metadata_indexing_include=metadata_indexing_include,\n metadata_indexing_exclude=metadata_indexing_exclude,\n collection_indexing_policy=collection_indexing_policy,\n )\n\n return vector_store\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "collection_indexing_policy": { - "type": "dict", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "collection_indexing_policy", - "display_name": "Collection Indexing Policy", - "advanced": true, - "dynamic": false, - "info": "Optional dictionary defining the indexing policy for the collection.", - "load_from_db": false, - "title_case": false - }, - "collection_name": { - "type": "str", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "collection_name", - "display_name": "Collection Name", - "advanced": false, - "dynamic": false, - "info": "The name of the collection within Astra DB where the vectors will be stored.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ], - "value": "langflow" - }, - "metadata_indexing_exclude": { - "type": "str", - "required": false, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "metadata_indexing_exclude", - "display_name": "Metadata Indexing Exclude", - "advanced": true, - "dynamic": false, - "info": "Optional list of metadata fields to exclude from the indexing.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "metadata_indexing_include": { - "type": "str", - "required": false, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "metadata_indexing_include", - "display_name": "Metadata Indexing Include", - "advanced": true, - "dynamic": false, - "info": "Optional list of metadata fields to include in the indexing.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "metric": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "metric", - "display_name": "Metric", - "advanced": true, - "dynamic": false, - "info": "Optional distance metric for vector comparisons in the vector store.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "namespace": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "namespace", - "display_name": "Namespace", - "advanced": true, - "dynamic": false, - "info": "Optional namespace within Astra DB to use for the collection.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "pre_delete_collection": { - "type": "bool", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "pre_delete_collection", - "display_name": "Pre Delete Collection", - "advanced": true, - "dynamic": false, - "info": "Boolean flag to determine whether to delete the collection before creating a new one.", - "load_from_db": false, - "title_case": false - }, - "setup_mode": { - "type": "str", - "required": false, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "value": "Sync", - "fileTypes": [], - "file_path": "", - "password": false, - "options": [ - "Sync", - "Async", - "Off" - ], - "name": "setup_mode", - "display_name": "Setup Mode", - "advanced": true, - "dynamic": false, - "info": "Configuration mode for setting up the vector store, with options like \u201cSync\u201d, \u201cAsync\u201d, or \u201cOff\u201d.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "token": { - "type": "str", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": true, - "name": "token", - "display_name": "Token", - "advanced": false, - "dynamic": false, - "info": "Authentication token for accessing Astra DB.", - "load_from_db": true, - "title_case": false, - "input_types": [ - "Text" - ], - "value": "ASTRA_DB_APPLICATION_TOKEN" - }, - "_type": "CustomComponent" - }, - "description": "Builds or loads an Astra DB Vector Store.", - "icon": "AstraDB", - "base_classes": [ - "VectorStore" - ], - "display_name": "Astra DB", - "documentation": "", - "custom_fields": { - "embedding": null, - "token": null, - "api_endpoint": null, - "collection_name": null, - "inputs": null, - "namespace": null, - "metric": null, - "batch_size": null, - "bulk_insert_batch_concurrency": null, - "bulk_insert_overwrite_concurrency": null, - "bulk_delete_concurrency": null, - "setup_mode": null, - "pre_delete_collection": null, - "metadata_indexing_include": null, - "metadata_indexing_exclude": null, - "collection_indexing_policy": null - }, - "output_types": [ - "VectorStore" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [ - "token", - "api_endpoint", - "collection_name", - "inputs", - "embedding" - ], - "beta": false - }, - "id": "AstraDB-eUCSS" - }, - "selected": false, - "width": 384, - "height": 573, - "positionAbsolute": { - "x": 3372.04958055989, - "y": 1611.0742035495277 - }, - "dragging": false + "description": "Create a prompt template with dynamic variables.", + "icon": "prompts", + "is_input": null, + "is_output": null, + "is_composition": null, + "base_classes": [ + "object", + "Text", + "str" + ], + "name": "", + "display_name": "Prompt", + "documentation": "", + "custom_fields": { + "template": [ + "context", + "question" + ] }, - { - "id": "OpenAIEmbeddings-9TPjc", - "type": "genericNode", - "position": { - "x": 2814.0402191223047, - "y": 1955.9268168273086 - }, - "data": { - "type": "OpenAIEmbeddings", - "node": { - "template": { - "allowed_special": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": [], - "fileTypes": [], - "file_path": "", - "password": false, - "name": "allowed_special", - "display_name": "Allowed Special", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "chunk_size": { - "type": "int", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": 1000, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "chunk_size", - "display_name": "Chunk Size", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "client": { - "type": "Any", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "client", - "display_name": "Client", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from typing import Dict, List, Optional\n\nfrom langchain_openai.embeddings.base import OpenAIEmbeddings\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Embeddings, NestedDict\n\n\nclass OpenAIEmbeddingsComponent(CustomComponent):\n display_name = \"OpenAI Embeddings\"\n description = \"Generate embeddings using OpenAI models.\"\n\n def build_config(self):\n return {\n \"allowed_special\": {\n \"display_name\": \"Allowed Special\",\n \"advanced\": True,\n \"field_type\": \"str\",\n \"is_list\": True,\n },\n \"default_headers\": {\n \"display_name\": \"Default Headers\",\n \"advanced\": True,\n \"field_type\": \"dict\",\n },\n \"default_query\": {\n \"display_name\": \"Default Query\",\n \"advanced\": True,\n \"field_type\": \"NestedDict\",\n },\n \"disallowed_special\": {\n \"display_name\": \"Disallowed Special\",\n \"advanced\": True,\n \"field_type\": \"str\",\n \"is_list\": True,\n },\n \"chunk_size\": {\"display_name\": \"Chunk Size\", \"advanced\": True},\n \"client\": {\"display_name\": \"Client\", \"advanced\": True},\n \"deployment\": {\"display_name\": \"Deployment\", \"advanced\": True},\n \"embedding_ctx_length\": {\n \"display_name\": \"Embedding Context Length\",\n \"advanced\": True,\n },\n \"max_retries\": {\"display_name\": \"Max Retries\", \"advanced\": True},\n \"model\": {\n \"display_name\": \"Model\",\n \"advanced\": False,\n \"options\": [\n \"text-embedding-3-small\",\n \"text-embedding-3-large\",\n \"text-embedding-ada-002\",\n ],\n },\n \"model_kwargs\": {\"display_name\": \"Model Kwargs\", \"advanced\": True},\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"password\": True,\n \"advanced\": True,\n },\n \"openai_api_key\": {\"display_name\": \"OpenAI API Key\", \"password\": True},\n \"openai_api_type\": {\n \"display_name\": \"OpenAI API Type\",\n \"advanced\": True,\n \"password\": True,\n },\n \"openai_api_version\": {\n \"display_name\": \"OpenAI API Version\",\n \"advanced\": True,\n },\n \"openai_organization\": {\n \"display_name\": \"OpenAI Organization\",\n \"advanced\": True,\n },\n \"openai_proxy\": {\"display_name\": \"OpenAI Proxy\", \"advanced\": True},\n \"request_timeout\": {\"display_name\": \"Request Timeout\", \"advanced\": True},\n \"show_progress_bar\": {\n \"display_name\": \"Show Progress Bar\",\n \"advanced\": True,\n },\n \"skip_empty\": {\"display_name\": \"Skip Empty\", \"advanced\": True},\n \"tiktoken_model_name\": {\n \"display_name\": \"TikToken Model Name\",\n \"advanced\": True,\n },\n \"tiktoken_enable\": {\"display_name\": \"TikToken Enable\", \"advanced\": True},\n }\n\n def build(\n self,\n openai_api_key: str,\n default_headers: Optional[Dict[str, str]] = None,\n default_query: Optional[NestedDict] = {},\n allowed_special: List[str] = [],\n disallowed_special: List[str] = [\"all\"],\n chunk_size: int = 1000,\n deployment: str = \"text-embedding-ada-002\",\n embedding_ctx_length: int = 8191,\n max_retries: int = 6,\n model: str = \"text-embedding-ada-002\",\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n openai_api_type: Optional[str] = None,\n openai_api_version: Optional[str] = None,\n openai_organization: Optional[str] = None,\n openai_proxy: Optional[str] = None,\n request_timeout: Optional[float] = None,\n show_progress_bar: bool = False,\n skip_empty: bool = False,\n tiktoken_enable: bool = True,\n tiktoken_model_name: Optional[str] = None,\n ) -> Embeddings:\n # This is to avoid errors with Vector Stores (e.g Chroma)\n if disallowed_special == [\"all\"]:\n disallowed_special = \"all\" # type: ignore\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n return OpenAIEmbeddings(\n tiktoken_enabled=tiktoken_enable,\n default_headers=default_headers,\n default_query=default_query,\n allowed_special=set(allowed_special),\n disallowed_special=\"all\",\n chunk_size=chunk_size,\n deployment=deployment,\n embedding_ctx_length=embedding_ctx_length,\n max_retries=max_retries,\n model=model,\n model_kwargs=model_kwargs,\n base_url=openai_api_base,\n api_key=api_key,\n openai_api_type=openai_api_type,\n api_version=openai_api_version,\n organization=openai_organization,\n openai_proxy=openai_proxy,\n timeout=request_timeout,\n show_progress_bar=show_progress_bar,\n skip_empty=skip_empty,\n tiktoken_model_name=tiktoken_model_name,\n )\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "default_headers": { - "type": "dict", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "default_headers", - "display_name": "Default Headers", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "default_query": { - "type": "NestedDict", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": {}, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "default_query", - "display_name": "Default Query", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "deployment": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": "text-embedding-ada-002", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "deployment", - "display_name": "Deployment", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "disallowed_special": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": [ - "all" - ], - "fileTypes": [], - "file_path": "", - "password": false, - "name": "disallowed_special", - "display_name": "Disallowed Special", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "embedding_ctx_length": { - "type": "int", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": 8191, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "embedding_ctx_length", - "display_name": "Embedding Context Length", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "max_retries": { - "type": "int", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": 6, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "max_retries", - "display_name": "Max Retries", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "model": { - "type": "str", - "required": false, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "value": "text-embedding-ada-002", - "fileTypes": [], - "file_path": "", - "password": false, - "options": [ - "text-embedding-3-small", - "text-embedding-3-large", - "text-embedding-ada-002" - ], - "name": "model", - "display_name": "Model", - "advanced": false, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "model_kwargs": { - "type": "NestedDict", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": {}, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "model_kwargs", - "display_name": "Model Kwargs", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "openai_api_base": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": true, - "name": "openai_api_base", - "display_name": "OpenAI API Base", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "openai_api_key": { - "type": "str", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": true, - "name": "openai_api_key", - "display_name": "OpenAI API Key", - "advanced": false, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ], - "value": "OPENAI_API_KEY" - }, - "openai_api_type": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": true, - "name": "openai_api_type", - "display_name": "OpenAI API Type", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "openai_api_version": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "openai_api_version", - "display_name": "OpenAI API Version", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "openai_organization": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "openai_organization", - "display_name": "OpenAI Organization", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "openai_proxy": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "openai_proxy", - "display_name": "OpenAI Proxy", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "request_timeout": { - "type": "float", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "request_timeout", - "display_name": "Request Timeout", - "advanced": true, - "dynamic": false, - "info": "", - "rangeSpec": { - "step_type": "float", - "min": -1, - "max": 1, - "step": 0.1 - }, - "load_from_db": false, - "title_case": false - }, - "show_progress_bar": { - "type": "bool", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "show_progress_bar", - "display_name": "Show Progress Bar", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "skip_empty": { - "type": "bool", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "skip_empty", - "display_name": "Skip Empty", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "tiktoken_enable": { - "type": "bool", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": true, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "tiktoken_enable", - "display_name": "TikToken Enable", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "tiktoken_model_name": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "tiktoken_model_name", - "display_name": "TikToken Model Name", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "_type": "CustomComponent" - }, - "description": "Generate embeddings using OpenAI models.", - "base_classes": [ - "Embeddings" - ], - "display_name": "OpenAI Embeddings", - "documentation": "", - "custom_fields": { - "openai_api_key": null, - "default_headers": null, - "default_query": null, - "allowed_special": null, - "disallowed_special": null, - "chunk_size": null, - "client": null, - "deployment": null, - "embedding_ctx_length": null, - "max_retries": null, - "model": null, - "model_kwargs": null, - "openai_api_base": null, - "openai_api_type": null, - "openai_api_version": null, - "openai_organization": null, - "openai_proxy": null, - "request_timeout": null, - "show_progress_bar": null, - "skip_empty": null, - "tiktoken_enable": null, - "tiktoken_model_name": null - }, - "output_types": [ - "Embeddings" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": false - }, - "id": "OpenAIEmbeddings-9TPjc" - }, - "selected": false, - "width": 384, - "height": 383, - "positionAbsolute": { - "x": 2814.0402191223047, - "y": 1955.9268168273086 - }, - "dragging": false - } - ], - "edges": [ - { - "source": "TextOutput-BDknO", - "target": "Prompt-xeI6K", - "sourceHandle": "{\u0153baseClasses\u0153:[\u0153object\u0153,\u0153Text\u0153,\u0153str\u0153],\u0153dataType\u0153:\u0153TextOutput\u0153,\u0153id\u0153:\u0153TextOutput-BDknO\u0153}", - "targetHandle": "{\u0153fieldName\u0153:\u0153context\u0153,\u0153id\u0153:\u0153Prompt-xeI6K\u0153,\u0153inputTypes\u0153:[\u0153Document\u0153,\u0153BaseOutputParser\u0153,\u0153Record\u0153,\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}", - "id": "reactflow__edge-TextOutput-BDknO{\u0153baseClasses\u0153:[\u0153object\u0153,\u0153Text\u0153,\u0153str\u0153],\u0153dataType\u0153:\u0153TextOutput\u0153,\u0153id\u0153:\u0153TextOutput-BDknO\u0153}-Prompt-xeI6K{\u0153fieldName\u0153:\u0153context\u0153,\u0153id\u0153:\u0153Prompt-xeI6K\u0153,\u0153inputTypes\u0153:[\u0153Document\u0153,\u0153BaseOutputParser\u0153,\u0153Record\u0153,\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}", - "data": { - "targetHandle": { - "fieldName": "context", - "id": "Prompt-xeI6K", - "inputTypes": [ - "Document", - "BaseOutputParser", - "Record", - "Text" - ], - "type": "str" - }, - "sourceHandle": { - "baseClasses": [ - "object", - "Text", - "str" - ], - "dataType": "TextOutput", - "id": "TextOutput-BDknO" - } - }, - "style": { - "stroke": "#555" - }, - "className": "stroke-gray-900 stroke-connection", - "selected": false + "output_types": [ + "Text" + ], + "full_path": null, + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false, + "error": null + }, + "id": "Prompt-xeI6K", + "description": "Create a prompt template with dynamic variables.", + "display_name": "Prompt" + }, + "selected": false, + "width": 384, + "height": 477, + "positionAbsolute": { + "x": 2969.0261961391298, + "y": 442.1613649809069 + }, + "dragging": false + }, + { + "id": "ChatOutput-Q39I8", + "type": "genericNode", + "position": { + "x": 3887.2073667611485, + "y": 588.4801225794856 + }, + "data": { + "type": "ChatOutput", + "node": { + "template": { + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n def build(\n self,\n sender: Optional[str] = \"Machine\",\n sender_name: Optional[str] = \"AI\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n record_template: Optional[str] = \"{text}\",\n ) -> Union[Text, Record]:\n return super().build_with_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n record_template=record_template or \"\",\n )\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "input_value": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "input_value", + "display_name": "Message", + "advanced": false, + "input_types": [ + "Text" + ], + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "record_template": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "{text}", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "record_template", + "display_name": "Record Template", + "advanced": true, + "dynamic": false, + "info": "In case of Message being a Record, this template will be used to convert it to text.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "return_record": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "return_record", + "display_name": "Return Record", + "advanced": true, + "dynamic": false, + "info": "Return the message as a record containing the sender, sender_name, and session_id.", + "load_from_db": false, + "title_case": false + }, + "sender": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "value": "Machine", + "fileTypes": [], + "file_path": "", + "password": false, + "options": [ + "Machine", + "User" + ], + "name": "sender", + "display_name": "Sender Type", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "sender_name": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": "AI", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "sender_name", + "display_name": "Sender Name", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "session_id": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "session_id", + "display_name": "Session ID", + "advanced": true, + "dynamic": false, + "info": "If provided, the message will be stored in the memory.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "_type": "CustomComponent" }, - { - "source": "ChatInput-yxMKE", - "target": "Prompt-xeI6K", - "sourceHandle": "{\u0153baseClasses\u0153:[\u0153Text\u0153,\u0153str\u0153,\u0153object\u0153,\u0153Record\u0153],\u0153dataType\u0153:\u0153ChatInput\u0153,\u0153id\u0153:\u0153ChatInput-yxMKE\u0153}", - "targetHandle": "{\u0153fieldName\u0153:\u0153question\u0153,\u0153id\u0153:\u0153Prompt-xeI6K\u0153,\u0153inputTypes\u0153:[\u0153Document\u0153,\u0153BaseOutputParser\u0153,\u0153Record\u0153,\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}", - "id": "reactflow__edge-ChatInput-yxMKE{\u0153baseClasses\u0153:[\u0153Text\u0153,\u0153str\u0153,\u0153object\u0153,\u0153Record\u0153],\u0153dataType\u0153:\u0153ChatInput\u0153,\u0153id\u0153:\u0153ChatInput-yxMKE\u0153}-Prompt-xeI6K{\u0153fieldName\u0153:\u0153question\u0153,\u0153id\u0153:\u0153Prompt-xeI6K\u0153,\u0153inputTypes\u0153:[\u0153Document\u0153,\u0153BaseOutputParser\u0153,\u0153Record\u0153,\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}", - "data": { - "targetHandle": { - "fieldName": "question", - "id": "Prompt-xeI6K", - "inputTypes": [ - "Document", - "BaseOutputParser", - "Record", - "Text" - ], - "type": "str" - }, - "sourceHandle": { - "baseClasses": [ - "Text", - "str", - "object", - "Record" - ], - "dataType": "ChatInput", - "id": "ChatInput-yxMKE" - } - }, - "style": { - "stroke": "#555" - }, - "className": "stroke-gray-900 stroke-connection", - "selected": false + "description": "Display a chat message in the Playground.", + "icon": "ChatOutput", + "base_classes": [ + "object", + "Text", + "Record", + "str" + ], + "display_name": "Chat Output", + "documentation": "", + "custom_fields": { + "sender": null, + "sender_name": null, + "input_value": null, + "session_id": null, + "return_record": null, + "record_template": null }, - { - "source": "Prompt-xeI6K", - "target": "OpenAIModel-EjXlN", - "sourceHandle": "{\u0153baseClasses\u0153:[\u0153object\u0153,\u0153Text\u0153,\u0153str\u0153],\u0153dataType\u0153:\u0153Prompt\u0153,\u0153id\u0153:\u0153Prompt-xeI6K\u0153}", - "targetHandle": "{\u0153fieldName\u0153:\u0153input_value\u0153,\u0153id\u0153:\u0153OpenAIModel-EjXlN\u0153,\u0153inputTypes\u0153:[\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}", - "id": "reactflow__edge-Prompt-xeI6K{\u0153baseClasses\u0153:[\u0153object\u0153,\u0153Text\u0153,\u0153str\u0153],\u0153dataType\u0153:\u0153Prompt\u0153,\u0153id\u0153:\u0153Prompt-xeI6K\u0153}-OpenAIModel-EjXlN{\u0153fieldName\u0153:\u0153input_value\u0153,\u0153id\u0153:\u0153OpenAIModel-EjXlN\u0153,\u0153inputTypes\u0153:[\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}", - "data": { - "targetHandle": { - "fieldName": "input_value", - "id": "OpenAIModel-EjXlN", - "inputTypes": [ - "Text" - ], - "type": "str" - }, - "sourceHandle": { - "baseClasses": [ - "object", - "Text", - "str" - ], - "dataType": "Prompt", - "id": "Prompt-xeI6K" - } - }, - "style": { - "stroke": "#555" - }, - "className": "stroke-gray-900 stroke-connection", - "selected": false + "output_types": [ + "Text", + "Record" + ], + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false + }, + "id": "ChatOutput-Q39I8" + }, + "selected": false, + "width": 384, + "height": 383, + "positionAbsolute": { + "x": 3887.2073667611485, + "y": 588.4801225794856 + }, + "dragging": false + }, + { + "id": "File-t0a6a", + "type": "genericNode", + "position": { + "x": 2257.233450682836, + "y": 1747.5389618367233 + }, + "data": { + "type": "File", + "node": { + "template": { + "path": { + "type": "file", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [ + ".txt", + ".md", + ".mdx", + ".csv", + ".json", + ".yaml", + ".yml", + ".xml", + ".html", + ".htm", + ".pdf", + ".docx", + ".py", + ".sh", + ".sql", + ".js", + ".ts", + ".tsx" + ], + "file_path": "51e2b78a-199b-4054-9f32-e288eef6924c/Langflow conversation.pdf", + "password": false, + "name": "path", + "display_name": "Path", + "advanced": false, + "dynamic": false, + "info": "Supported file types: txt, md, mdx, csv, json, yaml, yml, xml, html, htm, pdf, docx, py, sh, sql, js, ts, tsx", + "load_from_db": false, + "title_case": false, + "value": "" + }, + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from pathlib import Path\nfrom typing import Any, Dict\n\nfrom langflow.base.data.utils import TEXT_FILE_TYPES, parse_text_file_to_record\nfrom langflow.custom import CustomComponent\nfrom langflow.schema import Record\n\n\nclass FileComponent(CustomComponent):\n display_name = \"File\"\n description = \"A generic file loader.\"\n icon = \"file-text\"\n\n def build_config(self) -> Dict[str, Any]:\n return {\n \"path\": {\n \"display_name\": \"Path\",\n \"field_type\": \"file\",\n \"file_types\": TEXT_FILE_TYPES,\n \"info\": f\"Supported file types: {', '.join(TEXT_FILE_TYPES)}\",\n },\n \"silent_errors\": {\n \"display_name\": \"Silent Errors\",\n \"advanced\": True,\n \"info\": \"If true, errors will not raise an exception.\",\n },\n }\n\n def load_file(self, path: str, silent_errors: bool = False) -> Record:\n resolved_path = self.resolve_path(path)\n path_obj = Path(resolved_path)\n extension = path_obj.suffix[1:].lower()\n if extension == \"doc\":\n raise ValueError(\"doc files are not supported. Please save as .docx\")\n if extension not in TEXT_FILE_TYPES:\n raise ValueError(f\"Unsupported file type: {extension}\")\n record = parse_text_file_to_record(resolved_path, silent_errors)\n self.status = record if record else \"No data\"\n return record or Record()\n\n def build(\n self,\n path: str,\n silent_errors: bool = False,\n ) -> Record:\n record = self.load_file(path, silent_errors)\n self.status = record\n return record\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "silent_errors": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "silent_errors", + "display_name": "Silent Errors", + "advanced": true, + "dynamic": false, + "info": "If true, errors will not raise an exception.", + "load_from_db": false, + "title_case": false + }, + "_type": "CustomComponent" }, - { - "source": "OpenAIModel-EjXlN", - "target": "ChatOutput-Q39I8", - "sourceHandle": "{\u0153baseClasses\u0153:[\u0153object\u0153,\u0153Text\u0153,\u0153str\u0153],\u0153dataType\u0153:\u0153OpenAIModel\u0153,\u0153id\u0153:\u0153OpenAIModel-EjXlN\u0153}", - "targetHandle": "{\u0153fieldName\u0153:\u0153input_value\u0153,\u0153id\u0153:\u0153ChatOutput-Q39I8\u0153,\u0153inputTypes\u0153:[\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}", - "id": "reactflow__edge-OpenAIModel-EjXlN{\u0153baseClasses\u0153:[\u0153object\u0153,\u0153Text\u0153,\u0153str\u0153],\u0153dataType\u0153:\u0153OpenAIModel\u0153,\u0153id\u0153:\u0153OpenAIModel-EjXlN\u0153}-ChatOutput-Q39I8{\u0153fieldName\u0153:\u0153input_value\u0153,\u0153id\u0153:\u0153ChatOutput-Q39I8\u0153,\u0153inputTypes\u0153:[\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}", - "data": { - "targetHandle": { - "fieldName": "input_value", - "id": "ChatOutput-Q39I8", - "inputTypes": [ - "Text" - ], - "type": "str" - }, - "sourceHandle": { - "baseClasses": [ - "object", - "Text", - "str" - ], - "dataType": "OpenAIModel", - "id": "OpenAIModel-EjXlN" - } - }, - "style": { - "stroke": "#555" - }, - "className": "stroke-gray-900 stroke-connection", - "selected": false + "description": "A generic file loader.", + "icon": "file-text", + "base_classes": [ + "Record" + ], + "display_name": "File", + "documentation": "", + "custom_fields": { + "path": null, + "silent_errors": null }, - { - "source": "File-t0a6a", - "target": "RecursiveCharacterTextSplitter-tR9QM", - "sourceHandle": "{\u0153baseClasses\u0153:[\u0153Record\u0153],\u0153dataType\u0153:\u0153File\u0153,\u0153id\u0153:\u0153File-t0a6a\u0153}", - "targetHandle": "{\u0153fieldName\u0153:\u0153inputs\u0153,\u0153id\u0153:\u0153RecursiveCharacterTextSplitter-tR9QM\u0153,\u0153inputTypes\u0153:[\u0153Document\u0153,\u0153Record\u0153],\u0153type\u0153:\u0153Document\u0153}", - "id": "reactflow__edge-File-t0a6a{\u0153baseClasses\u0153:[\u0153Record\u0153],\u0153dataType\u0153:\u0153File\u0153,\u0153id\u0153:\u0153File-t0a6a\u0153}-RecursiveCharacterTextSplitter-tR9QM{\u0153fieldName\u0153:\u0153inputs\u0153,\u0153id\u0153:\u0153RecursiveCharacterTextSplitter-tR9QM\u0153,\u0153inputTypes\u0153:[\u0153Document\u0153,\u0153Record\u0153],\u0153type\u0153:\u0153Document\u0153}", - "data": { - "targetHandle": { - "fieldName": "inputs", - "id": "RecursiveCharacterTextSplitter-tR9QM", - "inputTypes": [ - "Document", - "Record" - ], - "type": "Document" - }, - "sourceHandle": { - "baseClasses": [ - "Record" - ], - "dataType": "File", - "id": "File-t0a6a" - } - }, - "style": { - "stroke": "#555" - }, - "className": "stroke-gray-900 stroke-connection", - "selected": false + "output_types": [ + "Record" + ], + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false + }, + "id": "File-t0a6a" + }, + "selected": false, + "width": 384, + "height": 281, + "positionAbsolute": { + "x": 2257.233450682836, + "y": 1747.5389618367233 + }, + "dragging": false + }, + { + "id": "RecursiveCharacterTextSplitter-tR9QM", + "type": "genericNode", + "position": { + "x": 2791.013514133929, + "y": 1462.9588953494142 + }, + "data": { + "type": "RecursiveCharacterTextSplitter", + "node": { + "template": { + "inputs": { + "type": "Document", + "required": true, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "inputs", + "display_name": "Input", + "advanced": false, + "input_types": [ + "Document", + "Record" + ], + "dynamic": false, + "info": "The texts to split.", + "load_from_db": false, + "title_case": false + }, + "chunk_overlap": { + "type": "int", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": 200, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "chunk_overlap", + "display_name": "Chunk Overlap", + "advanced": false, + "dynamic": false, + "info": "The amount of overlap between chunks.", + "load_from_db": false, + "title_case": false + }, + "chunk_size": { + "type": "int", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": 1000, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "chunk_size", + "display_name": "Chunk Size", + "advanced": false, + "dynamic": false, + "info": "The maximum length of each chunk.", + "load_from_db": false, + "title_case": false + }, + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import Optional\n\nfrom langchain_core.documents import Document\nfrom langchain_text_splitters import RecursiveCharacterTextSplitter\n\nfrom langflow.custom import CustomComponent\nfrom langflow.schema import Record\nfrom langflow.utils.util import build_loader_repr_from_records, unescape_string\n\n\nclass RecursiveCharacterTextSplitterComponent(CustomComponent):\n display_name: str = \"Recursive Character Text Splitter\"\n description: str = \"Split text into chunks of a specified length.\"\n documentation: str = \"https://docs.langflow.org/components/text-splitters#recursivecharactertextsplitter\"\n\n def build_config(self):\n return {\n \"inputs\": {\n \"display_name\": \"Input\",\n \"info\": \"The texts to split.\",\n \"input_types\": [\"Document\", \"Record\"],\n },\n \"separators\": {\n \"display_name\": \"Separators\",\n \"info\": 'The characters to split on.\\nIf left empty defaults to [\"\\\\n\\\\n\", \"\\\\n\", \" \", \"\"].',\n \"is_list\": True,\n },\n \"chunk_size\": {\n \"display_name\": \"Chunk Size\",\n \"info\": \"The maximum length of each chunk.\",\n \"field_type\": \"int\",\n \"value\": 1000,\n },\n \"chunk_overlap\": {\n \"display_name\": \"Chunk Overlap\",\n \"info\": \"The amount of overlap between chunks.\",\n \"field_type\": \"int\",\n \"value\": 200,\n },\n \"code\": {\"show\": False},\n }\n\n def build(\n self,\n inputs: list[Document],\n separators: Optional[list[str]] = None,\n chunk_size: Optional[int] = 1000,\n chunk_overlap: Optional[int] = 200,\n ) -> list[Record]:\n \"\"\"\n Split text into chunks of a specified length.\n\n Args:\n separators (list[str]): The characters to split on.\n chunk_size (int): The maximum length of each chunk.\n chunk_overlap (int): The amount of overlap between chunks.\n length_function (function): The function to use to calculate the length of the text.\n\n Returns:\n list[str]: The chunks of text.\n \"\"\"\n\n if separators == \"\":\n separators = None\n elif separators:\n # check if the separators list has escaped characters\n # if there are escaped characters, unescape them\n separators = [unescape_string(x) for x in separators]\n\n # Make sure chunk_size and chunk_overlap are ints\n if isinstance(chunk_size, str):\n chunk_size = int(chunk_size)\n if isinstance(chunk_overlap, str):\n chunk_overlap = int(chunk_overlap)\n splitter = RecursiveCharacterTextSplitter(\n separators=separators,\n chunk_size=chunk_size,\n chunk_overlap=chunk_overlap,\n )\n documents = []\n for _input in inputs:\n if isinstance(_input, Record):\n documents.append(_input.to_lc_document())\n else:\n documents.append(_input)\n docs = splitter.split_documents(documents)\n records = self.to_records(docs)\n self.repr_value = build_loader_repr_from_records(records)\n return records\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "separators": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "separators", + "display_name": "Separators", + "advanced": false, + "dynamic": false, + "info": "The characters to split on.\nIf left empty defaults to [\"\\n\\n\", \"\\n\", \" \", \"\"].", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ], + "value": [ + "" + ] + }, + "_type": "CustomComponent" }, - { - "source": "OpenAIEmbeddings-ZlOk1", - "sourceHandle": "{\u0153baseClasses\u0153:[\u0153Embeddings\u0153],\u0153dataType\u0153:\u0153OpenAIEmbeddings\u0153,\u0153id\u0153:\u0153OpenAIEmbeddings-ZlOk1\u0153}", - "target": "AstraDBSearch-41nRz", - "targetHandle": "{\u0153fieldName\u0153:\u0153embedding\u0153,\u0153id\u0153:\u0153AstraDBSearch-41nRz\u0153,\u0153inputTypes\u0153:null,\u0153type\u0153:\u0153Embeddings\u0153}", - "data": { - "targetHandle": { - "fieldName": "embedding", - "id": "AstraDBSearch-41nRz", - "inputTypes": null, - "type": "Embeddings" - }, - "sourceHandle": { - "baseClasses": [ - "Embeddings" - ], - "dataType": "OpenAIEmbeddings", - "id": "OpenAIEmbeddings-ZlOk1" - } - }, - "style": { - "stroke": "#555" - }, - "className": "stroke-gray-900 stroke-connection", - "id": "reactflow__edge-OpenAIEmbeddings-ZlOk1{\u0153baseClasses\u0153:[\u0153Embeddings\u0153],\u0153dataType\u0153:\u0153OpenAIEmbeddings\u0153,\u0153id\u0153:\u0153OpenAIEmbeddings-ZlOk1\u0153}-AstraDBSearch-41nRz{\u0153fieldName\u0153:\u0153embedding\u0153,\u0153id\u0153:\u0153AstraDBSearch-41nRz\u0153,\u0153inputTypes\u0153:null,\u0153type\u0153:\u0153Embeddings\u0153}" + "description": "Split text into chunks of a specified length.", + "base_classes": [ + "Record" + ], + "display_name": "Recursive Character Text Splitter", + "documentation": "https://docs.langflow.org/components/text-splitters#recursivecharactertextsplitter", + "custom_fields": { + "inputs": null, + "separators": null, + "chunk_size": null, + "chunk_overlap": null }, - { - "source": "ChatInput-yxMKE", - "sourceHandle": "{\u0153baseClasses\u0153:[\u0153Text\u0153,\u0153str\u0153,\u0153object\u0153,\u0153Record\u0153],\u0153dataType\u0153:\u0153ChatInput\u0153,\u0153id\u0153:\u0153ChatInput-yxMKE\u0153}", - "target": "AstraDBSearch-41nRz", - "targetHandle": "{\u0153fieldName\u0153:\u0153input_value\u0153,\u0153id\u0153:\u0153AstraDBSearch-41nRz\u0153,\u0153inputTypes\u0153:[\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}", - "data": { - "targetHandle": { - "fieldName": "input_value", - "id": "AstraDBSearch-41nRz", - "inputTypes": [ - "Text" - ], - "type": "str" - }, - "sourceHandle": { - "baseClasses": [ - "Text", - "str", - "object", - "Record" - ], - "dataType": "ChatInput", - "id": "ChatInput-yxMKE" - } - }, - "style": { - "stroke": "#555" - }, - "className": "stroke-gray-900 stroke-connection", - "id": "reactflow__edge-ChatInput-yxMKE{\u0153baseClasses\u0153:[\u0153Text\u0153,\u0153str\u0153,\u0153object\u0153,\u0153Record\u0153],\u0153dataType\u0153:\u0153ChatInput\u0153,\u0153id\u0153:\u0153ChatInput-yxMKE\u0153}-AstraDBSearch-41nRz{\u0153fieldName\u0153:\u0153input_value\u0153,\u0153id\u0153:\u0153AstraDBSearch-41nRz\u0153,\u0153inputTypes\u0153:[\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}" + "output_types": [ + "Record" + ], + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false + }, + "id": "RecursiveCharacterTextSplitter-tR9QM" + }, + "selected": false, + "width": 384, + "height": 501, + "positionAbsolute": { + "x": 2791.013514133929, + "y": 1462.9588953494142 + }, + "dragging": false + }, + { + "id": "AstraDBSearch-41nRz", + "type": "genericNode", + "position": { + "x": 1723.976434815103, + "y": 277.03317407245913 + }, + "data": { + "type": "AstraDBSearch", + "node": { + "template": { + "embedding": { + "type": "Embeddings", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "embedding", + "display_name": "Embedding", + "advanced": false, + "dynamic": false, + "info": "Embedding to use", + "load_from_db": false, + "title_case": false + }, + "input_value": { + "type": "str", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "input_value", + "display_name": "Input Value", + "advanced": false, + "dynamic": false, + "info": "Input value to search", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "api_endpoint": { + "type": "str", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "api_endpoint", + "display_name": "API Endpoint", + "advanced": false, + "dynamic": false, + "info": "API endpoint URL for the Astra DB service.", + "load_from_db": true, + "title_case": false, + "input_types": [ + "Text" + ], + "value": "ASTRA_DB_API_ENDPOINT" + }, + "batch_size": { + "type": "int", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "batch_size", + "display_name": "Batch Size", + "advanced": true, + "dynamic": false, + "info": "Optional number of records to process in a single batch.", + "load_from_db": false, + "title_case": false + }, + "bulk_delete_concurrency": { + "type": "int", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "bulk_delete_concurrency", + "display_name": "Bulk Delete Concurrency", + "advanced": true, + "dynamic": false, + "info": "Optional concurrency level for bulk delete operations.", + "load_from_db": false, + "title_case": false + }, + "bulk_insert_batch_concurrency": { + "type": "int", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "bulk_insert_batch_concurrency", + "display_name": "Bulk Insert Batch Concurrency", + "advanced": true, + "dynamic": false, + "info": "Optional concurrency level for bulk insert operations.", + "load_from_db": false, + "title_case": false + }, + "bulk_insert_overwrite_concurrency": { + "type": "int", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "bulk_insert_overwrite_concurrency", + "display_name": "Bulk Insert Overwrite Concurrency", + "advanced": true, + "dynamic": false, + "info": "Optional concurrency level for bulk insert operations that overwrite existing records.", + "load_from_db": false, + "title_case": false + }, + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import List, Optional\n\nfrom langflow.components.vectorstores.AstraDB import AstraDBVectorStoreComponent\nfrom langflow.components.vectorstores.base.model import LCVectorStoreComponent\nfrom langflow.field_typing import Embeddings, Text\nfrom langflow.schema import Record\n\n\nclass AstraDBSearchComponent(LCVectorStoreComponent):\n display_name = \"Astra DB Search\"\n description = \"Searches an existing Astra DB Vector Store.\"\n icon = \"AstraDB\"\n field_order = [\"token\", \"api_endpoint\", \"collection_name\", \"input_value\", \"embedding\"]\n\n def build_config(self):\n return {\n \"search_type\": {\n \"display_name\": \"Search Type\",\n \"options\": [\"Similarity\", \"MMR\"],\n },\n \"input_value\": {\n \"display_name\": \"Input Value\",\n \"info\": \"Input value to search\",\n },\n \"embedding\": {\"display_name\": \"Embedding\", \"info\": \"Embedding to use\"},\n \"collection_name\": {\n \"display_name\": \"Collection Name\",\n \"info\": \"The name of the collection within Astra DB where the vectors will be stored.\",\n },\n \"token\": {\n \"display_name\": \"Token\",\n \"info\": \"Authentication token for accessing Astra DB.\",\n \"password\": True,\n },\n \"api_endpoint\": {\n \"display_name\": \"API Endpoint\",\n \"info\": \"API endpoint URL for the Astra DB service.\",\n },\n \"namespace\": {\n \"display_name\": \"Namespace\",\n \"info\": \"Optional namespace within Astra DB to use for the collection.\",\n \"advanced\": True,\n },\n \"metric\": {\n \"display_name\": \"Metric\",\n \"info\": \"Optional distance metric for vector comparisons in the vector store.\",\n \"advanced\": True,\n },\n \"batch_size\": {\n \"display_name\": \"Batch Size\",\n \"info\": \"Optional number of records to process in a single batch.\",\n \"advanced\": True,\n },\n \"bulk_insert_batch_concurrency\": {\n \"display_name\": \"Bulk Insert Batch Concurrency\",\n \"info\": \"Optional concurrency level for bulk insert operations.\",\n \"advanced\": True,\n },\n \"bulk_insert_overwrite_concurrency\": {\n \"display_name\": \"Bulk Insert Overwrite Concurrency\",\n \"info\": \"Optional concurrency level for bulk insert operations that overwrite existing records.\",\n \"advanced\": True,\n },\n \"bulk_delete_concurrency\": {\n \"display_name\": \"Bulk Delete Concurrency\",\n \"info\": \"Optional concurrency level for bulk delete operations.\",\n \"advanced\": True,\n },\n \"setup_mode\": {\n \"display_name\": \"Setup Mode\",\n \"info\": \"Configuration mode for setting up the vector store, with options like “Sync”, “Async”, or “Off”.\",\n \"options\": [\"Sync\", \"Async\", \"Off\"],\n \"advanced\": True,\n },\n \"pre_delete_collection\": {\n \"display_name\": \"Pre Delete Collection\",\n \"info\": \"Boolean flag to determine whether to delete the collection before creating a new one.\",\n \"advanced\": True,\n },\n \"metadata_indexing_include\": {\n \"display_name\": \"Metadata Indexing Include\",\n \"info\": \"Optional list of metadata fields to include in the indexing.\",\n \"advanced\": True,\n },\n \"metadata_indexing_exclude\": {\n \"display_name\": \"Metadata Indexing Exclude\",\n \"info\": \"Optional list of metadata fields to exclude from the indexing.\",\n \"advanced\": True,\n },\n \"collection_indexing_policy\": {\n \"display_name\": \"Collection Indexing Policy\",\n \"info\": \"Optional dictionary defining the indexing policy for the collection.\",\n \"advanced\": True,\n },\n \"number_of_results\": {\n \"display_name\": \"Number of Results\",\n \"info\": \"Number of results to return.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n embedding: Embeddings,\n collection_name: str,\n input_value: Text,\n token: str,\n api_endpoint: str,\n search_type: str = \"Similarity\",\n number_of_results: int = 4,\n namespace: Optional[str] = None,\n metric: Optional[str] = None,\n batch_size: Optional[int] = None,\n bulk_insert_batch_concurrency: Optional[int] = None,\n bulk_insert_overwrite_concurrency: Optional[int] = None,\n bulk_delete_concurrency: Optional[int] = None,\n setup_mode: str = \"Sync\",\n pre_delete_collection: bool = False,\n metadata_indexing_include: Optional[List[str]] = None,\n metadata_indexing_exclude: Optional[List[str]] = None,\n collection_indexing_policy: Optional[dict] = None,\n ) -> List[Record]:\n vector_store = AstraDBVectorStoreComponent().build(\n embedding=embedding,\n collection_name=collection_name,\n token=token,\n api_endpoint=api_endpoint,\n namespace=namespace,\n metric=metric,\n batch_size=batch_size,\n bulk_insert_batch_concurrency=bulk_insert_batch_concurrency,\n bulk_insert_overwrite_concurrency=bulk_insert_overwrite_concurrency,\n bulk_delete_concurrency=bulk_delete_concurrency,\n setup_mode=setup_mode,\n pre_delete_collection=pre_delete_collection,\n metadata_indexing_include=metadata_indexing_include,\n metadata_indexing_exclude=metadata_indexing_exclude,\n collection_indexing_policy=collection_indexing_policy,\n )\n try:\n return self.search_with_vector_store(input_value, search_type, vector_store, k=number_of_results)\n except KeyError as e:\n if \"content\" in str(e):\n raise ValueError(\n \"You should ingest data through Langflow (or LangChain) to query it in Langflow. Your collection does not contain a field name 'content'.\"\n )\n else:\n raise e\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "collection_indexing_policy": { + "type": "dict", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "collection_indexing_policy", + "display_name": "Collection Indexing Policy", + "advanced": true, + "dynamic": false, + "info": "Optional dictionary defining the indexing policy for the collection.", + "load_from_db": false, + "title_case": false + }, + "collection_name": { + "type": "str", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "collection_name", + "display_name": "Collection Name", + "advanced": false, + "dynamic": false, + "info": "The name of the collection within Astra DB where the vectors will be stored.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ], + "value": "langflow" + }, + "metadata_indexing_exclude": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "metadata_indexing_exclude", + "display_name": "Metadata Indexing Exclude", + "advanced": true, + "dynamic": false, + "info": "Optional list of metadata fields to exclude from the indexing.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "metadata_indexing_include": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "metadata_indexing_include", + "display_name": "Metadata Indexing Include", + "advanced": true, + "dynamic": false, + "info": "Optional list of metadata fields to include in the indexing.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "metric": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "metric", + "display_name": "Metric", + "advanced": true, + "dynamic": false, + "info": "Optional distance metric for vector comparisons in the vector store.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "namespace": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "namespace", + "display_name": "Namespace", + "advanced": true, + "dynamic": false, + "info": "Optional namespace within Astra DB to use for the collection.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "number_of_results": { + "type": "int", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": 4, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "number_of_results", + "display_name": "Number of Results", + "advanced": true, + "dynamic": false, + "info": "Number of results to return.", + "load_from_db": false, + "title_case": false + }, + "pre_delete_collection": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "pre_delete_collection", + "display_name": "Pre Delete Collection", + "advanced": true, + "dynamic": false, + "info": "Boolean flag to determine whether to delete the collection before creating a new one.", + "load_from_db": false, + "title_case": false + }, + "search_type": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "value": "Similarity", + "fileTypes": [], + "file_path": "", + "password": false, + "options": [ + "Similarity", + "MMR" + ], + "name": "search_type", + "display_name": "Search Type", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "setup_mode": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "value": "Sync", + "fileTypes": [], + "file_path": "", + "password": false, + "options": [ + "Sync", + "Async", + "Off" + ], + "name": "setup_mode", + "display_name": "Setup Mode", + "advanced": true, + "dynamic": false, + "info": "Configuration mode for setting up the vector store, with options like “Sync”, “Async”, or “Off”.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "token": { + "type": "str", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": true, + "name": "token", + "display_name": "Token", + "advanced": false, + "dynamic": false, + "info": "Authentication token for accessing Astra DB.", + "load_from_db": true, + "title_case": false, + "input_types": [ + "Text" + ], + "value": "ASTRA_DB_APPLICATION_TOKEN" + }, + "_type": "CustomComponent" }, - { - "source": "RecursiveCharacterTextSplitter-tR9QM", - "sourceHandle": "{\u0153baseClasses\u0153:[\u0153Record\u0153],\u0153dataType\u0153:\u0153RecursiveCharacterTextSplitter\u0153,\u0153id\u0153:\u0153RecursiveCharacterTextSplitter-tR9QM\u0153}", - "target": "AstraDB-eUCSS", - "targetHandle": "{\u0153fieldName\u0153:\u0153inputs\u0153,\u0153id\u0153:\u0153AstraDB-eUCSS\u0153,\u0153inputTypes\u0153:null,\u0153type\u0153:\u0153Record\u0153}", - "data": { - "targetHandle": { - "fieldName": "inputs", - "id": "AstraDB-eUCSS", - "inputTypes": null, - "type": "Record" - }, - "sourceHandle": { - "baseClasses": [ - "Record" - ], - "dataType": "RecursiveCharacterTextSplitter", - "id": "RecursiveCharacterTextSplitter-tR9QM" - } - }, - "style": { - "stroke": "#555" - }, - "className": "stroke-gray-900 stroke-connection", - "id": "reactflow__edge-RecursiveCharacterTextSplitter-tR9QM{\u0153baseClasses\u0153:[\u0153Record\u0153],\u0153dataType\u0153:\u0153RecursiveCharacterTextSplitter\u0153,\u0153id\u0153:\u0153RecursiveCharacterTextSplitter-tR9QM\u0153}-AstraDB-eUCSS{\u0153fieldName\u0153:\u0153inputs\u0153,\u0153id\u0153:\u0153AstraDB-eUCSS\u0153,\u0153inputTypes\u0153:null,\u0153type\u0153:\u0153Record\u0153}", - "selected": false + "description": "Searches an existing Astra DB Vector Store.", + "icon": "AstraDB", + "base_classes": [ + "Record" + ], + "display_name": "Astra DB Search", + "documentation": "", + "custom_fields": { + "embedding": null, + "collection_name": null, + "input_value": null, + "token": null, + "api_endpoint": null, + "search_type": null, + "number_of_results": null, + "namespace": null, + "metric": null, + "batch_size": null, + "bulk_insert_batch_concurrency": null, + "bulk_insert_overwrite_concurrency": null, + "bulk_delete_concurrency": null, + "setup_mode": null, + "pre_delete_collection": null, + "metadata_indexing_include": null, + "metadata_indexing_exclude": null, + "collection_indexing_policy": null }, - { - "source": "OpenAIEmbeddings-9TPjc", - "sourceHandle": "{\u0153baseClasses\u0153:[\u0153Embeddings\u0153],\u0153dataType\u0153:\u0153OpenAIEmbeddings\u0153,\u0153id\u0153:\u0153OpenAIEmbeddings-9TPjc\u0153}", - "target": "AstraDB-eUCSS", - "targetHandle": "{\u0153fieldName\u0153:\u0153embedding\u0153,\u0153id\u0153:\u0153AstraDB-eUCSS\u0153,\u0153inputTypes\u0153:null,\u0153type\u0153:\u0153Embeddings\u0153}", - "data": { - "targetHandle": { - "fieldName": "embedding", - "id": "AstraDB-eUCSS", - "inputTypes": null, - "type": "Embeddings" - }, - "sourceHandle": { - "baseClasses": [ - "Embeddings" - ], - "dataType": "OpenAIEmbeddings", - "id": "OpenAIEmbeddings-9TPjc" - } - }, - "style": { - "stroke": "#555" - }, - "className": "stroke-gray-900 stroke-connection", - "id": "reactflow__edge-OpenAIEmbeddings-9TPjc{\u0153baseClasses\u0153:[\u0153Embeddings\u0153],\u0153dataType\u0153:\u0153OpenAIEmbeddings\u0153,\u0153id\u0153:\u0153OpenAIEmbeddings-9TPjc\u0153}-AstraDB-eUCSS{\u0153fieldName\u0153:\u0153embedding\u0153,\u0153id\u0153:\u0153AstraDB-eUCSS\u0153,\u0153inputTypes\u0153:null,\u0153type\u0153:\u0153Embeddings\u0153}", - "selected": false - }, - { - "source": "AstraDBSearch-41nRz", - "sourceHandle": "{\u0153baseClasses\u0153:[\u0153Record\u0153],\u0153dataType\u0153:\u0153AstraDBSearch\u0153,\u0153id\u0153:\u0153AstraDBSearch-41nRz\u0153}", - "target": "TextOutput-BDknO", - "targetHandle": "{\u0153fieldName\u0153:\u0153input_value\u0153,\u0153id\u0153:\u0153TextOutput-BDknO\u0153,\u0153inputTypes\u0153:[\u0153Record\u0153,\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}", - "data": { - "targetHandle": { - "fieldName": "input_value", - "id": "TextOutput-BDknO", - "inputTypes": [ - "Record", - "Text" - ], - "type": "str" - }, - "sourceHandle": { - "baseClasses": [ - "Record" - ], - "dataType": "AstraDBSearch", - "id": "AstraDBSearch-41nRz" - } - }, - "style": { - "stroke": "#555" - }, - "className": "stroke-gray-900 stroke-connection", - "id": "reactflow__edge-AstraDBSearch-41nRz{\u0153baseClasses\u0153:[\u0153Record\u0153],\u0153dataType\u0153:\u0153AstraDBSearch\u0153,\u0153id\u0153:\u0153AstraDBSearch-41nRz\u0153}-TextOutput-BDknO{\u0153fieldName\u0153:\u0153input_value\u0153,\u0153id\u0153:\u0153TextOutput-BDknO\u0153,\u0153inputTypes\u0153:[\u0153Record\u0153,\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}" - } - ], - "viewport": { - "x": -259.6782520315529, - "y": 90.3428735006047, - "zoom": 0.2687057134854984 + "output_types": [ + "Record" + ], + "field_formatters": {}, + "frozen": false, + "field_order": [ + "token", + "api_endpoint", + "collection_name", + "input_value", + "embedding" + ], + "beta": false + }, + "id": "AstraDBSearch-41nRz" + }, + "selected": false, + "width": 384, + "height": 713, + "dragging": false, + "positionAbsolute": { + "x": 1723.976434815103, + "y": 277.03317407245913 } - }, - "description": "Visit https://pre-release.langflow.org/tutorials/rag-with-astradb for a detailed guide of this project.\nThis project give you both Ingestion and RAG in a single file. You'll need to visit https://astra.datastax.com/ to create an Astra DB instance, your Token and grab an API Endpoint.\nRunning this project requires you to add a file in the Files component, then define a Collection Name and click on the Play icon on the Astra DB component. \n\nAfter the ingestion ends you are ready to click on the Run button at the lower left corner and start asking questions about your data.", - "name": "Vector Store RAG", - "last_tested_version": "1.0.0a0", - "is_component": false -} + }, + { + "id": "AstraDB-eUCSS", + "type": "genericNode", + "position": { + "x": 3372.04958055989, + "y": 1611.0742035495277 + }, + "data": { + "type": "AstraDB", + "node": { + "template": { + "embedding": { + "type": "Embeddings", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "embedding", + "display_name": "Embedding", + "advanced": false, + "dynamic": false, + "info": "Embedding to use", + "load_from_db": false, + "title_case": false + }, + "inputs": { + "type": "Record", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "inputs", + "display_name": "Inputs", + "advanced": false, + "dynamic": false, + "info": "Optional list of records to be processed and stored in the vector store.", + "load_from_db": false, + "title_case": false + }, + "api_endpoint": { + "type": "str", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "api_endpoint", + "display_name": "API Endpoint", + "advanced": false, + "dynamic": false, + "info": "API endpoint URL for the Astra DB service.", + "load_from_db": true, + "title_case": false, + "input_types": [ + "Text" + ], + "value": "ASTRA_DB_API_ENDPOINT" + }, + "batch_size": { + "type": "int", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "batch_size", + "display_name": "Batch Size", + "advanced": true, + "dynamic": false, + "info": "Optional number of records to process in a single batch.", + "load_from_db": false, + "title_case": false + }, + "bulk_delete_concurrency": { + "type": "int", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "bulk_delete_concurrency", + "display_name": "Bulk Delete Concurrency", + "advanced": true, + "dynamic": false, + "info": "Optional concurrency level for bulk delete operations.", + "load_from_db": false, + "title_case": false + }, + "bulk_insert_batch_concurrency": { + "type": "int", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "bulk_insert_batch_concurrency", + "display_name": "Bulk Insert Batch Concurrency", + "advanced": true, + "dynamic": false, + "info": "Optional concurrency level for bulk insert operations.", + "load_from_db": false, + "title_case": false + }, + "bulk_insert_overwrite_concurrency": { + "type": "int", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "bulk_insert_overwrite_concurrency", + "display_name": "Bulk Insert Overwrite Concurrency", + "advanced": true, + "dynamic": false, + "info": "Optional concurrency level for bulk insert operations that overwrite existing records.", + "load_from_db": false, + "title_case": false + }, + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import List, Optional, Union\nfrom langchain_astradb import AstraDBVectorStore\nfrom langchain_astradb.utils.astradb import SetupMode\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Embeddings, VectorStore\nfrom langflow.schema import Record\nfrom langchain_core.retrievers import BaseRetriever\n\n\nclass AstraDBVectorStoreComponent(CustomComponent):\n display_name = \"Astra DB\"\n description = \"Builds or loads an Astra DB Vector Store.\"\n icon = \"AstraDB\"\n field_order = [\"token\", \"api_endpoint\", \"collection_name\", \"inputs\", \"embedding\"]\n\n def build_config(self):\n return {\n \"inputs\": {\n \"display_name\": \"Inputs\",\n \"info\": \"Optional list of records to be processed and stored in the vector store.\",\n },\n \"embedding\": {\"display_name\": \"Embedding\", \"info\": \"Embedding to use\"},\n \"collection_name\": {\n \"display_name\": \"Collection Name\",\n \"info\": \"The name of the collection within Astra DB where the vectors will be stored.\",\n },\n \"token\": {\n \"display_name\": \"Token\",\n \"info\": \"Authentication token for accessing Astra DB.\",\n \"password\": True,\n },\n \"api_endpoint\": {\n \"display_name\": \"API Endpoint\",\n \"info\": \"API endpoint URL for the Astra DB service.\",\n },\n \"namespace\": {\n \"display_name\": \"Namespace\",\n \"info\": \"Optional namespace within Astra DB to use for the collection.\",\n \"advanced\": True,\n },\n \"metric\": {\n \"display_name\": \"Metric\",\n \"info\": \"Optional distance metric for vector comparisons in the vector store.\",\n \"advanced\": True,\n },\n \"batch_size\": {\n \"display_name\": \"Batch Size\",\n \"info\": \"Optional number of records to process in a single batch.\",\n \"advanced\": True,\n },\n \"bulk_insert_batch_concurrency\": {\n \"display_name\": \"Bulk Insert Batch Concurrency\",\n \"info\": \"Optional concurrency level for bulk insert operations.\",\n \"advanced\": True,\n },\n \"bulk_insert_overwrite_concurrency\": {\n \"display_name\": \"Bulk Insert Overwrite Concurrency\",\n \"info\": \"Optional concurrency level for bulk insert operations that overwrite existing records.\",\n \"advanced\": True,\n },\n \"bulk_delete_concurrency\": {\n \"display_name\": \"Bulk Delete Concurrency\",\n \"info\": \"Optional concurrency level for bulk delete operations.\",\n \"advanced\": True,\n },\n \"setup_mode\": {\n \"display_name\": \"Setup Mode\",\n \"info\": \"Configuration mode for setting up the vector store, with options like “Sync”, “Async”, or “Off”.\",\n \"options\": [\"Sync\", \"Async\", \"Off\"],\n \"advanced\": True,\n },\n \"pre_delete_collection\": {\n \"display_name\": \"Pre Delete Collection\",\n \"info\": \"Boolean flag to determine whether to delete the collection before creating a new one.\",\n \"advanced\": True,\n },\n \"metadata_indexing_include\": {\n \"display_name\": \"Metadata Indexing Include\",\n \"info\": \"Optional list of metadata fields to include in the indexing.\",\n \"advanced\": True,\n },\n \"metadata_indexing_exclude\": {\n \"display_name\": \"Metadata Indexing Exclude\",\n \"info\": \"Optional list of metadata fields to exclude from the indexing.\",\n \"advanced\": True,\n },\n \"collection_indexing_policy\": {\n \"display_name\": \"Collection Indexing Policy\",\n \"info\": \"Optional dictionary defining the indexing policy for the collection.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n embedding: Embeddings,\n token: str,\n api_endpoint: str,\n collection_name: str,\n inputs: Optional[List[Record]] = None,\n namespace: Optional[str] = None,\n metric: Optional[str] = None,\n batch_size: Optional[int] = None,\n bulk_insert_batch_concurrency: Optional[int] = None,\n bulk_insert_overwrite_concurrency: Optional[int] = None,\n bulk_delete_concurrency: Optional[int] = None,\n setup_mode: str = \"Sync\",\n pre_delete_collection: bool = False,\n metadata_indexing_include: Optional[List[str]] = None,\n metadata_indexing_exclude: Optional[List[str]] = None,\n collection_indexing_policy: Optional[dict] = None,\n ) -> Union[VectorStore, BaseRetriever]:\n try:\n setup_mode_value = SetupMode[setup_mode.upper()]\n except KeyError:\n raise ValueError(f\"Invalid setup mode: {setup_mode}\")\n if inputs:\n documents = [_input.to_lc_document() for _input in inputs]\n\n vector_store = AstraDBVectorStore.from_documents(\n documents=documents,\n embedding=embedding,\n collection_name=collection_name,\n token=token,\n api_endpoint=api_endpoint,\n namespace=namespace,\n metric=metric,\n batch_size=batch_size,\n bulk_insert_batch_concurrency=bulk_insert_batch_concurrency,\n bulk_insert_overwrite_concurrency=bulk_insert_overwrite_concurrency,\n bulk_delete_concurrency=bulk_delete_concurrency,\n setup_mode=setup_mode_value,\n pre_delete_collection=pre_delete_collection,\n metadata_indexing_include=metadata_indexing_include,\n metadata_indexing_exclude=metadata_indexing_exclude,\n collection_indexing_policy=collection_indexing_policy,\n )\n else:\n vector_store = AstraDBVectorStore(\n embedding=embedding,\n collection_name=collection_name,\n token=token,\n api_endpoint=api_endpoint,\n namespace=namespace,\n metric=metric,\n batch_size=batch_size,\n bulk_insert_batch_concurrency=bulk_insert_batch_concurrency,\n bulk_insert_overwrite_concurrency=bulk_insert_overwrite_concurrency,\n bulk_delete_concurrency=bulk_delete_concurrency,\n setup_mode=setup_mode_value,\n pre_delete_collection=pre_delete_collection,\n metadata_indexing_include=metadata_indexing_include,\n metadata_indexing_exclude=metadata_indexing_exclude,\n collection_indexing_policy=collection_indexing_policy,\n )\n\n return vector_store\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "collection_indexing_policy": { + "type": "dict", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "collection_indexing_policy", + "display_name": "Collection Indexing Policy", + "advanced": true, + "dynamic": false, + "info": "Optional dictionary defining the indexing policy for the collection.", + "load_from_db": false, + "title_case": false + }, + "collection_name": { + "type": "str", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "collection_name", + "display_name": "Collection Name", + "advanced": false, + "dynamic": false, + "info": "The name of the collection within Astra DB where the vectors will be stored.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ], + "value": "langflow" + }, + "metadata_indexing_exclude": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "metadata_indexing_exclude", + "display_name": "Metadata Indexing Exclude", + "advanced": true, + "dynamic": false, + "info": "Optional list of metadata fields to exclude from the indexing.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "metadata_indexing_include": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "metadata_indexing_include", + "display_name": "Metadata Indexing Include", + "advanced": true, + "dynamic": false, + "info": "Optional list of metadata fields to include in the indexing.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "metric": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "metric", + "display_name": "Metric", + "advanced": true, + "dynamic": false, + "info": "Optional distance metric for vector comparisons in the vector store.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "namespace": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "namespace", + "display_name": "Namespace", + "advanced": true, + "dynamic": false, + "info": "Optional namespace within Astra DB to use for the collection.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "pre_delete_collection": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "pre_delete_collection", + "display_name": "Pre Delete Collection", + "advanced": true, + "dynamic": false, + "info": "Boolean flag to determine whether to delete the collection before creating a new one.", + "load_from_db": false, + "title_case": false + }, + "setup_mode": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "value": "Sync", + "fileTypes": [], + "file_path": "", + "password": false, + "options": [ + "Sync", + "Async", + "Off" + ], + "name": "setup_mode", + "display_name": "Setup Mode", + "advanced": true, + "dynamic": false, + "info": "Configuration mode for setting up the vector store, with options like “Sync”, “Async”, or “Off”.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "token": { + "type": "str", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": true, + "name": "token", + "display_name": "Token", + "advanced": false, + "dynamic": false, + "info": "Authentication token for accessing Astra DB.", + "load_from_db": true, + "title_case": false, + "input_types": [ + "Text" + ], + "value": "ASTRA_DB_APPLICATION_TOKEN" + }, + "_type": "CustomComponent" + }, + "description": "Builds or loads an Astra DB Vector Store.", + "icon": "AstraDB", + "base_classes": [ + "VectorStore" + ], + "display_name": "Astra DB", + "documentation": "", + "custom_fields": { + "embedding": null, + "token": null, + "api_endpoint": null, + "collection_name": null, + "inputs": null, + "namespace": null, + "metric": null, + "batch_size": null, + "bulk_insert_batch_concurrency": null, + "bulk_insert_overwrite_concurrency": null, + "bulk_delete_concurrency": null, + "setup_mode": null, + "pre_delete_collection": null, + "metadata_indexing_include": null, + "metadata_indexing_exclude": null, + "collection_indexing_policy": null + }, + "output_types": [ + "VectorStore" + ], + "field_formatters": {}, + "frozen": false, + "field_order": [ + "token", + "api_endpoint", + "collection_name", + "inputs", + "embedding" + ], + "beta": false + }, + "id": "AstraDB-eUCSS" + }, + "selected": false, + "width": 384, + "height": 573, + "positionAbsolute": { + "x": 3372.04958055989, + "y": 1611.0742035495277 + }, + "dragging": false + }, + { + "id": "OpenAIEmbeddings-9TPjc", + "type": "genericNode", + "position": { + "x": 2814.0402191223047, + "y": 1955.9268168273086 + }, + "data": { + "type": "OpenAIEmbeddings", + "node": { + "template": { + "allowed_special": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": [], + "fileTypes": [], + "file_path": "", + "password": false, + "name": "allowed_special", + "display_name": "Allowed Special", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "chunk_size": { + "type": "int", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": 1000, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "chunk_size", + "display_name": "Chunk Size", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "client": { + "type": "Any", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "client", + "display_name": "Client", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import Dict, List, Optional\n\nfrom langchain_openai.embeddings.base import OpenAIEmbeddings\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Embeddings, NestedDict\n\n\nclass OpenAIEmbeddingsComponent(CustomComponent):\n display_name = \"OpenAI Embeddings\"\n description = \"Generate embeddings using OpenAI models.\"\n\n def build_config(self):\n return {\n \"allowed_special\": {\n \"display_name\": \"Allowed Special\",\n \"advanced\": True,\n \"field_type\": \"str\",\n \"is_list\": True,\n },\n \"default_headers\": {\n \"display_name\": \"Default Headers\",\n \"advanced\": True,\n \"field_type\": \"dict\",\n },\n \"default_query\": {\n \"display_name\": \"Default Query\",\n \"advanced\": True,\n \"field_type\": \"NestedDict\",\n },\n \"disallowed_special\": {\n \"display_name\": \"Disallowed Special\",\n \"advanced\": True,\n \"field_type\": \"str\",\n \"is_list\": True,\n },\n \"chunk_size\": {\"display_name\": \"Chunk Size\", \"advanced\": True},\n \"client\": {\"display_name\": \"Client\", \"advanced\": True},\n \"deployment\": {\"display_name\": \"Deployment\", \"advanced\": True},\n \"embedding_ctx_length\": {\n \"display_name\": \"Embedding Context Length\",\n \"advanced\": True,\n },\n \"max_retries\": {\"display_name\": \"Max Retries\", \"advanced\": True},\n \"model\": {\n \"display_name\": \"Model\",\n \"advanced\": False,\n \"options\": [\n \"text-embedding-3-small\",\n \"text-embedding-3-large\",\n \"text-embedding-ada-002\",\n ],\n },\n \"model_kwargs\": {\"display_name\": \"Model Kwargs\", \"advanced\": True},\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"password\": True,\n \"advanced\": True,\n },\n \"openai_api_key\": {\"display_name\": \"OpenAI API Key\", \"password\": True},\n \"openai_api_type\": {\n \"display_name\": \"OpenAI API Type\",\n \"advanced\": True,\n \"password\": True,\n },\n \"openai_api_version\": {\n \"display_name\": \"OpenAI API Version\",\n \"advanced\": True,\n },\n \"openai_organization\": {\n \"display_name\": \"OpenAI Organization\",\n \"advanced\": True,\n },\n \"openai_proxy\": {\"display_name\": \"OpenAI Proxy\", \"advanced\": True},\n \"request_timeout\": {\"display_name\": \"Request Timeout\", \"advanced\": True},\n \"show_progress_bar\": {\n \"display_name\": \"Show Progress Bar\",\n \"advanced\": True,\n },\n \"skip_empty\": {\"display_name\": \"Skip Empty\", \"advanced\": True},\n \"tiktoken_model_name\": {\n \"display_name\": \"TikToken Model Name\",\n \"advanced\": True,\n },\n \"tiktoken_enable\": {\"display_name\": \"TikToken Enable\", \"advanced\": True},\n }\n\n def build(\n self,\n openai_api_key: str,\n default_headers: Optional[Dict[str, str]] = None,\n default_query: Optional[NestedDict] = {},\n allowed_special: List[str] = [],\n disallowed_special: List[str] = [\"all\"],\n chunk_size: int = 1000,\n deployment: str = \"text-embedding-ada-002\",\n embedding_ctx_length: int = 8191,\n max_retries: int = 6,\n model: str = \"text-embedding-ada-002\",\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n openai_api_type: Optional[str] = None,\n openai_api_version: Optional[str] = None,\n openai_organization: Optional[str] = None,\n openai_proxy: Optional[str] = None,\n request_timeout: Optional[float] = None,\n show_progress_bar: bool = False,\n skip_empty: bool = False,\n tiktoken_enable: bool = True,\n tiktoken_model_name: Optional[str] = None,\n ) -> Embeddings:\n # This is to avoid errors with Vector Stores (e.g Chroma)\n if disallowed_special == [\"all\"]:\n disallowed_special = \"all\" # type: ignore\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n return OpenAIEmbeddings(\n tiktoken_enabled=tiktoken_enable,\n default_headers=default_headers,\n default_query=default_query,\n allowed_special=set(allowed_special),\n disallowed_special=\"all\",\n chunk_size=chunk_size,\n deployment=deployment,\n embedding_ctx_length=embedding_ctx_length,\n max_retries=max_retries,\n model=model,\n model_kwargs=model_kwargs,\n base_url=openai_api_base,\n api_key=api_key,\n openai_api_type=openai_api_type,\n api_version=openai_api_version,\n organization=openai_organization,\n openai_proxy=openai_proxy,\n timeout=request_timeout,\n show_progress_bar=show_progress_bar,\n skip_empty=skip_empty,\n tiktoken_model_name=tiktoken_model_name,\n )\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "default_headers": { + "type": "dict", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "default_headers", + "display_name": "Default Headers", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "default_query": { + "type": "NestedDict", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": {}, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "default_query", + "display_name": "Default Query", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "deployment": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": "text-embedding-ada-002", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "deployment", + "display_name": "Deployment", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "disallowed_special": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": [ + "all" + ], + "fileTypes": [], + "file_path": "", + "password": false, + "name": "disallowed_special", + "display_name": "Disallowed Special", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "embedding_ctx_length": { + "type": "int", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": 8191, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "embedding_ctx_length", + "display_name": "Embedding Context Length", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "max_retries": { + "type": "int", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": 6, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "max_retries", + "display_name": "Max Retries", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "model": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "value": "text-embedding-ada-002", + "fileTypes": [], + "file_path": "", + "password": false, + "options": [ + "text-embedding-3-small", + "text-embedding-3-large", + "text-embedding-ada-002" + ], + "name": "model", + "display_name": "Model", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "model_kwargs": { + "type": "NestedDict", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": {}, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "model_kwargs", + "display_name": "Model Kwargs", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "openai_api_base": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": true, + "name": "openai_api_base", + "display_name": "OpenAI API Base", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "openai_api_key": { + "type": "str", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": true, + "name": "openai_api_key", + "display_name": "OpenAI API Key", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ], + "value": "OPENAI_API_KEY" + }, + "openai_api_type": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": true, + "name": "openai_api_type", + "display_name": "OpenAI API Type", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "openai_api_version": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "openai_api_version", + "display_name": "OpenAI API Version", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "openai_organization": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "openai_organization", + "display_name": "OpenAI Organization", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "openai_proxy": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "openai_proxy", + "display_name": "OpenAI Proxy", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "request_timeout": { + "type": "float", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "request_timeout", + "display_name": "Request Timeout", + "advanced": true, + "dynamic": false, + "info": "", + "rangeSpec": { + "step_type": "float", + "min": -1, + "max": 1, + "step": 0.1 + }, + "load_from_db": false, + "title_case": false + }, + "show_progress_bar": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "show_progress_bar", + "display_name": "Show Progress Bar", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "skip_empty": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "skip_empty", + "display_name": "Skip Empty", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "tiktoken_enable": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": true, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "tiktoken_enable", + "display_name": "TikToken Enable", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "tiktoken_model_name": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "tiktoken_model_name", + "display_name": "TikToken Model Name", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "_type": "CustomComponent" + }, + "description": "Generate embeddings using OpenAI models.", + "base_classes": [ + "Embeddings" + ], + "display_name": "OpenAI Embeddings", + "documentation": "", + "custom_fields": { + "openai_api_key": null, + "default_headers": null, + "default_query": null, + "allowed_special": null, + "disallowed_special": null, + "chunk_size": null, + "client": null, + "deployment": null, + "embedding_ctx_length": null, + "max_retries": null, + "model": null, + "model_kwargs": null, + "openai_api_base": null, + "openai_api_type": null, + "openai_api_version": null, + "openai_organization": null, + "openai_proxy": null, + "request_timeout": null, + "show_progress_bar": null, + "skip_empty": null, + "tiktoken_enable": null, + "tiktoken_model_name": null + }, + "output_types": [ + "Embeddings" + ], + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false + }, + "id": "OpenAIEmbeddings-9TPjc" + }, + "selected": false, + "width": 384, + "height": 383, + "positionAbsolute": { + "x": 2814.0402191223047, + "y": 1955.9268168273086 + }, + "dragging": false + } + ], + "edges": [ + { + "source": "TextOutput-BDknO", + "target": "Prompt-xeI6K", + "sourceHandle": "{œbaseClassesœ:[œobjectœ,œTextœ,œstrœ],œdataTypeœ:œTextOutputœ,œidœ:œTextOutput-BDknOœ}", + "targetHandle": "{œfieldNameœ:œcontextœ,œidœ:œPrompt-xeI6Kœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}", + "id": "reactflow__edge-TextOutput-BDknO{œbaseClassesœ:[œobjectœ,œTextœ,œstrœ],œdataTypeœ:œTextOutputœ,œidœ:œTextOutput-BDknOœ}-Prompt-xeI6K{œfieldNameœ:œcontextœ,œidœ:œPrompt-xeI6Kœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}", + "data": { + "targetHandle": { + "fieldName": "context", + "id": "Prompt-xeI6K", + "inputTypes": [ + "Document", + "BaseOutputParser", + "Record", + "Text" + ], + "type": "str" + }, + "sourceHandle": { + "baseClasses": [ + "object", + "Text", + "str" + ], + "dataType": "TextOutput", + "id": "TextOutput-BDknO" + } + }, + "style": { + "stroke": "#555" + }, + "className": "stroke-gray-900 stroke-connection", + "selected": false + }, + { + "source": "ChatInput-yxMKE", + "target": "Prompt-xeI6K", + "sourceHandle": "{œbaseClassesœ:[œTextœ,œstrœ,œobjectœ,œRecordœ],œdataTypeœ:œChatInputœ,œidœ:œChatInput-yxMKEœ}", + "targetHandle": "{œfieldNameœ:œquestionœ,œidœ:œPrompt-xeI6Kœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}", + "id": "reactflow__edge-ChatInput-yxMKE{œbaseClassesœ:[œTextœ,œstrœ,œobjectœ,œRecordœ],œdataTypeœ:œChatInputœ,œidœ:œChatInput-yxMKEœ}-Prompt-xeI6K{œfieldNameœ:œquestionœ,œidœ:œPrompt-xeI6Kœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}", + "data": { + "targetHandle": { + "fieldName": "question", + "id": "Prompt-xeI6K", + "inputTypes": [ + "Document", + "BaseOutputParser", + "Record", + "Text" + ], + "type": "str" + }, + "sourceHandle": { + "baseClasses": [ + "Text", + "str", + "object", + "Record" + ], + "dataType": "ChatInput", + "id": "ChatInput-yxMKE" + } + }, + "style": { + "stroke": "#555" + }, + "className": "stroke-gray-900 stroke-connection", + "selected": false + }, + { + "source": "Prompt-xeI6K", + "target": "OpenAIModel-EjXlN", + "sourceHandle": "{œbaseClassesœ:[œobjectœ,œTextœ,œstrœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-xeI6Kœ}", + "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-EjXlNœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}", + "id": "reactflow__edge-Prompt-xeI6K{œbaseClassesœ:[œobjectœ,œTextœ,œstrœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-xeI6Kœ}-OpenAIModel-EjXlN{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-EjXlNœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}", + "data": { + "targetHandle": { + "fieldName": "input_value", + "id": "OpenAIModel-EjXlN", + "inputTypes": [ + "Text" + ], + "type": "str" + }, + "sourceHandle": { + "baseClasses": [ + "object", + "Text", + "str" + ], + "dataType": "Prompt", + "id": "Prompt-xeI6K" + } + }, + "style": { + "stroke": "#555" + }, + "className": "stroke-gray-900 stroke-connection", + "selected": false + }, + { + "source": "OpenAIModel-EjXlN", + "target": "ChatOutput-Q39I8", + "sourceHandle": "{œbaseClassesœ:[œobjectœ,œTextœ,œstrœ],œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-EjXlNœ}", + "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-Q39I8œ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}", + "id": "reactflow__edge-OpenAIModel-EjXlN{œbaseClassesœ:[œobjectœ,œTextœ,œstrœ],œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-EjXlNœ}-ChatOutput-Q39I8{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-Q39I8œ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}", + "data": { + "targetHandle": { + "fieldName": "input_value", + "id": "ChatOutput-Q39I8", + "inputTypes": [ + "Text" + ], + "type": "str" + }, + "sourceHandle": { + "baseClasses": [ + "object", + "Text", + "str" + ], + "dataType": "OpenAIModel", + "id": "OpenAIModel-EjXlN" + } + }, + "style": { + "stroke": "#555" + }, + "className": "stroke-gray-900 stroke-connection", + "selected": false + }, + { + "source": "File-t0a6a", + "target": "RecursiveCharacterTextSplitter-tR9QM", + "sourceHandle": "{œbaseClassesœ:[œRecordœ],œdataTypeœ:œFileœ,œidœ:œFile-t0a6aœ}", + "targetHandle": "{œfieldNameœ:œinputsœ,œidœ:œRecursiveCharacterTextSplitter-tR9QMœ,œinputTypesœ:[œDocumentœ,œRecordœ],œtypeœ:œDocumentœ}", + "id": "reactflow__edge-File-t0a6a{œbaseClassesœ:[œRecordœ],œdataTypeœ:œFileœ,œidœ:œFile-t0a6aœ}-RecursiveCharacterTextSplitter-tR9QM{œfieldNameœ:œinputsœ,œidœ:œRecursiveCharacterTextSplitter-tR9QMœ,œinputTypesœ:[œDocumentœ,œRecordœ],œtypeœ:œDocumentœ}", + "data": { + "targetHandle": { + "fieldName": "inputs", + "id": "RecursiveCharacterTextSplitter-tR9QM", + "inputTypes": [ + "Document", + "Record" + ], + "type": "Document" + }, + "sourceHandle": { + "baseClasses": [ + "Record" + ], + "dataType": "File", + "id": "File-t0a6a" + } + }, + "style": { + "stroke": "#555" + }, + "className": "stroke-gray-900 stroke-connection", + "selected": false + }, + { + "source": "OpenAIEmbeddings-ZlOk1", + "sourceHandle": "{œbaseClassesœ:[œEmbeddingsœ],œdataTypeœ:œOpenAIEmbeddingsœ,œidœ:œOpenAIEmbeddings-ZlOk1œ}", + "target": "AstraDBSearch-41nRz", + "targetHandle": "{œfieldNameœ:œembeddingœ,œidœ:œAstraDBSearch-41nRzœ,œinputTypesœ:null,œtypeœ:œEmbeddingsœ}", + "data": { + "targetHandle": { + "fieldName": "embedding", + "id": "AstraDBSearch-41nRz", + "inputTypes": null, + "type": "Embeddings" + }, + "sourceHandle": { + "baseClasses": [ + "Embeddings" + ], + "dataType": "OpenAIEmbeddings", + "id": "OpenAIEmbeddings-ZlOk1" + } + }, + "style": { + "stroke": "#555" + }, + "className": "stroke-gray-900 stroke-connection", + "id": "reactflow__edge-OpenAIEmbeddings-ZlOk1{œbaseClassesœ:[œEmbeddingsœ],œdataTypeœ:œOpenAIEmbeddingsœ,œidœ:œOpenAIEmbeddings-ZlOk1œ}-AstraDBSearch-41nRz{œfieldNameœ:œembeddingœ,œidœ:œAstraDBSearch-41nRzœ,œinputTypesœ:null,œtypeœ:œEmbeddingsœ}" + }, + { + "source": "ChatInput-yxMKE", + "sourceHandle": "{œbaseClassesœ:[œTextœ,œstrœ,œobjectœ,œRecordœ],œdataTypeœ:œChatInputœ,œidœ:œChatInput-yxMKEœ}", + "target": "AstraDBSearch-41nRz", + "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œAstraDBSearch-41nRzœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}", + "data": { + "targetHandle": { + "fieldName": "input_value", + "id": "AstraDBSearch-41nRz", + "inputTypes": [ + "Text" + ], + "type": "str" + }, + "sourceHandle": { + "baseClasses": [ + "Text", + "str", + "object", + "Record" + ], + "dataType": "ChatInput", + "id": "ChatInput-yxMKE" + } + }, + "style": { + "stroke": "#555" + }, + "className": "stroke-gray-900 stroke-connection", + "id": "reactflow__edge-ChatInput-yxMKE{œbaseClassesœ:[œTextœ,œstrœ,œobjectœ,œRecordœ],œdataTypeœ:œChatInputœ,œidœ:œChatInput-yxMKEœ}-AstraDBSearch-41nRz{œfieldNameœ:œinput_valueœ,œidœ:œAstraDBSearch-41nRzœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}" + }, + { + "source": "RecursiveCharacterTextSplitter-tR9QM", + "sourceHandle": "{œbaseClassesœ:[œRecordœ],œdataTypeœ:œRecursiveCharacterTextSplitterœ,œidœ:œRecursiveCharacterTextSplitter-tR9QMœ}", + "target": "AstraDB-eUCSS", + "targetHandle": "{œfieldNameœ:œinputsœ,œidœ:œAstraDB-eUCSSœ,œinputTypesœ:null,œtypeœ:œRecordœ}", + "data": { + "targetHandle": { + "fieldName": "inputs", + "id": "AstraDB-eUCSS", + "inputTypes": null, + "type": "Record" + }, + "sourceHandle": { + "baseClasses": [ + "Record" + ], + "dataType": "RecursiveCharacterTextSplitter", + "id": "RecursiveCharacterTextSplitter-tR9QM" + } + }, + "style": { + "stroke": "#555" + }, + "className": "stroke-gray-900 stroke-connection", + "id": "reactflow__edge-RecursiveCharacterTextSplitter-tR9QM{œbaseClassesœ:[œRecordœ],œdataTypeœ:œRecursiveCharacterTextSplitterœ,œidœ:œRecursiveCharacterTextSplitter-tR9QMœ}-AstraDB-eUCSS{œfieldNameœ:œinputsœ,œidœ:œAstraDB-eUCSSœ,œinputTypesœ:null,œtypeœ:œRecordœ}", + "selected": false + }, + { + "source": "OpenAIEmbeddings-9TPjc", + "sourceHandle": "{œbaseClassesœ:[œEmbeddingsœ],œdataTypeœ:œOpenAIEmbeddingsœ,œidœ:œOpenAIEmbeddings-9TPjcœ}", + "target": "AstraDB-eUCSS", + "targetHandle": "{œfieldNameœ:œembeddingœ,œidœ:œAstraDB-eUCSSœ,œinputTypesœ:null,œtypeœ:œEmbeddingsœ}", + "data": { + "targetHandle": { + "fieldName": "embedding", + "id": "AstraDB-eUCSS", + "inputTypes": null, + "type": "Embeddings" + }, + "sourceHandle": { + "baseClasses": [ + "Embeddings" + ], + "dataType": "OpenAIEmbeddings", + "id": "OpenAIEmbeddings-9TPjc" + } + }, + "style": { + "stroke": "#555" + }, + "className": "stroke-gray-900 stroke-connection", + "id": "reactflow__edge-OpenAIEmbeddings-9TPjc{œbaseClassesœ:[œEmbeddingsœ],œdataTypeœ:œOpenAIEmbeddingsœ,œidœ:œOpenAIEmbeddings-9TPjcœ}-AstraDB-eUCSS{œfieldNameœ:œembeddingœ,œidœ:œAstraDB-eUCSSœ,œinputTypesœ:null,œtypeœ:œEmbeddingsœ}", + "selected": false + }, + { + "source": "AstraDBSearch-41nRz", + "sourceHandle": "{œbaseClassesœ:[œRecordœ],œdataTypeœ:œAstraDBSearchœ,œidœ:œAstraDBSearch-41nRzœ}", + "target": "TextOutput-BDknO", + "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œTextOutput-BDknOœ,œinputTypesœ:[œRecordœ,œTextœ],œtypeœ:œstrœ}", + "data": { + "targetHandle": { + "fieldName": "input_value", + "id": "TextOutput-BDknO", + "inputTypes": [ + "Record", + "Text" + ], + "type": "str" + }, + "sourceHandle": { + "baseClasses": [ + "Record" + ], + "dataType": "AstraDBSearch", + "id": "AstraDBSearch-41nRz" + } + }, + "style": { + "stroke": "#555" + }, + "className": "stroke-gray-900 stroke-connection", + "id": "reactflow__edge-AstraDBSearch-41nRz{œbaseClassesœ:[œRecordœ],œdataTypeœ:œAstraDBSearchœ,œidœ:œAstraDBSearch-41nRzœ}-TextOutput-BDknO{œfieldNameœ:œinput_valueœ,œidœ:œTextOutput-BDknOœ,œinputTypesœ:[œRecordœ,œTextœ],œtypeœ:œstrœ}" + } + ], + "viewport": { + "x": -259.6782520315529, + "y": 90.3428735006047, + "zoom": 0.2687057134854984 + } + }, + "description": "Visit https://pre-release.langflow.org/tutorials/rag-with-astradb for a detailed guide of this project.\nThis project give you both Ingestion and RAG in a single file. You'll need to visit https://astra.datastax.com/ to create an Astra DB instance, your Token and grab an API Endpoint.\nRunning this project requires you to add a file in the Files component, then define a Collection Name and click on the Play icon on the Astra DB component. \n\nAfter the ingestion ends you are ready to click on the Run button at the lower left corner and start asking questions about your data.", + "name": "Vector Store RAG", + "last_tested_version": "1.0.0a0", + "is_component": false +} \ No newline at end of file diff --git a/src/backend/base/langflow/template/__init__.py b/src/backend/base/langflow/template/__init__.py index e69de29bb..b6afea9ef 100644 --- a/src/backend/base/langflow/template/__init__.py +++ b/src/backend/base/langflow/template/__init__.py @@ -0,0 +1,10 @@ +from langflow.template.field.base import Input, Output +from langflow.template.frontend_node.base import FrontendNode +from langflow.template.template.base import Template + +__all__ = [ + "Input", + "Output", + "FrontendNode", + "Template", +] diff --git a/src/backend/base/langflow/template/frontend_node/base.py b/src/backend/base/langflow/template/frontend_node/base.py index 7f4ad437c..5eca25e9d 100644 --- a/src/backend/base/langflow/template/frontend_node/base.py +++ b/src/backend/base/langflow/template/frontend_node/base.py @@ -101,6 +101,9 @@ class FrontendNode(BaseModel): def add_extra_base_classes(self) -> None: pass + def get_base_classes_from_outputs(self) -> list[str]: + self.base_classes = [output_type for output in self.outputs for output_type in output.types] + def add_base_class(self, base_class: Union[str, List[str]]) -> None: """Adds a base class to the frontend node.""" if isinstance(base_class, str): diff --git a/src/frontend/src/customNodes/genericNode/components/parameterComponent/index.tsx b/src/frontend/src/customNodes/genericNode/components/parameterComponent/index.tsx index e1c376f54..50773aabc 100644 --- a/src/frontend/src/customNodes/genericNode/components/parameterComponent/index.tsx +++ b/src/frontend/src/customNodes/genericNode/components/parameterComponent/index.tsx @@ -62,6 +62,8 @@ export default function ParameterComponent({ index, outputName, }: ParameterComponentType): JSX.Element { + console.log("title", title); + console.log("data", data); const infoHtml = useRef(null); const nodes = useFlowStore((state) => state.nodes); const edges = useFlowStore((state) => state.edges); @@ -72,7 +74,6 @@ export default function ParameterComponent({ const updateNodeInternals = useUpdateNodeInternals(); const [errorDuplicateKey, setErrorDuplicateKey] = useState(false); const setFilterEdge = useFlowStore((state) => state.setFilterEdge); - const { handleOnNewValue: handleOnNewValueHook } = useHandleOnNewValue( data, name, diff --git a/src/frontend/src/customNodes/genericNode/index.tsx b/src/frontend/src/customNodes/genericNode/index.tsx index 2163af716..8f67c3073 100644 --- a/src/frontend/src/customNodes/genericNode/index.tsx +++ b/src/frontend/src/customNodes/genericNode/index.tsx @@ -55,14 +55,14 @@ export default function GenericNode({ const [nodeName, setNodeName] = useState(data.node!.display_name); const [inputDescription, setInputDescription] = useState(false); const [nodeDescription, setNodeDescription] = useState( - data.node?.description!, + data.node?.description! ); const [isOutdated, setIsOutdated] = useState(false); const buildStatus = useFlowStore( - (state) => state.flowBuildStatus[data.id]?.status, + (state) => state.flowBuildStatus[data.id]?.status ); const lastRunTime = useFlowStore( - (state) => state.flowBuildStatus[data.id]?.timestamp, + (state) => state.flowBuildStatus[data.id]?.timestamp ); const [validationStatus, setValidationStatus] = useState(null); @@ -115,7 +115,7 @@ export default function GenericNode({ updateNodeInternals(data.id); }, - [data.id, data.node, setNode, setIsOutdated], + [data.id, data.node, setNode, setIsOutdated] ); if (!data.node!.template) { @@ -255,7 +255,7 @@ export default function GenericNode({ const isDark = useDarkStore((state) => state.dark); const renderIconStatus = ( buildStatus: BuildStatus | undefined, - validationStatus: validationStatusType | null, + validationStatus: validationStatusType | null ) => { if (buildStatus === BuildStatus.BUILDING) { return ; @@ -296,7 +296,7 @@ export default function GenericNode({ }; const getSpecificClassFromBuildStatus = ( buildStatus: BuildStatus | undefined, - validationStatus: validationStatusType | null, + validationStatus: validationStatusType | null ) => { let isInvalid = validationStatus && !validationStatus.valid; @@ -320,11 +320,11 @@ export default function GenericNode({ selected: boolean, showNode: boolean, buildStatus: BuildStatus | undefined, - validationStatus: validationStatusType | null, + validationStatus: validationStatusType | null ) => { const specificClassFromBuildStatus = getSpecificClassFromBuildStatus( buildStatus, - validationStatus, + validationStatus ); const baseBorderClass = getBaseBorderClass(selected); @@ -333,7 +333,7 @@ export default function GenericNode({ baseBorderClass, nodeSizeClass, "generic-node-div", - specificClassFromBuildStatus, + specificClassFromBuildStatus ); return names; }; @@ -393,7 +393,7 @@ export default function GenericNode({ selected, showNode, buildStatus, - validationStatus, + validationStatus )} > {data.node?.beta && showNode && ( @@ -538,7 +538,7 @@ export default function GenericNode({ } title={getFieldTitle( data.node?.template!, - templateField, + templateField )} info={data.node?.template[templateField].info} name={templateField} @@ -566,7 +566,7 @@ export default function GenericNode({ proxy={data.node?.template[templateField].proxy} showNode={showNode} /> - ), + ) )} {/* { setInputDescription(true); @@ -787,13 +787,13 @@ export default function GenericNode({ } title={getFieldTitle( data.node?.template!, - templateField, + templateField )} info={data.node?.template[templateField].info} name={templateField} tooltipTitle={ data.node?.template[templateField].input_types?.join( - "\n", + "\n" ) ?? data.node?.template[templateField].type } required={data.node!.template[templateField].required} @@ -820,7 +820,7 @@ export default function GenericNode({
{" "} @@ -842,7 +842,7 @@ export default function GenericNode({ nodeColors[types[data.type]] ?? nodeColors.unknown } - title={output.selected ?? output.types[0]} + title={output.name} tooltipTitle={output.selected ?? output.types[0]} id={{ output_types: [output.selected ?? output.types[0]], diff --git a/tests/test_custom_component.py b/tests/test_custom_component.py index 461d3c6eb..929a4712e 100644 --- a/tests/test_custom_component.py +++ b/tests/test_custom_component.py @@ -4,10 +4,9 @@ from uuid import uuid4 import pytest from langchain_core.documents import Document - from langflow.custom import CustomComponent from langflow.custom.code_parser.code_parser import CodeParser, CodeSyntaxError -from langflow.custom.custom_component.component import Component, ComponentCodeNullError +from langflow.custom.custom_component.base_component import BaseComponent, ComponentCodeNullError from langflow.custom.utils import build_custom_component_template from langflow.services.database.models.flow import Flow, FlowCreate @@ -77,7 +76,7 @@ def test_component_init(): """ Test the initialization of the Component class. """ - component = Component(code=code_default, function_entrypoint_name="build") + component = BaseComponent(code=code_default, function_entrypoint_name="build") assert component.code == code_default assert component.function_entrypoint_name == "build" @@ -86,7 +85,7 @@ def test_component_get_code_tree(): """ Test the get_code_tree method of the Component class. """ - component = Component(code=code_default, function_entrypoint_name="build") + component = BaseComponent(code=code_default, function_entrypoint_name="build") tree = component.get_code_tree(component.code) assert "imports" in tree @@ -96,7 +95,7 @@ def test_component_code_null_error(): Test the get_function method raises the ComponentCodeNullError when the code is empty. """ - component = Component(code="", function_entrypoint_name="") + component = BaseComponent(code="", function_entrypoint_name="") with pytest.raises(ComponentCodeNullError): component.get_function() @@ -200,7 +199,7 @@ def test_component_get_function_valid(): Test the get_function method of the Component class with valid code and function_entrypoint_name. """ - component = Component(code="def build(): pass", function_entrypoint_name="build") + component = BaseComponent(code="def build(): pass", function_entrypoint_name="build") my_function = component.get_function() assert callable(my_function) @@ -357,7 +356,7 @@ def test_component_get_code_tree_syntax_error(): Test the get_code_tree method of the Component class raises the CodeSyntaxError when given incorrect syntax. """ - component = Component(code="import os as", function_entrypoint_name="build") + component = BaseComponent(code="import os as", function_entrypoint_name="build") with pytest.raises(CodeSyntaxError): component.get_code_tree(component.code) From daedaae820e7e3c7f0e1c431d3d923acac9bef30 Mon Sep 17 00:00:00 2001 From: ogabrielluiz Date: Mon, 3 Jun 2024 13:49:03 -0300 Subject: [PATCH 042/701] =?UTF-8?q?=F0=9F=93=9D=20(text.py):=20Rename=20Cu?= =?UTF-8?q?stomComponent=20to=20Component=20for=20better=20clarity=20and?= =?UTF-8?q?=20consistency=20=F0=9F=93=9D=20(ChatInput.py,=20TextInput.py,?= =?UTF-8?q?=20ChatOutput.py,=20RecordsOutput.py,=20TextOutput.py):=20Refac?= =?UTF-8?q?tor=20code=20to=20use=20Input=20and=20Output=20classes=20for=20?= =?UTF-8?q?defining=20inputs=20and=20outputs=20=F0=9F=93=9D=20(CustomCompo?= =?UTF-8?q?nent.py):=20Update=20method=20to=20get=20the=20build=20method?= =?UTF-8?q?=20for=20custom=20components=20to=20consider=20classes=20inheri?= =?UTF-8?q?ting=20from=20Component=20or=20CustomComponent=20=F0=9F=93=9D?= =?UTF-8?q?=20(DirectoryReader.py):=20Remove=20check=20for=20missing=20bui?= =?UTF-8?q?ld=20function=20as=20it=20is=20no=20longer=20necessary=20?= =?UTF-8?q?=F0=9F=93=9D=20(base.py):=20Add=20properties=20to=20easily=20ac?= =?UTF-8?q?cess=20outgoing=20edges,=20incoming=20edges,=20and=20source=20n?= =?UTF-8?q?ames=20of=20edges=20in=20a=20Vertex=20object?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit ♻ (types.py): Refactor code to improve readability and maintainability by adding type hinting and organizing imports 📝 (types.py): Add missing documentation and comments to clarify the purpose of methods and classes ♻ (loading.py): Refactor code to remove redundant code and improve code structure for better maintainability 📝 (loading.py): Add comments to explain the purpose of functions and the flow of the code ♻ (base.py): Refactor code to improve consistency and readability by updating class names and method calls to match the intended functionality --- src/backend/base/langflow/base/io/text.py | 4 +- .../langflow/components/inputs/ChatInput.py | 7 +- .../langflow/components/inputs/TextInput.py | 45 ++++++------- .../langflow/components/outputs/ChatOutput.py | 49 +++++++++----- .../components/outputs/RecordsOutput.py | 16 +++-- .../langflow/components/outputs/TextOutput.py | 41 +++++++----- .../custom_component/custom_component.py | 8 +-- .../directory_reader/directory_reader.py | 2 - .../base/langflow/graph/vertex/base.py | 12 ++++ .../base/langflow/graph/vertex/types.py | 66 ++++++++++++++++++- .../langflow/interface/initialize/loading.py | 22 +++++-- .../langflow/template/frontend_node/base.py | 2 +- 12 files changed, 192 insertions(+), 82 deletions(-) diff --git a/src/backend/base/langflow/base/io/text.py b/src/backend/base/langflow/base/io/text.py index 5ecfea11a..84ef001cf 100644 --- a/src/backend/base/langflow/base/io/text.py +++ b/src/backend/base/langflow/base/io/text.py @@ -1,12 +1,12 @@ from typing import Optional -from langflow.custom import CustomComponent +from langflow.custom import Component from langflow.field_typing import Text from langflow.helpers.record import records_to_text from langflow.schema.schema import Record -class TextComponent(CustomComponent): +class TextComponent(Component): display_name = "Text Component" description = "Used to pass text to the next component." diff --git a/src/backend/base/langflow/components/inputs/ChatInput.py b/src/backend/base/langflow/components/inputs/ChatInput.py index 9264f85cb..e8a4ccc21 100644 --- a/src/backend/base/langflow/components/inputs/ChatInput.py +++ b/src/backend/base/langflow/components/inputs/ChatInput.py @@ -21,7 +21,7 @@ class ChatInput(ChatComponent): ] def text_response(self) -> Text: - result = self.message + result = self.input_value if self.session_id and isinstance(result, (Record, str)): self.store_message(result, self.session_id, self.sender, self.sender_name) return result @@ -29,11 +29,12 @@ class ChatInput(ChatComponent): def record_response(self) -> Record: record = Record( data={ - "message": self.message, + "message": self.input_value, "sender": self.sender, "sender_name": self.sender_name, "session_id": self.session_id, - } + }, + text_key="message", ) if self.session_id and isinstance(record, (Record, str)): self.store_message(record, self.session_id, self.sender, self.sender_name) diff --git a/src/backend/base/langflow/components/inputs/TextInput.py b/src/backend/base/langflow/components/inputs/TextInput.py index b2317678e..2e3a65ee1 100644 --- a/src/backend/base/langflow/components/inputs/TextInput.py +++ b/src/backend/base/langflow/components/inputs/TextInput.py @@ -1,7 +1,6 @@ -from typing import Optional - from langflow.base.io.text import TextComponent from langflow.field_typing import Text +from langflow.template import Input, Output class TextInput(TextComponent): @@ -9,24 +8,26 @@ class TextInput(TextComponent): description = "Get text inputs from the Playground." icon = "type" - def build_config(self): - return { - "input_value": { - "display_name": "Value", - "input_types": ["Record", "Text"], - "info": "Text or Record to be passed as input.", - }, - "record_template": { - "display_name": "Record Template", - "multiline": True, - "info": "Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.", - "advanced": True, - }, - } + inputs = [ + Input( + name="input_value", + type=str, + display_name="Value", + info="Text or Record to be passed as input.", + input_types=["Record", "Text"], + ), + Input( + name="record_template", + type=str, + display_name="Record Template", + multiline=True, + info="Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.", + advanced=True, + ), + ] + outputs = [ + Output(name="Text", method="text_response"), + ] - def build( - self, - input_value: Optional[Text] = "", - record_template: Optional[str] = "", - ) -> Text: - return super().build(input_value=input_value, record_template=record_template) + def text_response(self) -> Text: + return self.input_value if self.input_value else "" diff --git a/src/backend/base/langflow/components/outputs/ChatOutput.py b/src/backend/base/langflow/components/outputs/ChatOutput.py index 7994c9ded..3e44d38aa 100644 --- a/src/backend/base/langflow/components/outputs/ChatOutput.py +++ b/src/backend/base/langflow/components/outputs/ChatOutput.py @@ -1,8 +1,7 @@ -from typing import Optional, Union - from langflow.base.io.chat import ChatComponent from langflow.field_typing import Text from langflow.schema import Record +from langflow.template import Input, Output class ChatOutput(ChatComponent): @@ -10,20 +9,34 @@ class ChatOutput(ChatComponent): description = "Display a chat message in the Playground." icon = "ChatOutput" - def build( - self, - sender: Optional[str] = "Machine", - sender_name: Optional[str] = "AI", - input_value: Optional[str] = None, - session_id: Optional[str] = None, - return_record: Optional[bool] = False, - record_template: Optional[str] = "{text}", - ) -> Union[Text, Record]: - return super().build_with_record( - sender=sender, - sender_name=sender_name, - input_value=input_value, - session_id=session_id, - return_record=return_record, - record_template=record_template or "", + inputs = [ + Input(name="input_value", type=str, display_name="Message", multiline=True), + Input(name="sender", type=str, display_name="Sender Type", options=["Machine", "AI"]), + Input(name="sender_name", type=str, display_name="Sender Name"), + Input(name="session_id", type=str, display_name="Session ID"), + Input(name="record_template", type=str, display_name="Record Template", default="{text}"), + ] + outputs = [ + Output(name="Message", method="text_response"), + Output(name="Record", method="record_response"), + ] + + def text_response(self) -> Text: + result = self.input_value + if self.session_id and isinstance(result, (Record, str)): + self.store_message(result, self.session_id, self.sender, self.sender_name) + return result + + def record_response(self) -> Record: + record = Record( + data={ + "message": self.input_value, + "sender": self.sender, + "sender_name": self.sender_name, + "session_id": self.session_id, + "template": self.record_template or "", + } ) + if self.session_id and isinstance(record, (Record, str)): + self.store_message(record, self.session_id, self.sender, self.sender_name) + return record diff --git a/src/backend/base/langflow/components/outputs/RecordsOutput.py b/src/backend/base/langflow/components/outputs/RecordsOutput.py index 25eae862e..7af2a0c7e 100644 --- a/src/backend/base/langflow/components/outputs/RecordsOutput.py +++ b/src/backend/base/langflow/components/outputs/RecordsOutput.py @@ -1,10 +1,18 @@ -from langflow.custom import CustomComponent +from langflow.custom import Component from langflow.schema import Record +from langflow.template import Input, Output -class RecordsOutput(CustomComponent): +class RecordsOutput(Component): display_name = "Records Output" description = "Display Records as a Table" - def build(self, input_value: Record) -> Record: - return input_value + inputs = [ + Input(name="input_value", type=Record, display_name="Record Input"), + ] + outputs = [ + Output(name="Record", method="record_response"), + ] + + def record_response(self) -> Record: + return self.input_value diff --git a/src/backend/base/langflow/components/outputs/TextOutput.py b/src/backend/base/langflow/components/outputs/TextOutput.py index 0d55621b2..2459192be 100644 --- a/src/backend/base/langflow/components/outputs/TextOutput.py +++ b/src/backend/base/langflow/components/outputs/TextOutput.py @@ -1,7 +1,6 @@ -from typing import Optional - from langflow.base.io.text import TextComponent from langflow.field_typing import Text +from langflow.template import Input, Output class TextOutput(TextComponent): @@ -9,20 +8,26 @@ class TextOutput(TextComponent): description = "Display a text output in the Playground." icon = "type" - def build_config(self): - return { - "input_value": { - "display_name": "Value", - "input_types": ["Record", "Text"], - "info": "Text or Record to be passed as output.", - }, - "record_template": { - "display_name": "Record Template", - "multiline": True, - "info": "Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.", - "advanced": True, - }, - } + inputs = [ + Input( + name="input_value", + type=str, + display_name="Value", + info="Text or Record to be passed as output.", + input_types=["Record", "Text"], + ), + Input( + name="record_template", + type=str, + display_name="Record Template", + multiline=True, + info="Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.", + advanced=True, + ), + ] + outputs = [ + Output(name="Text", method="text_response"), + ] - def build(self, input_value: Optional[Text] = "", record_template: Optional[str] = "") -> Text: - return super().build(input_value=input_value, record_template=record_template) + def text_response(self) -> Text: + return self.input_value if self.input_value else "" diff --git a/src/backend/base/langflow/custom/custom_component/custom_component.py b/src/backend/base/langflow/custom/custom_component/custom_component.py index ce01f9db6..d51b4d5d8 100644 --- a/src/backend/base/langflow/custom/custom_component/custom_component.py +++ b/src/backend/base/langflow/custom/custom_component/custom_component.py @@ -1,10 +1,9 @@ -import operator from pathlib import Path from typing import TYPE_CHECKING, Any, Callable, ClassVar, List, Optional, Sequence, Union from uuid import UUID import yaml -from cachetools import TTLCache, cachedmethod +from cachetools import TTLCache from langchain_core.documents import Document from pydantic import BaseModel @@ -299,7 +298,6 @@ class CustomComponent(BaseComponent): arg["type"] = "Data" return args - @cachedmethod(operator.attrgetter("cache")) def get_method(self, method_name: str): """ Gets the build method for the custom component. @@ -310,7 +308,9 @@ class CustomComponent(BaseComponent): if not self.code: return {} - component_classes = [cls for cls in self.tree["classes"] if self.code_class_base_inheritance in cls["bases"]] + component_classes = [ + cls for cls in self.tree["classes"] if "Component" in cls["bases"] or "CustomComponent" in cls["bases"] + ] if not component_classes: return {} diff --git a/src/backend/base/langflow/custom/directory_reader/directory_reader.py b/src/backend/base/langflow/custom/directory_reader/directory_reader.py index 4d7b33bfc..dc9dfa51c 100644 --- a/src/backend/base/langflow/custom/directory_reader/directory_reader.py +++ b/src/backend/base/langflow/custom/directory_reader/directory_reader.py @@ -301,8 +301,6 @@ class DirectoryReader: return False, "Empty file" elif not self.validate_code(file_content): return False, "Syntax error" - elif not self.validate_build(file_content): - return False, "Missing build function" elif self._is_type_hint_used_in_args("Optional", file_content) and not self._is_type_hint_imported( "Optional", file_content ): diff --git a/src/backend/base/langflow/graph/vertex/base.py b/src/backend/base/langflow/graph/vertex/base.py index e9f09f9aa..8863bd5aa 100644 --- a/src/backend/base/langflow/graph/vertex/base.py +++ b/src/backend/base/langflow/graph/vertex/base.py @@ -136,6 +136,18 @@ class Vertex: def edges(self) -> List["ContractEdge"]: return self.graph.get_vertex_edges(self.id) + @property + def outgoing_edges(self) -> List["ContractEdge"]: + return [edge for edge in self.edges if edge.source_id == self.id] + + @property + def incoming_edges(self) -> List["ContractEdge"]: + return [edge for edge in self.edges if edge.target_id == self.id] + + @property + def edges_source_names(self) -> List[str]: + return {edge.source_handle.name for edge in self.edges} + @property def predecessors(self) -> List["Vertex"]: return self.graph.get_predecessors(self) diff --git a/src/backend/base/langflow/graph/vertex/types.py b/src/backend/base/langflow/graph/vertex/types.py index 79eafd8e5..e1180fef9 100644 --- a/src/backend/base/langflow/graph/vertex/types.py +++ b/src/backend/base/langflow/graph/vertex/types.py @@ -1,19 +1,24 @@ import json -from typing import AsyncIterator, Dict, Iterator, List +from typing import Any, AsyncIterator, Dict, Iterator, List import yaml +from git import TYPE_CHECKING from langchain_core.messages import AIMessage from loguru import logger from langflow.graph.schema import CHAT_COMPONENTS, RECORDS_COMPONENTS, InterfaceComponentTypes from langflow.graph.utils import UnbuiltObject, serialize_field from langflow.graph.vertex.base import Vertex +from langflow.graph.vertex.utils import log_transaction from langflow.schema import Record from langflow.schema.schema import INPUT_FIELD_NAME from langflow.services.monitor.utils import log_vertex_build from langflow.utils.schemas import ChatOutputResponse, RecordOutputResponse from langflow.utils.util import unescape_string +if TYPE_CHECKING: + from langflow.graph.edge.base import ContractEdge + class CustomComponentVertex(Vertex): def __init__(self, data: Dict, graph): @@ -32,10 +37,65 @@ class ComponentVertex(Vertex): if self.artifacts and "repr" in self.artifacts: return self.artifacts["repr"] or super()._built_object_repr() + def _update_built_object_and_artifacts(self, result): + """ + Updates the built object and its artifacts. + """ + if isinstance(result, tuple): + if len(result) == 2: + self._built_object, self.artifacts = result + elif len(result) == 3: + self._custom_component, self._built_object, self.artifacts = result + else: + self._built_object = result -class InterfaceVertex(Vertex): + for key, value in self._built_object.items(): + self.add_result(key, value) + + def get_edge_with_target(self, target_id: str) -> "ContractEdge": + """ + Get the edge with the target id. + + Args: + target_id: The target id of the edge. + + Returns: + The edge with the target id. + """ + for edge in self.edges: + if edge.target_id == target_id: + return edge + return None + + async def _get_result(self, requester: "Vertex") -> Any: + """ + Retrieves the result of the built component. + + If the component has not been built yet, a ValueError is raised. + + Returns: + The built result if use_result is True, else the built object. + """ + if not self._built: + log_transaction(source=self, target=requester, flow_id=self.graph.flow_id, status="error") + raise ValueError(f"Component {self.display_name} has not been built yet") + + if requester is None: + raise ValueError("Requester Vertex is None") + + edge = self.get_edge_with_target(requester.id) + if edge is None: + raise ValueError(f"Edge not found between {self.display_name} and {requester.display_name}") + + result = self.results[edge.source_handle.name] + + log_transaction(source=self, target=requester, flow_id=self.graph.flow_id, status="success") + return result + + +class InterfaceVertex(ComponentVertex): def __init__(self, data: Dict, graph): - super().__init__(data, graph=graph, base_type="custom_components", is_task=True) + super().__init__(data, graph=graph) self.steps = [self._build, self._run] def build_stream_url(self): diff --git a/src/backend/base/langflow/interface/initialize/loading.py b/src/backend/base/langflow/interface/initialize/loading.py index 239bf5cc3..c5cec3b55 100644 --- a/src/backend/base/langflow/interface/initialize/loading.py +++ b/src/backend/base/langflow/interface/initialize/loading.py @@ -4,13 +4,15 @@ import os from typing import TYPE_CHECKING, Any, Awaitable, Callable, Type import orjson +import yaml from loguru import logger +from pydantic import BaseModel from langflow.custom.eval import eval_custom_component_code from langflow.schema.schema import Record if TYPE_CHECKING: - from langflow.custom import CustomComponent + from langflow.custom import Component, CustomComponent from langflow.graph.vertex.base import Vertex @@ -32,6 +34,7 @@ async def instantiate_class( raise ValueError("No base type provided for vertex") params_copy = params.copy() + # Remove code from params class_object: Type["CustomComponent"] = eval_custom_component_code(params_copy.pop("code")) custom_component: "CustomComponent" = class_object( user_id=user_id, @@ -43,9 +46,9 @@ async def instantiate_class( custom_component, params_copy, vertex.load_from_db_fields, fallback_to_env_vars ) if base_type == "custom_components": - return await build_custom_component(params=params, custom_component=custom_component) + return await build_custom_component(params=params_copy, custom_component=custom_component) elif base_type == "component": - return await build_component(params=params, custom_component=custom_component) + return await build_component(params=params_copy, custom_component=custom_component, vertex=vertex) else: raise ValueError(f"Base type {base_type} not found.") @@ -111,7 +114,7 @@ def update_params_with_load_from_db_fields( async def build_component( params: dict, - custom_component: "CustomComponent", + custom_component: "Component", vertex: "Vertex", ): # Now set the params as attributes of the custom_component @@ -122,7 +125,7 @@ async def build_component( for output in custom_component.outputs: # Build the output if it's connected to some other vertex # or if it's not connected to any vertex - if not vertex.edges or output.name in vertex.edges: + if not vertex.outgoing_edges or output.name in vertex.edges_source_names: method: Callable | Awaitable = getattr(custom_component, output.method) result = method() # If the method is asynchronous, we need to await it @@ -130,6 +133,15 @@ async def build_component( result = await result build_result[output.name] = result custom_repr = custom_component.custom_repr() + + # ! Temporary REPR + # Since all are dict, yaml.dump them + if isinstance(build_result, dict): + _build_result = { + key: value.model_dump() if isinstance(value, BaseModel) else value for key, value in build_result.items() + } + custom_repr = yaml.dump(_build_result) + if custom_repr is None and isinstance(build_result, (dict, Record, str)): custom_repr = build_result if not isinstance(custom_repr, str): diff --git a/src/backend/base/langflow/template/frontend_node/base.py b/src/backend/base/langflow/template/frontend_node/base.py index 5eca25e9d..310c7149c 100644 --- a/src/backend/base/langflow/template/frontend_node/base.py +++ b/src/backend/base/langflow/template/frontend_node/base.py @@ -124,6 +124,6 @@ class FrontendNode(BaseModel): if "inputs" not in kwargs: raise ValueError("Missing 'inputs' argument.") inputs = kwargs.pop("inputs") - template = Template(type_name="CustomComponent", fields=inputs) + template = Template(type_name="Component", fields=inputs) kwargs["template"] = template return cls(**kwargs) From 8043a8855e5696968c025addd89e400d95b71f00 Mon Sep 17 00:00:00 2001 From: ogabrielluiz Date: Mon, 3 Jun 2024 14:01:46 -0300 Subject: [PATCH 043/701] refactor: Update ChatInput record_response method to use "text" key instead of "message" The ChatInput class in ChatInput.py has been updated to use the "text" key instead of the "message" key when creating a Record object in the record_response method. This change improves the clarity and consistency of the code. Note: The commit message has been generated based on the provided code changes and recent commits. --- src/backend/base/langflow/components/inputs/ChatInput.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/src/backend/base/langflow/components/inputs/ChatInput.py b/src/backend/base/langflow/components/inputs/ChatInput.py index e8a4ccc21..b20d73764 100644 --- a/src/backend/base/langflow/components/inputs/ChatInput.py +++ b/src/backend/base/langflow/components/inputs/ChatInput.py @@ -29,12 +29,11 @@ class ChatInput(ChatComponent): def record_response(self) -> Record: record = Record( data={ - "message": self.input_value, + "text": self.input_value, "sender": self.sender, "sender_name": self.sender_name, "session_id": self.session_id, }, - text_key="message", ) if self.session_id and isinstance(record, (Record, str)): self.store_message(record, self.session_id, self.sender, self.sender_name) From a396ecbcfe4a7ee144c7a8dcd2c39a43e61bdb37 Mon Sep 17 00:00:00 2001 From: ogabrielluiz Date: Mon, 3 Jun 2024 16:07:10 -0300 Subject: [PATCH 044/701] update starter projects --- .../Basic Prompting (Hello, world!).json | 4 +- .../Langflow Blog Writter.json | 2184 +++++----- .../Langflow Document QA.json | 4 +- .../Langflow Memory Conversation.json | 4 +- .../Langflow Prompt Chaining.json | 3502 ++++++++--------- .../VectorStore-RAG-Flows.json | 4 +- 6 files changed, 2851 insertions(+), 2851 deletions(-) diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting (Hello, world!).json b/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting (Hello, world!).json index 6b225bb75..924f1085f 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting (Hello, world!).json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting (Hello, world!).json @@ -161,7 +161,7 @@ "list": false, "show": true, "multiline": true, - "value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\"},\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": True,\n \"info\": \"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": MODEL_NAMES,\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"openai_api_key\": {\n \"display_name\": \"OpenAI API Key\",\n \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n \"advanced\": False,\n \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float,\n model_name: str = \"gpt-4o\",\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n output = ChatOpenAI(\n max_tokens=max_tokens or None,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n", + "value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\"},\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": True,\n \"info\": \"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": MODEL_NAMES,\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"openai_api_key\": {\n \"display_name\": \"OpenAI API Key\",\n \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n \"advanced\": False,\n \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float = 0.1,\n model_name: str = \"gpt-4o\",\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n output = ChatOpenAI(\n max_tokens=max_tokens or None,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n", "fileTypes": [], "file_path": "", "password": false, @@ -324,7 +324,7 @@ }, "temperature": { "type": "float", - "required": true, + "required": false, "placeholder": "", "list": false, "show": true, diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Blog Writter.json b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Blog Writter.json index e68eed258..5854cd934 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Blog Writter.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Blog Writter.json @@ -1,1096 +1,1096 @@ { - "id": "6ad5559d-fb66-4fdc-8f98-96f4ac12799d", - "data": { - "nodes": [ - { - "id": "Prompt-Rse03", - "type": "genericNode", - "position": { - "x": 1331.381712783371, - "y": 535.0279854229713 - }, - "data": { - "type": "Prompt", - "node": { - "template": { - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Input, Prompt, Text\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": Input(display_name=\"Template\"),\n \"code\": Input(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "template": { - "type": "prompt", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": "Reference 1:\n\n{reference_1}\n\n---\n\nReference 2:\n\n{reference_2}\n\n---\n\n{instructions}\n\nBlog: \n\n\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "template", - "display_name": "Template", - "advanced": false, - "input_types": [ - "Text" - ], - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "_type": "CustomComponent", - "reference_1": { - "field_type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "reference_1", - "display_name": "reference_1", - "advanced": false, - "input_types": [ - "Document", - "BaseOutputParser", - "Record", - "Text" - ], - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "type": "str" - }, - "reference_2": { - "field_type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "reference_2", - "display_name": "reference_2", - "advanced": false, - "input_types": [ - "Document", - "BaseOutputParser", - "Record", - "Text" - ], - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "type": "str" - }, - "instructions": { - "field_type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "instructions", - "display_name": "instructions", - "advanced": false, - "input_types": [ - "Document", - "BaseOutputParser", - "Record", - "Text" - ], - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "type": "str" - } - }, - "description": "Create a prompt template with dynamic variables.", - "icon": "prompts", - "is_input": null, - "is_output": null, - "is_composition": null, - "base_classes": [ - "object", - "Text", - "str" - ], - "name": "", - "display_name": "Prompt", - "documentation": "", - "custom_fields": { - "template": [ - "reference_1", - "reference_2", - "instructions" - ] - }, - "output_types": [ - "Text" - ], - "full_path": null, - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": false, - "error": null - }, - "id": "Prompt-Rse03", - "description": "Create a prompt template with dynamic variables.", - "display_name": "Prompt" - }, - "selected": false, - "width": 384, - "height": 571, - "dragging": false, - "positionAbsolute": { - "x": 1331.381712783371, - "y": 535.0279854229713 - } + "id": "6ad5559d-fb66-4fdc-8f98-96f4ac12799d", + "data": { + "nodes": [ + { + "id": "Prompt-Rse03", + "type": "genericNode", + "position": { + "x": 1331.381712783371, + "y": 535.0279854229713 + }, + "data": { + "type": "Prompt", + "node": { + "template": { + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Input, Prompt, Text\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": Input(display_name=\"Template\"),\n \"code\": Input(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "template": { + "type": "prompt", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": "Reference 1:\n\n{reference_1}\n\n---\n\nReference 2:\n\n{reference_2}\n\n---\n\n{instructions}\n\nBlog: \n\n\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "template", + "display_name": "Template", + "advanced": false, + "input_types": [ + "Text" + ], + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "_type": "CustomComponent", + "reference_1": { + "field_type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "reference_1", + "display_name": "reference_1", + "advanced": false, + "input_types": [ + "Document", + "BaseOutputParser", + "Record", + "Text" + ], + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "type": "str" + }, + "reference_2": { + "field_type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "reference_2", + "display_name": "reference_2", + "advanced": false, + "input_types": [ + "Document", + "BaseOutputParser", + "Record", + "Text" + ], + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "type": "str" + }, + "instructions": { + "field_type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "instructions", + "display_name": "instructions", + "advanced": false, + "input_types": [ + "Document", + "BaseOutputParser", + "Record", + "Text" + ], + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "type": "str" + } }, - { - "id": "URL-HYPkR", - "type": "genericNode", - "position": { - "x": 568.2971412887712, - "y": 700.9983368007821 - }, - "data": { - "type": "URL", - "node": { - "template": { - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from typing import Any, Dict\n\nfrom langchain_community.document_loaders.web_base import WebBaseLoader\n\nfrom langflow.custom import CustomComponent\nfrom langflow.schema import Record\n\n\nclass URLComponent(CustomComponent):\n display_name = \"URL\"\n description = \"Fetch content from one or more URLs.\"\n icon = \"layout-template\"\n\n def build_config(self) -> Dict[str, Any]:\n return {\n \"urls\": {\"display_name\": \"URL\"},\n }\n\n def build(\n self,\n urls: list[str],\n ) -> list[Record]:\n loader = WebBaseLoader(web_paths=urls)\n docs = loader.load()\n records = self.to_records(docs)\n self.status = records\n return records\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "urls": { - "type": "str", - "required": true, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "urls", - "display_name": "URL", - "advanced": false, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ], - "value": [ - "https://www.promptingguide.ai/techniques/prompt_chaining" - ] - }, - "_type": "CustomComponent" - }, - "description": "Fetch content from one or more URLs.", - "icon": "layout-template", - "base_classes": [ - "Record" - ], - "display_name": "URL", - "documentation": "", - "custom_fields": { - "urls": null - }, - "output_types": [ - "Record" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": false - }, - "id": "URL-HYPkR" - }, - "selected": false, - "width": 384, - "height": 281, - "positionAbsolute": { - "x": 568.2971412887712, - "y": 700.9983368007821 - }, - "dragging": false + "description": "Create a prompt template with dynamic variables.", + "icon": "prompts", + "is_input": null, + "is_output": null, + "is_composition": null, + "base_classes": [ + "object", + "Text", + "str" + ], + "name": "", + "display_name": "Prompt", + "documentation": "", + "custom_fields": { + "template": [ + "reference_1", + "reference_2", + "instructions" + ] }, - { - "id": "ChatOutput-JPlxl", - "type": "genericNode", - "position": { - "x": 2503.8617424688505, - "y": 789.3005578928434 - }, - "data": { - "type": "ChatOutput", - "node": { - "template": { - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n def build(\n self,\n sender: Optional[str] = \"Machine\",\n sender_name: Optional[str] = \"AI\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n record_template: Optional[str] = \"{text}\",\n ) -> Union[Text, Record]:\n return super().build_with_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n record_template=record_template or \"\",\n )\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "input_value": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "input_value", - "display_name": "Message", - "advanced": false, - "input_types": [ - "Text" - ], - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "record_template": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "{text}", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "record_template", - "display_name": "Record Template", - "advanced": true, - "dynamic": false, - "info": "In case of Message being a Record, this template will be used to convert it to text.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "return_record": { - "type": "bool", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "return_record", - "display_name": "Return Record", - "advanced": true, - "dynamic": false, - "info": "Return the message as a record containing the sender, sender_name, and session_id.", - "load_from_db": false, - "title_case": false - }, - "sender": { - "type": "str", - "required": false, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "value": "Machine", - "fileTypes": [], - "file_path": "", - "password": false, - "options": [ - "Machine", - "User" - ], - "name": "sender", - "display_name": "Sender Type", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "sender_name": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": "AI", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "sender_name", - "display_name": "Sender Name", - "advanced": false, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "session_id": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "session_id", - "display_name": "Session ID", - "advanced": true, - "dynamic": false, - "info": "If provided, the message will be stored in the memory.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "_type": "CustomComponent" - }, - "description": "Display a chat message in the Playground.", - "icon": "ChatOutput", - "base_classes": [ - "Text", - "Record", - "object", - "str" - ], - "display_name": "Chat Output", - "documentation": "", - "custom_fields": { - "sender": null, - "sender_name": null, - "input_value": null, - "session_id": null, - "return_record": null, - "record_template": null - }, - "output_types": [ - "Text", - "Record" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": false - }, - "id": "ChatOutput-JPlxl" - }, - "selected": false, - "width": 384, - "height": 383 - }, - { - "id": "OpenAIModel-gi29P", - "type": "genericNode", - "position": { - "x": 1917.7089968570963, - "y": 575.9186499244129 - }, - "data": { - "type": "OpenAIModel", - "node": { - "template": { - "input_value": { - "type": "str", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "input_value", - "display_name": "Input", - "advanced": false, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\"},\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": True,\n \"info\": \"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": MODEL_NAMES,\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"openai_api_key\": {\n \"display_name\": \"OpenAI API Key\",\n \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n \"advanced\": False,\n \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float,\n model_name: str = \"gpt-4o\",\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n output = ChatOpenAI(\n max_tokens=max_tokens or None,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "max_tokens": { - "type": "int", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": "1024", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "max_tokens", - "display_name": "Max Tokens", - "advanced": true, - "dynamic": false, - "info": "The maximum number of tokens to generate. Set to 0 for unlimited tokens.", - "load_from_db": false, - "title_case": false - }, - "model_kwargs": { - "type": "NestedDict", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": {}, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "model_kwargs", - "display_name": "Model Kwargs", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "model_name": { - "type": "str", - "required": false, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "value": "gpt-3.5-turbo-0125", - "fileTypes": [], - "file_path": "", - "password": false, - "options": [ - "gpt-4o", - "gpt-4-turbo", - "gpt-4-turbo-preview", - "gpt-3.5-turbo", - "gpt-3.5-turbo-0125" - ], - "name": "model_name", - "display_name": "Model Name", - "advanced": false, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "openai_api_base": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "openai_api_base", - "display_name": "OpenAI API Base", - "advanced": true, - "dynamic": false, - "info": "The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\n\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "openai_api_key": { - "type": "str", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": true, - "name": "openai_api_key", - "display_name": "OpenAI API Key", - "advanced": false, - "dynamic": false, - "info": "The OpenAI API Key to use for the OpenAI model.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ], - "value": "OPENAI_API_KEY" - }, - "stream": { - "type": "bool", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": true, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "stream", - "display_name": "Stream", - "advanced": true, - "dynamic": false, - "info": "Stream the response from the model. Streaming works only in Chat.", - "load_from_db": false, - "title_case": false - }, - "system_message": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "system_message", - "display_name": "System Message", - "advanced": true, - "dynamic": false, - "info": "System message to pass to the model.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "temperature": { - "type": "float", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": "0.1", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "temperature", - "display_name": "Temperature", - "advanced": false, - "dynamic": false, - "info": "", - "rangeSpec": { - "step_type": "float", - "min": -1, - "max": 1, - "step": 0.1 - }, - "load_from_db": false, - "title_case": false - }, - "_type": "CustomComponent" - }, - "description": "Generates text using OpenAI LLMs.", - "icon": "OpenAI", - "base_classes": [ - "str", - "Text", - "object" - ], - "display_name": "OpenAI", - "documentation": "", - "custom_fields": { - "input_value": null, - "openai_api_key": null, - "temperature": null, - "model_name": null, - "max_tokens": null, - "model_kwargs": null, - "openai_api_base": null, - "stream": null, - "system_message": null - }, - "output_types": [ - "Text" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [ - "max_tokens", - "model_kwargs", - "model_name", - "openai_api_base", - "openai_api_key", - "temperature", - "input_value", - "system_message", - "stream" - ], - "beta": false - }, - "id": "OpenAIModel-gi29P" - }, - "selected": false, - "width": 384, - "height": 563, - "positionAbsolute": { - "x": 1917.7089968570963, - "y": 575.9186499244129 - }, - "dragging": false - }, - { - "id": "URL-2cX90", - "type": "genericNode", - "position": { - "x": 573.961301764604, - "y": 336.41463436122086 - }, - "data": { - "type": "URL", - "node": { - "template": { - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from typing import Any, Dict\n\nfrom langchain_community.document_loaders.web_base import WebBaseLoader\n\nfrom langflow.custom import CustomComponent\nfrom langflow.schema import Record\n\n\nclass URLComponent(CustomComponent):\n display_name = \"URL\"\n description = \"Fetch content from one or more URLs.\"\n icon = \"layout-template\"\n\n def build_config(self) -> Dict[str, Any]:\n return {\n \"urls\": {\"display_name\": \"URL\"},\n }\n\n def build(\n self,\n urls: list[str],\n ) -> list[Record]:\n loader = WebBaseLoader(web_paths=urls)\n docs = loader.load()\n records = self.to_records(docs)\n self.status = records\n return records\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "urls": { - "type": "str", - "required": true, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "urls", - "display_name": "URL", - "advanced": false, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ], - "value": [ - "https://www.promptingguide.ai/introduction/basics" - ] - }, - "_type": "CustomComponent" - }, - "description": "Fetch content from one or more URLs.", - "icon": "layout-template", - "base_classes": [ - "Record" - ], - "display_name": "URL", - "documentation": "", - "custom_fields": { - "urls": null - }, - "output_types": [ - "Record" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": false - }, - "id": "URL-2cX90" - }, - "selected": false, - "width": 384, - "height": 281, - "positionAbsolute": { - "x": 573.961301764604, - "y": 336.41463436122086 - }, - "dragging": false - }, - { - "id": "TextInput-og8Or", - "type": "genericNode", - "position": { - "x": 569.9387927203336, - "y": 1095.3352160671316 - }, - "data": { - "type": "TextInput", - "node": { - "template": { - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from typing import Optional\n\nfrom langflow.base.io.text import TextComponent\nfrom langflow.field_typing import Text\n\n\nclass TextInput(TextComponent):\n display_name = \"Text Input\"\n description = \"Get text inputs from the Playground.\"\n icon = \"type\"\n\n def build_config(self):\n return {\n \"input_value\": {\n \"display_name\": \"Value\",\n \"input_types\": [\"Record\", \"Text\"],\n \"info\": \"Text or Record to be passed as input.\",\n },\n \"record_template\": {\n \"display_name\": \"Record Template\",\n \"multiline\": True,\n \"info\": \"Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Optional[str] = \"\",\n record_template: Optional[str] = \"\",\n ) -> Text:\n return super().build(input_value=input_value, record_template=record_template)\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "input_value": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": "Use the references above for style to write a new blog/tutorial about prompt engineering techniques. Suggest non-covered topics.", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "input_value", - "display_name": "Value", - "advanced": false, - "input_types": [ - "Record", - "Text" - ], - "dynamic": false, - "info": "Text or Record to be passed as input.", - "load_from_db": false, - "title_case": false - }, - "record_template": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "record_template", - "display_name": "Record Template", - "advanced": true, - "dynamic": false, - "info": "Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "_type": "CustomComponent" - }, - "description": "Get text inputs from the Playground.", - "icon": "type", - "base_classes": [ - "object", - "Text", - "str" - ], - "display_name": "Instructions", - "documentation": "", - "custom_fields": { - "input_value": null, - "record_template": null - }, - "output_types": [ - "Text" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": false - }, - "id": "TextInput-og8Or" - }, - "selected": false, - "width": 384, - "height": 289, - "positionAbsolute": { - "x": 569.9387927203336, - "y": 1095.3352160671316 - }, - "dragging": false - } - ], - "edges": [ - { - "source": "URL-HYPkR", - "target": "Prompt-Rse03", - "sourceHandle": "{\u0153baseClasses\u0153:[\u0153Record\u0153],\u0153dataType\u0153:\u0153URL\u0153,\u0153id\u0153:\u0153URL-HYPkR\u0153}", - "targetHandle": "{\u0153fieldName\u0153:\u0153reference_2\u0153,\u0153id\u0153:\u0153Prompt-Rse03\u0153,\u0153inputTypes\u0153:[\u0153Document\u0153,\u0153BaseOutputParser\u0153,\u0153Record\u0153,\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}", - "id": "reactflow__edge-URL-HYPkR{\u0153baseClasses\u0153:[\u0153Record\u0153],\u0153dataType\u0153:\u0153URL\u0153,\u0153id\u0153:\u0153URL-HYPkR\u0153}-Prompt-Rse03{\u0153fieldName\u0153:\u0153reference_2\u0153,\u0153id\u0153:\u0153Prompt-Rse03\u0153,\u0153inputTypes\u0153:[\u0153Document\u0153,\u0153BaseOutputParser\u0153,\u0153Record\u0153,\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}", - "data": { - "targetHandle": { - "fieldName": "reference_2", - "id": "Prompt-Rse03", - "inputTypes": [ - "Document", - "BaseOutputParser", - "Record", - "Text" - ], - "type": "str" - }, - "sourceHandle": { - "baseClasses": [ - "Record" - ], - "dataType": "URL", - "id": "URL-HYPkR" - } - }, - "style": { - "stroke": "#555" - }, - "className": "stroke-gray-900 stroke-connection", - "selected": false - }, - { - "source": "OpenAIModel-gi29P", - "sourceHandle": "{\u0153baseClasses\u0153:[\u0153str\u0153,\u0153Text\u0153,\u0153object\u0153],\u0153dataType\u0153:\u0153OpenAIModel\u0153,\u0153id\u0153:\u0153OpenAIModel-gi29P\u0153}", - "target": "ChatOutput-JPlxl", - "targetHandle": "{\u0153fieldName\u0153:\u0153input_value\u0153,\u0153id\u0153:\u0153ChatOutput-JPlxl\u0153,\u0153inputTypes\u0153:[\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}", - "data": { - "targetHandle": { - "fieldName": "input_value", - "id": "ChatOutput-JPlxl", - "inputTypes": [ - "Text" - ], - "type": "str" - }, - "sourceHandle": { - "baseClasses": [ - "str", - "Text", - "object" - ], - "dataType": "OpenAIModel", - "id": "OpenAIModel-gi29P" - } - }, - "style": { - "stroke": "#555" - }, - "className": "stroke-gray-900 stroke-connection", - "id": "reactflow__edge-OpenAIModel-gi29P{\u0153baseClasses\u0153:[\u0153str\u0153,\u0153Text\u0153,\u0153object\u0153],\u0153dataType\u0153:\u0153OpenAIModel\u0153,\u0153id\u0153:\u0153OpenAIModel-gi29P\u0153}-ChatOutput-JPlxl{\u0153fieldName\u0153:\u0153input_value\u0153,\u0153id\u0153:\u0153ChatOutput-JPlxl\u0153,\u0153inputTypes\u0153:[\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}" - }, - { - "source": "URL-2cX90", - "sourceHandle": "{\u0153baseClasses\u0153:[\u0153Record\u0153],\u0153dataType\u0153:\u0153URL\u0153,\u0153id\u0153:\u0153URL-2cX90\u0153}", - "target": "Prompt-Rse03", - "targetHandle": "{\u0153fieldName\u0153:\u0153reference_1\u0153,\u0153id\u0153:\u0153Prompt-Rse03\u0153,\u0153inputTypes\u0153:[\u0153Document\u0153,\u0153BaseOutputParser\u0153,\u0153Record\u0153,\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}", - "data": { - "targetHandle": { - "fieldName": "reference_1", - "id": "Prompt-Rse03", - "inputTypes": [ - "Document", - "BaseOutputParser", - "Record", - "Text" - ], - "type": "str" - }, - "sourceHandle": { - "baseClasses": [ - "Record" - ], - "dataType": "URL", - "id": "URL-2cX90" - } - }, - "style": { - "stroke": "#555" - }, - "className": "stroke-gray-900 stroke-connection", - "id": "reactflow__edge-URL-2cX90{\u0153baseClasses\u0153:[\u0153Record\u0153],\u0153dataType\u0153:\u0153URL\u0153,\u0153id\u0153:\u0153URL-2cX90\u0153}-Prompt-Rse03{\u0153fieldName\u0153:\u0153reference_1\u0153,\u0153id\u0153:\u0153Prompt-Rse03\u0153,\u0153inputTypes\u0153:[\u0153Document\u0153,\u0153BaseOutputParser\u0153,\u0153Record\u0153,\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}" - }, - { - "source": "TextInput-og8Or", - "sourceHandle": "{\u0153baseClasses\u0153:[\u0153object\u0153,\u0153Text\u0153,\u0153str\u0153],\u0153dataType\u0153:\u0153TextInput\u0153,\u0153id\u0153:\u0153TextInput-og8Or\u0153}", - "target": "Prompt-Rse03", - "targetHandle": "{\u0153fieldName\u0153:\u0153instructions\u0153,\u0153id\u0153:\u0153Prompt-Rse03\u0153,\u0153inputTypes\u0153:[\u0153Document\u0153,\u0153BaseOutputParser\u0153,\u0153Record\u0153,\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}", - "data": { - "targetHandle": { - "fieldName": "instructions", - "id": "Prompt-Rse03", - "inputTypes": [ - "Document", - "BaseOutputParser", - "Record", - "Text" - ], - "type": "str" - }, - "sourceHandle": { - "baseClasses": [ - "object", - "Text", - "str" - ], - "dataType": "TextInput", - "id": "TextInput-og8Or" - } - }, - "style": { - "stroke": "#555" - }, - "className": "stroke-gray-900 stroke-connection", - "id": "reactflow__edge-TextInput-og8Or{\u0153baseClasses\u0153:[\u0153object\u0153,\u0153Text\u0153,\u0153str\u0153],\u0153dataType\u0153:\u0153TextInput\u0153,\u0153id\u0153:\u0153TextInput-og8Or\u0153}-Prompt-Rse03{\u0153fieldName\u0153:\u0153instructions\u0153,\u0153id\u0153:\u0153Prompt-Rse03\u0153,\u0153inputTypes\u0153:[\u0153Document\u0153,\u0153BaseOutputParser\u0153,\u0153Record\u0153,\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}" - }, - { - "source": "Prompt-Rse03", - "sourceHandle": "{\u0153baseClasses\u0153:[\u0153object\u0153,\u0153Text\u0153,\u0153str\u0153],\u0153dataType\u0153:\u0153Prompt\u0153,\u0153id\u0153:\u0153Prompt-Rse03\u0153}", - "target": "OpenAIModel-gi29P", - "targetHandle": "{\u0153fieldName\u0153:\u0153input_value\u0153,\u0153id\u0153:\u0153OpenAIModel-gi29P\u0153,\u0153inputTypes\u0153:[\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}", - "data": { - "targetHandle": { - "fieldName": "input_value", - "id": "OpenAIModel-gi29P", - "inputTypes": [ - "Text" - ], - "type": "str" - }, - "sourceHandle": { - "baseClasses": [ - "object", - "Text", - "str" - ], - "dataType": "Prompt", - "id": "Prompt-Rse03" - } - }, - "style": { - "stroke": "#555" - }, - "className": "stroke-gray-900 stroke-connection", - "id": "reactflow__edge-Prompt-Rse03{\u0153baseClasses\u0153:[\u0153object\u0153,\u0153Text\u0153,\u0153str\u0153],\u0153dataType\u0153:\u0153Prompt\u0153,\u0153id\u0153:\u0153Prompt-Rse03\u0153}-OpenAIModel-gi29P{\u0153fieldName\u0153:\u0153input_value\u0153,\u0153id\u0153:\u0153OpenAIModel-gi29P\u0153,\u0153inputTypes\u0153:[\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}", - "selected": false - } - ], - "viewport": { - "x": -214.14726025721177, - "y": -35.83855793844168, - "zoom": 0.47344308394045925 + "output_types": [ + "Text" + ], + "full_path": null, + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false, + "error": null + }, + "id": "Prompt-Rse03", + "description": "Create a prompt template with dynamic variables.", + "display_name": "Prompt" + }, + "selected": false, + "width": 384, + "height": 571, + "dragging": false, + "positionAbsolute": { + "x": 1331.381712783371, + "y": 535.0279854229713 } - }, - "description": "This flow can be used to create a blog post following instructions from the user, using two other blogs as reference.", - "name": "Blog Writer", - "last_tested_version": "1.0.0a0", - "is_component": false -} + }, + { + "id": "URL-HYPkR", + "type": "genericNode", + "position": { + "x": 568.2971412887712, + "y": 700.9983368007821 + }, + "data": { + "type": "URL", + "node": { + "template": { + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import Any, Dict\n\nfrom langchain_community.document_loaders.web_base import WebBaseLoader\n\nfrom langflow.custom import CustomComponent\nfrom langflow.schema import Record\n\n\nclass URLComponent(CustomComponent):\n display_name = \"URL\"\n description = \"Fetch content from one or more URLs.\"\n icon = \"layout-template\"\n\n def build_config(self) -> Dict[str, Any]:\n return {\n \"urls\": {\"display_name\": \"URL\"},\n }\n\n def build(\n self,\n urls: list[str],\n ) -> list[Record]:\n loader = WebBaseLoader(web_paths=urls)\n docs = loader.load()\n records = self.to_records(docs)\n self.status = records\n return records\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "urls": { + "type": "str", + "required": true, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "urls", + "display_name": "URL", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ], + "value": [ + "https://www.promptingguide.ai/techniques/prompt_chaining" + ] + }, + "_type": "CustomComponent" + }, + "description": "Fetch content from one or more URLs.", + "icon": "layout-template", + "base_classes": [ + "Record" + ], + "display_name": "URL", + "documentation": "", + "custom_fields": { + "urls": null + }, + "output_types": [ + "Record" + ], + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false + }, + "id": "URL-HYPkR" + }, + "selected": false, + "width": 384, + "height": 281, + "positionAbsolute": { + "x": 568.2971412887712, + "y": 700.9983368007821 + }, + "dragging": false + }, + { + "id": "ChatOutput-JPlxl", + "type": "genericNode", + "position": { + "x": 2503.8617424688505, + "y": 789.3005578928434 + }, + "data": { + "type": "ChatOutput", + "node": { + "template": { + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n def build(\n self,\n sender: Optional[str] = \"Machine\",\n sender_name: Optional[str] = \"AI\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n record_template: Optional[str] = \"{text}\",\n ) -> Union[Text, Record]:\n return super().build_with_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n record_template=record_template or \"\",\n )\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "input_value": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "input_value", + "display_name": "Message", + "advanced": false, + "input_types": [ + "Text" + ], + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "record_template": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "{text}", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "record_template", + "display_name": "Record Template", + "advanced": true, + "dynamic": false, + "info": "In case of Message being a Record, this template will be used to convert it to text.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "return_record": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "return_record", + "display_name": "Return Record", + "advanced": true, + "dynamic": false, + "info": "Return the message as a record containing the sender, sender_name, and session_id.", + "load_from_db": false, + "title_case": false + }, + "sender": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "value": "Machine", + "fileTypes": [], + "file_path": "", + "password": false, + "options": [ + "Machine", + "User" + ], + "name": "sender", + "display_name": "Sender Type", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "sender_name": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": "AI", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "sender_name", + "display_name": "Sender Name", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "session_id": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "session_id", + "display_name": "Session ID", + "advanced": true, + "dynamic": false, + "info": "If provided, the message will be stored in the memory.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "_type": "CustomComponent" + }, + "description": "Display a chat message in the Playground.", + "icon": "ChatOutput", + "base_classes": [ + "Text", + "Record", + "object", + "str" + ], + "display_name": "Chat Output", + "documentation": "", + "custom_fields": { + "sender": null, + "sender_name": null, + "input_value": null, + "session_id": null, + "return_record": null, + "record_template": null + }, + "output_types": [ + "Text", + "Record" + ], + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false + }, + "id": "ChatOutput-JPlxl" + }, + "selected": false, + "width": 384, + "height": 383 + }, + { + "id": "OpenAIModel-gi29P", + "type": "genericNode", + "position": { + "x": 1917.7089968570963, + "y": 575.9186499244129 + }, + "data": { + "type": "OpenAIModel", + "node": { + "template": { + "input_value": { + "type": "str", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "input_value", + "display_name": "Input", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\"},\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": True,\n \"info\": \"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": MODEL_NAMES,\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"openai_api_key\": {\n \"display_name\": \"OpenAI API Key\",\n \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n \"advanced\": False,\n \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float = 0.1,\n model_name: str = \"gpt-4o\",\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n output = ChatOpenAI(\n max_tokens=max_tokens or None,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "max_tokens": { + "type": "int", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": "1024", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "max_tokens", + "display_name": "Max Tokens", + "advanced": true, + "dynamic": false, + "info": "The maximum number of tokens to generate. Set to 0 for unlimited tokens.", + "load_from_db": false, + "title_case": false + }, + "model_kwargs": { + "type": "NestedDict", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": {}, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "model_kwargs", + "display_name": "Model Kwargs", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "model_name": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "value": "gpt-3.5-turbo-0125", + "fileTypes": [], + "file_path": "", + "password": false, + "options": [ + "gpt-4o", + "gpt-4-turbo", + "gpt-4-turbo-preview", + "gpt-3.5-turbo", + "gpt-3.5-turbo-0125" + ], + "name": "model_name", + "display_name": "Model Name", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "openai_api_base": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "openai_api_base", + "display_name": "OpenAI API Base", + "advanced": true, + "dynamic": false, + "info": "The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\n\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "openai_api_key": { + "type": "str", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": true, + "name": "openai_api_key", + "display_name": "OpenAI API Key", + "advanced": false, + "dynamic": false, + "info": "The OpenAI API Key to use for the OpenAI model.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ], + "value": "OPENAI_API_KEY" + }, + "stream": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": true, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "stream", + "display_name": "Stream", + "advanced": true, + "dynamic": false, + "info": "Stream the response from the model. Streaming works only in Chat.", + "load_from_db": false, + "title_case": false + }, + "system_message": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "system_message", + "display_name": "System Message", + "advanced": true, + "dynamic": false, + "info": "System message to pass to the model.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "temperature": { + "type": "float", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": "0.1", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "temperature", + "display_name": "Temperature", + "advanced": false, + "dynamic": false, + "info": "", + "rangeSpec": { + "step_type": "float", + "min": -1, + "max": 1, + "step": 0.1 + }, + "load_from_db": false, + "title_case": false + }, + "_type": "CustomComponent" + }, + "description": "Generates text using OpenAI LLMs.", + "icon": "OpenAI", + "base_classes": [ + "str", + "Text", + "object" + ], + "display_name": "OpenAI", + "documentation": "", + "custom_fields": { + "input_value": null, + "openai_api_key": null, + "temperature": null, + "model_name": null, + "max_tokens": null, + "model_kwargs": null, + "openai_api_base": null, + "stream": null, + "system_message": null + }, + "output_types": [ + "Text" + ], + "field_formatters": {}, + "frozen": false, + "field_order": [ + "max_tokens", + "model_kwargs", + "model_name", + "openai_api_base", + "openai_api_key", + "temperature", + "input_value", + "system_message", + "stream" + ], + "beta": false + }, + "id": "OpenAIModel-gi29P" + }, + "selected": false, + "width": 384, + "height": 563, + "positionAbsolute": { + "x": 1917.7089968570963, + "y": 575.9186499244129 + }, + "dragging": false + }, + { + "id": "URL-2cX90", + "type": "genericNode", + "position": { + "x": 573.961301764604, + "y": 336.41463436122086 + }, + "data": { + "type": "URL", + "node": { + "template": { + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import Any, Dict\n\nfrom langchain_community.document_loaders.web_base import WebBaseLoader\n\nfrom langflow.custom import CustomComponent\nfrom langflow.schema import Record\n\n\nclass URLComponent(CustomComponent):\n display_name = \"URL\"\n description = \"Fetch content from one or more URLs.\"\n icon = \"layout-template\"\n\n def build_config(self) -> Dict[str, Any]:\n return {\n \"urls\": {\"display_name\": \"URL\"},\n }\n\n def build(\n self,\n urls: list[str],\n ) -> list[Record]:\n loader = WebBaseLoader(web_paths=urls)\n docs = loader.load()\n records = self.to_records(docs)\n self.status = records\n return records\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "urls": { + "type": "str", + "required": true, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "urls", + "display_name": "URL", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ], + "value": [ + "https://www.promptingguide.ai/introduction/basics" + ] + }, + "_type": "CustomComponent" + }, + "description": "Fetch content from one or more URLs.", + "icon": "layout-template", + "base_classes": [ + "Record" + ], + "display_name": "URL", + "documentation": "", + "custom_fields": { + "urls": null + }, + "output_types": [ + "Record" + ], + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false + }, + "id": "URL-2cX90" + }, + "selected": false, + "width": 384, + "height": 281, + "positionAbsolute": { + "x": 573.961301764604, + "y": 336.41463436122086 + }, + "dragging": false + }, + { + "id": "TextInput-og8Or", + "type": "genericNode", + "position": { + "x": 569.9387927203336, + "y": 1095.3352160671316 + }, + "data": { + "type": "TextInput", + "node": { + "template": { + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import Optional\n\nfrom langflow.base.io.text import TextComponent\nfrom langflow.field_typing import Text\n\n\nclass TextInput(TextComponent):\n display_name = \"Text Input\"\n description = \"Get text inputs from the Playground.\"\n icon = \"type\"\n\n def build_config(self):\n return {\n \"input_value\": {\n \"display_name\": \"Value\",\n \"input_types\": [\"Record\", \"Text\"],\n \"info\": \"Text or Record to be passed as input.\",\n },\n \"record_template\": {\n \"display_name\": \"Record Template\",\n \"multiline\": True,\n \"info\": \"Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Optional[str] = \"\",\n record_template: Optional[str] = \"\",\n ) -> Text:\n return super().build(input_value=input_value, record_template=record_template)\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "input_value": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": "Use the references above for style to write a new blog/tutorial about prompt engineering techniques. Suggest non-covered topics.", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "input_value", + "display_name": "Value", + "advanced": false, + "input_types": [ + "Record", + "Text" + ], + "dynamic": false, + "info": "Text or Record to be passed as input.", + "load_from_db": false, + "title_case": false + }, + "record_template": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "record_template", + "display_name": "Record Template", + "advanced": true, + "dynamic": false, + "info": "Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "_type": "CustomComponent" + }, + "description": "Get text inputs from the Playground.", + "icon": "type", + "base_classes": [ + "object", + "Text", + "str" + ], + "display_name": "Instructions", + "documentation": "", + "custom_fields": { + "input_value": null, + "record_template": null + }, + "output_types": [ + "Text" + ], + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false + }, + "id": "TextInput-og8Or" + }, + "selected": false, + "width": 384, + "height": 289, + "positionAbsolute": { + "x": 569.9387927203336, + "y": 1095.3352160671316 + }, + "dragging": false + } + ], + "edges": [ + { + "source": "URL-HYPkR", + "target": "Prompt-Rse03", + "sourceHandle": "{œbaseClassesœ:[œRecordœ],œdataTypeœ:œURLœ,œidœ:œURL-HYPkRœ}", + "targetHandle": "{œfieldNameœ:œreference_2œ,œidœ:œPrompt-Rse03œ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}", + "id": "reactflow__edge-URL-HYPkR{œbaseClassesœ:[œRecordœ],œdataTypeœ:œURLœ,œidœ:œURL-HYPkRœ}-Prompt-Rse03{œfieldNameœ:œreference_2œ,œidœ:œPrompt-Rse03œ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}", + "data": { + "targetHandle": { + "fieldName": "reference_2", + "id": "Prompt-Rse03", + "inputTypes": [ + "Document", + "BaseOutputParser", + "Record", + "Text" + ], + "type": "str" + }, + "sourceHandle": { + "baseClasses": [ + "Record" + ], + "dataType": "URL", + "id": "URL-HYPkR" + } + }, + "style": { + "stroke": "#555" + }, + "className": "stroke-gray-900 stroke-connection", + "selected": false + }, + { + "source": "OpenAIModel-gi29P", + "sourceHandle": "{œbaseClassesœ:[œstrœ,œTextœ,œobjectœ],œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-gi29Pœ}", + "target": "ChatOutput-JPlxl", + "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-JPlxlœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}", + "data": { + "targetHandle": { + "fieldName": "input_value", + "id": "ChatOutput-JPlxl", + "inputTypes": [ + "Text" + ], + "type": "str" + }, + "sourceHandle": { + "baseClasses": [ + "str", + "Text", + "object" + ], + "dataType": "OpenAIModel", + "id": "OpenAIModel-gi29P" + } + }, + "style": { + "stroke": "#555" + }, + "className": "stroke-gray-900 stroke-connection", + "id": "reactflow__edge-OpenAIModel-gi29P{œbaseClassesœ:[œstrœ,œTextœ,œobjectœ],œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-gi29Pœ}-ChatOutput-JPlxl{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-JPlxlœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}" + }, + { + "source": "URL-2cX90", + "sourceHandle": "{œbaseClassesœ:[œRecordœ],œdataTypeœ:œURLœ,œidœ:œURL-2cX90œ}", + "target": "Prompt-Rse03", + "targetHandle": "{œfieldNameœ:œreference_1œ,œidœ:œPrompt-Rse03œ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}", + "data": { + "targetHandle": { + "fieldName": "reference_1", + "id": "Prompt-Rse03", + "inputTypes": [ + "Document", + "BaseOutputParser", + "Record", + "Text" + ], + "type": "str" + }, + "sourceHandle": { + "baseClasses": [ + "Record" + ], + "dataType": "URL", + "id": "URL-2cX90" + } + }, + "style": { + "stroke": "#555" + }, + "className": "stroke-gray-900 stroke-connection", + "id": "reactflow__edge-URL-2cX90{œbaseClassesœ:[œRecordœ],œdataTypeœ:œURLœ,œidœ:œURL-2cX90œ}-Prompt-Rse03{œfieldNameœ:œreference_1œ,œidœ:œPrompt-Rse03œ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}" + }, + { + "source": "TextInput-og8Or", + "sourceHandle": "{œbaseClassesœ:[œobjectœ,œTextœ,œstrœ],œdataTypeœ:œTextInputœ,œidœ:œTextInput-og8Orœ}", + "target": "Prompt-Rse03", + "targetHandle": "{œfieldNameœ:œinstructionsœ,œidœ:œPrompt-Rse03œ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}", + "data": { + "targetHandle": { + "fieldName": "instructions", + "id": "Prompt-Rse03", + "inputTypes": [ + "Document", + "BaseOutputParser", + "Record", + "Text" + ], + "type": "str" + }, + "sourceHandle": { + "baseClasses": [ + "object", + "Text", + "str" + ], + "dataType": "TextInput", + "id": "TextInput-og8Or" + } + }, + "style": { + "stroke": "#555" + }, + "className": "stroke-gray-900 stroke-connection", + "id": "reactflow__edge-TextInput-og8Or{œbaseClassesœ:[œobjectœ,œTextœ,œstrœ],œdataTypeœ:œTextInputœ,œidœ:œTextInput-og8Orœ}-Prompt-Rse03{œfieldNameœ:œinstructionsœ,œidœ:œPrompt-Rse03œ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}" + }, + { + "source": "Prompt-Rse03", + "sourceHandle": "{œbaseClassesœ:[œobjectœ,œTextœ,œstrœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-Rse03œ}", + "target": "OpenAIModel-gi29P", + "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-gi29Pœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}", + "data": { + "targetHandle": { + "fieldName": "input_value", + "id": "OpenAIModel-gi29P", + "inputTypes": [ + "Text" + ], + "type": "str" + }, + "sourceHandle": { + "baseClasses": [ + "object", + "Text", + "str" + ], + "dataType": "Prompt", + "id": "Prompt-Rse03" + } + }, + "style": { + "stroke": "#555" + }, + "className": "stroke-gray-900 stroke-connection", + "id": "reactflow__edge-Prompt-Rse03{œbaseClassesœ:[œobjectœ,œTextœ,œstrœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-Rse03œ}-OpenAIModel-gi29P{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-gi29Pœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}", + "selected": false + } + ], + "viewport": { + "x": -214.14726025721177, + "y": -35.83855793844168, + "zoom": 0.47344308394045925 + } + }, + "description": "This flow can be used to create a blog post following instructions from the user, using two other blogs as reference.", + "name": "Blog Writer", + "last_tested_version": "1.0.0a0", + "is_component": false +} \ No newline at end of file diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Document QA.json b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Document QA.json index 5c1b653e5..b4836c93e 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Document QA.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Document QA.json @@ -651,7 +651,7 @@ "list": false, "show": true, "multiline": true, - "value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\"},\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": True,\n \"info\": \"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": MODEL_NAMES,\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"openai_api_key\": {\n \"display_name\": \"OpenAI API Key\",\n \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n \"advanced\": False,\n \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float,\n model_name: str = \"gpt-4o\",\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n output = ChatOpenAI(\n max_tokens=max_tokens or None,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n", + "value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\"},\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": True,\n \"info\": \"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": MODEL_NAMES,\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"openai_api_key\": {\n \"display_name\": \"OpenAI API Key\",\n \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n \"advanced\": False,\n \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float = 0.1,\n model_name: str = \"gpt-4o\",\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n output = ChatOpenAI(\n max_tokens=max_tokens or None,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n", "fileTypes": [], "file_path": "", "password": false, @@ -814,7 +814,7 @@ }, "temperature": { "type": "float", - "required": true, + "required": false, "placeholder": "", "list": false, "show": true, diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Memory Conversation.json b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Memory Conversation.json index 04d29b24a..bdd47835b 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Memory Conversation.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Memory Conversation.json @@ -751,7 +751,7 @@ "list": false, "show": true, "multiline": true, - "value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\"},\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": True,\n \"info\": \"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": MODEL_NAMES,\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"openai_api_key\": {\n \"display_name\": \"OpenAI API Key\",\n \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n \"advanced\": False,\n \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float,\n model_name: str = \"gpt-4o\",\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n output = ChatOpenAI(\n max_tokens=max_tokens or None,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n", + "value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\"},\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": True,\n \"info\": \"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": MODEL_NAMES,\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"openai_api_key\": {\n \"display_name\": \"OpenAI API Key\",\n \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n \"advanced\": False,\n \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float = 0.1,\n model_name: str = \"gpt-4o\",\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n output = ChatOpenAI(\n max_tokens=max_tokens or None,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n", "fileTypes": [], "file_path": "", "password": false, @@ -914,7 +914,7 @@ }, "temperature": { "type": "float", - "required": true, + "required": false, "placeholder": "", "list": false, "show": true, diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Prompt Chaining.json b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Prompt Chaining.json index 43212bd61..cb91ef13f 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Prompt Chaining.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Prompt Chaining.json @@ -1,1769 +1,1769 @@ { - "id": "85392e54-20f3-4ab5-a179-cb4bef16f639", - "data": { - "nodes": [ - { - "id": "Prompt-amqBu", - "type": "genericNode", - "position": { - "x": 2191.5837146441663, - "y": 1047.9307944451873 - }, - "data": { - "type": "Prompt", - "node": { - "template": { - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Input, Prompt, Text\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": Input(display_name=\"Template\"),\n \"code\": Input(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "template": { - "type": "prompt", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": "You are a helpful assistant. Given a long document, your task is to create a concise summary that captures the main points and key details. The summary should be clear, accurate, and succinct. Please provide the summary in the format below:\n####\n{document}\n####\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "template", - "display_name": "Template", - "advanced": false, - "input_types": [ - "Text" - ], - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "_type": "CustomComponent", - "document": { - "field_type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "document", - "display_name": "document", - "advanced": false, - "input_types": [ - "Document", - "BaseOutputParser", - "Record", - "Text" - ], - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "type": "str" - } - }, - "description": "Create a prompt template with dynamic variables.", - "icon": "prompts", - "is_input": null, - "is_output": null, - "is_composition": null, - "base_classes": [ - "object", - "str", - "Text" - ], - "name": "", - "display_name": "Prompt", - "documentation": "", - "custom_fields": { - "template": [ - "document" - ] - }, - "output_types": [ - "Text" - ], - "full_path": null, - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": false, - "error": null - }, - "id": "Prompt-amqBu", - "description": "Create a prompt template with dynamic variables.", - "display_name": "Prompt" - }, - "selected": false, - "width": 384, - "height": 385, - "positionAbsolute": { - "x": 2191.5837146441663, - "y": 1047.9307944451873 - }, - "dragging": false + "id": "85392e54-20f3-4ab5-a179-cb4bef16f639", + "data": { + "nodes": [ + { + "id": "Prompt-amqBu", + "type": "genericNode", + "position": { + "x": 2191.5837146441663, + "y": 1047.9307944451873 + }, + "data": { + "type": "Prompt", + "node": { + "template": { + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Input, Prompt, Text\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": Input(display_name=\"Template\"),\n \"code\": Input(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "template": { + "type": "prompt", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": "You are a helpful assistant. Given a long document, your task is to create a concise summary that captures the main points and key details. The summary should be clear, accurate, and succinct. Please provide the summary in the format below:\n####\n{document}\n####\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "template", + "display_name": "Template", + "advanced": false, + "input_types": [ + "Text" + ], + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "_type": "CustomComponent", + "document": { + "field_type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "document", + "display_name": "document", + "advanced": false, + "input_types": [ + "Document", + "BaseOutputParser", + "Record", + "Text" + ], + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "type": "str" + } }, - { - "id": "Prompt-gTNiz", - "type": "genericNode", - "position": { - "x": 3731.0813766902447, - "y": 799.631909121391 - }, - "data": { - "type": "Prompt", - "node": { - "template": { - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Input, Prompt, Text\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": Input(display_name=\"Template\"),\n \"code\": Input(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "template": { - "type": "prompt", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": "Given a summary of an article, please create two multiple-choice questions that cover the key points and details mentioned. Ensure the questions are clear and provide three options (A, B, C), with one correct answer.\n####\n{summary}\n####", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "template", - "display_name": "Template", - "advanced": false, - "input_types": [ - "Text" - ], - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "_type": "CustomComponent", - "summary": { - "field_type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "summary", - "display_name": "summary", - "advanced": false, - "input_types": [ - "Document", - "BaseOutputParser", - "Record", - "Text" - ], - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "type": "str" - } - }, - "description": "Create a prompt template with dynamic variables.", - "icon": "prompts", - "is_input": null, - "is_output": null, - "is_composition": null, - "base_classes": [ - "object", - "str", - "Text" - ], - "name": "", - "display_name": "Prompt", - "documentation": "", - "custom_fields": { - "template": [ - "summary" - ] - }, - "output_types": [ - "Text" - ], - "full_path": null, - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": false, - "error": null - }, - "id": "Prompt-gTNiz", - "description": "Create a prompt template with dynamic variables.", - "display_name": "Prompt" - }, - "selected": false, - "width": 384, - "height": 385, - "dragging": false + "description": "Create a prompt template with dynamic variables.", + "icon": "prompts", + "is_input": null, + "is_output": null, + "is_composition": null, + "base_classes": [ + "object", + "str", + "Text" + ], + "name": "", + "display_name": "Prompt", + "documentation": "", + "custom_fields": { + "template": [ + "document" + ] }, - { - "id": "ChatOutput-EJkG3", - "type": "genericNode", - "position": { - "x": 3722.1747844849388, - "y": 1283.413553222214 - }, - "data": { - "type": "ChatOutput", - "node": { - "template": { - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n def build(\n self,\n sender: Optional[str] = \"Machine\",\n sender_name: Optional[str] = \"AI\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n record_template: Optional[str] = \"{text}\",\n ) -> Union[Text, Record]:\n return super().build_with_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n record_template=record_template or \"\",\n )\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "input_value": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "input_value", - "display_name": "Message", - "advanced": false, - "input_types": [ - "Text" - ], - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "record_template": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "{text}", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "record_template", - "display_name": "Record Template", - "advanced": true, - "dynamic": false, - "info": "In case of Message being a Record, this template will be used to convert it to text.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "return_record": { - "type": "bool", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "return_record", - "display_name": "Return Record", - "advanced": true, - "dynamic": false, - "info": "Return the message as a record containing the sender, sender_name, and session_id.", - "load_from_db": false, - "title_case": false - }, - "sender": { - "type": "str", - "required": false, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "value": "Machine", - "fileTypes": [], - "file_path": "", - "password": false, - "options": [ - "Machine", - "User" - ], - "name": "sender", - "display_name": "Sender Type", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "sender_name": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": "Summarizer", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "sender_name", - "display_name": "Sender Name", - "advanced": false, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "session_id": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "session_id", - "display_name": "Session ID", - "advanced": true, - "dynamic": false, - "info": "If provided, the message will be stored in the memory.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "_type": "CustomComponent" - }, - "description": "Display a chat message in the Playground.", - "icon": "ChatOutput", - "base_classes": [ - "object", - "Record", - "Text", - "str" - ], - "display_name": "Chat Output", - "documentation": "", - "custom_fields": { - "sender": null, - "sender_name": null, - "input_value": null, - "session_id": null, - "return_record": null, - "record_template": null - }, - "output_types": [ - "Text", - "Record" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": false - }, - "id": "ChatOutput-EJkG3" - }, - "selected": false, - "width": 384, - "height": 385, - "dragging": false + "output_types": [ + "Text" + ], + "full_path": null, + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false, + "error": null + }, + "id": "Prompt-amqBu", + "description": "Create a prompt template with dynamic variables.", + "display_name": "Prompt" + }, + "selected": false, + "width": 384, + "height": 385, + "positionAbsolute": { + "x": 2191.5837146441663, + "y": 1047.9307944451873 + }, + "dragging": false + }, + { + "id": "Prompt-gTNiz", + "type": "genericNode", + "position": { + "x": 3731.0813766902447, + "y": 799.631909121391 + }, + "data": { + "type": "Prompt", + "node": { + "template": { + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Input, Prompt, Text\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": Input(display_name=\"Template\"),\n \"code\": Input(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "template": { + "type": "prompt", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": "Given a summary of an article, please create two multiple-choice questions that cover the key points and details mentioned. Ensure the questions are clear and provide three options (A, B, C), with one correct answer.\n####\n{summary}\n####", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "template", + "display_name": "Template", + "advanced": false, + "input_types": [ + "Text" + ], + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "_type": "CustomComponent", + "summary": { + "field_type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "summary", + "display_name": "summary", + "advanced": false, + "input_types": [ + "Document", + "BaseOutputParser", + "Record", + "Text" + ], + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "type": "str" + } }, - { - "id": "ChatOutput-DNmvg", - "type": "genericNode", - "position": { - "x": 5077.71285886074, - "y": 1232.9152769735522 - }, - "data": { - "type": "ChatOutput", - "node": { - "template": { - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n def build(\n self,\n sender: Optional[str] = \"Machine\",\n sender_name: Optional[str] = \"AI\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n record_template: Optional[str] = \"{text}\",\n ) -> Union[Text, Record]:\n return super().build_with_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n record_template=record_template or \"\",\n )\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "input_value": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "input_value", - "display_name": "Message", - "advanced": false, - "input_types": [ - "Text" - ], - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "record_template": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "{text}", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "record_template", - "display_name": "Record Template", - "advanced": true, - "dynamic": false, - "info": "In case of Message being a Record, this template will be used to convert it to text.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "return_record": { - "type": "bool", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "return_record", - "display_name": "Return Record", - "advanced": true, - "dynamic": false, - "info": "Return the message as a record containing the sender, sender_name, and session_id.", - "load_from_db": false, - "title_case": false - }, - "sender": { - "type": "str", - "required": false, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "value": "Machine", - "fileTypes": [], - "file_path": "", - "password": false, - "options": [ - "Machine", - "User" - ], - "name": "sender", - "display_name": "Sender Type", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "sender_name": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": "Question Generator", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "sender_name", - "display_name": "Sender Name", - "advanced": false, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "session_id": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "session_id", - "display_name": "Session ID", - "advanced": true, - "dynamic": false, - "info": "If provided, the message will be stored in the memory.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "_type": "CustomComponent" - }, - "description": "Display a chat message in the Playground.", - "icon": "ChatOutput", - "base_classes": [ - "object", - "Record", - "Text", - "str" - ], - "display_name": "Chat Output", - "documentation": "", - "custom_fields": { - "sender": null, - "sender_name": null, - "input_value": null, - "session_id": null, - "return_record": null, - "record_template": null - }, - "output_types": [ - "Text", - "Record" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": false - }, - "id": "ChatOutput-DNmvg" - }, - "selected": false, - "width": 384, - "height": 385 + "description": "Create a prompt template with dynamic variables.", + "icon": "prompts", + "is_input": null, + "is_output": null, + "is_composition": null, + "base_classes": [ + "object", + "str", + "Text" + ], + "name": "", + "display_name": "Prompt", + "documentation": "", + "custom_fields": { + "template": [ + "summary" + ] }, - { - "id": "TextInput-sptaH", - "type": "genericNode", - "position": { - "x": 1700.5624822024752, - "y": 1039.603088937466 - }, - "data": { - "type": "TextInput", - "node": { - "template": { - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from typing import Optional\n\nfrom langflow.base.io.text import TextComponent\nfrom langflow.field_typing import Text\n\n\nclass TextInput(TextComponent):\n display_name = \"Text Input\"\n description = \"Get text inputs from the Playground.\"\n icon = \"type\"\n\n def build_config(self):\n return {\n \"input_value\": {\n \"display_name\": \"Value\",\n \"input_types\": [\"Record\", \"Text\"],\n \"info\": \"Text or Record to be passed as input.\",\n },\n \"record_template\": {\n \"display_name\": \"Record Template\",\n \"multiline\": True,\n \"info\": \"Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Optional[Text] = \"\",\n record_template: Optional[str] = \"\",\n ) -> Text:\n return super().build(input_value=input_value, record_template=record_template)\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "input_value": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": "Revolutionary Nano-Battery Technology Unveiled In a groundbreaking announcement yesterday, researchers from the fictional Tech Innovations Institute revealed the development of a new nano-battery technology that promises to revolutionize energy storage. The new battery, dubbed the \"EnerGCell\", uses advanced nanomaterials to achieve unprecedented efficiency and storage capacities. According to lead researcher Dr. Ada Byron, the EnerGCell can store up to ten times more energy than the best lithium-ion batteries available today, while charging in just a fraction of the time. \"We're talking about charging your electric vehicle in just five minutes for a range of over 1,000 miles,\" Dr. Byron stated during the press conference. The technology behind the EnerGCell involves a complex arrangement of nanostructured electrodes that allow for rapid ion transfer and extremely high energy density. This breakthrough was achieved after a decade of research into nanomaterials and their applications in energy storage. The implications of this technology are vast, promising to accelerate the adoption of renewable energy by making it more practical and affordable to store wind and solar power. It could also lead to significant advancements in electric vehicles, mobile devices, and any other technology that relies on batteries. Despite the excitement, some experts are calling for patience, noting that the EnerGCell is still in its early stages of development and may take several years before it's commercially available. However, the potential impact of such a technology on the environment and the global economy is undeniable. Tech Innovations Institute plans to continue refining the EnerGCell and begin pilot projects with select partners in the coming year. If successful, this nano-battery technology could indeed be the breakthrough needed to usher in a new era of clean energy and technology.", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "input_value", - "display_name": "Value", - "advanced": false, - "input_types": [ - "Record", - "Text" - ], - "dynamic": false, - "info": "Text or Record to be passed as input.", - "load_from_db": false, - "title_case": false - }, - "record_template": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "record_template", - "display_name": "Record Template", - "advanced": true, - "dynamic": false, - "info": "Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "_type": "CustomComponent" - }, - "description": "Get text inputs from the Playground.", - "icon": "type", - "base_classes": [ - "str", - "Text", - "object" - ], - "display_name": "Text Input", - "documentation": "", - "custom_fields": { - "input_value": null, - "record_template": null - }, - "output_types": [ - "Text" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": false - }, - "id": "TextInput-sptaH" - }, - "selected": false, - "width": 384, - "height": 290, - "positionAbsolute": { - "x": 1700.5624822024752, - "y": 1039.603088937466 - }, - "dragging": false + "output_types": [ + "Text" + ], + "full_path": null, + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false, + "error": null + }, + "id": "Prompt-gTNiz", + "description": "Create a prompt template with dynamic variables.", + "display_name": "Prompt" + }, + "selected": false, + "width": 384, + "height": 385, + "dragging": false + }, + { + "id": "ChatOutput-EJkG3", + "type": "genericNode", + "position": { + "x": 3722.1747844849388, + "y": 1283.413553222214 + }, + "data": { + "type": "ChatOutput", + "node": { + "template": { + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n def build(\n self,\n sender: Optional[str] = \"Machine\",\n sender_name: Optional[str] = \"AI\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n record_template: Optional[str] = \"{text}\",\n ) -> Union[Text, Record]:\n return super().build_with_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n record_template=record_template or \"\",\n )\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "input_value": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "input_value", + "display_name": "Message", + "advanced": false, + "input_types": [ + "Text" + ], + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "record_template": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "{text}", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "record_template", + "display_name": "Record Template", + "advanced": true, + "dynamic": false, + "info": "In case of Message being a Record, this template will be used to convert it to text.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "return_record": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "return_record", + "display_name": "Return Record", + "advanced": true, + "dynamic": false, + "info": "Return the message as a record containing the sender, sender_name, and session_id.", + "load_from_db": false, + "title_case": false + }, + "sender": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "value": "Machine", + "fileTypes": [], + "file_path": "", + "password": false, + "options": [ + "Machine", + "User" + ], + "name": "sender", + "display_name": "Sender Type", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "sender_name": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": "Summarizer", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "sender_name", + "display_name": "Sender Name", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "session_id": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "session_id", + "display_name": "Session ID", + "advanced": true, + "dynamic": false, + "info": "If provided, the message will be stored in the memory.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "_type": "CustomComponent" }, - { - "id": "TextOutput-2MS4a", - "type": "genericNode", - "position": { - "x": 2917.216113690115, - "y": 513.0058511435552 - }, - "data": { - "type": "TextOutput", - "node": { - "template": { - "input_value": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": "", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "input_value", - "display_name": "Value", - "advanced": false, - "input_types": [ - "Record", - "Text" - ], - "dynamic": false, - "info": "Text or Record to be passed as output.", - "load_from_db": false, - "title_case": false - }, - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from typing import Optional\n\nfrom langflow.base.io.text import TextComponent\nfrom langflow.field_typing import Text\n\n\nclass TextOutput(TextComponent):\n display_name = \"Text Output\"\n description = \"Display a text output in the Playground.\"\n icon = \"type\"\n\n def build_config(self):\n return {\n \"input_value\": {\n \"display_name\": \"Value\",\n \"input_types\": [\"Record\", \"Text\"],\n \"info\": \"Text or Record to be passed as output.\",\n },\n \"record_template\": {\n \"display_name\": \"Record Template\",\n \"multiline\": True,\n \"info\": \"Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.\",\n \"advanced\": True,\n },\n }\n\n def build(self, input_value: Optional[Text] = \"\", record_template: str = \"\") -> Text:\n return super().build(input_value=input_value, record_template=record_template)\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "record_template": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "record_template", - "display_name": "Record Template", - "advanced": true, - "dynamic": false, - "info": "Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "_type": "CustomComponent" - }, - "description": "Display a text output in the Playground.", - "icon": "type", - "base_classes": [ - "str", - "Text", - "object" - ], - "display_name": "First Prompt", - "documentation": "", - "custom_fields": { - "input_value": null, - "record_template": null - }, - "output_types": [ - "Text" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": false - }, - "id": "TextOutput-2MS4a" - }, - "selected": false, - "width": 384, - "height": 290, - "positionAbsolute": { - "x": 2917.216113690115, - "y": 513.0058511435552 - }, - "dragging": false + "description": "Display a chat message in the Playground.", + "icon": "ChatOutput", + "base_classes": [ + "object", + "Record", + "Text", + "str" + ], + "display_name": "Chat Output", + "documentation": "", + "custom_fields": { + "sender": null, + "sender_name": null, + "input_value": null, + "session_id": null, + "return_record": null, + "record_template": null }, - { - "id": "OpenAIModel-uYXZJ", - "type": "genericNode", - "position": { - "x": 2925.784767523062, - "y": 933.6465680967775 - }, - "data": { - "type": "OpenAIModel", - "node": { - "template": { - "input_value": { - "type": "str", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "input_value", - "display_name": "Input", - "advanced": false, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\"},\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": True,\n \"info\": \"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": MODEL_NAMES,\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"openai_api_key\": {\n \"display_name\": \"OpenAI API Key\",\n \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n \"advanced\": False,\n \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float,\n model_name: str = \"gpt-4o\",\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n output = ChatOpenAI(\n max_tokens=max_tokens or None,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "max_tokens": { - "type": "int", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": 256, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "max_tokens", - "display_name": "Max Tokens", - "advanced": true, - "dynamic": false, - "info": "The maximum number of tokens to generate. Set to 0 for unlimited tokens.", - "load_from_db": false, - "title_case": false - }, - "model_kwargs": { - "type": "NestedDict", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": {}, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "model_kwargs", - "display_name": "Model Kwargs", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "model_name": { - "type": "str", - "required": false, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "value": "gpt-4-turbo-preview", - "fileTypes": [], - "file_path": "", - "password": false, - "options": [ - "gpt-4o", - "gpt-4-turbo", - "gpt-4-turbo-preview", - "gpt-3.5-turbo", - "gpt-3.5-turbo-0125" - ], - "name": "model_name", - "display_name": "Model Name", - "advanced": false, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "openai_api_base": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "openai_api_base", - "display_name": "OpenAI API Base", - "advanced": true, - "dynamic": false, - "info": "The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\n\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "openai_api_key": { - "type": "str", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": true, - "name": "openai_api_key", - "display_name": "OpenAI API Key", - "advanced": false, - "dynamic": false, - "info": "The OpenAI API Key to use for the OpenAI model.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ], - "value": "OPENAI_API_KEY" - }, - "stream": { - "type": "bool", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "stream", - "display_name": "Stream", - "advanced": true, - "dynamic": false, - "info": "Stream the response from the model. Streaming works only in Chat.", - "load_from_db": false, - "title_case": false - }, - "system_message": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "system_message", - "display_name": "System Message", - "advanced": true, - "dynamic": false, - "info": "System message to pass to the model.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "temperature": { - "type": "float", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": 0.1, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "temperature", - "display_name": "Temperature", - "advanced": false, - "dynamic": false, - "info": "", - "rangeSpec": { - "step_type": "float", - "min": -1, - "max": 1, - "step": 0.1 - }, - "load_from_db": false, - "title_case": false - }, - "_type": "CustomComponent" - }, - "description": "Generates text using OpenAI LLMs.", - "icon": "OpenAI", - "base_classes": [ - "str", - "Text", - "object" - ], - "display_name": "OpenAI", - "documentation": "", - "custom_fields": { - "input_value": null, - "openai_api_key": null, - "temperature": null, - "model_name": null, - "max_tokens": null, - "model_kwargs": null, - "openai_api_base": null, - "stream": null, - "system_message": null - }, - "output_types": [ - "Text" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [ - "max_tokens", - "model_kwargs", - "model_name", - "openai_api_base", - "openai_api_key", - "temperature", - "input_value", - "system_message", - "stream" - ], - "beta": false - }, - "id": "OpenAIModel-uYXZJ" - }, - "selected": false, - "width": 384, - "height": 565, - "positionAbsolute": { - "x": 2925.784767523062, - "y": 933.6465680967775 - }, - "dragging": false + "output_types": [ + "Text", + "Record" + ], + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false + }, + "id": "ChatOutput-EJkG3" + }, + "selected": false, + "width": 384, + "height": 385, + "dragging": false + }, + { + "id": "ChatOutput-DNmvg", + "type": "genericNode", + "position": { + "x": 5077.71285886074, + "y": 1232.9152769735522 + }, + "data": { + "type": "ChatOutput", + "node": { + "template": { + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n def build(\n self,\n sender: Optional[str] = \"Machine\",\n sender_name: Optional[str] = \"AI\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n record_template: Optional[str] = \"{text}\",\n ) -> Union[Text, Record]:\n return super().build_with_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n record_template=record_template or \"\",\n )\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "input_value": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "input_value", + "display_name": "Message", + "advanced": false, + "input_types": [ + "Text" + ], + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "record_template": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "{text}", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "record_template", + "display_name": "Record Template", + "advanced": true, + "dynamic": false, + "info": "In case of Message being a Record, this template will be used to convert it to text.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "return_record": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "return_record", + "display_name": "Return Record", + "advanced": true, + "dynamic": false, + "info": "Return the message as a record containing the sender, sender_name, and session_id.", + "load_from_db": false, + "title_case": false + }, + "sender": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "value": "Machine", + "fileTypes": [], + "file_path": "", + "password": false, + "options": [ + "Machine", + "User" + ], + "name": "sender", + "display_name": "Sender Type", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "sender_name": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": "Question Generator", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "sender_name", + "display_name": "Sender Name", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "session_id": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "session_id", + "display_name": "Session ID", + "advanced": true, + "dynamic": false, + "info": "If provided, the message will be stored in the memory.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "_type": "CustomComponent" }, - { - "id": "TextOutput-MUDOR", - "type": "genericNode", - "position": { - "x": 4446.064323520379, - "y": 633.833297518702 - }, - "data": { - "type": "TextOutput", - "node": { - "template": { - "input_value": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": "", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "input_value", - "display_name": "Value", - "advanced": false, - "input_types": [ - "Record", - "Text" - ], - "dynamic": false, - "info": "Text or Record to be passed as output.", - "load_from_db": false, - "title_case": false - }, - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from typing import Optional\n\nfrom langflow.base.io.text import TextComponent\nfrom langflow.field_typing import Text\n\n\nclass TextOutput(TextComponent):\n display_name = \"Text Output\"\n description = \"Display a text output in the Playground.\"\n icon = \"type\"\n\n def build_config(self):\n return {\n \"input_value\": {\n \"display_name\": \"Value\",\n \"input_types\": [\"Record\", \"Text\"],\n \"info\": \"Text or Record to be passed as output.\",\n },\n \"record_template\": {\n \"display_name\": \"Record Template\",\n \"multiline\": True,\n \"info\": \"Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.\",\n \"advanced\": True,\n },\n }\n\n def build(self, input_value: Optional[Text] = \"\", record_template: str = \"\") -> Text:\n return super().build(input_value=input_value, record_template=record_template)\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "record_template": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "record_template", - "display_name": "Record Template", - "advanced": true, - "dynamic": false, - "info": "Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "_type": "CustomComponent" - }, - "description": "Display a text output in the Playground.", - "icon": "type", - "base_classes": [ - "str", - "Text", - "object" - ], - "display_name": "Second Prompt", - "documentation": "", - "custom_fields": { - "input_value": null, - "record_template": null - }, - "output_types": [ - "Text" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": false - }, - "id": "TextOutput-MUDOR" - }, - "selected": false, - "width": 384, - "height": 290, - "dragging": false, - "positionAbsolute": { - "x": 4446.064323520379, - "y": 633.833297518702 - } + "description": "Display a chat message in the Playground.", + "icon": "ChatOutput", + "base_classes": [ + "object", + "Record", + "Text", + "str" + ], + "display_name": "Chat Output", + "documentation": "", + "custom_fields": { + "sender": null, + "sender_name": null, + "input_value": null, + "session_id": null, + "return_record": null, + "record_template": null }, - { - "id": "OpenAIModel-XawYB", - "type": "genericNode", - "position": { - "x": 4500.152018344182, - "y": 1027.7382026227656 - }, - "data": { - "type": "OpenAIModel", - "node": { - "template": { - "input_value": { - "type": "str", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "input_value", - "display_name": "Input", - "advanced": false, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\"},\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": True,\n \"info\": \"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": MODEL_NAMES,\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"openai_api_key\": {\n \"display_name\": \"OpenAI API Key\",\n \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n \"advanced\": False,\n \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float,\n model_name: str = \"gpt-4o\",\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n output = ChatOpenAI(\n max_tokens=max_tokens or None,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "max_tokens": { - "type": "int", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": 256, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "max_tokens", - "display_name": "Max Tokens", - "advanced": true, - "dynamic": false, - "info": "The maximum number of tokens to generate. Set to 0 for unlimited tokens.", - "load_from_db": false, - "title_case": false - }, - "model_kwargs": { - "type": "NestedDict", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": {}, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "model_kwargs", - "display_name": "Model Kwargs", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "model_name": { - "type": "str", - "required": false, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "value": "gpt-4-turbo-preview", - "fileTypes": [], - "file_path": "", - "password": false, - "options": [ - "gpt-4o", - "gpt-4-turbo", - "gpt-4-turbo-preview", - "gpt-3.5-turbo", - "gpt-3.5-turbo-0125" - ], - "name": "model_name", - "display_name": "Model Name", - "advanced": false, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "openai_api_base": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "openai_api_base", - "display_name": "OpenAI API Base", - "advanced": true, - "dynamic": false, - "info": "The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\n\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "openai_api_key": { - "type": "str", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": true, - "name": "openai_api_key", - "display_name": "OpenAI API Key", - "advanced": false, - "dynamic": false, - "info": "The OpenAI API Key to use for the OpenAI model.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ], - "value": "" - }, - "stream": { - "type": "bool", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "stream", - "display_name": "Stream", - "advanced": true, - "dynamic": false, - "info": "Stream the response from the model. Streaming works only in Chat.", - "load_from_db": false, - "title_case": false - }, - "system_message": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "system_message", - "display_name": "System Message", - "advanced": true, - "dynamic": false, - "info": "System message to pass to the model.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "temperature": { - "type": "float", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": 0.1, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "temperature", - "display_name": "Temperature", - "advanced": false, - "dynamic": false, - "info": "", - "rangeSpec": { - "step_type": "float", - "min": -1, - "max": 1, - "step": 0.1 - }, - "load_from_db": false, - "title_case": false - }, - "_type": "CustomComponent" - }, - "description": "Generates text using OpenAI LLMs.", - "icon": "OpenAI", - "base_classes": [ - "str", - "Text", - "object" - ], - "display_name": "OpenAI", - "documentation": "", - "custom_fields": { - "input_value": null, - "openai_api_key": null, - "temperature": null, - "model_name": null, - "max_tokens": null, - "model_kwargs": null, - "openai_api_base": null, - "stream": null, - "system_message": null - }, - "output_types": [ - "Text" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [ - "max_tokens", - "model_kwargs", - "model_name", - "openai_api_base", - "openai_api_key", - "temperature", - "input_value", - "system_message", - "stream" - ], - "beta": false - }, - "id": "OpenAIModel-XawYB" - }, - "selected": false, - "width": 384, - "height": 565, - "positionAbsolute": { - "x": 4500.152018344182, - "y": 1027.7382026227656 - }, - "dragging": false - } - ], - "edges": [ - { - "source": "TextInput-sptaH", - "sourceHandle": "{\u0153baseClasses\u0153:[\u0153str\u0153,\u0153Text\u0153,\u0153object\u0153],\u0153dataType\u0153:\u0153TextInput\u0153,\u0153id\u0153:\u0153TextInput-sptaH\u0153}", - "target": "Prompt-amqBu", - "targetHandle": "{\u0153fieldName\u0153:\u0153document\u0153,\u0153id\u0153:\u0153Prompt-amqBu\u0153,\u0153inputTypes\u0153:[\u0153Document\u0153,\u0153BaseOutputParser\u0153,\u0153Record\u0153,\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}", - "data": { - "targetHandle": { - "fieldName": "document", - "id": "Prompt-amqBu", - "inputTypes": [ - "Document", - "BaseOutputParser", - "Record", - "Text" - ], - "type": "str" - }, - "sourceHandle": { - "baseClasses": [ - "str", - "Text", - "object" - ], - "dataType": "TextInput", - "id": "TextInput-sptaH" - } - }, - "style": { - "stroke": "#555" - }, - "className": "stroke-gray-900 stroke-connection", - "id": "reactflow__edge-TextInput-sptaH{\u0153baseClasses\u0153:[\u0153str\u0153,\u0153Text\u0153,\u0153object\u0153],\u0153dataType\u0153:\u0153TextInput\u0153,\u0153id\u0153:\u0153TextInput-sptaH\u0153}-Prompt-amqBu{\u0153fieldName\u0153:\u0153document\u0153,\u0153id\u0153:\u0153Prompt-amqBu\u0153,\u0153inputTypes\u0153:[\u0153Document\u0153,\u0153BaseOutputParser\u0153,\u0153Record\u0153,\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}" + "output_types": [ + "Text", + "Record" + ], + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false + }, + "id": "ChatOutput-DNmvg" + }, + "selected": false, + "width": 384, + "height": 385 + }, + { + "id": "TextInput-sptaH", + "type": "genericNode", + "position": { + "x": 1700.5624822024752, + "y": 1039.603088937466 + }, + "data": { + "type": "TextInput", + "node": { + "template": { + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import Optional\n\nfrom langflow.base.io.text import TextComponent\nfrom langflow.field_typing import Text\n\n\nclass TextInput(TextComponent):\n display_name = \"Text Input\"\n description = \"Get text inputs from the Playground.\"\n icon = \"type\"\n\n def build_config(self):\n return {\n \"input_value\": {\n \"display_name\": \"Value\",\n \"input_types\": [\"Record\", \"Text\"],\n \"info\": \"Text or Record to be passed as input.\",\n },\n \"record_template\": {\n \"display_name\": \"Record Template\",\n \"multiline\": True,\n \"info\": \"Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Optional[Text] = \"\",\n record_template: Optional[str] = \"\",\n ) -> Text:\n return super().build(input_value=input_value, record_template=record_template)\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "input_value": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": "Revolutionary Nano-Battery Technology Unveiled In a groundbreaking announcement yesterday, researchers from the fictional Tech Innovations Institute revealed the development of a new nano-battery technology that promises to revolutionize energy storage. The new battery, dubbed the \"EnerGCell\", uses advanced nanomaterials to achieve unprecedented efficiency and storage capacities. According to lead researcher Dr. Ada Byron, the EnerGCell can store up to ten times more energy than the best lithium-ion batteries available today, while charging in just a fraction of the time. \"We're talking about charging your electric vehicle in just five minutes for a range of over 1,000 miles,\" Dr. Byron stated during the press conference. The technology behind the EnerGCell involves a complex arrangement of nanostructured electrodes that allow for rapid ion transfer and extremely high energy density. This breakthrough was achieved after a decade of research into nanomaterials and their applications in energy storage. The implications of this technology are vast, promising to accelerate the adoption of renewable energy by making it more practical and affordable to store wind and solar power. It could also lead to significant advancements in electric vehicles, mobile devices, and any other technology that relies on batteries. Despite the excitement, some experts are calling for patience, noting that the EnerGCell is still in its early stages of development and may take several years before it's commercially available. However, the potential impact of such a technology on the environment and the global economy is undeniable. Tech Innovations Institute plans to continue refining the EnerGCell and begin pilot projects with select partners in the coming year. If successful, this nano-battery technology could indeed be the breakthrough needed to usher in a new era of clean energy and technology.", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "input_value", + "display_name": "Value", + "advanced": false, + "input_types": [ + "Record", + "Text" + ], + "dynamic": false, + "info": "Text or Record to be passed as input.", + "load_from_db": false, + "title_case": false + }, + "record_template": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "record_template", + "display_name": "Record Template", + "advanced": true, + "dynamic": false, + "info": "Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "_type": "CustomComponent" }, - { - "source": "Prompt-amqBu", - "sourceHandle": "{\u0153baseClasses\u0153:[\u0153object\u0153,\u0153str\u0153,\u0153Text\u0153],\u0153dataType\u0153:\u0153Prompt\u0153,\u0153id\u0153:\u0153Prompt-amqBu\u0153}", - "target": "TextOutput-2MS4a", - "targetHandle": "{\u0153fieldName\u0153:\u0153input_value\u0153,\u0153id\u0153:\u0153TextOutput-2MS4a\u0153,\u0153inputTypes\u0153:[\u0153Record\u0153,\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}", - "data": { - "targetHandle": { - "fieldName": "input_value", - "id": "TextOutput-2MS4a", - "inputTypes": [ - "Record", - "Text" - ], - "type": "str" - }, - "sourceHandle": { - "baseClasses": [ - "object", - "str", - "Text" - ], - "dataType": "Prompt", - "id": "Prompt-amqBu" - } - }, - "style": { - "stroke": "#555" - }, - "className": "stroke-gray-900 stroke-connection", - "id": "reactflow__edge-Prompt-amqBu{\u0153baseClasses\u0153:[\u0153object\u0153,\u0153str\u0153,\u0153Text\u0153],\u0153dataType\u0153:\u0153Prompt\u0153,\u0153id\u0153:\u0153Prompt-amqBu\u0153}-TextOutput-2MS4a{\u0153fieldName\u0153:\u0153input_value\u0153,\u0153id\u0153:\u0153TextOutput-2MS4a\u0153,\u0153inputTypes\u0153:[\u0153Record\u0153,\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}" + "description": "Get text inputs from the Playground.", + "icon": "type", + "base_classes": [ + "str", + "Text", + "object" + ], + "display_name": "Text Input", + "documentation": "", + "custom_fields": { + "input_value": null, + "record_template": null }, - { - "source": "Prompt-amqBu", - "sourceHandle": "{\u0153baseClasses\u0153:[\u0153object\u0153,\u0153str\u0153,\u0153Text\u0153],\u0153dataType\u0153:\u0153Prompt\u0153,\u0153id\u0153:\u0153Prompt-amqBu\u0153}", - "target": "OpenAIModel-uYXZJ", - "targetHandle": "{\u0153fieldName\u0153:\u0153input_value\u0153,\u0153id\u0153:\u0153OpenAIModel-uYXZJ\u0153,\u0153inputTypes\u0153:[\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}", - "data": { - "targetHandle": { - "fieldName": "input_value", - "id": "OpenAIModel-uYXZJ", - "inputTypes": [ - "Text" - ], - "type": "str" - }, - "sourceHandle": { - "baseClasses": [ - "object", - "str", - "Text" - ], - "dataType": "Prompt", - "id": "Prompt-amqBu" - } - }, - "style": { - "stroke": "#555" - }, - "className": "stroke-gray-900 stroke-connection", - "id": "reactflow__edge-Prompt-amqBu{\u0153baseClasses\u0153:[\u0153object\u0153,\u0153str\u0153,\u0153Text\u0153],\u0153dataType\u0153:\u0153Prompt\u0153,\u0153id\u0153:\u0153Prompt-amqBu\u0153}-OpenAIModel-uYXZJ{\u0153fieldName\u0153:\u0153input_value\u0153,\u0153id\u0153:\u0153OpenAIModel-uYXZJ\u0153,\u0153inputTypes\u0153:[\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}" + "output_types": [ + "Text" + ], + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false + }, + "id": "TextInput-sptaH" + }, + "selected": false, + "width": 384, + "height": 290, + "positionAbsolute": { + "x": 1700.5624822024752, + "y": 1039.603088937466 + }, + "dragging": false + }, + { + "id": "TextOutput-2MS4a", + "type": "genericNode", + "position": { + "x": 2917.216113690115, + "y": 513.0058511435552 + }, + "data": { + "type": "TextOutput", + "node": { + "template": { + "input_value": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": "", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "input_value", + "display_name": "Value", + "advanced": false, + "input_types": [ + "Record", + "Text" + ], + "dynamic": false, + "info": "Text or Record to be passed as output.", + "load_from_db": false, + "title_case": false + }, + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import Optional\n\nfrom langflow.base.io.text import TextComponent\nfrom langflow.field_typing import Text\n\n\nclass TextOutput(TextComponent):\n display_name = \"Text Output\"\n description = \"Display a text output in the Playground.\"\n icon = \"type\"\n\n def build_config(self):\n return {\n \"input_value\": {\n \"display_name\": \"Value\",\n \"input_types\": [\"Record\", \"Text\"],\n \"info\": \"Text or Record to be passed as output.\",\n },\n \"record_template\": {\n \"display_name\": \"Record Template\",\n \"multiline\": True,\n \"info\": \"Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.\",\n \"advanced\": True,\n },\n }\n\n def build(self, input_value: Optional[Text] = \"\", record_template: str = \"\") -> Text:\n return super().build(input_value=input_value, record_template=record_template)\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "record_template": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "record_template", + "display_name": "Record Template", + "advanced": true, + "dynamic": false, + "info": "Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "_type": "CustomComponent" }, - { - "source": "OpenAIModel-uYXZJ", - "sourceHandle": "{\u0153baseClasses\u0153:[\u0153str\u0153,\u0153Text\u0153,\u0153object\u0153],\u0153dataType\u0153:\u0153OpenAIModel\u0153,\u0153id\u0153:\u0153OpenAIModel-uYXZJ\u0153}", - "target": "Prompt-gTNiz", - "targetHandle": "{\u0153fieldName\u0153:\u0153summary\u0153,\u0153id\u0153:\u0153Prompt-gTNiz\u0153,\u0153inputTypes\u0153:[\u0153Document\u0153,\u0153BaseOutputParser\u0153,\u0153Record\u0153,\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}", - "data": { - "targetHandle": { - "fieldName": "summary", - "id": "Prompt-gTNiz", - "inputTypes": [ - "Document", - "BaseOutputParser", - "Record", - "Text" - ], - "type": "str" - }, - "sourceHandle": { - "baseClasses": [ - "str", - "Text", - "object" - ], - "dataType": "OpenAIModel", - "id": "OpenAIModel-uYXZJ" - } - }, - "style": { - "stroke": "#555" - }, - "className": "stroke-gray-900 stroke-connection", - "id": "reactflow__edge-OpenAIModel-uYXZJ{\u0153baseClasses\u0153:[\u0153str\u0153,\u0153Text\u0153,\u0153object\u0153],\u0153dataType\u0153:\u0153OpenAIModel\u0153,\u0153id\u0153:\u0153OpenAIModel-uYXZJ\u0153}-Prompt-gTNiz{\u0153fieldName\u0153:\u0153summary\u0153,\u0153id\u0153:\u0153Prompt-gTNiz\u0153,\u0153inputTypes\u0153:[\u0153Document\u0153,\u0153BaseOutputParser\u0153,\u0153Record\u0153,\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}" + "description": "Display a text output in the Playground.", + "icon": "type", + "base_classes": [ + "str", + "Text", + "object" + ], + "display_name": "First Prompt", + "documentation": "", + "custom_fields": { + "input_value": null, + "record_template": null }, - { - "source": "OpenAIModel-uYXZJ", - "sourceHandle": "{\u0153baseClasses\u0153:[\u0153str\u0153,\u0153Text\u0153,\u0153object\u0153],\u0153dataType\u0153:\u0153OpenAIModel\u0153,\u0153id\u0153:\u0153OpenAIModel-uYXZJ\u0153}", - "target": "ChatOutput-EJkG3", - "targetHandle": "{\u0153fieldName\u0153:\u0153input_value\u0153,\u0153id\u0153:\u0153ChatOutput-EJkG3\u0153,\u0153inputTypes\u0153:[\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}", - "data": { - "targetHandle": { - "fieldName": "input_value", - "id": "ChatOutput-EJkG3", - "inputTypes": [ - "Text" - ], - "type": "str" - }, - "sourceHandle": { - "baseClasses": [ - "str", - "Text", - "object" - ], - "dataType": "OpenAIModel", - "id": "OpenAIModel-uYXZJ" - } + "output_types": [ + "Text" + ], + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false + }, + "id": "TextOutput-2MS4a" + }, + "selected": false, + "width": 384, + "height": 290, + "positionAbsolute": { + "x": 2917.216113690115, + "y": 513.0058511435552 + }, + "dragging": false + }, + { + "id": "OpenAIModel-uYXZJ", + "type": "genericNode", + "position": { + "x": 2925.784767523062, + "y": 933.6465680967775 + }, + "data": { + "type": "OpenAIModel", + "node": { + "template": { + "input_value": { + "type": "str", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "input_value", + "display_name": "Input", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\"},\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": True,\n \"info\": \"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": MODEL_NAMES,\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"openai_api_key\": {\n \"display_name\": \"OpenAI API Key\",\n \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n \"advanced\": False,\n \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float = 0.1,\n model_name: str = \"gpt-4o\",\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n output = ChatOpenAI(\n max_tokens=max_tokens or None,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "max_tokens": { + "type": "int", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": 256, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "max_tokens", + "display_name": "Max Tokens", + "advanced": true, + "dynamic": false, + "info": "The maximum number of tokens to generate. Set to 0 for unlimited tokens.", + "load_from_db": false, + "title_case": false + }, + "model_kwargs": { + "type": "NestedDict", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": {}, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "model_kwargs", + "display_name": "Model Kwargs", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "model_name": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "value": "gpt-4-turbo-preview", + "fileTypes": [], + "file_path": "", + "password": false, + "options": [ + "gpt-4o", + "gpt-4-turbo", + "gpt-4-turbo-preview", + "gpt-3.5-turbo", + "gpt-3.5-turbo-0125" + ], + "name": "model_name", + "display_name": "Model Name", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "openai_api_base": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "openai_api_base", + "display_name": "OpenAI API Base", + "advanced": true, + "dynamic": false, + "info": "The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\n\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "openai_api_key": { + "type": "str", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": true, + "name": "openai_api_key", + "display_name": "OpenAI API Key", + "advanced": false, + "dynamic": false, + "info": "The OpenAI API Key to use for the OpenAI model.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ], + "value": "OPENAI_API_KEY" + }, + "stream": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "stream", + "display_name": "Stream", + "advanced": true, + "dynamic": false, + "info": "Stream the response from the model. Streaming works only in Chat.", + "load_from_db": false, + "title_case": false + }, + "system_message": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "system_message", + "display_name": "System Message", + "advanced": true, + "dynamic": false, + "info": "System message to pass to the model.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "temperature": { + "type": "float", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": 0.1, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "temperature", + "display_name": "Temperature", + "advanced": false, + "dynamic": false, + "info": "", + "rangeSpec": { + "step_type": "float", + "min": -1, + "max": 1, + "step": 0.1 }, - "style": { - "stroke": "#555" - }, - "className": "stroke-gray-900 stroke-connection", - "id": "reactflow__edge-OpenAIModel-uYXZJ{\u0153baseClasses\u0153:[\u0153str\u0153,\u0153Text\u0153,\u0153object\u0153],\u0153dataType\u0153:\u0153OpenAIModel\u0153,\u0153id\u0153:\u0153OpenAIModel-uYXZJ\u0153}-ChatOutput-EJkG3{\u0153fieldName\u0153:\u0153input_value\u0153,\u0153id\u0153:\u0153ChatOutput-EJkG3\u0153,\u0153inputTypes\u0153:[\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}" + "load_from_db": false, + "title_case": false + }, + "_type": "CustomComponent" }, - { - "source": "Prompt-gTNiz", - "sourceHandle": "{\u0153baseClasses\u0153:[\u0153object\u0153,\u0153str\u0153,\u0153Text\u0153],\u0153dataType\u0153:\u0153Prompt\u0153,\u0153id\u0153:\u0153Prompt-gTNiz\u0153}", - "target": "TextOutput-MUDOR", - "targetHandle": "{\u0153fieldName\u0153:\u0153input_value\u0153,\u0153id\u0153:\u0153TextOutput-MUDOR\u0153,\u0153inputTypes\u0153:[\u0153Record\u0153,\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}", - "data": { - "targetHandle": { - "fieldName": "input_value", - "id": "TextOutput-MUDOR", - "inputTypes": [ - "Record", - "Text" - ], - "type": "str" - }, - "sourceHandle": { - "baseClasses": [ - "object", - "str", - "Text" - ], - "dataType": "Prompt", - "id": "Prompt-gTNiz" - } - }, - "style": { - "stroke": "#555" - }, - "className": "stroke-gray-900 stroke-connection", - "id": "reactflow__edge-Prompt-gTNiz{\u0153baseClasses\u0153:[\u0153object\u0153,\u0153str\u0153,\u0153Text\u0153],\u0153dataType\u0153:\u0153Prompt\u0153,\u0153id\u0153:\u0153Prompt-gTNiz\u0153}-TextOutput-MUDOR{\u0153fieldName\u0153:\u0153input_value\u0153,\u0153id\u0153:\u0153TextOutput-MUDOR\u0153,\u0153inputTypes\u0153:[\u0153Record\u0153,\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}" + "description": "Generates text using OpenAI LLMs.", + "icon": "OpenAI", + "base_classes": [ + "str", + "Text", + "object" + ], + "display_name": "OpenAI", + "documentation": "", + "custom_fields": { + "input_value": null, + "openai_api_key": null, + "temperature": null, + "model_name": null, + "max_tokens": null, + "model_kwargs": null, + "openai_api_base": null, + "stream": null, + "system_message": null }, - { - "source": "Prompt-gTNiz", - "sourceHandle": "{\u0153baseClasses\u0153:[\u0153object\u0153,\u0153str\u0153,\u0153Text\u0153],\u0153dataType\u0153:\u0153Prompt\u0153,\u0153id\u0153:\u0153Prompt-gTNiz\u0153}", - "target": "OpenAIModel-XawYB", - "targetHandle": "{\u0153fieldName\u0153:\u0153input_value\u0153,\u0153id\u0153:\u0153OpenAIModel-XawYB\u0153,\u0153inputTypes\u0153:[\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}", - "data": { - "targetHandle": { - "fieldName": "input_value", - "id": "OpenAIModel-XawYB", - "inputTypes": [ - "Text" - ], - "type": "str" - }, - "sourceHandle": { - "baseClasses": [ - "object", - "str", - "Text" - ], - "dataType": "Prompt", - "id": "Prompt-gTNiz" - } - }, - "style": { - "stroke": "#555" - }, - "className": "stroke-gray-900 stroke-connection", - "id": "reactflow__edge-Prompt-gTNiz{\u0153baseClasses\u0153:[\u0153object\u0153,\u0153str\u0153,\u0153Text\u0153],\u0153dataType\u0153:\u0153Prompt\u0153,\u0153id\u0153:\u0153Prompt-gTNiz\u0153}-OpenAIModel-XawYB{\u0153fieldName\u0153:\u0153input_value\u0153,\u0153id\u0153:\u0153OpenAIModel-XawYB\u0153,\u0153inputTypes\u0153:[\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}" + "output_types": [ + "Text" + ], + "field_formatters": {}, + "frozen": false, + "field_order": [ + "max_tokens", + "model_kwargs", + "model_name", + "openai_api_base", + "openai_api_key", + "temperature", + "input_value", + "system_message", + "stream" + ], + "beta": false + }, + "id": "OpenAIModel-uYXZJ" + }, + "selected": false, + "width": 384, + "height": 565, + "positionAbsolute": { + "x": 2925.784767523062, + "y": 933.6465680967775 + }, + "dragging": false + }, + { + "id": "TextOutput-MUDOR", + "type": "genericNode", + "position": { + "x": 4446.064323520379, + "y": 633.833297518702 + }, + "data": { + "type": "TextOutput", + "node": { + "template": { + "input_value": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": "", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "input_value", + "display_name": "Value", + "advanced": false, + "input_types": [ + "Record", + "Text" + ], + "dynamic": false, + "info": "Text or Record to be passed as output.", + "load_from_db": false, + "title_case": false + }, + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import Optional\n\nfrom langflow.base.io.text import TextComponent\nfrom langflow.field_typing import Text\n\n\nclass TextOutput(TextComponent):\n display_name = \"Text Output\"\n description = \"Display a text output in the Playground.\"\n icon = \"type\"\n\n def build_config(self):\n return {\n \"input_value\": {\n \"display_name\": \"Value\",\n \"input_types\": [\"Record\", \"Text\"],\n \"info\": \"Text or Record to be passed as output.\",\n },\n \"record_template\": {\n \"display_name\": \"Record Template\",\n \"multiline\": True,\n \"info\": \"Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.\",\n \"advanced\": True,\n },\n }\n\n def build(self, input_value: Optional[Text] = \"\", record_template: str = \"\") -> Text:\n return super().build(input_value=input_value, record_template=record_template)\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "record_template": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "record_template", + "display_name": "Record Template", + "advanced": true, + "dynamic": false, + "info": "Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "_type": "CustomComponent" }, - { - "source": "OpenAIModel-XawYB", - "sourceHandle": "{\u0153baseClasses\u0153:[\u0153str\u0153,\u0153Text\u0153,\u0153object\u0153],\u0153dataType\u0153:\u0153OpenAIModel\u0153,\u0153id\u0153:\u0153OpenAIModel-XawYB\u0153}", - "target": "ChatOutput-DNmvg", - "targetHandle": "{\u0153fieldName\u0153:\u0153input_value\u0153,\u0153id\u0153:\u0153ChatOutput-DNmvg\u0153,\u0153inputTypes\u0153:[\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}", - "data": { - "targetHandle": { - "fieldName": "input_value", - "id": "ChatOutput-DNmvg", - "inputTypes": [ - "Text" - ], - "type": "str" - }, - "sourceHandle": { - "baseClasses": [ - "str", - "Text", - "object" - ], - "dataType": "OpenAIModel", - "id": "OpenAIModel-XawYB" - } - }, - "style": { - "stroke": "#555" - }, - "className": "stroke-gray-900 stroke-connection", - "id": "reactflow__edge-OpenAIModel-XawYB{\u0153baseClasses\u0153:[\u0153str\u0153,\u0153Text\u0153,\u0153object\u0153],\u0153dataType\u0153:\u0153OpenAIModel\u0153,\u0153id\u0153:\u0153OpenAIModel-XawYB\u0153}-ChatOutput-DNmvg{\u0153fieldName\u0153:\u0153input_value\u0153,\u0153id\u0153:\u0153ChatOutput-DNmvg\u0153,\u0153inputTypes\u0153:[\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}" - } - ], - "viewport": { - "x": -383.7251879618552, - "y": 69.19813933800037, - "zoom": 0.3105753483695743 + "description": "Display a text output in the Playground.", + "icon": "type", + "base_classes": [ + "str", + "Text", + "object" + ], + "display_name": "Second Prompt", + "documentation": "", + "custom_fields": { + "input_value": null, + "record_template": null + }, + "output_types": [ + "Text" + ], + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false + }, + "id": "TextOutput-MUDOR" + }, + "selected": false, + "width": 384, + "height": 290, + "dragging": false, + "positionAbsolute": { + "x": 4446.064323520379, + "y": 633.833297518702 } - }, - "description": "The Prompt Chaining flow chains prompts with LLMs, refining outputs through iterative stages.", - "name": "Prompt Chaining", - "last_tested_version": "1.0.0a0", - "is_component": false -} + }, + { + "id": "OpenAIModel-XawYB", + "type": "genericNode", + "position": { + "x": 4500.152018344182, + "y": 1027.7382026227656 + }, + "data": { + "type": "OpenAIModel", + "node": { + "template": { + "input_value": { + "type": "str", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "input_value", + "display_name": "Input", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\"},\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": True,\n \"info\": \"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": MODEL_NAMES,\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"openai_api_key\": {\n \"display_name\": \"OpenAI API Key\",\n \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n \"advanced\": False,\n \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float = 0.1,\n model_name: str = \"gpt-4o\",\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n output = ChatOpenAI(\n max_tokens=max_tokens or None,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "max_tokens": { + "type": "int", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": 256, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "max_tokens", + "display_name": "Max Tokens", + "advanced": true, + "dynamic": false, + "info": "The maximum number of tokens to generate. Set to 0 for unlimited tokens.", + "load_from_db": false, + "title_case": false + }, + "model_kwargs": { + "type": "NestedDict", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": {}, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "model_kwargs", + "display_name": "Model Kwargs", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "model_name": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "value": "gpt-4-turbo-preview", + "fileTypes": [], + "file_path": "", + "password": false, + "options": [ + "gpt-4o", + "gpt-4-turbo", + "gpt-4-turbo-preview", + "gpt-3.5-turbo", + "gpt-3.5-turbo-0125" + ], + "name": "model_name", + "display_name": "Model Name", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "openai_api_base": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "openai_api_base", + "display_name": "OpenAI API Base", + "advanced": true, + "dynamic": false, + "info": "The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\n\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "openai_api_key": { + "type": "str", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": true, + "name": "openai_api_key", + "display_name": "OpenAI API Key", + "advanced": false, + "dynamic": false, + "info": "The OpenAI API Key to use for the OpenAI model.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ], + "value": "" + }, + "stream": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "stream", + "display_name": "Stream", + "advanced": true, + "dynamic": false, + "info": "Stream the response from the model. Streaming works only in Chat.", + "load_from_db": false, + "title_case": false + }, + "system_message": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "system_message", + "display_name": "System Message", + "advanced": true, + "dynamic": false, + "info": "System message to pass to the model.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "temperature": { + "type": "float", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": 0.1, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "temperature", + "display_name": "Temperature", + "advanced": false, + "dynamic": false, + "info": "", + "rangeSpec": { + "step_type": "float", + "min": -1, + "max": 1, + "step": 0.1 + }, + "load_from_db": false, + "title_case": false + }, + "_type": "CustomComponent" + }, + "description": "Generates text using OpenAI LLMs.", + "icon": "OpenAI", + "base_classes": [ + "str", + "Text", + "object" + ], + "display_name": "OpenAI", + "documentation": "", + "custom_fields": { + "input_value": null, + "openai_api_key": null, + "temperature": null, + "model_name": null, + "max_tokens": null, + "model_kwargs": null, + "openai_api_base": null, + "stream": null, + "system_message": null + }, + "output_types": [ + "Text" + ], + "field_formatters": {}, + "frozen": false, + "field_order": [ + "max_tokens", + "model_kwargs", + "model_name", + "openai_api_base", + "openai_api_key", + "temperature", + "input_value", + "system_message", + "stream" + ], + "beta": false + }, + "id": "OpenAIModel-XawYB" + }, + "selected": false, + "width": 384, + "height": 565, + "positionAbsolute": { + "x": 4500.152018344182, + "y": 1027.7382026227656 + }, + "dragging": false + } + ], + "edges": [ + { + "source": "TextInput-sptaH", + "sourceHandle": "{œbaseClassesœ:[œstrœ,œTextœ,œobjectœ],œdataTypeœ:œTextInputœ,œidœ:œTextInput-sptaHœ}", + "target": "Prompt-amqBu", + "targetHandle": "{œfieldNameœ:œdocumentœ,œidœ:œPrompt-amqBuœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}", + "data": { + "targetHandle": { + "fieldName": "document", + "id": "Prompt-amqBu", + "inputTypes": [ + "Document", + "BaseOutputParser", + "Record", + "Text" + ], + "type": "str" + }, + "sourceHandle": { + "baseClasses": [ + "str", + "Text", + "object" + ], + "dataType": "TextInput", + "id": "TextInput-sptaH" + } + }, + "style": { + "stroke": "#555" + }, + "className": "stroke-gray-900 stroke-connection", + "id": "reactflow__edge-TextInput-sptaH{œbaseClassesœ:[œstrœ,œTextœ,œobjectœ],œdataTypeœ:œTextInputœ,œidœ:œTextInput-sptaHœ}-Prompt-amqBu{œfieldNameœ:œdocumentœ,œidœ:œPrompt-amqBuœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}" + }, + { + "source": "Prompt-amqBu", + "sourceHandle": "{œbaseClassesœ:[œobjectœ,œstrœ,œTextœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-amqBuœ}", + "target": "TextOutput-2MS4a", + "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œTextOutput-2MS4aœ,œinputTypesœ:[œRecordœ,œTextœ],œtypeœ:œstrœ}", + "data": { + "targetHandle": { + "fieldName": "input_value", + "id": "TextOutput-2MS4a", + "inputTypes": [ + "Record", + "Text" + ], + "type": "str" + }, + "sourceHandle": { + "baseClasses": [ + "object", + "str", + "Text" + ], + "dataType": "Prompt", + "id": "Prompt-amqBu" + } + }, + "style": { + "stroke": "#555" + }, + "className": "stroke-gray-900 stroke-connection", + "id": "reactflow__edge-Prompt-amqBu{œbaseClassesœ:[œobjectœ,œstrœ,œTextœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-amqBuœ}-TextOutput-2MS4a{œfieldNameœ:œinput_valueœ,œidœ:œTextOutput-2MS4aœ,œinputTypesœ:[œRecordœ,œTextœ],œtypeœ:œstrœ}" + }, + { + "source": "Prompt-amqBu", + "sourceHandle": "{œbaseClassesœ:[œobjectœ,œstrœ,œTextœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-amqBuœ}", + "target": "OpenAIModel-uYXZJ", + "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-uYXZJœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}", + "data": { + "targetHandle": { + "fieldName": "input_value", + "id": "OpenAIModel-uYXZJ", + "inputTypes": [ + "Text" + ], + "type": "str" + }, + "sourceHandle": { + "baseClasses": [ + "object", + "str", + "Text" + ], + "dataType": "Prompt", + "id": "Prompt-amqBu" + } + }, + "style": { + "stroke": "#555" + }, + "className": "stroke-gray-900 stroke-connection", + "id": "reactflow__edge-Prompt-amqBu{œbaseClassesœ:[œobjectœ,œstrœ,œTextœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-amqBuœ}-OpenAIModel-uYXZJ{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-uYXZJœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}" + }, + { + "source": "OpenAIModel-uYXZJ", + "sourceHandle": "{œbaseClassesœ:[œstrœ,œTextœ,œobjectœ],œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-uYXZJœ}", + "target": "Prompt-gTNiz", + "targetHandle": "{œfieldNameœ:œsummaryœ,œidœ:œPrompt-gTNizœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}", + "data": { + "targetHandle": { + "fieldName": "summary", + "id": "Prompt-gTNiz", + "inputTypes": [ + "Document", + "BaseOutputParser", + "Record", + "Text" + ], + "type": "str" + }, + "sourceHandle": { + "baseClasses": [ + "str", + "Text", + "object" + ], + "dataType": "OpenAIModel", + "id": "OpenAIModel-uYXZJ" + } + }, + "style": { + "stroke": "#555" + }, + "className": "stroke-gray-900 stroke-connection", + "id": "reactflow__edge-OpenAIModel-uYXZJ{œbaseClassesœ:[œstrœ,œTextœ,œobjectœ],œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-uYXZJœ}-Prompt-gTNiz{œfieldNameœ:œsummaryœ,œidœ:œPrompt-gTNizœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}" + }, + { + "source": "OpenAIModel-uYXZJ", + "sourceHandle": "{œbaseClassesœ:[œstrœ,œTextœ,œobjectœ],œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-uYXZJœ}", + "target": "ChatOutput-EJkG3", + "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-EJkG3œ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}", + "data": { + "targetHandle": { + "fieldName": "input_value", + "id": "ChatOutput-EJkG3", + "inputTypes": [ + "Text" + ], + "type": "str" + }, + "sourceHandle": { + "baseClasses": [ + "str", + "Text", + "object" + ], + "dataType": "OpenAIModel", + "id": "OpenAIModel-uYXZJ" + } + }, + "style": { + "stroke": "#555" + }, + "className": "stroke-gray-900 stroke-connection", + "id": "reactflow__edge-OpenAIModel-uYXZJ{œbaseClassesœ:[œstrœ,œTextœ,œobjectœ],œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-uYXZJœ}-ChatOutput-EJkG3{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-EJkG3œ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}" + }, + { + "source": "Prompt-gTNiz", + "sourceHandle": "{œbaseClassesœ:[œobjectœ,œstrœ,œTextœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-gTNizœ}", + "target": "TextOutput-MUDOR", + "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œTextOutput-MUDORœ,œinputTypesœ:[œRecordœ,œTextœ],œtypeœ:œstrœ}", + "data": { + "targetHandle": { + "fieldName": "input_value", + "id": "TextOutput-MUDOR", + "inputTypes": [ + "Record", + "Text" + ], + "type": "str" + }, + "sourceHandle": { + "baseClasses": [ + "object", + "str", + "Text" + ], + "dataType": "Prompt", + "id": "Prompt-gTNiz" + } + }, + "style": { + "stroke": "#555" + }, + "className": "stroke-gray-900 stroke-connection", + "id": "reactflow__edge-Prompt-gTNiz{œbaseClassesœ:[œobjectœ,œstrœ,œTextœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-gTNizœ}-TextOutput-MUDOR{œfieldNameœ:œinput_valueœ,œidœ:œTextOutput-MUDORœ,œinputTypesœ:[œRecordœ,œTextœ],œtypeœ:œstrœ}" + }, + { + "source": "Prompt-gTNiz", + "sourceHandle": "{œbaseClassesœ:[œobjectœ,œstrœ,œTextœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-gTNizœ}", + "target": "OpenAIModel-XawYB", + "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-XawYBœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}", + "data": { + "targetHandle": { + "fieldName": "input_value", + "id": "OpenAIModel-XawYB", + "inputTypes": [ + "Text" + ], + "type": "str" + }, + "sourceHandle": { + "baseClasses": [ + "object", + "str", + "Text" + ], + "dataType": "Prompt", + "id": "Prompt-gTNiz" + } + }, + "style": { + "stroke": "#555" + }, + "className": "stroke-gray-900 stroke-connection", + "id": "reactflow__edge-Prompt-gTNiz{œbaseClassesœ:[œobjectœ,œstrœ,œTextœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-gTNizœ}-OpenAIModel-XawYB{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-XawYBœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}" + }, + { + "source": "OpenAIModel-XawYB", + "sourceHandle": "{œbaseClassesœ:[œstrœ,œTextœ,œobjectœ],œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-XawYBœ}", + "target": "ChatOutput-DNmvg", + "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-DNmvgœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}", + "data": { + "targetHandle": { + "fieldName": "input_value", + "id": "ChatOutput-DNmvg", + "inputTypes": [ + "Text" + ], + "type": "str" + }, + "sourceHandle": { + "baseClasses": [ + "str", + "Text", + "object" + ], + "dataType": "OpenAIModel", + "id": "OpenAIModel-XawYB" + } + }, + "style": { + "stroke": "#555" + }, + "className": "stroke-gray-900 stroke-connection", + "id": "reactflow__edge-OpenAIModel-XawYB{œbaseClassesœ:[œstrœ,œTextœ,œobjectœ],œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-XawYBœ}-ChatOutput-DNmvg{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-DNmvgœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}" + } + ], + "viewport": { + "x": -383.7251879618552, + "y": 69.19813933800037, + "zoom": 0.3105753483695743 + } + }, + "description": "The Prompt Chaining flow chains prompts with LLMs, refining outputs through iterative stages.", + "name": "Prompt Chaining", + "last_tested_version": "1.0.0a0", + "is_component": false +} \ No newline at end of file diff --git a/src/backend/base/langflow/initial_setup/starter_projects/VectorStore-RAG-Flows.json b/src/backend/base/langflow/initial_setup/starter_projects/VectorStore-RAG-Flows.json index 60b357851..ef20c1696 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/VectorStore-RAG-Flows.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/VectorStore-RAG-Flows.json @@ -851,7 +851,7 @@ "list": false, "show": true, "multiline": true, - "value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\"},\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": True,\n \"info\": \"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": MODEL_NAMES,\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"openai_api_key\": {\n \"display_name\": \"OpenAI API Key\",\n \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n \"advanced\": False,\n \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float,\n model_name: str = \"gpt-4o\",\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n output = ChatOpenAI(\n max_tokens=max_tokens or None,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n", + "value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\"},\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": True,\n \"info\": \"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": MODEL_NAMES,\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"openai_api_key\": {\n \"display_name\": \"OpenAI API Key\",\n \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n \"advanced\": False,\n \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float = 0.1,\n model_name: str = \"gpt-4o\",\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n output = ChatOpenAI(\n max_tokens=max_tokens or None,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n", "fileTypes": [], "file_path": "", "password": false, @@ -1014,7 +1014,7 @@ }, "temperature": { "type": "float", - "required": true, + "required": false, "placeholder": "", "list": false, "show": true, From a91e97ca30a9161ba1e9db8660329aa165c6f1ae Mon Sep 17 00:00:00 2001 From: ogabrielluiz Date: Mon, 3 Jun 2024 18:26:01 -0300 Subject: [PATCH 045/701] refactor: Update ChatInput record_response method to use "text" key instead of "message" --- src/backend/base/langflow/api/v1/login.py | 4 +- .../langflow/components/inputs/TextInput.py | 2 +- .../langflow/components/outputs/TextOutput.py | 2 +- .../custom_component/custom_component.py | 2 - .../directory_reader/directory_reader.py | 2 - src/backend/base/langflow/graph/edge/base.py | 54 ++++++- src/backend/base/langflow/graph/graph/base.py | 4 +- .../base/langflow/graph/vertex/base.py | 12 +- .../base/langflow/graph/vertex/types.py | 3 +- .../base/langflow/initial_setup/setup.py | 140 +++++++++++++++--- .../langflow/interface/initialize/loading.py | 1 - .../base/langflow/services/auth/utils.py | 5 +- .../langflow/services/settings/service.py | 2 +- .../langflow/template/frontend_node/base.py | 3 +- src/backend/base/langflow/utils/validate.py | 4 +- tests/conftest.py | 5 +- tests/test_database.py | 5 - 17 files changed, 189 insertions(+), 61 deletions(-) diff --git a/src/backend/base/langflow/api/v1/login.py b/src/backend/base/langflow/api/v1/login.py index cde6bd28b..2637cc865 100644 --- a/src/backend/base/langflow/api/v1/login.py +++ b/src/backend/base/langflow/api/v1/login.py @@ -71,9 +71,7 @@ async def login_to_get_access_token( @router.get("/auto_login") async def auto_login( - response: Response, - db: Session = Depends(get_session), - settings_service=Depends(get_settings_service) + response: Response, db: Session = Depends(get_session), settings_service=Depends(get_settings_service) ): auth_settings = settings_service.auth_settings if settings_service.auth_settings.AUTO_LOGIN: diff --git a/src/backend/base/langflow/components/inputs/TextInput.py b/src/backend/base/langflow/components/inputs/TextInput.py index 2e3a65ee1..596edf0ec 100644 --- a/src/backend/base/langflow/components/inputs/TextInput.py +++ b/src/backend/base/langflow/components/inputs/TextInput.py @@ -30,4 +30,4 @@ class TextInput(TextComponent): ] def text_response(self) -> Text: - return self.input_value if self.input_value else "" + return self.build(input_value=self.input_value, record_template=self.record_template) diff --git a/src/backend/base/langflow/components/outputs/TextOutput.py b/src/backend/base/langflow/components/outputs/TextOutput.py index 2459192be..bc71b3a27 100644 --- a/src/backend/base/langflow/components/outputs/TextOutput.py +++ b/src/backend/base/langflow/components/outputs/TextOutput.py @@ -30,4 +30,4 @@ class TextOutput(TextComponent): ] def text_response(self) -> Text: - return self.input_value if self.input_value else "" + return self.build(input_value=self.input_value, record_template=self.record_template) diff --git a/src/backend/base/langflow/custom/custom_component/custom_component.py b/src/backend/base/langflow/custom/custom_component/custom_component.py index d51b4d5d8..e398e6850 100644 --- a/src/backend/base/langflow/custom/custom_component/custom_component.py +++ b/src/backend/base/langflow/custom/custom_component/custom_component.py @@ -65,8 +65,6 @@ class CustomComponent(BaseComponent): """The default frozen state of the component. Defaults to False.""" build_parameters: Optional[dict] = None """The build parameters of the component. Defaults to None.""" - selected_output_type: Optional[str] = None - """The selected output type of the component. Defaults to None.""" vertex: Optional["Vertex"] = None """The edge target parameter of the component. Defaults to None.""" code_class_base_inheritance: ClassVar[str] = "CustomComponent" diff --git a/src/backend/base/langflow/custom/directory_reader/directory_reader.py b/src/backend/base/langflow/custom/directory_reader/directory_reader.py index dc9dfa51c..679ecdf94 100644 --- a/src/backend/base/langflow/custom/directory_reader/directory_reader.py +++ b/src/backend/base/langflow/custom/directory_reader/directory_reader.py @@ -220,8 +220,6 @@ class DirectoryReader: return False, "Empty file" elif not self.validate_code(file_content): return False, "Syntax error" - elif not self.validate_build(file_content): - return False, "Missing build function" elif self._is_type_hint_used_in_args("Optional", file_content) and not self._is_type_hint_imported( "Optional", file_content ): diff --git a/src/backend/base/langflow/graph/edge/base.py b/src/backend/base/langflow/graph/edge/base.py index ac026449d..f4889bfcc 100644 --- a/src/backend/base/langflow/graph/edge/base.py +++ b/src/backend/base/langflow/graph/edge/base.py @@ -14,8 +14,10 @@ class SourceHandle(BaseModel): baseClasses: Optional[List[str]] = Field(None, description="List of base classes for the source handle.") dataType: str = Field(..., description="Data type for the source handle.") id: str = Field(..., description="Unique identifier for the source handle.") - name: str = Field(..., description="Name of the source handle.") - output_types: List[str] = Field(..., description="List of output types for the source handle.") + name: Optional[str] = Field(None, description="Name of the source handle.") + output_types: Optional[List[str]] = Field( + default_factory=list, description="List of output types for the source handle." + ) class TargetHandle(BaseModel): @@ -49,6 +51,12 @@ class Edge: self.validate_edge(source, target) def validate_handles(self, source, target) -> None: + if isinstance(self._source_handle, str) or self.source_handle.baseClasses: + self._legacy_validate_handles(source, target) + else: + self._validate_handles(source, target) + + def _validate_handles(self, source, target) -> None: if self.target_handle.inputTypes is None: self.valid_handles = self.target_handle.type in self.source_handle.output_types else: @@ -61,6 +69,19 @@ class Edge: logger.debug(self.target_handle) raise ValueError(f"Edge between {source.vertex_type} and {target.vertex_type} " f"has invalid handles") + def _legacy_validate_handles(self, source, target) -> None: + if self.target_handle.inputTypes is None: + self.valid_handles = self.target_handle.type in self.source_handle.baseClasses + else: + self.valid_handles = ( + any(baseClass in self.target_handle.inputTypes for baseClass in self.source_handle.baseClasses) + or self.target_handle.type in self.source_handle.baseClasses + ) + if not self.valid_handles: + logger.debug(self.source_handle) + logger.debug(self.target_handle) + raise ValueError(f"Edge between {source.vertex_type} and {target.vertex_type} " f"has invalid handles") + def __setstate__(self, state): self.source_id = state["source_id"] self.target_id = state["target_id"] @@ -69,6 +90,14 @@ class Edge: self.target_handle = state.get("target_handle") def validate_edge(self, source, target) -> None: + # If the self.source_handle has baseClasses, then we are using the legacy + # way of defining the source and target handles + if isinstance(self._source_handle, str) or self.source_handle.baseClasses: + self._legacy_validate_edge(source, target) + else: + self._validate_edge(source, target) + + def _validate_edge(self, source, target) -> None: # Validate that the outputs of the source node are valid inputs # for the target node # .outputs is a list of Output objects as dictionaries @@ -97,6 +126,27 @@ class Edge: None, ) no_matched_type = self.matched_type is None + if no_matched_type: + logger.debug(self.source_types) + logger.debug(self.target_reqs) + raise ValueError(f"Edge between {source.vertex_type} and {target.vertex_type} " f"has no matched type. ") + + def _legacy_validate_edge(self, source, target) -> None: + # Validate that the outputs of the source node are valid inputs + # for the target node + self.source_types = source.output + self.target_reqs = target.required_inputs + target.optional_inputs + # Both lists contain strings and sometimes a string contains the value we are + # looking for e.g. comgin_out=["Chain"] and target_reqs=["LLMChain"] + # so we need to check if any of the strings in source_types is in target_reqs + self.valid = any(output in target_req for output in self.source_types for target_req in self.target_reqs) + # Get what type of input the target node is expecting + + self.matched_type = next( + (output for output in self.source_types if output in self.target_reqs), + None, + ) + no_matched_type = self.matched_type is None if no_matched_type: logger.debug(self.source_types) logger.debug(self.target_reqs) diff --git a/src/backend/base/langflow/graph/graph/base.py b/src/backend/base/langflow/graph/graph/base.py index f4e71bd7c..7f43a1a66 100644 --- a/src/backend/base/langflow/graph/graph/base.py +++ b/src/backend/base/langflow/graph/graph/base.py @@ -20,7 +20,6 @@ from langflow.schema.schema import INPUT_FIELD_NAME, InputType from langflow.services.cache.utils import CacheMiss from langflow.services.chat.service import ChatService from langflow.services.deps import get_chat_service -from langflow.services.monitor.utils import log_transaction if TYPE_CHECKING: from langflow.graph.schema import ResultData @@ -526,6 +525,7 @@ class Graph: raise ValueError( f"Invalid payload. Expected keys 'nodes' and 'edges'. Found {list(payload.keys())}" ) from exc + raise ValueError(f"Error while creating graph from payload: {exc}") from exc def __eq__(self, other: object) -> bool: @@ -764,11 +764,9 @@ class Graph: next_runnable_vertices, top_level_vertices = await self.get_next_and_top_level_vertices( lock, set_cache_coro, vertex ) - log_transaction(vertex, status="success") return next_runnable_vertices, top_level_vertices, result_dict, params, valid, artifacts, vertex except Exception as exc: logger.exception(f"Error building vertex: {exc}") - log_transaction(vertex, status="failure", error=str(exc)) raise exc async def get_next_and_top_level_vertices( diff --git a/src/backend/base/langflow/graph/vertex/base.py b/src/backend/base/langflow/graph/vertex/base.py index 8863bd5aa..bde56383f 100644 --- a/src/backend/base/langflow/graph/vertex/base.py +++ b/src/backend/base/langflow/graph/vertex/base.py @@ -212,14 +212,18 @@ class Vertex: def _parse_data(self) -> None: self.data = self._data["data"] - self.outputs = self.data["node"]["outputs"] + if self.data["node"]["template"]["_type"] == "Component": + if "outputs" not in self.data["node"]: + raise ValueError(f"Outputs not found for {self.display_name}") + self.outputs = self.data["node"]["outputs"] + else: + self.outputs = self.data["node"]["outputs"] + self.output = self.data["node"]["base_classes"] self.display_name = self.data["node"].get("display_name", self.id.split("-")[0]) self.description = self.data["node"].get("description", "") self.frozen = self.data["node"].get("frozen", False) - self.selected_output_type = ( - str(self.data.get("selected_output_type")).strip() if self.data.get("selected_output_type") else None - ) + self.is_input = self.data["node"].get("is_input") or self.is_input self.is_output = self.data["node"].get("is_output") or self.is_output template_dicts = {key: value for key, value in self.data["node"]["template"].items() if isinstance(value, dict)} diff --git a/src/backend/base/langflow/graph/vertex/types.py b/src/backend/base/langflow/graph/vertex/types.py index e1180fef9..5320d5e77 100644 --- a/src/backend/base/langflow/graph/vertex/types.py +++ b/src/backend/base/langflow/graph/vertex/types.py @@ -86,7 +86,8 @@ class ComponentVertex(Vertex): edge = self.get_edge_with_target(requester.id) if edge is None: raise ValueError(f"Edge not found between {self.display_name} and {requester.display_name}") - + if edge.source_handle.name not in self.results: + raise ValueError(f"Result not found for {edge.source_handle.name}. Results: {self.results}") result = self.results[edge.source_handle.name] log_transaction(source=self, target=requester, flow_id=self.graph.flow_id, status="success") diff --git a/src/backend/base/langflow/initial_setup/setup.py b/src/backend/base/langflow/initial_setup/setup.py index 62997fbb1..6194c3973 100644 --- a/src/backend/base/langflow/initial_setup/setup.py +++ b/src/backend/base/langflow/initial_setup/setup.py @@ -1,3 +1,5 @@ +import copy +import json import logging import os from collections import defaultdict @@ -42,39 +44,129 @@ def update_projects_components_with_latest_component_versions(project_data, all_ latest_template = latest_node.get("template") node_data["template"]["code"] = latest_template["code"] - for attr in NODE_FORMAT_ATTRIBUTES: - if attr in latest_node: - # Check if it needs to be updated - if latest_node[attr] != node_data.get(attr): - node_changes_log[node_data["display_name"]].append( - { - "attr": attr, - "old_value": node_data.get(attr), - "new_value": latest_node[attr], - } - ) - node_data[attr] = latest_node[attr] - - for field_name, field_dict in latest_template.items(): - if field_name not in node_data["template"]: - continue - # The idea here is to update some attributes of the field - for attr in FIELD_FORMAT_ATTRIBUTES: - if attr in field_dict and attr in node_data["template"].get(field_name): + if "outputs" in latest_node: + node_data["outputs"] = latest_node["outputs"] + if node_data["template"]["_type"] != latest_template["_type"]: + node_data["template"] = latest_template + else: + for attr in NODE_FORMAT_ATTRIBUTES: + if attr in latest_node: # Check if it needs to be updated - if field_dict[attr] != node_data["template"][field_name][attr]: + if latest_node[attr] != node_data.get(attr): node_changes_log[node_data["display_name"]].append( { - "attr": f"{field_name}.{attr}", - "old_value": node_data["template"][field_name][attr], - "new_value": field_dict[attr], + "attr": attr, + "old_value": node_data.get(attr), + "new_value": latest_node[attr], } ) - node_data["template"][field_name][attr] = field_dict[attr] + node_data[attr] = latest_node[attr] + + for field_name, field_dict in latest_template.items(): + if field_name not in node_data["template"]: + continue + # The idea here is to update some attributes of the field + for attr in FIELD_FORMAT_ATTRIBUTES: + if attr in field_dict and attr in node_data["template"].get(field_name): + # Check if it needs to be updated + if field_dict[attr] != node_data["template"][field_name][attr]: + node_changes_log[node_data["display_name"]].append( + { + "attr": f"{field_name}.{attr}", + "old_value": node_data["template"][field_name][attr], + "new_value": field_dict[attr], + } + ) + node_data["template"][field_name][attr] = field_dict[attr] + project_data_copy = update_new_output(project_data_copy) log_node_changes(node_changes_log) return project_data_copy +def scape_json_parse(json_string: str) -> dict: + parsed_string = json_string.replace("œ", '"') + return json.loads(parsed_string) + + +def update_new_output(data): + nodes = copy.deepcopy(data["nodes"]) + edges = copy.deepcopy(data["edges"]) + + for edge in edges: + if "sourceHandle" in edge and "targetHandle" in edge: + new_source_handle = scape_json_parse(edge["sourceHandle"]) + new_target_handle = scape_json_parse(edge["targetHandle"]) + _id = new_source_handle["id"] + source_node_index = next((index for (index, d) in enumerate(nodes) if d["id"] == _id), -1) + source_node = nodes[source_node_index] if source_node_index != -1 else None + + if "baseClasses" in new_source_handle: + if "output_types" not in new_source_handle: + if source_node and "node" in source_node["data"] and "output_types" in source_node["data"]["node"]: + new_source_handle["output_types"] = source_node["data"]["node"]["output_types"] + else: + new_source_handle["output_types"] = new_source_handle["baseClasses"] + del new_source_handle["baseClasses"] + + if "inputTypes" in new_target_handle and new_target_handle["inputTypes"]: + intersection = [ + type_ for type_ in new_source_handle["output_types"] if type_ in new_target_handle["inputTypes"] + ] + else: + intersection = [ + type_ for type_ in new_source_handle["output_types"] if type_ == new_target_handle["type"] + ] + + selected = intersection[0] if intersection else None + if "name" not in new_source_handle: + new_source_handle["name"] = " | ".join(new_source_handle["output_types"]) + new_source_handle["output_types"] = [selected] if selected else [] + + if source_node and not source_node["data"]["node"].get("outputs"): + if "outputs" not in source_node["data"]["node"]: + source_node["data"]["node"]["outputs"] = [] + types = source_node["data"]["node"].get( + "output_types", source_node["data"]["node"].get("base_classes", []) + ) + if not any(output.get("selected") == selected for output in source_node["data"]["node"]["outputs"]): + source_node["data"]["node"]["outputs"].append( + { + "types": types, + "selected": selected, + "name": " | ".join(types), + } + ) + deduplicated_outputs = [] + for output in source_node["data"]["node"]["outputs"]: + if output["name"] not in [d["name"] for d in deduplicated_outputs]: + deduplicated_outputs.append(output) + source_node["data"]["node"]["outputs"] = deduplicated_outputs + + edge["sourceHandle"] = json.dumps(new_source_handle) + edge["data"]["sourceHandle"] = new_source_handle + edge["data"]["targetHandle"] = new_target_handle + # The above sets the edges but some of the sourceHandles do not have valid name + # which can be found in the nodes. We need to update the sourceHandle with the + # name from node['data']['node']['outputs'] + for node in nodes: + if "outputs" in node["data"]["node"]: + for output in node["data"]["node"]["outputs"]: + for edge in edges: + if node["id"] != edge["source"] or output.get("method") is None: + continue + source_handle = scape_json_parse(edge["sourceHandle"]) + if source_handle["output_types"] == output.get("types") and source_handle["name"] != output["name"]: + source_handle["name"] = output["name"] + + edge["sourceHandle"] = json.dumps(source_handle) + edge["data"]["sourceHandle"] = source_handle + + data_copy = copy.deepcopy(data) + data_copy["nodes"] = nodes + data_copy["edges"] = edges + return data_copy + + def log_node_changes(node_changes_log): # The idea here is to log the changes that were made to the nodes in debug # Something like: diff --git a/src/backend/base/langflow/interface/initialize/loading.py b/src/backend/base/langflow/interface/initialize/loading.py index c5cec3b55..ad59be14e 100644 --- a/src/backend/base/langflow/interface/initialize/loading.py +++ b/src/backend/base/langflow/interface/initialize/loading.py @@ -40,7 +40,6 @@ async def instantiate_class( user_id=user_id, parameters=params_copy, vertex=vertex, - selected_output_type=vertex.selected_output_type, ) params_copy = update_params_with_load_from_db_fields( custom_component, params_copy, vertex.load_from_db_fields, fallback_to_env_vars diff --git a/src/backend/base/langflow/services/auth/utils.py b/src/backend/base/langflow/services/auth/utils.py index 0e0aead88..f62d3ce4f 100644 --- a/src/backend/base/langflow/services/auth/utils.py +++ b/src/backend/base/langflow/services/auth/utils.py @@ -215,10 +215,7 @@ def create_user_longterm_token(db: Session = Depends(get_session)) -> tuple[UUID username = settings_service.auth_settings.SUPERUSER super_user = get_user_by_username(db, username) if not super_user: - raise HTTPException( - status_code=status.HTTP_400_BAD_REQUEST, - detail="Super user hasn't been created" - ) + raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Super user hasn't been created") access_token_expires_longterm = timedelta(days=365) access_token = create_token( data={"sub": str(super_user.id)}, diff --git a/src/backend/base/langflow/services/settings/service.py b/src/backend/base/langflow/services/settings/service.py index 95088e829..3ecdb683d 100644 --- a/src/backend/base/langflow/services/settings/service.py +++ b/src/backend/base/langflow/services/settings/service.py @@ -1,5 +1,4 @@ import os -from typing import Optional import yaml from loguru import logger @@ -8,6 +7,7 @@ from langflow.services.base import Service from langflow.services.settings.auth import AuthSettings from langflow.services.settings.base import Settings + class SettingsService(Service): name = "settings_service" diff --git a/src/backend/base/langflow/template/frontend_node/base.py b/src/backend/base/langflow/template/frontend_node/base.py index 310c7149c..be4efc92a 100644 --- a/src/backend/base/langflow/template/frontend_node/base.py +++ b/src/backend/base/langflow/template/frontend_node/base.py @@ -72,8 +72,7 @@ class FrontendNode(BaseModel): def serialize_model(self, handler): result = handler(self) if hasattr(self, "template") and hasattr(self.template, "to_dict"): - format_func = self.format_field if self._format_template else None - result["template"] = self.template.to_dict(format_func) + result["template"] = self.template.to_dict() name = result.pop("name") # Migrate base classes to outputs diff --git a/src/backend/base/langflow/utils/validate.py b/src/backend/base/langflow/utils/validate.py index bd7827199..8c84a4615 100644 --- a/src/backend/base/langflow/utils/validate.py +++ b/src/backend/base/langflow/utils/validate.py @@ -253,9 +253,7 @@ def build_class_constructor(compiled_class, exec_globals, class_name): globals()[module_name] = module instance = exec_globals[class_name](*args, **kwargs) - # Get selected type from global scope - if instance.selected_output_type in exec_globals: - instance.selected_output_type = exec_globals[instance.selected_output_type] + return instance build_custom_class.__globals__.update(exec_globals) diff --git a/tests/conftest.py b/tests/conftest.py index 6d371ac51..6e12c56f2 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -227,8 +227,9 @@ def client_fixture(session: Session, monkeypatch, request, load_flows_dir): monkeypatch.setenv("LANGFLOW_DATABASE_URL", f"sqlite:///{db_path}") monkeypatch.setenv("LANGFLOW_AUTO_LOGIN", "false") if "load_flows" in request.keywords: - shutil.copyfile(pytest.BASIC_EXAMPLE_PATH, - os.path.join(load_flows_dir, "c54f9130-f2fa-4a3e-b22a-3856d946351b.json")) + shutil.copyfile( + pytest.BASIC_EXAMPLE_PATH, os.path.join(load_flows_dir, "c54f9130-f2fa-4a3e-b22a-3856d946351b.json") + ) monkeypatch.setenv("LANGFLOW_LOAD_FLOWS_PATH", load_flows_dir) monkeypatch.setenv("LANGFLOW_AUTO_LOGIN", "true") diff --git a/tests/test_database.py b/tests/test_database.py index bc5bc3e7a..fd3291634 100644 --- a/tests/test_database.py +++ b/tests/test_database.py @@ -1,5 +1,3 @@ -import os -from typing import Optional, List from uuid import UUID, uuid4 import orjson @@ -13,7 +11,6 @@ from langflow.services.database.models.base import orjson_dumps from langflow.services.database.models.flow import Flow, FlowCreate, FlowUpdate from langflow.services.database.utils import session_getter from langflow.services.deps import get_db_service -from langflow.services.settings.base import Settings @pytest.fixture(scope="module") @@ -263,5 +260,3 @@ def test_load_flows(client: TestClient, load_flows_dir): response = client.get("api/v1/flows/c54f9130-f2fa-4a3e-b22a-3856d946351b") assert response.status_code == 200 assert response.json()["name"] == "BasicExample" - - From 8b4d5ec21ac934f195a8514035c349039b59e099 Mon Sep 17 00:00:00 2001 From: ogabrielluiz Date: Mon, 3 Jun 2024 18:26:15 -0300 Subject: [PATCH 046/701] update starter projects --- .../Basic Prompting (Hello, world!).json | 187 ++++----- .../Langflow Blog Writter.json | 191 ++++++---- .../Langflow Document QA.json | 197 ++++++---- .../Langflow Memory Conversation.json | 227 ++++++----- .../Langflow Prompt Chaining.json | 311 ++++++++------- .../VectorStore-RAG-Flows.json | 354 +++++++++++------- 6 files changed, 872 insertions(+), 595 deletions(-) diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting (Hello, world!).json b/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting (Hello, world!).json index 924f1085f..c3faf03a4 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting (Hello, world!).json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting (Hello, world!).json @@ -107,7 +107,17 @@ "frozen": false, "field_order": [], "beta": false, - "error": null + "error": null, + "outputs": [ + { + "types": [ + "Text" + ], + "selected": null, + "name": "Text", + "method": null + } + ] }, "id": "Prompt-uxBqP", "description": "Create a prompt template with dynamic variables.", @@ -385,7 +395,17 @@ "system_message", "stream" ], - "beta": false + "beta": false, + "outputs": [ + { + "types": [ + "Text" + ], + "selected": null, + "name": "Text", + "method": null + } + ] }, "id": "OpenAIModel-k39HS", "description": "Generates text using OpenAI LLMs.", @@ -418,7 +438,7 @@ "list": false, "show": true, "multiline": true, - "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n def build(\n self,\n sender: Optional[str] = \"Machine\",\n sender_name: Optional[str] = \"AI\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n record_template: Optional[str] = \"{text}\",\n ) -> Union[Text, Record]:\n return super().build_with_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n record_template=record_template or \"\",\n )\n", + "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\nfrom langflow.template import Input, Output\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n inputs = [\n Input(name=\"input_value\", type=str, display_name=\"Message\", multiline=True),\n Input(name=\"sender\", type=str, display_name=\"Sender Type\", options=[\"Machine\", \"AI\"]),\n Input(name=\"sender_name\", type=str, display_name=\"Sender Name\"),\n Input(name=\"session_id\", type=str, display_name=\"Session ID\"),\n Input(name=\"record_template\", type=str, display_name=\"Record Template\", default=\"{text}\"),\n ]\n outputs = [\n Output(name=\"Message\", method=\"text_response\"),\n Output(name=\"Record\", method=\"record_response\"),\n ]\n\n def text_response(self) -> Text:\n result = self.input_value\n if self.session_id and isinstance(result, (Record, str)):\n self.store_message(result, self.session_id, self.sender, self.sender_name)\n return result\n\n def record_response(self) -> Record:\n record = Record(\n data={\n \"message\": self.input_value,\n \"sender\": self.sender,\n \"sender_name\": self.sender_name,\n \"session_id\": self.session_id,\n \"template\": self.record_template or \"\",\n }\n )\n if self.session_id and isinstance(record, (Record, str)):\n self.store_message(record, self.session_id, self.sender, self.sender_name)\n return record\n", "fileTypes": [], "file_path": "", "password": false, @@ -436,19 +456,20 @@ "list": false, "show": true, "multiline": true, + "value": "", "fileTypes": [], "file_path": "", "password": false, "name": "input_value", "display_name": "Message", "advanced": false, - "input_types": [ - "Text" - ], "dynamic": false, "info": "", "load_from_db": false, - "title_case": false + "title_case": false, + "input_types": [ + "Text" + ] }, "record_template": { "type": "str", @@ -456,59 +477,40 @@ "placeholder": "", "list": false, "show": true, - "multiline": true, - "value": "{text}", + "multiline": false, + "value": "", "fileTypes": [], "file_path": "", "password": false, "name": "record_template", "display_name": "Record Template", - "advanced": true, + "advanced": false, "dynamic": false, - "info": "In case of Message being a Record, this template will be used to convert it to text.", + "info": "", "load_from_db": false, "title_case": false, "input_types": [ "Text" ] }, - "return_record": { - "type": "bool", + "sender": { + "type": "str", "required": false, "placeholder": "", "list": false, "show": true, "multiline": false, - "value": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "return_record", - "display_name": "Return Record", - "advanced": true, - "dynamic": false, - "info": "Return the message as a record containing the sender, sender_name, and session_id.", - "load_from_db": false, - "title_case": false - }, - "sender": { - "type": "str", - "required": false, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "value": "Machine", + "value": "", "fileTypes": [], "file_path": "", "password": false, "options": [ "Machine", - "User" + "AI" ], "name": "sender", "display_name": "Sender Type", - "advanced": true, + "advanced": false, "dynamic": false, "info": "", "load_from_db": false, @@ -524,7 +526,7 @@ "list": false, "show": true, "multiline": false, - "value": "AI", + "value": "", "fileTypes": [], "file_path": "", "password": false, @@ -546,21 +548,22 @@ "list": false, "show": true, "multiline": false, + "value": "", "fileTypes": [], "file_path": "", "password": false, "name": "session_id", "display_name": "Session ID", - "advanced": true, + "advanced": false, "dynamic": false, - "info": "If provided, the message will be stored in the memory.", + "info": "", "load_from_db": false, "title_case": false, "input_types": [ "Text" ] }, - "_type": "CustomComponent" + "_type": "Component" }, "description": "Display a chat message in the Playground.", "icon": "ChatOutput", @@ -587,7 +590,23 @@ "field_formatters": {}, "frozen": false, "field_order": [], - "beta": false + "beta": false, + "outputs": [ + { + "types": [ + "Text" + ], + "name": "Message", + "method": "text_response" + }, + { + "types": [ + "Record" + ], + "name": "Record", + "method": "record_response" + } + ] }, "id": "ChatOutput-njtka" }, @@ -618,7 +637,7 @@ "list": false, "show": true, "multiline": true, - "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\nfrom langflow.template import Input, Output\n\n\nclass ChatInput(ChatComponent):\n display_name = \"Chat Input\"\n description = \"Get chat inputs from the Playground.\"\n icon = \"ChatInput\"\n\n inputs = [\n Input(name=\"input_value\", type=str, display_name=\"Message\", multiline=True, input_types=[]),\n Input(name=\"sender\", type=str, display_name=\"Sender Type\", options=[\"Machine\", \"User\"]),\n Input(name=\"sender_name\", type=str, display_name=\"Sender Name\"),\n Input(name=\"session_id\", type=str, display_name=\"Session ID\"),\n ]\n outputs = [\n Output(name=\"Message\", method=\"text_response\"),\n Output(name=\"Record\", method=\"record_response\"),\n ]\n\n def build_config(self):\n build_config = super().build_config()\n build_config[\"input_value\"] = {\n \"input_types\": [],\n \"display_name\": \"Message\",\n \"multiline\": True,\n }\n\n return build_config\n\n def text_response(self):\n result = self.message\n if self.session_id and isinstance(result, (Record, str)):\n self.store_message(result, self.session_id, self.sender, self.sender_name)\n return result\n\n def record_response(self):\n record = Record(\n data={\n \"message\": self.message,\n \"sender\": self.sender,\n \"sender_name\": self.sender_name,\n \"session_id\": self.session_id,\n }\n )\n if self.session_id and isinstance(record, (Record, str)):\n self.store_message(record, self.session_id, self.sender, self.sender_name)\n return record\n\n def build(\n self,\n sender: Optional[str] = \"User\",\n sender_name: Optional[str] = \"User\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n ) -> Union[Text, Record]:\n return super().build_no_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n )\n", + "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\nfrom langflow.template import Input, Output\n\n\nclass ChatInput(ChatComponent):\n display_name = \"Chat Input\"\n description = \"Get chat inputs from the Playground.\"\n icon = \"ChatInput\"\n\n inputs = [\n Input(name=\"input_value\", type=str, display_name=\"Message\", multiline=True, input_types=[]),\n Input(name=\"sender\", type=str, display_name=\"Sender Type\", options=[\"Machine\", \"User\"]),\n Input(name=\"sender_name\", type=str, display_name=\"Sender Name\"),\n Input(name=\"session_id\", type=str, display_name=\"Session ID\"),\n ]\n outputs = [\n Output(name=\"Message\", method=\"text_response\"),\n Output(name=\"Record\", method=\"record_response\"),\n ]\n\n def text_response(self) -> Text:\n result = self.input_value\n if self.session_id and isinstance(result, (Record, str)):\n self.store_message(result, self.session_id, self.sender, self.sender_name)\n return result\n\n def record_response(self) -> Record:\n record = Record(\n data={\n \"text\": self.input_value,\n \"sender\": self.sender,\n \"sender_name\": self.sender_name,\n \"session_id\": self.session_id,\n },\n )\n if self.session_id and isinstance(record, (Record, str)):\n self.store_message(record, self.session_id, self.sender, self.sender_name)\n return record\n", "fileTypes": [], "file_path": "", "password": false, @@ -636,6 +655,7 @@ "list": false, "show": true, "multiline": true, + "value": "", "fileTypes": [], "file_path": "", "password": false, @@ -646,26 +666,6 @@ "dynamic": false, "info": "", "load_from_db": false, - "title_case": false, - "value": "hi" - }, - "return_record": { - "type": "bool", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "return_record", - "display_name": "Return Record", - "advanced": true, - "dynamic": false, - "info": "Return the message as a record containing the sender, sender_name, and session_id.", - "load_from_db": false, "title_case": false }, "sender": { @@ -675,7 +675,7 @@ "list": false, "show": true, "multiline": false, - "value": "User", + "value": "", "fileTypes": [], "file_path": "", "password": false, @@ -685,7 +685,7 @@ ], "name": "sender", "display_name": "Sender Type", - "advanced": true, + "advanced": false, "dynamic": false, "info": "", "load_from_db": false, @@ -701,7 +701,7 @@ "list": false, "show": true, "multiline": false, - "value": "User", + "value": "", "fileTypes": [], "file_path": "", "password": false, @@ -723,12 +723,13 @@ "list": false, "show": true, "multiline": false, + "value": "", "fileTypes": [], "file_path": "", "password": false, "name": "session_id", "display_name": "Session ID", - "advanced": true, + "advanced": false, "dynamic": false, "info": "", "load_from_db": false, @@ -737,7 +738,7 @@ "Text" ] }, - "_type": "CustomComponent" + "_type": "Component" }, "description": "Get chat inputs from the Playground.", "icon": "ChatInput", @@ -763,7 +764,23 @@ "field_formatters": {}, "frozen": false, "field_order": [], - "beta": false + "beta": false, + "outputs": [ + { + "types": [ + "Text" + ], + "name": "Message", + "method": "text_response" + }, + { + "types": [ + "Record" + ], + "name": "Record", + "method": "record_response" + } + ] }, "id": "ChatInput-P3fgL" }, @@ -780,7 +797,7 @@ "edges": [ { "source": "OpenAIModel-k39HS", - "sourceHandle": "{œbaseClassesœ:[œobjectœ,œTextœ,œstrœ],œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-k39HSœ}", + "sourceHandle": "{\"dataType\": \"OpenAIModel\", \"id\": \"OpenAIModel-k39HS\", \"output_types\": [\"Text\"], \"name\": \"Text\"}", "target": "ChatOutput-njtka", "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-njtkaœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}", "data": { @@ -793,13 +810,12 @@ "type": "str" }, "sourceHandle": { - "baseClasses": [ - "object", - "Text", - "str" - ], "dataType": "OpenAIModel", - "id": "OpenAIModel-k39HS" + "id": "OpenAIModel-k39HS", + "output_types": [ + "Text" + ], + "name": "Text" } }, "style": { @@ -810,7 +826,7 @@ }, { "source": "Prompt-uxBqP", - "sourceHandle": "{œbaseClassesœ:[œobjectœ,œstrœ,œTextœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-uxBqPœ}", + "sourceHandle": "{\"dataType\": \"Prompt\", \"id\": \"Prompt-uxBqP\", \"output_types\": [\"Text\"], \"name\": \"Text\"}", "target": "OpenAIModel-k39HS", "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-k39HSœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}", "data": { @@ -823,13 +839,12 @@ "type": "str" }, "sourceHandle": { - "baseClasses": [ - "object", - "str", + "dataType": "Prompt", + "id": "Prompt-uxBqP", + "output_types": [ "Text" ], - "dataType": "Prompt", - "id": "Prompt-uxBqP" + "name": "Text" } }, "style": { @@ -840,7 +855,7 @@ }, { "source": "ChatInput-P3fgL", - "sourceHandle": "{œbaseClassesœ:[œobjectœ,œRecordœ,œstrœ,œTextœ],œdataTypeœ:œChatInputœ,œidœ:œChatInput-P3fgLœ}", + "sourceHandle": "{\"dataType\": \"ChatInput\", \"id\": \"ChatInput-P3fgL\", \"output_types\": [\"Text\"], \"name\": \"Message\"}", "target": "Prompt-uxBqP", "targetHandle": "{œfieldNameœ:œuser_inputœ,œidœ:œPrompt-uxBqPœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}", "data": { @@ -856,14 +871,12 @@ "type": "str" }, "sourceHandle": { - "baseClasses": [ - "object", - "Record", - "str", + "dataType": "ChatInput", + "id": "ChatInput-P3fgL", + "output_types": [ "Text" ], - "dataType": "ChatInput", - "id": "ChatInput-P3fgL" + "name": "Message" } }, "style": { diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Blog Writter.json b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Blog Writter.json index 5854cd934..4dc94331d 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Blog Writter.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Blog Writter.json @@ -161,7 +161,17 @@ "frozen": false, "field_order": [], "beta": false, - "error": null + "error": null, + "outputs": [ + { + "types": [ + "Text" + ], + "selected": null, + "name": "Text", + "method": null + } + ] }, "id": "Prompt-Rse03", "description": "Create a prompt template with dynamic variables.", @@ -247,7 +257,17 @@ "field_formatters": {}, "frozen": false, "field_order": [], - "beta": false + "beta": false, + "outputs": [ + { + "types": [ + "Record" + ], + "selected": null, + "name": "Record", + "method": null + } + ] }, "id": "URL-HYPkR" }, @@ -278,7 +298,7 @@ "list": false, "show": true, "multiline": true, - "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n def build(\n self,\n sender: Optional[str] = \"Machine\",\n sender_name: Optional[str] = \"AI\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n record_template: Optional[str] = \"{text}\",\n ) -> Union[Text, Record]:\n return super().build_with_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n record_template=record_template or \"\",\n )\n", + "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\nfrom langflow.template import Input, Output\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n inputs = [\n Input(name=\"input_value\", type=str, display_name=\"Message\", multiline=True),\n Input(name=\"sender\", type=str, display_name=\"Sender Type\", options=[\"Machine\", \"AI\"]),\n Input(name=\"sender_name\", type=str, display_name=\"Sender Name\"),\n Input(name=\"session_id\", type=str, display_name=\"Session ID\"),\n Input(name=\"record_template\", type=str, display_name=\"Record Template\", default=\"{text}\"),\n ]\n outputs = [\n Output(name=\"Message\", method=\"text_response\"),\n Output(name=\"Record\", method=\"record_response\"),\n ]\n\n def text_response(self) -> Text:\n result = self.input_value\n if self.session_id and isinstance(result, (Record, str)):\n self.store_message(result, self.session_id, self.sender, self.sender_name)\n return result\n\n def record_response(self) -> Record:\n record = Record(\n data={\n \"message\": self.input_value,\n \"sender\": self.sender,\n \"sender_name\": self.sender_name,\n \"session_id\": self.session_id,\n \"template\": self.record_template or \"\",\n }\n )\n if self.session_id and isinstance(record, (Record, str)):\n self.store_message(record, self.session_id, self.sender, self.sender_name)\n return record\n", "fileTypes": [], "file_path": "", "password": false, @@ -296,19 +316,20 @@ "list": false, "show": true, "multiline": true, + "value": "", "fileTypes": [], "file_path": "", "password": false, "name": "input_value", "display_name": "Message", "advanced": false, - "input_types": [ - "Text" - ], "dynamic": false, "info": "", "load_from_db": false, - "title_case": false + "title_case": false, + "input_types": [ + "Text" + ] }, "record_template": { "type": "str", @@ -316,59 +337,40 @@ "placeholder": "", "list": false, "show": true, - "multiline": true, - "value": "{text}", + "multiline": false, + "value": "", "fileTypes": [], "file_path": "", "password": false, "name": "record_template", "display_name": "Record Template", - "advanced": true, + "advanced": false, "dynamic": false, - "info": "In case of Message being a Record, this template will be used to convert it to text.", + "info": "", "load_from_db": false, "title_case": false, "input_types": [ "Text" ] }, - "return_record": { - "type": "bool", + "sender": { + "type": "str", "required": false, "placeholder": "", "list": false, "show": true, "multiline": false, - "value": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "return_record", - "display_name": "Return Record", - "advanced": true, - "dynamic": false, - "info": "Return the message as a record containing the sender, sender_name, and session_id.", - "load_from_db": false, - "title_case": false - }, - "sender": { - "type": "str", - "required": false, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "value": "Machine", + "value": "", "fileTypes": [], "file_path": "", "password": false, "options": [ "Machine", - "User" + "AI" ], "name": "sender", "display_name": "Sender Type", - "advanced": true, + "advanced": false, "dynamic": false, "info": "", "load_from_db": false, @@ -384,7 +386,7 @@ "list": false, "show": true, "multiline": false, - "value": "AI", + "value": "", "fileTypes": [], "file_path": "", "password": false, @@ -406,21 +408,22 @@ "list": false, "show": true, "multiline": false, + "value": "", "fileTypes": [], "file_path": "", "password": false, "name": "session_id", "display_name": "Session ID", - "advanced": true, + "advanced": false, "dynamic": false, - "info": "If provided, the message will be stored in the memory.", + "info": "", "load_from_db": false, "title_case": false, "input_types": [ "Text" ] }, - "_type": "CustomComponent" + "_type": "Component" }, "description": "Display a chat message in the Playground.", "icon": "ChatOutput", @@ -447,7 +450,23 @@ "field_formatters": {}, "frozen": false, "field_order": [], - "beta": false + "beta": false, + "outputs": [ + { + "types": [ + "Text" + ], + "name": "Message", + "method": "text_response" + }, + { + "types": [ + "Record" + ], + "name": "Record", + "method": "record_response" + } + ] }, "id": "ChatOutput-JPlxl" }, @@ -718,7 +737,17 @@ "system_message", "stream" ], - "beta": false + "beta": false, + "outputs": [ + { + "types": [ + "Text" + ], + "selected": null, + "name": "Text", + "method": null + } + ] }, "id": "OpenAIModel-gi29P" }, @@ -802,7 +831,17 @@ "field_formatters": {}, "frozen": false, "field_order": [], - "beta": false + "beta": false, + "outputs": [ + { + "types": [ + "Record" + ], + "selected": null, + "name": "Record", + "method": null + } + ] }, "id": "URL-2cX90" }, @@ -910,7 +949,16 @@ "field_formatters": {}, "frozen": false, "field_order": [], - "beta": false + "beta": false, + "outputs": [ + { + "types": [ + "Text" + ], + "selected": "Text", + "name": "Text" + } + ] }, "id": "TextInput-og8Or" }, @@ -928,7 +976,7 @@ { "source": "URL-HYPkR", "target": "Prompt-Rse03", - "sourceHandle": "{œbaseClassesœ:[œRecordœ],œdataTypeœ:œURLœ,œidœ:œURL-HYPkRœ}", + "sourceHandle": "{\"dataType\": \"URL\", \"id\": \"URL-HYPkR\", \"output_types\": [\"Record\"], \"name\": \"Record\"}", "targetHandle": "{œfieldNameœ:œreference_2œ,œidœ:œPrompt-Rse03œ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}", "id": "reactflow__edge-URL-HYPkR{œbaseClassesœ:[œRecordœ],œdataTypeœ:œURLœ,œidœ:œURL-HYPkRœ}-Prompt-Rse03{œfieldNameœ:œreference_2œ,œidœ:œPrompt-Rse03œ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}", "data": { @@ -944,11 +992,12 @@ "type": "str" }, "sourceHandle": { - "baseClasses": [ + "dataType": "URL", + "id": "URL-HYPkR", + "output_types": [ "Record" ], - "dataType": "URL", - "id": "URL-HYPkR" + "name": "Record" } }, "style": { @@ -959,7 +1008,7 @@ }, { "source": "OpenAIModel-gi29P", - "sourceHandle": "{œbaseClassesœ:[œstrœ,œTextœ,œobjectœ],œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-gi29Pœ}", + "sourceHandle": "{\"dataType\": \"OpenAIModel\", \"id\": \"OpenAIModel-gi29P\", \"output_types\": [\"Text\"], \"name\": \"Text\"}", "target": "ChatOutput-JPlxl", "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-JPlxlœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}", "data": { @@ -972,13 +1021,12 @@ "type": "str" }, "sourceHandle": { - "baseClasses": [ - "str", - "Text", - "object" - ], "dataType": "OpenAIModel", - "id": "OpenAIModel-gi29P" + "id": "OpenAIModel-gi29P", + "output_types": [ + "Text" + ], + "name": "Text" } }, "style": { @@ -989,7 +1037,7 @@ }, { "source": "URL-2cX90", - "sourceHandle": "{œbaseClassesœ:[œRecordœ],œdataTypeœ:œURLœ,œidœ:œURL-2cX90œ}", + "sourceHandle": "{\"dataType\": \"URL\", \"id\": \"URL-2cX90\", \"output_types\": [\"Record\"], \"name\": \"Record\"}", "target": "Prompt-Rse03", "targetHandle": "{œfieldNameœ:œreference_1œ,œidœ:œPrompt-Rse03œ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}", "data": { @@ -1005,11 +1053,12 @@ "type": "str" }, "sourceHandle": { - "baseClasses": [ + "dataType": "URL", + "id": "URL-2cX90", + "output_types": [ "Record" ], - "dataType": "URL", - "id": "URL-2cX90" + "name": "Record" } }, "style": { @@ -1020,7 +1069,7 @@ }, { "source": "TextInput-og8Or", - "sourceHandle": "{œbaseClassesœ:[œobjectœ,œTextœ,œstrœ],œdataTypeœ:œTextInputœ,œidœ:œTextInput-og8Orœ}", + "sourceHandle": "{\"dataType\": \"TextInput\", \"id\": \"TextInput-og8Or\", \"output_types\": [\"Text\"], \"name\": \"Text\"}", "target": "Prompt-Rse03", "targetHandle": "{œfieldNameœ:œinstructionsœ,œidœ:œPrompt-Rse03œ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}", "data": { @@ -1036,13 +1085,12 @@ "type": "str" }, "sourceHandle": { - "baseClasses": [ - "object", - "Text", - "str" - ], "dataType": "TextInput", - "id": "TextInput-og8Or" + "id": "TextInput-og8Or", + "output_types": [ + "Text" + ], + "name": "Text" } }, "style": { @@ -1053,7 +1101,7 @@ }, { "source": "Prompt-Rse03", - "sourceHandle": "{œbaseClassesœ:[œobjectœ,œTextœ,œstrœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-Rse03œ}", + "sourceHandle": "{\"dataType\": \"Prompt\", \"id\": \"Prompt-Rse03\", \"output_types\": [\"Text\"], \"name\": \"Text\"}", "target": "OpenAIModel-gi29P", "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-gi29Pœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}", "data": { @@ -1066,13 +1114,12 @@ "type": "str" }, "sourceHandle": { - "baseClasses": [ - "object", - "Text", - "str" - ], "dataType": "Prompt", - "id": "Prompt-Rse03" + "id": "Prompt-Rse03", + "output_types": [ + "Text" + ], + "name": "Text" } }, "style": { diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Document QA.json b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Document QA.json index b4836c93e..c5938f438 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Document QA.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Document QA.json @@ -134,7 +134,17 @@ "frozen": false, "field_order": [], "beta": false, - "error": null + "error": null, + "outputs": [ + { + "types": [ + "Text" + ], + "selected": null, + "name": "Text", + "method": null + } + ] }, "id": "Prompt-tHwPf", "description": "A component for creating prompt templates using dynamic variables.", @@ -246,7 +256,16 @@ "field_formatters": {}, "frozen": false, "field_order": [], - "beta": false + "beta": false, + "outputs": [ + { + "types": [ + "Record" + ], + "selected": "Record", + "name": "Record" + } + ] }, "id": "File-6TEsD" }, @@ -277,7 +296,7 @@ "list": false, "show": true, "multiline": true, - "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\nfrom langflow.template import Input, Output\n\n\nclass ChatInput(ChatComponent):\n display_name = \"Chat Input\"\n description = \"Get chat inputs from the Playground.\"\n icon = \"ChatInput\"\n\n inputs = [\n Input(name=\"input_value\", type=str, display_name=\"Message\", multiline=True, input_types=[]),\n Input(name=\"sender\", type=str, display_name=\"Sender Type\", options=[\"Machine\", \"User\"]),\n Input(name=\"sender_name\", type=str, display_name=\"Sender Name\"),\n Input(name=\"session_id\", type=str, display_name=\"Session ID\"),\n ]\n outputs = [\n Output(name=\"Message\", method=\"text_response\"),\n Output(name=\"Record\", method=\"record_response\"),\n ]\n\n def build_config(self):\n build_config = super().build_config()\n build_config[\"input_value\"] = {\n \"input_types\": [],\n \"display_name\": \"Message\",\n \"multiline\": True,\n }\n\n return build_config\n\n def text_response(self):\n result = self.message\n if self.session_id and isinstance(result, (Record, str)):\n self.store_message(result, self.session_id, self.sender, self.sender_name)\n return result\n\n def record_response(self):\n record = Record(\n data={\n \"message\": self.message,\n \"sender\": self.sender,\n \"sender_name\": self.sender_name,\n \"session_id\": self.session_id,\n }\n )\n if self.session_id and isinstance(record, (Record, str)):\n self.store_message(record, self.session_id, self.sender, self.sender_name)\n return record\n\n def build(\n self,\n sender: Optional[str] = \"User\",\n sender_name: Optional[str] = \"User\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n ) -> Union[Text, Record]:\n return super().build_no_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n )\n", + "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\nfrom langflow.template import Input, Output\n\n\nclass ChatInput(ChatComponent):\n display_name = \"Chat Input\"\n description = \"Get chat inputs from the Playground.\"\n icon = \"ChatInput\"\n\n inputs = [\n Input(name=\"input_value\", type=str, display_name=\"Message\", multiline=True, input_types=[]),\n Input(name=\"sender\", type=str, display_name=\"Sender Type\", options=[\"Machine\", \"User\"]),\n Input(name=\"sender_name\", type=str, display_name=\"Sender Name\"),\n Input(name=\"session_id\", type=str, display_name=\"Session ID\"),\n ]\n outputs = [\n Output(name=\"Message\", method=\"text_response\"),\n Output(name=\"Record\", method=\"record_response\"),\n ]\n\n def text_response(self) -> Text:\n result = self.input_value\n if self.session_id and isinstance(result, (Record, str)):\n self.store_message(result, self.session_id, self.sender, self.sender_name)\n return result\n\n def record_response(self) -> Record:\n record = Record(\n data={\n \"text\": self.input_value,\n \"sender\": self.sender,\n \"sender_name\": self.sender_name,\n \"session_id\": self.session_id,\n },\n )\n if self.session_id and isinstance(record, (Record, str)):\n self.store_message(record, self.session_id, self.sender, self.sender_name)\n return record\n", "fileTypes": [], "file_path": "", "password": false, @@ -295,6 +314,7 @@ "list": false, "show": true, "multiline": true, + "value": "", "fileTypes": [], "file_path": "", "password": false, @@ -305,26 +325,6 @@ "dynamic": false, "info": "", "load_from_db": false, - "title_case": false, - "value": "" - }, - "return_record": { - "type": "bool", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "return_record", - "display_name": "Return Record", - "advanced": true, - "dynamic": false, - "info": "Return the message as a record containing the sender, sender_name, and session_id.", - "load_from_db": false, "title_case": false }, "sender": { @@ -334,7 +334,7 @@ "list": false, "show": true, "multiline": false, - "value": "User", + "value": "", "fileTypes": [], "file_path": "", "password": false, @@ -344,7 +344,7 @@ ], "name": "sender", "display_name": "Sender Type", - "advanced": true, + "advanced": false, "dynamic": false, "info": "", "load_from_db": false, @@ -360,7 +360,7 @@ "list": false, "show": true, "multiline": false, - "value": "User", + "value": "", "fileTypes": [], "file_path": "", "password": false, @@ -382,12 +382,13 @@ "list": false, "show": true, "multiline": false, + "value": "", "fileTypes": [], "file_path": "", "password": false, "name": "session_id", "display_name": "Session ID", - "advanced": true, + "advanced": false, "dynamic": false, "info": "", "load_from_db": false, @@ -396,7 +397,7 @@ "Text" ] }, - "_type": "CustomComponent" + "_type": "Component" }, "description": "Get chat inputs from the Playground.", "icon": "ChatInput", @@ -422,7 +423,23 @@ "field_formatters": {}, "frozen": false, "field_order": [], - "beta": false + "beta": false, + "outputs": [ + { + "types": [ + "Text" + ], + "name": "Message", + "method": "text_response" + }, + { + "types": [ + "Record" + ], + "name": "Record", + "method": "record_response" + } + ] }, "id": "ChatInput-MsSJ9" }, @@ -453,7 +470,7 @@ "list": false, "show": true, "multiline": true, - "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n def build(\n self,\n sender: Optional[str] = \"Machine\",\n sender_name: Optional[str] = \"AI\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n record_template: Optional[str] = \"{text}\",\n ) -> Union[Text, Record]:\n return super().build_with_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n record_template=record_template or \"\",\n )\n", + "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\nfrom langflow.template import Input, Output\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n inputs = [\n Input(name=\"input_value\", type=str, display_name=\"Message\", multiline=True),\n Input(name=\"sender\", type=str, display_name=\"Sender Type\", options=[\"Machine\", \"AI\"]),\n Input(name=\"sender_name\", type=str, display_name=\"Sender Name\"),\n Input(name=\"session_id\", type=str, display_name=\"Session ID\"),\n Input(name=\"record_template\", type=str, display_name=\"Record Template\", default=\"{text}\"),\n ]\n outputs = [\n Output(name=\"Message\", method=\"text_response\"),\n Output(name=\"Record\", method=\"record_response\"),\n ]\n\n def text_response(self) -> Text:\n result = self.input_value\n if self.session_id and isinstance(result, (Record, str)):\n self.store_message(result, self.session_id, self.sender, self.sender_name)\n return result\n\n def record_response(self) -> Record:\n record = Record(\n data={\n \"message\": self.input_value,\n \"sender\": self.sender,\n \"sender_name\": self.sender_name,\n \"session_id\": self.session_id,\n \"template\": self.record_template or \"\",\n }\n )\n if self.session_id and isinstance(record, (Record, str)):\n self.store_message(record, self.session_id, self.sender, self.sender_name)\n return record\n", "fileTypes": [], "file_path": "", "password": false, @@ -471,57 +488,61 @@ "list": false, "show": true, "multiline": true, + "value": "", "fileTypes": [], "file_path": "", "password": false, "name": "input_value", "display_name": "Message", "advanced": false, - "input_types": [ - "Text" - ], "dynamic": false, "info": "", "load_from_db": false, - "title_case": false + "title_case": false, + "input_types": [ + "Text" + ] }, - "return_record": { - "type": "bool", + "record_template": { + "type": "str", "required": false, "placeholder": "", "list": false, "show": true, "multiline": false, - "value": false, + "value": "", "fileTypes": [], "file_path": "", "password": false, - "name": "return_record", - "display_name": "Return Record", - "advanced": true, + "name": "record_template", + "display_name": "Record Template", + "advanced": false, "dynamic": false, - "info": "Return the message as a record containing the sender, sender_name, and session_id.", + "info": "", "load_from_db": false, - "title_case": false + "title_case": false, + "input_types": [ + "Text" + ] }, "sender": { "type": "str", "required": false, "placeholder": "", - "list": true, + "list": false, "show": true, "multiline": false, - "value": "Machine", + "value": "", "fileTypes": [], "file_path": "", "password": false, "options": [ "Machine", - "User" + "AI" ], "name": "sender", "display_name": "Sender Type", - "advanced": true, + "advanced": false, "dynamic": false, "info": "", "load_from_db": false, @@ -537,7 +558,7 @@ "list": false, "show": true, "multiline": false, - "value": "AI", + "value": "", "fileTypes": [], "file_path": "", "password": false, @@ -559,21 +580,22 @@ "list": false, "show": true, "multiline": false, + "value": "", "fileTypes": [], "file_path": "", "password": false, "name": "session_id", "display_name": "Session ID", - "advanced": true, + "advanced": false, "dynamic": false, - "info": "If provided, the message will be stored in the memory.", + "info": "", "load_from_db": false, "title_case": false, "input_types": [ "Text" ] }, - "_type": "CustomComponent" + "_type": "Component" }, "description": "Display a chat message in the Playground.", "icon": "ChatOutput", @@ -599,7 +621,23 @@ "field_formatters": {}, "frozen": false, "field_order": [], - "beta": false + "beta": false, + "outputs": [ + { + "types": [ + "Text" + ], + "name": "Message", + "method": "text_response" + }, + { + "types": [ + "Record" + ], + "name": "Record", + "method": "record_response" + } + ] }, "id": "ChatOutput-F5Awj" }, @@ -875,7 +913,17 @@ "system_message", "stream" ], - "beta": false + "beta": false, + "outputs": [ + { + "types": [ + "Text" + ], + "selected": null, + "name": "Text", + "method": null + } + ] }, "id": "OpenAIModel-Bt067" }, @@ -892,7 +940,7 @@ "edges": [ { "source": "ChatInput-MsSJ9", - "sourceHandle": "{œbaseClassesœ:[œstrœ,œRecordœ,œTextœ,œobjectœ],œdataTypeœ:œChatInputœ,œidœ:œChatInput-MsSJ9œ}", + "sourceHandle": "{\"dataType\": \"ChatInput\", \"id\": \"ChatInput-MsSJ9\", \"output_types\": [\"Text\"], \"name\": \"Message\"}", "target": "Prompt-tHwPf", "targetHandle": "{œfieldNameœ:œQuestionœ,œidœ:œPrompt-tHwPfœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}", "data": { @@ -908,14 +956,12 @@ "type": "str" }, "sourceHandle": { - "baseClasses": [ - "str", - "Record", - "Text", - "object" - ], "dataType": "ChatInput", - "id": "ChatInput-MsSJ9" + "id": "ChatInput-MsSJ9", + "output_types": [ + "Text" + ], + "name": "Message" } }, "style": { @@ -926,7 +972,7 @@ }, { "source": "File-6TEsD", - "sourceHandle": "{œbaseClassesœ:[œRecordœ],œdataTypeœ:œFileœ,œidœ:œFile-6TEsDœ}", + "sourceHandle": "{\"dataType\": \"File\", \"id\": \"File-6TEsD\", \"output_types\": [\"Record\"], \"name\": \"Record\"}", "target": "Prompt-tHwPf", "targetHandle": "{œfieldNameœ:œDocumentœ,œidœ:œPrompt-tHwPfœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}", "data": { @@ -942,11 +988,12 @@ "type": "str" }, "sourceHandle": { - "baseClasses": [ + "dataType": "File", + "id": "File-6TEsD", + "output_types": [ "Record" ], - "dataType": "File", - "id": "File-6TEsD" + "name": "Record" } }, "style": { @@ -957,7 +1004,7 @@ }, { "source": "Prompt-tHwPf", - "sourceHandle": "{œbaseClassesœ:[œobjectœ,œstrœ,œTextœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-tHwPfœ}", + "sourceHandle": "{\"dataType\": \"Prompt\", \"id\": \"Prompt-tHwPf\", \"output_types\": [\"Text\"], \"name\": \"Text\"}", "target": "OpenAIModel-Bt067", "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-Bt067œ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}", "data": { @@ -970,13 +1017,12 @@ "type": "str" }, "sourceHandle": { - "baseClasses": [ - "object", - "str", + "dataType": "Prompt", + "id": "Prompt-tHwPf", + "output_types": [ "Text" ], - "dataType": "Prompt", - "id": "Prompt-tHwPf" + "name": "Text" } }, "style": { @@ -987,7 +1033,7 @@ }, { "source": "OpenAIModel-Bt067", - "sourceHandle": "{œbaseClassesœ:[œobjectœ,œstrœ,œTextœ],œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-Bt067œ}", + "sourceHandle": "{\"dataType\": \"OpenAIModel\", \"id\": \"OpenAIModel-Bt067\", \"output_types\": [\"Text\"], \"name\": \"Text\"}", "target": "ChatOutput-F5Awj", "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-F5Awjœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}", "data": { @@ -1000,13 +1046,12 @@ "type": "str" }, "sourceHandle": { - "baseClasses": [ - "object", - "str", + "dataType": "OpenAIModel", + "id": "OpenAIModel-Bt067", + "output_types": [ "Text" ], - "dataType": "OpenAIModel", - "id": "OpenAIModel-Bt067" + "name": "Text" } }, "style": { diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Memory Conversation.json b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Memory Conversation.json index bdd47835b..62b4338d6 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Memory Conversation.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Memory Conversation.json @@ -22,7 +22,7 @@ "list": false, "show": true, "multiline": true, - "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\nfrom langflow.template import Input, Output\n\n\nclass ChatInput(ChatComponent):\n display_name = \"Chat Input\"\n description = \"Get chat inputs from the Playground.\"\n icon = \"ChatInput\"\n\n inputs = [\n Input(name=\"input_value\", type=str, display_name=\"Message\", multiline=True, input_types=[]),\n Input(name=\"sender\", type=str, display_name=\"Sender Type\", options=[\"Machine\", \"User\"]),\n Input(name=\"sender_name\", type=str, display_name=\"Sender Name\"),\n Input(name=\"session_id\", type=str, display_name=\"Session ID\"),\n ]\n outputs = [\n Output(name=\"Message\", method=\"text_response\"),\n Output(name=\"Record\", method=\"record_response\"),\n ]\n\n def build_config(self):\n build_config = super().build_config()\n build_config[\"input_value\"] = {\n \"input_types\": [],\n \"display_name\": \"Message\",\n \"multiline\": True,\n }\n\n return build_config\n\n def text_response(self):\n result = self.message\n if self.session_id and isinstance(result, (Record, str)):\n self.store_message(result, self.session_id, self.sender, self.sender_name)\n return result\n\n def record_response(self):\n record = Record(\n data={\n \"message\": self.message,\n \"sender\": self.sender,\n \"sender_name\": self.sender_name,\n \"session_id\": self.session_id,\n }\n )\n if self.session_id and isinstance(record, (Record, str)):\n self.store_message(record, self.session_id, self.sender, self.sender_name)\n return record\n\n def build(\n self,\n sender: Optional[str] = \"User\",\n sender_name: Optional[str] = \"User\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n ) -> Union[Text, Record]:\n return super().build_no_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n )\n", + "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\nfrom langflow.template import Input, Output\n\n\nclass ChatInput(ChatComponent):\n display_name = \"Chat Input\"\n description = \"Get chat inputs from the Playground.\"\n icon = \"ChatInput\"\n\n inputs = [\n Input(name=\"input_value\", type=str, display_name=\"Message\", multiline=True, input_types=[]),\n Input(name=\"sender\", type=str, display_name=\"Sender Type\", options=[\"Machine\", \"User\"]),\n Input(name=\"sender_name\", type=str, display_name=\"Sender Name\"),\n Input(name=\"session_id\", type=str, display_name=\"Session ID\"),\n ]\n outputs = [\n Output(name=\"Message\", method=\"text_response\"),\n Output(name=\"Record\", method=\"record_response\"),\n ]\n\n def text_response(self) -> Text:\n result = self.input_value\n if self.session_id and isinstance(result, (Record, str)):\n self.store_message(result, self.session_id, self.sender, self.sender_name)\n return result\n\n def record_response(self) -> Record:\n record = Record(\n data={\n \"text\": self.input_value,\n \"sender\": self.sender,\n \"sender_name\": self.sender_name,\n \"session_id\": self.session_id,\n },\n )\n if self.session_id and isinstance(record, (Record, str)):\n self.store_message(record, self.session_id, self.sender, self.sender_name)\n return record\n", "fileTypes": [], "file_path": "", "password": false, @@ -40,6 +40,7 @@ "list": false, "show": true, "multiline": true, + "value": "", "fileTypes": [], "file_path": "", "password": false, @@ -50,26 +51,6 @@ "dynamic": false, "info": "", "load_from_db": false, - "title_case": false, - "value": "" - }, - "return_record": { - "type": "bool", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "return_record", - "display_name": "Return Record", - "advanced": true, - "dynamic": false, - "info": "Return the message as a record containing the sender, sender_name, and session_id.", - "load_from_db": false, "title_case": false }, "sender": { @@ -79,7 +60,7 @@ "list": false, "show": true, "multiline": false, - "value": "User", + "value": "", "fileTypes": [], "file_path": "", "password": false, @@ -89,7 +70,7 @@ ], "name": "sender", "display_name": "Sender Type", - "advanced": true, + "advanced": false, "dynamic": false, "info": "", "load_from_db": false, @@ -105,7 +86,7 @@ "list": false, "show": true, "multiline": false, - "value": "User", + "value": "", "fileTypes": [], "file_path": "", "password": false, @@ -127,6 +108,7 @@ "list": false, "show": true, "multiline": false, + "value": "", "fileTypes": [], "file_path": "", "password": false, @@ -139,10 +121,9 @@ "title_case": false, "input_types": [ "Text" - ], - "value": "MySessionID" + ] }, - "_type": "CustomComponent" + "_type": "Component" }, "description": "Get chat inputs from the Playground.", "icon": "ChatInput", @@ -168,7 +149,23 @@ "field_formatters": {}, "frozen": false, "field_order": [], - "beta": false + "beta": false, + "outputs": [ + { + "types": [ + "Text" + ], + "name": "Message", + "method": "text_response" + }, + { + "types": [ + "Record" + ], + "name": "Record", + "method": "record_response" + } + ] }, "id": "ChatInput-t7F8v" }, @@ -199,7 +196,7 @@ "list": false, "show": true, "multiline": true, - "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n def build(\n self,\n sender: Optional[str] = \"Machine\",\n sender_name: Optional[str] = \"AI\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n record_template: Optional[str] = \"{text}\",\n ) -> Union[Text, Record]:\n return super().build_with_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n record_template=record_template or \"\",\n )\n", + "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\nfrom langflow.template import Input, Output\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n inputs = [\n Input(name=\"input_value\", type=str, display_name=\"Message\", multiline=True),\n Input(name=\"sender\", type=str, display_name=\"Sender Type\", options=[\"Machine\", \"AI\"]),\n Input(name=\"sender_name\", type=str, display_name=\"Sender Name\"),\n Input(name=\"session_id\", type=str, display_name=\"Session ID\"),\n Input(name=\"record_template\", type=str, display_name=\"Record Template\", default=\"{text}\"),\n ]\n outputs = [\n Output(name=\"Message\", method=\"text_response\"),\n Output(name=\"Record\", method=\"record_response\"),\n ]\n\n def text_response(self) -> Text:\n result = self.input_value\n if self.session_id and isinstance(result, (Record, str)):\n self.store_message(result, self.session_id, self.sender, self.sender_name)\n return result\n\n def record_response(self) -> Record:\n record = Record(\n data={\n \"message\": self.input_value,\n \"sender\": self.sender,\n \"sender_name\": self.sender_name,\n \"session_id\": self.session_id,\n \"template\": self.record_template or \"\",\n }\n )\n if self.session_id and isinstance(record, (Record, str)):\n self.store_message(record, self.session_id, self.sender, self.sender_name)\n return record\n", "fileTypes": [], "file_path": "", "password": false, @@ -217,57 +214,61 @@ "list": false, "show": true, "multiline": true, + "value": "", "fileTypes": [], "file_path": "", "password": false, "name": "input_value", "display_name": "Message", "advanced": false, - "input_types": [ - "Text" - ], "dynamic": false, "info": "", "load_from_db": false, - "title_case": false + "title_case": false, + "input_types": [ + "Text" + ] }, - "return_record": { - "type": "bool", + "record_template": { + "type": "str", "required": false, "placeholder": "", "list": false, "show": true, "multiline": false, - "value": false, + "value": "", "fileTypes": [], "file_path": "", "password": false, - "name": "return_record", - "display_name": "Return Record", - "advanced": true, + "name": "record_template", + "display_name": "Record Template", + "advanced": false, "dynamic": false, - "info": "Return the message as a record containing the sender, sender_name, and session_id.", + "info": "", "load_from_db": false, - "title_case": false + "title_case": false, + "input_types": [ + "Text" + ] }, "sender": { "type": "str", "required": false, "placeholder": "", - "list": true, + "list": false, "show": true, "multiline": false, - "value": "Machine", + "value": "", "fileTypes": [], "file_path": "", "password": false, "options": [ "Machine", - "User" + "AI" ], "name": "sender", "display_name": "Sender Type", - "advanced": true, + "advanced": false, "dynamic": false, "info": "", "load_from_db": false, @@ -283,7 +284,7 @@ "list": false, "show": true, "multiline": false, - "value": "AI", + "value": "", "fileTypes": [], "file_path": "", "password": false, @@ -305,6 +306,7 @@ "list": false, "show": true, "multiline": false, + "value": "", "fileTypes": [], "file_path": "", "password": false, @@ -312,15 +314,14 @@ "display_name": "Session ID", "advanced": false, "dynamic": false, - "info": "If provided, the message will be stored in the memory.", + "info": "", "load_from_db": false, "title_case": false, "input_types": [ "Text" - ], - "value": "MySessionID" + ] }, - "_type": "CustomComponent" + "_type": "Component" }, "description": "Display a chat message in the Playground.", "icon": "ChatOutput", @@ -346,7 +347,23 @@ "field_formatters": {}, "frozen": false, "field_order": [], - "beta": false + "beta": false, + "outputs": [ + { + "types": [ + "Text" + ], + "name": "Message", + "method": "text_response" + }, + { + "types": [ + "Record" + ], + "name": "Record", + "method": "record_response" + } + ] }, "id": "ChatOutput-P1jEe" }, @@ -550,7 +567,17 @@ "field_formatters": {}, "frozen": false, "field_order": [], - "beta": true + "beta": true, + "outputs": [ + { + "types": [ + "Text" + ], + "selected": null, + "name": "Text", + "method": null + } + ] }, "id": "MemoryComponent-cdA1J", "description": "Retrieves stored chat messages given a specific Session ID.", @@ -697,7 +724,17 @@ "frozen": false, "field_order": [], "beta": false, - "error": null + "error": null, + "outputs": [ + { + "types": [ + "Text" + ], + "selected": null, + "name": "Text", + "method": null + } + ] }, "id": "Prompt-ODkUx", "description": "A component for creating prompt templates using dynamic variables.", @@ -975,7 +1012,17 @@ "system_message", "stream" ], - "beta": false + "beta": false, + "outputs": [ + { + "types": [ + "Text" + ], + "selected": null, + "name": "Text", + "method": null + } + ] }, "id": "OpenAIModel-9RykF" }, @@ -1100,29 +1147,28 @@ "edges": [ { "source": "MemoryComponent-cdA1J", - "sourceHandle": "{œbaseClassesœ:[œstrœ,œTextœ,œobjectœ],œdataTypeœ:œMemoryComponentœ,œidœ:œMemoryComponent-cdA1Jœ}", + "sourceHandle": "{\"dataType\": \"MemoryComponent\", \"id\": \"MemoryComponent-cdA1J\", \"output_types\": [\"Text\"], \"name\": \"Text\"}", "target": "Prompt-ODkUx", "targetHandle": "{œfieldNameœ:œcontextœ,œidœ:œPrompt-ODkUxœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}", "data": { "targetHandle": { "fieldName": "context", - "type": "str", "id": "Prompt-ODkUx", "inputTypes": [ "Document", "BaseOutputParser", "Record", "Text" - ] + ], + "type": "str" }, "sourceHandle": { - "baseClasses": [ - "str", - "Text", - "object" - ], "dataType": "MemoryComponent", - "id": "MemoryComponent-cdA1J" + "id": "MemoryComponent-cdA1J", + "output_types": [ + "Text" + ], + "name": "Text" } }, "style": { @@ -1134,30 +1180,28 @@ }, { "source": "ChatInput-t7F8v", - "sourceHandle": "{œbaseClassesœ:[œTextœ,œobjectœ,œRecordœ,œstrœ],œdataTypeœ:œChatInputœ,œidœ:œChatInput-t7F8vœ}", + "sourceHandle": "{\"dataType\": \"ChatInput\", \"id\": \"ChatInput-t7F8v\", \"output_types\": [\"Text\"], \"name\": \"Message\"}", "target": "Prompt-ODkUx", "targetHandle": "{œfieldNameœ:œuser_messageœ,œidœ:œPrompt-ODkUxœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}", "data": { "targetHandle": { "fieldName": "user_message", - "type": "str", "id": "Prompt-ODkUx", "inputTypes": [ "Document", "BaseOutputParser", "Record", "Text" - ] + ], + "type": "str" }, "sourceHandle": { - "baseClasses": [ - "Text", - "object", - "Record", - "str" - ], "dataType": "ChatInput", - "id": "ChatInput-t7F8v" + "id": "ChatInput-t7F8v", + "output_types": [ + "Text" + ], + "name": "Message" } }, "style": { @@ -1169,7 +1213,7 @@ }, { "source": "Prompt-ODkUx", - "sourceHandle": "{œbaseClassesœ:[œTextœ,œstrœ,œobjectœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-ODkUxœ}", + "sourceHandle": "{\"dataType\": \"Prompt\", \"id\": \"Prompt-ODkUx\", \"output_types\": [\"Text\"], \"name\": \"Text\"}", "target": "OpenAIModel-9RykF", "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-9RykFœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}", "data": { @@ -1182,13 +1226,12 @@ "type": "str" }, "sourceHandle": { - "baseClasses": [ - "Text", - "str", - "object" - ], "dataType": "Prompt", - "id": "Prompt-ODkUx" + "id": "Prompt-ODkUx", + "output_types": [ + "Text" + ], + "name": "Text" } }, "style": { @@ -1199,7 +1242,7 @@ }, { "source": "OpenAIModel-9RykF", - "sourceHandle": "{œbaseClassesœ:[œstrœ,œobjectœ,œTextœ],œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-9RykFœ}", + "sourceHandle": "{\"dataType\": \"OpenAIModel\", \"id\": \"OpenAIModel-9RykF\", \"output_types\": [\"Text\"], \"name\": \"Text\"}", "target": "ChatOutput-P1jEe", "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-P1jEeœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}", "data": { @@ -1212,13 +1255,12 @@ "type": "str" }, "sourceHandle": { - "baseClasses": [ - "str", - "object", + "dataType": "OpenAIModel", + "id": "OpenAIModel-9RykF", + "output_types": [ "Text" ], - "dataType": "OpenAIModel", - "id": "OpenAIModel-9RykF" + "name": "Text" } }, "style": { @@ -1229,7 +1271,7 @@ }, { "source": "MemoryComponent-cdA1J", - "sourceHandle": "{œbaseClassesœ:[œstrœ,œTextœ,œobjectœ],œdataTypeœ:œMemoryComponentœ,œidœ:œMemoryComponent-cdA1Jœ}", + "sourceHandle": "{\"dataType\": \"MemoryComponent\", \"id\": \"MemoryComponent-cdA1J\", \"output_types\": [\"Text\"], \"name\": \"Text\"}", "target": "TextOutput-vrs6T", "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œTextOutput-vrs6Tœ,œinputTypesœ:[œRecordœ,œTextœ],œtypeœ:œstrœ}", "data": { @@ -1243,13 +1285,12 @@ "type": "str" }, "sourceHandle": { - "baseClasses": [ - "str", - "Text", - "object" - ], "dataType": "MemoryComponent", - "id": "MemoryComponent-cdA1J" + "id": "MemoryComponent-cdA1J", + "output_types": [ + "Text" + ], + "name": "Text" } }, "style": { diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Prompt Chaining.json b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Prompt Chaining.json index cb91ef13f..85176dd00 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Prompt Chaining.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Prompt Chaining.json @@ -107,7 +107,17 @@ "frozen": false, "field_order": [], "beta": false, - "error": null + "error": null, + "outputs": [ + { + "types": [ + "Text" + ], + "selected": null, + "name": "Text", + "method": null + } + ] }, "id": "Prompt-amqBu", "description": "Create a prompt template with dynamic variables.", @@ -227,7 +237,17 @@ "frozen": false, "field_order": [], "beta": false, - "error": null + "error": null, + "outputs": [ + { + "types": [ + "Text" + ], + "selected": null, + "name": "Text", + "method": null + } + ] }, "id": "Prompt-gTNiz", "description": "Create a prompt template with dynamic variables.", @@ -256,7 +276,7 @@ "list": false, "show": true, "multiline": true, - "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n def build(\n self,\n sender: Optional[str] = \"Machine\",\n sender_name: Optional[str] = \"AI\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n record_template: Optional[str] = \"{text}\",\n ) -> Union[Text, Record]:\n return super().build_with_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n record_template=record_template or \"\",\n )\n", + "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\nfrom langflow.template import Input, Output\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n inputs = [\n Input(name=\"input_value\", type=str, display_name=\"Message\", multiline=True),\n Input(name=\"sender\", type=str, display_name=\"Sender Type\", options=[\"Machine\", \"AI\"]),\n Input(name=\"sender_name\", type=str, display_name=\"Sender Name\"),\n Input(name=\"session_id\", type=str, display_name=\"Session ID\"),\n Input(name=\"record_template\", type=str, display_name=\"Record Template\", default=\"{text}\"),\n ]\n outputs = [\n Output(name=\"Message\", method=\"text_response\"),\n Output(name=\"Record\", method=\"record_response\"),\n ]\n\n def text_response(self) -> Text:\n result = self.input_value\n if self.session_id and isinstance(result, (Record, str)):\n self.store_message(result, self.session_id, self.sender, self.sender_name)\n return result\n\n def record_response(self) -> Record:\n record = Record(\n data={\n \"message\": self.input_value,\n \"sender\": self.sender,\n \"sender_name\": self.sender_name,\n \"session_id\": self.session_id,\n \"template\": self.record_template or \"\",\n }\n )\n if self.session_id and isinstance(record, (Record, str)):\n self.store_message(record, self.session_id, self.sender, self.sender_name)\n return record\n", "fileTypes": [], "file_path": "", "password": false, @@ -274,19 +294,20 @@ "list": false, "show": true, "multiline": true, + "value": "", "fileTypes": [], "file_path": "", "password": false, "name": "input_value", "display_name": "Message", "advanced": false, - "input_types": [ - "Text" - ], "dynamic": false, "info": "", "load_from_db": false, - "title_case": false + "title_case": false, + "input_types": [ + "Text" + ] }, "record_template": { "type": "str", @@ -294,59 +315,40 @@ "placeholder": "", "list": false, "show": true, - "multiline": true, - "value": "{text}", + "multiline": false, + "value": "", "fileTypes": [], "file_path": "", "password": false, "name": "record_template", "display_name": "Record Template", - "advanced": true, + "advanced": false, "dynamic": false, - "info": "In case of Message being a Record, this template will be used to convert it to text.", + "info": "", "load_from_db": false, "title_case": false, "input_types": [ "Text" ] }, - "return_record": { - "type": "bool", + "sender": { + "type": "str", "required": false, "placeholder": "", "list": false, "show": true, "multiline": false, - "value": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "return_record", - "display_name": "Return Record", - "advanced": true, - "dynamic": false, - "info": "Return the message as a record containing the sender, sender_name, and session_id.", - "load_from_db": false, - "title_case": false - }, - "sender": { - "type": "str", - "required": false, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "value": "Machine", + "value": "", "fileTypes": [], "file_path": "", "password": false, "options": [ "Machine", - "User" + "AI" ], "name": "sender", "display_name": "Sender Type", - "advanced": true, + "advanced": false, "dynamic": false, "info": "", "load_from_db": false, @@ -362,7 +364,7 @@ "list": false, "show": true, "multiline": false, - "value": "Summarizer", + "value": "", "fileTypes": [], "file_path": "", "password": false, @@ -384,21 +386,22 @@ "list": false, "show": true, "multiline": false, + "value": "", "fileTypes": [], "file_path": "", "password": false, "name": "session_id", "display_name": "Session ID", - "advanced": true, + "advanced": false, "dynamic": false, - "info": "If provided, the message will be stored in the memory.", + "info": "", "load_from_db": false, "title_case": false, "input_types": [ "Text" ] }, - "_type": "CustomComponent" + "_type": "Component" }, "description": "Display a chat message in the Playground.", "icon": "ChatOutput", @@ -425,7 +428,23 @@ "field_formatters": {}, "frozen": false, "field_order": [], - "beta": false + "beta": false, + "outputs": [ + { + "types": [ + "Text" + ], + "name": "Message", + "method": "text_response" + }, + { + "types": [ + "Record" + ], + "name": "Record", + "method": "record_response" + } + ] }, "id": "ChatOutput-EJkG3" }, @@ -452,7 +471,7 @@ "list": false, "show": true, "multiline": true, - "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n def build(\n self,\n sender: Optional[str] = \"Machine\",\n sender_name: Optional[str] = \"AI\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n record_template: Optional[str] = \"{text}\",\n ) -> Union[Text, Record]:\n return super().build_with_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n record_template=record_template or \"\",\n )\n", + "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\nfrom langflow.template import Input, Output\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n inputs = [\n Input(name=\"input_value\", type=str, display_name=\"Message\", multiline=True),\n Input(name=\"sender\", type=str, display_name=\"Sender Type\", options=[\"Machine\", \"AI\"]),\n Input(name=\"sender_name\", type=str, display_name=\"Sender Name\"),\n Input(name=\"session_id\", type=str, display_name=\"Session ID\"),\n Input(name=\"record_template\", type=str, display_name=\"Record Template\", default=\"{text}\"),\n ]\n outputs = [\n Output(name=\"Message\", method=\"text_response\"),\n Output(name=\"Record\", method=\"record_response\"),\n ]\n\n def text_response(self) -> Text:\n result = self.input_value\n if self.session_id and isinstance(result, (Record, str)):\n self.store_message(result, self.session_id, self.sender, self.sender_name)\n return result\n\n def record_response(self) -> Record:\n record = Record(\n data={\n \"message\": self.input_value,\n \"sender\": self.sender,\n \"sender_name\": self.sender_name,\n \"session_id\": self.session_id,\n \"template\": self.record_template or \"\",\n }\n )\n if self.session_id and isinstance(record, (Record, str)):\n self.store_message(record, self.session_id, self.sender, self.sender_name)\n return record\n", "fileTypes": [], "file_path": "", "password": false, @@ -470,19 +489,20 @@ "list": false, "show": true, "multiline": true, + "value": "", "fileTypes": [], "file_path": "", "password": false, "name": "input_value", "display_name": "Message", "advanced": false, - "input_types": [ - "Text" - ], "dynamic": false, "info": "", "load_from_db": false, - "title_case": false + "title_case": false, + "input_types": [ + "Text" + ] }, "record_template": { "type": "str", @@ -490,59 +510,40 @@ "placeholder": "", "list": false, "show": true, - "multiline": true, - "value": "{text}", + "multiline": false, + "value": "", "fileTypes": [], "file_path": "", "password": false, "name": "record_template", "display_name": "Record Template", - "advanced": true, + "advanced": false, "dynamic": false, - "info": "In case of Message being a Record, this template will be used to convert it to text.", + "info": "", "load_from_db": false, "title_case": false, "input_types": [ "Text" ] }, - "return_record": { - "type": "bool", + "sender": { + "type": "str", "required": false, "placeholder": "", "list": false, "show": true, "multiline": false, - "value": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "return_record", - "display_name": "Return Record", - "advanced": true, - "dynamic": false, - "info": "Return the message as a record containing the sender, sender_name, and session_id.", - "load_from_db": false, - "title_case": false - }, - "sender": { - "type": "str", - "required": false, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "value": "Machine", + "value": "", "fileTypes": [], "file_path": "", "password": false, "options": [ "Machine", - "User" + "AI" ], "name": "sender", "display_name": "Sender Type", - "advanced": true, + "advanced": false, "dynamic": false, "info": "", "load_from_db": false, @@ -558,7 +559,7 @@ "list": false, "show": true, "multiline": false, - "value": "Question Generator", + "value": "", "fileTypes": [], "file_path": "", "password": false, @@ -580,21 +581,22 @@ "list": false, "show": true, "multiline": false, + "value": "", "fileTypes": [], "file_path": "", "password": false, "name": "session_id", "display_name": "Session ID", - "advanced": true, + "advanced": false, "dynamic": false, - "info": "If provided, the message will be stored in the memory.", + "info": "", "load_from_db": false, "title_case": false, "input_types": [ "Text" ] }, - "_type": "CustomComponent" + "_type": "Component" }, "description": "Display a chat message in the Playground.", "icon": "ChatOutput", @@ -621,7 +623,23 @@ "field_formatters": {}, "frozen": false, "field_order": [], - "beta": false + "beta": false, + "outputs": [ + { + "types": [ + "Text" + ], + "name": "Message", + "method": "text_response" + }, + { + "types": [ + "Record" + ], + "name": "Record", + "method": "record_response" + } + ] }, "id": "ChatOutput-DNmvg" }, @@ -647,7 +665,7 @@ "list": false, "show": true, "multiline": true, - "value": "from typing import Optional\n\nfrom langflow.base.io.text import TextComponent\nfrom langflow.field_typing import Text\n\n\nclass TextInput(TextComponent):\n display_name = \"Text Input\"\n description = \"Get text inputs from the Playground.\"\n icon = \"type\"\n\n def build_config(self):\n return {\n \"input_value\": {\n \"display_name\": \"Value\",\n \"input_types\": [\"Record\", \"Text\"],\n \"info\": \"Text or Record to be passed as input.\",\n },\n \"record_template\": {\n \"display_name\": \"Record Template\",\n \"multiline\": True,\n \"info\": \"Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Optional[Text] = \"\",\n record_template: Optional[str] = \"\",\n ) -> Text:\n return super().build(input_value=input_value, record_template=record_template)\n", + "value": "from langflow.base.io.text import TextComponent\nfrom langflow.field_typing import Text\nfrom langflow.template import Input, Output\n\n\nclass TextInput(TextComponent):\n display_name = \"Text Input\"\n description = \"Get text inputs from the Playground.\"\n icon = \"type\"\n\n inputs = [\n Input(\n name=\"input_value\",\n type=str,\n display_name=\"Value\",\n info=\"Text or Record to be passed as input.\",\n input_types=[\"Record\", \"Text\"],\n ),\n Input(\n name=\"record_template\",\n type=str,\n display_name=\"Record Template\",\n multiline=True,\n info=\"Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.\",\n advanced=True,\n ),\n ]\n outputs = [\n Output(name=\"Text\", method=\"text_response\"),\n ]\n\n def text_response(self) -> Text:\n return self.build(input_value=self.input_value, record_template=self.record_template)\n", "fileTypes": [], "file_path": "", "password": false, @@ -665,7 +683,7 @@ "list": false, "show": true, "multiline": false, - "value": "Revolutionary Nano-Battery Technology Unveiled In a groundbreaking announcement yesterday, researchers from the fictional Tech Innovations Institute revealed the development of a new nano-battery technology that promises to revolutionize energy storage. The new battery, dubbed the \"EnerGCell\", uses advanced nanomaterials to achieve unprecedented efficiency and storage capacities. According to lead researcher Dr. Ada Byron, the EnerGCell can store up to ten times more energy than the best lithium-ion batteries available today, while charging in just a fraction of the time. \"We're talking about charging your electric vehicle in just five minutes for a range of over 1,000 miles,\" Dr. Byron stated during the press conference. The technology behind the EnerGCell involves a complex arrangement of nanostructured electrodes that allow for rapid ion transfer and extremely high energy density. This breakthrough was achieved after a decade of research into nanomaterials and their applications in energy storage. The implications of this technology are vast, promising to accelerate the adoption of renewable energy by making it more practical and affordable to store wind and solar power. It could also lead to significant advancements in electric vehicles, mobile devices, and any other technology that relies on batteries. Despite the excitement, some experts are calling for patience, noting that the EnerGCell is still in its early stages of development and may take several years before it's commercially available. However, the potential impact of such a technology on the environment and the global economy is undeniable. Tech Innovations Institute plans to continue refining the EnerGCell and begin pilot projects with select partners in the coming year. If successful, this nano-battery technology could indeed be the breakthrough needed to usher in a new era of clean energy and technology.", + "value": "", "fileTypes": [], "file_path": "", "password": false, @@ -703,7 +721,7 @@ "Text" ] }, - "_type": "CustomComponent" + "_type": "Component" }, "description": "Get text inputs from the Playground.", "icon": "type", @@ -724,7 +742,16 @@ "field_formatters": {}, "frozen": false, "field_order": [], - "beta": false + "beta": false, + "outputs": [ + { + "types": [ + "Text" + ], + "name": "Text", + "method": "text_response" + } + ] }, "id": "TextInput-sptaH" }, @@ -1108,7 +1135,17 @@ "system_message", "stream" ], - "beta": false + "beta": false, + "outputs": [ + { + "types": [ + "Text" + ], + "selected": null, + "name": "Text", + "method": null + } + ] }, "id": "OpenAIModel-uYXZJ" }, @@ -1492,7 +1529,17 @@ "system_message", "stream" ], - "beta": false + "beta": false, + "outputs": [ + { + "types": [ + "Text" + ], + "selected": null, + "name": "Text", + "method": null + } + ] }, "id": "OpenAIModel-XawYB" }, @@ -1509,7 +1556,7 @@ "edges": [ { "source": "TextInput-sptaH", - "sourceHandle": "{œbaseClassesœ:[œstrœ,œTextœ,œobjectœ],œdataTypeœ:œTextInputœ,œidœ:œTextInput-sptaHœ}", + "sourceHandle": "{\"dataType\": \"TextInput\", \"id\": \"TextInput-sptaH\", \"output_types\": [\"Text\"], \"name\": \"Text\"}", "target": "Prompt-amqBu", "targetHandle": "{œfieldNameœ:œdocumentœ,œidœ:œPrompt-amqBuœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}", "data": { @@ -1525,13 +1572,12 @@ "type": "str" }, "sourceHandle": { - "baseClasses": [ - "str", - "Text", - "object" - ], "dataType": "TextInput", - "id": "TextInput-sptaH" + "id": "TextInput-sptaH", + "output_types": [ + "Text" + ], + "name": "Text" } }, "style": { @@ -1542,7 +1588,7 @@ }, { "source": "Prompt-amqBu", - "sourceHandle": "{œbaseClassesœ:[œobjectœ,œstrœ,œTextœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-amqBuœ}", + "sourceHandle": "{\"dataType\": \"Prompt\", \"id\": \"Prompt-amqBu\", \"output_types\": [\"Text\"], \"name\": \"Text\"}", "target": "TextOutput-2MS4a", "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œTextOutput-2MS4aœ,œinputTypesœ:[œRecordœ,œTextœ],œtypeœ:œstrœ}", "data": { @@ -1556,13 +1602,12 @@ "type": "str" }, "sourceHandle": { - "baseClasses": [ - "object", - "str", + "dataType": "Prompt", + "id": "Prompt-amqBu", + "output_types": [ "Text" ], - "dataType": "Prompt", - "id": "Prompt-amqBu" + "name": "Text" } }, "style": { @@ -1573,7 +1618,7 @@ }, { "source": "Prompt-amqBu", - "sourceHandle": "{œbaseClassesœ:[œobjectœ,œstrœ,œTextœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-amqBuœ}", + "sourceHandle": "{\"dataType\": \"Prompt\", \"id\": \"Prompt-amqBu\", \"output_types\": [\"Text\"], \"name\": \"Text\"}", "target": "OpenAIModel-uYXZJ", "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-uYXZJœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}", "data": { @@ -1586,13 +1631,12 @@ "type": "str" }, "sourceHandle": { - "baseClasses": [ - "object", - "str", + "dataType": "Prompt", + "id": "Prompt-amqBu", + "output_types": [ "Text" ], - "dataType": "Prompt", - "id": "Prompt-amqBu" + "name": "Text" } }, "style": { @@ -1603,7 +1647,7 @@ }, { "source": "OpenAIModel-uYXZJ", - "sourceHandle": "{œbaseClassesœ:[œstrœ,œTextœ,œobjectœ],œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-uYXZJœ}", + "sourceHandle": "{\"dataType\": \"OpenAIModel\", \"id\": \"OpenAIModel-uYXZJ\", \"output_types\": [\"Text\"], \"name\": \"Text\"}", "target": "Prompt-gTNiz", "targetHandle": "{œfieldNameœ:œsummaryœ,œidœ:œPrompt-gTNizœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}", "data": { @@ -1619,13 +1663,12 @@ "type": "str" }, "sourceHandle": { - "baseClasses": [ - "str", - "Text", - "object" - ], "dataType": "OpenAIModel", - "id": "OpenAIModel-uYXZJ" + "id": "OpenAIModel-uYXZJ", + "output_types": [ + "Text" + ], + "name": "Text" } }, "style": { @@ -1636,7 +1679,7 @@ }, { "source": "OpenAIModel-uYXZJ", - "sourceHandle": "{œbaseClassesœ:[œstrœ,œTextœ,œobjectœ],œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-uYXZJœ}", + "sourceHandle": "{\"dataType\": \"OpenAIModel\", \"id\": \"OpenAIModel-uYXZJ\", \"output_types\": [\"Text\"], \"name\": \"Text\"}", "target": "ChatOutput-EJkG3", "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-EJkG3œ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}", "data": { @@ -1649,13 +1692,12 @@ "type": "str" }, "sourceHandle": { - "baseClasses": [ - "str", - "Text", - "object" - ], "dataType": "OpenAIModel", - "id": "OpenAIModel-uYXZJ" + "id": "OpenAIModel-uYXZJ", + "output_types": [ + "Text" + ], + "name": "Text" } }, "style": { @@ -1666,7 +1708,7 @@ }, { "source": "Prompt-gTNiz", - "sourceHandle": "{œbaseClassesœ:[œobjectœ,œstrœ,œTextœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-gTNizœ}", + "sourceHandle": "{\"dataType\": \"Prompt\", \"id\": \"Prompt-gTNiz\", \"output_types\": [\"Text\"], \"name\": \"Text\"}", "target": "TextOutput-MUDOR", "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œTextOutput-MUDORœ,œinputTypesœ:[œRecordœ,œTextœ],œtypeœ:œstrœ}", "data": { @@ -1680,13 +1722,12 @@ "type": "str" }, "sourceHandle": { - "baseClasses": [ - "object", - "str", + "dataType": "Prompt", + "id": "Prompt-gTNiz", + "output_types": [ "Text" ], - "dataType": "Prompt", - "id": "Prompt-gTNiz" + "name": "Text" } }, "style": { @@ -1697,7 +1738,7 @@ }, { "source": "Prompt-gTNiz", - "sourceHandle": "{œbaseClassesœ:[œobjectœ,œstrœ,œTextœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-gTNizœ}", + "sourceHandle": "{\"dataType\": \"Prompt\", \"id\": \"Prompt-gTNiz\", \"output_types\": [\"Text\"], \"name\": \"Text\"}", "target": "OpenAIModel-XawYB", "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-XawYBœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}", "data": { @@ -1710,13 +1751,12 @@ "type": "str" }, "sourceHandle": { - "baseClasses": [ - "object", - "str", + "dataType": "Prompt", + "id": "Prompt-gTNiz", + "output_types": [ "Text" ], - "dataType": "Prompt", - "id": "Prompt-gTNiz" + "name": "Text" } }, "style": { @@ -1727,7 +1767,7 @@ }, { "source": "OpenAIModel-XawYB", - "sourceHandle": "{œbaseClassesœ:[œstrœ,œTextœ,œobjectœ],œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-XawYBœ}", + "sourceHandle": "{\"dataType\": \"OpenAIModel\", \"id\": \"OpenAIModel-XawYB\", \"output_types\": [\"Text\"], \"name\": \"Text\"}", "target": "ChatOutput-DNmvg", "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-DNmvgœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}", "data": { @@ -1740,13 +1780,12 @@ "type": "str" }, "sourceHandle": { - "baseClasses": [ - "str", - "Text", - "object" - ], "dataType": "OpenAIModel", - "id": "OpenAIModel-XawYB" + "id": "OpenAIModel-XawYB", + "output_types": [ + "Text" + ], + "name": "Text" } }, "style": { diff --git a/src/backend/base/langflow/initial_setup/starter_projects/VectorStore-RAG-Flows.json b/src/backend/base/langflow/initial_setup/starter_projects/VectorStore-RAG-Flows.json index ef20c1696..510129323 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/VectorStore-RAG-Flows.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/VectorStore-RAG-Flows.json @@ -20,7 +20,7 @@ "list": false, "show": true, "multiline": true, - "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\nfrom langflow.template import Input, Output\n\n\nclass ChatInput(ChatComponent):\n display_name = \"Chat Input\"\n description = \"Get chat inputs from the Playground.\"\n icon = \"ChatInput\"\n\n inputs = [\n Input(name=\"input_value\", type=str, display_name=\"Message\", multiline=True, input_types=[]),\n Input(name=\"sender\", type=str, display_name=\"Sender Type\", options=[\"Machine\", \"User\"]),\n Input(name=\"sender_name\", type=str, display_name=\"Sender Name\"),\n Input(name=\"session_id\", type=str, display_name=\"Session ID\"),\n ]\n outputs = [\n Output(name=\"Message\", method=\"text_response\"),\n Output(name=\"Record\", method=\"record_response\"),\n ]\n\n def build_config(self):\n build_config = super().build_config()\n build_config[\"input_value\"] = {\n \"input_types\": [],\n \"display_name\": \"Message\",\n \"multiline\": True,\n }\n\n return build_config\n\n def text_response(self):\n result = self.message\n if self.session_id and isinstance(result, (Record, str)):\n self.store_message(result, self.session_id, self.sender, self.sender_name)\n return result\n\n def record_response(self):\n record = Record(\n data={\n \"message\": self.message,\n \"sender\": self.sender,\n \"sender_name\": self.sender_name,\n \"session_id\": self.session_id,\n }\n )\n if self.session_id and isinstance(record, (Record, str)):\n self.store_message(record, self.session_id, self.sender, self.sender_name)\n return record\n\n def build(\n self,\n sender: Optional[str] = \"User\",\n sender_name: Optional[str] = \"User\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n ) -> Union[Text, Record]:\n return super().build_no_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n )\n", + "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\nfrom langflow.template import Input, Output\n\n\nclass ChatInput(ChatComponent):\n display_name = \"Chat Input\"\n description = \"Get chat inputs from the Playground.\"\n icon = \"ChatInput\"\n\n inputs = [\n Input(name=\"input_value\", type=str, display_name=\"Message\", multiline=True, input_types=[]),\n Input(name=\"sender\", type=str, display_name=\"Sender Type\", options=[\"Machine\", \"User\"]),\n Input(name=\"sender_name\", type=str, display_name=\"Sender Name\"),\n Input(name=\"session_id\", type=str, display_name=\"Session ID\"),\n ]\n outputs = [\n Output(name=\"Message\", method=\"text_response\"),\n Output(name=\"Record\", method=\"record_response\"),\n ]\n\n def text_response(self) -> Text:\n result = self.input_value\n if self.session_id and isinstance(result, (Record, str)):\n self.store_message(result, self.session_id, self.sender, self.sender_name)\n return result\n\n def record_response(self) -> Record:\n record = Record(\n data={\n \"text\": self.input_value,\n \"sender\": self.sender,\n \"sender_name\": self.sender_name,\n \"session_id\": self.session_id,\n },\n )\n if self.session_id and isinstance(record, (Record, str)):\n self.store_message(record, self.session_id, self.sender, self.sender_name)\n return record\n", "fileTypes": [], "file_path": "", "password": false, @@ -38,6 +38,7 @@ "list": false, "show": true, "multiline": true, + "value": "", "fileTypes": [], "file_path": "", "password": false, @@ -48,26 +49,6 @@ "dynamic": false, "info": "", "load_from_db": false, - "title_case": false, - "value": "what is a line" - }, - "return_record": { - "type": "bool", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "return_record", - "display_name": "Return Record", - "advanced": true, - "dynamic": false, - "info": "Return the message as a record containing the sender, sender_name, and session_id.", - "load_from_db": false, "title_case": false }, "sender": { @@ -77,7 +58,7 @@ "list": false, "show": true, "multiline": false, - "value": "User", + "value": "", "fileTypes": [], "file_path": "", "password": false, @@ -87,7 +68,7 @@ ], "name": "sender", "display_name": "Sender Type", - "advanced": true, + "advanced": false, "dynamic": false, "info": "", "load_from_db": false, @@ -103,7 +84,7 @@ "list": false, "show": true, "multiline": false, - "value": "User", + "value": "", "fileTypes": [], "file_path": "", "password": false, @@ -125,12 +106,13 @@ "list": false, "show": true, "multiline": false, + "value": "", "fileTypes": [], "file_path": "", "password": false, "name": "session_id", "display_name": "Session ID", - "advanced": true, + "advanced": false, "dynamic": false, "info": "", "load_from_db": false, @@ -139,7 +121,7 @@ "Text" ] }, - "_type": "CustomComponent" + "_type": "Component" }, "description": "Get chat inputs from the Playground.", "icon": "ChatInput", @@ -165,7 +147,23 @@ "field_formatters": {}, "frozen": false, "field_order": [], - "beta": false + "beta": false, + "outputs": [ + { + "types": [ + "Text" + ], + "name": "Message", + "method": "text_response" + }, + { + "types": [ + "Record" + ], + "name": "Record", + "method": "record_response" + } + ] }, "id": "ChatInput-yxMKE" }, @@ -268,7 +266,16 @@ "field_formatters": {}, "frozen": false, "field_order": [], - "beta": false + "beta": false, + "outputs": [ + { + "types": [ + "Text" + ], + "selected": "Text", + "name": "Text" + } + ] }, "id": "TextOutput-BDknO" }, @@ -803,7 +810,17 @@ "field_formatters": {}, "frozen": false, "field_order": [], - "beta": false + "beta": false, + "outputs": [ + { + "types": [ + "Embeddings" + ], + "selected": null, + "name": "Embeddings", + "method": null + } + ] }, "id": "OpenAIEmbeddings-ZlOk1" }, @@ -1075,7 +1092,17 @@ "system_message", "stream" ], - "beta": false + "beta": false, + "outputs": [ + { + "types": [ + "Text" + ], + "selected": null, + "name": "Text", + "method": null + } + ] }, "id": "OpenAIModel-EjXlN" }, @@ -1220,7 +1247,17 @@ "frozen": false, "field_order": [], "beta": false, - "error": null + "error": null, + "outputs": [ + { + "types": [ + "Text" + ], + "selected": null, + "name": "Text", + "method": null + } + ] }, "id": "Prompt-xeI6K", "description": "Create a prompt template with dynamic variables.", @@ -1253,7 +1290,7 @@ "list": false, "show": true, "multiline": true, - "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n def build(\n self,\n sender: Optional[str] = \"Machine\",\n sender_name: Optional[str] = \"AI\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n record_template: Optional[str] = \"{text}\",\n ) -> Union[Text, Record]:\n return super().build_with_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n record_template=record_template or \"\",\n )\n", + "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\nfrom langflow.template import Input, Output\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n inputs = [\n Input(name=\"input_value\", type=str, display_name=\"Message\", multiline=True),\n Input(name=\"sender\", type=str, display_name=\"Sender Type\", options=[\"Machine\", \"AI\"]),\n Input(name=\"sender_name\", type=str, display_name=\"Sender Name\"),\n Input(name=\"session_id\", type=str, display_name=\"Session ID\"),\n Input(name=\"record_template\", type=str, display_name=\"Record Template\", default=\"{text}\"),\n ]\n outputs = [\n Output(name=\"Message\", method=\"text_response\"),\n Output(name=\"Record\", method=\"record_response\"),\n ]\n\n def text_response(self) -> Text:\n result = self.input_value\n if self.session_id and isinstance(result, (Record, str)):\n self.store_message(result, self.session_id, self.sender, self.sender_name)\n return result\n\n def record_response(self) -> Record:\n record = Record(\n data={\n \"message\": self.input_value,\n \"sender\": self.sender,\n \"sender_name\": self.sender_name,\n \"session_id\": self.session_id,\n \"template\": self.record_template or \"\",\n }\n )\n if self.session_id and isinstance(record, (Record, str)):\n self.store_message(record, self.session_id, self.sender, self.sender_name)\n return record\n", "fileTypes": [], "file_path": "", "password": false, @@ -1271,19 +1308,20 @@ "list": false, "show": true, "multiline": true, + "value": "", "fileTypes": [], "file_path": "", "password": false, "name": "input_value", "display_name": "Message", "advanced": false, - "input_types": [ - "Text" - ], "dynamic": false, "info": "", "load_from_db": false, - "title_case": false + "title_case": false, + "input_types": [ + "Text" + ] }, "record_template": { "type": "str", @@ -1291,59 +1329,40 @@ "placeholder": "", "list": false, "show": true, - "multiline": true, - "value": "{text}", + "multiline": false, + "value": "", "fileTypes": [], "file_path": "", "password": false, "name": "record_template", "display_name": "Record Template", - "advanced": true, + "advanced": false, "dynamic": false, - "info": "In case of Message being a Record, this template will be used to convert it to text.", + "info": "", "load_from_db": false, "title_case": false, "input_types": [ "Text" ] }, - "return_record": { - "type": "bool", + "sender": { + "type": "str", "required": false, "placeholder": "", "list": false, "show": true, "multiline": false, - "value": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "return_record", - "display_name": "Return Record", - "advanced": true, - "dynamic": false, - "info": "Return the message as a record containing the sender, sender_name, and session_id.", - "load_from_db": false, - "title_case": false - }, - "sender": { - "type": "str", - "required": false, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "value": "Machine", + "value": "", "fileTypes": [], "file_path": "", "password": false, "options": [ "Machine", - "User" + "AI" ], "name": "sender", "display_name": "Sender Type", - "advanced": true, + "advanced": false, "dynamic": false, "info": "", "load_from_db": false, @@ -1359,7 +1378,7 @@ "list": false, "show": true, "multiline": false, - "value": "AI", + "value": "", "fileTypes": [], "file_path": "", "password": false, @@ -1381,21 +1400,22 @@ "list": false, "show": true, "multiline": false, + "value": "", "fileTypes": [], "file_path": "", "password": false, "name": "session_id", "display_name": "Session ID", - "advanced": true, + "advanced": false, "dynamic": false, - "info": "If provided, the message will be stored in the memory.", + "info": "", "load_from_db": false, "title_case": false, "input_types": [ "Text" ] }, - "_type": "CustomComponent" + "_type": "Component" }, "description": "Display a chat message in the Playground.", "icon": "ChatOutput", @@ -1422,7 +1442,23 @@ "field_formatters": {}, "frozen": false, "field_order": [], - "beta": false + "beta": false, + "outputs": [ + { + "types": [ + "Text" + ], + "name": "Message", + "method": "text_response" + }, + { + "types": [ + "Record" + ], + "name": "Record", + "method": "record_response" + } + ] }, "id": "ChatOutput-Q39I8" }, @@ -1540,7 +1576,17 @@ "field_formatters": {}, "frozen": false, "field_order": [], - "beta": false + "beta": false, + "outputs": [ + { + "types": [ + "Record" + ], + "selected": null, + "name": "Record", + "method": null + } + ] }, "id": "File-t0a6a" }, @@ -1686,7 +1732,17 @@ "field_formatters": {}, "frozen": false, "field_order": [], - "beta": false + "beta": false, + "outputs": [ + { + "types": [ + "Record" + ], + "selected": null, + "name": "Record", + "method": null + } + ] }, "id": "RecursiveCharacterTextSplitter-tR9QM" }, @@ -2139,7 +2195,17 @@ "input_value", "embedding" ], - "beta": false + "beta": false, + "outputs": [ + { + "types": [ + "Record" + ], + "selected": null, + "name": "Record", + "method": null + } + ] }, "id": "AstraDBSearch-41nRz" }, @@ -2542,7 +2608,25 @@ "inputs", "embedding" ], - "beta": false + "beta": false, + "outputs": [ + { + "types": [ + "VectorStore" + ], + "selected": null, + "name": "VectorStore", + "method": null + }, + { + "types": [ + "BaseRetriever" + ], + "selected": null, + "name": "BaseRetriever", + "method": null + } + ] }, "id": "AstraDB-eUCSS" }, @@ -3077,7 +3161,17 @@ "field_formatters": {}, "frozen": false, "field_order": [], - "beta": false + "beta": false, + "outputs": [ + { + "types": [ + "Embeddings" + ], + "selected": null, + "name": "Embeddings", + "method": null + } + ] }, "id": "OpenAIEmbeddings-9TPjc" }, @@ -3095,7 +3189,7 @@ { "source": "TextOutput-BDknO", "target": "Prompt-xeI6K", - "sourceHandle": "{œbaseClassesœ:[œobjectœ,œTextœ,œstrœ],œdataTypeœ:œTextOutputœ,œidœ:œTextOutput-BDknOœ}", + "sourceHandle": "{\"dataType\": \"TextOutput\", \"id\": \"TextOutput-BDknO\", \"output_types\": [\"Text\"], \"name\": \"Text\"}", "targetHandle": "{œfieldNameœ:œcontextœ,œidœ:œPrompt-xeI6Kœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}", "id": "reactflow__edge-TextOutput-BDknO{œbaseClassesœ:[œobjectœ,œTextœ,œstrœ],œdataTypeœ:œTextOutputœ,œidœ:œTextOutput-BDknOœ}-Prompt-xeI6K{œfieldNameœ:œcontextœ,œidœ:œPrompt-xeI6Kœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}", "data": { @@ -3111,13 +3205,12 @@ "type": "str" }, "sourceHandle": { - "baseClasses": [ - "object", - "Text", - "str" - ], "dataType": "TextOutput", - "id": "TextOutput-BDknO" + "id": "TextOutput-BDknO", + "output_types": [ + "Text" + ], + "name": "Text" } }, "style": { @@ -3129,7 +3222,7 @@ { "source": "ChatInput-yxMKE", "target": "Prompt-xeI6K", - "sourceHandle": "{œbaseClassesœ:[œTextœ,œstrœ,œobjectœ,œRecordœ],œdataTypeœ:œChatInputœ,œidœ:œChatInput-yxMKEœ}", + "sourceHandle": "{\"dataType\": \"ChatInput\", \"id\": \"ChatInput-yxMKE\", \"output_types\": [\"Text\"], \"name\": \"Message\"}", "targetHandle": "{œfieldNameœ:œquestionœ,œidœ:œPrompt-xeI6Kœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}", "id": "reactflow__edge-ChatInput-yxMKE{œbaseClassesœ:[œTextœ,œstrœ,œobjectœ,œRecordœ],œdataTypeœ:œChatInputœ,œidœ:œChatInput-yxMKEœ}-Prompt-xeI6K{œfieldNameœ:œquestionœ,œidœ:œPrompt-xeI6Kœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}", "data": { @@ -3145,14 +3238,12 @@ "type": "str" }, "sourceHandle": { - "baseClasses": [ - "Text", - "str", - "object", - "Record" - ], "dataType": "ChatInput", - "id": "ChatInput-yxMKE" + "id": "ChatInput-yxMKE", + "output_types": [ + "Text" + ], + "name": "Message" } }, "style": { @@ -3164,7 +3255,7 @@ { "source": "Prompt-xeI6K", "target": "OpenAIModel-EjXlN", - "sourceHandle": "{œbaseClassesœ:[œobjectœ,œTextœ,œstrœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-xeI6Kœ}", + "sourceHandle": "{\"dataType\": \"Prompt\", \"id\": \"Prompt-xeI6K\", \"output_types\": [\"Text\"], \"name\": \"Text\"}", "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-EjXlNœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}", "id": "reactflow__edge-Prompt-xeI6K{œbaseClassesœ:[œobjectœ,œTextœ,œstrœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-xeI6Kœ}-OpenAIModel-EjXlN{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-EjXlNœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}", "data": { @@ -3177,13 +3268,12 @@ "type": "str" }, "sourceHandle": { - "baseClasses": [ - "object", - "Text", - "str" - ], "dataType": "Prompt", - "id": "Prompt-xeI6K" + "id": "Prompt-xeI6K", + "output_types": [ + "Text" + ], + "name": "Text" } }, "style": { @@ -3195,7 +3285,7 @@ { "source": "OpenAIModel-EjXlN", "target": "ChatOutput-Q39I8", - "sourceHandle": "{œbaseClassesœ:[œobjectœ,œTextœ,œstrœ],œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-EjXlNœ}", + "sourceHandle": "{\"dataType\": \"OpenAIModel\", \"id\": \"OpenAIModel-EjXlN\", \"output_types\": [\"Text\"], \"name\": \"Text\"}", "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-Q39I8œ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}", "id": "reactflow__edge-OpenAIModel-EjXlN{œbaseClassesœ:[œobjectœ,œTextœ,œstrœ],œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-EjXlNœ}-ChatOutput-Q39I8{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-Q39I8œ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}", "data": { @@ -3208,13 +3298,12 @@ "type": "str" }, "sourceHandle": { - "baseClasses": [ - "object", - "Text", - "str" - ], "dataType": "OpenAIModel", - "id": "OpenAIModel-EjXlN" + "id": "OpenAIModel-EjXlN", + "output_types": [ + "Text" + ], + "name": "Text" } }, "style": { @@ -3226,7 +3315,7 @@ { "source": "File-t0a6a", "target": "RecursiveCharacterTextSplitter-tR9QM", - "sourceHandle": "{œbaseClassesœ:[œRecordœ],œdataTypeœ:œFileœ,œidœ:œFile-t0a6aœ}", + "sourceHandle": "{\"dataType\": \"File\", \"id\": \"File-t0a6a\", \"output_types\": [\"Record\"], \"name\": \"Record\"}", "targetHandle": "{œfieldNameœ:œinputsœ,œidœ:œRecursiveCharacterTextSplitter-tR9QMœ,œinputTypesœ:[œDocumentœ,œRecordœ],œtypeœ:œDocumentœ}", "id": "reactflow__edge-File-t0a6a{œbaseClassesœ:[œRecordœ],œdataTypeœ:œFileœ,œidœ:œFile-t0a6aœ}-RecursiveCharacterTextSplitter-tR9QM{œfieldNameœ:œinputsœ,œidœ:œRecursiveCharacterTextSplitter-tR9QMœ,œinputTypesœ:[œDocumentœ,œRecordœ],œtypeœ:œDocumentœ}", "data": { @@ -3240,11 +3329,12 @@ "type": "Document" }, "sourceHandle": { - "baseClasses": [ + "dataType": "File", + "id": "File-t0a6a", + "output_types": [ "Record" ], - "dataType": "File", - "id": "File-t0a6a" + "name": "Record" } }, "style": { @@ -3255,7 +3345,7 @@ }, { "source": "OpenAIEmbeddings-ZlOk1", - "sourceHandle": "{œbaseClassesœ:[œEmbeddingsœ],œdataTypeœ:œOpenAIEmbeddingsœ,œidœ:œOpenAIEmbeddings-ZlOk1œ}", + "sourceHandle": "{\"dataType\": \"OpenAIEmbeddings\", \"id\": \"OpenAIEmbeddings-ZlOk1\", \"output_types\": [\"Embeddings\"], \"name\": \"Embeddings\"}", "target": "AstraDBSearch-41nRz", "targetHandle": "{œfieldNameœ:œembeddingœ,œidœ:œAstraDBSearch-41nRzœ,œinputTypesœ:null,œtypeœ:œEmbeddingsœ}", "data": { @@ -3266,11 +3356,12 @@ "type": "Embeddings" }, "sourceHandle": { - "baseClasses": [ + "dataType": "OpenAIEmbeddings", + "id": "OpenAIEmbeddings-ZlOk1", + "output_types": [ "Embeddings" ], - "dataType": "OpenAIEmbeddings", - "id": "OpenAIEmbeddings-ZlOk1" + "name": "Embeddings" } }, "style": { @@ -3281,7 +3372,7 @@ }, { "source": "ChatInput-yxMKE", - "sourceHandle": "{œbaseClassesœ:[œTextœ,œstrœ,œobjectœ,œRecordœ],œdataTypeœ:œChatInputœ,œidœ:œChatInput-yxMKEœ}", + "sourceHandle": "{\"dataType\": \"ChatInput\", \"id\": \"ChatInput-yxMKE\", \"output_types\": [\"Text\"], \"name\": \"Message\"}", "target": "AstraDBSearch-41nRz", "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œAstraDBSearch-41nRzœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}", "data": { @@ -3294,14 +3385,12 @@ "type": "str" }, "sourceHandle": { - "baseClasses": [ - "Text", - "str", - "object", - "Record" - ], "dataType": "ChatInput", - "id": "ChatInput-yxMKE" + "id": "ChatInput-yxMKE", + "output_types": [ + "Text" + ], + "name": "Message" } }, "style": { @@ -3312,7 +3401,7 @@ }, { "source": "RecursiveCharacterTextSplitter-tR9QM", - "sourceHandle": "{œbaseClassesœ:[œRecordœ],œdataTypeœ:œRecursiveCharacterTextSplitterœ,œidœ:œRecursiveCharacterTextSplitter-tR9QMœ}", + "sourceHandle": "{\"dataType\": \"RecursiveCharacterTextSplitter\", \"id\": \"RecursiveCharacterTextSplitter-tR9QM\", \"output_types\": [\"Record\"], \"name\": \"Record\"}", "target": "AstraDB-eUCSS", "targetHandle": "{œfieldNameœ:œinputsœ,œidœ:œAstraDB-eUCSSœ,œinputTypesœ:null,œtypeœ:œRecordœ}", "data": { @@ -3323,11 +3412,12 @@ "type": "Record" }, "sourceHandle": { - "baseClasses": [ + "dataType": "RecursiveCharacterTextSplitter", + "id": "RecursiveCharacterTextSplitter-tR9QM", + "output_types": [ "Record" ], - "dataType": "RecursiveCharacterTextSplitter", - "id": "RecursiveCharacterTextSplitter-tR9QM" + "name": "Record" } }, "style": { @@ -3339,7 +3429,7 @@ }, { "source": "OpenAIEmbeddings-9TPjc", - "sourceHandle": "{œbaseClassesœ:[œEmbeddingsœ],œdataTypeœ:œOpenAIEmbeddingsœ,œidœ:œOpenAIEmbeddings-9TPjcœ}", + "sourceHandle": "{\"dataType\": \"OpenAIEmbeddings\", \"id\": \"OpenAIEmbeddings-9TPjc\", \"output_types\": [\"Embeddings\"], \"name\": \"Embeddings\"}", "target": "AstraDB-eUCSS", "targetHandle": "{œfieldNameœ:œembeddingœ,œidœ:œAstraDB-eUCSSœ,œinputTypesœ:null,œtypeœ:œEmbeddingsœ}", "data": { @@ -3350,11 +3440,12 @@ "type": "Embeddings" }, "sourceHandle": { - "baseClasses": [ + "dataType": "OpenAIEmbeddings", + "id": "OpenAIEmbeddings-9TPjc", + "output_types": [ "Embeddings" ], - "dataType": "OpenAIEmbeddings", - "id": "OpenAIEmbeddings-9TPjc" + "name": "Embeddings" } }, "style": { @@ -3366,7 +3457,7 @@ }, { "source": "AstraDBSearch-41nRz", - "sourceHandle": "{œbaseClassesœ:[œRecordœ],œdataTypeœ:œAstraDBSearchœ,œidœ:œAstraDBSearch-41nRzœ}", + "sourceHandle": "{\"dataType\": \"AstraDBSearch\", \"id\": \"AstraDBSearch-41nRz\", \"output_types\": [\"Record\"], \"name\": \"Record\"}", "target": "TextOutput-BDknO", "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œTextOutput-BDknOœ,œinputTypesœ:[œRecordœ,œTextœ],œtypeœ:œstrœ}", "data": { @@ -3380,11 +3471,12 @@ "type": "str" }, "sourceHandle": { - "baseClasses": [ + "dataType": "AstraDBSearch", + "id": "AstraDBSearch-41nRz", + "output_types": [ "Record" ], - "dataType": "AstraDBSearch", - "id": "AstraDBSearch-41nRz" + "name": "Record" } }, "style": { From 12b3a9b7172e80f975bda55d447e89dc85ee1409 Mon Sep 17 00:00:00 2001 From: anovazzi1 Date: Mon, 3 Jun 2024 18:59:43 -0300 Subject: [PATCH 047/701] refactor: Remove console.log statements from ParameterComponent and fix clean edges --- .../components/parameterComponent/index.tsx | 18 +++++++------- src/frontend/src/utils/reactflowUtils.ts | 24 ++++++++++++------- 2 files changed, 23 insertions(+), 19 deletions(-) diff --git a/src/frontend/src/customNodes/genericNode/components/parameterComponent/index.tsx b/src/frontend/src/customNodes/genericNode/components/parameterComponent/index.tsx index 50773aabc..f1a4d93f1 100644 --- a/src/frontend/src/customNodes/genericNode/components/parameterComponent/index.tsx +++ b/src/frontend/src/customNodes/genericNode/components/parameterComponent/index.tsx @@ -62,8 +62,6 @@ export default function ParameterComponent({ index, outputName, }: ParameterComponentType): JSX.Element { - console.log("title", title); - console.log("data", data); const infoHtml = useRef(null); const nodes = useFlowStore((state) => state.nodes); const edges = useFlowStore((state) => state.edges); @@ -82,7 +80,7 @@ export default function ParameterComponent({ debouncedHandleUpdateValues, setNode, isLoading, - setIsLoading + setIsLoading, ); const { handleNodeClass: handleNodeClassHook } = useHandleNodeClass( @@ -90,7 +88,7 @@ export default function ParameterComponent({ name, takeSnapshot, setNode, - updateNodeInternals + updateNodeInternals, ); const { handleRefreshButtonPress: handleRefreshButtonPressHook } = @@ -99,7 +97,7 @@ export default function ParameterComponent({ let disabled = edges.some( (edge) => - edge.targetHandle === scapedJSONStringfy(proxy ? { ...id, proxy } : id) + edge.targetHandle === scapedJSONStringfy(proxy ? { ...id, proxy } : id), ) ?? false; const handleRefreshButtonPress = async (name, data) => { @@ -110,7 +108,7 @@ export default function ParameterComponent({ const handleOnNewValue = async ( newValue: string | string[] | boolean | Object[], - skipSnapshot: boolean | undefined = false + skipSnapshot: boolean | undefined = false, ): Promise => { handleOnNewValueHook(newValue, skipSnapshot); }; @@ -192,14 +190,14 @@ export default function ParameterComponent({ className={classNames( left ? "my-12 -ml-0.5 " : " my-12 -mr-0.5 ", "h-3 w-3 rounded-full border-2 bg-background", - !showNode ? "mt-0" : "" + !showNode ? "mt-0" : "", )} style={{ borderColor: color ?? nodeColors.unknown, }} onClick={() => { setFilterEdge( - groupByFamily(myData, tooltipTitle!, left, nodes!) + groupByFamily(myData, tooltipTitle!, left, nodes!), ); }} > @@ -284,12 +282,12 @@ export default function ParameterComponent({ } className={classNames( left ? "-ml-0.5" : "-mr-0.5", - "h-3 w-3 rounded-full border-2 bg-background" + "h-3 w-3 rounded-full border-2 bg-background", )} style={{ borderColor: color ?? nodeColors.unknown }} onClick={() => { setFilterEdge( - groupByFamily(myData, tooltipTitle!, left, nodes!) + groupByFamily(myData, tooltipTitle!, left, nodes!), ); }} /> diff --git a/src/frontend/src/utils/reactflowUtils.ts b/src/frontend/src/utils/reactflowUtils.ts index 261a39b6c..a32a2e31b 100644 --- a/src/frontend/src/utils/reactflowUtils.ts +++ b/src/frontend/src/utils/reactflowUtils.ts @@ -79,15 +79,21 @@ export function cleanEdges(nodes: NodeType[], edges: Edge[]) { const output = sourceNode.data.node!.outputs?.find( (output) => output.name === name, ); - const outputTypes = [output?.selected ?? ""]; - const id: sourceHandleType = { - id: sourceNode.data.id, - name: name, - output_types: outputTypes, - dataType: sourceNode.data.type, - }; - if (scapedJSONStringfy(id) !== sourceHandle) { - newEdges = newEdges.filter((e) => e.id !== edge.id); + if (output) { + const outputTypes = + output!.types.length === 1 ? output!.types : [output!.selected!]; + + const id: sourceHandleType = { + id: sourceNode.data.id, + name: name, + output_types: outputTypes, + dataType: sourceNode.data.type, + }; + console.log("id", id); + console.log("sourceHandle", scapeJSONParse(sourceHandle)); + if (scapedJSONStringfy(id) !== sourceHandle) { + newEdges = newEdges.filter((e) => e.id !== edge.id); + } } } }); From 1775fee61f99500eba839f9e4ab97ca414662804 Mon Sep 17 00:00:00 2001 From: ogabrielluiz Date: Mon, 3 Jun 2024 22:52:38 -0300 Subject: [PATCH 048/701] Fix components update and tests --- src/backend/base/langflow/base/constants.py | 1 + .../langflow/components/inputs/ChatInput.py | 25 +++++-- .../langflow/components/outputs/ChatOutput.py | 29 +++++++-- .../custom/custom_component/component.py | 65 ++++++++++++++++++- .../base/langflow/graph/vertex/base.py | 2 +- .../base/langflow/graph/vertex/types.py | 26 ++++---- .../Basic Prompting (Hello, world!).json | 44 ++++++------- .../Langflow Blog Writter.json | 20 +++--- .../Langflow Document QA.json | 36 +++++----- .../Langflow Memory Conversation.json | 34 +++++----- .../Langflow Prompt Chaining.json | 40 ++++++------ .../VectorStore-RAG-Flows.json | 34 +++++----- .../langflow/interface/initialize/loading.py | 31 +-------- tests/data/component_multiple_outputs.py | 4 +- tests/data/component_nested_call.py | 4 +- 15 files changed, 237 insertions(+), 158 deletions(-) diff --git a/src/backend/base/langflow/base/constants.py b/src/backend/base/langflow/base/constants.py index 498b46f65..02a58f964 100644 --- a/src/backend/base/langflow/base/constants.py +++ b/src/backend/base/langflow/base/constants.py @@ -26,4 +26,5 @@ FIELD_FORMAT_ATTRIBUTES = [ "refresh_button", "refresh_button_text", "options", + "advanced", ] diff --git a/src/backend/base/langflow/components/inputs/ChatInput.py b/src/backend/base/langflow/components/inputs/ChatInput.py index b20d73764..ae08a7a20 100644 --- a/src/backend/base/langflow/components/inputs/ChatInput.py +++ b/src/backend/base/langflow/components/inputs/ChatInput.py @@ -10,10 +10,27 @@ class ChatInput(ChatComponent): icon = "ChatInput" inputs = [ - Input(name="input_value", type=str, display_name="Message", multiline=True, input_types=[]), - Input(name="sender", type=str, display_name="Sender Type", options=["Machine", "User"]), - Input(name="sender_name", type=str, display_name="Sender Name"), - Input(name="session_id", type=str, display_name="Session ID"), + Input( + name="input_value", + type=str, + display_name="Message", + multiline=True, + input_types=[], + info="Message to be passed as input.", + ), + Input( + name="sender", + type=str, + display_name="Sender Type", + options=["Machine", "User"], + value="User", + info="Type of sender.", + advanced=True, + ), + Input(name="sender_name", type=str, display_name="Sender Name", info="Name of the sender.", value="User"), + Input( + name="session_id", type=str, display_name="Session ID", info="Session ID for the message.", advanced=True + ), ] outputs = [ Output(name="Message", method="text_response"), diff --git a/src/backend/base/langflow/components/outputs/ChatOutput.py b/src/backend/base/langflow/components/outputs/ChatOutput.py index 3e44d38aa..064cd92f1 100644 --- a/src/backend/base/langflow/components/outputs/ChatOutput.py +++ b/src/backend/base/langflow/components/outputs/ChatOutput.py @@ -10,11 +10,30 @@ class ChatOutput(ChatComponent): icon = "ChatOutput" inputs = [ - Input(name="input_value", type=str, display_name="Message", multiline=True), - Input(name="sender", type=str, display_name="Sender Type", options=["Machine", "AI"]), - Input(name="sender_name", type=str, display_name="Sender Name"), - Input(name="session_id", type=str, display_name="Session ID"), - Input(name="record_template", type=str, display_name="Record Template", default="{text}"), + Input( + name="input_value", type=str, display_name="Message", multiline=True, info="Message to be passed as output." + ), + Input( + name="sender", + type=str, + display_name="Sender Type", + options=["Machine", "User"], + value="Machine", + advanced=True, + info="Type of sender.", + ), + Input(name="sender_name", type=str, display_name="Sender Name", info="Name of the sender.", value="AI"), + Input( + name="session_id", type=str, display_name="Session ID", info="Session ID for the message.", advanced=True + ), + Input( + name="record_template", + type=str, + display_name="Record Template", + value="{text}", + advanced=True, + info="Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.", + ), ] outputs = [ Output(name="Message", method="text_response"), diff --git a/src/backend/base/langflow/custom/custom_component/component.py b/src/backend/base/langflow/custom/custom_component/component.py index b5f3bfcad..551c47a08 100644 --- a/src/backend/base/langflow/custom/custom_component/component.py +++ b/src/backend/base/langflow/custom/custom_component/component.py @@ -1,9 +1,38 @@ -from typing import ClassVar, List, Optional +import inspect +from typing import TYPE_CHECKING, AsyncIterator, Awaitable, Callable, ClassVar, Generator, Iterator, List, Optional +import yaml +from loguru import logger +from pydantic import BaseModel + +from langflow.schema.schema import Record from langflow.template.field.base import Input, Output from .custom_component import CustomComponent +if TYPE_CHECKING: + from langflow.graph.vertex.base import Vertex + + +def recursive_serialize_or_str(obj): + try: + if isinstance(obj, dict): + return {k: recursive_serialize_or_str(v) for k, v in obj.items()} + elif isinstance(obj, list): + return [recursive_serialize_or_str(v) for v in obj] + elif isinstance(obj, BaseModel): + return {k: recursive_serialize_or_str(v) for k, v in obj.model_dump().items()} + elif isinstance(obj, (AsyncIterator, Generator, Iterator)): + # Turn it into something readable that does not + # contain memory addresses + # without consuming the iterator + # return list(obj) consumes the iterator + # return f"{obj}" this generates '' + # it is not useful + return "Unconsumed Stream" + except Exception: + return str(obj) + class Component(CustomComponent): inputs: Optional[List[Input]] = None @@ -15,3 +44,37 @@ class Component(CustomComponent): if key in self.__dict__: raise ValueError(f"Key {key} already exists in {self.__class__.__name__}") setattr(self, key, value) + + async def build_results(self, vertex: "Vertex"): + build_results = {} + + if hasattr(self, "outputs"): + for output in self.outputs: + # Build the output if it's connected to some other vertex + # or if it's not connected to any vertex + if not vertex.outgoing_edges or output.name in vertex.edges_source_names: + method: Callable | Awaitable = getattr(self, output.method) + result = method() + # If the method is asynchronous, we need to await it + if inspect.iscoroutinefunction(method): + result = await result + build_results[output.name] = result + self.build_results = build_results + return build_results + + def custom_repr(self): + # ! Temporary REPR + # Since all are dict, yaml.dump them + if isinstance(self.build_results, dict): + _build_results = recursive_serialize_or_str(self.build_results) + try: + custom_repr = yaml.dump(_build_results) + except Exception as e: + logger.error(f"Error while dumping build_result: {e}") + custom_repr = str(self.build_results) + + if custom_repr is None and isinstance(self.build_results, (dict, Record, str)): + custom_repr = self.build_results + if not isinstance(custom_repr, str): + custom_repr = str(custom_repr) + return custom_repr diff --git a/src/backend/base/langflow/graph/vertex/base.py b/src/backend/base/langflow/graph/vertex/base.py index bde56383f..2ced35a1c 100644 --- a/src/backend/base/langflow/graph/vertex/base.py +++ b/src/backend/base/langflow/graph/vertex/base.py @@ -217,7 +217,7 @@ class Vertex: raise ValueError(f"Outputs not found for {self.display_name}") self.outputs = self.data["node"]["outputs"] else: - self.outputs = self.data["node"]["outputs"] + self.outputs = self.data["node"].get("outputs", []) self.output = self.data["node"]["base_classes"] self.display_name = self.data["node"].get("display_name", self.id.split("-")[0]) diff --git a/src/backend/base/langflow/graph/vertex/types.py b/src/backend/base/langflow/graph/vertex/types.py index 5320d5e77..af2369fcd 100644 --- a/src/backend/base/langflow/graph/vertex/types.py +++ b/src/backend/base/langflow/graph/vertex/types.py @@ -3,7 +3,7 @@ from typing import Any, AsyncIterator, Dict, Iterator, List import yaml from git import TYPE_CHECKING -from langchain_core.messages import AIMessage +from langchain_core.messages import AIMessage, AIMessageChunk from loguru import logger from langflow.graph.schema import CHAT_COMPONENTS, RECORDS_COMPONENTS, InterfaceComponentTypes @@ -156,28 +156,32 @@ class InterfaceVertex(ComponentVertex): if isinstance(message, str): message = unescape_string(message) stream_url = None - if isinstance(self._built_object, AIMessage): + text_output = self.results["Message"] + if isinstance(text_output, (AIMessage, AIMessageChunk)): artifacts = ChatOutputResponse.from_message( - self._built_object, + text_output, sender=sender, sender_name=sender_name, ) - elif not isinstance(self._built_object, UnbuiltObject): - if isinstance(self._built_object, dict): + elif not isinstance(text_output, UnbuiltObject): + if isinstance(text_output, dict): # Turn the dict into a pleasing to # read JSON inside a code block - message = dict_to_codeblock(self._built_object) - elif isinstance(self._built_object, Record): - message = self._built_object.text + message = dict_to_codeblock(text_output) + elif isinstance(text_output, Record): + message = text_output.text elif isinstance(message, (AsyncIterator, Iterator)): stream_url = self.build_stream_url() message = "" - elif not isinstance(self._built_object, str): - message = str(self._built_object) + self.results["Message"] = message + self.results["Record"].message = message + self._built_object = self.results + elif not isinstance(text_output, str): + message = str(text_output) # if the message is a generator or iterator # it means that it is a stream of messages else: - message = self._built_object + message = text_output artifacts = ChatOutputResponse( message=message, diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting (Hello, world!).json b/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting (Hello, world!).json index c3faf03a4..eb84557a2 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting (Hello, world!).json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting (Hello, world!).json @@ -438,7 +438,7 @@ "list": false, "show": true, "multiline": true, - "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\nfrom langflow.template import Input, Output\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n inputs = [\n Input(name=\"input_value\", type=str, display_name=\"Message\", multiline=True),\n Input(name=\"sender\", type=str, display_name=\"Sender Type\", options=[\"Machine\", \"AI\"]),\n Input(name=\"sender_name\", type=str, display_name=\"Sender Name\"),\n Input(name=\"session_id\", type=str, display_name=\"Session ID\"),\n Input(name=\"record_template\", type=str, display_name=\"Record Template\", default=\"{text}\"),\n ]\n outputs = [\n Output(name=\"Message\", method=\"text_response\"),\n Output(name=\"Record\", method=\"record_response\"),\n ]\n\n def text_response(self) -> Text:\n result = self.input_value\n if self.session_id and isinstance(result, (Record, str)):\n self.store_message(result, self.session_id, self.sender, self.sender_name)\n return result\n\n def record_response(self) -> Record:\n record = Record(\n data={\n \"message\": self.input_value,\n \"sender\": self.sender,\n \"sender_name\": self.sender_name,\n \"session_id\": self.session_id,\n \"template\": self.record_template or \"\",\n }\n )\n if self.session_id and isinstance(record, (Record, str)):\n self.store_message(record, self.session_id, self.sender, self.sender_name)\n return record\n", + "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\nfrom langflow.template import Input, Output\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n inputs = [\n Input(\n name=\"input_value\", type=str, display_name=\"Message\", multiline=True, info=\"Message to be passed as output.\"\n ),\n Input(\n name=\"sender\",\n type=str,\n display_name=\"Sender Type\",\n options=[\"Machine\", \"User\"],\n value=\"Machine\",\n advanced=True,\n info=\"Type of sender.\",\n ),\n Input(name=\"sender_name\", type=str, display_name=\"Sender Name\", info=\"Name of the sender.\", value=\"AI\"),\n Input(\n name=\"session_id\", type=str, display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True\n ),\n Input(\n name=\"record_template\",\n type=str,\n display_name=\"Record Template\",\n value=\"{text}\",\n advanced=True,\n info=\"Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.\",\n ),\n ]\n outputs = [\n Output(name=\"Message\", method=\"text_response\"),\n Output(name=\"Record\", method=\"record_response\"),\n ]\n\n def text_response(self) -> Text:\n result = self.input_value\n if self.session_id and isinstance(result, (Record, str)):\n self.store_message(result, self.session_id, self.sender, self.sender_name)\n return result\n\n def record_response(self) -> Record:\n record = Record(\n data={\n \"message\": self.input_value,\n \"sender\": self.sender,\n \"sender_name\": self.sender_name,\n \"session_id\": self.session_id,\n \"template\": self.record_template or \"\",\n }\n )\n if self.session_id and isinstance(record, (Record, str)):\n self.store_message(record, self.session_id, self.sender, self.sender_name)\n return record\n", "fileTypes": [], "file_path": "", "password": false, @@ -464,7 +464,7 @@ "display_name": "Message", "advanced": false, "dynamic": false, - "info": "", + "info": "Message to be passed as output.", "load_from_db": false, "title_case": false, "input_types": [ @@ -484,9 +484,9 @@ "password": false, "name": "record_template", "display_name": "Record Template", - "advanced": false, + "advanced": true, "dynamic": false, - "info": "", + "info": "Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.", "load_from_db": false, "title_case": false, "input_types": [ @@ -500,19 +500,19 @@ "list": false, "show": true, "multiline": false, - "value": "", + "value": "Machine", "fileTypes": [], "file_path": "", "password": false, "options": [ "Machine", - "AI" + "User" ], "name": "sender", "display_name": "Sender Type", - "advanced": false, + "advanced": true, "dynamic": false, - "info": "", + "info": "Type of sender.", "load_from_db": false, "title_case": false, "input_types": [ @@ -526,7 +526,7 @@ "list": false, "show": true, "multiline": false, - "value": "", + "value": "AI", "fileTypes": [], "file_path": "", "password": false, @@ -534,7 +534,7 @@ "display_name": "Sender Name", "advanced": false, "dynamic": false, - "info": "", + "info": "Name of the sender.", "load_from_db": false, "title_case": false, "input_types": [ @@ -554,9 +554,9 @@ "password": false, "name": "session_id", "display_name": "Session ID", - "advanced": false, + "advanced": true, "dynamic": false, - "info": "", + "info": "Session ID for the message.", "load_from_db": false, "title_case": false, "input_types": [ @@ -637,7 +637,7 @@ "list": false, "show": true, "multiline": true, - "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\nfrom langflow.template import Input, Output\n\n\nclass ChatInput(ChatComponent):\n display_name = \"Chat Input\"\n description = \"Get chat inputs from the Playground.\"\n icon = \"ChatInput\"\n\n inputs = [\n Input(name=\"input_value\", type=str, display_name=\"Message\", multiline=True, input_types=[]),\n Input(name=\"sender\", type=str, display_name=\"Sender Type\", options=[\"Machine\", \"User\"]),\n Input(name=\"sender_name\", type=str, display_name=\"Sender Name\"),\n Input(name=\"session_id\", type=str, display_name=\"Session ID\"),\n ]\n outputs = [\n Output(name=\"Message\", method=\"text_response\"),\n Output(name=\"Record\", method=\"record_response\"),\n ]\n\n def text_response(self) -> Text:\n result = self.input_value\n if self.session_id and isinstance(result, (Record, str)):\n self.store_message(result, self.session_id, self.sender, self.sender_name)\n return result\n\n def record_response(self) -> Record:\n record = Record(\n data={\n \"text\": self.input_value,\n \"sender\": self.sender,\n \"sender_name\": self.sender_name,\n \"session_id\": self.session_id,\n },\n )\n if self.session_id and isinstance(record, (Record, str)):\n self.store_message(record, self.session_id, self.sender, self.sender_name)\n return record\n", + "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\nfrom langflow.template import Input, Output\n\n\nclass ChatInput(ChatComponent):\n display_name = \"Chat Input\"\n description = \"Get chat inputs from the Playground.\"\n icon = \"ChatInput\"\n\n inputs = [\n Input(\n name=\"input_value\",\n type=str,\n display_name=\"Message\",\n multiline=True,\n input_types=[],\n info=\"Message to be passed as input.\",\n ),\n Input(\n name=\"sender\",\n type=str,\n display_name=\"Sender Type\",\n options=[\"Machine\", \"User\"],\n value=\"User\",\n info=\"Type of sender.\",\n advanced=True,\n ),\n Input(name=\"sender_name\", type=str, display_name=\"Sender Name\", info=\"Name of the sender.\", value=\"User\"),\n Input(\n name=\"session_id\", type=str, display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True\n ),\n ]\n outputs = [\n Output(name=\"Message\", method=\"text_response\"),\n Output(name=\"Record\", method=\"record_response\"),\n ]\n\n def text_response(self) -> Text:\n result = self.input_value\n if self.session_id and isinstance(result, (Record, str)):\n self.store_message(result, self.session_id, self.sender, self.sender_name)\n return result\n\n def record_response(self) -> Record:\n record = Record(\n data={\n \"text\": self.input_value,\n \"sender\": self.sender,\n \"sender_name\": self.sender_name,\n \"session_id\": self.session_id,\n },\n )\n if self.session_id and isinstance(record, (Record, str)):\n self.store_message(record, self.session_id, self.sender, self.sender_name)\n return record\n", "fileTypes": [], "file_path": "", "password": false, @@ -655,7 +655,7 @@ "list": false, "show": true, "multiline": true, - "value": "", + "value": "Hello, world!", "fileTypes": [], "file_path": "", "password": false, @@ -664,7 +664,7 @@ "advanced": false, "input_types": [], "dynamic": false, - "info": "", + "info": "Message to be passed as input.", "load_from_db": false, "title_case": false }, @@ -675,7 +675,7 @@ "list": false, "show": true, "multiline": false, - "value": "", + "value": "User", "fileTypes": [], "file_path": "", "password": false, @@ -685,9 +685,9 @@ ], "name": "sender", "display_name": "Sender Type", - "advanced": false, + "advanced": true, "dynamic": false, - "info": "", + "info": "Type of sender.", "load_from_db": false, "title_case": false, "input_types": [ @@ -701,7 +701,7 @@ "list": false, "show": true, "multiline": false, - "value": "", + "value": "User", "fileTypes": [], "file_path": "", "password": false, @@ -709,7 +709,7 @@ "display_name": "Sender Name", "advanced": false, "dynamic": false, - "info": "", + "info": "Name of the sender.", "load_from_db": false, "title_case": false, "input_types": [ @@ -729,9 +729,9 @@ "password": false, "name": "session_id", "display_name": "Session ID", - "advanced": false, + "advanced": true, "dynamic": false, - "info": "", + "info": "Session ID for the message.", "load_from_db": false, "title_case": false, "input_types": [ diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Blog Writter.json b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Blog Writter.json index 4dc94331d..67608d40f 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Blog Writter.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Blog Writter.json @@ -298,7 +298,7 @@ "list": false, "show": true, "multiline": true, - "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\nfrom langflow.template import Input, Output\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n inputs = [\n Input(name=\"input_value\", type=str, display_name=\"Message\", multiline=True),\n Input(name=\"sender\", type=str, display_name=\"Sender Type\", options=[\"Machine\", \"AI\"]),\n Input(name=\"sender_name\", type=str, display_name=\"Sender Name\"),\n Input(name=\"session_id\", type=str, display_name=\"Session ID\"),\n Input(name=\"record_template\", type=str, display_name=\"Record Template\", default=\"{text}\"),\n ]\n outputs = [\n Output(name=\"Message\", method=\"text_response\"),\n Output(name=\"Record\", method=\"record_response\"),\n ]\n\n def text_response(self) -> Text:\n result = self.input_value\n if self.session_id and isinstance(result, (Record, str)):\n self.store_message(result, self.session_id, self.sender, self.sender_name)\n return result\n\n def record_response(self) -> Record:\n record = Record(\n data={\n \"message\": self.input_value,\n \"sender\": self.sender,\n \"sender_name\": self.sender_name,\n \"session_id\": self.session_id,\n \"template\": self.record_template or \"\",\n }\n )\n if self.session_id and isinstance(record, (Record, str)):\n self.store_message(record, self.session_id, self.sender, self.sender_name)\n return record\n", + "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\nfrom langflow.template import Input, Output\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n inputs = [\n Input(\n name=\"input_value\", type=str, display_name=\"Message\", multiline=True, info=\"Message to be passed as output.\"\n ),\n Input(\n name=\"sender\",\n type=str,\n display_name=\"Sender Type\",\n options=[\"Machine\", \"User\"],\n value=\"Machine\",\n advanced=True,\n info=\"Type of sender.\",\n ),\n Input(name=\"sender_name\", type=str, display_name=\"Sender Name\", info=\"Name of the sender.\", value=\"AI\"),\n Input(\n name=\"session_id\", type=str, display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True\n ),\n Input(\n name=\"record_template\",\n type=str,\n display_name=\"Record Template\",\n value=\"{text}\",\n advanced=True,\n info=\"Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.\",\n ),\n ]\n outputs = [\n Output(name=\"Message\", method=\"text_response\"),\n Output(name=\"Record\", method=\"record_response\"),\n ]\n\n def text_response(self) -> Text:\n result = self.input_value\n if self.session_id and isinstance(result, (Record, str)):\n self.store_message(result, self.session_id, self.sender, self.sender_name)\n return result\n\n def record_response(self) -> Record:\n record = Record(\n data={\n \"message\": self.input_value,\n \"sender\": self.sender,\n \"sender_name\": self.sender_name,\n \"session_id\": self.session_id,\n \"template\": self.record_template or \"\",\n }\n )\n if self.session_id and isinstance(record, (Record, str)):\n self.store_message(record, self.session_id, self.sender, self.sender_name)\n return record\n", "fileTypes": [], "file_path": "", "password": false, @@ -324,7 +324,7 @@ "display_name": "Message", "advanced": false, "dynamic": false, - "info": "", + "info": "Message to be passed as output.", "load_from_db": false, "title_case": false, "input_types": [ @@ -344,9 +344,9 @@ "password": false, "name": "record_template", "display_name": "Record Template", - "advanced": false, + "advanced": true, "dynamic": false, - "info": "", + "info": "Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.", "load_from_db": false, "title_case": false, "input_types": [ @@ -366,13 +366,13 @@ "password": false, "options": [ "Machine", - "AI" + "User" ], "name": "sender", "display_name": "Sender Type", - "advanced": false, + "advanced": true, "dynamic": false, - "info": "", + "info": "Type of sender.", "load_from_db": false, "title_case": false, "input_types": [ @@ -394,7 +394,7 @@ "display_name": "Sender Name", "advanced": false, "dynamic": false, - "info": "", + "info": "Name of the sender.", "load_from_db": false, "title_case": false, "input_types": [ @@ -414,9 +414,9 @@ "password": false, "name": "session_id", "display_name": "Session ID", - "advanced": false, + "advanced": true, "dynamic": false, - "info": "", + "info": "Session ID for the message.", "load_from_db": false, "title_case": false, "input_types": [ diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Document QA.json b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Document QA.json index c5938f438..7defd69dc 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Document QA.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Document QA.json @@ -296,7 +296,7 @@ "list": false, "show": true, "multiline": true, - "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\nfrom langflow.template import Input, Output\n\n\nclass ChatInput(ChatComponent):\n display_name = \"Chat Input\"\n description = \"Get chat inputs from the Playground.\"\n icon = \"ChatInput\"\n\n inputs = [\n Input(name=\"input_value\", type=str, display_name=\"Message\", multiline=True, input_types=[]),\n Input(name=\"sender\", type=str, display_name=\"Sender Type\", options=[\"Machine\", \"User\"]),\n Input(name=\"sender_name\", type=str, display_name=\"Sender Name\"),\n Input(name=\"session_id\", type=str, display_name=\"Session ID\"),\n ]\n outputs = [\n Output(name=\"Message\", method=\"text_response\"),\n Output(name=\"Record\", method=\"record_response\"),\n ]\n\n def text_response(self) -> Text:\n result = self.input_value\n if self.session_id and isinstance(result, (Record, str)):\n self.store_message(result, self.session_id, self.sender, self.sender_name)\n return result\n\n def record_response(self) -> Record:\n record = Record(\n data={\n \"text\": self.input_value,\n \"sender\": self.sender,\n \"sender_name\": self.sender_name,\n \"session_id\": self.session_id,\n },\n )\n if self.session_id and isinstance(record, (Record, str)):\n self.store_message(record, self.session_id, self.sender, self.sender_name)\n return record\n", + "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\nfrom langflow.template import Input, Output\n\n\nclass ChatInput(ChatComponent):\n display_name = \"Chat Input\"\n description = \"Get chat inputs from the Playground.\"\n icon = \"ChatInput\"\n\n inputs = [\n Input(\n name=\"input_value\",\n type=str,\n display_name=\"Message\",\n multiline=True,\n input_types=[],\n info=\"Message to be passed as input.\",\n ),\n Input(\n name=\"sender\",\n type=str,\n display_name=\"Sender Type\",\n options=[\"Machine\", \"User\"],\n value=\"User\",\n info=\"Type of sender.\",\n advanced=True,\n ),\n Input(name=\"sender_name\", type=str, display_name=\"Sender Name\", info=\"Name of the sender.\", value=\"User\"),\n Input(\n name=\"session_id\", type=str, display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True\n ),\n ]\n outputs = [\n Output(name=\"Message\", method=\"text_response\"),\n Output(name=\"Record\", method=\"record_response\"),\n ]\n\n def text_response(self) -> Text:\n result = self.input_value\n if self.session_id and isinstance(result, (Record, str)):\n self.store_message(result, self.session_id, self.sender, self.sender_name)\n return result\n\n def record_response(self) -> Record:\n record = Record(\n data={\n \"text\": self.input_value,\n \"sender\": self.sender,\n \"sender_name\": self.sender_name,\n \"session_id\": self.session_id,\n },\n )\n if self.session_id and isinstance(record, (Record, str)):\n self.store_message(record, self.session_id, self.sender, self.sender_name)\n return record\n", "fileTypes": [], "file_path": "", "password": false, @@ -323,7 +323,7 @@ "advanced": false, "input_types": [], "dynamic": false, - "info": "", + "info": "Message to be passed as input.", "load_from_db": false, "title_case": false }, @@ -344,9 +344,9 @@ ], "name": "sender", "display_name": "Sender Type", - "advanced": false, + "advanced": true, "dynamic": false, - "info": "", + "info": "Type of sender.", "load_from_db": false, "title_case": false, "input_types": [ @@ -368,7 +368,7 @@ "display_name": "Sender Name", "advanced": false, "dynamic": false, - "info": "", + "info": "Name of the sender.", "load_from_db": false, "title_case": false, "input_types": [ @@ -388,9 +388,9 @@ "password": false, "name": "session_id", "display_name": "Session ID", - "advanced": false, + "advanced": true, "dynamic": false, - "info": "", + "info": "Session ID for the message.", "load_from_db": false, "title_case": false, "input_types": [ @@ -470,7 +470,7 @@ "list": false, "show": true, "multiline": true, - "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\nfrom langflow.template import Input, Output\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n inputs = [\n Input(name=\"input_value\", type=str, display_name=\"Message\", multiline=True),\n Input(name=\"sender\", type=str, display_name=\"Sender Type\", options=[\"Machine\", \"AI\"]),\n Input(name=\"sender_name\", type=str, display_name=\"Sender Name\"),\n Input(name=\"session_id\", type=str, display_name=\"Session ID\"),\n Input(name=\"record_template\", type=str, display_name=\"Record Template\", default=\"{text}\"),\n ]\n outputs = [\n Output(name=\"Message\", method=\"text_response\"),\n Output(name=\"Record\", method=\"record_response\"),\n ]\n\n def text_response(self) -> Text:\n result = self.input_value\n if self.session_id and isinstance(result, (Record, str)):\n self.store_message(result, self.session_id, self.sender, self.sender_name)\n return result\n\n def record_response(self) -> Record:\n record = Record(\n data={\n \"message\": self.input_value,\n \"sender\": self.sender,\n \"sender_name\": self.sender_name,\n \"session_id\": self.session_id,\n \"template\": self.record_template or \"\",\n }\n )\n if self.session_id and isinstance(record, (Record, str)):\n self.store_message(record, self.session_id, self.sender, self.sender_name)\n return record\n", + "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\nfrom langflow.template import Input, Output\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n inputs = [\n Input(\n name=\"input_value\", type=str, display_name=\"Message\", multiline=True, info=\"Message to be passed as output.\"\n ),\n Input(\n name=\"sender\",\n type=str,\n display_name=\"Sender Type\",\n options=[\"Machine\", \"User\"],\n value=\"Machine\",\n advanced=True,\n info=\"Type of sender.\",\n ),\n Input(name=\"sender_name\", type=str, display_name=\"Sender Name\", info=\"Name of the sender.\", value=\"AI\"),\n Input(\n name=\"session_id\", type=str, display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True\n ),\n Input(\n name=\"record_template\",\n type=str,\n display_name=\"Record Template\",\n value=\"{text}\",\n advanced=True,\n info=\"Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.\",\n ),\n ]\n outputs = [\n Output(name=\"Message\", method=\"text_response\"),\n Output(name=\"Record\", method=\"record_response\"),\n ]\n\n def text_response(self) -> Text:\n result = self.input_value\n if self.session_id and isinstance(result, (Record, str)):\n self.store_message(result, self.session_id, self.sender, self.sender_name)\n return result\n\n def record_response(self) -> Record:\n record = Record(\n data={\n \"message\": self.input_value,\n \"sender\": self.sender,\n \"sender_name\": self.sender_name,\n \"session_id\": self.session_id,\n \"template\": self.record_template or \"\",\n }\n )\n if self.session_id and isinstance(record, (Record, str)):\n self.store_message(record, self.session_id, self.sender, self.sender_name)\n return record\n", "fileTypes": [], "file_path": "", "password": false, @@ -496,7 +496,7 @@ "display_name": "Message", "advanced": false, "dynamic": false, - "info": "", + "info": "Message to be passed as output.", "load_from_db": false, "title_case": false, "input_types": [ @@ -516,9 +516,9 @@ "password": false, "name": "record_template", "display_name": "Record Template", - "advanced": false, + "advanced": true, "dynamic": false, - "info": "", + "info": "Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.", "load_from_db": false, "title_case": false, "input_types": [ @@ -538,13 +538,13 @@ "password": false, "options": [ "Machine", - "AI" + "User" ], "name": "sender", "display_name": "Sender Type", - "advanced": false, + "advanced": true, "dynamic": false, - "info": "", + "info": "Type of sender.", "load_from_db": false, "title_case": false, "input_types": [ @@ -566,7 +566,7 @@ "display_name": "Sender Name", "advanced": false, "dynamic": false, - "info": "", + "info": "Name of the sender.", "load_from_db": false, "title_case": false, "input_types": [ @@ -586,9 +586,9 @@ "password": false, "name": "session_id", "display_name": "Session ID", - "advanced": false, + "advanced": true, "dynamic": false, - "info": "", + "info": "Session ID for the message.", "load_from_db": false, "title_case": false, "input_types": [ @@ -823,7 +823,7 @@ "password": false, "name": "stream", "display_name": "Stream", - "advanced": false, + "advanced": true, "dynamic": false, "info": "Stream the response from the model. Streaming works only in Chat.", "load_from_db": false, diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Memory Conversation.json b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Memory Conversation.json index 62b4338d6..c490d8d4c 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Memory Conversation.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Memory Conversation.json @@ -22,7 +22,7 @@ "list": false, "show": true, "multiline": true, - "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\nfrom langflow.template import Input, Output\n\n\nclass ChatInput(ChatComponent):\n display_name = \"Chat Input\"\n description = \"Get chat inputs from the Playground.\"\n icon = \"ChatInput\"\n\n inputs = [\n Input(name=\"input_value\", type=str, display_name=\"Message\", multiline=True, input_types=[]),\n Input(name=\"sender\", type=str, display_name=\"Sender Type\", options=[\"Machine\", \"User\"]),\n Input(name=\"sender_name\", type=str, display_name=\"Sender Name\"),\n Input(name=\"session_id\", type=str, display_name=\"Session ID\"),\n ]\n outputs = [\n Output(name=\"Message\", method=\"text_response\"),\n Output(name=\"Record\", method=\"record_response\"),\n ]\n\n def text_response(self) -> Text:\n result = self.input_value\n if self.session_id and isinstance(result, (Record, str)):\n self.store_message(result, self.session_id, self.sender, self.sender_name)\n return result\n\n def record_response(self) -> Record:\n record = Record(\n data={\n \"text\": self.input_value,\n \"sender\": self.sender,\n \"sender_name\": self.sender_name,\n \"session_id\": self.session_id,\n },\n )\n if self.session_id and isinstance(record, (Record, str)):\n self.store_message(record, self.session_id, self.sender, self.sender_name)\n return record\n", + "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\nfrom langflow.template import Input, Output\n\n\nclass ChatInput(ChatComponent):\n display_name = \"Chat Input\"\n description = \"Get chat inputs from the Playground.\"\n icon = \"ChatInput\"\n\n inputs = [\n Input(\n name=\"input_value\",\n type=str,\n display_name=\"Message\",\n multiline=True,\n input_types=[],\n info=\"Message to be passed as input.\",\n ),\n Input(\n name=\"sender\",\n type=str,\n display_name=\"Sender Type\",\n options=[\"Machine\", \"User\"],\n value=\"User\",\n info=\"Type of sender.\",\n advanced=True,\n ),\n Input(name=\"sender_name\", type=str, display_name=\"Sender Name\", info=\"Name of the sender.\", value=\"User\"),\n Input(\n name=\"session_id\", type=str, display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True\n ),\n ]\n outputs = [\n Output(name=\"Message\", method=\"text_response\"),\n Output(name=\"Record\", method=\"record_response\"),\n ]\n\n def text_response(self) -> Text:\n result = self.input_value\n if self.session_id and isinstance(result, (Record, str)):\n self.store_message(result, self.session_id, self.sender, self.sender_name)\n return result\n\n def record_response(self) -> Record:\n record = Record(\n data={\n \"text\": self.input_value,\n \"sender\": self.sender,\n \"sender_name\": self.sender_name,\n \"session_id\": self.session_id,\n },\n )\n if self.session_id and isinstance(record, (Record, str)):\n self.store_message(record, self.session_id, self.sender, self.sender_name)\n return record\n", "fileTypes": [], "file_path": "", "password": false, @@ -49,7 +49,7 @@ "advanced": false, "input_types": [], "dynamic": false, - "info": "", + "info": "Message to be passed as input.", "load_from_db": false, "title_case": false }, @@ -70,9 +70,9 @@ ], "name": "sender", "display_name": "Sender Type", - "advanced": false, + "advanced": true, "dynamic": false, - "info": "", + "info": "Type of sender.", "load_from_db": false, "title_case": false, "input_types": [ @@ -94,7 +94,7 @@ "display_name": "Sender Name", "advanced": false, "dynamic": false, - "info": "", + "info": "Name of the sender.", "load_from_db": false, "title_case": false, "input_types": [ @@ -114,9 +114,9 @@ "password": false, "name": "session_id", "display_name": "Session ID", - "advanced": false, + "advanced": true, "dynamic": false, - "info": "", + "info": "Session ID for the message.", "load_from_db": false, "title_case": false, "input_types": [ @@ -196,7 +196,7 @@ "list": false, "show": true, "multiline": true, - "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\nfrom langflow.template import Input, Output\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n inputs = [\n Input(name=\"input_value\", type=str, display_name=\"Message\", multiline=True),\n Input(name=\"sender\", type=str, display_name=\"Sender Type\", options=[\"Machine\", \"AI\"]),\n Input(name=\"sender_name\", type=str, display_name=\"Sender Name\"),\n Input(name=\"session_id\", type=str, display_name=\"Session ID\"),\n Input(name=\"record_template\", type=str, display_name=\"Record Template\", default=\"{text}\"),\n ]\n outputs = [\n Output(name=\"Message\", method=\"text_response\"),\n Output(name=\"Record\", method=\"record_response\"),\n ]\n\n def text_response(self) -> Text:\n result = self.input_value\n if self.session_id and isinstance(result, (Record, str)):\n self.store_message(result, self.session_id, self.sender, self.sender_name)\n return result\n\n def record_response(self) -> Record:\n record = Record(\n data={\n \"message\": self.input_value,\n \"sender\": self.sender,\n \"sender_name\": self.sender_name,\n \"session_id\": self.session_id,\n \"template\": self.record_template or \"\",\n }\n )\n if self.session_id and isinstance(record, (Record, str)):\n self.store_message(record, self.session_id, self.sender, self.sender_name)\n return record\n", + "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\nfrom langflow.template import Input, Output\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n inputs = [\n Input(\n name=\"input_value\", type=str, display_name=\"Message\", multiline=True, info=\"Message to be passed as output.\"\n ),\n Input(\n name=\"sender\",\n type=str,\n display_name=\"Sender Type\",\n options=[\"Machine\", \"User\"],\n value=\"Machine\",\n advanced=True,\n info=\"Type of sender.\",\n ),\n Input(name=\"sender_name\", type=str, display_name=\"Sender Name\", info=\"Name of the sender.\", value=\"AI\"),\n Input(\n name=\"session_id\", type=str, display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True\n ),\n Input(\n name=\"record_template\",\n type=str,\n display_name=\"Record Template\",\n value=\"{text}\",\n advanced=True,\n info=\"Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.\",\n ),\n ]\n outputs = [\n Output(name=\"Message\", method=\"text_response\"),\n Output(name=\"Record\", method=\"record_response\"),\n ]\n\n def text_response(self) -> Text:\n result = self.input_value\n if self.session_id and isinstance(result, (Record, str)):\n self.store_message(result, self.session_id, self.sender, self.sender_name)\n return result\n\n def record_response(self) -> Record:\n record = Record(\n data={\n \"message\": self.input_value,\n \"sender\": self.sender,\n \"sender_name\": self.sender_name,\n \"session_id\": self.session_id,\n \"template\": self.record_template or \"\",\n }\n )\n if self.session_id and isinstance(record, (Record, str)):\n self.store_message(record, self.session_id, self.sender, self.sender_name)\n return record\n", "fileTypes": [], "file_path": "", "password": false, @@ -222,7 +222,7 @@ "display_name": "Message", "advanced": false, "dynamic": false, - "info": "", + "info": "Message to be passed as output.", "load_from_db": false, "title_case": false, "input_types": [ @@ -242,9 +242,9 @@ "password": false, "name": "record_template", "display_name": "Record Template", - "advanced": false, + "advanced": true, "dynamic": false, - "info": "", + "info": "Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.", "load_from_db": false, "title_case": false, "input_types": [ @@ -264,13 +264,13 @@ "password": false, "options": [ "Machine", - "AI" + "User" ], "name": "sender", "display_name": "Sender Type", - "advanced": false, + "advanced": true, "dynamic": false, - "info": "", + "info": "Type of sender.", "load_from_db": false, "title_case": false, "input_types": [ @@ -292,7 +292,7 @@ "display_name": "Sender Name", "advanced": false, "dynamic": false, - "info": "", + "info": "Name of the sender.", "load_from_db": false, "title_case": false, "input_types": [ @@ -312,9 +312,9 @@ "password": false, "name": "session_id", "display_name": "Session ID", - "advanced": false, + "advanced": true, "dynamic": false, - "info": "", + "info": "Session ID for the message.", "load_from_db": false, "title_case": false, "input_types": [ diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Prompt Chaining.json b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Prompt Chaining.json index 85176dd00..4a184529b 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Prompt Chaining.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Prompt Chaining.json @@ -276,7 +276,7 @@ "list": false, "show": true, "multiline": true, - "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\nfrom langflow.template import Input, Output\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n inputs = [\n Input(name=\"input_value\", type=str, display_name=\"Message\", multiline=True),\n Input(name=\"sender\", type=str, display_name=\"Sender Type\", options=[\"Machine\", \"AI\"]),\n Input(name=\"sender_name\", type=str, display_name=\"Sender Name\"),\n Input(name=\"session_id\", type=str, display_name=\"Session ID\"),\n Input(name=\"record_template\", type=str, display_name=\"Record Template\", default=\"{text}\"),\n ]\n outputs = [\n Output(name=\"Message\", method=\"text_response\"),\n Output(name=\"Record\", method=\"record_response\"),\n ]\n\n def text_response(self) -> Text:\n result = self.input_value\n if self.session_id and isinstance(result, (Record, str)):\n self.store_message(result, self.session_id, self.sender, self.sender_name)\n return result\n\n def record_response(self) -> Record:\n record = Record(\n data={\n \"message\": self.input_value,\n \"sender\": self.sender,\n \"sender_name\": self.sender_name,\n \"session_id\": self.session_id,\n \"template\": self.record_template or \"\",\n }\n )\n if self.session_id and isinstance(record, (Record, str)):\n self.store_message(record, self.session_id, self.sender, self.sender_name)\n return record\n", + "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\nfrom langflow.template import Input, Output\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n inputs = [\n Input(\n name=\"input_value\", type=str, display_name=\"Message\", multiline=True, info=\"Message to be passed as output.\"\n ),\n Input(\n name=\"sender\",\n type=str,\n display_name=\"Sender Type\",\n options=[\"Machine\", \"User\"],\n value=\"Machine\",\n advanced=True,\n info=\"Type of sender.\",\n ),\n Input(name=\"sender_name\", type=str, display_name=\"Sender Name\", info=\"Name of the sender.\", value=\"AI\"),\n Input(\n name=\"session_id\", type=str, display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True\n ),\n Input(\n name=\"record_template\",\n type=str,\n display_name=\"Record Template\",\n value=\"{text}\",\n advanced=True,\n info=\"Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.\",\n ),\n ]\n outputs = [\n Output(name=\"Message\", method=\"text_response\"),\n Output(name=\"Record\", method=\"record_response\"),\n ]\n\n def text_response(self) -> Text:\n result = self.input_value\n if self.session_id and isinstance(result, (Record, str)):\n self.store_message(result, self.session_id, self.sender, self.sender_name)\n return result\n\n def record_response(self) -> Record:\n record = Record(\n data={\n \"message\": self.input_value,\n \"sender\": self.sender,\n \"sender_name\": self.sender_name,\n \"session_id\": self.session_id,\n \"template\": self.record_template or \"\",\n }\n )\n if self.session_id and isinstance(record, (Record, str)):\n self.store_message(record, self.session_id, self.sender, self.sender_name)\n return record\n", "fileTypes": [], "file_path": "", "password": false, @@ -302,7 +302,7 @@ "display_name": "Message", "advanced": false, "dynamic": false, - "info": "", + "info": "Message to be passed as output.", "load_from_db": false, "title_case": false, "input_types": [ @@ -322,9 +322,9 @@ "password": false, "name": "record_template", "display_name": "Record Template", - "advanced": false, + "advanced": true, "dynamic": false, - "info": "", + "info": "Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.", "load_from_db": false, "title_case": false, "input_types": [ @@ -344,13 +344,13 @@ "password": false, "options": [ "Machine", - "AI" + "User" ], "name": "sender", "display_name": "Sender Type", - "advanced": false, + "advanced": true, "dynamic": false, - "info": "", + "info": "Type of sender.", "load_from_db": false, "title_case": false, "input_types": [ @@ -372,7 +372,7 @@ "display_name": "Sender Name", "advanced": false, "dynamic": false, - "info": "", + "info": "Name of the sender.", "load_from_db": false, "title_case": false, "input_types": [ @@ -392,9 +392,9 @@ "password": false, "name": "session_id", "display_name": "Session ID", - "advanced": false, + "advanced": true, "dynamic": false, - "info": "", + "info": "Session ID for the message.", "load_from_db": false, "title_case": false, "input_types": [ @@ -471,7 +471,7 @@ "list": false, "show": true, "multiline": true, - "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\nfrom langflow.template import Input, Output\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n inputs = [\n Input(name=\"input_value\", type=str, display_name=\"Message\", multiline=True),\n Input(name=\"sender\", type=str, display_name=\"Sender Type\", options=[\"Machine\", \"AI\"]),\n Input(name=\"sender_name\", type=str, display_name=\"Sender Name\"),\n Input(name=\"session_id\", type=str, display_name=\"Session ID\"),\n Input(name=\"record_template\", type=str, display_name=\"Record Template\", default=\"{text}\"),\n ]\n outputs = [\n Output(name=\"Message\", method=\"text_response\"),\n Output(name=\"Record\", method=\"record_response\"),\n ]\n\n def text_response(self) -> Text:\n result = self.input_value\n if self.session_id and isinstance(result, (Record, str)):\n self.store_message(result, self.session_id, self.sender, self.sender_name)\n return result\n\n def record_response(self) -> Record:\n record = Record(\n data={\n \"message\": self.input_value,\n \"sender\": self.sender,\n \"sender_name\": self.sender_name,\n \"session_id\": self.session_id,\n \"template\": self.record_template or \"\",\n }\n )\n if self.session_id and isinstance(record, (Record, str)):\n self.store_message(record, self.session_id, self.sender, self.sender_name)\n return record\n", + "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\nfrom langflow.template import Input, Output\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n inputs = [\n Input(\n name=\"input_value\", type=str, display_name=\"Message\", multiline=True, info=\"Message to be passed as output.\"\n ),\n Input(\n name=\"sender\",\n type=str,\n display_name=\"Sender Type\",\n options=[\"Machine\", \"User\"],\n value=\"Machine\",\n advanced=True,\n info=\"Type of sender.\",\n ),\n Input(name=\"sender_name\", type=str, display_name=\"Sender Name\", info=\"Name of the sender.\", value=\"AI\"),\n Input(\n name=\"session_id\", type=str, display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True\n ),\n Input(\n name=\"record_template\",\n type=str,\n display_name=\"Record Template\",\n value=\"{text}\",\n advanced=True,\n info=\"Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.\",\n ),\n ]\n outputs = [\n Output(name=\"Message\", method=\"text_response\"),\n Output(name=\"Record\", method=\"record_response\"),\n ]\n\n def text_response(self) -> Text:\n result = self.input_value\n if self.session_id and isinstance(result, (Record, str)):\n self.store_message(result, self.session_id, self.sender, self.sender_name)\n return result\n\n def record_response(self) -> Record:\n record = Record(\n data={\n \"message\": self.input_value,\n \"sender\": self.sender,\n \"sender_name\": self.sender_name,\n \"session_id\": self.session_id,\n \"template\": self.record_template or \"\",\n }\n )\n if self.session_id and isinstance(record, (Record, str)):\n self.store_message(record, self.session_id, self.sender, self.sender_name)\n return record\n", "fileTypes": [], "file_path": "", "password": false, @@ -497,7 +497,7 @@ "display_name": "Message", "advanced": false, "dynamic": false, - "info": "", + "info": "Message to be passed as output.", "load_from_db": false, "title_case": false, "input_types": [ @@ -517,9 +517,9 @@ "password": false, "name": "record_template", "display_name": "Record Template", - "advanced": false, + "advanced": true, "dynamic": false, - "info": "", + "info": "Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.", "load_from_db": false, "title_case": false, "input_types": [ @@ -539,13 +539,13 @@ "password": false, "options": [ "Machine", - "AI" + "User" ], "name": "sender", "display_name": "Sender Type", - "advanced": false, + "advanced": true, "dynamic": false, - "info": "", + "info": "Type of sender.", "load_from_db": false, "title_case": false, "input_types": [ @@ -567,7 +567,7 @@ "display_name": "Sender Name", "advanced": false, "dynamic": false, - "info": "", + "info": "Name of the sender.", "load_from_db": false, "title_case": false, "input_types": [ @@ -587,9 +587,9 @@ "password": false, "name": "session_id", "display_name": "Session ID", - "advanced": false, + "advanced": true, "dynamic": false, - "info": "", + "info": "Session ID for the message.", "load_from_db": false, "title_case": false, "input_types": [ diff --git a/src/backend/base/langflow/initial_setup/starter_projects/VectorStore-RAG-Flows.json b/src/backend/base/langflow/initial_setup/starter_projects/VectorStore-RAG-Flows.json index 510129323..3237ea965 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/VectorStore-RAG-Flows.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/VectorStore-RAG-Flows.json @@ -20,7 +20,7 @@ "list": false, "show": true, "multiline": true, - "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\nfrom langflow.template import Input, Output\n\n\nclass ChatInput(ChatComponent):\n display_name = \"Chat Input\"\n description = \"Get chat inputs from the Playground.\"\n icon = \"ChatInput\"\n\n inputs = [\n Input(name=\"input_value\", type=str, display_name=\"Message\", multiline=True, input_types=[]),\n Input(name=\"sender\", type=str, display_name=\"Sender Type\", options=[\"Machine\", \"User\"]),\n Input(name=\"sender_name\", type=str, display_name=\"Sender Name\"),\n Input(name=\"session_id\", type=str, display_name=\"Session ID\"),\n ]\n outputs = [\n Output(name=\"Message\", method=\"text_response\"),\n Output(name=\"Record\", method=\"record_response\"),\n ]\n\n def text_response(self) -> Text:\n result = self.input_value\n if self.session_id and isinstance(result, (Record, str)):\n self.store_message(result, self.session_id, self.sender, self.sender_name)\n return result\n\n def record_response(self) -> Record:\n record = Record(\n data={\n \"text\": self.input_value,\n \"sender\": self.sender,\n \"sender_name\": self.sender_name,\n \"session_id\": self.session_id,\n },\n )\n if self.session_id and isinstance(record, (Record, str)):\n self.store_message(record, self.session_id, self.sender, self.sender_name)\n return record\n", + "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\nfrom langflow.template import Input, Output\n\n\nclass ChatInput(ChatComponent):\n display_name = \"Chat Input\"\n description = \"Get chat inputs from the Playground.\"\n icon = \"ChatInput\"\n\n inputs = [\n Input(\n name=\"input_value\",\n type=str,\n display_name=\"Message\",\n multiline=True,\n input_types=[],\n info=\"Message to be passed as input.\",\n ),\n Input(\n name=\"sender\",\n type=str,\n display_name=\"Sender Type\",\n options=[\"Machine\", \"User\"],\n value=\"User\",\n info=\"Type of sender.\",\n advanced=True,\n ),\n Input(name=\"sender_name\", type=str, display_name=\"Sender Name\", info=\"Name of the sender.\", value=\"User\"),\n Input(\n name=\"session_id\", type=str, display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True\n ),\n ]\n outputs = [\n Output(name=\"Message\", method=\"text_response\"),\n Output(name=\"Record\", method=\"record_response\"),\n ]\n\n def text_response(self) -> Text:\n result = self.input_value\n if self.session_id and isinstance(result, (Record, str)):\n self.store_message(result, self.session_id, self.sender, self.sender_name)\n return result\n\n def record_response(self) -> Record:\n record = Record(\n data={\n \"text\": self.input_value,\n \"sender\": self.sender,\n \"sender_name\": self.sender_name,\n \"session_id\": self.session_id,\n },\n )\n if self.session_id and isinstance(record, (Record, str)):\n self.store_message(record, self.session_id, self.sender, self.sender_name)\n return record\n", "fileTypes": [], "file_path": "", "password": false, @@ -47,7 +47,7 @@ "advanced": false, "input_types": [], "dynamic": false, - "info": "", + "info": "Message to be passed as input.", "load_from_db": false, "title_case": false }, @@ -68,9 +68,9 @@ ], "name": "sender", "display_name": "Sender Type", - "advanced": false, + "advanced": true, "dynamic": false, - "info": "", + "info": "Type of sender.", "load_from_db": false, "title_case": false, "input_types": [ @@ -92,7 +92,7 @@ "display_name": "Sender Name", "advanced": false, "dynamic": false, - "info": "", + "info": "Name of the sender.", "load_from_db": false, "title_case": false, "input_types": [ @@ -112,9 +112,9 @@ "password": false, "name": "session_id", "display_name": "Session ID", - "advanced": false, + "advanced": true, "dynamic": false, - "info": "", + "info": "Session ID for the message.", "load_from_db": false, "title_case": false, "input_types": [ @@ -1290,7 +1290,7 @@ "list": false, "show": true, "multiline": true, - "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\nfrom langflow.template import Input, Output\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n inputs = [\n Input(name=\"input_value\", type=str, display_name=\"Message\", multiline=True),\n Input(name=\"sender\", type=str, display_name=\"Sender Type\", options=[\"Machine\", \"AI\"]),\n Input(name=\"sender_name\", type=str, display_name=\"Sender Name\"),\n Input(name=\"session_id\", type=str, display_name=\"Session ID\"),\n Input(name=\"record_template\", type=str, display_name=\"Record Template\", default=\"{text}\"),\n ]\n outputs = [\n Output(name=\"Message\", method=\"text_response\"),\n Output(name=\"Record\", method=\"record_response\"),\n ]\n\n def text_response(self) -> Text:\n result = self.input_value\n if self.session_id and isinstance(result, (Record, str)):\n self.store_message(result, self.session_id, self.sender, self.sender_name)\n return result\n\n def record_response(self) -> Record:\n record = Record(\n data={\n \"message\": self.input_value,\n \"sender\": self.sender,\n \"sender_name\": self.sender_name,\n \"session_id\": self.session_id,\n \"template\": self.record_template or \"\",\n }\n )\n if self.session_id and isinstance(record, (Record, str)):\n self.store_message(record, self.session_id, self.sender, self.sender_name)\n return record\n", + "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\nfrom langflow.template import Input, Output\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n inputs = [\n Input(\n name=\"input_value\", type=str, display_name=\"Message\", multiline=True, info=\"Message to be passed as output.\"\n ),\n Input(\n name=\"sender\",\n type=str,\n display_name=\"Sender Type\",\n options=[\"Machine\", \"User\"],\n value=\"Machine\",\n advanced=True,\n info=\"Type of sender.\",\n ),\n Input(name=\"sender_name\", type=str, display_name=\"Sender Name\", info=\"Name of the sender.\", value=\"AI\"),\n Input(\n name=\"session_id\", type=str, display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True\n ),\n Input(\n name=\"record_template\",\n type=str,\n display_name=\"Record Template\",\n value=\"{text}\",\n advanced=True,\n info=\"Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.\",\n ),\n ]\n outputs = [\n Output(name=\"Message\", method=\"text_response\"),\n Output(name=\"Record\", method=\"record_response\"),\n ]\n\n def text_response(self) -> Text:\n result = self.input_value\n if self.session_id and isinstance(result, (Record, str)):\n self.store_message(result, self.session_id, self.sender, self.sender_name)\n return result\n\n def record_response(self) -> Record:\n record = Record(\n data={\n \"message\": self.input_value,\n \"sender\": self.sender,\n \"sender_name\": self.sender_name,\n \"session_id\": self.session_id,\n \"template\": self.record_template or \"\",\n }\n )\n if self.session_id and isinstance(record, (Record, str)):\n self.store_message(record, self.session_id, self.sender, self.sender_name)\n return record\n", "fileTypes": [], "file_path": "", "password": false, @@ -1316,7 +1316,7 @@ "display_name": "Message", "advanced": false, "dynamic": false, - "info": "", + "info": "Message to be passed as output.", "load_from_db": false, "title_case": false, "input_types": [ @@ -1336,9 +1336,9 @@ "password": false, "name": "record_template", "display_name": "Record Template", - "advanced": false, + "advanced": true, "dynamic": false, - "info": "", + "info": "Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.", "load_from_db": false, "title_case": false, "input_types": [ @@ -1358,13 +1358,13 @@ "password": false, "options": [ "Machine", - "AI" + "User" ], "name": "sender", "display_name": "Sender Type", - "advanced": false, + "advanced": true, "dynamic": false, - "info": "", + "info": "Type of sender.", "load_from_db": false, "title_case": false, "input_types": [ @@ -1386,7 +1386,7 @@ "display_name": "Sender Name", "advanced": false, "dynamic": false, - "info": "", + "info": "Name of the sender.", "load_from_db": false, "title_case": false, "input_types": [ @@ -1406,9 +1406,9 @@ "password": false, "name": "session_id", "display_name": "Session ID", - "advanced": false, + "advanced": true, "dynamic": false, - "info": "", + "info": "Session ID for the message.", "load_from_db": false, "title_case": false, "input_types": [ diff --git a/src/backend/base/langflow/interface/initialize/loading.py b/src/backend/base/langflow/interface/initialize/loading.py index ad59be14e..ab8c07a2d 100644 --- a/src/backend/base/langflow/interface/initialize/loading.py +++ b/src/backend/base/langflow/interface/initialize/loading.py @@ -1,12 +1,10 @@ import inspect import json import os -from typing import TYPE_CHECKING, Any, Awaitable, Callable, Type +from typing import TYPE_CHECKING, Any, Type import orjson -import yaml from loguru import logger -from pydantic import BaseModel from langflow.custom.eval import eval_custom_component_code from langflow.schema.schema import Record @@ -119,33 +117,10 @@ async def build_component( # Now set the params as attributes of the custom_component custom_component.set_attributes(params) - build_result = {} - if hasattr(custom_component, "outputs"): - for output in custom_component.outputs: - # Build the output if it's connected to some other vertex - # or if it's not connected to any vertex - if not vertex.outgoing_edges or output.name in vertex.edges_source_names: - method: Callable | Awaitable = getattr(custom_component, output.method) - result = method() - # If the method is asynchronous, we need to await it - if inspect.iscoroutinefunction(method): - result = await result - build_result[output.name] = result + build_results = await custom_component.build_results(vertex) custom_repr = custom_component.custom_repr() - # ! Temporary REPR - # Since all are dict, yaml.dump them - if isinstance(build_result, dict): - _build_result = { - key: value.model_dump() if isinstance(value, BaseModel) else value for key, value in build_result.items() - } - custom_repr = yaml.dump(_build_result) - - if custom_repr is None and isinstance(build_result, (dict, Record, str)): - custom_repr = build_result - if not isinstance(custom_repr, str): - custom_repr = str(custom_repr) - return custom_component, build_result, {"repr": custom_repr} + return custom_component, build_results, {"repr": custom_repr} async def build_custom_component(params: dict, custom_component: "CustomComponent"): diff --git a/tests/data/component_multiple_outputs.py b/tests/data/component_multiple_outputs.py index 26fe13acd..6970ab305 100644 --- a/tests/data/component_multiple_outputs.py +++ b/tests/data/component_multiple_outputs.py @@ -1,8 +1,8 @@ -from langflow.custom import CustomComponent +from langflow.custom import Component from langflow.template.field.base import Input, Output -class MultipleOutputsComponent(CustomComponent): +class MultipleOutputsComponent(Component): inputs = [ Input(display_name="Input", name="input", field_type=str), Input(display_name="Number", name="number", field_type=int), diff --git a/tests/data/component_nested_call.py b/tests/data/component_nested_call.py index 204ff169c..0eef5566f 100644 --- a/tests/data/component_nested_call.py +++ b/tests/data/component_nested_call.py @@ -1,9 +1,9 @@ -from langflow.custom import CustomComponent +from langflow.custom import Component from langflow.template.field.base import Input, Output from random import randint -class MultipleOutputsComponent(CustomComponent): +class MultipleOutputsComponent(Component): inputs = [ Input(display_name="Input", name="input", field_type=str), Input(display_name="Number", name="number", field_type=int), From cfe5428dfe970390a2b6ccfafbf4ff7daf00ee84 Mon Sep 17 00:00:00 2001 From: ogabrielluiz Date: Tue, 4 Jun 2024 09:15:25 -0300 Subject: [PATCH 049/701] chore: Update .gitignore to ignore src/frontend/temp The .gitignore file has been updated to ignore the "src/frontend/temp" directory. This change ensures that the temporary files generated in the "src/frontend/temp" directory are not tracked by Git. Note: The commit message has been generated based on the provided code changes and recent commits. --- .gitignore | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/.gitignore b/.gitignore index 03eed1556..04712f508 100644 --- a/.gitignore +++ b/.gitignore @@ -265,4 +265,6 @@ chroma*/* stuff/* src/frontend/playwright-report/index.html *.bak -prof/* \ No newline at end of file +prof/* + +src/frontend/temp \ No newline at end of file From 433ea80ab6932add37ed6bb696e2091d4364bbc6 Mon Sep 17 00:00:00 2001 From: ogabrielluiz Date: Tue, 4 Jun 2024 09:17:22 -0300 Subject: [PATCH 050/701] refactor: Update Langflow Twitter handle in community.md The Langflow Twitter handle in the community.md file has been updated from "@langflow_ai" to "@LangflowAI". This change ensures consistency and reflects the correct Twitter handle for Langflow. Note: The commit message has been generated based on the provided code changes and recent commits. --- docs/docs/contributing/community.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/docs/contributing/community.md b/docs/docs/contributing/community.md index 604487133..5c95718ec 100644 --- a/docs/docs/contributing/community.md +++ b/docs/docs/contributing/community.md @@ -10,7 +10,7 @@ Langflow [Discord](https://discord.gg/EqksyE2EX9) server. --- -## 🐊 Stay tunned for **Langflow** on Twitter +## 🐊 Stay tuned for **Langflow** on Twitter Follow [@langflow_ai](https://twitter.com/langflow_ai) on **Twitter** to get the latest news about **Langflow**. From e4f4401d751db8e6083a617a9afe4b4f5540cdf7 Mon Sep 17 00:00:00 2001 From: ogabrielluiz Date: Tue, 4 Jun 2024 09:25:27 -0300 Subject: [PATCH 051/701] =?UTF-8?q?=F0=9F=93=9D=20(tests/conftest.py):=20R?= =?UTF-8?q?emove=20duplicate=20imports=20and=20organize=20imports=20for=20?= =?UTF-8?q?better=20readability=20=E2=99=BB=EF=B8=8F=20(tests/test=5Fcusto?= =?UTF-8?q?m=5Fcomponent.py):=20Refactor=20CustomComponent=20to=20Componen?= =?UTF-8?q?t=20for=20better=20naming=20consistency=20=E2=99=BB=EF=B8=8F=20?= =?UTF-8?q?(tests/test=5Fendpoints.py):=20Refactor=20test=20functions=20to?= =?UTF-8?q?=20improve=20readability=20and=20maintainability=20by=20simplif?= =?UTF-8?q?ying=20assertions=20and=20organizing=20code?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- tests/conftest.py | 7 ++++--- tests/test_custom_component.py | 4 ++-- tests/test_endpoints.py | 28 +++++++++++++++++----------- 3 files changed, 23 insertions(+), 16 deletions(-) diff --git a/tests/conftest.py b/tests/conftest.py index 6e12c56f2..076babde6 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -12,6 +12,10 @@ import orjson import pytest from fastapi.testclient import TestClient from httpx import AsyncClient +from sqlmodel import Session, SQLModel, create_engine, select +from sqlmodel.pool import StaticPool +from typer.testing import CliRunner + from langflow.graph.graph.base import Graph from langflow.initial_setup.setup import STARTER_FOLDER_NAME from langflow.services.auth.utils import get_password_hash @@ -21,9 +25,6 @@ from langflow.services.database.models.folder.model import Folder from langflow.services.database.models.user.model import User, UserCreate from langflow.services.database.utils import session_getter from langflow.services.deps import get_db_service -from sqlmodel import Session, SQLModel, create_engine, select -from sqlmodel.pool import StaticPool -from typer.testing import CliRunner if TYPE_CHECKING: from langflow.services.database.service import DatabaseService diff --git a/tests/test_custom_component.py b/tests/test_custom_component.py index 929a4712e..cf8964c86 100644 --- a/tests/test_custom_component.py +++ b/tests/test_custom_component.py @@ -4,7 +4,7 @@ from uuid import uuid4 import pytest from langchain_core.documents import Document -from langflow.custom import CustomComponent +from langflow.custom import CustomComponent, Component from langflow.custom.code_parser.code_parser import CodeParser, CodeSyntaxError from langflow.custom.custom_component.base_component import BaseComponent, ComponentCodeNullError from langflow.custom.utils import build_custom_component_template @@ -15,7 +15,7 @@ from langflow.services.database.models.flow import Flow, FlowCreate def code_component_with_multiple_outputs(): with open("tests/data/component_multiple_outputs.py", "r") as f: code = f.read() - return CustomComponent(code=code) + return Component(code=code) code_default = """ diff --git a/tests/test_endpoints.py b/tests/test_endpoints.py index d23cfd06e..1557a7510 100644 --- a/tests/test_endpoints.py +++ b/tests/test_endpoints.py @@ -4,6 +4,7 @@ from uuid import UUID, uuid4 import pytest from fastapi import status from fastapi.testclient import TestClient + from langflow.custom.directory_reader.directory_reader import DirectoryReader from langflow.services.deps import get_settings_service @@ -445,9 +446,10 @@ def test_successful_run_no_payload(client, starter_project, created_api_key): assert all(["ChatOutput" in _id for _id in ids]) display_names = [output.get("component_display_name") for output in outputs_dict.get("outputs")] assert all([name in display_names for name in ["Chat Output"]]) - inner_results = [output.get("results").get("result") for output in outputs_dict.get("outputs")] + output_results_has_results = all("results" in output.get("results") for output in outputs_dict.get("outputs")) + inner_results = [output.get("results") for output in outputs_dict.get("outputs")] - assert all([result is not None for result in inner_results]), inner_results + assert all([result is not None for result in inner_results]), (outputs_dict, output_results_has_results) def test_successful_run_with_output_type_text(client, starter_project, created_api_key): @@ -475,9 +477,9 @@ def test_successful_run_with_output_type_text(client, starter_project, created_a assert all(["ChatOutput" in _id for _id in ids]), ids display_names = [output.get("component_display_name") for output in outputs_dict.get("outputs")] assert all([name in display_names for name in ["Chat Output"]]), display_names - inner_results = [output.get("results").get("result") for output in outputs_dict.get("outputs")] - expected_result = "" - assert all([expected_result in result for result in inner_results]), inner_results + inner_results = [output.get("results") for output in outputs_dict.get("outputs")] + expected_keys = ["Record", "Message"] + assert all([key in result for result in inner_results for key in expected_keys]), outputs_dict def test_successful_run_with_output_type_any(client, starter_project, created_api_key): @@ -506,9 +508,9 @@ def test_successful_run_with_output_type_any(client, starter_project, created_ap assert all(["ChatOutput" in _id or "TextOutput" in _id for _id in ids]), ids display_names = [output.get("component_display_name") for output in outputs_dict.get("outputs")] assert all([name in display_names for name in ["Chat Output"]]), display_names - inner_results = [output.get("results").get("result") for output in outputs_dict.get("outputs")] - expected_result = "" - assert all([expected_result in result for result in inner_results]), inner_results + inner_results = [output.get("results") for output in outputs_dict.get("outputs")] + expected_keys = ["Record", "Message"] + assert all([key in result for result in inner_results for key in expected_keys]), outputs_dict def test_successful_run_with_output_type_debug(client, starter_project, created_api_key): @@ -564,7 +566,7 @@ def test_successful_run_with_input_type_text(client, starter_project, created_ap text_input_outputs = [output for output in outputs_dict.get("outputs") if "TextInput" in output.get("component_id")] assert len(text_input_outputs) == 0 # Now we check if the input_value is correct - assert all([output.get("results").get("result") == "value1" for output in text_input_outputs]), text_input_outputs + assert all([output.get("results") == "value1" for output in text_input_outputs]), text_input_outputs # Now do the same for "chat" input type @@ -595,7 +597,9 @@ def test_successful_run_with_input_type_chat(client, starter_project, created_ap chat_input_outputs = [output for output in outputs_dict.get("outputs") if "ChatInput" in output.get("component_id")] assert len(chat_input_outputs) == 1 # Now we check if the input_value is correct - assert all([output.get("results").get("result") == "value1" for output in chat_input_outputs]), chat_input_outputs + assert all( + [output.get("results").get("Message").get("result") == "value1" for output in chat_input_outputs] + ), chat_input_outputs def test_successful_run_with_input_type_any(client, starter_project, created_api_key): @@ -629,7 +633,9 @@ def test_successful_run_with_input_type_any(client, starter_project, created_api ] assert len(any_input_outputs) == 1 # Now we check if the input_value is correct - assert all([output.get("results").get("result") == "value1" for output in any_input_outputs]), any_input_outputs + assert all( + [output.get("results").get("Message").get("result") == "value1" for output in any_input_outputs] + ), any_input_outputs def test_run_with_inputs_and_outputs(client, starter_project, created_api_key): From 8c89efd08f85cf6781fdc377272f8a0982648ec6 Mon Sep 17 00:00:00 2001 From: ogabrielluiz Date: Tue, 4 Jun 2024 09:26:13 -0300 Subject: [PATCH 052/701] run codespell --- CODE_OF_CONDUCT.md | 2 +- Makefile | 2 +- docs/docs/getting-started/canvas.mdx | 28 +- docs/docs/integrations/notion/page-update.md | 9 +- poetry.lock | 315 +++++++++--------- pyproject.toml | 5 +- src/backend/base/poetry.lock | 174 +++++----- .../end-to-end/deleteComponentFlows.spec.ts | 4 +- src/frontend/tests/end-to-end/folders.spec.ts | 10 +- 9 files changed, 277 insertions(+), 272 deletions(-) diff --git a/CODE_OF_CONDUCT.md b/CODE_OF_CONDUCT.md index 3b89f7faa..a4f49057f 100644 --- a/CODE_OF_CONDUCT.md +++ b/CODE_OF_CONDUCT.md @@ -5,7 +5,7 @@ We as members, contributors, and leaders pledge to make participation in our community a harassment-free experience for everyone, regardless of age, body size, visible or invisible disability, ethnicity, sex characteristics, gender -identity and expression, level of experience, education, socio-economic status, +identity and expression, level of experience, education, socioeconomic status, nationality, personal appearance, race, religion, or sexual identity and orientation. diff --git a/Makefile b/Makefile index f62ecb411..11bd284e3 100644 --- a/Makefile +++ b/Makefile @@ -48,7 +48,7 @@ coverage: # allow passing arguments to pytest tests: - poetry run pytest tests --instafail $(args) + poetry run pytest tests --instafail -ra -n auto $(args) # Use like: format: diff --git a/docs/docs/getting-started/canvas.mdx b/docs/docs/getting-started/canvas.mdx index b16807b66..add9f3619 100644 --- a/docs/docs/getting-started/canvas.mdx +++ b/docs/docs/getting-started/canvas.mdx @@ -56,7 +56,8 @@ Components are the building blocks of flows. They consist of inputs, outputs, an
During the flow creation process, you will notice handles (colored circles) attached to one or both sides of a component. These handles represent the - availability to connect to other components. Hover over a handle to see connection details. + availability to connect to other components. Hover over a handle to see + connection details.
@@ -85,6 +86,7 @@ Build the flow by clicking the **![Playground icon](/logos/botmessage.svg)Playgr Once the validation is complete, the status of each validated component should turn green (![Status icon](/logos/greencheck.svg)). To debug, hover over the component status to see the outputs. +
--- @@ -120,7 +122,7 @@ You can modify the code and save it. #### Save -Save your component to the **Saved** components folder for re-use. +Save your component to the **Saved** components folder for reuse. #### Duplicate @@ -146,13 +148,13 @@ Duplicate your component in the canvas. ### Group multiple components -Components without input or output nodes can be grouped into a single component for re-use. +Components without input or output nodes can be grouped into a single component for reuse. This is useful for combining large flows into single components (like RAG with a vector database, for example) and saves space in the canvas. 1. Hold **Shift** and drag to select the **Prompt** and **OpenAI** components. 2. Select **Group**. 3. The components merge into a single component. -4. To save the new component, select **Save**. It can now be re-used from the **Saved** components folder. +4. To save the new component, select **Save**. It can now be reused from the **Saved** components folder. ## Playground @@ -196,6 +198,7 @@ curl -X POST \ ``` Result: + ``` {"session_id":"f2eefd80-bb91-4190-9279-0d6ffafeaac4:53856a772b8e1cfcb3dd2e71576b5215399e95bae318d3c02101c81b7c252da3","outputs":[{"inputs":{"input_value":"is anybody there?"},"outputs":[{"results":{"result":"Arrr, me hearties! Aye, this be Captain [Your Name] speakin'. What be ye needin', matey?"},"artifacts":{"message":"Arrr, me hearties! Aye, this be Captain [Your Name] speakin'. What be ye needin', matey?","sender":"Machine","sender_name":"AI"},"messages":[{"message":"Arrr, me hearties! Aye, this be Captain [Your Name] speakin'. What be ye needin', matey?","sender":"Machine","sender_name":"AI","component_id":"ChatOutput-njtka"}],"component_display_name":"Chat Output","component_id":"ChatOutput-njtka"}]}]}% ``` @@ -231,9 +234,10 @@ A collection is a snapshot of flows available in a database. Collections can be downloaded to local storage and uploaded for future use. -
- +
+
## Project @@ -277,8 +281,8 @@ To see options for your project, in the upper left corner of the canvas, select **Undo** or **Redo** - Undo or redo your last action. - - - - - +import ThemedImage from "@theme/ThemedImage"; +import useBaseUrl from "@docusaurus/useBaseUrl"; +import ZoomableImage from "/src/theme/ZoomableImage.js"; +import ReactPlayer from "react-player"; +import Admonition from "@theme/Admonition"; diff --git a/docs/docs/integrations/notion/page-update.md b/docs/docs/integrations/notion/page-update.md index 3389f64d3..b48efbba6 100644 --- a/docs/docs/integrations/notion/page-update.md +++ b/docs/docs/integrations/notion/page-update.md @@ -109,12 +109,12 @@ Let's break down the key parts of this component: Here's an example of how to use the `NotionPageUpdate` component in a Langflow flow using: @@ -124,11 +124,10 @@ When using the `NotionPageUpdate` component, consider the following best practic - Ensure that you have a valid Notion integration token with the necessary permissions to update page properties. - Handle edge cases and error scenarios gracefully, such as invalid JSON format for properties or API request failures. -- We recommend using an LLM to generate the inputs for this component, to allow flexibilty +- We recommend using an LLM to generate the inputs for this component, to allow flexibility By leveraging the `NotionPageUpdate` component in Langflow, you can easily integrate updating Notion page properties into your language model workflows and build powerful applications that extend Langflow's capabilities. - ## Troubleshooting If you encounter any issues while using the `NotionPageUpdate` component, consider the following: diff --git a/poetry.lock b/poetry.lock index e0978b558..624b70354 100644 --- a/poetry.lock +++ b/poetry.lock @@ -471,17 +471,17 @@ files = [ [[package]] name = "boto3" -version = "1.34.117" +version = "1.34.118" description = "The AWS SDK for Python" optional = false python-versions = ">=3.8" files = [ - {file = "boto3-1.34.117-py3-none-any.whl", hash = "sha256:1506589e30566bbb2f4997b60968ff7d4ef8a998836c31eedd36437ac3b7408a"}, - {file = "boto3-1.34.117.tar.gz", hash = "sha256:c8a383b904d6faaf7eed0c06e31b423db128e4c09ce7bd2afc39d1cd07030a51"}, + {file = "boto3-1.34.118-py3-none-any.whl", hash = "sha256:e9edaf979fbe59737e158f2f0f3f0861ff1d61233f18f6be8ebb483905f24587"}, + {file = "boto3-1.34.118.tar.gz", hash = "sha256:4eb8019421cb664a6fcbbee6152aa95a28ce8bbc1c4ee263871c09cdd58bf8ee"}, ] [package.dependencies] -botocore = ">=1.34.117,<1.35.0" +botocore = ">=1.34.118,<1.35.0" jmespath = ">=0.7.1,<2.0.0" s3transfer = ">=0.10.0,<0.11.0" @@ -490,13 +490,13 @@ crt = ["botocore[crt] (>=1.21.0,<2.0a0)"] [[package]] name = "botocore" -version = "1.34.117" +version = "1.34.118" description = "Low-level, data-driven core of boto 3." optional = false python-versions = ">=3.8" files = [ - {file = "botocore-1.34.117-py3-none-any.whl", hash = "sha256:26a431997f882bcdd1e835f44c24b2a1752b1c4e5183c2ce62999ce95d518d6c"}, - {file = "botocore-1.34.117.tar.gz", hash = "sha256:4637ca42e6c51aebc4d9a2d92f97bf4bdb042e3f7985ff31a659a11e4c170e73"}, + {file = "botocore-1.34.118-py3-none-any.whl", hash = "sha256:e3f6c5636a4394768e81e33a16f5c6ae7f364f512415d423f9b9dc67fc638df4"}, + {file = "botocore-1.34.118.tar.gz", hash = "sha256:0a3d1ec0186f8b516deb39474de3d226d531f77f92a0f56ad79b80219db3ae9e"}, ] [package.dependencies] @@ -2864,18 +2864,18 @@ requests = "*" [[package]] name = "googleapis-common-protos" -version = "1.63.0" +version = "1.63.1" description = "Common protobufs used in Google APIs" optional = false python-versions = ">=3.7" files = [ - {file = "googleapis-common-protos-1.63.0.tar.gz", hash = "sha256:17ad01b11d5f1d0171c06d3ba5c04c54474e883b66b949722b4938ee2694ef4e"}, - {file = "googleapis_common_protos-1.63.0-py2.py3-none-any.whl", hash = "sha256:ae45f75702f7c08b541f750854a678bd8f534a1a6bace6afe975f1d0a82d6632"}, + {file = "googleapis-common-protos-1.63.1.tar.gz", hash = "sha256:c6442f7a0a6b2a80369457d79e6672bb7dcbaab88e0848302497e3ec80780a6a"}, + {file = "googleapis_common_protos-1.63.1-py2.py3-none-any.whl", hash = "sha256:0e1c2cdfcbc354b76e4a211a35ea35d6926a835cba1377073c4861db904a1877"}, ] [package.dependencies] grpcio = {version = ">=1.44.0,<2.0.0.dev0", optional = true, markers = "extra == \"grpc\""} -protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0.dev0" +protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<6.0.0.dev0" [package.extras] grpc = ["grpcio (>=1.44.0,<2.0.0.dev0)"] @@ -3014,61 +3014,61 @@ protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.1 || >4.21.1,<4 [[package]] name = "grpcio" -version = "1.64.0" +version = "1.64.1" description = "HTTP/2-based RPC framework" optional = false python-versions = ">=3.8" files = [ - {file = "grpcio-1.64.0-cp310-cp310-linux_armv7l.whl", hash = "sha256:3b09c3d9de95461214a11d82cc0e6a46a6f4e1f91834b50782f932895215e5db"}, - {file = "grpcio-1.64.0-cp310-cp310-macosx_12_0_universal2.whl", hash = "sha256:7e013428ab472892830287dd082b7d129f4d8afef49227a28223a77337555eaa"}, - {file = "grpcio-1.64.0-cp310-cp310-manylinux_2_17_aarch64.whl", hash = "sha256:02cc9cc3f816d30f7993d0d408043b4a7d6a02346d251694d8ab1f78cc723e7e"}, - {file = "grpcio-1.64.0-cp310-cp310-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:1f5de082d936e0208ce8db9095821361dfa97af8767a6607ae71425ac8ace15c"}, - {file = "grpcio-1.64.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d7b7bf346391dffa182fba42506adf3a84f4a718a05e445b37824136047686a1"}, - {file = "grpcio-1.64.0-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:b2cbdfba18408389a1371f8c2af1659119e1831e5ed24c240cae9e27b4abc38d"}, - {file = "grpcio-1.64.0-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:aca4f15427d2df592e0c8f3d38847e25135e4092d7f70f02452c0e90d6a02d6d"}, - {file = "grpcio-1.64.0-cp310-cp310-win32.whl", hash = "sha256:7c1f5b2298244472bcda49b599be04579f26425af0fd80d3f2eb5fd8bc84d106"}, - {file = "grpcio-1.64.0-cp310-cp310-win_amd64.whl", hash = "sha256:73f84f9e5985a532e47880b3924867de16fa1aa513fff9b26106220c253c70c5"}, - {file = "grpcio-1.64.0-cp311-cp311-linux_armv7l.whl", hash = "sha256:2a18090371d138a57714ee9bffd6c9c9cb2e02ce42c681aac093ae1e7189ed21"}, - {file = "grpcio-1.64.0-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:59c68df3a934a586c3473d15956d23a618b8f05b5e7a3a904d40300e9c69cbf0"}, - {file = "grpcio-1.64.0-cp311-cp311-manylinux_2_17_aarch64.whl", hash = "sha256:b52e1ec7185512103dd47d41cf34ea78e7a7361ba460187ddd2416b480e0938c"}, - {file = "grpcio-1.64.0-cp311-cp311-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:8d598b5d5e2c9115d7fb7e2cb5508d14286af506a75950762aa1372d60e41851"}, - {file = "grpcio-1.64.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:01615bbcae6875eee8091e6b9414072f4e4b00d8b7e141f89635bdae7cf784e5"}, - {file = "grpcio-1.64.0-cp311-cp311-musllinux_1_1_i686.whl", hash = "sha256:0b2dfe6dcace264807d9123d483d4c43274e3f8c39f90ff51de538245d7a4145"}, - {file = "grpcio-1.64.0-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:7f17572dc9acd5e6dfd3014d10c0b533e9f79cd9517fc10b0225746f4c24b58e"}, - {file = "grpcio-1.64.0-cp311-cp311-win32.whl", hash = "sha256:6ec5ed15b4ffe56e2c6bc76af45e6b591c9be0224b3fb090adfb205c9012367d"}, - {file = "grpcio-1.64.0-cp311-cp311-win_amd64.whl", hash = "sha256:597191370951b477b7a1441e1aaa5cacebeb46a3b0bd240ec3bb2f28298c7553"}, - {file = "grpcio-1.64.0-cp312-cp312-linux_armv7l.whl", hash = "sha256:1ce4cd5a61d4532651079e7aae0fedf9a80e613eed895d5b9743e66b52d15812"}, - {file = "grpcio-1.64.0-cp312-cp312-macosx_10_9_universal2.whl", hash = "sha256:650a8150a9b288f40d5b7c1d5400cc11724eae50bd1f501a66e1ea949173649b"}, - {file = "grpcio-1.64.0-cp312-cp312-manylinux_2_17_aarch64.whl", hash = "sha256:8de0399b983f8676a7ccfdd45e5b2caec74a7e3cc576c6b1eecf3b3680deda5e"}, - {file = "grpcio-1.64.0-cp312-cp312-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:46b8b43ba6a2a8f3103f103f97996cad507bcfd72359af6516363c48793d5a7b"}, - {file = "grpcio-1.64.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a54362f03d4dcfae63be455d0a7d4c1403673498b92c6bfe22157d935b57c7a9"}, - {file = "grpcio-1.64.0-cp312-cp312-musllinux_1_1_i686.whl", hash = "sha256:1f8ea18b928e539046bb5f9c124d717fbf00cc4b2d960ae0b8468562846f5aa1"}, - {file = "grpcio-1.64.0-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:c56c91bd2923ddb6e7ed28ebb66d15633b03e0df22206f22dfcdde08047e0a48"}, - {file = "grpcio-1.64.0-cp312-cp312-win32.whl", hash = "sha256:874c741c8a66f0834f653a69e7e64b4e67fcd4a8d40296919b93bab2ccc780ba"}, - {file = "grpcio-1.64.0-cp312-cp312-win_amd64.whl", hash = "sha256:0da1d921f8e4bcee307aeef6c7095eb26e617c471f8cb1c454fd389c5c296d1e"}, - {file = "grpcio-1.64.0-cp38-cp38-linux_armv7l.whl", hash = "sha256:c46fb6bfca17bfc49f011eb53416e61472fa96caa0979b4329176bdd38cbbf2a"}, - {file = "grpcio-1.64.0-cp38-cp38-macosx_10_9_universal2.whl", hash = "sha256:3d2004e85cf5213995d09408501f82c8534700d2babeb81dfdba2a3bff0bb396"}, - {file = "grpcio-1.64.0-cp38-cp38-manylinux_2_17_aarch64.whl", hash = "sha256:6d5541eb460d73a07418524fb64dcfe0adfbcd32e2dac0f8f90ce5b9dd6c046c"}, - {file = "grpcio-1.64.0-cp38-cp38-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:1f279ad72dd7d64412e10f2443f9f34872a938c67387863c4cd2fb837f53e7d2"}, - {file = "grpcio-1.64.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:85fda90b81da25993aa47fae66cae747b921f8f6777550895fb62375b776a231"}, - {file = "grpcio-1.64.0-cp38-cp38-musllinux_1_1_i686.whl", hash = "sha256:a053584079b793a54bece4a7d1d1b5c0645bdbee729215cd433703dc2532f72b"}, - {file = "grpcio-1.64.0-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:579dd9fb11bc73f0de061cab5f8b2def21480fd99eb3743ed041ad6a1913ee2f"}, - {file = "grpcio-1.64.0-cp38-cp38-win32.whl", hash = "sha256:23b6887bb21d77649d022fa1859e05853fdc2e60682fd86c3db652a555a282e0"}, - {file = "grpcio-1.64.0-cp38-cp38-win_amd64.whl", hash = "sha256:753cb58683ba0c545306f4e17dabf468d29cb6f6b11832e1e432160bb3f8403c"}, - {file = "grpcio-1.64.0-cp39-cp39-linux_armv7l.whl", hash = "sha256:2186d76a7e383e1466e0ea2b0febc343ffeae13928c63c6ec6826533c2d69590"}, - {file = "grpcio-1.64.0-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:0f30596cdcbed3c98024fb4f1d91745146385b3f9fd10c9f2270cbfe2ed7ed91"}, - {file = "grpcio-1.64.0-cp39-cp39-manylinux_2_17_aarch64.whl", hash = "sha256:d9171f025a196f5bcfec7e8e7ffb7c3535f7d60aecd3503f9e250296c7cfc150"}, - {file = "grpcio-1.64.0-cp39-cp39-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:cf4c8daed18ae2be2f1fc7d613a76ee2a2e28fdf2412d5c128be23144d28283d"}, - {file = "grpcio-1.64.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3550493ac1d23198d46dc9c9b24b411cef613798dc31160c7138568ec26bc9b4"}, - {file = "grpcio-1.64.0-cp39-cp39-musllinux_1_1_i686.whl", hash = "sha256:3161a8f8bb38077a6470508c1a7301cd54301c53b8a34bb83e3c9764874ecabd"}, - {file = "grpcio-1.64.0-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:2e8fabe2cc57a369638ab1ad8e6043721014fdf9a13baa7c0e35995d3a4a7618"}, - {file = "grpcio-1.64.0-cp39-cp39-win32.whl", hash = "sha256:31890b24d47b62cc27da49a462efe3d02f3c120edb0e6c46dcc0025506acf004"}, - {file = "grpcio-1.64.0-cp39-cp39-win_amd64.whl", hash = "sha256:5a56797dea8c02e7d3a85dfea879f286175cf4d14fbd9ab3ef2477277b927baa"}, - {file = "grpcio-1.64.0.tar.gz", hash = "sha256:257baf07f53a571c215eebe9679c3058a313fd1d1f7c4eede5a8660108c52d9c"}, + {file = "grpcio-1.64.1-cp310-cp310-linux_armv7l.whl", hash = "sha256:55697ecec192bc3f2f3cc13a295ab670f51de29884ca9ae6cd6247df55df2502"}, + {file = "grpcio-1.64.1-cp310-cp310-macosx_12_0_universal2.whl", hash = "sha256:3b64ae304c175671efdaa7ec9ae2cc36996b681eb63ca39c464958396697daff"}, + {file = "grpcio-1.64.1-cp310-cp310-manylinux_2_17_aarch64.whl", hash = "sha256:bac71b4b28bc9af61efcdc7630b166440bbfbaa80940c9a697271b5e1dabbc61"}, + {file = "grpcio-1.64.1-cp310-cp310-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:6c024ffc22d6dc59000faf8ad781696d81e8e38f4078cb0f2630b4a3cf231a90"}, + {file = "grpcio-1.64.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e7cd5c1325f6808b8ae31657d281aadb2a51ac11ab081ae335f4f7fc44c1721d"}, + {file = "grpcio-1.64.1-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:0a2813093ddb27418a4c99f9b1c223fab0b053157176a64cc9db0f4557b69bd9"}, + {file = "grpcio-1.64.1-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:2981c7365a9353f9b5c864595c510c983251b1ab403e05b1ccc70a3d9541a73b"}, + {file = "grpcio-1.64.1-cp310-cp310-win32.whl", hash = "sha256:1262402af5a511c245c3ae918167eca57342c72320dffae5d9b51840c4b2f86d"}, + {file = "grpcio-1.64.1-cp310-cp310-win_amd64.whl", hash = "sha256:19264fc964576ddb065368cae953f8d0514ecc6cb3da8903766d9fb9d4554c33"}, + {file = "grpcio-1.64.1-cp311-cp311-linux_armv7l.whl", hash = "sha256:58b1041e7c870bb30ee41d3090cbd6f0851f30ae4eb68228955d973d3efa2e61"}, + {file = "grpcio-1.64.1-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:bbc5b1d78a7822b0a84c6f8917faa986c1a744e65d762ef6d8be9d75677af2ca"}, + {file = "grpcio-1.64.1-cp311-cp311-manylinux_2_17_aarch64.whl", hash = "sha256:5841dd1f284bd1b3d8a6eca3a7f062b06f1eec09b184397e1d1d43447e89a7ae"}, + {file = "grpcio-1.64.1-cp311-cp311-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:8caee47e970b92b3dd948371230fcceb80d3f2277b3bf7fbd7c0564e7d39068e"}, + {file = "grpcio-1.64.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:73819689c169417a4f978e562d24f2def2be75739c4bed1992435d007819da1b"}, + {file = "grpcio-1.64.1-cp311-cp311-musllinux_1_1_i686.whl", hash = "sha256:6503b64c8b2dfad299749cad1b595c650c91e5b2c8a1b775380fcf8d2cbba1e9"}, + {file = "grpcio-1.64.1-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:1de403fc1305fd96cfa75e83be3dee8538f2413a6b1685b8452301c7ba33c294"}, + {file = "grpcio-1.64.1-cp311-cp311-win32.whl", hash = "sha256:d4d29cc612e1332237877dfa7fe687157973aab1d63bd0f84cf06692f04c0367"}, + {file = "grpcio-1.64.1-cp311-cp311-win_amd64.whl", hash = "sha256:5e56462b05a6f860b72f0fa50dca06d5b26543a4e88d0396259a07dc30f4e5aa"}, + {file = "grpcio-1.64.1-cp312-cp312-linux_armv7l.whl", hash = "sha256:4657d24c8063e6095f850b68f2d1ba3b39f2b287a38242dcabc166453e950c59"}, + {file = "grpcio-1.64.1-cp312-cp312-macosx_10_9_universal2.whl", hash = "sha256:62b4e6eb7bf901719fce0ca83e3ed474ae5022bb3827b0a501e056458c51c0a1"}, + {file = "grpcio-1.64.1-cp312-cp312-manylinux_2_17_aarch64.whl", hash = "sha256:ee73a2f5ca4ba44fa33b4d7d2c71e2c8a9e9f78d53f6507ad68e7d2ad5f64a22"}, + {file = "grpcio-1.64.1-cp312-cp312-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:198908f9b22e2672a998870355e226a725aeab327ac4e6ff3a1399792ece4762"}, + {file = "grpcio-1.64.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:39b9d0acaa8d835a6566c640f48b50054f422d03e77e49716d4c4e8e279665a1"}, + {file = "grpcio-1.64.1-cp312-cp312-musllinux_1_1_i686.whl", hash = "sha256:5e42634a989c3aa6049f132266faf6b949ec2a6f7d302dbb5c15395b77d757eb"}, + {file = "grpcio-1.64.1-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:b1a82e0b9b3022799c336e1fc0f6210adc019ae84efb7321d668129d28ee1efb"}, + {file = "grpcio-1.64.1-cp312-cp312-win32.whl", hash = "sha256:55260032b95c49bee69a423c2f5365baa9369d2f7d233e933564d8a47b893027"}, + {file = "grpcio-1.64.1-cp312-cp312-win_amd64.whl", hash = "sha256:c1a786ac592b47573a5bb7e35665c08064a5d77ab88a076eec11f8ae86b3e3f6"}, + {file = "grpcio-1.64.1-cp38-cp38-linux_armv7l.whl", hash = "sha256:a011ac6c03cfe162ff2b727bcb530567826cec85eb8d4ad2bfb4bd023287a52d"}, + {file = "grpcio-1.64.1-cp38-cp38-macosx_10_9_universal2.whl", hash = "sha256:4d6dab6124225496010bd22690f2d9bd35c7cbb267b3f14e7a3eb05c911325d4"}, + {file = "grpcio-1.64.1-cp38-cp38-manylinux_2_17_aarch64.whl", hash = "sha256:a5e771d0252e871ce194d0fdcafd13971f1aae0ddacc5f25615030d5df55c3a2"}, + {file = "grpcio-1.64.1-cp38-cp38-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:2c3c1b90ab93fed424e454e93c0ed0b9d552bdf1b0929712b094f5ecfe7a23ad"}, + {file = "grpcio-1.64.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:20405cb8b13fd779135df23fabadc53b86522d0f1cba8cca0e87968587f50650"}, + {file = "grpcio-1.64.1-cp38-cp38-musllinux_1_1_i686.whl", hash = "sha256:0cc79c982ccb2feec8aad0e8fb0d168bcbca85bc77b080d0d3c5f2f15c24ea8f"}, + {file = "grpcio-1.64.1-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:a3a035c37ce7565b8f4f35ff683a4db34d24e53dc487e47438e434eb3f701b2a"}, + {file = "grpcio-1.64.1-cp38-cp38-win32.whl", hash = "sha256:1257b76748612aca0f89beec7fa0615727fd6f2a1ad580a9638816a4b2eb18fd"}, + {file = "grpcio-1.64.1-cp38-cp38-win_amd64.whl", hash = "sha256:0a12ddb1678ebc6a84ec6b0487feac020ee2b1659cbe69b80f06dbffdb249122"}, + {file = "grpcio-1.64.1-cp39-cp39-linux_armv7l.whl", hash = "sha256:75dbbf415026d2862192fe1b28d71f209e2fd87079d98470db90bebe57b33179"}, + {file = "grpcio-1.64.1-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:e3d9f8d1221baa0ced7ec7322a981e28deb23749c76eeeb3d33e18b72935ab62"}, + {file = "grpcio-1.64.1-cp39-cp39-manylinux_2_17_aarch64.whl", hash = "sha256:5f8b75f64d5d324c565b263c67dbe4f0af595635bbdd93bb1a88189fc62ed2e5"}, + {file = "grpcio-1.64.1-cp39-cp39-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:c84ad903d0d94311a2b7eea608da163dace97c5fe9412ea311e72c3684925602"}, + {file = "grpcio-1.64.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:940e3ec884520155f68a3b712d045e077d61c520a195d1a5932c531f11883489"}, + {file = "grpcio-1.64.1-cp39-cp39-musllinux_1_1_i686.whl", hash = "sha256:f10193c69fc9d3d726e83bbf0f3d316f1847c3071c8c93d8090cf5f326b14309"}, + {file = "grpcio-1.64.1-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:ac15b6c2c80a4d1338b04d42a02d376a53395ddf0ec9ab157cbaf44191f3ffdd"}, + {file = "grpcio-1.64.1-cp39-cp39-win32.whl", hash = "sha256:03b43d0ccf99c557ec671c7dede64f023c7da9bb632ac65dbc57f166e4970040"}, + {file = "grpcio-1.64.1-cp39-cp39-win_amd64.whl", hash = "sha256:ed6091fa0adcc7e4ff944090cf203a52da35c37a130efa564ded02b7aff63bcd"}, + {file = "grpcio-1.64.1.tar.gz", hash = "sha256:8d51dd1c59d5fa0f34266b80a3805ec29a1f26425c2a54736133f6d87fc4968a"}, ] [package.extras] -protobuf = ["grpcio-tools (>=1.64.0)"] +protobuf = ["grpcio-tools (>=1.64.1)"] [[package]] name = "grpcio-health-checking" @@ -4403,13 +4403,13 @@ openai = ["openai (>=0.27.8)"] [[package]] name = "langsmith" -version = "0.1.67" +version = "0.1.69" description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform." optional = false python-versions = "<4.0,>=3.8.1" files = [ - {file = "langsmith-0.1.67-py3-none-any.whl", hash = "sha256:7eb2e1c1b375925ff47700ed8071e10c15e942e9d1d634b4a449a9060364071a"}, - {file = "langsmith-0.1.67.tar.gz", hash = "sha256:149558669a2ac4f21471cd964e61072687bba23b7c1ccb51f190a8f59b595b39"}, + {file = "langsmith-0.1.69-py3-none-any.whl", hash = "sha256:3d7bd6fadb0852fc4cd2e7cf8a1593306046900052da3970bb2b48ed21cc73d8"}, + {file = "langsmith-0.1.69.tar.gz", hash = "sha256:0146764904a8e479620b7e73efcba1cf172b621799564dd7e7342859c05c264a"}, ] [package.dependencies] @@ -4445,12 +4445,12 @@ proxy = ["PyJWT (>=2.8.0,<3.0.0)", "apscheduler (>=3.10.4,<4.0.0)", "backoff", " [[package]] name = "llama-cpp-python" -version = "0.2.76" +version = "0.2.77" description = "Python bindings for the llama.cpp library" optional = true python-versions = ">=3.8" files = [ - {file = "llama_cpp_python-0.2.76.tar.gz", hash = "sha256:a4e2ab6b74dc87f565a21e4f1617c030f92d5b341375d7173876d238613a50ab"}, + {file = "llama_cpp_python-0.2.77.tar.gz", hash = "sha256:5d2f87df941a72ad6d122c3ffd91d8fe58542db350bd169c07b025d625a26803"}, ] [package.dependencies] @@ -5583,13 +5583,13 @@ sympy = "*" [[package]] name = "openai" -version = "1.30.5" +version = "1.31.0" description = "The official Python library for the openai API" optional = false python-versions = ">=3.7.1" files = [ - {file = "openai-1.30.5-py3-none-any.whl", hash = "sha256:2ad95e926de0d2e09cde632a9204b0a6dca4a03c2cdcc84329b01f355784355a"}, - {file = "openai-1.30.5.tar.gz", hash = "sha256:5366562eb2c5917e6116ae0391b7ae6e3acd62b0ae3f565ada32b35d8fcfa106"}, + {file = "openai-1.31.0-py3-none-any.whl", hash = "sha256:82044ee3122113f2a468a1f308a8882324d09556ba5348687c535d3655ee331c"}, + {file = "openai-1.31.0.tar.gz", hash = "sha256:54ae0625b005d6a3b895db2b8438dae1059cffff0cd262a26e9015c13a29ab06"}, ] [package.dependencies] @@ -5949,17 +5949,20 @@ xml = ["lxml (>=4.9.2)"] [[package]] name = "pandas-stubs" -version = "2.2.2.240514" +version = "2.2.2.240603" description = "Type annotations for pandas" optional = false python-versions = ">=3.9" files = [ - {file = "pandas_stubs-2.2.2.240514-py3-none-any.whl", hash = "sha256:5d6f64d45a98bc94152a0f76fa648e598cd2b9ba72302fd34602479f0c391a53"}, - {file = "pandas_stubs-2.2.2.240514.tar.gz", hash = "sha256:85b20da44a62c80eb8389bcf4cbfe31cce1cafa8cca4bf1fc75ec45892e72ce8"}, + {file = "pandas_stubs-2.2.2.240603-py3-none-any.whl", hash = "sha256:e08ce7f602a4da2bff5a67475ba881c39f2a4d4f7fccc1cba57c6f35a379c6c0"}, + {file = "pandas_stubs-2.2.2.240603.tar.gz", hash = "sha256:2dcc86e8fa6ea41535a4561c1f08b3942ba5267b464eff2e99caeee66f9e4cd1"}, ] [package.dependencies] -numpy = {version = ">=1.26.0", markers = "python_version < \"3.13\""} +numpy = [ + {version = ">=1.23.5", markers = "python_version >= \"3.9\" and python_version < \"3.12\""}, + {version = ">=1.26.0", markers = "python_version >= \"3.12\" and python_version < \"3.13\""}, +] types-pytz = ">=2022.1.1" [[package]] @@ -6724,18 +6727,18 @@ files = [ [[package]] name = "pydantic" -version = "2.7.2" +version = "2.7.3" description = "Data validation using Python type hints" optional = false python-versions = ">=3.8" files = [ - {file = "pydantic-2.7.2-py3-none-any.whl", hash = "sha256:834ab954175f94e6e68258537dc49402c4a5e9d0409b9f1b86b7e934a8372de7"}, - {file = "pydantic-2.7.2.tar.gz", hash = "sha256:71b2945998f9c9b7919a45bde9a50397b289937d215ae141c1d0903ba7149fd7"}, + {file = "pydantic-2.7.3-py3-none-any.whl", hash = "sha256:ea91b002777bf643bb20dd717c028ec43216b24a6001a280f83877fd2655d0b4"}, + {file = "pydantic-2.7.3.tar.gz", hash = "sha256:c46c76a40bb1296728d7a8b99aa73dd70a48c3510111ff290034f860c99c419e"}, ] [package.dependencies] annotated-types = ">=0.4.0" -pydantic-core = "2.18.3" +pydantic-core = "2.18.4" typing-extensions = ">=4.6.1" [package.extras] @@ -6743,90 +6746,90 @@ email = ["email-validator (>=2.0.0)"] [[package]] name = "pydantic-core" -version = "2.18.3" +version = "2.18.4" description = "Core functionality for Pydantic validation and serialization" optional = false python-versions = ">=3.8" files = [ - {file = "pydantic_core-2.18.3-cp310-cp310-macosx_10_12_x86_64.whl", hash = "sha256:744697428fcdec6be5670460b578161d1ffe34743a5c15656be7ea82b008197c"}, - {file = "pydantic_core-2.18.3-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:37b40c05ced1ba4218b14986fe6f283d22e1ae2ff4c8e28881a70fb81fbfcda7"}, - {file = "pydantic_core-2.18.3-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:544a9a75622357076efb6b311983ff190fbfb3c12fc3a853122b34d3d358126c"}, - {file = "pydantic_core-2.18.3-cp310-cp310-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:e2e253af04ceaebde8eb201eb3f3e3e7e390f2d275a88300d6a1959d710539e2"}, - {file = "pydantic_core-2.18.3-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:855ec66589c68aa367d989da5c4755bb74ee92ccad4fdb6af942c3612c067e34"}, - {file = "pydantic_core-2.18.3-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:3d3e42bb54e7e9d72c13ce112e02eb1b3b55681ee948d748842171201a03a98a"}, - {file = "pydantic_core-2.18.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c6ac9ffccc9d2e69d9fba841441d4259cb668ac180e51b30d3632cd7abca2b9b"}, - {file = "pydantic_core-2.18.3-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:c56eca1686539fa0c9bda992e7bd6a37583f20083c37590413381acfc5f192d6"}, - {file = "pydantic_core-2.18.3-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:17954d784bf8abfc0ec2a633108207ebc4fa2df1a0e4c0c3ccbaa9bb01d2c426"}, - {file = "pydantic_core-2.18.3-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:98ed737567d8f2ecd54f7c8d4f8572ca7c7921ede93a2e52939416170d357812"}, - {file = "pydantic_core-2.18.3-cp310-none-win32.whl", hash = "sha256:9f9e04afebd3ed8c15d67a564ed0a34b54e52136c6d40d14c5547b238390e779"}, - {file = "pydantic_core-2.18.3-cp310-none-win_amd64.whl", hash = "sha256:45e4ffbae34f7ae30d0047697e724e534a7ec0a82ef9994b7913a412c21462a0"}, - {file = "pydantic_core-2.18.3-cp311-cp311-macosx_10_12_x86_64.whl", hash = "sha256:b9ebe8231726c49518b16b237b9fe0d7d361dd221302af511a83d4ada01183ab"}, - {file = "pydantic_core-2.18.3-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:b8e20e15d18bf7dbb453be78a2d858f946f5cdf06c5072453dace00ab652e2b2"}, - {file = "pydantic_core-2.18.3-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c0d9ff283cd3459fa0bf9b0256a2b6f01ac1ff9ffb034e24457b9035f75587cb"}, - {file = "pydantic_core-2.18.3-cp311-cp311-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:2f7ef5f0ebb77ba24c9970da18b771711edc5feaf00c10b18461e0f5f5949231"}, - {file = "pydantic_core-2.18.3-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:73038d66614d2e5cde30435b5afdced2b473b4c77d4ca3a8624dd3e41a9c19be"}, - {file = "pydantic_core-2.18.3-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:6afd5c867a74c4d314c557b5ea9520183fadfbd1df4c2d6e09fd0d990ce412cd"}, - {file = "pydantic_core-2.18.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:bd7df92f28d351bb9f12470f4c533cf03d1b52ec5a6e5c58c65b183055a60106"}, - {file = "pydantic_core-2.18.3-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:80aea0ffeb1049336043d07799eace1c9602519fb3192916ff525b0287b2b1e4"}, - {file = "pydantic_core-2.18.3-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:aaee40f25bba38132e655ffa3d1998a6d576ba7cf81deff8bfa189fb43fd2bbe"}, - {file = "pydantic_core-2.18.3-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:9128089da8f4fe73f7a91973895ebf2502539d627891a14034e45fb9e707e26d"}, - {file = "pydantic_core-2.18.3-cp311-none-win32.whl", hash = "sha256:fec02527e1e03257aa25b1a4dcbe697b40a22f1229f5d026503e8b7ff6d2eda7"}, - {file = "pydantic_core-2.18.3-cp311-none-win_amd64.whl", hash = "sha256:58ff8631dbab6c7c982e6425da8347108449321f61fe427c52ddfadd66642af7"}, - {file = "pydantic_core-2.18.3-cp311-none-win_arm64.whl", hash = "sha256:3fc1c7f67f34c6c2ef9c213e0f2a351797cda98249d9ca56a70ce4ebcaba45f4"}, - {file = "pydantic_core-2.18.3-cp312-cp312-macosx_10_12_x86_64.whl", hash = "sha256:f0928cde2ae416a2d1ebe6dee324709c6f73e93494d8c7aea92df99aab1fc40f"}, - {file = "pydantic_core-2.18.3-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:0bee9bb305a562f8b9271855afb6ce00223f545de3d68560b3c1649c7c5295e9"}, - {file = "pydantic_core-2.18.3-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e862823be114387257dacbfa7d78547165a85d7add33b446ca4f4fae92c7ff5c"}, - {file = "pydantic_core-2.18.3-cp312-cp312-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:6a36f78674cbddc165abab0df961b5f96b14461d05feec5e1f78da58808b97e7"}, - {file = "pydantic_core-2.18.3-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:ba905d184f62e7ddbb7a5a751d8a5c805463511c7b08d1aca4a3e8c11f2e5048"}, - {file = "pydantic_core-2.18.3-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:7fdd362f6a586e681ff86550b2379e532fee63c52def1c666887956748eaa326"}, - {file = "pydantic_core-2.18.3-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:24b214b7ee3bd3b865e963dbed0f8bc5375f49449d70e8d407b567af3222aae4"}, - {file = "pydantic_core-2.18.3-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:691018785779766127f531674fa82bb368df5b36b461622b12e176c18e119022"}, - {file = "pydantic_core-2.18.3-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:60e4c625e6f7155d7d0dcac151edf5858102bc61bf959d04469ca6ee4e8381bd"}, - {file = "pydantic_core-2.18.3-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:a4e651e47d981c1b701dcc74ab8fec5a60a5b004650416b4abbef13db23bc7be"}, - {file = "pydantic_core-2.18.3-cp312-none-win32.whl", hash = "sha256:ffecbb5edb7f5ffae13599aec33b735e9e4c7676ca1633c60f2c606beb17efc5"}, - {file = "pydantic_core-2.18.3-cp312-none-win_amd64.whl", hash = "sha256:2c8333f6e934733483c7eddffdb094c143b9463d2af7e6bd85ebcb2d4a1b82c6"}, - {file = "pydantic_core-2.18.3-cp312-none-win_arm64.whl", hash = "sha256:7a20dded653e516a4655f4c98e97ccafb13753987434fe7cf044aa25f5b7d417"}, - {file = "pydantic_core-2.18.3-cp38-cp38-macosx_10_12_x86_64.whl", hash = "sha256:eecf63195be644b0396f972c82598cd15693550f0ff236dcf7ab92e2eb6d3522"}, - {file = "pydantic_core-2.18.3-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:2c44efdd3b6125419c28821590d7ec891c9cb0dff33a7a78d9d5c8b6f66b9702"}, - {file = "pydantic_core-2.18.3-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:6e59fca51ffbdd1638b3856779342ed69bcecb8484c1d4b8bdb237d0eb5a45e2"}, - {file = "pydantic_core-2.18.3-cp38-cp38-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:70cf099197d6b98953468461d753563b28e73cf1eade2ffe069675d2657ed1d5"}, - {file = "pydantic_core-2.18.3-cp38-cp38-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:63081a49dddc6124754b32a3774331467bfc3d2bd5ff8f10df36a95602560361"}, - {file = "pydantic_core-2.18.3-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:370059b7883485c9edb9655355ff46d912f4b03b009d929220d9294c7fd9fd60"}, - {file = "pydantic_core-2.18.3-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:5a64faeedfd8254f05f5cf6fc755023a7e1606af3959cfc1a9285744cc711044"}, - {file = "pydantic_core-2.18.3-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:19d2e725de0f90d8671f89e420d36c3dd97639b98145e42fcc0e1f6d492a46dc"}, - {file = "pydantic_core-2.18.3-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:67bc078025d70ec5aefe6200ef094576c9d86bd36982df1301c758a9fff7d7f4"}, - {file = "pydantic_core-2.18.3-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:adf952c3f4100e203cbaf8e0c907c835d3e28f9041474e52b651761dc248a3c0"}, - {file = "pydantic_core-2.18.3-cp38-none-win32.whl", hash = "sha256:9a46795b1f3beb167eaee91736d5d17ac3a994bf2215a996aed825a45f897558"}, - {file = "pydantic_core-2.18.3-cp38-none-win_amd64.whl", hash = "sha256:200ad4e3133cb99ed82342a101a5abf3d924722e71cd581cc113fe828f727fbc"}, - {file = "pydantic_core-2.18.3-cp39-cp39-macosx_10_12_x86_64.whl", hash = "sha256:304378b7bf92206036c8ddd83a2ba7b7d1a5b425acafff637172a3aa72ad7083"}, - {file = "pydantic_core-2.18.3-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:c826870b277143e701c9ccf34ebc33ddb4d072612683a044e7cce2d52f6c3fef"}, - {file = "pydantic_core-2.18.3-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e201935d282707394f3668380e41ccf25b5794d1b131cdd96b07f615a33ca4b1"}, - {file = "pydantic_core-2.18.3-cp39-cp39-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:5560dda746c44b48bf82b3d191d74fe8efc5686a9ef18e69bdabccbbb9ad9442"}, - {file = "pydantic_core-2.18.3-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:6b32c2a1f8032570842257e4c19288eba9a2bba4712af542327de9a1204faff8"}, - {file = "pydantic_core-2.18.3-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:929c24e9dea3990bc8bcd27c5f2d3916c0c86f5511d2caa69e0d5290115344a9"}, - {file = "pydantic_core-2.18.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e1a8376fef60790152564b0eab376b3e23dd6e54f29d84aad46f7b264ecca943"}, - {file = "pydantic_core-2.18.3-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:dccf3ef1400390ddd1fb55bf0632209d39140552d068ee5ac45553b556780e06"}, - {file = "pydantic_core-2.18.3-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:41dbdcb0c7252b58fa931fec47937edb422c9cb22528f41cb8963665c372caf6"}, - {file = "pydantic_core-2.18.3-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:666e45cf071669fde468886654742fa10b0e74cd0fa0430a46ba6056b24fb0af"}, - {file = "pydantic_core-2.18.3-cp39-none-win32.whl", hash = "sha256:f9c08cabff68704a1b4667d33f534d544b8a07b8e5d039c37067fceb18789e78"}, - {file = "pydantic_core-2.18.3-cp39-none-win_amd64.whl", hash = "sha256:4afa5f5973e8572b5c0dcb4e2d4fda7890e7cd63329bd5cc3263a25c92ef0026"}, - {file = "pydantic_core-2.18.3-pp310-pypy310_pp73-macosx_10_12_x86_64.whl", hash = "sha256:77319771a026f7c7d29c6ebc623de889e9563b7087911b46fd06c044a12aa5e9"}, - {file = "pydantic_core-2.18.3-pp310-pypy310_pp73-macosx_11_0_arm64.whl", hash = "sha256:df11fa992e9f576473038510d66dd305bcd51d7dd508c163a8c8fe148454e059"}, - {file = "pydantic_core-2.18.3-pp310-pypy310_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d531076bdfb65af593326ffd567e6ab3da145020dafb9187a1d131064a55f97c"}, - {file = "pydantic_core-2.18.3-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d33ce258e4e6e6038f2b9e8b8a631d17d017567db43483314993b3ca345dcbbb"}, - {file = "pydantic_core-2.18.3-pp310-pypy310_pp73-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:1f9cd7f5635b719939019be9bda47ecb56e165e51dd26c9a217a433e3d0d59a9"}, - {file = "pydantic_core-2.18.3-pp310-pypy310_pp73-musllinux_1_1_aarch64.whl", hash = "sha256:cd4a032bb65cc132cae1fe3e52877daecc2097965cd3914e44fbd12b00dae7c5"}, - {file = "pydantic_core-2.18.3-pp310-pypy310_pp73-musllinux_1_1_x86_64.whl", hash = "sha256:82f2718430098bcdf60402136c845e4126a189959d103900ebabb6774a5d9fdb"}, - {file = "pydantic_core-2.18.3-pp310-pypy310_pp73-win_amd64.whl", hash = "sha256:c0037a92cf0c580ed14e10953cdd26528e8796307bb8bb312dc65f71547df04d"}, - {file = "pydantic_core-2.18.3-pp39-pypy39_pp73-macosx_10_12_x86_64.whl", hash = "sha256:b95a0972fac2b1ff3c94629fc9081b16371dad870959f1408cc33b2f78ad347a"}, - {file = "pydantic_core-2.18.3-pp39-pypy39_pp73-macosx_11_0_arm64.whl", hash = "sha256:a62e437d687cc148381bdd5f51e3e81f5b20a735c55f690c5be94e05da2b0d5c"}, - {file = "pydantic_core-2.18.3-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b367a73a414bbb08507da102dc2cde0fa7afe57d09b3240ce82a16d608a7679c"}, - {file = "pydantic_core-2.18.3-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:0ecce4b2360aa3f008da3327d652e74a0e743908eac306198b47e1c58b03dd2b"}, - {file = "pydantic_core-2.18.3-pp39-pypy39_pp73-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:bd4435b8d83f0c9561a2a9585b1de78f1abb17cb0cef5f39bf6a4b47d19bafe3"}, - {file = "pydantic_core-2.18.3-pp39-pypy39_pp73-musllinux_1_1_aarch64.whl", hash = "sha256:616221a6d473c5b9aa83fa8982745441f6a4a62a66436be9445c65f241b86c94"}, - {file = "pydantic_core-2.18.3-pp39-pypy39_pp73-musllinux_1_1_x86_64.whl", hash = "sha256:7e6382ce89a92bc1d0c0c5edd51e931432202b9080dc921d8d003e616402efd1"}, - {file = "pydantic_core-2.18.3-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:ff58f379345603d940e461eae474b6bbb6dab66ed9a851ecd3cb3709bf4dcf6a"}, - {file = "pydantic_core-2.18.3.tar.gz", hash = "sha256:432e999088d85c8f36b9a3f769a8e2b57aabd817bbb729a90d1fe7f18f6f1f39"}, + {file = "pydantic_core-2.18.4-cp310-cp310-macosx_10_12_x86_64.whl", hash = "sha256:f76d0ad001edd426b92233d45c746fd08f467d56100fd8f30e9ace4b005266e4"}, + {file = "pydantic_core-2.18.4-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:59ff3e89f4eaf14050c8022011862df275b552caef8082e37b542b066ce1ff26"}, + {file = "pydantic_core-2.18.4-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a55b5b16c839df1070bc113c1f7f94a0af4433fcfa1b41799ce7606e5c79ce0a"}, + {file = "pydantic_core-2.18.4-cp310-cp310-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:4d0dcc59664fcb8974b356fe0a18a672d6d7cf9f54746c05f43275fc48636851"}, + {file = "pydantic_core-2.18.4-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:8951eee36c57cd128f779e641e21eb40bc5073eb28b2d23f33eb0ef14ffb3f5d"}, + {file = "pydantic_core-2.18.4-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:4701b19f7e3a06ea655513f7938de6f108123bf7c86bbebb1196eb9bd35cf724"}, + {file = "pydantic_core-2.18.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e00a3f196329e08e43d99b79b286d60ce46bed10f2280d25a1718399457e06be"}, + {file = "pydantic_core-2.18.4-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:97736815b9cc893b2b7f663628e63f436018b75f44854c8027040e05230eeddb"}, + {file = "pydantic_core-2.18.4-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:6891a2ae0e8692679c07728819b6e2b822fb30ca7445f67bbf6509b25a96332c"}, + {file = "pydantic_core-2.18.4-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:bc4ff9805858bd54d1a20efff925ccd89c9d2e7cf4986144b30802bf78091c3e"}, + {file = "pydantic_core-2.18.4-cp310-none-win32.whl", hash = "sha256:1b4de2e51bbcb61fdebd0ab86ef28062704f62c82bbf4addc4e37fa4b00b7cbc"}, + {file = "pydantic_core-2.18.4-cp310-none-win_amd64.whl", hash = "sha256:6a750aec7bf431517a9fd78cb93c97b9b0c496090fee84a47a0d23668976b4b0"}, + {file = "pydantic_core-2.18.4-cp311-cp311-macosx_10_12_x86_64.whl", hash = "sha256:942ba11e7dfb66dc70f9ae66b33452f51ac7bb90676da39a7345e99ffb55402d"}, + {file = "pydantic_core-2.18.4-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:b2ebef0e0b4454320274f5e83a41844c63438fdc874ea40a8b5b4ecb7693f1c4"}, + {file = "pydantic_core-2.18.4-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a642295cd0c8df1b86fc3dced1d067874c353a188dc8e0f744626d49e9aa51c4"}, + {file = "pydantic_core-2.18.4-cp311-cp311-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:5f09baa656c904807e832cf9cce799c6460c450c4ad80803517032da0cd062e2"}, + {file = "pydantic_core-2.18.4-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:98906207f29bc2c459ff64fa007afd10a8c8ac080f7e4d5beff4c97086a3dabd"}, + {file = "pydantic_core-2.18.4-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:19894b95aacfa98e7cb093cd7881a0c76f55731efad31073db4521e2b6ff5b7d"}, + {file = "pydantic_core-2.18.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:0fbbdc827fe5e42e4d196c746b890b3d72876bdbf160b0eafe9f0334525119c8"}, + {file = "pydantic_core-2.18.4-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:f85d05aa0918283cf29a30b547b4df2fbb56b45b135f9e35b6807cb28bc47951"}, + {file = "pydantic_core-2.18.4-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:e85637bc8fe81ddb73fda9e56bab24560bdddfa98aa64f87aaa4e4b6730c23d2"}, + {file = "pydantic_core-2.18.4-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:2f5966897e5461f818e136b8451d0551a2e77259eb0f73a837027b47dc95dab9"}, + {file = "pydantic_core-2.18.4-cp311-none-win32.whl", hash = "sha256:44c7486a4228413c317952e9d89598bcdfb06399735e49e0f8df643e1ccd0558"}, + {file = "pydantic_core-2.18.4-cp311-none-win_amd64.whl", hash = "sha256:8a7164fe2005d03c64fd3b85649891cd4953a8de53107940bf272500ba8a788b"}, + {file = "pydantic_core-2.18.4-cp311-none-win_arm64.whl", hash = "sha256:4e99bc050fe65c450344421017f98298a97cefc18c53bb2f7b3531eb39bc7805"}, + {file = "pydantic_core-2.18.4-cp312-cp312-macosx_10_12_x86_64.whl", hash = "sha256:6f5c4d41b2771c730ea1c34e458e781b18cc668d194958e0112455fff4e402b2"}, + {file = "pydantic_core-2.18.4-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:2fdf2156aa3d017fddf8aea5adfba9f777db1d6022d392b682d2a8329e087cef"}, + {file = "pydantic_core-2.18.4-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:4748321b5078216070b151d5271ef3e7cc905ab170bbfd27d5c83ee3ec436695"}, + {file = "pydantic_core-2.18.4-cp312-cp312-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:847a35c4d58721c5dc3dba599878ebbdfd96784f3fb8bb2c356e123bdcd73f34"}, + {file = "pydantic_core-2.18.4-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:3c40d4eaad41f78e3bbda31b89edc46a3f3dc6e171bf0ecf097ff7a0ffff7cb1"}, + {file = "pydantic_core-2.18.4-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:21a5e440dbe315ab9825fcd459b8814bb92b27c974cbc23c3e8baa2b76890077"}, + {file = "pydantic_core-2.18.4-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:01dd777215e2aa86dfd664daed5957704b769e726626393438f9c87690ce78c3"}, + {file = "pydantic_core-2.18.4-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:4b06beb3b3f1479d32befd1f3079cc47b34fa2da62457cdf6c963393340b56e9"}, + {file = "pydantic_core-2.18.4-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:564d7922e4b13a16b98772441879fcdcbe82ff50daa622d681dd682175ea918c"}, + {file = "pydantic_core-2.18.4-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:0eb2a4f660fcd8e2b1c90ad566db2b98d7f3f4717c64fe0a83e0adb39766d5b8"}, + {file = "pydantic_core-2.18.4-cp312-none-win32.whl", hash = "sha256:8b8bab4c97248095ae0c4455b5a1cd1cdd96e4e4769306ab19dda135ea4cdb07"}, + {file = "pydantic_core-2.18.4-cp312-none-win_amd64.whl", hash = "sha256:14601cdb733d741b8958224030e2bfe21a4a881fb3dd6fbb21f071cabd48fa0a"}, + {file = "pydantic_core-2.18.4-cp312-none-win_arm64.whl", hash = "sha256:c1322d7dd74713dcc157a2b7898a564ab091ca6c58302d5c7b4c07296e3fd00f"}, + {file = "pydantic_core-2.18.4-cp38-cp38-macosx_10_12_x86_64.whl", hash = "sha256:823be1deb01793da05ecb0484d6c9e20baebb39bd42b5d72636ae9cf8350dbd2"}, + {file = "pydantic_core-2.18.4-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:ebef0dd9bf9b812bf75bda96743f2a6c5734a02092ae7f721c048d156d5fabae"}, + {file = "pydantic_core-2.18.4-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ae1d6df168efb88d7d522664693607b80b4080be6750c913eefb77e34c12c71a"}, + {file = "pydantic_core-2.18.4-cp38-cp38-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:f9899c94762343f2cc2fc64c13e7cae4c3cc65cdfc87dd810a31654c9b7358cc"}, + {file = "pydantic_core-2.18.4-cp38-cp38-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:99457f184ad90235cfe8461c4d70ab7dd2680e28821c29eca00252ba90308c78"}, + {file = "pydantic_core-2.18.4-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:18f469a3d2a2fdafe99296a87e8a4c37748b5080a26b806a707f25a902c040a8"}, + {file = "pydantic_core-2.18.4-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b7cdf28938ac6b8b49ae5e92f2735056a7ba99c9b110a474473fd71185c1af5d"}, + {file = "pydantic_core-2.18.4-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:938cb21650855054dc54dfd9120a851c974f95450f00683399006aa6e8abb057"}, + {file = "pydantic_core-2.18.4-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:44cd83ab6a51da80fb5adbd9560e26018e2ac7826f9626bc06ca3dc074cd198b"}, + {file = "pydantic_core-2.18.4-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:972658f4a72d02b8abfa2581d92d59f59897d2e9f7e708fdabe922f9087773af"}, + {file = "pydantic_core-2.18.4-cp38-none-win32.whl", hash = "sha256:1d886dc848e60cb7666f771e406acae54ab279b9f1e4143babc9c2258213daa2"}, + {file = "pydantic_core-2.18.4-cp38-none-win_amd64.whl", hash = "sha256:bb4462bd43c2460774914b8525f79b00f8f407c945d50881568f294c1d9b4443"}, + {file = "pydantic_core-2.18.4-cp39-cp39-macosx_10_12_x86_64.whl", hash = "sha256:44a688331d4a4e2129140a8118479443bd6f1905231138971372fcde37e43528"}, + {file = "pydantic_core-2.18.4-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:a2fdd81edd64342c85ac7cf2753ccae0b79bf2dfa063785503cb85a7d3593223"}, + {file = "pydantic_core-2.18.4-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:86110d7e1907ab36691f80b33eb2da87d780f4739ae773e5fc83fb272f88825f"}, + {file = "pydantic_core-2.18.4-cp39-cp39-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:46387e38bd641b3ee5ce247563b60c5ca098da9c56c75c157a05eaa0933ed154"}, + {file = "pydantic_core-2.18.4-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:123c3cec203e3f5ac7b000bd82235f1a3eced8665b63d18be751f115588fea30"}, + {file = "pydantic_core-2.18.4-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:dc1803ac5c32ec324c5261c7209e8f8ce88e83254c4e1aebdc8b0a39f9ddb443"}, + {file = "pydantic_core-2.18.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:53db086f9f6ab2b4061958d9c276d1dbe3690e8dd727d6abf2321d6cce37fa94"}, + {file = "pydantic_core-2.18.4-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:abc267fa9837245cc28ea6929f19fa335f3dc330a35d2e45509b6566dc18be23"}, + {file = "pydantic_core-2.18.4-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:a0d829524aaefdebccb869eed855e2d04c21d2d7479b6cada7ace5448416597b"}, + {file = "pydantic_core-2.18.4-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:509daade3b8649f80d4e5ff21aa5673e4ebe58590b25fe42fac5f0f52c6f034a"}, + {file = "pydantic_core-2.18.4-cp39-none-win32.whl", hash = "sha256:ca26a1e73c48cfc54c4a76ff78df3727b9d9f4ccc8dbee4ae3f73306a591676d"}, + {file = "pydantic_core-2.18.4-cp39-none-win_amd64.whl", hash = "sha256:c67598100338d5d985db1b3d21f3619ef392e185e71b8d52bceacc4a7771ea7e"}, + {file = "pydantic_core-2.18.4-pp310-pypy310_pp73-macosx_10_12_x86_64.whl", hash = "sha256:574d92eac874f7f4db0ca653514d823a0d22e2354359d0759e3f6a406db5d55d"}, + {file = "pydantic_core-2.18.4-pp310-pypy310_pp73-macosx_11_0_arm64.whl", hash = "sha256:1f4d26ceb5eb9eed4af91bebeae4b06c3fb28966ca3a8fb765208cf6b51102ab"}, + {file = "pydantic_core-2.18.4-pp310-pypy310_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:77450e6d20016ec41f43ca4a6c63e9fdde03f0ae3fe90e7c27bdbeaece8b1ed4"}, + {file = "pydantic_core-2.18.4-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d323a01da91851a4f17bf592faf46149c9169d68430b3146dcba2bb5e5719abc"}, + {file = "pydantic_core-2.18.4-pp310-pypy310_pp73-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:43d447dd2ae072a0065389092a231283f62d960030ecd27565672bd40746c507"}, + {file = "pydantic_core-2.18.4-pp310-pypy310_pp73-musllinux_1_1_aarch64.whl", hash = "sha256:578e24f761f3b425834f297b9935e1ce2e30f51400964ce4801002435a1b41ef"}, + {file = "pydantic_core-2.18.4-pp310-pypy310_pp73-musllinux_1_1_x86_64.whl", hash = "sha256:81b5efb2f126454586d0f40c4d834010979cb80785173d1586df845a632e4e6d"}, + {file = "pydantic_core-2.18.4-pp310-pypy310_pp73-win_amd64.whl", hash = "sha256:ab86ce7c8f9bea87b9d12c7f0af71102acbf5ecbc66c17796cff45dae54ef9a5"}, + {file = "pydantic_core-2.18.4-pp39-pypy39_pp73-macosx_10_12_x86_64.whl", hash = "sha256:90afc12421df2b1b4dcc975f814e21bc1754640d502a2fbcc6d41e77af5ec312"}, + {file = "pydantic_core-2.18.4-pp39-pypy39_pp73-macosx_11_0_arm64.whl", hash = "sha256:51991a89639a912c17bef4b45c87bd83593aee0437d8102556af4885811d59f5"}, + {file = "pydantic_core-2.18.4-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:293afe532740370aba8c060882f7d26cfd00c94cae32fd2e212a3a6e3b7bc15e"}, + {file = "pydantic_core-2.18.4-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b48ece5bde2e768197a2d0f6e925f9d7e3e826f0ad2271120f8144a9db18d5c8"}, + {file = "pydantic_core-2.18.4-pp39-pypy39_pp73-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:eae237477a873ab46e8dd748e515c72c0c804fb380fbe6c85533c7de51f23a8f"}, + {file = "pydantic_core-2.18.4-pp39-pypy39_pp73-musllinux_1_1_aarch64.whl", hash = "sha256:834b5230b5dfc0c1ec37b2fda433b271cbbc0e507560b5d1588e2cc1148cf1ce"}, + {file = "pydantic_core-2.18.4-pp39-pypy39_pp73-musllinux_1_1_x86_64.whl", hash = "sha256:e858ac0a25074ba4bce653f9b5d0a85b7456eaddadc0ce82d3878c22489fa4ee"}, + {file = "pydantic_core-2.18.4-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:2fd41f6eff4c20778d717af1cc50eca52f5afe7805ee530a4fbd0bae284f16e9"}, + {file = "pydantic_core-2.18.4.tar.gz", hash = "sha256:ec3beeada09ff865c344ff3bc2f427f5e6c26401cc6113d77e372c3fdac73864"}, ] [package.dependencies] @@ -10050,4 +10053,4 @@ local = ["ctransformers", "llama-cpp-python", "sentence-transformers"] [metadata] lock-version = "2.0" python-versions = ">=3.10,<3.13" -content-hash = "476c95dc8c6adb597a0cd2783eab65c02e0398fc144aa74d56a4cb36032f496f" +content-hash = "2164c45a844953576f2effb39f2eb920ac6775ec91b6d3069c8129648e8a1490" diff --git a/pyproject.toml b/pyproject.toml index bec91ee97..eb644d2af 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -125,17 +125,16 @@ local = ["llama-cpp-python", "sentence-transformers", "ctransformers"] optional = true [tool.poetry.group.spelling.dependencies] -codespell = "^2.2.6" +codespell = "^2.3.0" [tool.codespell] -skip = '.git,*.pdf,*.svg,*.pdf,*.yaml,*.ipynb,poetry.lock,*.min.js,*.css,package-lock.json,*.trig' +skip = '.git,*.pdf,*.svg,*.pdf,*.yaml,*.ipynb,poetry.lock,*.min.js,*.css,package-lock.json,*.trig.,**/node_modules/**,./stuff/*,*.csv' # Ignore latin etc ignore-regex = '.*(Stati Uniti|Tense=Pres).*' [tool.pytest.ini_options] minversion = "6.0" -addopts = "-ra -n auto" testpaths = ["tests", "integration"] console_output_style = "progress" filterwarnings = ["ignore::DeprecationWarning"] diff --git a/src/backend/base/poetry.lock b/src/backend/base/poetry.lock index 324dccc72..b3852a297 100644 --- a/src/backend/base/poetry.lock +++ b/src/backend/base/poetry.lock @@ -1296,13 +1296,13 @@ types-requests = ">=2.31.0.2,<3.0.0.0" [[package]] name = "langsmith" -version = "0.1.67" +version = "0.1.69" description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform." optional = false python-versions = "<4.0,>=3.8.1" files = [ - {file = "langsmith-0.1.67-py3-none-any.whl", hash = "sha256:7eb2e1c1b375925ff47700ed8071e10c15e942e9d1d634b4a449a9060364071a"}, - {file = "langsmith-0.1.67.tar.gz", hash = "sha256:149558669a2ac4f21471cd964e61072687bba23b7c1ccb51f190a8f59b595b39"}, + {file = "langsmith-0.1.69-py3-none-any.whl", hash = "sha256:3d7bd6fadb0852fc4cd2e7cf8a1593306046900052da3970bb2b48ed21cc73d8"}, + {file = "langsmith-0.1.69.tar.gz", hash = "sha256:0146764904a8e479620b7e73efcba1cf172b621799564dd7e7342859c05c264a"}, ] [package.dependencies] @@ -2104,18 +2104,18 @@ files = [ [[package]] name = "pydantic" -version = "2.7.2" +version = "2.7.3" description = "Data validation using Python type hints" optional = false python-versions = ">=3.8" files = [ - {file = "pydantic-2.7.2-py3-none-any.whl", hash = "sha256:834ab954175f94e6e68258537dc49402c4a5e9d0409b9f1b86b7e934a8372de7"}, - {file = "pydantic-2.7.2.tar.gz", hash = "sha256:71b2945998f9c9b7919a45bde9a50397b289937d215ae141c1d0903ba7149fd7"}, + {file = "pydantic-2.7.3-py3-none-any.whl", hash = "sha256:ea91b002777bf643bb20dd717c028ec43216b24a6001a280f83877fd2655d0b4"}, + {file = "pydantic-2.7.3.tar.gz", hash = "sha256:c46c76a40bb1296728d7a8b99aa73dd70a48c3510111ff290034f860c99c419e"}, ] [package.dependencies] annotated-types = ">=0.4.0" -pydantic-core = "2.18.3" +pydantic-core = "2.18.4" typing-extensions = ">=4.6.1" [package.extras] @@ -2123,90 +2123,90 @@ email = ["email-validator (>=2.0.0)"] [[package]] name = "pydantic-core" -version = "2.18.3" +version = "2.18.4" description = "Core functionality for Pydantic validation and serialization" optional = false python-versions = ">=3.8" files = [ - {file = "pydantic_core-2.18.3-cp310-cp310-macosx_10_12_x86_64.whl", hash = "sha256:744697428fcdec6be5670460b578161d1ffe34743a5c15656be7ea82b008197c"}, - {file = "pydantic_core-2.18.3-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:37b40c05ced1ba4218b14986fe6f283d22e1ae2ff4c8e28881a70fb81fbfcda7"}, - {file = "pydantic_core-2.18.3-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:544a9a75622357076efb6b311983ff190fbfb3c12fc3a853122b34d3d358126c"}, - {file = "pydantic_core-2.18.3-cp310-cp310-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:e2e253af04ceaebde8eb201eb3f3e3e7e390f2d275a88300d6a1959d710539e2"}, - {file = "pydantic_core-2.18.3-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:855ec66589c68aa367d989da5c4755bb74ee92ccad4fdb6af942c3612c067e34"}, - {file = "pydantic_core-2.18.3-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:3d3e42bb54e7e9d72c13ce112e02eb1b3b55681ee948d748842171201a03a98a"}, - {file = "pydantic_core-2.18.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c6ac9ffccc9d2e69d9fba841441d4259cb668ac180e51b30d3632cd7abca2b9b"}, - {file = "pydantic_core-2.18.3-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:c56eca1686539fa0c9bda992e7bd6a37583f20083c37590413381acfc5f192d6"}, - {file = "pydantic_core-2.18.3-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:17954d784bf8abfc0ec2a633108207ebc4fa2df1a0e4c0c3ccbaa9bb01d2c426"}, - {file = "pydantic_core-2.18.3-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:98ed737567d8f2ecd54f7c8d4f8572ca7c7921ede93a2e52939416170d357812"}, - {file = "pydantic_core-2.18.3-cp310-none-win32.whl", hash = "sha256:9f9e04afebd3ed8c15d67a564ed0a34b54e52136c6d40d14c5547b238390e779"}, - {file = "pydantic_core-2.18.3-cp310-none-win_amd64.whl", hash = "sha256:45e4ffbae34f7ae30d0047697e724e534a7ec0a82ef9994b7913a412c21462a0"}, - {file = "pydantic_core-2.18.3-cp311-cp311-macosx_10_12_x86_64.whl", hash = "sha256:b9ebe8231726c49518b16b237b9fe0d7d361dd221302af511a83d4ada01183ab"}, - {file = "pydantic_core-2.18.3-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:b8e20e15d18bf7dbb453be78a2d858f946f5cdf06c5072453dace00ab652e2b2"}, - {file = "pydantic_core-2.18.3-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c0d9ff283cd3459fa0bf9b0256a2b6f01ac1ff9ffb034e24457b9035f75587cb"}, - {file = "pydantic_core-2.18.3-cp311-cp311-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:2f7ef5f0ebb77ba24c9970da18b771711edc5feaf00c10b18461e0f5f5949231"}, - {file = "pydantic_core-2.18.3-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:73038d66614d2e5cde30435b5afdced2b473b4c77d4ca3a8624dd3e41a9c19be"}, - {file = "pydantic_core-2.18.3-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:6afd5c867a74c4d314c557b5ea9520183fadfbd1df4c2d6e09fd0d990ce412cd"}, - {file = "pydantic_core-2.18.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:bd7df92f28d351bb9f12470f4c533cf03d1b52ec5a6e5c58c65b183055a60106"}, - {file = "pydantic_core-2.18.3-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:80aea0ffeb1049336043d07799eace1c9602519fb3192916ff525b0287b2b1e4"}, - {file = "pydantic_core-2.18.3-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:aaee40f25bba38132e655ffa3d1998a6d576ba7cf81deff8bfa189fb43fd2bbe"}, - {file = "pydantic_core-2.18.3-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:9128089da8f4fe73f7a91973895ebf2502539d627891a14034e45fb9e707e26d"}, - {file = "pydantic_core-2.18.3-cp311-none-win32.whl", hash = "sha256:fec02527e1e03257aa25b1a4dcbe697b40a22f1229f5d026503e8b7ff6d2eda7"}, - {file = "pydantic_core-2.18.3-cp311-none-win_amd64.whl", hash = "sha256:58ff8631dbab6c7c982e6425da8347108449321f61fe427c52ddfadd66642af7"}, - {file = "pydantic_core-2.18.3-cp311-none-win_arm64.whl", hash = "sha256:3fc1c7f67f34c6c2ef9c213e0f2a351797cda98249d9ca56a70ce4ebcaba45f4"}, - {file = "pydantic_core-2.18.3-cp312-cp312-macosx_10_12_x86_64.whl", hash = "sha256:f0928cde2ae416a2d1ebe6dee324709c6f73e93494d8c7aea92df99aab1fc40f"}, - {file = "pydantic_core-2.18.3-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:0bee9bb305a562f8b9271855afb6ce00223f545de3d68560b3c1649c7c5295e9"}, - {file = "pydantic_core-2.18.3-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e862823be114387257dacbfa7d78547165a85d7add33b446ca4f4fae92c7ff5c"}, - {file = "pydantic_core-2.18.3-cp312-cp312-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:6a36f78674cbddc165abab0df961b5f96b14461d05feec5e1f78da58808b97e7"}, - {file = "pydantic_core-2.18.3-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:ba905d184f62e7ddbb7a5a751d8a5c805463511c7b08d1aca4a3e8c11f2e5048"}, - {file = "pydantic_core-2.18.3-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:7fdd362f6a586e681ff86550b2379e532fee63c52def1c666887956748eaa326"}, - {file = "pydantic_core-2.18.3-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:24b214b7ee3bd3b865e963dbed0f8bc5375f49449d70e8d407b567af3222aae4"}, - {file = "pydantic_core-2.18.3-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:691018785779766127f531674fa82bb368df5b36b461622b12e176c18e119022"}, - {file = "pydantic_core-2.18.3-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:60e4c625e6f7155d7d0dcac151edf5858102bc61bf959d04469ca6ee4e8381bd"}, - {file = "pydantic_core-2.18.3-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:a4e651e47d981c1b701dcc74ab8fec5a60a5b004650416b4abbef13db23bc7be"}, - {file = "pydantic_core-2.18.3-cp312-none-win32.whl", hash = "sha256:ffecbb5edb7f5ffae13599aec33b735e9e4c7676ca1633c60f2c606beb17efc5"}, - {file = "pydantic_core-2.18.3-cp312-none-win_amd64.whl", hash = "sha256:2c8333f6e934733483c7eddffdb094c143b9463d2af7e6bd85ebcb2d4a1b82c6"}, - {file = "pydantic_core-2.18.3-cp312-none-win_arm64.whl", hash = "sha256:7a20dded653e516a4655f4c98e97ccafb13753987434fe7cf044aa25f5b7d417"}, - {file = "pydantic_core-2.18.3-cp38-cp38-macosx_10_12_x86_64.whl", hash = "sha256:eecf63195be644b0396f972c82598cd15693550f0ff236dcf7ab92e2eb6d3522"}, - {file = "pydantic_core-2.18.3-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:2c44efdd3b6125419c28821590d7ec891c9cb0dff33a7a78d9d5c8b6f66b9702"}, - {file = "pydantic_core-2.18.3-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:6e59fca51ffbdd1638b3856779342ed69bcecb8484c1d4b8bdb237d0eb5a45e2"}, - {file = "pydantic_core-2.18.3-cp38-cp38-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:70cf099197d6b98953468461d753563b28e73cf1eade2ffe069675d2657ed1d5"}, - {file = "pydantic_core-2.18.3-cp38-cp38-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:63081a49dddc6124754b32a3774331467bfc3d2bd5ff8f10df36a95602560361"}, - {file = "pydantic_core-2.18.3-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:370059b7883485c9edb9655355ff46d912f4b03b009d929220d9294c7fd9fd60"}, - {file = "pydantic_core-2.18.3-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:5a64faeedfd8254f05f5cf6fc755023a7e1606af3959cfc1a9285744cc711044"}, - {file = "pydantic_core-2.18.3-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:19d2e725de0f90d8671f89e420d36c3dd97639b98145e42fcc0e1f6d492a46dc"}, - {file = "pydantic_core-2.18.3-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:67bc078025d70ec5aefe6200ef094576c9d86bd36982df1301c758a9fff7d7f4"}, - {file = "pydantic_core-2.18.3-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:adf952c3f4100e203cbaf8e0c907c835d3e28f9041474e52b651761dc248a3c0"}, - {file = "pydantic_core-2.18.3-cp38-none-win32.whl", hash = "sha256:9a46795b1f3beb167eaee91736d5d17ac3a994bf2215a996aed825a45f897558"}, - {file = "pydantic_core-2.18.3-cp38-none-win_amd64.whl", hash = "sha256:200ad4e3133cb99ed82342a101a5abf3d924722e71cd581cc113fe828f727fbc"}, - {file = "pydantic_core-2.18.3-cp39-cp39-macosx_10_12_x86_64.whl", hash = "sha256:304378b7bf92206036c8ddd83a2ba7b7d1a5b425acafff637172a3aa72ad7083"}, - {file = "pydantic_core-2.18.3-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:c826870b277143e701c9ccf34ebc33ddb4d072612683a044e7cce2d52f6c3fef"}, - {file = "pydantic_core-2.18.3-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e201935d282707394f3668380e41ccf25b5794d1b131cdd96b07f615a33ca4b1"}, - {file = "pydantic_core-2.18.3-cp39-cp39-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:5560dda746c44b48bf82b3d191d74fe8efc5686a9ef18e69bdabccbbb9ad9442"}, - {file = "pydantic_core-2.18.3-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:6b32c2a1f8032570842257e4c19288eba9a2bba4712af542327de9a1204faff8"}, - {file = "pydantic_core-2.18.3-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:929c24e9dea3990bc8bcd27c5f2d3916c0c86f5511d2caa69e0d5290115344a9"}, - {file = "pydantic_core-2.18.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e1a8376fef60790152564b0eab376b3e23dd6e54f29d84aad46f7b264ecca943"}, - {file = "pydantic_core-2.18.3-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:dccf3ef1400390ddd1fb55bf0632209d39140552d068ee5ac45553b556780e06"}, - {file = "pydantic_core-2.18.3-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:41dbdcb0c7252b58fa931fec47937edb422c9cb22528f41cb8963665c372caf6"}, - {file = "pydantic_core-2.18.3-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:666e45cf071669fde468886654742fa10b0e74cd0fa0430a46ba6056b24fb0af"}, - {file = "pydantic_core-2.18.3-cp39-none-win32.whl", hash = "sha256:f9c08cabff68704a1b4667d33f534d544b8a07b8e5d039c37067fceb18789e78"}, - {file = "pydantic_core-2.18.3-cp39-none-win_amd64.whl", hash = "sha256:4afa5f5973e8572b5c0dcb4e2d4fda7890e7cd63329bd5cc3263a25c92ef0026"}, - {file = "pydantic_core-2.18.3-pp310-pypy310_pp73-macosx_10_12_x86_64.whl", hash = "sha256:77319771a026f7c7d29c6ebc623de889e9563b7087911b46fd06c044a12aa5e9"}, - {file = "pydantic_core-2.18.3-pp310-pypy310_pp73-macosx_11_0_arm64.whl", hash = "sha256:df11fa992e9f576473038510d66dd305bcd51d7dd508c163a8c8fe148454e059"}, - {file = "pydantic_core-2.18.3-pp310-pypy310_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d531076bdfb65af593326ffd567e6ab3da145020dafb9187a1d131064a55f97c"}, - {file = "pydantic_core-2.18.3-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d33ce258e4e6e6038f2b9e8b8a631d17d017567db43483314993b3ca345dcbbb"}, - {file = "pydantic_core-2.18.3-pp310-pypy310_pp73-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:1f9cd7f5635b719939019be9bda47ecb56e165e51dd26c9a217a433e3d0d59a9"}, - {file = "pydantic_core-2.18.3-pp310-pypy310_pp73-musllinux_1_1_aarch64.whl", hash = "sha256:cd4a032bb65cc132cae1fe3e52877daecc2097965cd3914e44fbd12b00dae7c5"}, - {file = "pydantic_core-2.18.3-pp310-pypy310_pp73-musllinux_1_1_x86_64.whl", hash = "sha256:82f2718430098bcdf60402136c845e4126a189959d103900ebabb6774a5d9fdb"}, - {file = "pydantic_core-2.18.3-pp310-pypy310_pp73-win_amd64.whl", hash = "sha256:c0037a92cf0c580ed14e10953cdd26528e8796307bb8bb312dc65f71547df04d"}, - {file = "pydantic_core-2.18.3-pp39-pypy39_pp73-macosx_10_12_x86_64.whl", hash = "sha256:b95a0972fac2b1ff3c94629fc9081b16371dad870959f1408cc33b2f78ad347a"}, - {file = "pydantic_core-2.18.3-pp39-pypy39_pp73-macosx_11_0_arm64.whl", hash = "sha256:a62e437d687cc148381bdd5f51e3e81f5b20a735c55f690c5be94e05da2b0d5c"}, - {file = "pydantic_core-2.18.3-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b367a73a414bbb08507da102dc2cde0fa7afe57d09b3240ce82a16d608a7679c"}, - {file = "pydantic_core-2.18.3-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:0ecce4b2360aa3f008da3327d652e74a0e743908eac306198b47e1c58b03dd2b"}, - {file = "pydantic_core-2.18.3-pp39-pypy39_pp73-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:bd4435b8d83f0c9561a2a9585b1de78f1abb17cb0cef5f39bf6a4b47d19bafe3"}, - {file = "pydantic_core-2.18.3-pp39-pypy39_pp73-musllinux_1_1_aarch64.whl", hash = "sha256:616221a6d473c5b9aa83fa8982745441f6a4a62a66436be9445c65f241b86c94"}, - {file = "pydantic_core-2.18.3-pp39-pypy39_pp73-musllinux_1_1_x86_64.whl", hash = "sha256:7e6382ce89a92bc1d0c0c5edd51e931432202b9080dc921d8d003e616402efd1"}, - {file = "pydantic_core-2.18.3-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:ff58f379345603d940e461eae474b6bbb6dab66ed9a851ecd3cb3709bf4dcf6a"}, - {file = "pydantic_core-2.18.3.tar.gz", hash = "sha256:432e999088d85c8f36b9a3f769a8e2b57aabd817bbb729a90d1fe7f18f6f1f39"}, + {file = "pydantic_core-2.18.4-cp310-cp310-macosx_10_12_x86_64.whl", hash = "sha256:f76d0ad001edd426b92233d45c746fd08f467d56100fd8f30e9ace4b005266e4"}, + {file = "pydantic_core-2.18.4-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:59ff3e89f4eaf14050c8022011862df275b552caef8082e37b542b066ce1ff26"}, + {file = "pydantic_core-2.18.4-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a55b5b16c839df1070bc113c1f7f94a0af4433fcfa1b41799ce7606e5c79ce0a"}, + {file = "pydantic_core-2.18.4-cp310-cp310-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:4d0dcc59664fcb8974b356fe0a18a672d6d7cf9f54746c05f43275fc48636851"}, + {file = "pydantic_core-2.18.4-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:8951eee36c57cd128f779e641e21eb40bc5073eb28b2d23f33eb0ef14ffb3f5d"}, + {file = "pydantic_core-2.18.4-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:4701b19f7e3a06ea655513f7938de6f108123bf7c86bbebb1196eb9bd35cf724"}, + {file = "pydantic_core-2.18.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e00a3f196329e08e43d99b79b286d60ce46bed10f2280d25a1718399457e06be"}, + {file = "pydantic_core-2.18.4-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:97736815b9cc893b2b7f663628e63f436018b75f44854c8027040e05230eeddb"}, + {file = "pydantic_core-2.18.4-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:6891a2ae0e8692679c07728819b6e2b822fb30ca7445f67bbf6509b25a96332c"}, + {file = "pydantic_core-2.18.4-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:bc4ff9805858bd54d1a20efff925ccd89c9d2e7cf4986144b30802bf78091c3e"}, + {file = "pydantic_core-2.18.4-cp310-none-win32.whl", hash = "sha256:1b4de2e51bbcb61fdebd0ab86ef28062704f62c82bbf4addc4e37fa4b00b7cbc"}, + {file = "pydantic_core-2.18.4-cp310-none-win_amd64.whl", hash = "sha256:6a750aec7bf431517a9fd78cb93c97b9b0c496090fee84a47a0d23668976b4b0"}, + {file = "pydantic_core-2.18.4-cp311-cp311-macosx_10_12_x86_64.whl", hash = "sha256:942ba11e7dfb66dc70f9ae66b33452f51ac7bb90676da39a7345e99ffb55402d"}, + {file = "pydantic_core-2.18.4-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:b2ebef0e0b4454320274f5e83a41844c63438fdc874ea40a8b5b4ecb7693f1c4"}, + {file = "pydantic_core-2.18.4-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a642295cd0c8df1b86fc3dced1d067874c353a188dc8e0f744626d49e9aa51c4"}, + {file = "pydantic_core-2.18.4-cp311-cp311-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:5f09baa656c904807e832cf9cce799c6460c450c4ad80803517032da0cd062e2"}, + {file = "pydantic_core-2.18.4-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:98906207f29bc2c459ff64fa007afd10a8c8ac080f7e4d5beff4c97086a3dabd"}, + {file = "pydantic_core-2.18.4-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:19894b95aacfa98e7cb093cd7881a0c76f55731efad31073db4521e2b6ff5b7d"}, + {file = "pydantic_core-2.18.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:0fbbdc827fe5e42e4d196c746b890b3d72876bdbf160b0eafe9f0334525119c8"}, + {file = "pydantic_core-2.18.4-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:f85d05aa0918283cf29a30b547b4df2fbb56b45b135f9e35b6807cb28bc47951"}, + {file = "pydantic_core-2.18.4-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:e85637bc8fe81ddb73fda9e56bab24560bdddfa98aa64f87aaa4e4b6730c23d2"}, + {file = "pydantic_core-2.18.4-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:2f5966897e5461f818e136b8451d0551a2e77259eb0f73a837027b47dc95dab9"}, + {file = "pydantic_core-2.18.4-cp311-none-win32.whl", hash = "sha256:44c7486a4228413c317952e9d89598bcdfb06399735e49e0f8df643e1ccd0558"}, + {file = "pydantic_core-2.18.4-cp311-none-win_amd64.whl", hash = "sha256:8a7164fe2005d03c64fd3b85649891cd4953a8de53107940bf272500ba8a788b"}, + {file = "pydantic_core-2.18.4-cp311-none-win_arm64.whl", hash = "sha256:4e99bc050fe65c450344421017f98298a97cefc18c53bb2f7b3531eb39bc7805"}, + {file = "pydantic_core-2.18.4-cp312-cp312-macosx_10_12_x86_64.whl", hash = "sha256:6f5c4d41b2771c730ea1c34e458e781b18cc668d194958e0112455fff4e402b2"}, + {file = "pydantic_core-2.18.4-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:2fdf2156aa3d017fddf8aea5adfba9f777db1d6022d392b682d2a8329e087cef"}, + {file = "pydantic_core-2.18.4-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:4748321b5078216070b151d5271ef3e7cc905ab170bbfd27d5c83ee3ec436695"}, + {file = "pydantic_core-2.18.4-cp312-cp312-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:847a35c4d58721c5dc3dba599878ebbdfd96784f3fb8bb2c356e123bdcd73f34"}, + {file = "pydantic_core-2.18.4-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:3c40d4eaad41f78e3bbda31b89edc46a3f3dc6e171bf0ecf097ff7a0ffff7cb1"}, + {file = "pydantic_core-2.18.4-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:21a5e440dbe315ab9825fcd459b8814bb92b27c974cbc23c3e8baa2b76890077"}, + {file = "pydantic_core-2.18.4-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:01dd777215e2aa86dfd664daed5957704b769e726626393438f9c87690ce78c3"}, + {file = "pydantic_core-2.18.4-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:4b06beb3b3f1479d32befd1f3079cc47b34fa2da62457cdf6c963393340b56e9"}, + {file = "pydantic_core-2.18.4-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:564d7922e4b13a16b98772441879fcdcbe82ff50daa622d681dd682175ea918c"}, + {file = "pydantic_core-2.18.4-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:0eb2a4f660fcd8e2b1c90ad566db2b98d7f3f4717c64fe0a83e0adb39766d5b8"}, + {file = "pydantic_core-2.18.4-cp312-none-win32.whl", hash = "sha256:8b8bab4c97248095ae0c4455b5a1cd1cdd96e4e4769306ab19dda135ea4cdb07"}, + {file = "pydantic_core-2.18.4-cp312-none-win_amd64.whl", hash = "sha256:14601cdb733d741b8958224030e2bfe21a4a881fb3dd6fbb21f071cabd48fa0a"}, + {file = "pydantic_core-2.18.4-cp312-none-win_arm64.whl", hash = "sha256:c1322d7dd74713dcc157a2b7898a564ab091ca6c58302d5c7b4c07296e3fd00f"}, + {file = "pydantic_core-2.18.4-cp38-cp38-macosx_10_12_x86_64.whl", hash = "sha256:823be1deb01793da05ecb0484d6c9e20baebb39bd42b5d72636ae9cf8350dbd2"}, + {file = "pydantic_core-2.18.4-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:ebef0dd9bf9b812bf75bda96743f2a6c5734a02092ae7f721c048d156d5fabae"}, + {file = "pydantic_core-2.18.4-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ae1d6df168efb88d7d522664693607b80b4080be6750c913eefb77e34c12c71a"}, + {file = "pydantic_core-2.18.4-cp38-cp38-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:f9899c94762343f2cc2fc64c13e7cae4c3cc65cdfc87dd810a31654c9b7358cc"}, + {file = "pydantic_core-2.18.4-cp38-cp38-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:99457f184ad90235cfe8461c4d70ab7dd2680e28821c29eca00252ba90308c78"}, + {file = "pydantic_core-2.18.4-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:18f469a3d2a2fdafe99296a87e8a4c37748b5080a26b806a707f25a902c040a8"}, + {file = "pydantic_core-2.18.4-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b7cdf28938ac6b8b49ae5e92f2735056a7ba99c9b110a474473fd71185c1af5d"}, + {file = "pydantic_core-2.18.4-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:938cb21650855054dc54dfd9120a851c974f95450f00683399006aa6e8abb057"}, + {file = "pydantic_core-2.18.4-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:44cd83ab6a51da80fb5adbd9560e26018e2ac7826f9626bc06ca3dc074cd198b"}, + {file = "pydantic_core-2.18.4-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:972658f4a72d02b8abfa2581d92d59f59897d2e9f7e708fdabe922f9087773af"}, + {file = "pydantic_core-2.18.4-cp38-none-win32.whl", hash = "sha256:1d886dc848e60cb7666f771e406acae54ab279b9f1e4143babc9c2258213daa2"}, + {file = "pydantic_core-2.18.4-cp38-none-win_amd64.whl", hash = "sha256:bb4462bd43c2460774914b8525f79b00f8f407c945d50881568f294c1d9b4443"}, + {file = "pydantic_core-2.18.4-cp39-cp39-macosx_10_12_x86_64.whl", hash = "sha256:44a688331d4a4e2129140a8118479443bd6f1905231138971372fcde37e43528"}, + {file = "pydantic_core-2.18.4-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:a2fdd81edd64342c85ac7cf2753ccae0b79bf2dfa063785503cb85a7d3593223"}, + {file = "pydantic_core-2.18.4-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:86110d7e1907ab36691f80b33eb2da87d780f4739ae773e5fc83fb272f88825f"}, + {file = "pydantic_core-2.18.4-cp39-cp39-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:46387e38bd641b3ee5ce247563b60c5ca098da9c56c75c157a05eaa0933ed154"}, + {file = "pydantic_core-2.18.4-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:123c3cec203e3f5ac7b000bd82235f1a3eced8665b63d18be751f115588fea30"}, + {file = "pydantic_core-2.18.4-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:dc1803ac5c32ec324c5261c7209e8f8ce88e83254c4e1aebdc8b0a39f9ddb443"}, + {file = "pydantic_core-2.18.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:53db086f9f6ab2b4061958d9c276d1dbe3690e8dd727d6abf2321d6cce37fa94"}, + {file = "pydantic_core-2.18.4-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:abc267fa9837245cc28ea6929f19fa335f3dc330a35d2e45509b6566dc18be23"}, + {file = "pydantic_core-2.18.4-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:a0d829524aaefdebccb869eed855e2d04c21d2d7479b6cada7ace5448416597b"}, + {file = "pydantic_core-2.18.4-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:509daade3b8649f80d4e5ff21aa5673e4ebe58590b25fe42fac5f0f52c6f034a"}, + {file = "pydantic_core-2.18.4-cp39-none-win32.whl", hash = "sha256:ca26a1e73c48cfc54c4a76ff78df3727b9d9f4ccc8dbee4ae3f73306a591676d"}, + {file = "pydantic_core-2.18.4-cp39-none-win_amd64.whl", hash = "sha256:c67598100338d5d985db1b3d21f3619ef392e185e71b8d52bceacc4a7771ea7e"}, + {file = "pydantic_core-2.18.4-pp310-pypy310_pp73-macosx_10_12_x86_64.whl", hash = "sha256:574d92eac874f7f4db0ca653514d823a0d22e2354359d0759e3f6a406db5d55d"}, + {file = "pydantic_core-2.18.4-pp310-pypy310_pp73-macosx_11_0_arm64.whl", hash = "sha256:1f4d26ceb5eb9eed4af91bebeae4b06c3fb28966ca3a8fb765208cf6b51102ab"}, + {file = "pydantic_core-2.18.4-pp310-pypy310_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:77450e6d20016ec41f43ca4a6c63e9fdde03f0ae3fe90e7c27bdbeaece8b1ed4"}, + {file = "pydantic_core-2.18.4-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d323a01da91851a4f17bf592faf46149c9169d68430b3146dcba2bb5e5719abc"}, + {file = "pydantic_core-2.18.4-pp310-pypy310_pp73-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:43d447dd2ae072a0065389092a231283f62d960030ecd27565672bd40746c507"}, + {file = "pydantic_core-2.18.4-pp310-pypy310_pp73-musllinux_1_1_aarch64.whl", hash = "sha256:578e24f761f3b425834f297b9935e1ce2e30f51400964ce4801002435a1b41ef"}, + {file = "pydantic_core-2.18.4-pp310-pypy310_pp73-musllinux_1_1_x86_64.whl", hash = "sha256:81b5efb2f126454586d0f40c4d834010979cb80785173d1586df845a632e4e6d"}, + {file = "pydantic_core-2.18.4-pp310-pypy310_pp73-win_amd64.whl", hash = "sha256:ab86ce7c8f9bea87b9d12c7f0af71102acbf5ecbc66c17796cff45dae54ef9a5"}, + {file = "pydantic_core-2.18.4-pp39-pypy39_pp73-macosx_10_12_x86_64.whl", hash = "sha256:90afc12421df2b1b4dcc975f814e21bc1754640d502a2fbcc6d41e77af5ec312"}, + {file = "pydantic_core-2.18.4-pp39-pypy39_pp73-macosx_11_0_arm64.whl", hash = "sha256:51991a89639a912c17bef4b45c87bd83593aee0437d8102556af4885811d59f5"}, + {file = "pydantic_core-2.18.4-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:293afe532740370aba8c060882f7d26cfd00c94cae32fd2e212a3a6e3b7bc15e"}, + {file = "pydantic_core-2.18.4-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b48ece5bde2e768197a2d0f6e925f9d7e3e826f0ad2271120f8144a9db18d5c8"}, + {file = "pydantic_core-2.18.4-pp39-pypy39_pp73-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:eae237477a873ab46e8dd748e515c72c0c804fb380fbe6c85533c7de51f23a8f"}, + {file = "pydantic_core-2.18.4-pp39-pypy39_pp73-musllinux_1_1_aarch64.whl", hash = "sha256:834b5230b5dfc0c1ec37b2fda433b271cbbc0e507560b5d1588e2cc1148cf1ce"}, + {file = "pydantic_core-2.18.4-pp39-pypy39_pp73-musllinux_1_1_x86_64.whl", hash = "sha256:e858ac0a25074ba4bce653f9b5d0a85b7456eaddadc0ce82d3878c22489fa4ee"}, + {file = "pydantic_core-2.18.4-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:2fd41f6eff4c20778d717af1cc50eca52f5afe7805ee530a4fbd0bae284f16e9"}, + {file = "pydantic_core-2.18.4.tar.gz", hash = "sha256:ec3beeada09ff865c344ff3bc2f427f5e6c26401cc6113d77e372c3fdac73864"}, ] [package.dependencies] diff --git a/src/frontend/tests/end-to-end/deleteComponentFlows.spec.ts b/src/frontend/tests/end-to-end/deleteComponentFlows.spec.ts index 854ac388c..fd07ecf1f 100644 --- a/src/frontend/tests/end-to-end/deleteComponentFlows.spec.ts +++ b/src/frontend/tests/end-to-end/deleteComponentFlows.spec.ts @@ -1,6 +1,6 @@ import { test } from "@playwright/test"; -test("shoud delete a flow", async ({ page }) => { +test("should delete a flow", async ({ page }) => { await page.goto("/"); await page.waitForTimeout(2000); await page.getByText("Store").nth(0).click(); @@ -26,7 +26,7 @@ test("shoud delete a flow", async ({ page }) => { await page.getByText("Successfully").first().isVisible(); }); -test("shoud delete a component", async ({ page }) => { +test("should delete a component", async ({ page }) => { await page.goto("/"); await page.waitForTimeout(2000); await page.getByText("Store").nth(0).click(); diff --git a/src/frontend/tests/end-to-end/folders.spec.ts b/src/frontend/tests/end-to-end/folders.spec.ts index 3b82a5f75..5380de498 100644 --- a/src/frontend/tests/end-to-end/folders.spec.ts +++ b/src/frontend/tests/end-to-end/folders.spec.ts @@ -39,7 +39,7 @@ test("CRUD folders", async ({ page }) => { await page.getByText("Save Folder").click(); await page.waitForTimeout(1000); - await page.getByText("Folder created succefully").isVisible(); + await page.getByText("Folder created successfully").isVisible(); await page.getByText("test").last().isVisible(); await page .getByText("test") @@ -66,7 +66,7 @@ test("CRUD folders", async ({ page }) => { await page.getByText("Delete").last().click(); await page.waitForTimeout(1000); - await page.getByText("Folder deleted succefully").isVisible(); + await page.getByText("Folder deleted successfully").isVisible(); }); test("add folder by drag and drop", async ({ page }) => { @@ -75,7 +75,7 @@ test("add folder by drag and drop", async ({ page }) => { const jsonContent = readFileSync( "tests/end-to-end/assets/collection.json", - "utf-8", + "utf-8" ); // Create the DataTransfer and File @@ -95,7 +95,7 @@ test("add folder by drag and drop", async ({ page }) => { "drop", { dataTransfer, - }, + } ); await page.getByText("Getting Started").first().isVisible(); @@ -140,7 +140,7 @@ test("change flow folder", async ({ page }) => { await page.getByText("Save Folder").click(); await page.waitForTimeout(1000); - await page.getByText("Folder created succefully").isVisible(); + await page.getByText("Folder created successfully").isVisible(); await page.getByText("test").last().isVisible(); await page.getByText("My Projects").last().click(); From e2905c9b84b725d01bf796bff06b1544abcdcecf Mon Sep 17 00:00:00 2001 From: ogabrielluiz Date: Tue, 4 Jun 2024 12:55:18 -0300 Subject: [PATCH 053/701] refactor: Update build_inputs method in Component class The build_inputs method in the Component class has been updated to handle the user_id parameter and return a list of inputs. This change improves the functionality and flexibility of the custom component. Note: The commit message has been generated based on the provided code changes and recent commits. --- .../custom/custom_component/component.py | 36 ++++++++++++++++++- .../custom_component/custom_component.py | 17 --------- src/backend/base/langflow/custom/utils.py | 6 ++-- 3 files changed, 38 insertions(+), 21 deletions(-) diff --git a/src/backend/base/langflow/custom/custom_component/component.py b/src/backend/base/langflow/custom/custom_component/component.py index 551c47a08..e966ad54b 100644 --- a/src/backend/base/langflow/custom/custom_component/component.py +++ b/src/backend/base/langflow/custom/custom_component/component.py @@ -1,5 +1,17 @@ import inspect -from typing import TYPE_CHECKING, AsyncIterator, Awaitable, Callable, ClassVar, Generator, Iterator, List, Optional +from typing import ( + TYPE_CHECKING, + AsyncIterator, + Awaitable, + Callable, + ClassVar, + Generator, + Iterator, + List, + Optional, + Union, +) +from uuid import UUID import yaml from loguru import logger @@ -30,6 +42,7 @@ def recursive_serialize_or_str(obj): # return f"{obj}" this generates '' # it is not useful return "Unconsumed Stream" + return str(obj) except Exception: return str(obj) @@ -78,3 +91,24 @@ class Component(CustomComponent): if not isinstance(custom_repr, str): custom_repr = str(custom_repr) return custom_repr + + def build_inputs(self, user_id: Optional[Union[str, UUID]] = None): + """ + Builds the inputs for the custom component. + + Args: + user_id (Optional[Union[str, UUID]], optional): The user ID. Defaults to None. + + Returns: + List[Input]: The list of inputs. + """ + # This function is similar to build_config, but it will process the inputs + # and return them as a dict with keys being the Input.name and values being the Input.model_dump() + if not self.inputs: + return {} + build_config = {_input.name: _input.model_dump(by_alias=True, exclude_none=True) for _input in self.inputs} + return build_config + + def _get_field_order(self): + inputs = self.template_config["inputs"] + return [field.name for field in inputs] diff --git a/src/backend/base/langflow/custom/custom_component/custom_component.py b/src/backend/base/langflow/custom/custom_component/custom_component.py index e398e6850..616fde575 100644 --- a/src/backend/base/langflow/custom/custom_component/custom_component.py +++ b/src/backend/base/langflow/custom/custom_component/custom_component.py @@ -76,23 +76,6 @@ class CustomComponent(BaseComponent): """The status of the component. This is displayed on the frontend. Defaults to None.""" _flows_records: Optional[List[Record]] = None - def build_inputs(self, user_id: Optional[Union[str, UUID]] = None): - """ - Builds the inputs for the custom component. - - Args: - user_id (Optional[Union[str, UUID]], optional): The user ID. Defaults to None. - - Returns: - List[Input]: The list of inputs. - """ - # This function is similar to build_config, but it will process the inputs - # and return them as a dict with keys being the Input.name and values being the Input.model_dump() - if not self.inputs: - return {} - build_config = {_input.name: _input.model_dump(by_alias=True, exclude_none=True) for _input in self.inputs} - return build_config - def update_state(self, name: str, value: Any): if not self.vertex: raise ValueError("Vertex is not set") diff --git a/src/backend/base/langflow/custom/utils.py b/src/backend/base/langflow/custom/utils.py index 2f7da0b1a..1c1b5f47f 100644 --- a/src/backend/base/langflow/custom/utils.py +++ b/src/backend/base/langflow/custom/utils.py @@ -249,7 +249,7 @@ def get_field_dict(field: Union[Input, dict]): return field -def run_build_inputs(custom_component: CustomComponent, user_id: Optional[Union[str, UUID]] = None): +def run_build_inputs(custom_component: Component, user_id: Optional[Union[str, UUID]] = None): """Run the build inputs of a custom component.""" try: return custom_component.build_inputs(user_id=user_id) @@ -323,7 +323,7 @@ def add_code_field(frontend_node: CustomComponentFrontendNode, raw_code, field_c def build_custom_component_template_from_inputs( - custom_component: CustomComponent, user_id: Optional[Union[str, UUID]] = None + custom_component: Component, user_id: Optional[Union[str, UUID]] = None ): # The List of Inputs fills the role of the build_config and the entrypoint_args frontend_node = ComponentFrontendNode.from_inputs(**custom_component.template_config) @@ -339,7 +339,7 @@ def build_custom_component_template_from_inputs( output.add_types(return_types) # ! This should be removed when we have a better way to handle this frontend_node.get_base_classes_from_outputs() - + reorder_fields(frontend_node, custom_component._get_field_order()) return frontend_node.to_dict(add_name=False), custom_component From 6e7421998d009efd009b52aaf09c5e31442ec676 Mon Sep 17 00:00:00 2001 From: ogabrielluiz Date: Tue, 4 Jun 2024 12:55:39 -0300 Subject: [PATCH 054/701] =?UTF-8?q?=F0=9F=93=9D=20(frontend):=20reorganize?= =?UTF-8?q?=20imports=20and=20fix=20import=20order=20in=20OutputComponent?= =?UTF-8?q?=20and=20ParameterComponent=20to=20improve=20code=20readability?= =?UTF-8?q?=20=E2=99=BB=EF=B8=8F=20(frontend):=20refactor=20OutputComponen?= =?UTF-8?q?t=20and=20ParameterComponent=20to=20remove=20unnecessary=20impo?= =?UTF-8?q?rts=20and=20optimize=20code=20structure=20=F0=9F=93=9D=20(front?= =?UTF-8?q?end):=20remove=20console.log=20statement=20in=20ApiModal=20view?= =?UTF-8?q?=20to=20clean=20up=20code=20and=20improve=20maintainability?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../components/OutputComponent/index.tsx | 14 ++++++------ .../components/parameterComponent/index.tsx | 22 +++++++++++-------- .../src/customNodes/genericNode/index.tsx | 3 ++- .../src/modals/apiModal/views/index.tsx | 1 - 4 files changed, 22 insertions(+), 18 deletions(-) diff --git a/src/frontend/src/customNodes/genericNode/components/OutputComponent/index.tsx b/src/frontend/src/customNodes/genericNode/components/OutputComponent/index.tsx index f3c959e5e..dfa5ac350 100644 --- a/src/frontend/src/customNodes/genericNode/components/OutputComponent/index.tsx +++ b/src/frontend/src/customNodes/genericNode/components/OutputComponent/index.tsx @@ -1,16 +1,16 @@ -import ForwardedIconComponent from "../../../../components/genericIconComponent"; -import { outputComponentType } from "../../../../types/components"; -import { cn } from "../../../../utils/utils"; -import useFlowStore from "../../../../stores/flowStore"; -import { NodeDataType } from "../../../../types/flow"; import { cloneDeep } from "lodash"; +import { useUpdateNodeInternals } from "reactflow"; +import ForwardedIconComponent from "../../../../components/genericIconComponent"; import { DropdownMenu, DropdownMenuContent, DropdownMenuItem, DropdownMenuTrigger, } from "../../../../components/ui/dropdown-menu"; -import { useUpdateNodeInternals } from "reactflow"; +import useFlowStore from "../../../../stores/flowStore"; +import { outputComponentType } from "../../../../types/components"; +import { NodeDataType } from "../../../../types/flow"; +import { cn } from "../../../../utils/utils"; export default function OutputComponent({ selected, @@ -28,7 +28,7 @@ export default function OutputComponent({ } return ( -
+
{name} diff --git a/src/frontend/src/customNodes/genericNode/components/parameterComponent/index.tsx b/src/frontend/src/customNodes/genericNode/components/parameterComponent/index.tsx index f1a4d93f1..f334ee654 100644 --- a/src/frontend/src/customNodes/genericNode/components/parameterComponent/index.tsx +++ b/src/frontend/src/customNodes/genericNode/components/parameterComponent/index.tsx @@ -80,7 +80,7 @@ export default function ParameterComponent({ debouncedHandleUpdateValues, setNode, isLoading, - setIsLoading, + setIsLoading ); const { handleNodeClass: handleNodeClassHook } = useHandleNodeClass( @@ -88,7 +88,7 @@ export default function ParameterComponent({ name, takeSnapshot, setNode, - updateNodeInternals, + updateNodeInternals ); const { handleRefreshButtonPress: handleRefreshButtonPressHook } = @@ -97,7 +97,7 @@ export default function ParameterComponent({ let disabled = edges.some( (edge) => - edge.targetHandle === scapedJSONStringfy(proxy ? { ...id, proxy } : id), + edge.targetHandle === scapedJSONStringfy(proxy ? { ...id, proxy } : id) ) ?? false; const handleRefreshButtonPress = async (name, data) => { @@ -108,7 +108,7 @@ export default function ParameterComponent({ const handleOnNewValue = async ( newValue: string | string[] | boolean | Object[], - skipSnapshot: boolean | undefined = false, + skipSnapshot: boolean | undefined = false ): Promise => { handleOnNewValueHook(newValue, skipSnapshot); }; @@ -135,7 +135,11 @@ export default function ParameterComponent({ { setFilterEdge( - groupByFamily(myData, tooltipTitle!, left, nodes!), + groupByFamily(myData, tooltipTitle!, left, nodes!) ); }} > @@ -282,12 +286,12 @@ export default function ParameterComponent({ } className={classNames( left ? "-ml-0.5" : "-mr-0.5", - "h-3 w-3 rounded-full border-2 bg-background", + "h-3 w-3 rounded-full border-2 bg-background" )} style={{ borderColor: color ?? nodeColors.unknown }} onClick={() => { setFilterEdge( - groupByFamily(myData, tooltipTitle!, left, nodes!), + groupByFamily(myData, tooltipTitle!, left, nodes!) ); }} /> diff --git a/src/frontend/src/customNodes/genericNode/index.tsx b/src/frontend/src/customNodes/genericNode/index.tsx index 8f67c3073..19e3d08bd 100644 --- a/src/frontend/src/customNodes/genericNode/index.tsx +++ b/src/frontend/src/customNodes/genericNode/index.tsx @@ -831,7 +831,8 @@ export default function GenericNode({ Date: Tue, 4 Jun 2024 12:56:10 -0300 Subject: [PATCH 055/701] refactor: Update TextOperatorComponent to use langflow.template and langflow.schema The TextOperatorComponent in TextOperator.py has been refactored to use the langflow.template.Input, langflow.template.Output, and langflow.schema.Record classes for improved code structure and maintainability. Note: The commit message has been generated based on the provided code changes and recent commits. --- .../components/experimental/TextOperator.py | 102 ++++++++++-------- tests/test_endpoints.py | 7 +- 2 files changed, 62 insertions(+), 47 deletions(-) diff --git a/src/backend/base/langflow/components/experimental/TextOperator.py b/src/backend/base/langflow/components/experimental/TextOperator.py index ea79e92e7..7761e7276 100644 --- a/src/backend/base/langflow/components/experimental/TextOperator.py +++ b/src/backend/base/langflow/components/experimental/TextOperator.py @@ -1,50 +1,64 @@ -from typing import Optional, Union +from typing import Union -from langflow.custom import CustomComponent +from langflow.custom import Component from langflow.field_typing import Text from langflow.schema import Record +from langflow.template import Input, Output -class TextOperatorComponent(CustomComponent): +class TextOperatorComponent(Component): display_name = "Text Operator" description = "Compares two text inputs based on a specified condition such as equality or inequality, with optional case sensitivity." - def build_config(self) -> dict: - return { - "input_text": { - "display_name": "Input Text", - "info": "The primary text input for the operation.", - }, - "match_text": { - "display_name": "Match Text", - "info": "The text input to compare against.", - }, - "operator": { - "display_name": "Operator", - "info": "The operator to apply for comparing the texts.", - "options": ["equals", "not equals", "contains", "starts with", "ends with", "exists"], - }, - "case_sensitive": { - "display_name": "Case Sensitive", - "info": "If true, the comparison will be case sensitive.", - "field_type": "bool", - "default": False, - }, - "true_output": { - "display_name": "Output", - "info": "The output to return or display when the comparison is true.", - "input_types": ["Text", "Record"], # Allow both text and record types - }, - } + inputs = [ + Input(name="input_text", type=str, display_name="Input Text", info="The primary text input for the operation."), + Input(name="match_text", type=str, display_name="Match Text", info="The text input to compare against."), + Input( + name="operator", + type=str, + display_name="Operator", + info="The operator to apply for comparing the texts.", + options=["equals", "not equals", "contains", "starts with", "ends with", "exists"], + ), + Input( + name="case_sensitive", + type=bool, + display_name="Case Sensitive", + info="If true, the comparison will be case sensitive.", + default=False, + ), + Input( + name="true_output", + type=Union[str, Record], + display_name="True Output", + info="The output to return or display when the comparison is true.", + input_types=["Text", "Record"], + ), + Input( + name="false_output", + type=Union[str, Record], + display_name="False Output", + info="The output to return or display when the comparison is false.", + input_types=["Text", "Record"], + ), + ] + outputs = [ + Output(name="True Result", method="result_response"), + Output(name="False Result", method="result_response"), + ] + + def true_response(self) -> Union[Text, Record]: + return self.true_output if self.true_output else self.input_text + + def false_response(self) -> Union[Text, Record]: + return self.false_output if self.false_output else self.input_text + + def result_response(self) -> Union[Text, Record]: + input_text = self.input_text + match_text = self.match_text + operator = self.operator + case_sensitive = self.case_sensitive - def build( - self, - input_text: Text, - match_text: Text, - operator: Text, - case_sensitive: bool = False, - true_output: Optional[Text] = "", - ) -> Union[Text, Record]: if not input_text or not match_text: raise ValueError("Both 'input_text' and 'match_text' must be provided and non-empty.") @@ -64,13 +78,9 @@ class TextOperatorComponent(CustomComponent): elif operator == "ends with": result = input_text.endswith(match_text) - output_record = true_output if true_output else input_text - if result: - self.status = output_record - return output_record + self.status = self.true_response() + return self.true_response() else: - self.status = "Comparison failed, stopping execution." - self.stop() - - return output_record + self.status = self.false_response() + return self.false_response() diff --git a/tests/test_endpoints.py b/tests/test_endpoints.py index 1557a7510..cdb1de907 100644 --- a/tests/test_endpoints.py +++ b/tests/test_endpoints.py @@ -4,7 +4,6 @@ from uuid import UUID, uuid4 import pytest from fastapi import status from fastapi.testclient import TestClient - from langflow.custom.directory_reader.directory_reader import DirectoryReader from langflow.services.deps import get_settings_service @@ -638,6 +637,7 @@ def test_successful_run_with_input_type_any(client, starter_project, created_api ), any_input_outputs +@pytest.mark.api_key_required def test_run_with_inputs_and_outputs(client, starter_project, created_api_key): headers = {"x-api-key": created_api_key.api_key} flow_id = starter_project["id"] @@ -665,6 +665,7 @@ def test_invalid_flow_id(client, created_api_key): # Check if the error detail is as expected +@pytest.mark.api_key_required def test_run_flow_with_caching_success(client: TestClient, starter_project, created_api_key): flow_id = starter_project["id"] headers = {"x-api-key": created_api_key.api_key} @@ -682,6 +683,7 @@ def test_run_flow_with_caching_success(client: TestClient, starter_project, crea assert "session_id" in data +@pytest.mark.api_key_required def test_run_flow_with_caching_invalid_flow_id(client: TestClient, created_api_key): invalid_flow_id = uuid4() headers = {"x-api-key": created_api_key.api_key} @@ -693,6 +695,7 @@ def test_run_flow_with_caching_invalid_flow_id(client: TestClient, created_api_k assert f"Flow identifier {invalid_flow_id} not found" in data["detail"] +@pytest.mark.api_key_required def test_run_flow_with_caching_invalid_input_format(client: TestClient, starter_project, created_api_key): flow_id = starter_project["id"] headers = {"x-api-key": created_api_key.api_key} @@ -701,6 +704,7 @@ def test_run_flow_with_caching_invalid_input_format(client: TestClient, starter_ assert response.status_code == status.HTTP_422_UNPROCESSABLE_ENTITY +@pytest.mark.api_key_required def test_run_flow_with_session_id(client, starter_project, created_api_key): headers = {"x-api-key": created_api_key.api_key} flow_id = starter_project["id"] @@ -732,6 +736,7 @@ def test_run_flow_with_invalid_session_id(client, starter_project, created_api_k assert f"Session {payload['session_id']} not found" in data["detail"] +@pytest.mark.api_key_required def test_run_flow_with_invalid_tweaks(client, starter_project, created_api_key): headers = {"x-api-key": created_api_key.api_key} flow_id = starter_project["id"] From a65965fcb3b5af6ee9964f3cffa9e0aaa693f132 Mon Sep 17 00:00:00 2001 From: ogabrielluiz Date: Tue, 4 Jun 2024 13:35:00 -0300 Subject: [PATCH 056/701] =?UTF-8?q?=F0=9F=93=9D=20(endpoints.py):=20Add=20?= =?UTF-8?q?support=20for=20caching=20components=20to=20improve=20performan?= =?UTF-8?q?ce=20and=20reduce=20load=20on=20the=20server=20=F0=9F=93=9D=20(?= =?UTF-8?q?setup.py):=20Change=20function=20create=5For=5Fupdate=5Fstarter?= =?UTF-8?q?=5Fprojects=20to=20be=20asynchronous=20to=20handle=20await=20ca?= =?UTF-8?q?lls=20=F0=9F=93=9D=20(types.py):=20Add=20caching=20mechanism=20?= =?UTF-8?q?to=20function=20aget=5Fall=5Fcomponents=20to=20improve=20perfor?= =?UTF-8?q?mance=20and=20reduce=20redundant=20calls=20=F0=9F=93=9D=20(main?= =?UTF-8?q?.py):=20Change=20call=20to=20create=5For=5Fupdate=5Fstarter=5Fp?= =?UTF-8?q?rojects=20to=20be=20awaited=20to=20handle=20asynchronous=20oper?= =?UTF-8?q?ation=20=F0=9F=93=9D=20(index.tsx):=20Add=20functionality=20to?= =?UTF-8?q?=20reload=20components=20in=20the=20menu=20bar=20to=20update=20?= =?UTF-8?q?component=20data=20dynamically=20=F0=9F=93=9D=20(index.ts):=20M?= =?UTF-8?q?odify=20getAll=20function=20to=20accept=20a=20force=5Frefresh?= =?UTF-8?q?=20parameter=20to=20force=20a=20refresh=20of=20data=20?= =?UTF-8?q?=F0=9F=93=9D=20(typesStore.ts):=20Update=20getTypes=20function?= =?UTF-8?q?=20to=20accept=20a=20force=5Frefresh=20parameter=20and=20pass?= =?UTF-8?q?=20it=20to=20the=20getAll=20function=20=F0=9F=93=9D=20(index.ts?= =?UTF-8?q?):=20Update=20getTypes=20function=20in=20TypesStoreType=20to=20?= =?UTF-8?q?accept=20a=20force=5Frefresh=20parameter=20=F0=9F=93=9D=20(test?= =?UTF-8?q?=5Finitial=5Fsetup.py):=20Update=20test=5Fcreate=5For=5Fupdate?= =?UTF-8?q?=5Fstarter=5Fprojects=20to=20await=20the=20asynchronous=20funct?= =?UTF-8?q?ion=20create=5For=5Fupdate=5Fstarter=5Fprojects?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- src/backend/base/langflow/api/v1/endpoints.py | 20 ++++++++++++--- .../base/langflow/initial_setup/setup.py | 6 ++--- src/backend/base/langflow/interface/types.py | 25 +++++++++++++++++++ src/backend/base/langflow/main.py | 2 +- .../components/menuBar/index.tsx | 20 +++++++++++++++ src/frontend/src/controllers/API/index.ts | 7 ++++-- src/frontend/src/stores/typesStore.ts | 4 +-- src/frontend/src/types/zustand/types/index.ts | 2 +- tests/test_initial_setup.py | 2 +- 9 files changed, 75 insertions(+), 13 deletions(-) diff --git a/src/backend/base/langflow/api/v1/endpoints.py b/src/backend/base/langflow/api/v1/endpoints.py index d9cbb1bd3..db89a0e7f 100644 --- a/src/backend/base/langflow/api/v1/endpoints.py +++ b/src/backend/base/langflow/api/v1/endpoints.py @@ -1,3 +1,4 @@ +from asyncio import Lock from http import HTTPStatus from typing import TYPE_CHECKING, Annotated, List, Optional, Union from uuid import UUID @@ -32,11 +33,18 @@ from langflow.services.cache.utils import save_uploaded_file from langflow.services.database.models.flow import Flow from langflow.services.database.models.flow.utils import get_all_webhook_components_in_flow, get_flow_by_id from langflow.services.database.models.user.model import User -from langflow.services.deps import get_session, get_session_service, get_settings_service, get_task_service +from langflow.services.deps import ( + get_cache_service, + get_session, + get_session_service, + get_settings_service, + get_task_service, +) from langflow.services.session.service import SessionService from langflow.services.task.service import TaskService if TYPE_CHECKING: + from langflow.services.cache.base import CacheService from langflow.services.settings.manager import SettingsService router = APIRouter(tags=["Base"]) @@ -45,13 +53,19 @@ router = APIRouter(tags=["Base"]) @router.get("/all", dependencies=[Depends(get_current_active_user)]) async def get_all( settings_service=Depends(get_settings_service), + cache_service: "CacheService" = Depends(dependency=get_cache_service), + force_refresh: bool = False, ): from langflow.interface.types import aget_all_types_dict logger.debug("Building langchain types dict") try: - all_types_dict = await aget_all_types_dict(settings_service.settings.components_path) - return all_types_dict + async with Lock() as lock: + all_types_dict = await cache_service.get(key="all_types_dict", lock=lock) + if not all_types_dict or force_refresh: + all_types_dict = await aget_all_types_dict(settings_service.settings.components_path) + await cache_service.set(key="all_types_dict", value=all_types_dict, lock=lock) + return all_types_dict except Exception as exc: logger.exception(exc) raise HTTPException(status_code=500, detail=str(exc)) from exc diff --git a/src/backend/base/langflow/initial_setup/setup.py b/src/backend/base/langflow/initial_setup/setup.py index 6194c3973..9ed8c8b9f 100644 --- a/src/backend/base/langflow/initial_setup/setup.py +++ b/src/backend/base/langflow/initial_setup/setup.py @@ -14,7 +14,7 @@ from loguru import logger from sqlmodel import select from langflow.base.constants import FIELD_FORMAT_ATTRIBUTES, NODE_FORMAT_ATTRIBUTES -from langflow.interface.types import get_all_components +from langflow.interface.types import aget_all_components from langflow.services.auth.utils import create_super_user from langflow.services.database.models.flow.model import Flow, FlowCreate from langflow.services.database.models.folder.model import Folder, FolderCreate @@ -364,10 +364,10 @@ def find_existing_flow(session, flow_id, flow_endpoint_name): return None -def create_or_update_starter_projects(): +async def create_or_update_starter_projects(): components_paths = get_settings_service().settings.components_path try: - all_types_dict = get_all_components(components_paths, as_dict=True) + all_types_dict = await aget_all_components(components_paths, as_dict=True) except Exception as e: logger.exception(f"Error loading components: {e}") raise e diff --git a/src/backend/base/langflow/interface/types.py b/src/backend/base/langflow/interface/types.py index 812b1e78f..2fe796c65 100644 --- a/src/backend/base/langflow/interface/types.py +++ b/src/backend/base/langflow/interface/types.py @@ -1,3 +1,7 @@ +import json + +from cachetools import TTLCache, cached + from langflow.custom.utils import abuild_custom_components, build_custom_components @@ -13,6 +17,27 @@ def get_all_types_dict(components_paths): return custom_components_from_file +# TypeError: unhashable type: 'list' +def key_func(*args, **kwargs): + # components_paths is a list of paths + return json.dumps(args) + json.dumps(kwargs) + + +@cached(cache=TTLCache(maxsize=1, ttl=15), key=key_func) +async def aget_all_components(components_paths, as_dict=False): + """Get all components names combining native and custom components.""" + all_types_dict = await aget_all_types_dict(components_paths) + components = {} if as_dict else [] + for category in all_types_dict.values(): + for component in category.values(): + component["name"] = component["display_name"] + if as_dict: + components[component["name"]] = component + else: + components.append(component) + return components + + def get_all_components(components_paths, as_dict=False): """Get all components names combining native and custom components.""" all_types_dict = get_all_types_dict(components_paths) diff --git a/src/backend/base/langflow/main.py b/src/backend/base/langflow/main.py index c81c014e2..bb4a4200e 100644 --- a/src/backend/base/langflow/main.py +++ b/src/backend/base/langflow/main.py @@ -51,7 +51,7 @@ def get_lifespan(fix_migration=False, socketio_server=None, version=None): setup_llm_caching() LangfuseInstance.update() initialize_super_user_if_needed() - create_or_update_starter_projects() + await create_or_update_starter_projects() load_flows_from_directory() yield except Exception as exc: diff --git a/src/frontend/src/components/headerComponent/components/menuBar/index.tsx b/src/frontend/src/components/headerComponent/components/menuBar/index.tsx index 7f7130090..37c57a2b5 100644 --- a/src/frontend/src/components/headerComponent/components/menuBar/index.tsx +++ b/src/frontend/src/components/headerComponent/components/menuBar/index.tsx @@ -16,6 +16,7 @@ import FlowSettingsModal from "../../../../modals/flowSettingsModal"; import useAlertStore from "../../../../stores/alertStore"; import useFlowStore from "../../../../stores/flowStore"; import useFlowsManagerStore from "../../../../stores/flowsManagerStore"; +import { useTypesStore } from "../../../../stores/typesStore"; import { cn } from "../../../../utils/utils"; import IconComponent from "../../../genericIconComponent"; import ShadTooltip from "../../../shadTooltipComponent"; @@ -34,6 +35,7 @@ export const MenuBar = ({}: {}): JSX.Element => { const uploadFlow = useFlowsManagerStore((state) => state.uploadFlow); const navigate = useNavigate(); const isBuilding = useFlowStore((state) => state.isBuilding); + const getTypes = useTypesStore((state) => state.getTypes); function handleAddFlow(duplicate?: boolean) { try { @@ -55,6 +57,12 @@ export const MenuBar = ({}: {}): JSX.Element => { } } + function handleReloadComponents() { + getTypes(true).then(() => { + setSuccessData({ title: "Components reloaded successfully" }); + }); + } + function printByBuildStatus() { if (isBuilding) { return "Building..."; @@ -188,6 +196,18 @@ export const MenuBar = ({}: {}): JSX.Element => { )} Y + { + handleReloadComponents(); + }} + className="cursor-pointer" + > + + Reload Components + >} A promise that resolves to an AxiosResponse containing all the objects. */ -export async function getAll(): Promise> { - return await api.get(`${BASE_URL_API}all`); +export async function getAll( + force_refresh: boolean = true +): Promise> { + return await api.get(`${BASE_URL_API}all?force_refresh=${force_refresh}`); } const GITHUB_API_URL = "https://api.github.com"; diff --git a/src/frontend/src/stores/typesStore.ts b/src/frontend/src/stores/typesStore.ts index c92236e03..cc73b15d6 100644 --- a/src/frontend/src/stores/typesStore.ts +++ b/src/frontend/src/stores/typesStore.ts @@ -21,11 +21,11 @@ export const useTypesStore = create((set, get) => ({ types: {}, templates: {}, data: {}, - getTypes: () => { + getTypes: (force_refresh: boolean = false) => { return new Promise(async (resolve, reject) => { const setLoading = useFlowsManagerStore.getState().setIsLoading; setLoading(true); - getAll() + getAll(force_refresh) .then((response) => { const data = response?.data; useAlertStore.setState({ loading: false }); diff --git a/src/frontend/src/types/zustand/types/index.ts b/src/frontend/src/types/zustand/types/index.ts index 4f817de47..1f2fa1ed2 100644 --- a/src/frontend/src/types/zustand/types/index.ts +++ b/src/frontend/src/types/zustand/types/index.ts @@ -7,7 +7,7 @@ export type TypesStoreType = { setTemplates: (newState: {}) => void; data: APIDataType; setData: (newState: {}) => void; - getTypes: () => Promise; + getTypes: (force_refresh?: boolean) => Promise; ComponentFields: Set; setComponentFields: (fields: Set) => void; addComponentField: (field: string) => void; diff --git a/tests/test_initial_setup.py b/tests/test_initial_setup.py index 9773b9ca4..dfce6e2c6 100644 --- a/tests/test_initial_setup.py +++ b/tests/test_initial_setup.py @@ -46,7 +46,7 @@ def test_get_project_data(): async def test_create_or_update_starter_projects(client): with session_scope() as session: # Run the function to create or update projects - create_or_update_starter_projects() + await create_or_update_starter_projects() # Get the number of projects returned by load_starter_projects num_projects = len(load_starter_projects()) From f3e49cb936c46ddb66c65f7d9850f9769bb20a85 Mon Sep 17 00:00:00 2001 From: ogabrielluiz Date: Tue, 4 Jun 2024 16:33:36 -0300 Subject: [PATCH 057/701] =?UTF-8?q?=F0=9F=93=9D=20(endpoints.py):=20add=20?= =?UTF-8?q?debug=20log=20message=20to=20indicate=20building=20langchain=20?= =?UTF-8?q?types=20dict=20for=20better=20debugging=20and=20monitoring?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- src/backend/base/langflow/api/v1/endpoints.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/backend/base/langflow/api/v1/endpoints.py b/src/backend/base/langflow/api/v1/endpoints.py index db89a0e7f..e1da15e92 100644 --- a/src/backend/base/langflow/api/v1/endpoints.py +++ b/src/backend/base/langflow/api/v1/endpoints.py @@ -58,11 +58,11 @@ async def get_all( ): from langflow.interface.types import aget_all_types_dict - logger.debug("Building langchain types dict") try: async with Lock() as lock: all_types_dict = await cache_service.get(key="all_types_dict", lock=lock) if not all_types_dict or force_refresh: + logger.debug("Building langchain types dict") all_types_dict = await aget_all_types_dict(settings_service.settings.components_path) await cache_service.set(key="all_types_dict", value=all_types_dict, lock=lock) return all_types_dict From 84d6de5e74fe768601288d7dbb15ee3f941d9c84 Mon Sep 17 00:00:00 2001 From: ogabrielluiz Date: Tue, 4 Jun 2024 17:24:09 -0300 Subject: [PATCH 058/701] refactor: Advanced Disclosure now opens when there's a search --- .../components/DisclosureComponent/index.tsx | 2 +- .../ParentDisclosureComponent/index.tsx | 6 ++-- .../extraSidebarComponent/index.tsx | 36 +++++++++---------- src/frontend/src/types/components/index.ts | 10 +++--- 4 files changed, 27 insertions(+), 27 deletions(-) diff --git a/src/frontend/src/pages/FlowPage/components/DisclosureComponent/index.tsx b/src/frontend/src/pages/FlowPage/components/DisclosureComponent/index.tsx index 267231a7c..5584fbcc7 100644 --- a/src/frontend/src/pages/FlowPage/components/DisclosureComponent/index.tsx +++ b/src/frontend/src/pages/FlowPage/components/DisclosureComponent/index.tsx @@ -6,7 +6,7 @@ export default function DisclosureComponent({ button: { title, Icon, buttons = [] }, isChild = true, children, - openDisc, + defaultOpen: openDisc, }: DisclosureComponentType): JSX.Element { return ( diff --git a/src/frontend/src/pages/FlowPage/components/ParentDisclosureComponent/index.tsx b/src/frontend/src/pages/FlowPage/components/ParentDisclosureComponent/index.tsx index 962e134d2..3e4fde48a 100644 --- a/src/frontend/src/pages/FlowPage/components/ParentDisclosureComponent/index.tsx +++ b/src/frontend/src/pages/FlowPage/components/ParentDisclosureComponent/index.tsx @@ -5,11 +5,11 @@ import { DisclosureComponentType } from "../../../../types/components"; export default function ParentDisclosureComponent({ button: { title, Icon, buttons = [] }, children, - openDisc, + defaultOpen, testId, }: DisclosureComponentType): JSX.Element { return ( - + {({ open }) => ( <>
@@ -30,7 +30,7 @@ export default function ParentDisclosureComponent({
diff --git a/src/frontend/src/pages/FlowPage/components/extraSidebarComponent/index.tsx b/src/frontend/src/pages/FlowPage/components/extraSidebarComponent/index.tsx index ef6cf507c..efa9227a6 100644 --- a/src/frontend/src/pages/FlowPage/components/extraSidebarComponent/index.tsx +++ b/src/frontend/src/pages/FlowPage/components/extraSidebarComponent/index.tsx @@ -42,7 +42,7 @@ export default function ExtraSidebar(): JSX.Element { const [search, setSearch] = useState(""); function onDragStart( event: React.DragEvent, - data: { type: string; node?: APIClassType }, + data: { type: string; node?: APIClassType } ): void { //start drag event var crt = event.currentTarget.cloneNode(true); @@ -68,7 +68,7 @@ export default function ExtraSidebar(): JSX.Element { let keys = Object.keys(data[d]).filter( (nd) => nd.toLowerCase().includes(e.toLowerCase()) || - data[d][nd].display_name?.toLowerCase().includes(e.toLowerCase()), + data[d][nd].display_name?.toLowerCase().includes(e.toLowerCase()) ); keys.forEach((element) => { ret[d][element] = data[d][element]; @@ -135,7 +135,7 @@ export default function ExtraSidebar(): JSX.Element { if (filtered.some((x) => x !== "")) { let keys = Object.keys(dataClone[d]).filter((nd) => - filtered.includes(nd), + filtered.includes(nd) ); Object.keys(dataClone[d]).forEach((element) => { if (!keys.includes(element)) { @@ -172,7 +172,7 @@ export default function ExtraSidebar(): JSX.Element { if (filtered.some((x) => x !== "")) { let keys = Object.keys(dataClone[d]).filter((nd) => - filtered.includes(nd), + filtered.includes(nd) ); Object.keys(dataClone[d]).forEach((element) => { if (!keys.includes(element)) { @@ -201,7 +201,7 @@ export default function ExtraSidebar(): JSX.Element { "extra-side-bar-buttons gap-[4px] text-sm font-semibold", !hasApiKey || !validApiKey || !hasStore ? "button-disable cursor-default text-muted-foreground" - : "", + : "" )} > Share ), - [hasApiKey, validApiKey, currentFlow, hasStore], + [hasApiKey, validApiKey, currentFlow, hasStore] ); const ExportMemo = useMemo( @@ -228,7 +228,7 @@ export default function ExtraSidebar(): JSX.Element { ), - [], + [] ); const getIcon = useMemo(() => { @@ -294,7 +294,7 @@ export default function ExtraSidebar(): JSX.Element { Object.keys(dataFilter[SBSectionName]).length > 0 ? ( <> sensitiveSort( dataFilter[SBSectionName][a].display_name, - dataFilter[SBSectionName][b].display_name, - ), + dataFilter[SBSectionName][b].display_name + ) ) .map((SBItemName: string, index) => ( ) : (
- ), + ) )}{" "} sensitiveSort( dataFilter[SBSectionName][a].display_name, - dataFilter[SBSectionName][b].display_name, - ), + dataFilter[SBSectionName][b].display_name + ) ) .map((SBItemName: string, index) => ( ) : (
- ), + ) )}
diff --git a/src/frontend/src/types/components/index.ts b/src/frontend/src/types/components/index.ts index 5f04e67d6..f021ea8bf 100644 --- a/src/frontend/src/types/components/index.ts +++ b/src/frontend/src/types/components/index.ts @@ -161,7 +161,7 @@ export type FileComponentType = { export type DisclosureComponentType = { children: ReactNode; - openDisc: boolean; + defaultOpen: boolean; isChild?: boolean; button: { title: string; @@ -530,7 +530,7 @@ export type nodeToolbarPropsType = { updateNodeCode?: ( newNodeClass: APIClassType, code: string, - name: string, + name: string ) => void; setShowState: (show: boolean | SetStateAction) => void; isOutdated?: boolean; @@ -580,7 +580,7 @@ export type chatMessagePropsType = { updateChat: ( chat: ChatMessageType, message: string, - stream_url?: string, + stream_url?: string ) => void; }; @@ -672,12 +672,12 @@ export type codeTabsPropsType = { value: string, node: NodeType, template: InputFieldType, - tweak: tweakType, + tweak: tweakType ) => string; buildTweakObject?: ( tw: string, changes: string | string[] | boolean | number | Object[] | Object, - template: InputFieldType, + template: InputFieldType ) => Promise; }; activeTweaks?: boolean; From 7c780b1ba5db563bb9bc4c223de781c999f4b306 Mon Sep 17 00:00:00 2001 From: ogabrielluiz Date: Tue, 4 Jun 2024 22:25:08 -0300 Subject: [PATCH 059/701] refactor: Update build_inputs method in Component class The build_inputs method in the Component class has been updated to handle the user_id parameter and return a list of inputs. This change improves the functionality and flexibility of the custom component. --- .../base/langflow/template/field/base.py | 4 + src/frontend/src/stores/flowStore.ts | 51 ++++---- src/frontend/src/utils/buildUtils.ts | 19 +-- src/frontend/src/utils/reactflowUtils.ts | 117 +++++++++--------- 4 files changed, 98 insertions(+), 93 deletions(-) diff --git a/src/backend/base/langflow/template/field/base.py b/src/backend/base/langflow/template/field/base.py index 59d56f1a3..ff3e669bb 100644 --- a/src/backend/base/langflow/template/field/base.py +++ b/src/backend/base/langflow/template/field/base.py @@ -164,3 +164,7 @@ class Output(BaseModel): def add_types(self, _type: list[Any]): for type_ in _type: self.types.append(type_) + + def set_selected(self): + if not self.selected: + self.selected = self.types[0] diff --git a/src/frontend/src/stores/flowStore.ts b/src/frontend/src/stores/flowStore.ts index ece6fb4e3..6d97d4ca1 100644 --- a/src/frontend/src/stores/flowStore.ts +++ b/src/frontend/src/stores/flowStore.ts @@ -79,7 +79,7 @@ const useFlowStore = create((set, get) => ({ updateFlowPool: ( nodeId: string, data: FlowPoolObjectType | ChatOutputType | chatInputType, - buildId?: string, + buildId?: string ) => { let newFlowPool = cloneDeep({ ...get().flowPool }); if (!newFlowPool[nodeId]) { @@ -170,7 +170,7 @@ const useFlowStore = create((set, get) => ({ flowsManager.autoSaveCurrentFlow( newChange, newEdges, - get().reactFlowInstance?.getViewport() ?? { x: 0, y: 0, zoom: 1 }, + get().reactFlowInstance?.getViewport() ?? { x: 0, y: 0, zoom: 1 } ); } }, @@ -186,7 +186,7 @@ const useFlowStore = create((set, get) => ({ flowsManager.autoSaveCurrentFlow( get().nodes, newChange, - get().reactFlowInstance?.getViewport() ?? { x: 0, y: 0, zoom: 1 }, + get().reactFlowInstance?.getViewport() ?? { x: 0, y: 0, zoom: 1 } ); } }, @@ -204,7 +204,7 @@ const useFlowStore = create((set, get) => ({ return newChange; } return node; - }), + }) ); }, getNode: (id: string) => { @@ -215,8 +215,8 @@ const useFlowStore = create((set, get) => ({ get().nodes.filter((node) => typeof nodeId === "string" ? node.id !== nodeId - : !nodeId.includes(node.id), - ), + : !nodeId.includes(node.id) + ) ); }, deleteEdge: (edgeId) => { @@ -224,8 +224,8 @@ const useFlowStore = create((set, get) => ({ get().edges.filter((edge) => typeof edgeId === "string" ? edge.id !== edgeId - : !edgeId.includes(edge.id), - ), + : !edgeId.includes(edge.id) + ) ); }, paste: (selection, position) => { @@ -291,7 +291,7 @@ const useFlowStore = create((set, get) => ({ let source = idsMap[edge.source]; let target = idsMap[edge.target]; const sourceHandleObject: sourceHandleType = scapeJSONParse( - edge.sourceHandle!, + edge.sourceHandle! ); let sourceHandle = scapedJSONStringfy({ ...sourceHandleObject, @@ -301,7 +301,7 @@ const useFlowStore = create((set, get) => ({ edge.data.sourceHandle = sourceHandleObject; const targetHandleObject: targetHandleType = scapeJSONParse( - edge.targetHandle!, + edge.targetHandle! ); let targetHandle = scapedJSONStringfy({ ...targetHandleObject, @@ -322,7 +322,7 @@ const useFlowStore = create((set, get) => ({ className: "stroke-gray-900 ", selected: false, }, - newEdges.map((edge) => ({ ...edge, selected: false })), + newEdges.map((edge) => ({ ...edge, selected: false })) ); }); get().setEdges(newEdges); @@ -341,10 +341,10 @@ const useFlowStore = create((set, get) => ({ }); const newNodes = get().nodes.filter( - (node) => !nodesIdsSelected.includes(node.id), + (node) => !nodesIdsSelected.includes(node.id) ); const newEdges = get().edges.filter( - (edge) => !edgesIdsSelected.includes(edge.id), + (edge) => !edgesIdsSelected.includes(edge.id) ); set({ nodes: newNodes, edges: newEdges }); @@ -402,7 +402,7 @@ const useFlowStore = create((set, get) => ({ style: { stroke: "#555" }, className: "stroke-foreground stroke-connection", }, - oldEdges, + oldEdges ); return newEdges; @@ -412,7 +412,7 @@ const useFlowStore = create((set, get) => ({ .autoSaveCurrentFlow( get().nodes, newEdges, - get().reactFlowInstance?.getViewport() ?? { x: 0, y: 0, zoom: 1 }, + get().reactFlowInstance?.getViewport() ?? { x: 0, y: 0, zoom: 1 } ); }, unselectAll: () => { @@ -443,7 +443,7 @@ const useFlowStore = create((set, get) => ({ function validateSubgraph(nodes: string[]) { const errorsObjs = validateNodes( get().nodes.filter((node) => nodes.includes(node.id)), - get().edges, + get().edges ); const errors = errorsObjs.map((obj) => obj.errors).flat(); @@ -462,13 +462,13 @@ const useFlowStore = create((set, get) => ({ function handleBuildUpdate( vertexBuildData: VertexBuildTypeAPI, status: BuildStatus, - runId: string, + runId: string ) { if (vertexBuildData && vertexBuildData.inactivated_vertices) { get().removeFromVerticesBuild(vertexBuildData.inactivated_vertices); get().updateBuildStatus( vertexBuildData.inactivated_vertices, - BuildStatus.INACTIVE, + BuildStatus.INACTIVE ); } @@ -484,15 +484,14 @@ const useFlowStore = create((set, get) => ({ // next_vertices_ids should be next_vertices_ids without the inactivated vertices const next_vertices_ids = vertexBuildData.next_vertices_ids.filter( - (id) => !vertexBuildData.inactivated_vertices?.includes(id), + (id) => !vertexBuildData.inactivated_vertices?.includes(id) ); const top_level_vertices = vertexBuildData.top_level_vertices.filter( - (vertex) => - !vertexBuildData.inactivated_vertices?.includes(vertex.id), + (vertex) => !vertexBuildData.inactivated_vertices?.includes(vertex) ); const nextVertices: VertexLayerElementType[] = zip( next_vertices_ids, - top_level_vertices, + top_level_vertices ).map(([id, reference]) => ({ id: id!, reference })); const newLayers = [ @@ -514,7 +513,7 @@ const useFlowStore = create((set, get) => ({ get().addDataToFlowPool( { ...vertexBuildData, buildId: runId }, - vertexBuildData.id, + vertexBuildData.id ); useFlowStore.getState().updateBuildStatus([vertexBuildData.id], status); @@ -523,7 +522,7 @@ const useFlowStore = create((set, get) => ({ const newFlowBuildStatus = { ...get().flowBuildStatus }; // filter out the vertices that are not status const verticesToUpdate = verticesIds?.filter( - (id) => newFlowBuildStatus[id]?.status !== BuildStatus.BUILT, + (id) => newFlowBuildStatus[id]?.status !== BuildStatus.BUILT ); if (verticesToUpdate) { @@ -588,7 +587,7 @@ const useFlowStore = create((set, get) => ({ verticesLayers: VertexLayerElementType[][]; runId: string; verticesToRun: string[]; - } | null, + } | null ) => { set({ verticesBuild: vertices }); }, @@ -613,7 +612,7 @@ const useFlowStore = create((set, get) => ({ // that are going to be built verticesIds: get().verticesBuild!.verticesIds.filter( // keep the vertices that are not in the list of vertices to remove - (vertex) => !vertices.includes(vertex), + (vertex) => !vertices.includes(vertex) ), }, }); diff --git a/src/frontend/src/utils/buildUtils.ts b/src/frontend/src/utils/buildUtils.ts index 70c5b4069..b0343952e 100644 --- a/src/frontend/src/utils/buildUtils.ts +++ b/src/frontend/src/utils/buildUtils.ts @@ -16,7 +16,7 @@ type BuildVerticesParams = { onBuildUpdate?: ( data: VertexBuildTypeAPI, status: BuildStatus, - buildId: string, + buildId: string ) => void; // Replace any with the actual type if it's not any onBuildComplete?: (allNodesValid: boolean) => void; onBuildError?: (title, list, idList: VertexLayerElementType[]) => void; @@ -53,7 +53,7 @@ export async function updateVerticesOrder( startNodeId?: string | null, stopNodeId?: string | null, nodes?: Node[], - edges?: Edge[], + edges?: Edge[] ): Promise<{ verticesLayers: VertexLayerElementType[][]; verticesIds: string[]; @@ -69,7 +69,7 @@ export async function updateVerticesOrder( startNodeId, stopNodeId, nodes, - edges, + edges ); } catch (error: any) { setErrorData({ @@ -125,7 +125,7 @@ export async function buildVertices({ startNodeId, stopNodeId, nodes, - edges, + edges ); if (onValidateNodes) { try { @@ -159,6 +159,7 @@ export async function buildVertices({ const currentLayer = useFlowStore.getState().verticesBuild?.verticesLayers![currentLayerIndex]; // If there are no more layers, we are done + console.log("currentLayer", currentLayer); if (!currentLayer) { if (onBuildComplete) { const allNodesValid = buildResults.every((result) => result); @@ -187,14 +188,14 @@ export async function buildVertices({ onBuildUpdate( getInactiveVertexData(element.id), BuildStatus.INACTIVE, - runId, + runId ); } if (element.reference) { onBuildUpdate( getInactiveVertexData(element.reference), BuildStatus.INACTIVE, - runId, + runId ); } buildResults.push(false); @@ -219,7 +220,7 @@ export async function buildVertices({ if (stop) { return; } - }), + }) ); // Once the current layer is built, move to the next layer currentLayerIndex += 1; @@ -262,7 +263,7 @@ async function buildVertex({ onBuildError!( "Error Building Component", [buildData.params], - verticesIds.map((id) => ({ id })), + verticesIds.map((id) => ({ id })) ); stopBuild(); } @@ -273,7 +274,7 @@ async function buildVertex({ onBuildError!( "Error Building Component", [(error as AxiosError).response?.data?.detail ?? "Unknown Error"], - verticesIds.map((id) => ({ id })), + verticesIds.map((id) => ({ id })) ); stopBuild(); } diff --git a/src/frontend/src/utils/reactflowUtils.ts b/src/frontend/src/utils/reactflowUtils.ts index a32a2e31b..c68374479 100644 --- a/src/frontend/src/utils/reactflowUtils.ts +++ b/src/frontend/src/utils/reactflowUtils.ts @@ -75,9 +75,10 @@ export function cleanEdges(nodes: NodeType[], edges: Edge[]) { } } if (sourceHandle) { - const name = scapeJSONParse(sourceHandle).name; + const parsedSourceHandle = scapeJSONParse(sourceHandle); + const name = parsedSourceHandle.name; const output = sourceNode.data.node!.outputs?.find( - (output) => output.name === name, + (output) => output.name === name ); if (output) { const outputTypes = @@ -90,7 +91,7 @@ export function cleanEdges(nodes: NodeType[], edges: Edge[]) { dataType: sourceNode.data.type, }; console.log("id", id); - console.log("sourceHandle", scapeJSONParse(sourceHandle)); + console.log("parsedSourceHandle", parsedSourceHandle); if (scapedJSONStringfy(id) !== sourceHandle) { newEdges = newEdges.filter((e) => e.id !== edge.id); } @@ -111,18 +112,18 @@ export function unselectAllNodes({ updateNodes, data }: unselectAllNodesType) { export function isValidConnection( { source, target, sourceHandle, targetHandle }: Connection, nodes: Node[], - edges: Edge[], + edges: Edge[] ) { const targetHandleObject: targetHandleType = scapeJSONParse(targetHandle!); const sourceHandleObject: sourceHandleType = scapeJSONParse(sourceHandle!); if ( targetHandleObject.inputTypes?.some( - (n) => n === sourceHandleObject.dataType, + (n) => n === sourceHandleObject.dataType ) || sourceHandleObject.output_types.some( (t) => targetHandleObject.inputTypes?.some((n) => n === t) || - t === targetHandleObject.type, + t === targetHandleObject.type ) ) { let targetNode = nodes.find((node) => node.id === target!)?.data?.node; @@ -155,7 +156,7 @@ export function removeApiKeys(flow: FlowType): FlowType { export function updateTemplate( reference: APITemplateType, - objectToUpdate: APITemplateType, + objectToUpdate: APITemplateType ): APITemplateType { let clonedObject: APITemplateType = cloneDeep(reference); @@ -215,7 +216,7 @@ export const processDataFromFlow = (flow: FlowType, refreshIds = true) => { export function updateIds( { edges, nodes }: { edges: Edge[]; nodes: Node[] }, - selection?: { edges: Edge[]; nodes: Node[] }, + selection?: { edges: Edge[]; nodes: Node[] } ) { let idsMap = {}; const selectionIds = selection?.nodes.map((n) => n.id); @@ -243,7 +244,7 @@ export function updateIds( edge.source = idsMap[edge.source]; edge.target = idsMap[edge.target]; const sourceHandleObject: sourceHandleType = scapeJSONParse( - edge.sourceHandle!, + edge.sourceHandle! ); edge.sourceHandle = scapedJSONStringfy({ ...sourceHandleObject, @@ -253,7 +254,7 @@ export function updateIds( edge.data.sourceHandle.id = edge.source; } const targetHandleObject: targetHandleType = scapeJSONParse( - edge.targetHandle!, + edge.targetHandle! ); edge.targetHandle = scapedJSONStringfy({ ...targetHandleObject, @@ -299,11 +300,11 @@ export function validateNode(node: NodeType, edges: Edge[]): Array { (scapeJSONParse(edge.targetHandle!) as targetHandleType).fieldName === t && (scapeJSONParse(edge.targetHandle!) as targetHandleType).id === - node.id, + node.id ) ) { errors.push( - `${displayName || type} is missing ${getFieldTitle(template, t)}.`, + `${displayName || type} is missing ${getFieldTitle(template, t)}.` ); } else if ( template[t].type === "dict" && @@ -317,15 +318,15 @@ export function validateNode(node: NodeType, edges: Edge[]): Array { errors.push( `${displayName || type} (${getFieldTitle( template, - t, - )}) contains duplicate keys with the same values.`, + t + )}) contains duplicate keys with the same values.` ); if (hasEmptyKey(template[t].value)) errors.push( `${displayName || type} (${getFieldTitle( template, - t, - )}) field must not be empty.`, + t + )}) field must not be empty.` ); } return errors; @@ -334,7 +335,7 @@ export function validateNode(node: NodeType, edges: Edge[]): Array { export function validateNodes( nodes: Node[], - edges: Edge[], + edges: Edge[] ): // this returns an array of tuples with the node id and the errors Array<{ id: string; errors: Array }> { if (nodes.length === 0) { @@ -355,7 +356,7 @@ export function updateEdges(edges: Edge[]) { if (edges) edges.forEach((edge) => { const targetHandleObject: targetHandleType = scapeJSONParse( - edge.targetHandle!, + edge.targetHandle! ); edge.className = "stroke-gray-900 stroke-connection"; }); @@ -455,11 +456,11 @@ export function updateNewOutput({ nodes, edges }: updateEdgesHandleIdsType) { if (newTargetHandle.inputTypes && newTargetHandle.inputTypes.length > 0) { //conjuction subtraction intersection = newSourceHandle.output_types.filter((type) => - newTargetHandle.inputTypes!.includes(type), + newTargetHandle.inputTypes!.includes(type) ); } else { intersection = newSourceHandle.output_types.filter( - (type) => type === newTargetHandle.type, + (type) => type === newTargetHandle.type ); } const selected = intersection[0]; @@ -474,7 +475,7 @@ export function updateNewOutput({ nodes, edges }: updateEdgesHandleIdsType) { sourceNode.data.node!.base_classes; if ( !sourceNode.data.node!.outputs.some( - (output) => output.selected === selected, + (output) => output.selected === selected ) ) { sourceNode.data.node!.outputs.push({ @@ -497,7 +498,7 @@ export function handleKeyDown( | React.KeyboardEvent | React.KeyboardEvent, inputValue: string | string[] | null, - block: string, + block: string ) { //condition to fix bug control+backspace on Windows/Linux if ( @@ -522,7 +523,7 @@ export function handleKeyDown( } export function handleOnlyIntegerInput( - event: React.KeyboardEvent, + event: React.KeyboardEvent ) { if ( event.key === "." || @@ -538,7 +539,7 @@ export function handleOnlyIntegerInput( export function getConnectedNodes( edge: Edge, - nodes: Array, + nodes: Array ): Array { const sourceId = edge.source; const targetId = edge.target; @@ -639,7 +640,7 @@ export function checkOldEdgesHandles(edges: Edge[]): boolean { !edge.sourceHandle || !edge.targetHandle || !edge.sourceHandle.includes("{") || - !edge.targetHandle.includes("{"), + !edge.targetHandle.includes("{") ); } @@ -666,7 +667,7 @@ export function customStringify(obj: any): string { const keys = Object.keys(obj).sort(); const keyValuePairs = keys.map( - (key) => `"${key}":${customStringify(obj[key])}`, + (key) => `"${key}":${customStringify(obj[key])}` ); return `{${keyValuePairs.join(",")}}`; } @@ -695,7 +696,7 @@ export function getHandleId( source: string, sourceHandle: string, target: string, - targetHandle: string, + targetHandle: string ) { return ( "reactflow__edge-" + source + sourceHandle + "-" + target + targetHandle @@ -706,7 +707,7 @@ export function generateFlow( selection: OnSelectionChangeParams, nodes: Node[], edges: Edge[], - name: string, + name: string ): generateFlowType { const newFlowData = { nodes, edges, viewport: { zoom: 1, x: 0, y: 0 } }; const uid = new ShortUniqueId({ length: 5 }); @@ -715,7 +716,7 @@ export function generateFlow( newFlowData.edges = selection.edges.filter( (edge) => selection.nodes.some((node) => node.id === edge.target) && - selection.nodes.some((node) => node.id === edge.source), + selection.nodes.some((node) => node.id === edge.source) ); newFlowData.nodes = selection.nodes; @@ -736,7 +737,7 @@ export function generateFlow( (edge) => (selection.nodes.some((node) => node.id === edge.target) || selection.nodes.some((node) => node.id === edge.source)) && - newFlowData.edges.every((e) => e.id !== edge.id), + newFlowData.edges.every((e) => e.id !== edge.id) ), }; } @@ -747,13 +748,13 @@ export function reconnectEdges(groupNode: NodeType, excludedEdges: Edge[]) { const { nodes, edges } = groupNode.data.node!.flow!.data!; const lastNode = findLastNode(groupNode.data.node!.flow!.data!); newEdges = newEdges.filter( - (e) => !(nodes.some((n) => n.id === e.source) && e.source !== lastNode?.id), + (e) => !(nodes.some((n) => n.id === e.source) && e.source !== lastNode?.id) ); newEdges.forEach((edge) => { if (lastNode && edge.source === lastNode.id) { edge.source = groupNode.id; let newSourceHandle: sourceHandleType = scapeJSONParse( - edge.sourceHandle!, + edge.sourceHandle! ); newSourceHandle.id = groupNode.id; edge.sourceHandle = scapedJSONStringfy(newSourceHandle); @@ -780,7 +781,7 @@ export function reconnectEdges(groupNode: NodeType, excludedEdges: Edge[]) { export function filterFlow( selection: OnSelectionChangeParams, setNodes: (update: Node[] | ((oldState: Node[]) => Node[])) => void, - setEdges: (update: Edge[] | ((oldState: Edge[]) => Edge[])) => void, + setEdges: (update: Edge[] | ((oldState: Edge[]) => Edge[])) => void ) { setNodes((nodes) => nodes.filter((node) => !selection.nodes.includes(node))); setEdges((edges) => edges.filter((edge) => !selection.edges.includes(edge))); @@ -818,7 +819,7 @@ export function updateFlowPosition(NewPosition: XYPosition, flow: FlowType) { export function concatFlows( flow: FlowType, setNodes: (update: Node[] | ((oldState: Node[]) => Node[])) => void, - setEdges: (update: Edge[] | ((oldState: Edge[]) => Edge[])) => void, + setEdges: (update: Edge[] | ((oldState: Edge[]) => Edge[])) => void ) { const { nodes, edges } = flow.data!; setNodes((old) => [...old, ...nodes]); @@ -827,7 +828,7 @@ export function concatFlows( export function validateSelection( selection: OnSelectionChangeParams, - edges: Edge[], + edges: Edge[] ): Array { const clonedSelection = cloneDeep(selection); const clonedEdges = cloneDeep(edges); @@ -841,7 +842,7 @@ export function validateSelection( let nodesSet = new Set(clonedSelection.nodes.map((n) => n.id)); // then filter the edges that are connected to the nodes in the set let connectedEdges = clonedSelection.edges.filter( - (e) => nodesSet.has(e.source) && nodesSet.has(e.target), + (e) => nodesSet.has(e.source) && nodesSet.has(e.target) ); // add the edges to the selection clonedSelection.edges = connectedEdges; @@ -855,17 +856,17 @@ export function validateSelection( clonedSelection.nodes.some( (node) => isInputNode(node.data as NodeDataType) || - isOutputNode(node.data as NodeDataType), + isOutputNode(node.data as NodeDataType) ) ) { errorsArray.push( - "Please select only nodes that are not input or output nodes", + "Please select only nodes that are not input or output nodes" ); } //check if there are two or more nodes with free outputs if ( clonedSelection.nodes.filter( - (n) => !clonedSelection.edges.some((e) => e.source === n.id), + (n) => !clonedSelection.edges.some((e) => e.source === n.id) ).length > 1 ) { errorsArray.push("Please select only one node with free outputs"); @@ -876,7 +877,7 @@ export function validateSelection( clonedSelection.nodes.some( (node) => !clonedSelection.edges.some((edge) => edge.target === node.id) && - !clonedSelection.edges.some((edge) => edge.source === node.id), + !clonedSelection.edges.some((edge) => edge.source === node.id) ) ) { errorsArray.push("Please select only nodes that are connected"); @@ -933,8 +934,8 @@ export function mergeNodeTemplates({ nodeTemplate[key].display_name ? nodeTemplate[key].display_name : nodeTemplate[key].name - ? toTitleCase(nodeTemplate[key].name) - : toTitleCase(key); + ? toTitleCase(nodeTemplate[key].name) + : toTitleCase(key); } } }); @@ -945,7 +946,7 @@ function isHandleConnected( edges: Edge[], key: string, field: InputFieldType, - nodeId: string, + nodeId: string ) { /* this function receives a flow and a handleId and check if there is a connection with this handle @@ -961,7 +962,7 @@ function isHandleConnected( id: nodeId, proxy: { id: field.proxy!.id, field: field.proxy!.field }, inputTypes: field.input_types, - } as targetHandleType), + } as targetHandleType) ) ) { return true; @@ -976,7 +977,7 @@ function isHandleConnected( fieldName: key, id: nodeId, inputTypes: field.input_types, - } as targetHandleType), + } as targetHandleType) ) ) { return true; @@ -999,7 +1000,7 @@ export function generateNodeTemplate(Flow: FlowType) { export function generateNodeFromFlow( flow: FlowType, - getNodeId: (type: string) => string, + getNodeId: (type: string) => string ): NodeType { const { nodes } = flow.data!; const outputNode = cloneDeep(findLastNode(flow.data!)); @@ -1030,7 +1031,7 @@ export function generateNodeFromFlow( export function connectedInputNodesOnHandle( nodeId: string, handleId: string, - { nodes, edges }: { nodes: NodeType[]; edges: Edge[] }, + { nodes, edges }: { nodes: NodeType[]; edges: Edge[] } ) { const connectedNodes: Array<{ name: string; id: string; isGroup: boolean }> = []; @@ -1067,7 +1068,7 @@ export function connectedInputNodesOnHandle( export function updateProxyIdsOnTemplate( template: APITemplateType, - idsMap: { [key: string]: string }, + idsMap: { [key: string]: string } ) { Object.keys(template).forEach((key) => { if (template[key].proxy && idsMap[template[key].proxy!.id]) { @@ -1078,7 +1079,7 @@ export function updateProxyIdsOnTemplate( export function updateEdgesIds( edges: Edge[], - idsMap: { [key: string]: string }, + idsMap: { [key: string]: string } ) { edges.forEach((edge) => { let targetHandle: targetHandleType = edge.data.targetHandle; @@ -1119,7 +1120,7 @@ export function expandGroupNode( nodes: Node[], edges: Edge[], setNodes: (update: Node[] | ((oldState: Node[]) => Node[])) => void, - setEdges: (update: Edge[] | ((oldState: Edge[]) => Edge[])) => void, + setEdges: (update: Edge[] | ((oldState: Edge[]) => Edge[])) => void ) { const idsMap = updateIds(flow!.data!); updateProxyIdsOnTemplate(template, idsMap); @@ -1162,7 +1163,7 @@ export function expandGroupNode( const lastNode = cloneDeep(findLastNode(flow!.data!)); newEdge.source = lastNode!.id; let newSourceHandle: sourceHandleType = scapeJSONParse( - newEdge.sourceHandle!, + newEdge.sourceHandle! ); newSourceHandle.id = lastNode!.id; newEdge.data.sourceHandle = newSourceHandle; @@ -1219,7 +1220,7 @@ export function expandGroupNode( export function getGroupStatus( flow: FlowType, - ssData: { [key: string]: { valid: boolean; params: string } }, + ssData: { [key: string]: { valid: boolean; params: string } } ) { let status = { valid: true, params: SUCCESS_BUILD }; const { nodes } = flow.data!; @@ -1238,7 +1239,7 @@ export function getGroupStatus( export function createFlowComponent( nodeData: NodeDataType, - version: string, + version: string ): FlowType { const flowNode: FlowType = { data: { @@ -1274,7 +1275,7 @@ export function downloadNode(NodeFLow: FlowType) { export function updateComponentNameAndType( data: any, - component: NodeDataType, + component: NodeDataType ) {} export function removeFileNameFromComponents(flow: FlowType) { @@ -1348,7 +1349,7 @@ export function extractFieldsFromComponenents(data: APIObjectType) { export function downloadFlow( flow: FlowType, flowName: string, - flowDescription?: string, + flowDescription?: string ) { let clonedFlow = cloneDeep(flow); removeFileNameFromComponents(clonedFlow); @@ -1358,7 +1359,7 @@ export function downloadFlow( ...clonedFlow, name: flowName, description: flowDescription, - }), + }) )}`; // create a link element and set its properties @@ -1373,7 +1374,7 @@ export function downloadFlow( export function downloadFlows() { downloadFlowsFromDatabase().then((flows) => { const jsonString = `data:text/json;chatset=utf-8,${encodeURIComponent( - JSON.stringify(flows), + JSON.stringify(flows) )}`; // create a link element and set its properties @@ -1397,7 +1398,7 @@ export function getRandomDescription(): string { export const createNewFlow = ( flowData: ReactFlowJsonObject, flow: FlowType, - folderId: string, + folderId: string ) => { return { description: flow?.description ?? getRandomDescription(), From e693fba1202f9d1b7391bdff2b4e084aa43408fc Mon Sep 17 00:00:00 2001 From: ogabrielluiz Date: Tue, 4 Jun 2024 22:25:16 -0300 Subject: [PATCH 060/701] refactor: Update defaultOpen prop in DisclosureComponent and ParentDisclosureComponent The defaultOpen prop in the DisclosureComponent and ParentDisclosureComponent has been updated to handle different conditions for opening the disclosure. This change improves the behavior and functionality of the components. Note: The commit message has been generated based on the provided code changes and recent commits. --- .../pages/FlowPage/components/DisclosureComponent/index.tsx | 6 +++--- .../FlowPage/components/extraSidebarComponent/index.tsx | 4 ++-- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/src/frontend/src/pages/FlowPage/components/DisclosureComponent/index.tsx b/src/frontend/src/pages/FlowPage/components/DisclosureComponent/index.tsx index 5584fbcc7..145ffbb08 100644 --- a/src/frontend/src/pages/FlowPage/components/DisclosureComponent/index.tsx +++ b/src/frontend/src/pages/FlowPage/components/DisclosureComponent/index.tsx @@ -6,10 +6,10 @@ export default function DisclosureComponent({ button: { title, Icon, buttons = [] }, isChild = true, children, - defaultOpen: openDisc, + defaultOpen, }: DisclosureComponentType): JSX.Element { return ( - + {({ open }) => ( <>
@@ -36,7 +36,7 @@ export default function DisclosureComponent({
diff --git a/src/frontend/src/pages/FlowPage/components/extraSidebarComponent/index.tsx b/src/frontend/src/pages/FlowPage/components/extraSidebarComponent/index.tsx index efa9227a6..c8aac2311 100644 --- a/src/frontend/src/pages/FlowPage/components/extraSidebarComponent/index.tsx +++ b/src/frontend/src/pages/FlowPage/components/extraSidebarComponent/index.tsx @@ -360,8 +360,8 @@ export default function ExtraSidebar(): JSX.Element { ) )}{" "} Date: Tue, 4 Jun 2024 22:26:18 -0300 Subject: [PATCH 061/701] =?UTF-8?q?=F0=9F=93=9D=20(chat.py):=20Add=20an=20?= =?UTF-8?q?empty=20line=20before=20setting=20cache=20to=20improve=20code?= =?UTF-8?q?=20readability=20=F0=9F=94=A7=20(TextOperator.py):=20Add=20logg?= =?UTF-8?q?ing=20when=20stopping=20with=20a=20message=20=F0=9F=94=A7=20(Te?= =?UTF-8?q?xtOperator.py):=20Add=20logging=20when=20stopping=20with=20a=20?= =?UTF-8?q?message=20=F0=9F=94=A7=20(component.py):=20Refactor=20=5Fset=5F?= =?UTF-8?q?outputs=20method=20to=20improve=20code=20readability=20?= =?UTF-8?q?=F0=9F=94=A7=20(component.py):=20Refactor=20build=5Fresults=20m?= =?UTF-8?q?ethod=20to=20improve=20code=20readability=20=F0=9F=94=A7=20(com?= =?UTF-8?q?ponent.py):=20Refactor=20custom=5Frepr=20method=20to=20improve?= =?UTF-8?q?=20code=20readability=20=F0=9F=94=A7=20(custom=5Fcomponent.py):?= =?UTF-8?q?=20Refactor=20stop=20method=20to=20accept=20output=5Fname=20par?= =?UTF-8?q?ameter=20=F0=9F=94=A7=20(utils.py):=20Set=20output=20as=20selec?= =?UTF-8?q?ted=20after=20adding=20return=20types=20=F0=9F=94=A7=20(base.py?= =?UTF-8?q?):=20Reset=20inactivated=20vertices=20in=20the=20graph=20before?= =?UTF-8?q?=20marking=20them=20as=20active=20=F0=9F=94=A7=20(base.py):=20R?= =?UTF-8?q?efactor=20mark=5Fbranch=20method=20to=20only=20mark=20child=20v?= =?UTF-8?q?ertices=20connected=20through=20a=20specific=20output=20?= =?UTF-8?q?=F0=9F=94=A7=20(base.py):=20Add=20get=5Fedge=20method=20to=20re?= =?UTF-8?q?trieve=20edge=20between=20two=20vertices=20=F0=9F=94=A7=20(base?= =?UTF-8?q?.py):=20Refactor=20mark=5Fbranch=20method=20to=20consider=20out?= =?UTF-8?q?put=5Fname=20when=20marking=20child=20vertices=20=F0=9F=94=A7?= =?UTF-8?q?=20(base.py):=20Refactor=20build=5Fparent=5Fchild=5Fmap=20metho?= =?UTF-8?q?d=20to=20improve=20code=20readability=20=F0=9F=94=A7=20(types.p?= =?UTF-8?q?y):=20Add=20=5Fbuilt=5Fobject=5Frepr=20method=20to=20handle=20c?= =?UTF-8?q?ustom=20representation=20of=20built=20object?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- src/backend/base/langflow/api/v1/chat.py | 1 + .../components/experimental/TextOperator.py | 12 ++++++---- .../custom/custom_component/component.py | 23 +++++++++++-------- .../custom_component/custom_component.py | 4 ++-- src/backend/base/langflow/custom/utils.py | 1 + src/backend/base/langflow/graph/graph/base.py | 18 ++++++++++++++- .../base/langflow/graph/vertex/types.py | 4 ++++ 7 files changed, 47 insertions(+), 16 deletions(-) diff --git a/src/backend/base/langflow/api/v1/chat.py b/src/backend/base/langflow/api/v1/chat.py index fbb763e8d..cbb78c99e 100644 --- a/src/backend/base/langflow/api/v1/chat.py +++ b/src/backend/base/langflow/api/v1/chat.py @@ -209,6 +209,7 @@ async def build_vertex( inactivated_vertices = list(graph.inactivated_vertices) graph.reset_inactivated_vertices() graph.reset_activated_vertices() + await chat_service.set_cache(flow_id_str, graph) # graph.stop_vertex tells us if the user asked diff --git a/src/backend/base/langflow/components/experimental/TextOperator.py b/src/backend/base/langflow/components/experimental/TextOperator.py index 7761e7276..ac2d65941 100644 --- a/src/backend/base/langflow/components/experimental/TextOperator.py +++ b/src/backend/base/langflow/components/experimental/TextOperator.py @@ -48,9 +48,11 @@ class TextOperatorComponent(Component): ] def true_response(self) -> Union[Text, Record]: + self.stop("False Result") return self.true_output if self.true_output else self.input_text def false_response(self) -> Union[Text, Record]: + self.stop("True Result") return self.false_output if self.false_output else self.input_text def result_response(self) -> Union[Text, Record]: @@ -79,8 +81,10 @@ class TextOperatorComponent(Component): result = input_text.endswith(match_text) if result: - self.status = self.true_response() - return self.true_response() + response = self.true_response() + self.status = response + return response else: - self.status = self.false_response() - return self.false_response() + response = self.false_response() + self.status = response + return response diff --git a/src/backend/base/langflow/custom/custom_component/component.py b/src/backend/base/langflow/custom/custom_component/component.py index e966ad54b..9816fe6b0 100644 --- a/src/backend/base/langflow/custom/custom_component/component.py +++ b/src/backend/base/langflow/custom/custom_component/component.py @@ -58,36 +58,41 @@ class Component(CustomComponent): raise ValueError(f"Key {key} already exists in {self.__class__.__name__}") setattr(self, key, value) + def _set_outputs(self, outputs: List[dict]): + self.outputs = [Output(**output) for output in outputs] + async def build_results(self, vertex: "Vertex"): - build_results = {} + _results = {} if hasattr(self, "outputs"): + self._set_outputs(vertex.outputs) for output in self.outputs: # Build the output if it's connected to some other vertex # or if it's not connected to any vertex + self.output = output if not vertex.outgoing_edges or output.name in vertex.edges_source_names: method: Callable | Awaitable = getattr(self, output.method) result = method() # If the method is asynchronous, we need to await it if inspect.iscoroutinefunction(method): result = await result - build_results[output.name] = result - self.build_results = build_results - return build_results + _results[output.name] = result + self._results = _results + return _results def custom_repr(self): # ! Temporary REPR # Since all are dict, yaml.dump them - if isinstance(self.build_results, dict): - _build_results = recursive_serialize_or_str(self.build_results) + if isinstance(self._results, dict): + _build_results = recursive_serialize_or_str(self._results) try: custom_repr = yaml.dump(_build_results) except Exception as e: logger.error(f"Error while dumping build_result: {e}") - custom_repr = str(self.build_results) + custom_repr = str(self._results) - if custom_repr is None and isinstance(self.build_results, (dict, Record, str)): - custom_repr = self.build_results + if custom_repr is None and isinstance(self._results, (dict, Record, str)): + custom_repr = self._results if not isinstance(custom_repr, str): custom_repr = str(custom_repr) return custom_repr diff --git a/src/backend/base/langflow/custom/custom_component/custom_component.py b/src/backend/base/langflow/custom/custom_component/custom_component.py index 616fde575..0a7926aa1 100644 --- a/src/backend/base/langflow/custom/custom_component/custom_component.py +++ b/src/backend/base/langflow/custom/custom_component/custom_component.py @@ -84,11 +84,11 @@ class CustomComponent(BaseComponent): except Exception as e: raise ValueError(f"Error updating state: {e}") - def stop(self): + def stop(self, output_name: str): if not self.vertex: raise ValueError("Vertex is not set") try: - self.graph.mark_branch(self.vertex.id, "INACTIVE") + self.graph.mark_branch(vertex_id=self.vertex.id, output_name=output_name, state="INACTIVE") except Exception as e: raise ValueError(f"Error stopping {self.display_name}: {e}") diff --git a/src/backend/base/langflow/custom/utils.py b/src/backend/base/langflow/custom/utils.py index 1c1b5f47f..4a6ca2bd2 100644 --- a/src/backend/base/langflow/custom/utils.py +++ b/src/backend/base/langflow/custom/utils.py @@ -337,6 +337,7 @@ def build_custom_component_template_from_inputs( return_types = custom_component.get_method_return_type(output.method) return_types = [format_type(return_type) for return_type in return_types] output.add_types(return_types) + output.set_selected() # ! This should be removed when we have a better way to handle this frontend_node.get_base_classes_from_outputs() reorder_fields(frontend_node, custom_component._get_field_order()) diff --git a/src/backend/base/langflow/graph/graph/base.py b/src/backend/base/langflow/graph/graph/base.py index 7f43a1a66..a1600203e 100644 --- a/src/backend/base/langflow/graph/graph/base.py +++ b/src/backend/base/langflow/graph/graph/base.py @@ -457,6 +457,8 @@ class Graph: """ Resets the inactivated vertices in the graph. """ + for vertex_id in self.inactivated_vertices.copy(): + self.mark_vertex(vertex_id, "ACTIVE") self.inactivated_vertices = [] self.inactivated_vertices = set() @@ -470,7 +472,7 @@ class Graph: vertex = self.get_vertex(vertex_id) vertex.set_state(state) - def mark_branch(self, vertex_id: str, state: str, visited: Optional[set] = None): + def mark_branch(self, vertex_id: str, state: str, visited: Optional[set] = None, output_name: Optional[str] = None): """Marks a branch of the graph.""" if visited is None: visited = set() @@ -481,8 +483,21 @@ class Graph: self.mark_vertex(vertex_id, state) for child_id in self.parent_child_map[vertex_id]: + # Only child_id that have an edge with the vertex_id through the output_name + # should be marked + if output_name: + edge = self.get_edge(vertex_id, child_id) + if edge.source_handle.name != output_name: + continue self.mark_branch(child_id, state) + def get_edge(self, source_id: str, target_id: str) -> Optional[ContractEdge]: + """Returns the edge between two vertices.""" + for edge in self.edges: + if edge.source_id == source_id and edge.target_id == target_id: + return edge + return None + def build_parent_child_map(self, vertices: List[Vertex]): parent_child_map = defaultdict(list) for vertex in vertices: @@ -1132,6 +1147,7 @@ class Graph: ) layers: List[List[str]] = [] visited = set(queue) + current_layer = 0 while queue: layers.append([]) # Start a new layer diff --git a/src/backend/base/langflow/graph/vertex/types.py b/src/backend/base/langflow/graph/vertex/types.py index af2369fcd..20b6b393c 100644 --- a/src/backend/base/langflow/graph/vertex/types.py +++ b/src/backend/base/langflow/graph/vertex/types.py @@ -309,6 +309,10 @@ class StateVertex(Vertex): successors = self.graph.successor_map.get(self.id, []) return successors + self.graph.activated_vertices + def _built_object_repr(self): + if self.artifacts and "repr" in self.artifacts: + return self.artifacts["repr"] or super()._built_object_repr() + def dict_to_codeblock(d: dict) -> str: serialized = {key: serialize_field(val) for key, val in d.items()} From b98fc0b53467b69d75b509ea51f65c9c46b7f4d9 Mon Sep 17 00:00:00 2001 From: ogabrielluiz Date: Tue, 4 Jun 2024 22:26:33 -0300 Subject: [PATCH 062/701] update projects --- .../starter_projects/Basic Prompting (Hello, world!).json | 4 ++++ .../starter_projects/Langflow Blog Writter.json | 2 ++ .../initial_setup/starter_projects/Langflow Document QA.json | 4 ++++ .../starter_projects/Langflow Memory Conversation.json | 4 ++++ .../starter_projects/Langflow Prompt Chaining.json | 5 +++++ .../starter_projects/VectorStore-RAG-Flows.json | 4 ++++ 6 files changed, 23 insertions(+) diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting (Hello, world!).json b/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting (Hello, world!).json index eb84557a2..8b75ec3c9 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting (Hello, world!).json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting (Hello, world!).json @@ -596,6 +596,7 @@ "types": [ "Text" ], + "selected": "Text", "name": "Message", "method": "text_response" }, @@ -603,6 +604,7 @@ "types": [ "Record" ], + "selected": "Record", "name": "Record", "method": "record_response" } @@ -770,6 +772,7 @@ "types": [ "Text" ], + "selected": "Text", "name": "Message", "method": "text_response" }, @@ -777,6 +780,7 @@ "types": [ "Record" ], + "selected": "Record", "name": "Record", "method": "record_response" } diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Blog Writter.json b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Blog Writter.json index 67608d40f..b0969d6a2 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Blog Writter.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Blog Writter.json @@ -456,6 +456,7 @@ "types": [ "Text" ], + "selected": "Text", "name": "Message", "method": "text_response" }, @@ -463,6 +464,7 @@ "types": [ "Record" ], + "selected": "Record", "name": "Record", "method": "record_response" } diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Document QA.json b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Document QA.json index 7defd69dc..0ac16f7f3 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Document QA.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Document QA.json @@ -429,6 +429,7 @@ "types": [ "Text" ], + "selected": "Text", "name": "Message", "method": "text_response" }, @@ -436,6 +437,7 @@ "types": [ "Record" ], + "selected": "Record", "name": "Record", "method": "record_response" } @@ -627,6 +629,7 @@ "types": [ "Text" ], + "selected": "Text", "name": "Message", "method": "text_response" }, @@ -634,6 +637,7 @@ "types": [ "Record" ], + "selected": "Record", "name": "Record", "method": "record_response" } diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Memory Conversation.json b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Memory Conversation.json index c490d8d4c..4a0ba9e83 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Memory Conversation.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Memory Conversation.json @@ -155,6 +155,7 @@ "types": [ "Text" ], + "selected": "Text", "name": "Message", "method": "text_response" }, @@ -162,6 +163,7 @@ "types": [ "Record" ], + "selected": "Record", "name": "Record", "method": "record_response" } @@ -353,6 +355,7 @@ "types": [ "Text" ], + "selected": "Text", "name": "Message", "method": "text_response" }, @@ -360,6 +363,7 @@ "types": [ "Record" ], + "selected": "Record", "name": "Record", "method": "record_response" } diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Prompt Chaining.json b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Prompt Chaining.json index 4a184529b..53d770806 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Prompt Chaining.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Prompt Chaining.json @@ -434,6 +434,7 @@ "types": [ "Text" ], + "selected": "Text", "name": "Message", "method": "text_response" }, @@ -441,6 +442,7 @@ "types": [ "Record" ], + "selected": "Record", "name": "Record", "method": "record_response" } @@ -629,6 +631,7 @@ "types": [ "Text" ], + "selected": "Text", "name": "Message", "method": "text_response" }, @@ -636,6 +639,7 @@ "types": [ "Record" ], + "selected": "Record", "name": "Record", "method": "record_response" } @@ -748,6 +752,7 @@ "types": [ "Text" ], + "selected": "Text", "name": "Text", "method": "text_response" } diff --git a/src/backend/base/langflow/initial_setup/starter_projects/VectorStore-RAG-Flows.json b/src/backend/base/langflow/initial_setup/starter_projects/VectorStore-RAG-Flows.json index 3237ea965..dd85f47fd 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/VectorStore-RAG-Flows.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/VectorStore-RAG-Flows.json @@ -153,6 +153,7 @@ "types": [ "Text" ], + "selected": "Text", "name": "Message", "method": "text_response" }, @@ -160,6 +161,7 @@ "types": [ "Record" ], + "selected": "Record", "name": "Record", "method": "record_response" } @@ -1448,6 +1450,7 @@ "types": [ "Text" ], + "selected": "Text", "name": "Message", "method": "text_response" }, @@ -1455,6 +1458,7 @@ "types": [ "Record" ], + "selected": "Record", "name": "Record", "method": "record_response" } From 41b0660dfd6cb01640e8d10b0694da88de750da4 Mon Sep 17 00:00:00 2001 From: Lucas Oliveira Date: Wed, 5 Jun 2024 16:51:30 -0300 Subject: [PATCH 063/701] Styled Output Type --- poetry.lock | 45 ++++++++++++++++++- pyproject.toml | 1 + .../components/OutputComponent/index.tsx | 23 ++++++---- 3 files changed, 60 insertions(+), 9 deletions(-) diff --git a/poetry.lock b/poetry.lock index 5d85d6b81..521a9e42b 100644 --- a/poetry.lock +++ b/poetry.lock @@ -2488,6 +2488,38 @@ benchmarks = ["httplib2", "httpx", "requests", "urllib3"] dev = ["dpkt", "pytest", "requests"] examples = ["oauth2"] +[[package]] +name = "gitdb" +version = "4.0.11" +description = "Git Object Database" +optional = false +python-versions = ">=3.7" +files = [ + {file = "gitdb-4.0.11-py3-none-any.whl", hash = "sha256:81a3407ddd2ee8df444cbacea00e2d038e40150acfa3001696fe0dcf1d3adfa4"}, + {file = "gitdb-4.0.11.tar.gz", hash = "sha256:bf5421126136d6d0af55bc1e7c1af1c397a34f5b7bd79e776cd3e89785c2b04b"}, +] + +[package.dependencies] +smmap = ">=3.0.1,<6" + +[[package]] +name = "gitpython" +version = "3.1.43" +description = "GitPython is a Python library used to interact with Git repositories" +optional = false +python-versions = ">=3.7" +files = [ + {file = "GitPython-3.1.43-py3-none-any.whl", hash = "sha256:eec7ec56b92aad751f9912a73404bc02ba212a23adb2c7098ee668417051a1ff"}, + {file = "GitPython-3.1.43.tar.gz", hash = "sha256:35f314a9f878467f5453cc1fee295c3e18e52f1b99f10f6cf5b1682e968a9e7c"}, +] + +[package.dependencies] +gitdb = ">=4.0.1,<5" + +[package.extras] +doc = ["sphinx (==4.3.2)", "sphinx-autodoc-typehints", "sphinx-rtd-theme", "sphinxcontrib-applehelp (>=1.0.2,<=1.0.4)", "sphinxcontrib-devhelp (==1.0.2)", "sphinxcontrib-htmlhelp (>=2.0.0,<=2.0.1)", "sphinxcontrib-qthelp (==1.0.3)", "sphinxcontrib-serializinghtml (==1.1.5)"] +test = ["coverage[toml]", "ddt (>=1.1.1,!=1.4.3)", "mock", "mypy", "pre-commit", "pytest (>=7.3.1)", "pytest-cov", "pytest-instafail", "pytest-mock", "pytest-sugar", "typing-extensions"] + [[package]] name = "google-ai-generativelanguage" version = "0.6.4" @@ -8151,6 +8183,17 @@ files = [ {file = "six-1.16.0.tar.gz", hash = "sha256:1e61c37477a1626458e36f7b1d82aa5c9b094fa4802892072e49de9c60c4c926"}, ] +[[package]] +name = "smmap" +version = "5.0.1" +description = "A pure Python implementation of a sliding window memory map manager" +optional = false +python-versions = ">=3.7" +files = [ + {file = "smmap-5.0.1-py3-none-any.whl", hash = "sha256:e6d8668fa5f93e706934a62d7b4db19c8d9eb8cf2adbb75ef1b675aa332b69da"}, + {file = "smmap-5.0.1.tar.gz", hash = "sha256:dceeb6c0028fdb6734471eb07c0cd2aae706ccaecab45965ee83f11c8d3b1f62"}, +] + [[package]] name = "sniffio" version = "1.3.1" @@ -10054,4 +10097,4 @@ local = ["ctransformers", "llama-cpp-python", "sentence-transformers"] [metadata] lock-version = "2.0" python-versions = ">=3.10,<3.13" -content-hash = "83c94ed0fa28b968553221385251b871139a7440ab0420f867efbe16568b8411" +content-hash = "8fd6622a9bdd88dcac9ab9ca8136b6b7c7939f0abeb6cd3f4ee754cc38527648" diff --git a/pyproject.toml b/pyproject.toml index 18be560c7..dd3057bff 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -86,6 +86,7 @@ youtube-transcript-api = "^0.6.2" markdown = "^3.6" langchain-chroma = "^0.1.1" upstash-vector = "^0.4.0" +gitpython = "^3.1.43" [tool.poetry.group.dev.dependencies] diff --git a/src/frontend/src/customNodes/genericNode/components/OutputComponent/index.tsx b/src/frontend/src/customNodes/genericNode/components/OutputComponent/index.tsx index dfa5ac350..f684a902f 100644 --- a/src/frontend/src/customNodes/genericNode/components/OutputComponent/index.tsx +++ b/src/frontend/src/customNodes/genericNode/components/OutputComponent/index.tsx @@ -11,6 +11,7 @@ import useFlowStore from "../../../../stores/flowStore"; import { outputComponentType } from "../../../../types/components"; import { NodeDataType } from "../../../../types/flow"; import { cn } from "../../../../utils/utils"; +import { Button } from "../../../../components/ui/button"; export default function OutputComponent({ selected, @@ -28,16 +29,21 @@ export default function OutputComponent({ } return ( -
- {name} +
- - + {types.map((type) => ( @@ -58,6 +64,7 @@ export default function OutputComponent({ ))} + {name}
); } From 5121fb10e5b66c49cd4d96fb37d728837e7abb63 Mon Sep 17 00:00:00 2001 From: ogabrielluiz Date: Wed, 5 Jun 2024 19:01:12 -0300 Subject: [PATCH 064/701] refactor: Update output names in TextInput, TextOutput, RecordsOutput, ChatInput, and ChatOutput The output names in the TextInput, TextOutput, RecordsOutput, ChatInput, and ChatOutput components have been updated to use more descriptive names. This change improves the clarity and consistency of the output names across the components. Note: The commit message has been generated based on the provided code changes and recent commits. --- .../components/experimental/TextOperator.py | 4 ++-- .../base/langflow/components/inputs/ChatInput.py | 4 ++-- .../base/langflow/components/inputs/TextInput.py | 2 +- .../base/langflow/components/outputs/ChatOutput.py | 4 ++-- .../langflow/components/outputs/RecordsOutput.py | 2 +- .../base/langflow/components/outputs/TextOutput.py | 2 +- .../langflow/custom/custom_component/component.py | 7 ++++--- src/backend/base/langflow/template/field/base.py | 14 +++++++++++++- tests/data/component_multiple_outputs.py | 4 ++-- tests/data/component_nested_call.py | 7 ++++--- 10 files changed, 32 insertions(+), 18 deletions(-) diff --git a/src/backend/base/langflow/components/experimental/TextOperator.py b/src/backend/base/langflow/components/experimental/TextOperator.py index ac2d65941..0b9821240 100644 --- a/src/backend/base/langflow/components/experimental/TextOperator.py +++ b/src/backend/base/langflow/components/experimental/TextOperator.py @@ -43,8 +43,8 @@ class TextOperatorComponent(Component): ), ] outputs = [ - Output(name="True Result", method="result_response"), - Output(name="False Result", method="result_response"), + Output(display_name="True Result", name="true_result", method="result_response"), + Output(display_name="False Result", name="false_result", method="result_response"), ] def true_response(self) -> Union[Text, Record]: diff --git a/src/backend/base/langflow/components/inputs/ChatInput.py b/src/backend/base/langflow/components/inputs/ChatInput.py index ae08a7a20..1de364364 100644 --- a/src/backend/base/langflow/components/inputs/ChatInput.py +++ b/src/backend/base/langflow/components/inputs/ChatInput.py @@ -33,8 +33,8 @@ class ChatInput(ChatComponent): ), ] outputs = [ - Output(name="Message", method="text_response"), - Output(name="Record", method="record_response"), + Output(display_name="Message", name="message", method="text_response"), + Output(display_name="Record", name="record", method="record_response"), ] def text_response(self) -> Text: diff --git a/src/backend/base/langflow/components/inputs/TextInput.py b/src/backend/base/langflow/components/inputs/TextInput.py index 596edf0ec..7fdcbb501 100644 --- a/src/backend/base/langflow/components/inputs/TextInput.py +++ b/src/backend/base/langflow/components/inputs/TextInput.py @@ -26,7 +26,7 @@ class TextInput(TextComponent): ), ] outputs = [ - Output(name="Text", method="text_response"), + Output(display_name="Text", name="text", method="text_response"), ] def text_response(self) -> Text: diff --git a/src/backend/base/langflow/components/outputs/ChatOutput.py b/src/backend/base/langflow/components/outputs/ChatOutput.py index 064cd92f1..d1506310b 100644 --- a/src/backend/base/langflow/components/outputs/ChatOutput.py +++ b/src/backend/base/langflow/components/outputs/ChatOutput.py @@ -36,8 +36,8 @@ class ChatOutput(ChatComponent): ), ] outputs = [ - Output(name="Message", method="text_response"), - Output(name="Record", method="record_response"), + Output(display_name="Message", name="message", method="text_response"), + Output(display_name="Record", name="record", method="record_response"), ] def text_response(self) -> Text: diff --git a/src/backend/base/langflow/components/outputs/RecordsOutput.py b/src/backend/base/langflow/components/outputs/RecordsOutput.py index 7af2a0c7e..f03b0ccd6 100644 --- a/src/backend/base/langflow/components/outputs/RecordsOutput.py +++ b/src/backend/base/langflow/components/outputs/RecordsOutput.py @@ -11,7 +11,7 @@ class RecordsOutput(Component): Input(name="input_value", type=Record, display_name="Record Input"), ] outputs = [ - Output(name="Record", method="record_response"), + Output(display_name="Record", name="record", method="record_response"), ] def record_response(self) -> Record: diff --git a/src/backend/base/langflow/components/outputs/TextOutput.py b/src/backend/base/langflow/components/outputs/TextOutput.py index bc71b3a27..d4615b418 100644 --- a/src/backend/base/langflow/components/outputs/TextOutput.py +++ b/src/backend/base/langflow/components/outputs/TextOutput.py @@ -26,7 +26,7 @@ class TextOutput(TextComponent): ), ] outputs = [ - Output(name="Text", method="text_response"), + Output(display_name="Text", name="text", method="text_response"), ] def text_response(self) -> Text: diff --git a/src/backend/base/langflow/custom/custom_component/component.py b/src/backend/base/langflow/custom/custom_component/component.py index 9816fe6b0..6c88e51be 100644 --- a/src/backend/base/langflow/custom/custom_component/component.py +++ b/src/backend/base/langflow/custom/custom_component/component.py @@ -60,6 +60,8 @@ class Component(CustomComponent): def _set_outputs(self, outputs: List[dict]): self.outputs = [Output(**output) for output in outputs] + for output in self.outputs: + setattr(self, output.name, output) async def build_results(self, vertex: "Vertex"): _results = {} @@ -69,14 +71,13 @@ class Component(CustomComponent): for output in self.outputs: # Build the output if it's connected to some other vertex # or if it's not connected to any vertex - self.output = output - if not vertex.outgoing_edges or output.name in vertex.edges_source_names: + if not vertex.outgoing_edges or output.display_name in vertex.edges_source_names: method: Callable | Awaitable = getattr(self, output.method) result = method() # If the method is asynchronous, we need to await it if inspect.iscoroutinefunction(method): result = await result - _results[output.name] = result + _results[output.display_name] = result self._results = _results return _results diff --git a/src/backend/base/langflow/template/field/base.py b/src/backend/base/langflow/template/field/base.py index ff3e669bb..4e9f059fa 100644 --- a/src/backend/base/langflow/template/field/base.py +++ b/src/backend/base/langflow/template/field/base.py @@ -152,7 +152,10 @@ class Output(BaseModel): selected: Optional[str] = Field(default=None, serialization_alias="selected") """The selected output type for the field.""" - name: str = Field(default="", serialization_alias="name") + display_name: Optional[str] = Field(default=None, serialization_alias="name") + """The display name of the field.""" + + name: str = Field(default=None, serialization_alias="name") """The name of the field.""" method: Optional[str] = Field(default=None, serialization_alias="method") @@ -168,3 +171,12 @@ class Output(BaseModel): def set_selected(self): if not self.selected: self.selected = self.types[0] + + @field_validator("display_name", mode="before") + def validate_display_name(cls, v, info): + if not v: + if info.data.get("name"): + return info.data["name"] + else: + raise ValueError("If display_name is not set, name must be set") + return v diff --git a/tests/data/component_multiple_outputs.py b/tests/data/component_multiple_outputs.py index 6970ab305..5cdff8773 100644 --- a/tests/data/component_multiple_outputs.py +++ b/tests/data/component_multiple_outputs.py @@ -8,8 +8,8 @@ class MultipleOutputsComponent(Component): Input(display_name="Number", name="number", field_type=int), ] outputs = [ - Output(name="Certain Output", method="certain_output"), - Output(name="Other Output", method="other_output"), + Output(display_name="Certain Output", name="certain_output", method="certain_output"), + Output(display_name="Other Output", name="other_output", method="other_output"), ] def certain_output(self) -> str: diff --git a/tests/data/component_nested_call.py b/tests/data/component_nested_call.py index 0eef5566f..6cf64944a 100644 --- a/tests/data/component_nested_call.py +++ b/tests/data/component_nested_call.py @@ -1,6 +1,7 @@ +from random import randint + from langflow.custom import Component from langflow.template.field.base import Input, Output -from random import randint class MultipleOutputsComponent(Component): @@ -9,8 +10,8 @@ class MultipleOutputsComponent(Component): Input(display_name="Number", name="number", field_type=int), ] outputs = [ - Output(name="Certain Output", method="certain_output"), - Output(name="Other Output", method="other_output"), + Output(display_name="Certain Output", name="certain_output", method="certain_output"), + Output(display_name="Other Output", name="other_output", method="other_output"), ] def certain_output(self) -> int: From 24b41eb59d8cd5034cc9e0224014978de63164a6 Mon Sep 17 00:00:00 2001 From: ogabrielluiz Date: Wed, 5 Jun 2024 19:01:21 -0300 Subject: [PATCH 065/701] update examples --- .../Basic Prompting (Hello, world!).json | 18 +++++++----- .../Langflow Blog Writter.json | 10 +++++-- .../Langflow Document QA.json | 18 +++++++----- .../Langflow Memory Conversation.json | 19 +++++++----- .../Langflow Prompt Chaining.json | 24 ++++++++------- .../VectorStore-RAG-Flows.json | 29 ++++++++++++------- 6 files changed, 71 insertions(+), 47 deletions(-) diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting (Hello, world!).json b/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting (Hello, world!).json index 8b75ec3c9..4a485b9ca 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting (Hello, world!).json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting (Hello, world!).json @@ -114,6 +114,7 @@ "Text" ], "selected": null, + "display_name": null, "name": "Text", "method": null } @@ -402,6 +403,7 @@ "Text" ], "selected": null, + "display_name": null, "name": "Text", "method": null } @@ -438,7 +440,7 @@ "list": false, "show": true, "multiline": true, - "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\nfrom langflow.template import Input, Output\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n inputs = [\n Input(\n name=\"input_value\", type=str, display_name=\"Message\", multiline=True, info=\"Message to be passed as output.\"\n ),\n Input(\n name=\"sender\",\n type=str,\n display_name=\"Sender Type\",\n options=[\"Machine\", \"User\"],\n value=\"Machine\",\n advanced=True,\n info=\"Type of sender.\",\n ),\n Input(name=\"sender_name\", type=str, display_name=\"Sender Name\", info=\"Name of the sender.\", value=\"AI\"),\n Input(\n name=\"session_id\", type=str, display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True\n ),\n Input(\n name=\"record_template\",\n type=str,\n display_name=\"Record Template\",\n value=\"{text}\",\n advanced=True,\n info=\"Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.\",\n ),\n ]\n outputs = [\n Output(name=\"Message\", method=\"text_response\"),\n Output(name=\"Record\", method=\"record_response\"),\n ]\n\n def text_response(self) -> Text:\n result = self.input_value\n if self.session_id and isinstance(result, (Record, str)):\n self.store_message(result, self.session_id, self.sender, self.sender_name)\n return result\n\n def record_response(self) -> Record:\n record = Record(\n data={\n \"message\": self.input_value,\n \"sender\": self.sender,\n \"sender_name\": self.sender_name,\n \"session_id\": self.session_id,\n \"template\": self.record_template or \"\",\n }\n )\n if self.session_id and isinstance(record, (Record, str)):\n self.store_message(record, self.session_id, self.sender, self.sender_name)\n return record\n", + "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\nfrom langflow.template import Input, Output\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n inputs = [\n Input(\n name=\"input_value\", type=str, display_name=\"Message\", multiline=True, info=\"Message to be passed as output.\"\n ),\n Input(\n name=\"sender\",\n type=str,\n display_name=\"Sender Type\",\n options=[\"Machine\", \"User\"],\n value=\"Machine\",\n advanced=True,\n info=\"Type of sender.\",\n ),\n Input(name=\"sender_name\", type=str, display_name=\"Sender Name\", info=\"Name of the sender.\", value=\"AI\"),\n Input(\n name=\"session_id\", type=str, display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True\n ),\n Input(\n name=\"record_template\",\n type=str,\n display_name=\"Record Template\",\n value=\"{text}\",\n advanced=True,\n info=\"Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.\",\n ),\n ]\n outputs = [\n Output(display_name=\"Message\", name=\"message\", method=\"text_response\"),\n Output(display_name=\"Record\", name=\"record\", method=\"record_response\"),\n ]\n\n def text_response(self) -> Text:\n result = self.input_value\n if self.session_id and isinstance(result, (Record, str)):\n self.store_message(result, self.session_id, self.sender, self.sender_name)\n return result\n\n def record_response(self) -> Record:\n record = Record(\n data={\n \"message\": self.input_value,\n \"sender\": self.sender,\n \"sender_name\": self.sender_name,\n \"session_id\": self.session_id,\n \"template\": self.record_template or \"\",\n }\n )\n if self.session_id and isinstance(record, (Record, str)):\n self.store_message(record, self.session_id, self.sender, self.sender_name)\n return record\n", "fileTypes": [], "file_path": "", "password": false, @@ -597,7 +599,7 @@ "Text" ], "selected": "Text", - "name": "Message", + "name": "message", "method": "text_response" }, { @@ -605,7 +607,7 @@ "Record" ], "selected": "Record", - "name": "Record", + "name": "record", "method": "record_response" } ] @@ -639,7 +641,7 @@ "list": false, "show": true, "multiline": true, - "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\nfrom langflow.template import Input, Output\n\n\nclass ChatInput(ChatComponent):\n display_name = \"Chat Input\"\n description = \"Get chat inputs from the Playground.\"\n icon = \"ChatInput\"\n\n inputs = [\n Input(\n name=\"input_value\",\n type=str,\n display_name=\"Message\",\n multiline=True,\n input_types=[],\n info=\"Message to be passed as input.\",\n ),\n Input(\n name=\"sender\",\n type=str,\n display_name=\"Sender Type\",\n options=[\"Machine\", \"User\"],\n value=\"User\",\n info=\"Type of sender.\",\n advanced=True,\n ),\n Input(name=\"sender_name\", type=str, display_name=\"Sender Name\", info=\"Name of the sender.\", value=\"User\"),\n Input(\n name=\"session_id\", type=str, display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True\n ),\n ]\n outputs = [\n Output(name=\"Message\", method=\"text_response\"),\n Output(name=\"Record\", method=\"record_response\"),\n ]\n\n def text_response(self) -> Text:\n result = self.input_value\n if self.session_id and isinstance(result, (Record, str)):\n self.store_message(result, self.session_id, self.sender, self.sender_name)\n return result\n\n def record_response(self) -> Record:\n record = Record(\n data={\n \"text\": self.input_value,\n \"sender\": self.sender,\n \"sender_name\": self.sender_name,\n \"session_id\": self.session_id,\n },\n )\n if self.session_id and isinstance(record, (Record, str)):\n self.store_message(record, self.session_id, self.sender, self.sender_name)\n return record\n", + "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\nfrom langflow.template import Input, Output\n\n\nclass ChatInput(ChatComponent):\n display_name = \"Chat Input\"\n description = \"Get chat inputs from the Playground.\"\n icon = \"ChatInput\"\n\n inputs = [\n Input(\n name=\"input_value\",\n type=str,\n display_name=\"Message\",\n multiline=True,\n input_types=[],\n info=\"Message to be passed as input.\",\n ),\n Input(\n name=\"sender\",\n type=str,\n display_name=\"Sender Type\",\n options=[\"Machine\", \"User\"],\n value=\"User\",\n info=\"Type of sender.\",\n advanced=True,\n ),\n Input(name=\"sender_name\", type=str, display_name=\"Sender Name\", info=\"Name of the sender.\", value=\"User\"),\n Input(\n name=\"session_id\", type=str, display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True\n ),\n ]\n outputs = [\n Output(display_name=\"Message\", name=\"message\", method=\"text_response\"),\n Output(display_name=\"Record\", name=\"record\", method=\"record_response\"),\n ]\n\n def text_response(self) -> Text:\n result = self.input_value\n if self.session_id and isinstance(result, (Record, str)):\n self.store_message(result, self.session_id, self.sender, self.sender_name)\n return result\n\n def record_response(self) -> Record:\n record = Record(\n data={\n \"text\": self.input_value,\n \"sender\": self.sender,\n \"sender_name\": self.sender_name,\n \"session_id\": self.session_id,\n },\n )\n if self.session_id and isinstance(record, (Record, str)):\n self.store_message(record, self.session_id, self.sender, self.sender_name)\n return record\n", "fileTypes": [], "file_path": "", "password": false, @@ -773,7 +775,7 @@ "Text" ], "selected": "Text", - "name": "Message", + "name": "message", "method": "text_response" }, { @@ -781,7 +783,7 @@ "Record" ], "selected": "Record", - "name": "Record", + "name": "record", "method": "record_response" } ] @@ -859,7 +861,7 @@ }, { "source": "ChatInput-P3fgL", - "sourceHandle": "{\"dataType\": \"ChatInput\", \"id\": \"ChatInput-P3fgL\", \"output_types\": [\"Text\"], \"name\": \"Message\"}", + "sourceHandle": "{\"dataType\": \"ChatInput\", \"id\": \"ChatInput-P3fgL\", \"output_types\": [\"Text\"], \"name\": \"message\"}", "target": "Prompt-uxBqP", "targetHandle": "{œfieldNameœ:œuser_inputœ,œidœ:œPrompt-uxBqPœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}", "data": { @@ -880,7 +882,7 @@ "output_types": [ "Text" ], - "name": "Message" + "name": "message" } }, "style": { diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Blog Writter.json b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Blog Writter.json index b0969d6a2..466815700 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Blog Writter.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Blog Writter.json @@ -168,6 +168,7 @@ "Text" ], "selected": null, + "display_name": null, "name": "Text", "method": null } @@ -264,6 +265,7 @@ "Record" ], "selected": null, + "display_name": null, "name": "Record", "method": null } @@ -298,7 +300,7 @@ "list": false, "show": true, "multiline": true, - "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\nfrom langflow.template import Input, Output\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n inputs = [\n Input(\n name=\"input_value\", type=str, display_name=\"Message\", multiline=True, info=\"Message to be passed as output.\"\n ),\n Input(\n name=\"sender\",\n type=str,\n display_name=\"Sender Type\",\n options=[\"Machine\", \"User\"],\n value=\"Machine\",\n advanced=True,\n info=\"Type of sender.\",\n ),\n Input(name=\"sender_name\", type=str, display_name=\"Sender Name\", info=\"Name of the sender.\", value=\"AI\"),\n Input(\n name=\"session_id\", type=str, display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True\n ),\n Input(\n name=\"record_template\",\n type=str,\n display_name=\"Record Template\",\n value=\"{text}\",\n advanced=True,\n info=\"Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.\",\n ),\n ]\n outputs = [\n Output(name=\"Message\", method=\"text_response\"),\n Output(name=\"Record\", method=\"record_response\"),\n ]\n\n def text_response(self) -> Text:\n result = self.input_value\n if self.session_id and isinstance(result, (Record, str)):\n self.store_message(result, self.session_id, self.sender, self.sender_name)\n return result\n\n def record_response(self) -> Record:\n record = Record(\n data={\n \"message\": self.input_value,\n \"sender\": self.sender,\n \"sender_name\": self.sender_name,\n \"session_id\": self.session_id,\n \"template\": self.record_template or \"\",\n }\n )\n if self.session_id and isinstance(record, (Record, str)):\n self.store_message(record, self.session_id, self.sender, self.sender_name)\n return record\n", + "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\nfrom langflow.template import Input, Output\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n inputs = [\n Input(\n name=\"input_value\", type=str, display_name=\"Message\", multiline=True, info=\"Message to be passed as output.\"\n ),\n Input(\n name=\"sender\",\n type=str,\n display_name=\"Sender Type\",\n options=[\"Machine\", \"User\"],\n value=\"Machine\",\n advanced=True,\n info=\"Type of sender.\",\n ),\n Input(name=\"sender_name\", type=str, display_name=\"Sender Name\", info=\"Name of the sender.\", value=\"AI\"),\n Input(\n name=\"session_id\", type=str, display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True\n ),\n Input(\n name=\"record_template\",\n type=str,\n display_name=\"Record Template\",\n value=\"{text}\",\n advanced=True,\n info=\"Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.\",\n ),\n ]\n outputs = [\n Output(display_name=\"Message\", name=\"message\", method=\"text_response\"),\n Output(display_name=\"Record\", name=\"record\", method=\"record_response\"),\n ]\n\n def text_response(self) -> Text:\n result = self.input_value\n if self.session_id and isinstance(result, (Record, str)):\n self.store_message(result, self.session_id, self.sender, self.sender_name)\n return result\n\n def record_response(self) -> Record:\n record = Record(\n data={\n \"message\": self.input_value,\n \"sender\": self.sender,\n \"sender_name\": self.sender_name,\n \"session_id\": self.session_id,\n \"template\": self.record_template or \"\",\n }\n )\n if self.session_id and isinstance(record, (Record, str)):\n self.store_message(record, self.session_id, self.sender, self.sender_name)\n return record\n", "fileTypes": [], "file_path": "", "password": false, @@ -457,7 +459,7 @@ "Text" ], "selected": "Text", - "name": "Message", + "name": "message", "method": "text_response" }, { @@ -465,7 +467,7 @@ "Record" ], "selected": "Record", - "name": "Record", + "name": "record", "method": "record_response" } ] @@ -746,6 +748,7 @@ "Text" ], "selected": null, + "display_name": null, "name": "Text", "method": null } @@ -840,6 +843,7 @@ "Record" ], "selected": null, + "display_name": null, "name": "Record", "method": null } diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Document QA.json b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Document QA.json index 0ac16f7f3..dfcfc0144 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Document QA.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Document QA.json @@ -141,6 +141,7 @@ "Text" ], "selected": null, + "display_name": null, "name": "Text", "method": null } @@ -296,7 +297,7 @@ "list": false, "show": true, "multiline": true, - "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\nfrom langflow.template import Input, Output\n\n\nclass ChatInput(ChatComponent):\n display_name = \"Chat Input\"\n description = \"Get chat inputs from the Playground.\"\n icon = \"ChatInput\"\n\n inputs = [\n Input(\n name=\"input_value\",\n type=str,\n display_name=\"Message\",\n multiline=True,\n input_types=[],\n info=\"Message to be passed as input.\",\n ),\n Input(\n name=\"sender\",\n type=str,\n display_name=\"Sender Type\",\n options=[\"Machine\", \"User\"],\n value=\"User\",\n info=\"Type of sender.\",\n advanced=True,\n ),\n Input(name=\"sender_name\", type=str, display_name=\"Sender Name\", info=\"Name of the sender.\", value=\"User\"),\n Input(\n name=\"session_id\", type=str, display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True\n ),\n ]\n outputs = [\n Output(name=\"Message\", method=\"text_response\"),\n Output(name=\"Record\", method=\"record_response\"),\n ]\n\n def text_response(self) -> Text:\n result = self.input_value\n if self.session_id and isinstance(result, (Record, str)):\n self.store_message(result, self.session_id, self.sender, self.sender_name)\n return result\n\n def record_response(self) -> Record:\n record = Record(\n data={\n \"text\": self.input_value,\n \"sender\": self.sender,\n \"sender_name\": self.sender_name,\n \"session_id\": self.session_id,\n },\n )\n if self.session_id and isinstance(record, (Record, str)):\n self.store_message(record, self.session_id, self.sender, self.sender_name)\n return record\n", + "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\nfrom langflow.template import Input, Output\n\n\nclass ChatInput(ChatComponent):\n display_name = \"Chat Input\"\n description = \"Get chat inputs from the Playground.\"\n icon = \"ChatInput\"\n\n inputs = [\n Input(\n name=\"input_value\",\n type=str,\n display_name=\"Message\",\n multiline=True,\n input_types=[],\n info=\"Message to be passed as input.\",\n ),\n Input(\n name=\"sender\",\n type=str,\n display_name=\"Sender Type\",\n options=[\"Machine\", \"User\"],\n value=\"User\",\n info=\"Type of sender.\",\n advanced=True,\n ),\n Input(name=\"sender_name\", type=str, display_name=\"Sender Name\", info=\"Name of the sender.\", value=\"User\"),\n Input(\n name=\"session_id\", type=str, display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True\n ),\n ]\n outputs = [\n Output(display_name=\"Message\", name=\"message\", method=\"text_response\"),\n Output(display_name=\"Record\", name=\"record\", method=\"record_response\"),\n ]\n\n def text_response(self) -> Text:\n result = self.input_value\n if self.session_id and isinstance(result, (Record, str)):\n self.store_message(result, self.session_id, self.sender, self.sender_name)\n return result\n\n def record_response(self) -> Record:\n record = Record(\n data={\n \"text\": self.input_value,\n \"sender\": self.sender,\n \"sender_name\": self.sender_name,\n \"session_id\": self.session_id,\n },\n )\n if self.session_id and isinstance(record, (Record, str)):\n self.store_message(record, self.session_id, self.sender, self.sender_name)\n return record\n", "fileTypes": [], "file_path": "", "password": false, @@ -430,7 +431,7 @@ "Text" ], "selected": "Text", - "name": "Message", + "name": "message", "method": "text_response" }, { @@ -438,7 +439,7 @@ "Record" ], "selected": "Record", - "name": "Record", + "name": "record", "method": "record_response" } ] @@ -472,7 +473,7 @@ "list": false, "show": true, "multiline": true, - "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\nfrom langflow.template import Input, Output\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n inputs = [\n Input(\n name=\"input_value\", type=str, display_name=\"Message\", multiline=True, info=\"Message to be passed as output.\"\n ),\n Input(\n name=\"sender\",\n type=str,\n display_name=\"Sender Type\",\n options=[\"Machine\", \"User\"],\n value=\"Machine\",\n advanced=True,\n info=\"Type of sender.\",\n ),\n Input(name=\"sender_name\", type=str, display_name=\"Sender Name\", info=\"Name of the sender.\", value=\"AI\"),\n Input(\n name=\"session_id\", type=str, display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True\n ),\n Input(\n name=\"record_template\",\n type=str,\n display_name=\"Record Template\",\n value=\"{text}\",\n advanced=True,\n info=\"Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.\",\n ),\n ]\n outputs = [\n Output(name=\"Message\", method=\"text_response\"),\n Output(name=\"Record\", method=\"record_response\"),\n ]\n\n def text_response(self) -> Text:\n result = self.input_value\n if self.session_id and isinstance(result, (Record, str)):\n self.store_message(result, self.session_id, self.sender, self.sender_name)\n return result\n\n def record_response(self) -> Record:\n record = Record(\n data={\n \"message\": self.input_value,\n \"sender\": self.sender,\n \"sender_name\": self.sender_name,\n \"session_id\": self.session_id,\n \"template\": self.record_template or \"\",\n }\n )\n if self.session_id and isinstance(record, (Record, str)):\n self.store_message(record, self.session_id, self.sender, self.sender_name)\n return record\n", + "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\nfrom langflow.template import Input, Output\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n inputs = [\n Input(\n name=\"input_value\", type=str, display_name=\"Message\", multiline=True, info=\"Message to be passed as output.\"\n ),\n Input(\n name=\"sender\",\n type=str,\n display_name=\"Sender Type\",\n options=[\"Machine\", \"User\"],\n value=\"Machine\",\n advanced=True,\n info=\"Type of sender.\",\n ),\n Input(name=\"sender_name\", type=str, display_name=\"Sender Name\", info=\"Name of the sender.\", value=\"AI\"),\n Input(\n name=\"session_id\", type=str, display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True\n ),\n Input(\n name=\"record_template\",\n type=str,\n display_name=\"Record Template\",\n value=\"{text}\",\n advanced=True,\n info=\"Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.\",\n ),\n ]\n outputs = [\n Output(display_name=\"Message\", name=\"message\", method=\"text_response\"),\n Output(display_name=\"Record\", name=\"record\", method=\"record_response\"),\n ]\n\n def text_response(self) -> Text:\n result = self.input_value\n if self.session_id and isinstance(result, (Record, str)):\n self.store_message(result, self.session_id, self.sender, self.sender_name)\n return result\n\n def record_response(self) -> Record:\n record = Record(\n data={\n \"message\": self.input_value,\n \"sender\": self.sender,\n \"sender_name\": self.sender_name,\n \"session_id\": self.session_id,\n \"template\": self.record_template or \"\",\n }\n )\n if self.session_id and isinstance(record, (Record, str)):\n self.store_message(record, self.session_id, self.sender, self.sender_name)\n return record\n", "fileTypes": [], "file_path": "", "password": false, @@ -630,7 +631,7 @@ "Text" ], "selected": "Text", - "name": "Message", + "name": "message", "method": "text_response" }, { @@ -638,7 +639,7 @@ "Record" ], "selected": "Record", - "name": "Record", + "name": "record", "method": "record_response" } ] @@ -924,6 +925,7 @@ "Text" ], "selected": null, + "display_name": null, "name": "Text", "method": null } @@ -944,7 +946,7 @@ "edges": [ { "source": "ChatInput-MsSJ9", - "sourceHandle": "{\"dataType\": \"ChatInput\", \"id\": \"ChatInput-MsSJ9\", \"output_types\": [\"Text\"], \"name\": \"Message\"}", + "sourceHandle": "{\"dataType\": \"ChatInput\", \"id\": \"ChatInput-MsSJ9\", \"output_types\": [\"Text\"], \"name\": \"message\"}", "target": "Prompt-tHwPf", "targetHandle": "{œfieldNameœ:œQuestionœ,œidœ:œPrompt-tHwPfœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}", "data": { @@ -965,7 +967,7 @@ "output_types": [ "Text" ], - "name": "Message" + "name": "message" } }, "style": { diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Memory Conversation.json b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Memory Conversation.json index 4a0ba9e83..64d7eac81 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Memory Conversation.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Memory Conversation.json @@ -22,7 +22,7 @@ "list": false, "show": true, "multiline": true, - "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\nfrom langflow.template import Input, Output\n\n\nclass ChatInput(ChatComponent):\n display_name = \"Chat Input\"\n description = \"Get chat inputs from the Playground.\"\n icon = \"ChatInput\"\n\n inputs = [\n Input(\n name=\"input_value\",\n type=str,\n display_name=\"Message\",\n multiline=True,\n input_types=[],\n info=\"Message to be passed as input.\",\n ),\n Input(\n name=\"sender\",\n type=str,\n display_name=\"Sender Type\",\n options=[\"Machine\", \"User\"],\n value=\"User\",\n info=\"Type of sender.\",\n advanced=True,\n ),\n Input(name=\"sender_name\", type=str, display_name=\"Sender Name\", info=\"Name of the sender.\", value=\"User\"),\n Input(\n name=\"session_id\", type=str, display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True\n ),\n ]\n outputs = [\n Output(name=\"Message\", method=\"text_response\"),\n Output(name=\"Record\", method=\"record_response\"),\n ]\n\n def text_response(self) -> Text:\n result = self.input_value\n if self.session_id and isinstance(result, (Record, str)):\n self.store_message(result, self.session_id, self.sender, self.sender_name)\n return result\n\n def record_response(self) -> Record:\n record = Record(\n data={\n \"text\": self.input_value,\n \"sender\": self.sender,\n \"sender_name\": self.sender_name,\n \"session_id\": self.session_id,\n },\n )\n if self.session_id and isinstance(record, (Record, str)):\n self.store_message(record, self.session_id, self.sender, self.sender_name)\n return record\n", + "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\nfrom langflow.template import Input, Output\n\n\nclass ChatInput(ChatComponent):\n display_name = \"Chat Input\"\n description = \"Get chat inputs from the Playground.\"\n icon = \"ChatInput\"\n\n inputs = [\n Input(\n name=\"input_value\",\n type=str,\n display_name=\"Message\",\n multiline=True,\n input_types=[],\n info=\"Message to be passed as input.\",\n ),\n Input(\n name=\"sender\",\n type=str,\n display_name=\"Sender Type\",\n options=[\"Machine\", \"User\"],\n value=\"User\",\n info=\"Type of sender.\",\n advanced=True,\n ),\n Input(name=\"sender_name\", type=str, display_name=\"Sender Name\", info=\"Name of the sender.\", value=\"User\"),\n Input(\n name=\"session_id\", type=str, display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True\n ),\n ]\n outputs = [\n Output(display_name=\"Message\", name=\"message\", method=\"text_response\"),\n Output(display_name=\"Record\", name=\"record\", method=\"record_response\"),\n ]\n\n def text_response(self) -> Text:\n result = self.input_value\n if self.session_id and isinstance(result, (Record, str)):\n self.store_message(result, self.session_id, self.sender, self.sender_name)\n return result\n\n def record_response(self) -> Record:\n record = Record(\n data={\n \"text\": self.input_value,\n \"sender\": self.sender,\n \"sender_name\": self.sender_name,\n \"session_id\": self.session_id,\n },\n )\n if self.session_id and isinstance(record, (Record, str)):\n self.store_message(record, self.session_id, self.sender, self.sender_name)\n return record\n", "fileTypes": [], "file_path": "", "password": false, @@ -156,7 +156,7 @@ "Text" ], "selected": "Text", - "name": "Message", + "name": "message", "method": "text_response" }, { @@ -164,7 +164,7 @@ "Record" ], "selected": "Record", - "name": "Record", + "name": "record", "method": "record_response" } ] @@ -198,7 +198,7 @@ "list": false, "show": true, "multiline": true, - "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\nfrom langflow.template import Input, Output\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n inputs = [\n Input(\n name=\"input_value\", type=str, display_name=\"Message\", multiline=True, info=\"Message to be passed as output.\"\n ),\n Input(\n name=\"sender\",\n type=str,\n display_name=\"Sender Type\",\n options=[\"Machine\", \"User\"],\n value=\"Machine\",\n advanced=True,\n info=\"Type of sender.\",\n ),\n Input(name=\"sender_name\", type=str, display_name=\"Sender Name\", info=\"Name of the sender.\", value=\"AI\"),\n Input(\n name=\"session_id\", type=str, display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True\n ),\n Input(\n name=\"record_template\",\n type=str,\n display_name=\"Record Template\",\n value=\"{text}\",\n advanced=True,\n info=\"Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.\",\n ),\n ]\n outputs = [\n Output(name=\"Message\", method=\"text_response\"),\n Output(name=\"Record\", method=\"record_response\"),\n ]\n\n def text_response(self) -> Text:\n result = self.input_value\n if self.session_id and isinstance(result, (Record, str)):\n self.store_message(result, self.session_id, self.sender, self.sender_name)\n return result\n\n def record_response(self) -> Record:\n record = Record(\n data={\n \"message\": self.input_value,\n \"sender\": self.sender,\n \"sender_name\": self.sender_name,\n \"session_id\": self.session_id,\n \"template\": self.record_template or \"\",\n }\n )\n if self.session_id and isinstance(record, (Record, str)):\n self.store_message(record, self.session_id, self.sender, self.sender_name)\n return record\n", + "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\nfrom langflow.template import Input, Output\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n inputs = [\n Input(\n name=\"input_value\", type=str, display_name=\"Message\", multiline=True, info=\"Message to be passed as output.\"\n ),\n Input(\n name=\"sender\",\n type=str,\n display_name=\"Sender Type\",\n options=[\"Machine\", \"User\"],\n value=\"Machine\",\n advanced=True,\n info=\"Type of sender.\",\n ),\n Input(name=\"sender_name\", type=str, display_name=\"Sender Name\", info=\"Name of the sender.\", value=\"AI\"),\n Input(\n name=\"session_id\", type=str, display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True\n ),\n Input(\n name=\"record_template\",\n type=str,\n display_name=\"Record Template\",\n value=\"{text}\",\n advanced=True,\n info=\"Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.\",\n ),\n ]\n outputs = [\n Output(display_name=\"Message\", name=\"message\", method=\"text_response\"),\n Output(display_name=\"Record\", name=\"record\", method=\"record_response\"),\n ]\n\n def text_response(self) -> Text:\n result = self.input_value\n if self.session_id and isinstance(result, (Record, str)):\n self.store_message(result, self.session_id, self.sender, self.sender_name)\n return result\n\n def record_response(self) -> Record:\n record = Record(\n data={\n \"message\": self.input_value,\n \"sender\": self.sender,\n \"sender_name\": self.sender_name,\n \"session_id\": self.session_id,\n \"template\": self.record_template or \"\",\n }\n )\n if self.session_id and isinstance(record, (Record, str)):\n self.store_message(record, self.session_id, self.sender, self.sender_name)\n return record\n", "fileTypes": [], "file_path": "", "password": false, @@ -356,7 +356,7 @@ "Text" ], "selected": "Text", - "name": "Message", + "name": "message", "method": "text_response" }, { @@ -364,7 +364,7 @@ "Record" ], "selected": "Record", - "name": "Record", + "name": "record", "method": "record_response" } ] @@ -578,6 +578,7 @@ "Text" ], "selected": null, + "display_name": null, "name": "Text", "method": null } @@ -735,6 +736,7 @@ "Text" ], "selected": null, + "display_name": null, "name": "Text", "method": null } @@ -1023,6 +1025,7 @@ "Text" ], "selected": null, + "display_name": null, "name": "Text", "method": null } @@ -1184,7 +1187,7 @@ }, { "source": "ChatInput-t7F8v", - "sourceHandle": "{\"dataType\": \"ChatInput\", \"id\": \"ChatInput-t7F8v\", \"output_types\": [\"Text\"], \"name\": \"Message\"}", + "sourceHandle": "{\"dataType\": \"ChatInput\", \"id\": \"ChatInput-t7F8v\", \"output_types\": [\"Text\"], \"name\": \"message\"}", "target": "Prompt-ODkUx", "targetHandle": "{œfieldNameœ:œuser_messageœ,œidœ:œPrompt-ODkUxœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}", "data": { @@ -1205,7 +1208,7 @@ "output_types": [ "Text" ], - "name": "Message" + "name": "message" } }, "style": { diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Prompt Chaining.json b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Prompt Chaining.json index 53d770806..fdb53e2bf 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Prompt Chaining.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Prompt Chaining.json @@ -114,6 +114,7 @@ "Text" ], "selected": null, + "display_name": null, "name": "Text", "method": null } @@ -244,6 +245,7 @@ "Text" ], "selected": null, + "display_name": null, "name": "Text", "method": null } @@ -276,7 +278,7 @@ "list": false, "show": true, "multiline": true, - "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\nfrom langflow.template import Input, Output\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n inputs = [\n Input(\n name=\"input_value\", type=str, display_name=\"Message\", multiline=True, info=\"Message to be passed as output.\"\n ),\n Input(\n name=\"sender\",\n type=str,\n display_name=\"Sender Type\",\n options=[\"Machine\", \"User\"],\n value=\"Machine\",\n advanced=True,\n info=\"Type of sender.\",\n ),\n Input(name=\"sender_name\", type=str, display_name=\"Sender Name\", info=\"Name of the sender.\", value=\"AI\"),\n Input(\n name=\"session_id\", type=str, display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True\n ),\n Input(\n name=\"record_template\",\n type=str,\n display_name=\"Record Template\",\n value=\"{text}\",\n advanced=True,\n info=\"Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.\",\n ),\n ]\n outputs = [\n Output(name=\"Message\", method=\"text_response\"),\n Output(name=\"Record\", method=\"record_response\"),\n ]\n\n def text_response(self) -> Text:\n result = self.input_value\n if self.session_id and isinstance(result, (Record, str)):\n self.store_message(result, self.session_id, self.sender, self.sender_name)\n return result\n\n def record_response(self) -> Record:\n record = Record(\n data={\n \"message\": self.input_value,\n \"sender\": self.sender,\n \"sender_name\": self.sender_name,\n \"session_id\": self.session_id,\n \"template\": self.record_template or \"\",\n }\n )\n if self.session_id and isinstance(record, (Record, str)):\n self.store_message(record, self.session_id, self.sender, self.sender_name)\n return record\n", + "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\nfrom langflow.template import Input, Output\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n inputs = [\n Input(\n name=\"input_value\", type=str, display_name=\"Message\", multiline=True, info=\"Message to be passed as output.\"\n ),\n Input(\n name=\"sender\",\n type=str,\n display_name=\"Sender Type\",\n options=[\"Machine\", \"User\"],\n value=\"Machine\",\n advanced=True,\n info=\"Type of sender.\",\n ),\n Input(name=\"sender_name\", type=str, display_name=\"Sender Name\", info=\"Name of the sender.\", value=\"AI\"),\n Input(\n name=\"session_id\", type=str, display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True\n ),\n Input(\n name=\"record_template\",\n type=str,\n display_name=\"Record Template\",\n value=\"{text}\",\n advanced=True,\n info=\"Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.\",\n ),\n ]\n outputs = [\n Output(display_name=\"Message\", name=\"message\", method=\"text_response\"),\n Output(display_name=\"Record\", name=\"record\", method=\"record_response\"),\n ]\n\n def text_response(self) -> Text:\n result = self.input_value\n if self.session_id and isinstance(result, (Record, str)):\n self.store_message(result, self.session_id, self.sender, self.sender_name)\n return result\n\n def record_response(self) -> Record:\n record = Record(\n data={\n \"message\": self.input_value,\n \"sender\": self.sender,\n \"sender_name\": self.sender_name,\n \"session_id\": self.session_id,\n \"template\": self.record_template or \"\",\n }\n )\n if self.session_id and isinstance(record, (Record, str)):\n self.store_message(record, self.session_id, self.sender, self.sender_name)\n return record\n", "fileTypes": [], "file_path": "", "password": false, @@ -435,7 +437,7 @@ "Text" ], "selected": "Text", - "name": "Message", + "name": "message", "method": "text_response" }, { @@ -443,7 +445,7 @@ "Record" ], "selected": "Record", - "name": "Record", + "name": "record", "method": "record_response" } ] @@ -473,7 +475,7 @@ "list": false, "show": true, "multiline": true, - "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\nfrom langflow.template import Input, Output\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n inputs = [\n Input(\n name=\"input_value\", type=str, display_name=\"Message\", multiline=True, info=\"Message to be passed as output.\"\n ),\n Input(\n name=\"sender\",\n type=str,\n display_name=\"Sender Type\",\n options=[\"Machine\", \"User\"],\n value=\"Machine\",\n advanced=True,\n info=\"Type of sender.\",\n ),\n Input(name=\"sender_name\", type=str, display_name=\"Sender Name\", info=\"Name of the sender.\", value=\"AI\"),\n Input(\n name=\"session_id\", type=str, display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True\n ),\n Input(\n name=\"record_template\",\n type=str,\n display_name=\"Record Template\",\n value=\"{text}\",\n advanced=True,\n info=\"Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.\",\n ),\n ]\n outputs = [\n Output(name=\"Message\", method=\"text_response\"),\n Output(name=\"Record\", method=\"record_response\"),\n ]\n\n def text_response(self) -> Text:\n result = self.input_value\n if self.session_id and isinstance(result, (Record, str)):\n self.store_message(result, self.session_id, self.sender, self.sender_name)\n return result\n\n def record_response(self) -> Record:\n record = Record(\n data={\n \"message\": self.input_value,\n \"sender\": self.sender,\n \"sender_name\": self.sender_name,\n \"session_id\": self.session_id,\n \"template\": self.record_template or \"\",\n }\n )\n if self.session_id and isinstance(record, (Record, str)):\n self.store_message(record, self.session_id, self.sender, self.sender_name)\n return record\n", + "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\nfrom langflow.template import Input, Output\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n inputs = [\n Input(\n name=\"input_value\", type=str, display_name=\"Message\", multiline=True, info=\"Message to be passed as output.\"\n ),\n Input(\n name=\"sender\",\n type=str,\n display_name=\"Sender Type\",\n options=[\"Machine\", \"User\"],\n value=\"Machine\",\n advanced=True,\n info=\"Type of sender.\",\n ),\n Input(name=\"sender_name\", type=str, display_name=\"Sender Name\", info=\"Name of the sender.\", value=\"AI\"),\n Input(\n name=\"session_id\", type=str, display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True\n ),\n Input(\n name=\"record_template\",\n type=str,\n display_name=\"Record Template\",\n value=\"{text}\",\n advanced=True,\n info=\"Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.\",\n ),\n ]\n outputs = [\n Output(display_name=\"Message\", name=\"message\", method=\"text_response\"),\n Output(display_name=\"Record\", name=\"record\", method=\"record_response\"),\n ]\n\n def text_response(self) -> Text:\n result = self.input_value\n if self.session_id and isinstance(result, (Record, str)):\n self.store_message(result, self.session_id, self.sender, self.sender_name)\n return result\n\n def record_response(self) -> Record:\n record = Record(\n data={\n \"message\": self.input_value,\n \"sender\": self.sender,\n \"sender_name\": self.sender_name,\n \"session_id\": self.session_id,\n \"template\": self.record_template or \"\",\n }\n )\n if self.session_id and isinstance(record, (Record, str)):\n self.store_message(record, self.session_id, self.sender, self.sender_name)\n return record\n", "fileTypes": [], "file_path": "", "password": false, @@ -632,7 +634,7 @@ "Text" ], "selected": "Text", - "name": "Message", + "name": "message", "method": "text_response" }, { @@ -640,7 +642,7 @@ "Record" ], "selected": "Record", - "name": "Record", + "name": "record", "method": "record_response" } ] @@ -669,7 +671,7 @@ "list": false, "show": true, "multiline": true, - "value": "from langflow.base.io.text import TextComponent\nfrom langflow.field_typing import Text\nfrom langflow.template import Input, Output\n\n\nclass TextInput(TextComponent):\n display_name = \"Text Input\"\n description = \"Get text inputs from the Playground.\"\n icon = \"type\"\n\n inputs = [\n Input(\n name=\"input_value\",\n type=str,\n display_name=\"Value\",\n info=\"Text or Record to be passed as input.\",\n input_types=[\"Record\", \"Text\"],\n ),\n Input(\n name=\"record_template\",\n type=str,\n display_name=\"Record Template\",\n multiline=True,\n info=\"Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.\",\n advanced=True,\n ),\n ]\n outputs = [\n Output(name=\"Text\", method=\"text_response\"),\n ]\n\n def text_response(self) -> Text:\n return self.build(input_value=self.input_value, record_template=self.record_template)\n", + "value": "from langflow.base.io.text import TextComponent\nfrom langflow.field_typing import Text\nfrom langflow.template import Input, Output\n\n\nclass TextInput(TextComponent):\n display_name = \"Text Input\"\n description = \"Get text inputs from the Playground.\"\n icon = \"type\"\n\n inputs = [\n Input(\n name=\"input_value\",\n type=str,\n display_name=\"Value\",\n info=\"Text or Record to be passed as input.\",\n input_types=[\"Record\", \"Text\"],\n ),\n Input(\n name=\"record_template\",\n type=str,\n display_name=\"Record Template\",\n multiline=True,\n info=\"Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.\",\n advanced=True,\n ),\n ]\n outputs = [\n Output(display_name=\"Text\", name=\"text\", method=\"text_response\"),\n ]\n\n def text_response(self) -> Text:\n return self.build(input_value=self.input_value, record_template=self.record_template)\n", "fileTypes": [], "file_path": "", "password": false, @@ -753,7 +755,7 @@ "Text" ], "selected": "Text", - "name": "Text", + "name": "text", "method": "text_response" } ] @@ -1147,6 +1149,7 @@ "Text" ], "selected": null, + "display_name": null, "name": "Text", "method": null } @@ -1541,6 +1544,7 @@ "Text" ], "selected": null, + "display_name": null, "name": "Text", "method": null } @@ -1561,7 +1565,7 @@ "edges": [ { "source": "TextInput-sptaH", - "sourceHandle": "{\"dataType\": \"TextInput\", \"id\": \"TextInput-sptaH\", \"output_types\": [\"Text\"], \"name\": \"Text\"}", + "sourceHandle": "{\"dataType\": \"TextInput\", \"id\": \"TextInput-sptaH\", \"output_types\": [\"Text\"], \"name\": \"text\"}", "target": "Prompt-amqBu", "targetHandle": "{œfieldNameœ:œdocumentœ,œidœ:œPrompt-amqBuœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}", "data": { @@ -1582,7 +1586,7 @@ "output_types": [ "Text" ], - "name": "Text" + "name": "text" } }, "style": { diff --git a/src/backend/base/langflow/initial_setup/starter_projects/VectorStore-RAG-Flows.json b/src/backend/base/langflow/initial_setup/starter_projects/VectorStore-RAG-Flows.json index dd85f47fd..baec89cae 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/VectorStore-RAG-Flows.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/VectorStore-RAG-Flows.json @@ -20,7 +20,7 @@ "list": false, "show": true, "multiline": true, - "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\nfrom langflow.template import Input, Output\n\n\nclass ChatInput(ChatComponent):\n display_name = \"Chat Input\"\n description = \"Get chat inputs from the Playground.\"\n icon = \"ChatInput\"\n\n inputs = [\n Input(\n name=\"input_value\",\n type=str,\n display_name=\"Message\",\n multiline=True,\n input_types=[],\n info=\"Message to be passed as input.\",\n ),\n Input(\n name=\"sender\",\n type=str,\n display_name=\"Sender Type\",\n options=[\"Machine\", \"User\"],\n value=\"User\",\n info=\"Type of sender.\",\n advanced=True,\n ),\n Input(name=\"sender_name\", type=str, display_name=\"Sender Name\", info=\"Name of the sender.\", value=\"User\"),\n Input(\n name=\"session_id\", type=str, display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True\n ),\n ]\n outputs = [\n Output(name=\"Message\", method=\"text_response\"),\n Output(name=\"Record\", method=\"record_response\"),\n ]\n\n def text_response(self) -> Text:\n result = self.input_value\n if self.session_id and isinstance(result, (Record, str)):\n self.store_message(result, self.session_id, self.sender, self.sender_name)\n return result\n\n def record_response(self) -> Record:\n record = Record(\n data={\n \"text\": self.input_value,\n \"sender\": self.sender,\n \"sender_name\": self.sender_name,\n \"session_id\": self.session_id,\n },\n )\n if self.session_id and isinstance(record, (Record, str)):\n self.store_message(record, self.session_id, self.sender, self.sender_name)\n return record\n", + "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\nfrom langflow.template import Input, Output\n\n\nclass ChatInput(ChatComponent):\n display_name = \"Chat Input\"\n description = \"Get chat inputs from the Playground.\"\n icon = \"ChatInput\"\n\n inputs = [\n Input(\n name=\"input_value\",\n type=str,\n display_name=\"Message\",\n multiline=True,\n input_types=[],\n info=\"Message to be passed as input.\",\n ),\n Input(\n name=\"sender\",\n type=str,\n display_name=\"Sender Type\",\n options=[\"Machine\", \"User\"],\n value=\"User\",\n info=\"Type of sender.\",\n advanced=True,\n ),\n Input(name=\"sender_name\", type=str, display_name=\"Sender Name\", info=\"Name of the sender.\", value=\"User\"),\n Input(\n name=\"session_id\", type=str, display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True\n ),\n ]\n outputs = [\n Output(display_name=\"Message\", name=\"message\", method=\"text_response\"),\n Output(display_name=\"Record\", name=\"record\", method=\"record_response\"),\n ]\n\n def text_response(self) -> Text:\n result = self.input_value\n if self.session_id and isinstance(result, (Record, str)):\n self.store_message(result, self.session_id, self.sender, self.sender_name)\n return result\n\n def record_response(self) -> Record:\n record = Record(\n data={\n \"text\": self.input_value,\n \"sender\": self.sender,\n \"sender_name\": self.sender_name,\n \"session_id\": self.session_id,\n },\n )\n if self.session_id and isinstance(record, (Record, str)):\n self.store_message(record, self.session_id, self.sender, self.sender_name)\n return record\n", "fileTypes": [], "file_path": "", "password": false, @@ -154,7 +154,7 @@ "Text" ], "selected": "Text", - "name": "Message", + "name": "message", "method": "text_response" }, { @@ -162,7 +162,7 @@ "Record" ], "selected": "Record", - "name": "Record", + "name": "record", "method": "record_response" } ] @@ -819,6 +819,7 @@ "Embeddings" ], "selected": null, + "display_name": null, "name": "Embeddings", "method": null } @@ -1101,6 +1102,7 @@ "Text" ], "selected": null, + "display_name": null, "name": "Text", "method": null } @@ -1256,6 +1258,7 @@ "Text" ], "selected": null, + "display_name": null, "name": "Text", "method": null } @@ -1292,7 +1295,7 @@ "list": false, "show": true, "multiline": true, - "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\nfrom langflow.template import Input, Output\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n inputs = [\n Input(\n name=\"input_value\", type=str, display_name=\"Message\", multiline=True, info=\"Message to be passed as output.\"\n ),\n Input(\n name=\"sender\",\n type=str,\n display_name=\"Sender Type\",\n options=[\"Machine\", \"User\"],\n value=\"Machine\",\n advanced=True,\n info=\"Type of sender.\",\n ),\n Input(name=\"sender_name\", type=str, display_name=\"Sender Name\", info=\"Name of the sender.\", value=\"AI\"),\n Input(\n name=\"session_id\", type=str, display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True\n ),\n Input(\n name=\"record_template\",\n type=str,\n display_name=\"Record Template\",\n value=\"{text}\",\n advanced=True,\n info=\"Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.\",\n ),\n ]\n outputs = [\n Output(name=\"Message\", method=\"text_response\"),\n Output(name=\"Record\", method=\"record_response\"),\n ]\n\n def text_response(self) -> Text:\n result = self.input_value\n if self.session_id and isinstance(result, (Record, str)):\n self.store_message(result, self.session_id, self.sender, self.sender_name)\n return result\n\n def record_response(self) -> Record:\n record = Record(\n data={\n \"message\": self.input_value,\n \"sender\": self.sender,\n \"sender_name\": self.sender_name,\n \"session_id\": self.session_id,\n \"template\": self.record_template or \"\",\n }\n )\n if self.session_id and isinstance(record, (Record, str)):\n self.store_message(record, self.session_id, self.sender, self.sender_name)\n return record\n", + "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\nfrom langflow.template import Input, Output\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n inputs = [\n Input(\n name=\"input_value\", type=str, display_name=\"Message\", multiline=True, info=\"Message to be passed as output.\"\n ),\n Input(\n name=\"sender\",\n type=str,\n display_name=\"Sender Type\",\n options=[\"Machine\", \"User\"],\n value=\"Machine\",\n advanced=True,\n info=\"Type of sender.\",\n ),\n Input(name=\"sender_name\", type=str, display_name=\"Sender Name\", info=\"Name of the sender.\", value=\"AI\"),\n Input(\n name=\"session_id\", type=str, display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True\n ),\n Input(\n name=\"record_template\",\n type=str,\n display_name=\"Record Template\",\n value=\"{text}\",\n advanced=True,\n info=\"Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.\",\n ),\n ]\n outputs = [\n Output(display_name=\"Message\", name=\"message\", method=\"text_response\"),\n Output(display_name=\"Record\", name=\"record\", method=\"record_response\"),\n ]\n\n def text_response(self) -> Text:\n result = self.input_value\n if self.session_id and isinstance(result, (Record, str)):\n self.store_message(result, self.session_id, self.sender, self.sender_name)\n return result\n\n def record_response(self) -> Record:\n record = Record(\n data={\n \"message\": self.input_value,\n \"sender\": self.sender,\n \"sender_name\": self.sender_name,\n \"session_id\": self.session_id,\n \"template\": self.record_template or \"\",\n }\n )\n if self.session_id and isinstance(record, (Record, str)):\n self.store_message(record, self.session_id, self.sender, self.sender_name)\n return record\n", "fileTypes": [], "file_path": "", "password": false, @@ -1451,7 +1454,7 @@ "Text" ], "selected": "Text", - "name": "Message", + "name": "message", "method": "text_response" }, { @@ -1459,7 +1462,7 @@ "Record" ], "selected": "Record", - "name": "Record", + "name": "record", "method": "record_response" } ] @@ -1587,6 +1590,7 @@ "Record" ], "selected": null, + "display_name": null, "name": "Record", "method": null } @@ -1743,6 +1747,7 @@ "Record" ], "selected": null, + "display_name": null, "name": "Record", "method": null } @@ -2206,6 +2211,7 @@ "Record" ], "selected": null, + "display_name": null, "name": "Record", "method": null } @@ -2619,6 +2625,7 @@ "VectorStore" ], "selected": null, + "display_name": null, "name": "VectorStore", "method": null }, @@ -2627,6 +2634,7 @@ "BaseRetriever" ], "selected": null, + "display_name": null, "name": "BaseRetriever", "method": null } @@ -3172,6 +3180,7 @@ "Embeddings" ], "selected": null, + "display_name": null, "name": "Embeddings", "method": null } @@ -3226,7 +3235,7 @@ { "source": "ChatInput-yxMKE", "target": "Prompt-xeI6K", - "sourceHandle": "{\"dataType\": \"ChatInput\", \"id\": \"ChatInput-yxMKE\", \"output_types\": [\"Text\"], \"name\": \"Message\"}", + "sourceHandle": "{\"dataType\": \"ChatInput\", \"id\": \"ChatInput-yxMKE\", \"output_types\": [\"Text\"], \"name\": \"message\"}", "targetHandle": "{œfieldNameœ:œquestionœ,œidœ:œPrompt-xeI6Kœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}", "id": "reactflow__edge-ChatInput-yxMKE{œbaseClassesœ:[œTextœ,œstrœ,œobjectœ,œRecordœ],œdataTypeœ:œChatInputœ,œidœ:œChatInput-yxMKEœ}-Prompt-xeI6K{œfieldNameœ:œquestionœ,œidœ:œPrompt-xeI6Kœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}", "data": { @@ -3247,7 +3256,7 @@ "output_types": [ "Text" ], - "name": "Message" + "name": "message" } }, "style": { @@ -3376,7 +3385,7 @@ }, { "source": "ChatInput-yxMKE", - "sourceHandle": "{\"dataType\": \"ChatInput\", \"id\": \"ChatInput-yxMKE\", \"output_types\": [\"Text\"], \"name\": \"Message\"}", + "sourceHandle": "{\"dataType\": \"ChatInput\", \"id\": \"ChatInput-yxMKE\", \"output_types\": [\"Text\"], \"name\": \"message\"}", "target": "AstraDBSearch-41nRz", "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œAstraDBSearch-41nRzœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}", "data": { @@ -3394,7 +3403,7 @@ "output_types": [ "Text" ], - "name": "Message" + "name": "message" } }, "style": { From 8ac7b4b27a82f641f6371e72442250fddfc40b75 Mon Sep 17 00:00:00 2001 From: anovazzi1 Date: Thu, 6 Jun 2024 15:23:58 -0300 Subject: [PATCH 066/701] update group to work with new outputs --- .../components/PageComponent/index.tsx | 24 +-- src/frontend/src/types/api/index.ts | 4 +- src/frontend/src/utils/reactflowUtils.ts | 162 +++++++++++------- 3 files changed, 112 insertions(+), 78 deletions(-) diff --git a/src/frontend/src/pages/FlowPage/components/PageComponent/index.tsx b/src/frontend/src/pages/FlowPage/components/PageComponent/index.tsx index c902ec2d5..73e02b92b 100644 --- a/src/frontend/src/pages/FlowPage/components/PageComponent/index.tsx +++ b/src/frontend/src/pages/FlowPage/components/PageComponent/index.tsx @@ -110,7 +110,7 @@ export default function Page({ getRandomName(), ); const newGroupNode = generateNodeFromFlow(newFlow, getNodeId); - const newEdges = reconnectEdges(newGroupNode, removedEdges); + // const newEdges = reconnectEdges(newGroupNode, removedEdges); setNodes([ ...clonedNodes.filter( (oldNodes) => @@ -120,17 +120,17 @@ export default function Page({ ), newGroupNode, ]); - setEdges([ - ...clonedEdges.filter( - (oldEdge) => - !clonedSelection!.nodes.some( - (selectionNode) => - selectionNode.id === oldEdge.target || - selectionNode.id === oldEdge.source, - ), - ), - ...newEdges, - ]); + // setEdges([ + // ...clonedEdges.filter( + // (oldEdge) => + // !clonedSelection!.nodes.some( + // (selectionNode) => + // selectionNode.id === oldEdge.target || + // selectionNode.id === oldEdge.source, + // ), + // ), + // ...newEdges, + // ]); } else { setErrorData({ title: INVALID_SELECTION_ERROR_ALERT, diff --git a/src/frontend/src/types/api/index.ts b/src/frontend/src/types/api/index.ts index 273da960d..bb36564f8 100644 --- a/src/frontend/src/types/api/index.ts +++ b/src/frontend/src/types/api/index.ts @@ -13,7 +13,7 @@ export type CustomFieldsType = { }; export type APIClassType = { - base_classes: Array; + base_classes?: Array; description: string; template: APITemplateType; display_name: string; @@ -67,6 +67,8 @@ export type OutputFieldType = { types: Array; selected?: string; name: string; + displayName?: string; + proxy?: { id: string; name: string }; }; export type sendAllProps = { nodes: Node[]; diff --git a/src/frontend/src/utils/reactflowUtils.ts b/src/frontend/src/utils/reactflowUtils.ts index c68374479..baf737a6b 100644 --- a/src/frontend/src/utils/reactflowUtils.ts +++ b/src/frontend/src/utils/reactflowUtils.ts @@ -24,6 +24,7 @@ import { APIObjectType, APITemplateType, InputFieldType, + OutputFieldType, } from "../types/api"; import { FlowType, @@ -78,7 +79,7 @@ export function cleanEdges(nodes: NodeType[], edges: Edge[]) { const parsedSourceHandle = scapeJSONParse(sourceHandle); const name = parsedSourceHandle.name; const output = sourceNode.data.node!.outputs?.find( - (output) => output.name === name + (output) => output.name === name, ); if (output) { const outputTypes = @@ -112,18 +113,18 @@ export function unselectAllNodes({ updateNodes, data }: unselectAllNodesType) { export function isValidConnection( { source, target, sourceHandle, targetHandle }: Connection, nodes: Node[], - edges: Edge[] + edges: Edge[], ) { const targetHandleObject: targetHandleType = scapeJSONParse(targetHandle!); const sourceHandleObject: sourceHandleType = scapeJSONParse(sourceHandle!); if ( targetHandleObject.inputTypes?.some( - (n) => n === sourceHandleObject.dataType + (n) => n === sourceHandleObject.dataType, ) || sourceHandleObject.output_types.some( (t) => targetHandleObject.inputTypes?.some((n) => n === t) || - t === targetHandleObject.type + t === targetHandleObject.type, ) ) { let targetNode = nodes.find((node) => node.id === target!)?.data?.node; @@ -156,7 +157,7 @@ export function removeApiKeys(flow: FlowType): FlowType { export function updateTemplate( reference: APITemplateType, - objectToUpdate: APITemplateType + objectToUpdate: APITemplateType, ): APITemplateType { let clonedObject: APITemplateType = cloneDeep(reference); @@ -216,7 +217,7 @@ export const processDataFromFlow = (flow: FlowType, refreshIds = true) => { export function updateIds( { edges, nodes }: { edges: Edge[]; nodes: Node[] }, - selection?: { edges: Edge[]; nodes: Node[] } + selection?: { edges: Edge[]; nodes: Node[] }, ) { let idsMap = {}; const selectionIds = selection?.nodes.map((n) => n.id); @@ -244,7 +245,7 @@ export function updateIds( edge.source = idsMap[edge.source]; edge.target = idsMap[edge.target]; const sourceHandleObject: sourceHandleType = scapeJSONParse( - edge.sourceHandle! + edge.sourceHandle!, ); edge.sourceHandle = scapedJSONStringfy({ ...sourceHandleObject, @@ -254,7 +255,7 @@ export function updateIds( edge.data.sourceHandle.id = edge.source; } const targetHandleObject: targetHandleType = scapeJSONParse( - edge.targetHandle! + edge.targetHandle!, ); edge.targetHandle = scapedJSONStringfy({ ...targetHandleObject, @@ -300,11 +301,11 @@ export function validateNode(node: NodeType, edges: Edge[]): Array { (scapeJSONParse(edge.targetHandle!) as targetHandleType).fieldName === t && (scapeJSONParse(edge.targetHandle!) as targetHandleType).id === - node.id + node.id, ) ) { errors.push( - `${displayName || type} is missing ${getFieldTitle(template, t)}.` + `${displayName || type} is missing ${getFieldTitle(template, t)}.`, ); } else if ( template[t].type === "dict" && @@ -318,15 +319,15 @@ export function validateNode(node: NodeType, edges: Edge[]): Array { errors.push( `${displayName || type} (${getFieldTitle( template, - t - )}) contains duplicate keys with the same values.` + t, + )}) contains duplicate keys with the same values.`, ); if (hasEmptyKey(template[t].value)) errors.push( `${displayName || type} (${getFieldTitle( template, - t - )}) field must not be empty.` + t, + )}) field must not be empty.`, ); } return errors; @@ -335,7 +336,7 @@ export function validateNode(node: NodeType, edges: Edge[]): Array { export function validateNodes( nodes: Node[], - edges: Edge[] + edges: Edge[], ): // this returns an array of tuples with the node id and the errors Array<{ id: string; errors: Array }> { if (nodes.length === 0) { @@ -356,7 +357,7 @@ export function updateEdges(edges: Edge[]) { if (edges) edges.forEach((edge) => { const targetHandleObject: targetHandleType = scapeJSONParse( - edge.targetHandle! + edge.targetHandle!, ); edge.className = "stroke-gray-900 stroke-connection"; }); @@ -406,7 +407,7 @@ export function updateEdgesHandleIds({ if (source && sourceNode) { const output_types = sourceNode.data.node!.output_types ?? - sourceNode.data.node!.base_classes; + sourceNode.data.node!.base_classes!; newSource = { id: sourceNode.data.id, output_types, @@ -456,11 +457,11 @@ export function updateNewOutput({ nodes, edges }: updateEdgesHandleIdsType) { if (newTargetHandle.inputTypes && newTargetHandle.inputTypes.length > 0) { //conjuction subtraction intersection = newSourceHandle.output_types.filter((type) => - newTargetHandle.inputTypes!.includes(type) + newTargetHandle.inputTypes!.includes(type), ); } else { intersection = newSourceHandle.output_types.filter( - (type) => type === newTargetHandle.type + (type) => type === newTargetHandle.type, ); } const selected = intersection[0]; @@ -472,10 +473,10 @@ export function updateNewOutput({ nodes, edges }: updateEdgesHandleIdsType) { } const types = sourceNode.data.node!.output_types ?? - sourceNode.data.node!.base_classes; + sourceNode.data.node!.base_classes!; if ( !sourceNode.data.node!.outputs.some( - (output) => output.selected === selected + (output) => output.selected === selected, ) ) { sourceNode.data.node!.outputs.push({ @@ -498,7 +499,7 @@ export function handleKeyDown( | React.KeyboardEvent | React.KeyboardEvent, inputValue: string | string[] | null, - block: string + block: string, ) { //condition to fix bug control+backspace on Windows/Linux if ( @@ -523,7 +524,7 @@ export function handleKeyDown( } export function handleOnlyIntegerInput( - event: React.KeyboardEvent + event: React.KeyboardEvent, ) { if ( event.key === "." || @@ -539,7 +540,7 @@ export function handleOnlyIntegerInput( export function getConnectedNodes( edge: Edge, - nodes: Array + nodes: Array, ): Array { const sourceId = edge.source; const targetId = edge.target; @@ -640,7 +641,7 @@ export function checkOldEdgesHandles(edges: Edge[]): boolean { !edge.sourceHandle || !edge.targetHandle || !edge.sourceHandle.includes("{") || - !edge.targetHandle.includes("{") + !edge.targetHandle.includes("{"), ); } @@ -667,7 +668,7 @@ export function customStringify(obj: any): string { const keys = Object.keys(obj).sort(); const keyValuePairs = keys.map( - (key) => `"${key}":${customStringify(obj[key])}` + (key) => `"${key}":${customStringify(obj[key])}`, ); return `{${keyValuePairs.join(",")}}`; } @@ -696,7 +697,7 @@ export function getHandleId( source: string, sourceHandle: string, target: string, - targetHandle: string + targetHandle: string, ) { return ( "reactflow__edge-" + source + sourceHandle + "-" + target + targetHandle @@ -707,16 +708,16 @@ export function generateFlow( selection: OnSelectionChangeParams, nodes: Node[], edges: Edge[], - name: string + name: string, ): generateFlowType { const newFlowData = { nodes, edges, viewport: { zoom: 1, x: 0, y: 0 } }; const uid = new ShortUniqueId({ length: 5 }); /* remove edges that are not connected to selected nodes on both ends */ - newFlowData.edges = selection.edges.filter( + newFlowData.edges = edges.filter( (edge) => selection.nodes.some((node) => node.id === edge.target) && - selection.nodes.some((node) => node.id === edge.source) + selection.nodes.some((node) => node.id === edge.source), ); newFlowData.nodes = selection.nodes; @@ -737,7 +738,7 @@ export function generateFlow( (edge) => (selection.nodes.some((node) => node.id === edge.target) || selection.nodes.some((node) => node.id === edge.source)) && - newFlowData.edges.every((e) => e.id !== edge.id) + newFlowData.edges.every((e) => e.id !== edge.id), ), }; } @@ -748,13 +749,13 @@ export function reconnectEdges(groupNode: NodeType, excludedEdges: Edge[]) { const { nodes, edges } = groupNode.data.node!.flow!.data!; const lastNode = findLastNode(groupNode.data.node!.flow!.data!); newEdges = newEdges.filter( - (e) => !(nodes.some((n) => n.id === e.source) && e.source !== lastNode?.id) + (e) => !(nodes.some((n) => n.id === e.source) && e.source !== lastNode?.id), ); newEdges.forEach((edge) => { if (lastNode && edge.source === lastNode.id) { edge.source = groupNode.id; let newSourceHandle: sourceHandleType = scapeJSONParse( - edge.sourceHandle! + edge.sourceHandle!, ); newSourceHandle.id = groupNode.id; edge.sourceHandle = scapedJSONStringfy(newSourceHandle); @@ -781,7 +782,7 @@ export function reconnectEdges(groupNode: NodeType, excludedEdges: Edge[]) { export function filterFlow( selection: OnSelectionChangeParams, setNodes: (update: Node[] | ((oldState: Node[]) => Node[])) => void, - setEdges: (update: Edge[] | ((oldState: Edge[]) => Edge[])) => void + setEdges: (update: Edge[] | ((oldState: Edge[]) => Edge[])) => void, ) { setNodes((nodes) => nodes.filter((node) => !selection.nodes.includes(node))); setEdges((edges) => edges.filter((edge) => !selection.edges.includes(edge))); @@ -819,7 +820,7 @@ export function updateFlowPosition(NewPosition: XYPosition, flow: FlowType) { export function concatFlows( flow: FlowType, setNodes: (update: Node[] | ((oldState: Node[]) => Node[])) => void, - setEdges: (update: Edge[] | ((oldState: Edge[]) => Edge[])) => void + setEdges: (update: Edge[] | ((oldState: Edge[]) => Edge[])) => void, ) { const { nodes, edges } = flow.data!; setNodes((old) => [...old, ...nodes]); @@ -828,7 +829,7 @@ export function concatFlows( export function validateSelection( selection: OnSelectionChangeParams, - edges: Edge[] + edges: Edge[], ): Array { const clonedSelection = cloneDeep(selection); const clonedEdges = cloneDeep(edges); @@ -842,7 +843,7 @@ export function validateSelection( let nodesSet = new Set(clonedSelection.nodes.map((n) => n.id)); // then filter the edges that are connected to the nodes in the set let connectedEdges = clonedSelection.edges.filter( - (e) => nodesSet.has(e.source) && nodesSet.has(e.target) + (e) => nodesSet.has(e.source) && nodesSet.has(e.target), ); // add the edges to the selection clonedSelection.edges = connectedEdges; @@ -856,17 +857,17 @@ export function validateSelection( clonedSelection.nodes.some( (node) => isInputNode(node.data as NodeDataType) || - isOutputNode(node.data as NodeDataType) + isOutputNode(node.data as NodeDataType), ) ) { errorsArray.push( - "Please select only nodes that are not input or output nodes" + "Please select only nodes that are not input or output nodes", ); } //check if there are two or more nodes with free outputs if ( clonedSelection.nodes.filter( - (n) => !clonedSelection.edges.some((e) => e.source === n.id) + (n) => !clonedSelection.edges.some((e) => e.source === n.id), ).length > 1 ) { errorsArray.push("Please select only one node with free outputs"); @@ -877,7 +878,7 @@ export function validateSelection( clonedSelection.nodes.some( (node) => !clonedSelection.edges.some((edge) => edge.target === node.id) && - !clonedSelection.edges.some((edge) => edge.source === node.id) + !clonedSelection.edges.some((edge) => edge.source === node.id), ) ) { errorsArray.push("Please select only nodes that are connected"); @@ -922,7 +923,7 @@ export function mergeNodeTemplates({ Object.keys(nodeTemplate) .filter((field_name) => field_name.charAt(0) !== "_") .forEach((key) => { - if (!isHandleConnected(edges, key, nodeTemplate[key], node.id)) { + if (!isTargetHandleConnected(edges, key, nodeTemplate[key], node.id)) { template[key + "_" + node.id] = nodeTemplate[key]; template[key + "_" + node.id].proxy = { id: node.id, field: key }; if (node.type === "groupNode") { @@ -934,19 +935,19 @@ export function mergeNodeTemplates({ nodeTemplate[key].display_name ? nodeTemplate[key].display_name : nodeTemplate[key].name - ? toTitleCase(nodeTemplate[key].name) - : toTitleCase(key); + ? toTitleCase(nodeTemplate[key].name) + : toTitleCase(key); } } }); }); return template; } -function isHandleConnected( +function isTargetHandleConnected( edges: Edge[], key: string, field: InputFieldType, - nodeId: string + nodeId: string, ) { /* this function receives a flow and a handleId and check if there is a connection with this handle @@ -962,7 +963,7 @@ function isHandleConnected( id: nodeId, proxy: { id: field.proxy!.id, field: field.proxy!.field }, inputTypes: field.input_types, - } as targetHandleType) + } as targetHandleType), ) ) { return true; @@ -977,7 +978,7 @@ function isHandleConnected( fieldName: key, id: nodeId, inputTypes: field.input_types, - } as targetHandleType) + } as targetHandleType), ) ) { return true; @@ -1000,25 +1001,24 @@ export function generateNodeTemplate(Flow: FlowType) { export function generateNodeFromFlow( flow: FlowType, - getNodeId: (type: string) => string + getNodeId: (type: string) => string, ): NodeType { const { nodes } = flow.data!; const outputNode = cloneDeep(findLastNode(flow.data!)); const position = getMiddlePoint(nodes); let data = cloneDeep(flow); - const id = getNodeId(outputNode?.data.type!); + const id = getNodeId("groupComponent"); const newGroupNode: NodeType = { data: { id, - type: outputNode?.data.type!, + type: "GroupNode", node: { - output_types: outputNode!.data.node!.output_types, display_name: "Group", documentation: "", - base_classes: outputNode!.data.node!.base_classes, description: "", template: generateNodeTemplate(data), flow: data, + outputs: generateNodeOutputs(data), }, }, id, @@ -1028,10 +1028,42 @@ export function generateNodeFromFlow( return newGroupNode; } +function generateNodeOutputs(flow: FlowType) { + const { nodes, edges } = flow.data!; + const outputs: Array = []; + nodes.forEach((node: NodeType) => { + if (node.data.node?.outputs) { + const nodeOutputs = node.data.node.outputs; + nodeOutputs.forEach((output) => { + //filter outputs that are not connected + console.log(output); + console.log(edges); + if ( + !edges.some( + (edge) => + edge.source === node.id && + (edge.data.sourceHandle as sourceHandleType).name === output.name, + ) + ) { + outputs.push( + cloneDeep({ + ...output, + proxy: { id: node.id, name: output.name }, + name: node.id + "_" + output.name, + displayName: output.displayName, + }), + ); + } + }); + } + }); + return outputs; +} + export function connectedInputNodesOnHandle( nodeId: string, handleId: string, - { nodes, edges }: { nodes: NodeType[]; edges: Edge[] } + { nodes, edges }: { nodes: NodeType[]; edges: Edge[] }, ) { const connectedNodes: Array<{ name: string; id: string; isGroup: boolean }> = []; @@ -1068,7 +1100,7 @@ export function connectedInputNodesOnHandle( export function updateProxyIdsOnTemplate( template: APITemplateType, - idsMap: { [key: string]: string } + idsMap: { [key: string]: string }, ) { Object.keys(template).forEach((key) => { if (template[key].proxy && idsMap[template[key].proxy!.id]) { @@ -1079,7 +1111,7 @@ export function updateProxyIdsOnTemplate( export function updateEdgesIds( edges: Edge[], - idsMap: { [key: string]: string } + idsMap: { [key: string]: string }, ) { edges.forEach((edge) => { let targetHandle: targetHandleType = edge.data.targetHandle; @@ -1120,7 +1152,7 @@ export function expandGroupNode( nodes: Node[], edges: Edge[], setNodes: (update: Node[] | ((oldState: Node[]) => Node[])) => void, - setEdges: (update: Edge[] | ((oldState: Edge[]) => Edge[])) => void + setEdges: (update: Edge[] | ((oldState: Edge[]) => Edge[])) => void, ) { const idsMap = updateIds(flow!.data!); updateProxyIdsOnTemplate(template, idsMap); @@ -1163,7 +1195,7 @@ export function expandGroupNode( const lastNode = cloneDeep(findLastNode(flow!.data!)); newEdge.source = lastNode!.id; let newSourceHandle: sourceHandleType = scapeJSONParse( - newEdge.sourceHandle! + newEdge.sourceHandle!, ); newSourceHandle.id = lastNode!.id; newEdge.data.sourceHandle = newSourceHandle; @@ -1220,7 +1252,7 @@ export function expandGroupNode( export function getGroupStatus( flow: FlowType, - ssData: { [key: string]: { valid: boolean; params: string } } + ssData: { [key: string]: { valid: boolean; params: string } }, ) { let status = { valid: true, params: SUCCESS_BUILD }; const { nodes } = flow.data!; @@ -1239,7 +1271,7 @@ export function getGroupStatus( export function createFlowComponent( nodeData: NodeDataType, - version: string + version: string, ): FlowType { const flowNode: FlowType = { data: { @@ -1275,7 +1307,7 @@ export function downloadNode(NodeFLow: FlowType) { export function updateComponentNameAndType( data: any, - component: NodeDataType + component: NodeDataType, ) {} export function removeFileNameFromComponents(flow: FlowType) { @@ -1349,7 +1381,7 @@ export function extractFieldsFromComponenents(data: APIObjectType) { export function downloadFlow( flow: FlowType, flowName: string, - flowDescription?: string + flowDescription?: string, ) { let clonedFlow = cloneDeep(flow); removeFileNameFromComponents(clonedFlow); @@ -1359,7 +1391,7 @@ export function downloadFlow( ...clonedFlow, name: flowName, description: flowDescription, - }) + }), )}`; // create a link element and set its properties @@ -1374,7 +1406,7 @@ export function downloadFlow( export function downloadFlows() { downloadFlowsFromDatabase().then((flows) => { const jsonString = `data:text/json;chatset=utf-8,${encodeURIComponent( - JSON.stringify(flows) + JSON.stringify(flows), )}`; // create a link element and set its properties @@ -1398,7 +1430,7 @@ export function getRandomDescription(): string { export const createNewFlow = ( flowData: ReactFlowJsonObject, flow: FlowType, - folderId: string + folderId: string, ) => { return { description: flow?.description ?? getRandomDescription(), From 45e2691598e51c4797f3d7cf8da786c7f514b1c9 Mon Sep 17 00:00:00 2001 From: ogabrielluiz Date: Thu, 6 Jun 2024 15:34:00 -0300 Subject: [PATCH 067/701] Refactor build_inputs method to add extra fields in ComponentFrontendNode --- .../custom/custom_component/component.py | 1 + src/backend/base/langflow/custom/utils.py | 20 +++++++++++--- .../base/langflow/template/field/base.py | 26 +++++++++++-------- 3 files changed, 32 insertions(+), 15 deletions(-) diff --git a/src/backend/base/langflow/custom/custom_component/component.py b/src/backend/base/langflow/custom/custom_component/component.py index 6c88e51be..767f5fc7b 100644 --- a/src/backend/base/langflow/custom/custom_component/component.py +++ b/src/backend/base/langflow/custom/custom_component/component.py @@ -110,6 +110,7 @@ class Component(CustomComponent): """ # This function is similar to build_config, but it will process the inputs # and return them as a dict with keys being the Input.name and values being the Input.model_dump() + self.inputs = self.template_config.get("inputs", []) if not self.inputs: return {} build_config = {_input.name: _input.model_dump(by_alias=True, exclude_none=True) for _input in self.inputs} diff --git a/src/backend/base/langflow/custom/utils.py b/src/backend/base/langflow/custom/utils.py index 4a6ca2bd2..7b0220024 100644 --- a/src/backend/base/langflow/custom/utils.py +++ b/src/backend/base/langflow/custom/utils.py @@ -103,6 +103,8 @@ def extract_type_from_optional(field_type): Returns: str: The extracted type, or an empty string if no type was found. """ + if "optional" not in field_type.lower(): + return field_type match = re.search(r"\[(.*?)\]$", field_type) return match[1] if match else field_type @@ -249,10 +251,16 @@ def get_field_dict(field: Union[Input, dict]): return field -def run_build_inputs(custom_component: Component, user_id: Optional[Union[str, UUID]] = None): +def run_build_inputs( + frontend_node: ComponentFrontendNode, + custom_component: Component, + user_id: Optional[Union[str, UUID]] = None, +): """Run the build inputs of a custom component.""" try: - return custom_component.build_inputs(user_id=user_id) + field_config = custom_component.build_inputs(user_id=user_id) + add_extra_fields(frontend_node, field_config, field_config.values()) + return field_config except Exception as exc: logger.error(f"Error running build inputs: {exc}") raise HTTPException(status_code=500, detail=str(exc)) from exc @@ -326,14 +334,18 @@ def build_custom_component_template_from_inputs( custom_component: Component, user_id: Optional[Union[str, UUID]] = None ): # The List of Inputs fills the role of the build_config and the entrypoint_args - frontend_node = ComponentFrontendNode.from_inputs(**custom_component.template_config) + field_config = custom_component.template_config + frontend_node = ComponentFrontendNode.from_inputs(**field_config) field_config = run_build_inputs( - custom_component, + frontend_node=frontend_node, + custom_component=custom_component, user_id=user_id, ) frontend_node = add_code_field(frontend_node, custom_component.code, field_config.get("code", {})) # But we now need to calculate the return_type of the methods in the outputs for output in frontend_node.outputs: + if output.types: + continue return_types = custom_component.get_method_return_type(output.method) return_types = [format_type(return_type) for return_type in return_types] output.add_types(return_types) diff --git a/src/backend/base/langflow/template/field/base.py b/src/backend/base/langflow/template/field/base.py index 4e9f059fa..a073f8c93 100644 --- a/src/backend/base/langflow/template/field/base.py +++ b/src/backend/base/langflow/template/field/base.py @@ -1,18 +1,16 @@ +from types import GenericAlias from typing import Any, Callable, Optional, Union from pydantic import BaseModel, ConfigDict, Field, field_serializer, field_validator, model_serializer, model_validator +from langflow.field_typing import Text from langflow.field_typing.range_spec import RangeSpec -from langflow.helpers.custom import format_type class Input(BaseModel): - model_config = ConfigDict() + model_config = ConfigDict(arbitrary_types_allowed=True) - field_type: str = Field( - default="str", - serialization_alias="type", - ) + field_type: str | type | None = Field(default=str, serialization_alias="type") """The type of field this is. Default is a string.""" required: bool = False @@ -86,10 +84,10 @@ class Input(BaseModel): def serialize_model(self, handler): result = handler(self) # If the field is str, we add the Text input type - if self.field_type in ["str", "Text"]: + if self.field_type in [str, Text]: if "input_types" not in result: result["input_types"] = ["Text"] - if self.field_type == "Text": + if self.field_type == Text: result["type"] = "str" else: result["type"] = self.field_type @@ -111,15 +109,15 @@ class Input(BaseModel): # If the user passes CustomComponent as a type insteado of "CustomComponent" we need to convert it to a string # this should be done for all types # How to check if v is a type? - if isinstance(v, type): - return format_type(v) + if isinstance(v, (type, GenericAlias)): + return str(v) elif not isinstance(v, str): raise ValueError(f"type must be a string or a type, not {type(v)}") return v @field_serializer("field_type") def serialize_field_type(self, value, _info): - if value == "float" and self.range_spec is None: + if value == float and self.range_spec is None: self.range_spec = RangeSpec() return value @@ -180,3 +178,9 @@ class Output(BaseModel): else: raise ValueError("If display_name is not set, name must be set") return v + + @model_serializer(mode="wrap") + def serialize_model(self, handler): + result = handler(self) + + return result From 5fa5237f1d538012ebd7fbded4cc5da0b2369319 Mon Sep 17 00:00:00 2001 From: ogabrielluiz Date: Thu, 6 Jun 2024 15:34:11 -0300 Subject: [PATCH 068/701] update projects --- .../Basic Prompting (Hello, world!).json | 4 ++-- .../starter_projects/Langflow Blog Writter.json | 2 +- .../starter_projects/Langflow Document QA.json | 4 ++-- .../Langflow Memory Conversation.json | 4 ++-- .../starter_projects/Langflow Prompt Chaining.json | 4 ++-- .../starter_projects/VectorStore-RAG-Flows.json | 12 ++++++------ 6 files changed, 15 insertions(+), 15 deletions(-) diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting (Hello, world!).json b/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting (Hello, world!).json index 4a485b9ca..d190bb340 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting (Hello, world!).json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting (Hello, world!).json @@ -499,7 +499,7 @@ "type": "str", "required": false, "placeholder": "", - "list": false, + "list": true, "show": true, "multiline": false, "value": "Machine", @@ -676,7 +676,7 @@ "type": "str", "required": false, "placeholder": "", - "list": false, + "list": true, "show": true, "multiline": false, "value": "User", diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Blog Writter.json b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Blog Writter.json index 466815700..058009652 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Blog Writter.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Blog Writter.json @@ -359,7 +359,7 @@ "type": "str", "required": false, "placeholder": "", - "list": false, + "list": true, "show": true, "multiline": false, "value": "", diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Document QA.json b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Document QA.json index dfcfc0144..c3e6eeb46 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Document QA.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Document QA.json @@ -332,7 +332,7 @@ "type": "str", "required": false, "placeholder": "", - "list": false, + "list": true, "show": true, "multiline": false, "value": "", @@ -532,7 +532,7 @@ "type": "str", "required": false, "placeholder": "", - "list": false, + "list": true, "show": true, "multiline": false, "value": "", diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Memory Conversation.json b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Memory Conversation.json index 64d7eac81..e861e6a76 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Memory Conversation.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Memory Conversation.json @@ -57,7 +57,7 @@ "type": "str", "required": false, "placeholder": "", - "list": false, + "list": true, "show": true, "multiline": false, "value": "", @@ -257,7 +257,7 @@ "type": "str", "required": false, "placeholder": "", - "list": false, + "list": true, "show": true, "multiline": false, "value": "", diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Prompt Chaining.json b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Prompt Chaining.json index fdb53e2bf..aed8c91ce 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Prompt Chaining.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Prompt Chaining.json @@ -337,7 +337,7 @@ "type": "str", "required": false, "placeholder": "", - "list": false, + "list": true, "show": true, "multiline": false, "value": "", @@ -534,7 +534,7 @@ "type": "str", "required": false, "placeholder": "", - "list": false, + "list": true, "show": true, "multiline": false, "value": "", diff --git a/src/backend/base/langflow/initial_setup/starter_projects/VectorStore-RAG-Flows.json b/src/backend/base/langflow/initial_setup/starter_projects/VectorStore-RAG-Flows.json index baec89cae..898ebb119 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/VectorStore-RAG-Flows.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/VectorStore-RAG-Flows.json @@ -55,7 +55,7 @@ "type": "str", "required": false, "placeholder": "", - "list": false, + "list": true, "show": true, "multiline": false, "value": "", @@ -305,7 +305,7 @@ "type": "str", "required": false, "placeholder": "", - "list": false, + "list": true, "show": true, "multiline": false, "value": [], @@ -441,7 +441,7 @@ "type": "str", "required": false, "placeholder": "", - "list": false, + "list": true, "show": true, "multiline": false, "value": [ @@ -1354,7 +1354,7 @@ "type": "str", "required": false, "placeholder": "", - "list": false, + "list": true, "show": true, "multiline": false, "value": "", @@ -2666,7 +2666,7 @@ "type": "str", "required": false, "placeholder": "", - "list": false, + "list": true, "show": true, "multiline": false, "value": [], @@ -2802,7 +2802,7 @@ "type": "str", "required": false, "placeholder": "", - "list": false, + "list": true, "show": true, "multiline": false, "value": [ From 6ade4567676afee620c7f004f4bd2d1207a8da06 Mon Sep 17 00:00:00 2001 From: ogabrielluiz Date: Thu, 6 Jun 2024 19:49:38 -0300 Subject: [PATCH 069/701] refactor: Remove serialization_alias from display_name field in Output class The `serialization_alias` attribute has been removed from the `display_name` field in the `Output` class. This change simplifies the code and removes unnecessary serialization configuration. Note: The commit message has been generated based on the provided code changes and recent commits. --- src/backend/base/langflow/template/field/base.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/backend/base/langflow/template/field/base.py b/src/backend/base/langflow/template/field/base.py index a073f8c93..e3160b9a8 100644 --- a/src/backend/base/langflow/template/field/base.py +++ b/src/backend/base/langflow/template/field/base.py @@ -150,7 +150,7 @@ class Output(BaseModel): selected: Optional[str] = Field(default=None, serialization_alias="selected") """The selected output type for the field.""" - display_name: Optional[str] = Field(default=None, serialization_alias="name") + display_name: Optional[str] = Field(default=None) """The display name of the field.""" name: str = Field(default=None, serialization_alias="name") From 3ec7089edeadc6911afe7b25e0d52696f3e7a963 Mon Sep 17 00:00:00 2001 From: ogabrielluiz Date: Thu, 6 Jun 2024 19:52:26 -0300 Subject: [PATCH 070/701] update projects --- .../starter_projects/Basic Prompting (Hello, world!).json | 4 ++++ .../starter_projects/Langflow Blog Writter.json | 2 ++ .../initial_setup/starter_projects/Langflow Document QA.json | 4 ++++ .../starter_projects/Langflow Memory Conversation.json | 4 ++++ .../starter_projects/Langflow Prompt Chaining.json | 5 +++++ .../starter_projects/VectorStore-RAG-Flows.json | 4 ++++ 6 files changed, 23 insertions(+) diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting (Hello, world!).json b/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting (Hello, world!).json index d190bb340..c3014eab4 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting (Hello, world!).json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting (Hello, world!).json @@ -599,6 +599,7 @@ "Text" ], "selected": "Text", + "display_name": "Message", "name": "message", "method": "text_response" }, @@ -607,6 +608,7 @@ "Record" ], "selected": "Record", + "display_name": "Record", "name": "record", "method": "record_response" } @@ -775,6 +777,7 @@ "Text" ], "selected": "Text", + "display_name": "Message", "name": "message", "method": "text_response" }, @@ -783,6 +786,7 @@ "Record" ], "selected": "Record", + "display_name": "Record", "name": "record", "method": "record_response" } diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Blog Writter.json b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Blog Writter.json index 058009652..36b512f55 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Blog Writter.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Blog Writter.json @@ -459,6 +459,7 @@ "Text" ], "selected": "Text", + "display_name": "Message", "name": "message", "method": "text_response" }, @@ -467,6 +468,7 @@ "Record" ], "selected": "Record", + "display_name": "Record", "name": "record", "method": "record_response" } diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Document QA.json b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Document QA.json index c3e6eeb46..df07a7a58 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Document QA.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Document QA.json @@ -431,6 +431,7 @@ "Text" ], "selected": "Text", + "display_name": "Message", "name": "message", "method": "text_response" }, @@ -439,6 +440,7 @@ "Record" ], "selected": "Record", + "display_name": "Record", "name": "record", "method": "record_response" } @@ -631,6 +633,7 @@ "Text" ], "selected": "Text", + "display_name": "Message", "name": "message", "method": "text_response" }, @@ -639,6 +642,7 @@ "Record" ], "selected": "Record", + "display_name": "Record", "name": "record", "method": "record_response" } diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Memory Conversation.json b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Memory Conversation.json index e861e6a76..8ad3c73d1 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Memory Conversation.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Memory Conversation.json @@ -156,6 +156,7 @@ "Text" ], "selected": "Text", + "display_name": "Message", "name": "message", "method": "text_response" }, @@ -164,6 +165,7 @@ "Record" ], "selected": "Record", + "display_name": "Record", "name": "record", "method": "record_response" } @@ -356,6 +358,7 @@ "Text" ], "selected": "Text", + "display_name": "Message", "name": "message", "method": "text_response" }, @@ -364,6 +367,7 @@ "Record" ], "selected": "Record", + "display_name": "Record", "name": "record", "method": "record_response" } diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Prompt Chaining.json b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Prompt Chaining.json index aed8c91ce..3234512c8 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Prompt Chaining.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Prompt Chaining.json @@ -437,6 +437,7 @@ "Text" ], "selected": "Text", + "display_name": "Message", "name": "message", "method": "text_response" }, @@ -445,6 +446,7 @@ "Record" ], "selected": "Record", + "display_name": "Record", "name": "record", "method": "record_response" } @@ -634,6 +636,7 @@ "Text" ], "selected": "Text", + "display_name": "Message", "name": "message", "method": "text_response" }, @@ -642,6 +645,7 @@ "Record" ], "selected": "Record", + "display_name": "Record", "name": "record", "method": "record_response" } @@ -755,6 +759,7 @@ "Text" ], "selected": "Text", + "display_name": "Text", "name": "text", "method": "text_response" } diff --git a/src/backend/base/langflow/initial_setup/starter_projects/VectorStore-RAG-Flows.json b/src/backend/base/langflow/initial_setup/starter_projects/VectorStore-RAG-Flows.json index 898ebb119..ae5af6fde 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/VectorStore-RAG-Flows.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/VectorStore-RAG-Flows.json @@ -154,6 +154,7 @@ "Text" ], "selected": "Text", + "display_name": "Message", "name": "message", "method": "text_response" }, @@ -162,6 +163,7 @@ "Record" ], "selected": "Record", + "display_name": "Record", "name": "record", "method": "record_response" } @@ -1454,6 +1456,7 @@ "Text" ], "selected": "Text", + "display_name": "Message", "name": "message", "method": "text_response" }, @@ -1462,6 +1465,7 @@ "Record" ], "selected": "Record", + "display_name": "Record", "name": "record", "method": "record_response" } From 3d82417068c5931618b2e67d93541cf56608017a Mon Sep 17 00:00:00 2001 From: ogabrielluiz Date: Fri, 7 Jun 2024 10:04:39 -0300 Subject: [PATCH 071/701] Refactor run_flow function to improve readability and maintainability --- src/backend/base/langflow/helpers/flow.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/src/backend/base/langflow/helpers/flow.py b/src/backend/base/langflow/helpers/flow.py index 9a8a7c3b5..7bdc510c6 100644 --- a/src/backend/base/langflow/helpers/flow.py +++ b/src/backend/base/langflow/helpers/flow.py @@ -90,7 +90,9 @@ async def run_flow( fallback_to_env_vars = get_settings_service().settings.fallback_to_env_var - return await graph.arun(inputs_list, inputs_components=inputs_components, types=types, fallback_to_env_vars=fallback_to_env_vars) + return await graph.arun( + inputs_list, inputs_components=inputs_components, types=types, fallback_to_env_vars=fallback_to_env_vars + ) def generate_function_for_flow( @@ -249,7 +251,7 @@ def get_flow_by_id_or_endpoint_name( flow = db.get(Flow, flow_id) except ValueError: endpoint_name = flow_id_or_name - stmt = select(Flow).where(Flow.name == endpoint_name) + stmt = select(Flow).where(Flow.endpoint_name == endpoint_name) if user_id: stmt = stmt.where(Flow.user_id == user_id) flow = db.exec(stmt).first() From 4c87f7662cde9b5f5a5ab08036fbffc51af48c94 Mon Sep 17 00:00:00 2001 From: ogabrielluiz Date: Fri, 7 Jun 2024 10:42:22 -0300 Subject: [PATCH 072/701] Merge remote-tracking branch 'origin/dev' into two_edges --- .github/actions/poetry_caching/action.yml | 2 +- .github/workflows/create-release.yml | 2 +- .github/workflows/docker-build.yml | 9 +- .github/workflows/pre-release-base.yml | 2 +- .github/workflows/pre-release-langflow.yml | 2 +- .github/workflows/pre-release.yml | 6 +- .github/workflows/release.yml | 2 +- Makefile | 1 + README.PT.md | 171 + README.md | 7 +- README.zh_CN.md | 172 + docker/build_and_push.Dockerfile | 5 +- docs/docs/administration/api.mdx | 3 +- docs/docs/administration/cli.mdx | 169 +- docs/docs/administration/global-env.mdx | 87 +- docs/docs/components/custom.mdx | 77 +- docs/docs/components/inputs-and-outputs.mdx | 161 + docs/docs/components/inputs.mdx | 99 - docs/docs/components/outputs.mdx | 34 - docs/docs/components/prompts.mdx | 25 - docs/docs/components/text-and-record.mdx | 49 + docs/docs/components/vector-stores.mdx | 2 +- docs/docs/deployment/backend-only.md | 113 + docs/docs/deployment/docker.md | 65 + docs/docs/deployment/jina-deployment.md | 0 docs/docs/examples/chat-memory.mdx | 2 +- docs/docs/examples/combine-text.mdx | 2 +- docs/docs/examples/create-record.mdx | 2 +- docs/docs/examples/pass.mdx | 2 +- docs/docs/examples/store-message.mdx | 2 +- docs/docs/examples/sub-flow.mdx | 2 +- docs/docs/examples/text-operator.mdx | 2 +- docs/docs/getting-started/canvas.mdx | 6 - .../flows-components-collections.mdx | 14 +- .../docs/getting-started/install-langflow.mdx | 15 +- docs/docs/getting-started/quickstart.mdx | 33 +- docs/docs/index.mdx | 5 +- .../notion/add-content-to-page.md | 15 +- docs/docs/integrations/notion/intro.md | 6 +- .../notion/list-database-properties.md | 6 +- docs/docs/integrations/notion/list-pages.md | 13 +- docs/docs/integrations/notion/list-users.md | 21 +- .../notion/page-content-viewer.md | 12 +- docs/docs/integrations/notion/page-create.md | 10 +- docs/docs/integrations/notion/search.md | 7 +- docs/docs/integrations/notion/setup.md | 1 - docs/docs/migration/api.mdx | 0 .../component-status-and-data-passing.mdx | 0 .../connecting-output-components.mdx | 0 docs/docs/migration/custom-component.mdx | 0 .../migration/experimental-components.mdx | 0 docs/docs/migration/flow-of-data.mdx | 0 docs/docs/migration/global-variables.mdx | 118 - docs/docs/migration/inputs-and-outputs.mdx | 36 - .../migration/migrating-to-one-point-zero.mdx | 6 +- docs/docs/migration/multiple-flows.mdx | 0 .../new-categories-and-components.mdx | 0 .../migration/passing-tweaks-and-inputs.mdx | 0 .../possible-installation-issues.mdx | 9 +- .../renaming-and-editing-components.mdx | 0 .../sidebar-and-interaction-panel.mdx | 0 docs/docs/migration/state-management.mdx | 0 docs/docs/migration/supported-frameworks.mdx | 0 docs/docs/migration/text-and-record.mdx | 45 - .../docs/starter-projects/basic-prompting.mdx | 27 +- docs/docs/starter-projects/blog-writer.mdx | 22 +- docs/docs/starter-projects/document-qa.mdx | 34 +- docs/docs/starter-projects/memory-chatbot.mdx | 24 +- .../starter-projects/vector-store-rag.mdx | 59 +- .../docs/whats-new/a-new-chapter-langflow.mdx | 2 +- docs/sidebars.js | 14 +- docs/static/data/AstraDB-RAG-Flows.json | 6368 ++++++++--------- .../json_files/Notion_Components_bundle.json | 882 ++- docs/static/logos/twitter.svg | 4 +- poetry.lock | 492 +- pyproject.toml | 4 +- scripts/factory_restart_space.py | 22 +- scripts/gcp/GCP_DEPLOYMENT.md | 3 +- src/backend/base/langflow/__main__.py | 2 +- src/backend/base/langflow/api/v1/flows.py | 10 +- src/backend/base/langflow/api/v1/monitor.py | 40 +- src/backend/base/langflow/base/curl/parse.py | 100 +- src/backend/base/langflow/base/data/utils.py | 2 +- .../components/vectorsearch/__init__.py | 27 - .../components/vectorstores/__init__.py | 28 - .../custom/code_parser/code_parser.py | 2 +- .../directory_reader/directory_reader.py | 6 +- .../Basic Prompting (Hello, world!).json | 1871 +++-- .../Langflow Blog Writter.json | 279 +- .../Langflow Document QA.json | 295 +- .../Langflow Memory Conversation.json | 357 +- .../Langflow Prompt Chaining.json | 480 +- src/backend/base/langflow/load/__init__.py | 5 +- src/backend/base/langflow/load/utils.py | 89 + .../base/langflow/processing/process.py | 2 +- .../services/database/models/api_key/model.py | 1 + .../services/database/models/flow/model.py | 1 - .../base/langflow/services/monitor/schema.py | 7 + .../base/langflow/services/monitor/service.py | 25 +- .../base/langflow/services/settings/base.py | 1 - src/backend/base/poetry.lock | 57 +- src/backend/base/pyproject.toml | 2 +- src/frontend/package-lock.json | 53 +- src/frontend/package.json | 3 + src/frontend/playwright.config.ts | 26 +- src/frontend/src/App.css | 10 + src/frontend/src/App.tsx | 28 +- .../src/alerts/alertDropDown/index.tsx | 2 +- src/frontend/src/alerts/error/index.tsx | 8 +- src/frontend/src/alerts/notice/index.tsx | 4 +- src/frontend/src/alerts/success/index.tsx | 4 +- .../src/components/ImageViewer/index.tsx | 14 +- .../components/accordionComponent/index.tsx | 18 +- .../addNewVariableButton.tsx | 22 +- .../src/components/cardComponent/index.tsx | 20 +- .../components/cardsWrapComponent/index.tsx | 2 +- .../src/components/chatComponent/index.tsx | 8 +- .../components/codeTabsComponent/index.tsx | 40 +- .../components/csvOutputComponent/index.tsx | 2 +- .../components/dropdownComponent/index.tsx | 5 +- .../editFlowSettingsComponent/index.tsx | 4 +- .../components/fetchErrorComponent/index.tsx | 31 +- .../components/menuBar/index.tsx | 27 +- .../src/components/headerComponent/index.tsx | 46 +- .../components/popover/index.tsx | 31 +- .../components/popoverObject/index.tsx | 41 +- .../src/components/inputComponent/index.tsx | 13 +- .../components/inputFileComponent/index.tsx | 1 - .../components/inputGlobalComponent/index.tsx | 33 +- .../components/inputListComponent/index.tsx | 6 +- .../components/shadTooltipComponent/index.tsx | 3 +- .../components/sideBarButtons/index.tsx | 19 +- .../components/sideBarFolderButtons/index.tsx | 85 +- .../hooks/use-on-file-drop.tsx | 10 +- .../src/components/sidebarComponent/index.tsx | 28 +- .../components/ResetColumns/index.tsx | 29 + .../components}/tableAutoCellRender/index.tsx | 15 +- .../components/tableNodeCellRender/index.tsx | 266 + .../tableToggleCellRender/index.tsx | 24 + .../components/tableTooltipRender/index.tsx | 9 + .../src/components/tableComponent/index.tsx | 77 +- .../utils/reset-grid-columns.tsx | 12 + .../components/toggleShadComponent/index.tsx | 28 +- src/frontend/src/components/ui/accordion.tsx | 23 +- src/frontend/src/components/ui/button.tsx | 50 +- src/frontend/src/components/ui/card.tsx | 2 +- .../src/components/ui/refreshButton.tsx | 9 +- src/frontend/src/components/ui/toggle.tsx | 45 + src/frontend/src/components/ui/tooltip.tsx | 24 +- .../src/constants/alerts_constants.tsx | 2 + src/frontend/src/constants/constants.ts | 12 +- src/frontend/src/controllers/API/api.tsx | 24 +- .../API/helpers/check-duplicate-requests.ts | 30 + src/frontend/src/controllers/API/index.ts | 79 +- .../HandleTooltipComponent/index.tsx | 31 - .../components/OutputComponent/index.tsx | 70 - .../parameterComponent/constants.ts | 1 - .../components/parameterComponent/index.tsx | 580 -- .../tooltipRenderComponent/index.tsx | 91 - .../src/customNodes/genericNode/index.tsx | 866 --- .../hooks/use-fetch-data-on-mount.tsx | 52 - .../hooks/use-handle-new-value.tsx | 71 - .../hooks/use-handle-node-class.tsx | 37 - .../hooks/use-handle-refresh-buttons.tsx | 38 - .../src/customNodes/utils/get-field-title.tsx | 10 - .../src/customNodes/utils/sort-fields.tsx | 40 - .../BotMessageSquare/BotMessageSquare.jsx | 6 +- .../src/icons/Streamlit/SvgStreamlit.jsx | 25 + src/frontend/src/icons/Streamlit/index.tsx | 8 + .../BundleModal/hooks/submit-folder.tsx | 4 +- .../IOModal/components/IOFieldView/index.tsx | 2 +- .../components/chatView/chatMessage/index.tsx | 16 +- .../IOModal/components/chatView/index.tsx | 69 +- src/frontend/src/modals/IOModal/index.tsx | 64 +- .../utils/check-can-build-tweak-object.ts | 2 +- .../utils/get-nodes-with-default-value.ts | 4 +- .../apiModal/utils/get-python-api-code.tsx | 71 +- .../modals/apiModal/utils/get-widget-code.tsx | 2 +- .../src/modals/apiModal/views/index.tsx | 7 +- .../baseModal/helpers/switch-case-size.ts | 70 + src/frontend/src/modals/baseModal/index.tsx | 172 +- .../modals/deleteConfirmationModal/index.tsx | 2 +- .../editNodeModal/hooks/use-column-defs.tsx | 91 + .../editNodeModal/hooks/use-row-data.tsx | 37 + .../src/modals/editNodeModal/index.tsx | 635 +- src/frontend/src/modals/exportModal/index.tsx | 85 +- .../src/modals/flowLogsModal/index.tsx | 51 +- .../src/modals/flowSettingsModal/index.tsx | 31 +- .../modals/foldersModal/component/index.tsx | 1 - .../foldersModal/hooks/submit-folder.tsx | 4 +- .../src/modals/newFlowModal/index.tsx | 2 +- .../src/modals/secretKeyModal/index.tsx | 170 +- src/frontend/src/modals/shareModal/index.tsx | 58 +- .../modals/shareModal/utils/get-tags-ids.tsx | 2 +- .../src/modals/storeApiKeyModal/index.tsx | 145 - src/frontend/src/pages/ApiKeysPage/index.tsx | 291 - .../components/PageComponent/index.tsx | 40 +- .../PageComponent/utils/get-random-name.tsx | 2 +- .../SelectionMenuComponent/index.tsx | 2 +- .../components/nodeToolbarComponent/index.tsx | 43 +- src/frontend/src/pages/FlowPage/index.tsx | 2 +- .../components/componentsComponent/index.tsx | 99 +- .../components/emptyComponent/index.tsx | 43 +- .../components/headerComponent/index.tsx | 114 +- .../headerTabsSearchComponent/index.tsx | 12 +- .../components/inputSearchComponent/index.tsx | 4 +- .../myCollectionComponent/index.tsx | 5 +- .../pages/MainPage/pages/mainPage/index.tsx | 4 - .../MainPage/utils/handle-download-folder.ts | 2 +- src/frontend/src/pages/SettingsPage/index.tsx | 38 +- .../components/ApiKeyHeader/index.tsx | 58 + .../pages/ApiKeysPage/helpers/column-defs.ts | 40 + .../pages/ApiKeysPage/hooks/use-api-keys.tsx | 26 + .../hooks/use-handle-delete-key.tsx | 42 + .../SettingsPage/pages/ApiKeysPage/index.tsx | 69 + .../components/GeneralPageHeader/index.tsx | 23 + .../components/PasswordForm/index.tsx | 93 + .../components/ProfileGradientForm/index.tsx | 68 + .../components/StoreApiKeyForm/index.tsx | 102 + .../SettingsPage/pages/GeneralPage/index.tsx | 252 +- .../pages/GlobalVariablesPage/index.tsx | 33 +- .../pages/ShortcutsPage/index.tsx | 25 +- .../pages/hooks/use-patch-gradient.tsx | 37 + .../pages/hooks/use-patch-password.tsx | 36 + .../SettingsPage/pages/hooks/use-save-key.tsx | 48 + .../pages/hooks/use-scroll-to-element.tsx | 17 + .../components/headerMessages/index.tsx | 49 + .../messagesPage/hooks/use-messages-table.tsx | 24 + .../hooks/use-remove-messages.tsx | 30 + .../messagesPage/hooks/use-updateMessage.tsx | 29 + .../SettingsPage/pages/messagesPage/index.tsx | 77 + src/frontend/src/pages/StorePage/index.tsx | 37 +- src/frontend/src/routes.tsx | 25 +- src/frontend/src/stores/darkStore.ts | 2 +- src/frontend/src/stores/flowStore.ts | 26 +- src/frontend/src/stores/flowsManagerStore.ts | 33 +- src/frontend/src/stores/foldersStore.tsx | 34 +- .../globalVariablesStore/globalVariables.ts | 2 +- src/frontend/src/stores/messagesStore.ts | 43 + src/frontend/src/style/applies.css | 4 +- src/frontend/src/style/classes.css | 14 + src/frontend/src/types/components/index.ts | 24 +- src/frontend/src/types/messages/index.ts | 13 + src/frontend/src/types/zustand/flow/index.ts | 29 +- .../src/types/zustand/flowsManager/index.ts | 8 +- .../src/types/zustand/folders/index.ts | 2 +- .../types/zustand/globalVariables/index.ts | 2 +- .../src/types/zustand/messages/index.ts | 11 + src/frontend/src/utils/parameterUtils.ts | 4 +- src/frontend/src/utils/reactflowUtils.ts | 2 +- src/frontend/src/utils/storeUtils.ts | 14 +- src/frontend/src/utils/styleUtils.ts | 10 +- src/frontend/src/utils/utils.ts | 19 +- .../tests/end-to-end/chatInputOutput.spec.ts | 6 +- .../end-to-end/chatInputOutputUser.spec.ts | 5 +- .../end-to-end/codeAreaModalComponent.spec.ts | 2 +- .../end-to-end/deleteComponentFlows.spec.ts | 1 + .../tests/end-to-end/dragAndDrop.spec.ts | 2 +- .../end-to-end/dropdownComponent.spec.ts | 6 +- .../end-to-end/fileUploadComponent.spec.ts | 2 +- .../tests/end-to-end/filterEdge.spec.ts | 6 +- .../tests/end-to-end/floatComponent.spec.ts | 18 +- .../tests/end-to-end/flowPage.spec.ts | 2 +- .../tests/end-to-end/flowSettings.spec.ts | 4 +- src/frontend/tests/end-to-end/folders.spec.ts | 49 +- .../tests/end-to-end/generalBugs.spec.ts | 99 + .../tests/end-to-end/globalVariables.spec.ts | 3 +- .../tests/end-to-end/inputComponent.spec.ts | 14 +- .../end-to-end/inputListComponent.spec.ts | 4 +- .../tests/end-to-end/intComponent.spec.ts | 12 +- .../end-to-end/keyPairListComponent.spec.ts | 6 +- .../end-to-end/langflowShortcuts.spec.ts | 10 +- src/frontend/tests/end-to-end/logs.spec.ts | 2 + .../tests/end-to-end/nestedComponent.spec.ts | 4 +- .../end-to-end/promptModalComponent.spec.ts | 4 +- .../tests/end-to-end/saveComponents.spec.ts | 4 +- src/frontend/tests/end-to-end/store.spec.ts | 23 +- .../end-to-end/textAreaModalComponent.spec.ts | 2 +- .../tests/end-to-end/textInputOutput.spec.ts | 8 +- .../tests/end-to-end/toggleComponent.spec.ts | 8 +- .../tests/end-to-end/tweaks_test.spec.ts | 2 +- .../tests/end-to-end/userSettings.spec.ts | 38 +- tests/test_database.py | 15 + 283 files changed, 11734 insertions(+), 9297 deletions(-) create mode 100644 README.PT.md create mode 100644 README.zh_CN.md create mode 100644 docs/docs/components/inputs-and-outputs.mdx delete mode 100644 docs/docs/components/inputs.mdx delete mode 100644 docs/docs/components/outputs.mdx delete mode 100644 docs/docs/components/prompts.mdx create mode 100644 docs/docs/components/text-and-record.mdx create mode 100644 docs/docs/deployment/backend-only.md create mode 100644 docs/docs/deployment/docker.md delete mode 100644 docs/docs/deployment/jina-deployment.md delete mode 100644 docs/docs/migration/api.mdx delete mode 100644 docs/docs/migration/component-status-and-data-passing.mdx delete mode 100644 docs/docs/migration/connecting-output-components.mdx delete mode 100644 docs/docs/migration/custom-component.mdx delete mode 100644 docs/docs/migration/experimental-components.mdx delete mode 100644 docs/docs/migration/flow-of-data.mdx delete mode 100644 docs/docs/migration/global-variables.mdx delete mode 100644 docs/docs/migration/inputs-and-outputs.mdx delete mode 100644 docs/docs/migration/multiple-flows.mdx delete mode 100644 docs/docs/migration/new-categories-and-components.mdx delete mode 100644 docs/docs/migration/passing-tweaks-and-inputs.mdx delete mode 100644 docs/docs/migration/renaming-and-editing-components.mdx delete mode 100644 docs/docs/migration/sidebar-and-interaction-panel.mdx delete mode 100644 docs/docs/migration/state-management.mdx delete mode 100644 docs/docs/migration/supported-frameworks.mdx delete mode 100644 docs/docs/migration/text-and-record.mdx create mode 100644 src/backend/base/langflow/load/utils.py create mode 100644 src/frontend/src/components/tableComponent/components/ResetColumns/index.tsx rename src/frontend/src/components/{ => tableComponent/components}/tableAutoCellRender/index.tsx (78%) create mode 100644 src/frontend/src/components/tableComponent/components/tableNodeCellRender/index.tsx create mode 100644 src/frontend/src/components/tableComponent/components/tableToggleCellRender/index.tsx create mode 100644 src/frontend/src/components/tableComponent/components/tableTooltipRender/index.tsx create mode 100644 src/frontend/src/components/tableComponent/utils/reset-grid-columns.tsx create mode 100644 src/frontend/src/components/ui/toggle.tsx create mode 100644 src/frontend/src/controllers/API/helpers/check-duplicate-requests.ts delete mode 100644 src/frontend/src/customNodes/genericNode/components/HandleTooltipComponent/index.tsx delete mode 100644 src/frontend/src/customNodes/genericNode/components/OutputComponent/index.tsx delete mode 100644 src/frontend/src/customNodes/genericNode/components/parameterComponent/constants.ts delete mode 100644 src/frontend/src/customNodes/genericNode/components/parameterComponent/index.tsx delete mode 100644 src/frontend/src/customNodes/genericNode/components/tooltipRenderComponent/index.tsx delete mode 100644 src/frontend/src/customNodes/genericNode/index.tsx delete mode 100644 src/frontend/src/customNodes/hooks/use-fetch-data-on-mount.tsx delete mode 100644 src/frontend/src/customNodes/hooks/use-handle-new-value.tsx delete mode 100644 src/frontend/src/customNodes/hooks/use-handle-node-class.tsx delete mode 100644 src/frontend/src/customNodes/hooks/use-handle-refresh-buttons.tsx delete mode 100644 src/frontend/src/customNodes/utils/get-field-title.tsx delete mode 100644 src/frontend/src/customNodes/utils/sort-fields.tsx create mode 100644 src/frontend/src/icons/Streamlit/SvgStreamlit.jsx create mode 100644 src/frontend/src/icons/Streamlit/index.tsx create mode 100644 src/frontend/src/modals/baseModal/helpers/switch-case-size.ts create mode 100644 src/frontend/src/modals/editNodeModal/hooks/use-column-defs.tsx create mode 100644 src/frontend/src/modals/editNodeModal/hooks/use-row-data.tsx delete mode 100644 src/frontend/src/modals/storeApiKeyModal/index.tsx delete mode 100644 src/frontend/src/pages/ApiKeysPage/index.tsx create mode 100644 src/frontend/src/pages/SettingsPage/pages/ApiKeysPage/components/ApiKeyHeader/index.tsx create mode 100644 src/frontend/src/pages/SettingsPage/pages/ApiKeysPage/helpers/column-defs.ts create mode 100644 src/frontend/src/pages/SettingsPage/pages/ApiKeysPage/hooks/use-api-keys.tsx create mode 100644 src/frontend/src/pages/SettingsPage/pages/ApiKeysPage/hooks/use-handle-delete-key.tsx create mode 100644 src/frontend/src/pages/SettingsPage/pages/ApiKeysPage/index.tsx create mode 100644 src/frontend/src/pages/SettingsPage/pages/GeneralPage/components/GeneralPageHeader/index.tsx create mode 100644 src/frontend/src/pages/SettingsPage/pages/GeneralPage/components/PasswordForm/index.tsx create mode 100644 src/frontend/src/pages/SettingsPage/pages/GeneralPage/components/ProfileGradientForm/index.tsx create mode 100644 src/frontend/src/pages/SettingsPage/pages/GeneralPage/components/StoreApiKeyForm/index.tsx create mode 100644 src/frontend/src/pages/SettingsPage/pages/hooks/use-patch-gradient.tsx create mode 100644 src/frontend/src/pages/SettingsPage/pages/hooks/use-patch-password.tsx create mode 100644 src/frontend/src/pages/SettingsPage/pages/hooks/use-save-key.tsx create mode 100644 src/frontend/src/pages/SettingsPage/pages/hooks/use-scroll-to-element.tsx create mode 100644 src/frontend/src/pages/SettingsPage/pages/messagesPage/components/headerMessages/index.tsx create mode 100644 src/frontend/src/pages/SettingsPage/pages/messagesPage/hooks/use-messages-table.tsx create mode 100644 src/frontend/src/pages/SettingsPage/pages/messagesPage/hooks/use-remove-messages.tsx create mode 100644 src/frontend/src/pages/SettingsPage/pages/messagesPage/hooks/use-updateMessage.tsx create mode 100644 src/frontend/src/pages/SettingsPage/pages/messagesPage/index.tsx create mode 100644 src/frontend/src/stores/messagesStore.ts create mode 100644 src/frontend/src/types/messages/index.ts create mode 100644 src/frontend/src/types/zustand/messages/index.ts create mode 100644 src/frontend/tests/end-to-end/generalBugs.spec.ts diff --git a/.github/actions/poetry_caching/action.yml b/.github/actions/poetry_caching/action.yml index e185e7094..4bb6415ac 100644 --- a/.github/actions/poetry_caching/action.yml +++ b/.github/actions/poetry_caching/action.yml @@ -74,7 +74,7 @@ runs: if: steps.cache-bin-poetry.outputs.cache-hit != 'true' shell: bash env: - POETRY_VERSION: ${{ inputs.poetry-version }} + POETRY_VERSION: ${{ inputs.poetry-version || env.POETRY_VERSION }} PYTHON_VERSION: ${{ inputs.python-version }} # Install poetry using the python version installed by setup-python step. run: | diff --git a/.github/workflows/create-release.yml b/.github/workflows/create-release.yml index ef3c8f698..e1b806ccf 100644 --- a/.github/workflows/create-release.yml +++ b/.github/workflows/create-release.yml @@ -25,7 +25,7 @@ jobs: steps: - uses: actions/checkout@v4 - name: Install poetry - run: pipx install poetry==$POETRY_VERSION + run: pipx install poetry==${{ env.POETRY_VERSION }} - name: Set up Python 3.12 uses: actions/setup-python@v5 with: diff --git a/.github/workflows/docker-build.yml b/.github/workflows/docker-build.yml index cdab4c526..b5424b367 100644 --- a/.github/workflows/docker-build.yml +++ b/.github/workflows/docker-build.yml @@ -19,6 +19,8 @@ on: options: - base - main +env: + POETRY_VERSION: "1.8.2" jobs: docker_build: @@ -78,7 +80,10 @@ jobs: langflowai/langflow-frontend:1.0-alpha restart-space: + name: Restart HuggingFace Spaces + if: ${{ inputs.release_type == 'main' }} runs-on: ubuntu-latest + needs: docker_build strategy: matrix: python-version: @@ -98,6 +103,4 @@ jobs: - name: Restart HuggingFace Spaces Build run: | - poetry run python ./scripts/factory_restart_space.py - env: - HUGGINGFACE_API_TOKEN: ${{ secrets.HUGGINGFACE_API_TOKEN }} + poetry run python ./scripts/factory_restart_space.py --space "Langflow/Langflow-Preview" --token ${{ secrets.HUGGINGFACE_API_TOKEN }} diff --git a/.github/workflows/pre-release-base.yml b/.github/workflows/pre-release-base.yml index d087fc183..6045038be 100644 --- a/.github/workflows/pre-release-base.yml +++ b/.github/workflows/pre-release-base.yml @@ -22,7 +22,7 @@ jobs: steps: - uses: actions/checkout@v4 - name: Install poetry - run: pipx install poetry==$POETRY_VERSION + run: pipx install poetry==${{ env.POETRY_VERSION }} - name: Set up Python 3.10 uses: actions/setup-python@v5 with: diff --git a/.github/workflows/pre-release-langflow.yml b/.github/workflows/pre-release-langflow.yml index 15726385e..f3909f7b1 100644 --- a/.github/workflows/pre-release-langflow.yml +++ b/.github/workflows/pre-release-langflow.yml @@ -26,7 +26,7 @@ jobs: steps: - uses: actions/checkout@v4 - name: Install poetry - run: pipx install poetry==$POETRY_VERSION + run: pipx install poetry==${{ env.POETRY_VERSION }} - name: Set up Python 3.10 uses: actions/setup-python@v5 with: diff --git a/.github/workflows/pre-release.yml b/.github/workflows/pre-release.yml index b72def8b3..286a7a921 100644 --- a/.github/workflows/pre-release.yml +++ b/.github/workflows/pre-release.yml @@ -29,12 +29,16 @@ jobs: steps: - uses: actions/checkout@v4 - name: Install poetry - run: pipx install poetry==$POETRY_VERSION + run: pipx install poetry==${{ env.POETRY_VERSION }} - name: Set up Python 3.10 uses: actions/setup-python@v5 with: python-version: "3.10" cache: "poetry" + - name: Set up Nodejs 20 + uses: actions/setup-node@v4 + with: + node-version: "20" - name: Check Version id: check-version run: | diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index 3cd8fc2f0..851f06424 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -19,7 +19,7 @@ jobs: steps: - uses: actions/checkout@v4 - name: Install poetry - run: pipx install poetry==$POETRY_VERSION + run: pipx install poetry==${{ env.POETRY_VERSION }} - name: Set up Python 3.10 uses: actions/setup-python@v5 with: diff --git a/Makefile b/Makefile index 878eeca80..abf3e67ec 100644 --- a/Makefile +++ b/Makefile @@ -168,6 +168,7 @@ build_and_install: build_frontend: cd src/frontend && CI='' npm run build + rm -rf src/backend/base/langflow/frontend cp -r src/frontend/build src/backend/base/langflow/frontend build: diff --git a/README.PT.md b/README.PT.md new file mode 100644 index 000000000..8d3197dd7 --- /dev/null +++ b/README.PT.md @@ -0,0 +1,171 @@ + + +# [![Langflow](./docs/static/img/hero.png)](https://www.langflow.org) + +

+ Um framework visual para criar apps de agentes autÃŽnomos e RAG +

+

+ Open-source, construído em Python, totalmente personalizável, agnóstico em relação a modelos e databases +

+ +

+ Docs - + Junte-se ao nosso Discord - + Siga-nos no X - + Demonstração +

+ +

+ + + + + + +

+ +
+ README em Inglês + README em Chinês Simplificado +
+ +

+ Seu GIF +

+ +# 📝 Conteúdo + +- [📝 Conteúdo](#-conteúdo) +- [📊 Introdução](#-introdução) +- [🎚 Criar Fluxos](#-criar-fluxos) +- [Deploy](#deploy) + - [Deploy usando Google Cloud Platform](#deploy-usando-google-cloud-platform) + - [Deploy on Railway](#deploy-on-railway) + - [Deploy on Render](#deploy-on-render) +- [🖥 Interface de Linha de Comando (CLI)](#-interface-de-linha-de-comando-cli) + - [Uso](#uso) + - [Variáveis de Ambiente](#variáveis-de-ambiente) +- [👋 Contribuir](#-contribuir) +- [🌟 Contribuidores](#-contribuidores) +- [📄 Licença](#-licença) + +# 📊 Introdução + +Você pode instalar o Langflow com pip: + +```shell +# Certifique-se de ter >=Python 3.10 instalado no seu sistema. +# Instale a versão pré-lançamento (recomendada para as atualizações mais recentes) +python -m pip install langflow --pre --force-reinstall + +# ou versão estável +python -m pip install langflow -U +``` + +Então, execute o Langflow com: + +```shell +python -m langflow run +``` + +Você também pode visualizar o Langflow no [HuggingFace Spaces](https://huggingface.co/spaces/Langflow/Langflow-Preview). [Clone o Space usando este link](https://huggingface.co/spaces/Langflow/Langflow-Preview?duplicate=true) para criar seu próprio workspace do Langflow em minutos. + +# 🎚 Criar Fluxos + +Criar fluxos com Langflow é fácil. Basta arrastar componentes da barra lateral para o canvas e conectá-los para começar a construir sua aplicação. + +Explore editando os parâmetros do prompt, agrupando componentes e construindo seus próprios componentes personalizados (Custom Components). + +Quando terminar, você pode exportar seu fluxo como um arquivo JSON. + +Carregue o fluxo com: + +```python +from langflow.load import run_flow_from_json + +results = run_flow_from_json("path/to/flow.json", input_value="Hello, World!") +``` + +# Deploy + +## Deploy usando Google Cloud Platform + +Siga nosso passo a passo para fazer deploy do Langflow no Google Cloud Platform (GCP) usando o Google Cloud Shell. O guia está disponível no documento [**Langflow on Google Cloud Platform**](https://github.com/langflow-ai/langflow/blob/dev/docs/docs/deployment/gcp-deployment.md). + +Alternativamente, clique no botão **"Open in Cloud Shell"** abaixo para iniciar o Google Cloud Shell, clonar o repositório do Langflow e começar um **tutorial interativo** que o guiará pelo processo de configuração dos recursos necessários e deploy do Langflow no seu projeto GCP. + +[![Open on Cloud Shell](https://gstatic.com/cloudssh/images/open-btn.svg)](https://console.cloud.google.com/cloudshell/open?git_repo=https://github.com/langflow-ai/langflow&working_dir=scripts/gcp&shellonly=true&tutorial=walkthroughtutorial_spot.md) + +## Deploy on Railway + +Use este template para implantar o Langflow 1.0 Preview no Railway: + +[![Deploy 1.0 Preview on Railway](https://railway.app/button.svg)](https://railway.app/template/UsJ1uB?referralCode=MnPSdg) + +Ou este para implantar o Langflow 0.6.x: + +[![Deploy on Railway](https://railway.app/button.svg)](https://railway.app/template/JMXEWp?referralCode=MnPSdg) + +## Deploy on Render + + +Deploy to Render + + +# 🖥 Interface de Linha de Comando (CLI) + +O Langflow fornece uma interface de linha de comando (CLI) para fácil gerenciamento e configuração. + +## Uso + +Você pode executar o Langflow usando o seguinte comando: + +```shell +langflow run [OPTIONS] +``` + +Cada opção é detalhada abaixo: + +- `--help`: Exibe todas as opções disponíveis. +- `--host`: Define o host para vincular o servidor. Pode ser configurado usando a variável de ambiente `LANGFLOW_HOST`. O padrão é `127.0.0.1`. +- `--workers`: Define o número de processos. Pode ser configurado usando a variável de ambiente `LANGFLOW_WORKERS`. O padrão é `1`. +- `--timeout`: Define o tempo limite do worker em segundos. O padrão é `60`. +- `--port`: Define a porta para escutar. Pode ser configurado usando a variável de ambiente `LANGFLOW_PORT`. O padrão é `7860`. +- `--env-file`: Especifica o caminho para o arquivo .env contendo variáveis de ambiente. O padrão é `.env`. +- `--log-level`: Define o nível de log. Pode ser configurado usando a variável de ambiente `LANGFLOW_LOG_LEVEL`. O padrão é `critical`. +- `--components-path`: Especifica o caminho para o diretório contendo componentes personalizados. Pode ser configurado usando a variável de ambiente `LANGFLOW_COMPONENTS_PATH`. O padrão é `langflow/components`. +- `--log-file`: Especifica o caminho para o arquivo de log. Pode ser configurado usando a variável de ambiente `LANGFLOW_LOG_FILE`. O padrão é `logs/langflow.log`. +- `--cache`: Seleciona o tipo de cache a ser usado. As opções são `InMemoryCache` e `SQLiteCache`. Pode ser configurado usando a variável de ambiente `LANGFLOW_LANGCHAIN_CACHE`. O padrão é `SQLiteCache`. +- `--dev/--no-dev`: Alterna o modo de desenvolvimento. O padrão é `no-dev`. +- `--path`: Especifica o caminho para o diretório frontend contendo os arquivos de build. Esta opção é apenas para fins de desenvolvimento. Pode ser configurado usando a variável de ambiente `LANGFLOW_FRONTEND_PATH`. +- `--open-browser/--no-open-browser`: Alterna a opção de abrir o navegador após iniciar o servidor. Pode ser configurado usando a variável de ambiente `LANGFLOW_OPEN_BROWSER`. O padrão é `open-browser`. +- `--remove-api-keys/--no-remove-api-keys`: Alterna a opção de remover as chaves de API dos projetos salvos no banco de dados. Pode ser configurado usando a variável de ambiente `LANGFLOW_REMOVE_API_KEYS`. O padrão é `no-remove-api-keys`. +- `--install-completion [bash|zsh|fish|powershell|pwsh]`: Instala a conclusão para o shell especificado. +- `--show-completion [bash|zsh|fish|powershell|pwsh]`: Exibe a conclusão para o shell especificado, permitindo que você copie ou personalize a instalação. +- `--backend-only`: Este parâmetro, com valor padrão `False`, permite executar apenas o servidor backend sem o frontend. Também pode ser configurado usando a variável de ambiente `LANGFLOW_BACKEND_ONLY`. +- `--store`: Este parâmetro, com valor padrão `True`, ativa os recursos da loja, use `--no-store` para desativá-los. Pode ser configurado usando a variável de ambiente `LANGFLOW_STORE`. + +Esses parâmetros são importantes para usuários que precisam personalizar o comportamento do Langflow, especialmente em cenários de desenvolvimento ou deploy especializado. + +### Variáveis de Ambiente + +Você pode configurar muitas das opções de CLI usando variáveis de ambiente. Estas podem ser exportadas no seu sistema operacional ou adicionadas a um arquivo `.env` e carregadas usando a opção `--env-file`. + +Um arquivo de exemplo `.env` chamado `.env.example` está incluído no projeto. Copie este arquivo para um novo arquivo chamado `.env` e substitua os valores de exemplo pelas suas configurações reais. Se você estiver definindo valores tanto no seu sistema operacional quanto no arquivo `.env`, as configurações do `.env` terão precedência. + +# 👋 Contribuir + +Aceitamos contribuições de desenvolvedores de todos os níveis para nosso projeto open-source no GitHub. Se você deseja contribuir, por favor, confira nossas [diretrizes de contribuição](./CONTRIBUTING.md) e ajude a tornar o Langflow mais acessível. + +--- + +[![Star History Chart](https://api.star-history.com/svg?repos=langflow-ai/langflow&type=Timeline)](https://star-history.com/#langflow-ai/langflow&Date) + +# 🌟 Contribuidores + +[![langflow contributors](https://contrib.rocks/image?repo=langflow-ai/langflow)](https://github.com/langflow-ai/langflow/graphs/contributors) + +# 📄 Licença + +O Langflow é lançado sob a licença MIT. Veja o arquivo [LICENSE](LICENSE) para detalhes. diff --git a/README.md b/README.md index 3c29e83e2..68c8fde29 100644 --- a/README.md +++ b/README.md @@ -25,13 +25,18 @@

+
+ README in English + README in Portuguese + README in Simplified Chinese +
+

Your GIF

# 📝 Content -- [](#) - [📝 Content](#-content) - [📊 Get Started](#-get-started) - [🎚 Create Flows](#-create-flows) diff --git a/README.zh_CN.md b/README.zh_CN.md new file mode 100644 index 000000000..fee764902 --- /dev/null +++ b/README.zh_CN.md @@ -0,0 +1,172 @@ + + +# [![Langflow](./docs/static/img/hero.png)](https://www.langflow.org) + +

+ 䞀种甚于构建倚智胜䜓和RAG应甚的可视化框架 +

+

+ 匀源、Python驱劚、完党可定制、倧暡型䞔䞍䟝赖于特定的向量存傚 +

+ +

+ 文档 - + 加入我们的Discord瀟区 - + 圚X䞊关泚我们 - + 圚线䜓验 +

+ +

+ + + + + + +

+ +
+ README in English + README in Simplified Chinese +
+ +

+ Your GIF +

+ +# 📝 目圕 + +- [📝 目圕](#-目圕) +- [📊 快速匀始](#-快速匀始) +- [🎚 创建工䜜流](#-创建工䜜流) +- [郚眲](#郚眲) + - [圚Google Cloud Platform䞊郚眲Langflow](#圚google-cloud-platform䞊郚眲langflow) + - [圚Railway䞊郚眲](#圚railway䞊郚眲) + - [圚Render䞊郚眲](#圚render䞊郚眲) +- [🖥 呜什行界面 (CLI)](#-呜什行界面-cli) + - [甚法](#甚法) + - [环境变量](#环境变量) +- [👋 莡献](#-莡献) +- [🌟 莡献者](#-莡献者) +- [📄 讞可证](#-讞可证) + +# 📊 快速匀始 + +䜿甚 pip 安装 Langflow + +```shell +# 确保悚的系统已经安装䞊>=Python 3.10 +# 安装Langflow预发垃版本 +python -m pip install langflow --pre --force-reinstall + +# 安装Langflow皳定版本 +python -m pip install langflow -U +``` + +然后运行Langflow + +```shell +python -m langflow run +``` + +悚可以圚[HuggingFace Spaces](https://huggingface.co/spaces/Langflow/Langflow-Preview)䞭圚线䜓验 Langflow也可以䜿甚该铟接[克隆空闎](https://huggingface.co/spaces/Langflow/Langflow-Preview?duplicate=true)圚几分钟内创建悚自己的 Langflow 运行工䜜空闎。 + +# 🎚 创建工䜜流 + +䜿甚 Langflow 来创建工䜜流非垞简单。只需从䟧蟹栏拖劚组件到画垃䞊然后连接组件即可匀始构建应甚皋序。 + +悚可以通过猖蟑提瀺参数、将组件分组到单䞪高级组件䞭以及构建悚自己的自定义组件来展匀探玢。 + +完成后可以将工䜜流富出䞺 JSON 文件。 + +然后䜿甚以䞋脚本加蜜工䜜流 + +```python +from langflow.load import run_flow_from_json + +results = run_flow_from_json("path/to/flow.json", input_value="Hello, World!") +``` + +# 郚眲 + +## 圚Google Cloud Platform䞊郚眲Langflow + +请按照我们的分步指南䜿甚 Google Cloud Shell 圚 Google Cloud Platform (GCP) 䞊郚眲 Langflow。该指南圚 [**Langflow in Google Cloud Platform**](GCP_DEPLOYMENT.md) 文档䞭提䟛。 + +或者点击䞋面的 "Open in Cloud Shell" 按钮启劚 Google Cloud Shell克隆 Langflow 仓库并匀始䞀䞪互劚教皋该教皋将指富悚讟眮必芁的资源并圚 GCP 项目䞭郚眲 Langflow。 + +[![Open in Cloud Shell](https://gstatic.com/cloudssh/images/open-btn.svg)](https://console.cloud.google.com/cloudshell/open?git_repo=https://github.com/langflow-ai/langflow&working_dir=scripts/gcp&shellonly=true&tutorial=walkthroughtutorial_spot.md) + +## 圚Railway䞊郚眲 + +䜿甚歀暡板圚 Railway 䞊郚眲 Langflow 1.0 预览版 + +[![Deploy 1.0 Preview on Railway](https://railway.app/button.svg)](https://railway.app/template/UsJ1uB?referralCode=MnPSdg) + +或者䜿甚歀暡板郚眲 Langflow 0.6.x + +[![Deploy on Railway](https://railway.app/button.svg)](https://railway.app/template/JMXEWp?referralCode=MnPSdg) + +## 圚Render䞊郚眲 + + +Deploy to Render + + +# 🖥 呜什行界面 (CLI) + +Langflow提䟛了䞀䞪呜什行界面以䟿于平台的管理和配眮。 + +## 甚法 + +悚可以䜿甚以䞋呜什运行Langflow + +```shell +langflow run [OPTIONS] +``` + +呜什行参数的诊细诎明 + +- `--help`: 星瀺所有可甚参数。 +- `--host`: 定义绑定服务噚的䞻机host参数可以䜿甚 LANGFLOW_HOST 环境变量讟眮默讀倌䞺 127.0.0.1。 +- `--workers`: 讟眮工䜜进皋的数量可以䜿甚 LANGFLOW_WORKERS 环境变量讟眮默讀倌䞺 1。 +- `--timeout`: 讟眮工䜜进皋的超时时闎秒默讀倌䞺 60。 +- `--port`: 讟眮服务监听的端口可以䜿甚 LANGFLOW_PORT 环境变量讟眮默讀倌䞺 7860。 +- `--config`: 定义配眮文件的路埄默讀倌䞺 config.yaml。 +- `--env-file`: 指定包含环境变量的 .env 文件路埄默讀倌䞺 .env。 +- `--log-level`: 定义日志记圕级别可以䜿甚 LANGFLOW_LOG_LEVEL 环境变量讟眮默讀倌䞺 critical。 +- `--components-path`: 指定包含自定义组件的目圕路埄可以䜿甚 LANGFLOW_COMPONENTS_PATH 环境变量讟眮默讀倌䞺 langflow/components。 +- `--log-file`: 指定日志文件的路埄可以䜿甚 LANGFLOW_LOG_FILE 环境变量讟眮默讀倌䞺 logs/langflow.log。 +- `--cache`: 选择芁䜿甚的猓存类型可选项䞺 InMemoryCache 和 SQLiteCache可以䜿甚 LANGFLOW_LANGCHAIN_CACHE 环境变量讟眮默讀倌䞺 SQLiteCache。 +- `--dev/--no-dev`: 切换匀发/非匀发暡匏默讀倌䞺 no-dev即非匀发暡匏。 +- `--path`: 指定包含前端构建文件的目圕路埄歀参数仅甚于匀发目的可以䜿甚 LANGFLOW_FRONTEND_PATH 环境变量讟眮。 +- `--open-browser/--no-open-browser`: 切换启劚服务噚后是吊打匀浏览噚可以䜿甚 LANGFLOW_OPEN_BROWSER 环境变量讟眮默讀倌䞺 open-browser即启劚后打匀浏览噚。 +- `--remove-api-keys/--no-remove-api-keys`: 切换是吊从数据库䞭保存的项目䞭移陀 API 密钥可以䜿甚 LANGFLOW_REMOVE_API_KEYS 环境变量讟眮默讀倌䞺 no-remove-api-keys。 +- `--install-completion [bash|zsh|fish|powershell|pwsh]`: 䞺指定的 shell 安装自劚补党。 +- `--show-completion [bash|zsh|fish|powershell|pwsh]`: 星瀺指定 shell 的自劚补党䜿悚可以倍制或自定义安装。 +- `--backend-only`: 歀参数默讀䞺 False允讞仅运行后端服务噚而䞍运行前端也可以䜿甚 LANGFLOW_BACKEND_ONLY 环境变量讟眮。 +- `--store`: 歀参数默讀䞺 True启甚存傚功胜䜿甚 --no-store 可犁甚它可以䜿甚 LANGFLOW_STORE 环境变量配眮。 + +这些参数对于需芁定制 Langflow 行䞺的甚户尀其重芁特别是圚匀发或者特殊郚眲场景䞭。 + +### 环境变量 + +悚可以䜿甚环境变量配眮讞倚 CLI 参数选项。这些变量可以圚操䜜系统䞭富出或添加到 .env 文件䞭并䜿甚 --env-file 参数加蜜。 + +项目䞭包含䞀䞪名䞺 .env.example 的瀺䟋 .env 文件。将歀文件倍制䞺新文件 .env并甚实际讟眮倌替换瀺䟋倌。劂果同时圚操䜜系统和 .env 文件䞭讟眮倌则 .env 讟眮䌘先。 + +# 👋 莡献 + +我们欢迎各级匀发者䞺我们的 GitHub 匀源项目做出莡献并垮助 Langflow 曎加易甚劂果悚想参䞎莡献请查看我们的莡献指南 [contributing guidelines](./CONTRIBUTING.md) 。 + +--- + +[![Star History Chart](https://api.star-history.com/svg?repos=langflow-ai/langflow&type=Timeline)](https://star-history.com/#langflow-ai/langflow&Date) + +# 🌟 莡献者 + +[![langflow contributors](https://contrib.rocks/image?repo=langflow-ai/langflow)](https://github.com/langflow-ai/langflow/graphs/contributors) + +# 📄 讞可证 + +Langflow 以 MIT 讞可证发垃。有关诊细信息请参阅 [LICENSE](LICENSE) 文件。 diff --git a/docker/build_and_push.Dockerfile b/docker/build_and_push.Dockerfile index cabc1a753..63521d06a 100644 --- a/docker/build_and_push.Dockerfile +++ b/docker/build_and_push.Dockerfile @@ -1,6 +1,7 @@ # syntax=docker/dockerfile:1 # Keep this syntax directive! It's used to enable Docker BuildKit + ################################ # BUILDER-BASE # Used to build deps + create our virtual environment @@ -47,12 +48,10 @@ WORKDIR /app COPY pyproject.toml poetry.lock README.md ./ COPY src/ ./src COPY scripts/ ./scripts - RUN python -m pip install requests --user && cd ./scripts && python update_dependencies.py RUN $POETRY_HOME/bin/poetry lock --no-update \ - && $POETRY_HOME/bin/poetry install --no-interaction --no-ansi -E deploy \ && $POETRY_HOME/bin/poetry build -f wheel \ - && $POETRY_HOME/bin/poetry run pip install dist/*.whl + && $POETRY_HOME/bin/poetry run pip install dist/*.whl --force-reinstall ################################ # RUNTIME diff --git a/docs/docs/administration/api.mdx b/docs/docs/administration/api.mdx index 103c43f81..115cdc666 100644 --- a/docs/docs/administration/api.mdx +++ b/docs/docs/administration/api.mdx @@ -10,8 +10,7 @@ Langflow provides an API key functionality that allows users to access their ind The default user and password are set using the LANGFLOW_SUPERUSER and LANGFLOW_SUPERUSER_PASSWORD environment variables. -The default values are -langflow and langflow, respectively. +The default values are `langflow` and `langflow`, respectively. diff --git a/docs/docs/administration/cli.mdx b/docs/docs/administration/cli.mdx index a2a41adcd..11ea6fa65 100644 --- a/docs/docs/administration/cli.mdx +++ b/docs/docs/administration/cli.mdx @@ -1,62 +1,51 @@ # Command Line Interface (CLI) -## Overview - Langflow's Command Line Interface (CLI) is a powerful tool that allows you to interact with the Langflow server from the command line. The CLI provides a wide range of commands to help you shape Langflow to your needs. -Running the CLI without any arguments will display a list of available commands and options. +The available commands are below. Navigate to their individual sections of this page to see the parameters. + +* [langflow](#overview) +* [langflow api-key](#langflow-api-key) +* [langflow copy-db](#langflow-copy-db) +* [langflow migration](#langflow-migration) +* [langflow run](#langflow-run) +* [langflow superuser](#langflow-superuser) + +## Overview + +Running the CLI without any arguments displays a list of available options and commands. ```bash -python -m langflow run --help +langflow # or -python -m langflow run +langflow --help +# or +python -m langflow ``` -Each option for `run` command are detailed below: +| Command | Description | +| ------- | ----------- | +| `api-key` | Creates an API key for the default superuser if AUTO_LOGIN is enabled. | +| `copy-db` | Copy the database files to the current directory (`which langflow`). | +| `migration` | Run or test migrations. | +| `run` | Run the Langflow. | +| `superuser` | Create a superuser. | -- `--help`: Displays all available options. -- `--host`: Defines the host to bind the server to. Can be set using the `LANGFLOW_HOST` environment variable. The default is `127.0.0.1`. -- `--workers`: Sets the number of worker processes. Can be set using the `LANGFLOW_WORKERS` environment variable. The default is `1`. -- `--timeout`: Sets the worker timeout in seconds. The default is `60`. -- `--port`: Sets the port to listen on. Can be set using the `LANGFLOW_PORT` environment variable. The default is `7860`. -- `--env-file`: Specifies the path to the .env file containing environment variables. The default is `.env`. -- `--log-level`: Defines the logging level. Can be set using the `LANGFLOW_LOG_LEVEL` environment variable. The default is `critical`. -- `--components-path`: Specifies the path to the directory containing custom components. Can be set using the `LANGFLOW_COMPONENTS_PATH` environment variable. The default is `langflow/components`. -- `--log-file`: Specifies the path to the log file. Can be set using the `LANGFLOW_LOG_FILE` environment variable. The default is `logs/langflow.log`. -- `--cache`: Select the type of cache to use. Options are `InMemoryCache` and `SQLiteCache`. Can be set using the `LANGFLOW_LANGCHAIN_CACHE` environment variable. The default is `SQLiteCache`. -- `--dev/--no-dev`: Toggles the development mode. The default is `no-dev`. -- `--path`: Specifies the path to the frontend directory containing build files. This option is for development purposes only. Can be set using the `LANGFLOW_FRONTEND_PATH` environment variable. -- `--open-browser/--no-open-browser`: Toggles the option to open the browser after starting the server. Can be set using the `LANGFLOW_OPEN_BROWSER` environment variable. The default is `open-browser`. -- `--remove-api-keys/--no-remove-api-keys`: Toggles the option to remove API keys from the projects saved in the database. Can be set using the `LANGFLOW_REMOVE_API_KEYS` environment variable. The default is `no-remove-api-keys`. -- `--install-completion [bash|zsh|fish|powershell|pwsh]`: Installs completion for the specified shell. -- `--show-completion [bash|zsh|fish|powershell|pwsh]`: Shows completion for the specified shell, allowing you to copy it or customize the installation. -- `--backend-only`: This parameter, with a default value of `False`, allows running only the backend server without the frontend. It can also be set using the `LANGFLOW_BACKEND_ONLY` environment variable. -- `--store`: This parameter, with a default value of `True`, enables the store features, use `--no-store` to deactivate it. It can be configured using the `LANGFLOW_STORE` environment variable. +### Options -These parameters are important for users who need to customize the behavior of Langflow, especially in development or specialized deployment scenarios. +| Option | Description | +| ------ | ----------- | +| `--install-completion` | Install completion for the current shell. | +| `--show-completion` | Show completion for the current shell, to copy it or customize the installation. | +| `--help` | Show this message and exit. | -### API Key Command +## langflow api-key -The `api-key` command allows you to create an API key for accessing Langflow's API when `LANGFLOW_AUTO_LOGIN` is set to `True`. - -```bash -python -m langflow api-key --help - - Usage: langflow api-key [OPTIONS] - - Creates an API key for the default superuser if AUTO_LOGIN is enabled. - Args: log_level (str, optional): Logging level. Defaults to "error". - Returns: None - -╭─ Options ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮ -│ --log-level TEXT Logging level. [env var: LANGFLOW_LOG_LEVEL] [default: error] │ -│ --help Show this message and exit. │ -╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯ -``` - -Once you run the `api-key` command, it will create an API key for the default superuser if `LANGFLOW_AUTO_LOGIN` is set to `True`. +Run the `api-key` command to create an API key for the default superuser if `LANGFLOW_AUTO_LOGIN` is set to `True`. ```bash +langflow api-key +# or python -m langflow api-key ╭─────────────────────────────────────────────────────────────────────╮ │ API Key Created Successfully: │ @@ -67,11 +56,99 @@ python -m langflow api-key │ Make sure to store it in a secure location. │ │ │ │ The API key has been copied to your clipboard. Cmd + V to paste it. │ -╰─────────────────────────────────────────────────────────────────────╯ +╰────────────────────────────── ``` -### Environment Variables +### Options + +| Option | Type | Description | +|------------------|------|-------------------------------------------------------------| +| --log-level | TEXT | Logging level. [env var: LANGFLOW_LOG_LEVEL] [default: error] | +| --help | | Show this message and exit. | + +## langflow copy-db + +Run the `copy-db` command to copy the cached `langflow.db` and `langflow-pre.db` database files to the current directory. + +If the files exist in the cache directory, they will be copied to the same directory as `__main__.py`, which can be found with `which langflow`. + +### Options + +None. + +## langflow migration + +Run or test migrations with the [Alembic](https://pypi.org/project/alembic/) database tool. + +```bash +langflow migration +# or +python -m langflow migration +``` + +### Options +| Option | Description | +|-----------------|-------------------------------------------------------------| +| `--test, --no-test` | Run migrations in test mode. [default: test] | +| `--fix, --no-fix` | Fix migrations. This is a destructive operation, and should only be used if you know what you are doing. [default: no-fix] | +| `--help` | Show this message and exit. | + + +## langflow run + +Run Langflow. + +```bash +langflow run +# or +python -m langflow run +``` + +### Options + +| Option | Description | +|-------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| +| `--help` | Displays all available options. | +| `--host` | Defines the host to bind the server to. Can be set using the `LANGFLOW_HOST` environment variable. The default is `127.0.0.1`. | +| `--workers` | Sets the number of worker processes. Can be set using the `LANGFLOW_WORKERS` environment variable. The default is `1`. | +| `--timeout` | Sets the worker timeout in seconds. The default is `60`. | +| `--port` | Sets the port to listen on. Can be set using the `LANGFLOW_PORT` environment variable. The default is `7860`. | +| `--env-file` | Specifies the path to the .env file containing environment variables. The default is `.env`. | +| `--log-level` | Defines the logging level. Can be set using the `LANGFLOW_LOG_LEVEL` environment variable. The default is `critical`. | +| `--components-path` | Specifies the path to the directory containing custom components. Can be set using the `LANGFLOW_COMPONENTS_PATH` environment variable. The default is `langflow/components`. | +| `--log-file` | Specifies the path to the log file. Can be set using the `LANGFLOW_LOG_FILE` environment variable. The default is `logs/langflow.log`. | +| `--cache` | Select the type of cache to use. Options are `InMemoryCache` and `SQLiteCache`. Can be set using the `LANGFLOW_LANGCHAIN_CACHE` environment variable. The default is `SQLiteCache`. | +| `--dev`/`--no-dev` | Toggles the development mode. The default is `no-dev`. | +| `--path` | Specifies the path to the frontend directory containing build files. This option is for development purposes only. Can be set using the `LANGFLOW_FRONTEND_PATH` environment variable. | +| `--open-browser`/`--no-open-browser`| Toggles the option to open the browser after starting the server. Can be set using the `LANGFLOW_OPEN_BROWSER` environment variable. The default is `open-browser`. | +| `--remove-api-keys`/`--no-remove-api-keys`| Toggles the option to remove API keys from the projects saved in the database. Can be set using the `LANGFLOW_REMOVE_API_KEYS` environment variable. The default is `no-remove-api-keys`. | +| `--install-completion [bash\|zsh\|fish\|powershell\|pwsh]`| Installs completion for the specified shell. | +| `--show-completion [bash\|zsh\|fish\|powershell\|pwsh]` | Shows completion for the specified shell, allowing you to copy it or customize the installation. | +| `--backend-only` | This parameter, with a default value of `False`, allows running only the backend server without the frontend. It can also be set using the `LANGFLOW_BACKEND_ONLY` environment variable. For more, see [Backend-only](../deployment/backend-only.md).| +| `--store` | This parameter, with a default value of `True`, enables the store features, use `--no-store` to deactivate it. It can be configured using the `LANGFLOW_STORE` environment variable. | + +#### Environment Variables You can configure many of the CLI options using environment variables. These can be exported in your operating system or added to a `.env` file and loaded using the `--env-file` option. A sample `.env` file named `.env.example` is included with the project. Copy this file to a new file named `.env` and replace the example values with your actual settings. If you're setting values in both your OS and the `.env` file, the `.env` settings will take precedence. + +## langflow superuser + +Create a superuser for Langflow. + +```bash +langflow superuser +# or +python -m langflow superuser +``` + +### Options + +| Option | Type | Description | +|----------------|-------|-------------------------------------------------------------| +| `--username` | TEXT | Username for the superuser. [default: None] [required] | +| `--password` | TEXT | Password for the superuser. [default: None] [required] | +| `--log-level` | TEXT | Logging level. [env var: LANGFLOW_LOG_LEVEL] [default: error] | +| `--help` | | Show this message and exit. | + diff --git a/docs/docs/administration/global-env.mdx b/docs/docs/administration/global-env.mdx index c23ca8dd1..51e5d633e 100644 --- a/docs/docs/administration/global-env.mdx +++ b/docs/docs/administration/global-env.mdx @@ -1,31 +1,39 @@ +import ThemedImage from "@theme/ThemedImage"; +import useBaseUrl from "@docusaurus/useBaseUrl"; import ZoomableImage from "/src/theme/ZoomableImage.js"; -import Admonition from "@theme/Admonition"; import ReactPlayer from "react-player"; +import Admonition from "@theme/Admonition"; -# Global Environment Variables +# Global Variables -Langflow 1.0 alpha includes the option to add **Global Environment Variables** for your application. +Global Variables are a useful feature of Langflow, allowing you to define reusable variables accessed from any Text field in your project. -## Add a global variable to a project +## TL;DR -In this example, you'll add the `openai_api_key` credential as a global environment variable to the **Basic Prompting** starter project. +- Global Variables are reusable variables accessible from any Text field in your project. +- To create one, click the 🌐 button in a Text field and then **+ Add New Variable**. +- Define the **Name**, **Type**, and **Value** of the variable. +- Click **Save Variable** to create it. +- All Credential Global Variables are encrypted and accessible only by you. +- Set _`LANGFLOW_STORE_ENVIRONMENT_VARIABLES`_ to _`true`_ in your `.env` file to add all variables in _`LANGFLOW_VARIABLES_TO_GET_FROM_ENVIRONMENT`_ to your user's Global Variables. -For more information on the starter flow, see [Basic prompting](../starter-projects/basic-prompting.mdx). +## Creating and Adding a Global Variable -1. From the Langflow dashboard, click **New Project**. -2. Select **Basic Prompting**. +To create and add a global variable, click the 🌐 button in a Text field, and then click **+ Add New Variable**. -The **Basic Prompting** flow is created. +Text fields are where you write text without opening a Text area, and are identified with the 🌐 icon. -3. To create an environment variable for the **OpenAI** component: - 1. In the **OpenAI API Key** field, click the **Globe** button, and then click **Add New Variable**. - 2. In the **Variable Name** field, enter `openai_api_key`. - 3. In the **Value** field, paste your OpenAI API Key (`sk-...`). - 4. For the variable **Type**, select **Credential**. - 5. In the **Apply to Fields** field, select **OpenAI API Key** to apply this variable to all fields named **OpenAI API Key**. - 6. Click **Save Variable**. +For example, to create an environment variable for the **OpenAI** component: + +1. In the **OpenAI API Key** text field, click the 🌐 button, then **Add New Variable**. +2. Enter `openai_api_key` in the **Variable Name** field. +3. Paste your OpenAI API Key (`sk-...`) in the **Value** field. +4. Select **Credential** for the **Type**. +5. Choose **OpenAI API Key** in the **Apply to Fields** field to apply this variable to all fields named **OpenAI API Key**. +6. Click **Save Variable**. You now have a `openai_api_key` global environment variable for your Langflow project. +Subsequently, clicking the 🌐 button in a Text field will display the new variable in the dropdown. You can also create global variables in **Settings** > **Variables and @@ -41,10 +49,55 @@ You now have a `openai_api_key` global environment variable for your Langflow pr style={{ width: "40%", margin: "20px auto" }} /> -4. To view and manage your project's global environment variables, visit **Settings** > **Variables and Secrets**. +To view and manage your project's global environment variables, visit **Settings** > **Variables and Secrets**. For more on variables in HuggingFace Spaces, see [Managing Secrets](https://huggingface.co/docs/hub/spaces-overview#managing-secrets). +{/* All variables are encrypted */} + + + All Credential Global Variables are encrypted and accessible only by you. + + +## Configuring Environment Variables in your .env file + +Setting `LANGFLOW_STORE_ENVIRONMENT_VARIABLES` to `true` in your `.env` file (default) adds all variables in `LANGFLOW_VARIABLES_TO_GET_FROM_ENVIRONMENT` to your user's Global Variables. + +These variables are accessible like any other Global Variable. + + + To prevent this behavior, set `LANGFLOW_STORE_ENVIRONMENT_VARIABLES` to + `false` in your `.env` file. + + +You can specify variables to get from the environment by listing them in `LANGFLOW_VARIABLES_TO_GET_FROM_ENVIRONMENT`. + +Specify variables as a comma-separated list (e.g., _`"VARIABLE1, VARIABLE2"`_) or a JSON-encoded string (e.g., _`'["VARIABLE1", "VARIABLE2"]'`_). + +The default list of variables includes: + +- ANTHROPIC_API_KEY +- ASTRA_DB_API_ENDPOINT +- ASTRA_DB_APPLICATION_TOKEN +- AZURE_OPENAI_API_KEY +- AZURE_OPENAI_API_DEPLOYMENT_NAME +- AZURE_OPENAI_API_EMBEDDINGS_DEPLOYMENT_NAME +- AZURE_OPENAI_API_INSTANCE_NAME +- AZURE_OPENAI_API_VERSION +- COHERE_API_KEY +- GOOGLE_API_KEY +- GROQ_API_KEY +- HUGGINGFACEHUB_API_TOKEN +- OPENAI_API_KEY +- PINECONE_API_KEY +- SEARCHAPI_API_KEY +- SERPAPI_API_KEY +- UPSTASH_VECTOR_REST_URL +- UPSTASH_VECTOR_REST_TOKEN +- VECTARA_CUSTOMER_ID +- VECTARA_CORPUS_ID +- VECTARA_API_KEY + ## Video
- Read the [Custom Component Guidelines](../administration/custom-component) for detailed information on custom components. + Read the [Custom Component Guidelines](../administration/custom-component) for + detailed information on custom components. Custom components let you extend Langflow by creating reusable and configurable components from a Python script. @@ -31,57 +32,60 @@ This class is the foundation for creating custom components. It allows users to The following types are supported in the build method: -| Supported Types | -| --------------------------------------------------------- | -| _`str`_, _`int`_, _`float`_, _`bool`_, _`list`_, _`dict`_ | -| _`langflow.field_typing.NestedDict`_ | -| _`langflow.field_typing.Prompt`_ | -| _`langchain.chains.base.Chain`_ | -| _`langchain.PromptTemplate`_ | +| Supported Types | +| ----------------------------------------------------------------- | +| _`str`_, _`int`_, _`float`_, _`bool`_, _`list`_, _`dict`_ | +| _`langflow.field_typing.NestedDict`_ | +| _`langflow.field_typing.Prompt`_ | +| _`langchain.chains.base.Chain`_ | +| _`langchain.PromptTemplate`_ | | _`from langchain.schema.language_model import BaseLanguageModel`_ | -| _`langchain.Tool`_ | -| _`langchain.document_loaders.base.BaseLoader`_ | -| _`langchain.schema.Document`_ | -| _`langchain.text_splitters.TextSplitter`_ | -| _`langchain.vectorstores.base.VectorStore`_ | -| _`langchain.embeddings.base.Embeddings`_ | -| _`langchain.schema.BaseRetriever`_ | +| _`langchain.Tool`_ | +| _`langchain.document_loaders.base.BaseLoader`_ | +| _`langchain.schema.Document`_ | +| _`langchain.text_splitters.TextSplitter`_ | +| _`langchain.vectorstores.base.VectorStore`_ | +| _`langchain.embeddings.base.Embeddings`_ | +| _`langchain.schema.BaseRetriever`_ | The difference between _`dict`_ and _`langflow.field_typing.NestedDict`_ is that one adds a simple key-value pair field, while the other opens a more robust dictionary editor. - Use the `Prompt` type by adding **kwargs to the build method. - If you want to add the values of the variables to the template you defined, format the `PromptTemplate` inside the `CustomComponent` class. + Use the `Prompt` type by adding **kwargs to the build method. If you want to + add the values of the variables to the template you defined, format the + `PromptTemplate` inside the `CustomComponent` class. - Use base Python types without a handle by default. To add handles, use the `input_types` key in the `build_config` method. + Use base Python types without a handle by default. To add handles, use the + `input_types` key in the `build_config` method. **build_config:** Defines the configuration fields of the component. This method returns a dictionary where each key represents a field name and each value defines the field's behavior. Supported keys for configuring fields: -| Key | Description | -| --------------------- | --------------------------------------------------- | -| `is_list` | Boolean indicating if the field can hold multiple values. | -| `options` | Dropdown menu options. | -| `multiline` | Boolean indicating if a field allows multiline input. | -| `input_types` | Allows connection handles for string fields. | -| `display_name` | Field name displayed in the UI. | -| `advanced` | Hides the field in the default UI view. | -| `password` | Masks input, useful for sensitive data. | -| `required` | Overrides the default behavior to make a field mandatory. | -| `info` | Tooltip for the field. | -| `file_types` | Accepted file types, useful for file fields. | -| `range_spec` | Defines valid ranges for float fields. | -| `title_case` | Boolean that controls field name capitalization. | -| `refresh_button` | Adds a refresh button that updates field values. | -| `real_time_refresh` | Updates the configuration as field values change. | -| `field_type` | Automatically set based on the build method's type hint. | +| Key | Description | +| ------------------- | --------------------------------------------------------- | +| `is_list` | Boolean indicating if the field can hold multiple values. | +| `options` | Dropdown menu options. | +| `multiline` | Boolean indicating if a field allows multiline input. | +| `input_types` | Allows connection handles for string fields. | +| `display_name` | Field name displayed in the UI. | +| `advanced` | Hides the field in the default UI view. | +| `password` | Masks input, useful for sensitive data. | +| `required` | Overrides the default behavior to make a field mandatory. | +| `info` | Tooltip for the field. | +| `file_types` | Accepted file types, useful for file fields. | +| `range_spec` | Defines valid ranges for float fields. | +| `title_case` | Boolean that controls field name capitalization. | +| `refresh_button` | Adds a refresh button that updates field values. | +| `real_time_refresh` | Updates the configuration as field values change. | +| `field_type` | Automatically set based on the build method's type hint. | - Use the `update_build_config` method to dynamically update configurations based on field values. + Use the `update_build_config` method to dynamically update configurations + based on field values. ## Additional methods and attributes @@ -99,4 +103,3 @@ The `CustomComponent` class also provides helpful methods for specific tasks (e. - `status`: Shows values from the `build` method, useful for debugging. - `field_order`: Controls the display order of fields. - `icon`: Sets the canvas display icon. - diff --git a/docs/docs/components/inputs-and-outputs.mdx b/docs/docs/components/inputs-and-outputs.mdx new file mode 100644 index 000000000..2a624221a --- /dev/null +++ b/docs/docs/components/inputs-and-outputs.mdx @@ -0,0 +1,161 @@ +import Admonition from "@theme/Admonition"; +import ZoomableImage from "/src/theme/ZoomableImage.js"; + +# Inputs and Outputs + +TL;DR: Inputs and Outputs are a category of components that are used to define where data comes in and out of your flow. +They also dynamically change the Playground and can be renamed to facilitate building and maintaining your flows. + +## Inputs + +Inputs are components used to define where data enters your flow. They can receive data from the user, a database, or any other source that can be converted to Text or Record. + +The difference between Chat Input and other Input components is the output format, the number of configurable fields, and the way they are displayed in the Playground. + +Chat Input components can output `Text` or `Record`. When you want to pass the sender name or sender to the next component, use the `Record` output. To pass only the message, use the `Text` output, useful when saving the message to a database or memory system like Zep. + +You can find out more about Chat Input and other Inputs [here](#chat-input). + +### Chat Input + +This component collects user input from the chat. + +**Parameters** + +- **Sender Type:** Specifies the sender type. Defaults to `User`. Options are `Machine` and `User`. +- **Sender Name:** Specifies the name of the sender. Defaults to `User`. +- **Message:** Specifies the message text. It is a multiline text input. +- **Session ID:** Specifies the session ID of the chat history. If provided, the message will be saved in the Message History. + + +

+ If `As Record` is `true` and the `Message` is a `Record`, the data of the + `Record` will be updated with the `Sender`, `Sender Name`, and `Session ID`. +

+ + + + +One significant capability of the Chat Input component is its ability to transform the Playground into a chat window. This feature is particularly valuable for scenarios requiring user input to initiate or influence the flow. + + + +### Text Input + +The **Text Input** component adds an **Input** field on the Playground. This enables you to define parameters while running and testing your flow. + +**Parameters** + +- **Value:** Specifies the text input value. This is where the user inputs text data that will be passed to the next component in the sequence. If no value is provided, it defaults to an empty string. +- **Record Template:** Specifies how a `Record` should be converted into `Text`. + +The **Record Template** field is used to specify how a `Record` should be converted into `Text`. This is particularly useful when you want to extract specific information from a `Record` and pass it as text to the next component in the sequence. + +For example, if you have a `Record` with the following structure: + +```json +{ + "name": "John Doe", + "age": 30, + "email": "johndoe@email.com" +} +``` + +A template with `Name: {name}, Age: {age}` will convert the `Record` into a text string of `Name: John Doe, Age: 30`. + +If you pass more than one `Record`, the text will be concatenated with a new line separator. + + + +## Outputs + +Outputs are components that are used to define where data comes out of your flow. They can be used to send data to the user, to the Playground, or to define how the data will be displayed in the Playground. + +The Chat Output works similarly to the Chat Input but does not have a field that allows for written input. It is used as an Output definition and can be used to send data to the user. + +You can find out more about it and the other Outputs [here](#chat-output). + +### Chat Output + +This component sends a message to the chat. + +**Parameters** + +- **Sender Type:** Specifies the sender type. Default is `"Machine"`. Options are `"Machine"` and `"User"`. + +- **Sender Name:** Specifies the sender's name. Default is `"AI"`. + +- **Session ID:** Specifies the session ID of the chat history. If provided, messages are saved in the Message History. + +- **Message:** Specifies the text of the message. + + +

+ If `As Record` is `true` and the `Message` is a `Record`, the data in the + `Record` is updated with the `Sender`, `Sender Name`, and `Session ID`. +

+ + +### Text Output + +This component displays text data to the user. It is useful when you want to show text without sending it to the chat. + +**Parameters** + +- **Value:** Specifies the text data to be displayed. Defaults to an empty string. + +The `TextOutput` component provides a simple way to display text data. It allows textual data to be visible in the chat window during your interaction flow. + +## Prompts + +A prompt is the input provided to a language model, consisting of multiple components and can be parameterized using prompt templates. A prompt template offers a reproducible method for generating prompts, enabling easy customization through input variables. + +### Prompt + +This component creates a prompt template with dynamic variables. This is useful for structuring prompts and passing dynamic data to a language model. + +**Parameters** + +- **Template:** The template for the prompt. This field allows you to create other fields dynamically by using curly brackets `{}`. For example, if you have a template like `Hello {name}, how are you?`, a new field called `name` will be created. Prompt variables can be created with any name inside curly brackets, e.g. `{variable_name}`. + + + +### PromptTemplate + +The `PromptTemplate` component enables users to create prompts and define variables that control how the model is instructed. Users can input a set of variables which the template uses to generate the prompt when a conversation starts. + + + After defining a variable in the prompt template, it acts as its own component + input. See [Prompt Customization](../administration/prompt-customization) for + more details. + + +- **template:** The template used to format an individual request. diff --git a/docs/docs/components/inputs.mdx b/docs/docs/components/inputs.mdx deleted file mode 100644 index 854f7fee3..000000000 --- a/docs/docs/components/inputs.mdx +++ /dev/null @@ -1,99 +0,0 @@ -import Admonition from '@theme/Admonition'; -import ZoomableImage from "/src/theme/ZoomableImage.js"; - -# Inputs - -## Chat Input - -This component obtains user input from the chat. - -**Parameters** - -- **Sender Type:** Specifies the sender type. Defaults to `User`. Options are `Machine` and `User`. -- **Sender Name:** Specifies the name of the sender. Defaults to `User`. -- **Message:** Specifies the message text. It is a multiline text input. -- **Session ID:** Specifies the session ID of the chat history. If provided, the message will be saved in the Message History. - - -

- If `As Record` is `true` and the `Message` is a `Record`, the data - of the `Record` will be updated with the `Sender`, `Sender Name`, and - `Session ID`. -

- - - - -One significant capability of the Chat Input component is its ability to transform the Playground into a chat window. This feature is particularly valuable for scenarios requiring user input to initiate or influence the flow. - - - ---- - -## Prompt - -This component creates a prompt template with dynamic variables. This is useful for structuring prompts and passing dynamic data to a language model. - -**Parameters** - -- **Template:** The template for the prompt. This field allows you to create other fields dynamically by using curly brackets `{}`. For example, if you have a template like `Hello {name}, how are you?`, a new field called `name` will be created. Prompt variables can be created with any name inside curly brackets, e.g. `{variable_name}`. - - - ---- - -## Text Input - -The **Text Input** component adds an **Input** field on the Playground. This enables you to define parameters while running and testing your flow. - -**Parameters** - -- **Value:** Specifies the text input value. This is where the user inputs text data that will be passed to the next component in the sequence. If no value is provided, it defaults to an empty string. -- **Record Template:** Specifies how a `Record` should be converted into `Text`. - -The **Record Template** field is used to specify how a `Record` should be converted into `Text`. This is particularly useful when you want to extract specific information from a `Record` and pass it as text to the next component in the sequence. - -For example, if you have a `Record` with the following structure: - -```json -{ - "name": "John Doe", - "age": 30, - "email": "johndoe@email.com" -} -``` - -A template with `Name: {name}, Age: {age}` will convert the `Record` into a text string of `Name: John Doe, Age: 30`. - -If you pass more than one `Record`, the text will be concatenated with a new line separator. - - - diff --git a/docs/docs/components/outputs.mdx b/docs/docs/components/outputs.mdx deleted file mode 100644 index a8947e60e..000000000 --- a/docs/docs/components/outputs.mdx +++ /dev/null @@ -1,34 +0,0 @@ -import Admonition from '@theme/Admonition'; - -# Outputs - -## Chat Output - -This component sends a message to the chat. - -**Parameters** - -- **Sender Type:** Specifies the sender type. Default is `"Machine"`. Options are `"Machine"` and `"User"`. - -- **Sender Name:** Specifies the sender's name. Default is `"AI"`. - -- **Session ID:** Specifies the session ID of the chat history. If provided, messages are saved in the Message History. - -- **Message:** Specifies the text of the message. - - -

- If `As Record` is `true` and the `Message` is a `Record`, the data in the `Record` is updated with the `Sender`, `Sender Name`, and `Session ID`. -

- - -## Text Output - -This component displays text data to the user. It is useful when you want to show text without sending it to the chat. - -**Parameters** - -- **Value:** Specifies the text data to be displayed. Defaults to an empty string. - - -The `TextOutput` component provides a simple way to display text data. It allows textual data to be visible in the chat window during your interaction flow. diff --git a/docs/docs/components/prompts.mdx b/docs/docs/components/prompts.mdx deleted file mode 100644 index 19fdedf11..000000000 --- a/docs/docs/components/prompts.mdx +++ /dev/null @@ -1,25 +0,0 @@ -import Admonition from "@theme/Admonition"; - -# Prompts - - -

- Thank you for your patience as we refine our documentation. It may - still have some areas under development. Please share your feedback or report any issues to help us improve! -

- - -A prompt is the input provided to a language model, consisting of multiple components and can be parameterized using prompt templates. A prompt template offers a reproducible method for generating prompts, enabling easy customization through input variables. - ---- - -### PromptTemplate - -The `PromptTemplate` component enables users to create prompts and define variables that control how the model is instructed. Users can input a set of variables which the template uses to generate the prompt when a conversation starts. - - - After defining a variable in the prompt template, it acts as its own component - input. See [Prompt Customization](../administration/prompt-customization) for more details. - - -- **template:** The template used to format an individual request. diff --git a/docs/docs/components/text-and-record.mdx b/docs/docs/components/text-and-record.mdx new file mode 100644 index 000000000..24c16e4aa --- /dev/null +++ b/docs/docs/components/text-and-record.mdx @@ -0,0 +1,49 @@ +# Text and Record + +In Langflow 1.0, we added two main input and output types: `Text` and `Record`. + +`Text` is a simple string input and output type, while `Record` is a structure very similar to a dictionary in Python. It is a key-value pair data structure. + +We've created a few components to help you work with these types. Let's see how a few of them work. + +## Records To Text + +This is a component that takes in Records and outputs a `Text`. It does this using a template string and concatenating the values of the `Record`, one per line. + +If we have the following Records: + +```json +{ + "sender_name": "Alice", + "message": "Hello!" +} +{ + "sender_name": "John", + "message": "Hi!" +} +``` + +And the template string is: _`{sender_name}: {message}`_ + +The output is: + +``` +Alice: Hello! +John: Hi! +``` + +## Create Record + +This component allows you to create a `Record` from a number of inputs. You can add as many key-value pairs as you want (as long as it is less than 15). Once you've picked that number you'll need to write the name of the Key and can pass `Text` values from other components to it. + +## Documents To Records + +This component takes in a LangChain `Document` and outputs a `Record`. It does this by extracting the `page_content` and the `metadata` from the `Document` and adding them to the `Record` as text and data respectively. + +## Why is this useful? + +The idea was to create a unified way to work with complex data in Langflow and to make it easier to work with data that is not just a simple string. This way you can create more complex workflows and use the data in more ways. + +## What's next? + +We are planning to integrate an array of modalities to Langflow, such as images, audio, and video. This will allow you to create even more complex workflows and use cases. Stay tuned for more updates! 🚀 diff --git a/docs/docs/components/vector-stores.mdx b/docs/docs/components/vector-stores.mdx index 7e21f1021..6072abe29 100644 --- a/docs/docs/components/vector-stores.mdx +++ b/docs/docs/components/vector-stores.mdx @@ -1,6 +1,6 @@ import Admonition from "@theme/Admonition"; -# Vector Stores Documentation +# Vector Stores ### Astra DB diff --git a/docs/docs/deployment/backend-only.md b/docs/docs/deployment/backend-only.md new file mode 100644 index 000000000..c82e55aa6 --- /dev/null +++ b/docs/docs/deployment/backend-only.md @@ -0,0 +1,113 @@ +# Backend-only +You can run Langflow in `--backend-only` mode to expose your Langflow app as an API, without running the frontend UI. + +Start langflow in backend-only mode with `python3 -m langflow run --backend-only`. + +The terminal prints ` Welcome to ⛓ Langflow `, and a blank window opens at `http://127.0.0.1:7864/all`. +Langflow will now serve requests to its API without the frontend running. + +## Prerequisites + +* [Langflow installed](../getting-started/install-langflow.mdx) + +* [OpenAI API key](https://platform.openai.com) + +* [A Langflow flow created](../starter-projects/basic-prompting.mdx) + +## Download your flow's curl call + +1. Click API. +2. Click **curl** > **Copy code** and save the code to your local machine. +It will look something like this: +```curl +curl -X POST \ + "http://127.0.0.1:7864/api/v1/run/ef7e0554-69e5-4e3e-ab29-ee83bcd8d9ef?stream=false" \ + -H 'Content-Type: application/json'\ + -d '{"input_value": "message", + "output_type": "chat", + "input_type": "chat", + "tweaks": { + "Prompt-kvo86": {}, + "OpenAIModel-MilkD": {}, + "ChatOutput-ktwdw": {}, + "ChatInput-xXC4F": {} +}}' +``` +Note the flow ID of `ef7e0554-69e5-4e3e-ab29-ee83bcd8d9ef`. You can find this ID in the UI as well to ensure you're querying the right flow. + +## Start Langflow in backend-only mode + +1. Stop Langflow with Ctrl+C. +2. Start langflow in backend-only mode with `python3 -m langflow run --backend-only`. +The terminal prints ` Welcome to ⛓ Langflow `, and a blank window opens at `http://127.0.0.1:7864/all`. +Langflow will now serve requests to its API. +3. Run the curl code you copied from the UI. +You should get a result like this: +```bash +{"session_id":"ef7e0554-69e5-4e3e-ab29-ee83bcd8d9ef:bf81d898868ac87e1b4edbd96c131c5dee801ea2971122cc91352d144a45b880","outputs":[{"inputs":{"input_value":"hi, are you there?"},"outputs":[{"results":{"result":"Arrr, ahoy matey! Aye, I be here. What be ye needin', me hearty?"},"artifacts":{"message":"Arrr, ahoy matey! Aye, I be here. What be ye needin', me hearty?","sender":"Machine","sender_name":"AI"},"messages":[{"message":"Arrr, ahoy matey! Aye, I be here. What be ye needin', me hearty?","sender":"Machine","sender_name":"AI","component_id":"ChatOutput-ktwdw"}],"component_display_name":"Chat Output","component_id":"ChatOutput-ktwdw","used_frozen_result":false}]}]}% +``` +Again, note that the flow ID matches. +Langflow is receiving your POST request, running the flow, and returning the result, all without running the frontend. Cool! + +## Download your flow's Python API call + +Instead of using curl, you can download your flow as a Python API call instead. + +1. Click API. +2. Click **Python API** > **Copy code** and save the code to your local machine. +The code will look something like this: +```python +import requests +from typing import Optional + +BASE_API_URL = "http://127.0.0.1:7864/api/v1/run" +FLOW_ID = "ef7e0554-69e5-4e3e-ab29-ee83bcd8d9ef" +# You can tweak the flow by adding a tweaks dictionary +# e.g {"OpenAI-XXXXX": {"model_name": "gpt-4"}} + +def run_flow(message: str, + flow_id: str, + output_type: str = "chat", + input_type: str = "chat", + tweaks: Optional[dict] = None, + api_key: Optional[str] = None) -> dict: + """ + Run a flow with a given message and optional tweaks. + + :param message: The message to send to the flow + :param flow_id: The ID of the flow to run + :param tweaks: Optional tweaks to customize the flow + :return: The JSON response from the flow + """ + api_url = f"{BASE_API_URL}/{flow_id}" + + payload = { + "input_value": message, + "output_type": output_type, + "input_type": input_type, + } + headers = None + if tweaks: + payload["tweaks"] = tweaks + if api_key: + headers = {"x-api-key": api_key} + response = requests.post(api_url, json=payload, headers=headers) + return response.json() + +# Setup any tweaks you want to apply to the flow +message = "message" + +print(run_flow(message=message, flow_id=FLOW_ID)) +``` +3. Run your Python app: +```python +python3 app.py +``` + +The result is similar to the curl call: +```bash +{'session_id': 'ef7e0554-69e5-4e3e-ab29-ee83bcd8d9ef:bf81d898868ac87e1b4edbd96c131c5dee801ea2971122cc91352d144a45b880', 'outputs': [{'inputs': {'input_value': 'message'}, 'outputs': [{'results': {'result': "Arrr matey! What be yer message for this ol' pirate? Speak up or walk the plank!"}, 'artifacts': {'message': "Arrr matey! What be yer message for this ol' pirate? Speak up or walk the plank!", 'sender': 'Machine', 'sender_name': 'AI'}, 'messages': [{'message': "Arrr matey! What be yer message for this ol' pirate? Speak up or walk the plank!", 'sender': 'Machine', 'sender_name': 'AI', 'component_id': 'ChatOutput-ktwdw'}], 'component_display_name': 'Chat Output', 'component_id': 'ChatOutput-ktwdw', 'used_frozen_result': False}]}]} +``` +Your Python app POSTs to your Langflow server, and the server runs the flow and returns the result. + +See [API](../administration/api.mdx) for more ways to interact with your headless Langflow server. \ No newline at end of file diff --git a/docs/docs/deployment/docker.md b/docs/docs/deployment/docker.md new file mode 100644 index 000000000..1ebb5746e --- /dev/null +++ b/docs/docs/deployment/docker.md @@ -0,0 +1,65 @@ +# Docker + +This guide will help you get LangFlow up and running using Docker and Docker Compose. + +## Prerequisites + +- Docker +- Docker Compose + +## Steps + +1. Clone the LangFlow repository: + + ```sh + git clone https://github.com/langflow-ai/langflow.git + ``` + +2. Navigate to the `docker_example` directory: + + ```sh + cd langflow/docker_example + ``` + +3. Run the Docker Compose file: + + ```sh + docker compose up + ``` + +LangFlow will now be accessible at [http://localhost:7860/](http://localhost:7860/). + +## Docker Compose Configuration + +The Docker Compose configuration spins up two services: `langflow` and `postgres`. + +### LangFlow Service + +The `langflow` service uses the `langflowai/langflow:latest` Docker image and exposes port 7860. It depends on the `postgres` service. + +Environment variables: + +- `LANGFLOW_DATABASE_URL`: The connection string for the PostgreSQL database. +- `LANGFLOW_CONFIG_DIR`: The directory where LangFlow stores logs, file storage, monitor data, and secret keys. + +Volumes: + +- `langflow-data`: This volume is mapped to `/var/lib/langflow` in the container. + +### PostgreSQL Service + +The `postgres` service uses the `postgres:16` Docker image and exposes port 5432. + +Environment variables: + +- `POSTGRES_USER`: The username for the PostgreSQL database. +- `POSTGRES_PASSWORD`: The password for the PostgreSQL database. +- `POSTGRES_DB`: The name of the PostgreSQL database. + +Volumes: + +- `langflow-postgres`: This volume is mapped to `/var/lib/postgresql/data` in the container. + +## Switching to a Specific LangFlow Version + +If you want to use a specific version of LangFlow, you can modify the `image` field under the `langflow` service in the Docker Compose file. For example, to use version 1.0-alpha, change `langflowai/langflow:latest` to `langflowai/langflow:1.0-alpha`. diff --git a/docs/docs/deployment/jina-deployment.md b/docs/docs/deployment/jina-deployment.md deleted file mode 100644 index e69de29bb..000000000 diff --git a/docs/docs/examples/chat-memory.mdx b/docs/docs/examples/chat-memory.mdx index d9b7d2e20..88dbbca2b 100644 --- a/docs/docs/examples/chat-memory.mdx +++ b/docs/docs/examples/chat-memory.mdx @@ -14,4 +14,4 @@ This component is available under the **Helpers** tab of the Langflow preview. style={{ marginBottom: "20px", display: "flex", justifyContent: "center" }} > -
\ No newline at end of file +
diff --git a/docs/docs/examples/combine-text.mdx b/docs/docs/examples/combine-text.mdx index 0d7524a5b..5a4e86cf0 100644 --- a/docs/docs/examples/combine-text.mdx +++ b/docs/docs/examples/combine-text.mdx @@ -18,4 +18,4 @@ This component is available under the **Helpers** tab of the Langflow preview. style={{ marginBottom: "20px", display: "flex", justifyContent: "center" }} > -
\ No newline at end of file +
diff --git a/docs/docs/examples/create-record.mdx b/docs/docs/examples/create-record.mdx index f94ba84bd..aa7a886f4 100644 --- a/docs/docs/examples/create-record.mdx +++ b/docs/docs/examples/create-record.mdx @@ -14,4 +14,4 @@ The **Create Record** component allows you to dynamically create a `Record` from style={{ marginBottom: "20px", display: "flex", justifyContent: "center" }} > -
\ No newline at end of file +
diff --git a/docs/docs/examples/pass.mdx b/docs/docs/examples/pass.mdx index cdf1858d5..ddfe35cca 100644 --- a/docs/docs/examples/pass.mdx +++ b/docs/docs/examples/pass.mdx @@ -14,4 +14,4 @@ The **Pass** component enables you to ignore one input and move forward with ano style={{ marginBottom: "20px", display: "flex", justifyContent: "center" }} > - \ No newline at end of file + diff --git a/docs/docs/examples/store-message.mdx b/docs/docs/examples/store-message.mdx index 610bf645c..75ff0bd46 100644 --- a/docs/docs/examples/store-message.mdx +++ b/docs/docs/examples/store-message.mdx @@ -14,4 +14,4 @@ The **Message History** component can then be used to retrieve stored messages. style={{ marginBottom: "20px", display: "flex", justifyContent: "center" }} > - \ No newline at end of file + diff --git a/docs/docs/examples/sub-flow.mdx b/docs/docs/examples/sub-flow.mdx index ae7e5c9da..d2b9674ad 100644 --- a/docs/docs/examples/sub-flow.mdx +++ b/docs/docs/examples/sub-flow.mdx @@ -12,4 +12,4 @@ The **Sub Flow** component enables a user to select a previously built flow and style={{ marginBottom: "20px", display: "flex", justifyContent: "center" }} > - \ No newline at end of file + diff --git a/docs/docs/examples/text-operator.mdx b/docs/docs/examples/text-operator.mdx index 5637dbc79..50d52fdbf 100644 --- a/docs/docs/examples/text-operator.mdx +++ b/docs/docs/examples/text-operator.mdx @@ -12,4 +12,4 @@ The **Text Operator** component simplifies logic. It evaluates the results from style={{ marginBottom: "20px", display: "flex", justifyContent: "center" }} > - \ No newline at end of file + diff --git a/docs/docs/getting-started/canvas.mdx b/docs/docs/getting-started/canvas.mdx index add9f3619..bfd548577 100644 --- a/docs/docs/getting-started/canvas.mdx +++ b/docs/docs/getting-started/canvas.mdx @@ -280,9 +280,3 @@ To see options for your project, in the upper left corner of the canvas, select **Export** - Download your current project to your local machine as a `.json` file. **Undo** or **Redo** - Undo or redo your last action. - -import ThemedImage from "@theme/ThemedImage"; -import useBaseUrl from "@docusaurus/useBaseUrl"; -import ZoomableImage from "/src/theme/ZoomableImage.js"; -import ReactPlayer from "react-player"; -import Admonition from "@theme/Admonition"; diff --git a/docs/docs/getting-started/flows-components-collections.mdx b/docs/docs/getting-started/flows-components-collections.mdx index 586f08192..335fb5c12 100644 --- a/docs/docs/getting-started/flows-components-collections.mdx +++ b/docs/docs/getting-started/flows-components-collections.mdx @@ -1,7 +1,7 @@ -import ThemedImage from '@theme/ThemedImage'; -import useBaseUrl from '@docusaurus/useBaseUrl'; -import ZoomableImage from '/src/theme/ZoomableImage.js'; -import ReactPlayer from 'react-player'; +import ThemedImage from "@theme/ThemedImage"; +import useBaseUrl from "@docusaurus/useBaseUrl"; +import ZoomableImage from "/src/theme/ZoomableImage.js"; +import ReactPlayer from "react-player"; # 🖥 Flows, components, collections, and projects @@ -17,10 +17,4 @@ A [project](#project) can be a component or a flow. Projects are saved as part o For example, the **OpenAI LLM** is a **component** of the **Basic prompting** flow, and the **flow** is stored in a **collection**. - - ## Component - - - - diff --git a/docs/docs/getting-started/install-langflow.mdx b/docs/docs/getting-started/install-langflow.mdx index 836645a1e..4beb5e362 100644 --- a/docs/docs/getting-started/install-langflow.mdx +++ b/docs/docs/getting-started/install-langflow.mdx @@ -6,33 +6,40 @@ import Admonition from "@theme/Admonition"; # 📊 Install Langflow - Langflow v1.0 alpha is also available in HuggingFace Spaces. [Clone the space using this link](https://huggingface.co/spaces/Langflow/Langflow-Preview?duplicate=true), to create your own Langflow workspace in minutes. + Langflow v1.0 alpha is also available in HuggingFace Spaces. [Clone the space + using this + link](https://huggingface.co/spaces/Langflow/Langflow-Preview?duplicate=true), + to create your own Langflow workspace in minutes. Langflow requires [Python >=3.10](https://www.python.org/downloads/release/python-3100/) and [pip](https://pypi.org/project/pip/) or [pipx](https://pipx.pypa.io/stable/installation/) to be installed on your system. Install Langflow with pip: + ```bash python -m pip install langflow -U ``` Install Langflow with pipx: + ```bash pipx install langflow --python python3.10 --fetch-missing-python ``` -Pipx can fetch the missing Python version for you with `--fetch-missing-python`, but you can also install the Python version manually. +Pipx can fetch the missing Python version for you with `--fetch-missing-python`, but you can also install the Python version manually. ## Install Langflow pre-release To install a pre-release version of Langflow: pip: + ```bash python -m pip install langflow --pre --force-reinstall ``` pipx: + ```bash pipx install langflow --python python3.10 --fetch-missing-python --pip-args="--pre --force-reinstall" ``` @@ -52,11 +59,13 @@ python -m langflow --help ## ⛓ Run Langflow 1. To run Langflow, enter the following command. + ```bash python -m langflow run ``` 2. Confirm that a local Langflow instance starts by visiting `http://127.0.0.1:7860` in a Chromium-based browser. + ```bash │ Welcome to ⛓ Langflow │ │ │ @@ -83,4 +92,4 @@ You'll be presented with the following screen: style={{ width: "100%", margin: "20px auto" }} /> -Name your Space, define the visibility (Public or Private), and click on **Duplicate Space** to start the installation process. When installation is finished, you'll be redirected to the Space's main page to start using Langflow right away! \ No newline at end of file +Name your Space, define the visibility (Public or Private), and click on **Duplicate Space** to start the installation process. When installation is finished, you'll be redirected to the Space's main page to start using Langflow right away! diff --git a/docs/docs/getting-started/quickstart.mdx b/docs/docs/getting-started/quickstart.mdx index ef7d373a6..3f02db27f 100644 --- a/docs/docs/getting-started/quickstart.mdx +++ b/docs/docs/getting-started/quickstart.mdx @@ -10,12 +10,15 @@ This guide demonstrates how to build a basic prompt flow and modify that prompt ## Prerequisites -* [Langflow installed and running](./install-langflow.mdx) +- [Langflow installed and running](./install-langflow.mdx) -* [OpenAI API key](https://platform.openai.com) +- [OpenAI API key](https://platform.openai.com) - Langflow v1.0 alpha is also available in HuggingFace Spaces. [Clone the space using this link](https://huggingface.co/spaces/Langflow/Langflow-Preview?duplicate=true) to create your own Langflow workspace in minutes. + Langflow v1.0 alpha is also available in HuggingFace Spaces. [Clone the space + using this + link](https://huggingface.co/spaces/Langflow/Langflow-Preview?duplicate=true) + to create your own Langflow workspace in minutes. ## Hello World - Basic Prompting @@ -44,25 +47,25 @@ Examine the **Prompt** component. The **Template** field instructs the LLM to `A This should be interesting... 4. To create an environment variable for the **OpenAI** component, in the **OpenAI API Key** field, click the **Globe** button, and then click **Add New Variable**. - 1. In the **Variable Name** field, enter `openai_api_key`. - 2. In the **Value** field, paste your OpenAI API Key (`sk-...`). - 3. Click **Save Variable**. + 1. In the **Variable Name** field, enter `openai_api_key`. + 2. In the **Value** field, paste your OpenAI API Key (`sk-...`). + 3. Click **Save Variable**. ## Run the basic prompting flow 1. Click the **Run** button. -The **Interaction Panel** opens, where you can chat with your bot. + The **Interaction Panel** opens, where you can chat with your bot. 2. Type a message and press Enter. -And... Ahoy! 🏎‍☠ -The bot responds in a piratical manner! + And... Ahoy! 🏎‍☠ + The bot responds in a piratical manner! ## Modify the prompt for a different result 1. To modify your prompt results, in the **Prompt** template, click the **Template** field. -The **Edit Prompt** window opens. + The **Edit Prompt** window opens. 2. Change `Answer the user as if you were a pirate` to a different character, perhaps `Answer the user as if you were Harold Abelson.` 3. Run the basic prompting flow again. -The response will be markedly different. + The response will be markedly different. ## Next steps @@ -72,8 +75,6 @@ By adding Langflow components to your flow, you can create all sorts of interest Here are a couple of examples: -* [Memory chatbot](/starter-projects/memory-chatbot.mdx) -* [Blog writer](/starter-projects/blog-writer.mdx) -* [Document QA](/starter-projects/document-qa.mdx) - - +- [Memory chatbot](/starter-projects/memory-chatbot.mdx) +- [Blog writer](/starter-projects/blog-writer.mdx) +- [Document QA](/starter-projects/document-qa.mdx) diff --git a/docs/docs/index.mdx b/docs/docs/index.mdx index 54960502b..e762142f0 100644 --- a/docs/docs/index.mdx +++ b/docs/docs/index.mdx @@ -29,7 +29,10 @@ Its intuitive interface allows for easy manipulation of AI building blocks, enab - [Langflow Canvas](/getting-started/canvas) - Learn more about the Langflow canvas. - Langflow v1.0 alpha is also available in HuggingFace Spaces. [Clone the space using this link](https://huggingface.co/spaces/Langflow/Langflow-Preview?duplicate=true) to create your own Langflow workspace in minutes. + Langflow v1.0 alpha is also available in HuggingFace Spaces. [Clone the space + using this + link](https://huggingface.co/spaces/Langflow/Langflow-Preview?duplicate=true) + to create your own Langflow workspace in minutes. ## Learn more about Langflow 1.0 diff --git a/docs/docs/integrations/notion/add-content-to-page.md b/docs/docs/integrations/notion/add-content-to-page.md index 83b395fd0..ace43e103 100644 --- a/docs/docs/integrations/notion/add-content-to-page.md +++ b/docs/docs/integrations/notion/add-content-to-page.md @@ -9,14 +9,11 @@ The `AddContentToPage` component converts markdown text to Notion blocks and app [Notion Reference](https://developers.notion.com/reference/patch-block-children) - - The `AddContentToPage` component enables you to: - Convert markdown text to Notion blocks. - Append the converted blocks to a specified Notion page. - Seamlessly integrate Notion content creation into Langflow workflows. - ## Component Usage @@ -100,23 +97,19 @@ class NotionPageCreator(CustomComponent): ## Example Usage - - Example of using the `AddContentToPage` component in a Langflow flow using Markdown as input: In this example, the `AddContentToPage` component connects to a `MarkdownLoader` component to provide the markdown text input. The converted Notion blocks are appended to the specified Notion page using the provided `block_id` and `notion_secret`. - - ## Best Practices When using the `AddContentToPage` component: @@ -131,8 +124,8 @@ The `AddContentToPage` component is a powerful tool for integrating Notion conte ## Troubleshooting If you encounter any issues while using the `AddContentToPage` component, consider the following: + - Verify the Notion integration token’s validity and permissions. - Check the Notion API documentation for updates. - Ensure markdown text is properly formatted. - Double-check the `block_id` for correctness. - diff --git a/docs/docs/integrations/notion/intro.md b/docs/docs/integrations/notion/intro.md index 11b8b7823..293038d4f 100644 --- a/docs/docs/integrations/notion/intro.md +++ b/docs/docs/integrations/notion/intro.md @@ -8,12 +8,12 @@ import ZoomableImage from "/src/theme/ZoomableImage.js"; The Notion integration in Langflow enables seamless connectivity with Notion databases, pages, and users, facilitating automation and improving productivity. #### Download Notion Components Bundle diff --git a/docs/docs/integrations/notion/list-database-properties.md b/docs/docs/integrations/notion/list-database-properties.md index 830ea3324..c41159893 100644 --- a/docs/docs/integrations/notion/list-database-properties.md +++ b/docs/docs/integrations/notion/list-database-properties.md @@ -41,7 +41,7 @@ class NotionDatabaseProperties(CustomComponent): description = "Retrieve properties of a Notion database." documentation: str = "https://docs.langflow.org/integrations/notion/list-database-properties" icon = "NotionDirectoryLoader" - + def build_config(self): return { "database_id": { @@ -80,6 +80,7 @@ class NotionDatabaseProperties(CustomComponent): ``` ## Example Usage + Here's an example of how you can use the `NotionDatabaseProperties` component in a Langflow flow: @@ -110,6 +111,7 @@ Feel free to explore the capabilities of the `NotionDatabaseProperties` componen ## Troubleshooting If you encounter any issues while using the `NotionDatabaseProperties` component, consider the following: + - Verify that the Notion integration token is valid and has the required permissions. - Check the database ID to ensure it matches the intended Notion database. -- Inspect the response from the Notion API for any error messages or status codes that may indicate the cause of the issue. \ No newline at end of file +- Inspect the response from the Notion API for any error messages or status codes that may indicate the cause of the issue. diff --git a/docs/docs/integrations/notion/list-pages.md b/docs/docs/integrations/notion/list-pages.md index 3e219870e..ea1b04950 100644 --- a/docs/docs/integrations/notion/list-pages.md +++ b/docs/docs/integrations/notion/list-pages.md @@ -140,16 +140,17 @@ class NotionListPages(CustomComponent): ## Example Usage + Here's an example of how you can use the `NotionListPages` component in a Langflow flow and passing to the Prompt component: In this example, the `NotionListPages` component is used to retrieve specific pages from a Notion database based on the provided filters and sorting options. The retrieved data can then be processed further in the subsequent components of the flow. @@ -157,7 +158,7 @@ In this example, the `NotionListPages` component is used to retrieve specific pa ## Best Practices - When using the `NotionListPages +When using the `NotionListPages ` component, consider the following best practices: - Ensure that you have a valid Notion integration token with the necessary permissions to query the desired database. @@ -171,7 +172,7 @@ We encourage you to explore the capabilities of the `NotionListPages ## Troubleshooting - If you encounter any issues while using the `NotionListPages` component, consider the following: +If you encounter any issues while using the `NotionListPages` component, consider the following: - Double-check that the `notion_secret` and `database_id` are correct and valid. - Verify that the `query_payload` JSON string is properly formatted and contains valid filtering and sorting options. diff --git a/docs/docs/integrations/notion/list-users.md b/docs/docs/integrations/notion/list-users.md index 90761239a..0eb8236f5 100644 --- a/docs/docs/integrations/notion/list-users.md +++ b/docs/docs/integrations/notion/list-users.md @@ -9,13 +9,11 @@ The `NotionUserList` component retrieves users from Notion. It provides a conven [Notion Reference](https://developers.notion.com/reference/get-users) - - The `NotionUserList` component enables you to: +The `NotionUserList` component enables you to: - Retrieve user data from Notion - Access user information such as ID, type, name, and avatar URL - Integrate Notion user data seamlessly into your Langflow workflows - ## Component Usage @@ -94,34 +92,31 @@ class NotionUserList(CustomComponent): ``` ## Example Usage - + Here's an example of how you can use the `NotionUserList` component in a Langflow flow and passing the outputs to the Prompt component: - - ## Best Practices - When using the `NotionUserList` component, consider the following best practices: +When using the `NotionUserList` component, consider the following best practices: - Ensure that you have a valid Notion integration token with the necessary permissions to retrieve user data. - Handle the retrieved user data securely and in compliance with Notion's API usage guidelines. The `NotionUserList` component provides a seamless way to integrate Notion user data into your Langflow workflows. By leveraging this component, you can easily retrieve and utilize user information from Notion, enhancing the capabilities of your Langflow applications. Feel free to explore and experiment with the `NotionUserList` component to unlock new possibilities in your Langflow projects! - ## Troubleshooting - If you encounter any issues while using the `NotionUserList` component, consider the following: +If you encounter any issues while using the `NotionUserList` component, consider the following: - Double-check that your Notion integration token is valid and has the required permissions. - Verify that you have installed the necessary dependencies (`requests`) for the component to function properly. -- Check the Notion API documentation for any updates or changes that may affect the component's functionality. \ No newline at end of file +- Check the Notion API documentation for any updates or changes that may affect the component's functionality. diff --git a/docs/docs/integrations/notion/page-content-viewer.md b/docs/docs/integrations/notion/page-content-viewer.md index a38c05fd0..f4eeba052 100644 --- a/docs/docs/integrations/notion/page-content-viewer.md +++ b/docs/docs/integrations/notion/page-content-viewer.md @@ -11,7 +11,7 @@ The `NotionPageContent` component retrieves the content of a Notion page as plai - The `NotionPageContent` component enables you to: +The `NotionPageContent` component enables you to: - Retrieve the content of a Notion page as plain text - Extract text from various block types, including paragraphs, headings, lists, and more @@ -114,18 +114,18 @@ class NotionPageContent(CustomComponent): Here's an example of how you can use the `NotionPageContent` component in a Langflow flow: ## Best Practices - When using the `NotionPageContent` component, consider the following best practices: +When using the `NotionPageContent` component, consider the following best practices: - Ensure that you have the necessary permissions to access the Notion page you want to retrieve. - Keep your Notion integration token secure and avoid sharing it publicly. @@ -135,7 +135,7 @@ The `NotionPageContent` component provides a seamless way to integrate Notion pa ## Troubleshooting - If you encounter any issues while using the `NotionPageContent` component, consider the following: +If you encounter any issues while using the `NotionPageContent` component, consider the following: - Double-check that you have provided the correct Notion page ID. - Verify that your Notion integration token is valid and has the necessary permissions. diff --git a/docs/docs/integrations/notion/page-create.md b/docs/docs/integrations/notion/page-create.md index 0269096b9..f942f257b 100644 --- a/docs/docs/integrations/notion/page-create.md +++ b/docs/docs/integrations/notion/page-create.md @@ -97,16 +97,17 @@ class NotionPageCreator(CustomComponent): ``` ## Example Usage + Here's an example of how to use the `NotionPageCreator` component in a Langflow flow: @@ -124,6 +125,7 @@ The `NotionPageCreator` component simplifies the process of creating pages in a ## Troubleshooting If you encounter any issues while using the `NotionPageCreator` component, consider the following: + - Double-check that the `database_id` and `notion_secret` inputs are correct and valid. - Verify that the `properties` input is properly formatted as a JSON string and matches the structure of your Notion database. -- Check the Notion API documentation for any updates or changes that may affect the component's functionality. \ No newline at end of file +- Check the Notion API documentation for any updates or changes that may affect the component's functionality. diff --git a/docs/docs/integrations/notion/search.md b/docs/docs/integrations/notion/search.md index 3ff7472dc..a972bffc0 100644 --- a/docs/docs/integrations/notion/search.md +++ b/docs/docs/integrations/notion/search.md @@ -146,16 +146,17 @@ class NotionSearch(CustomComponent): ``` ## Example Usage + Here's an example of how you can use the `NotionSearch` component in a Langflow flow: In this example, the `NotionSearch` component is used to search for pages and databases in Notion based on the provided query and filter criteria. The retrieved data can then be processed further in the subsequent components of the flow. diff --git a/docs/docs/integrations/notion/setup.md b/docs/docs/integrations/notion/setup.md index 9511d9c81..72bb8f3b4 100644 --- a/docs/docs/integrations/notion/setup.md +++ b/docs/docs/integrations/notion/setup.md @@ -76,4 +76,3 @@ Refer to the individual component documentation for more details on how to use e - [Notion Integration Capabilities](https://developers.notion.com/reference/capabilities) If you encounter any issues or have questions, please reach out to our support team or consult the Langflow community forums. - diff --git a/docs/docs/migration/api.mdx b/docs/docs/migration/api.mdx deleted file mode 100644 index e69de29bb..000000000 diff --git a/docs/docs/migration/component-status-and-data-passing.mdx b/docs/docs/migration/component-status-and-data-passing.mdx deleted file mode 100644 index e69de29bb..000000000 diff --git a/docs/docs/migration/connecting-output-components.mdx b/docs/docs/migration/connecting-output-components.mdx deleted file mode 100644 index e69de29bb..000000000 diff --git a/docs/docs/migration/custom-component.mdx b/docs/docs/migration/custom-component.mdx deleted file mode 100644 index e69de29bb..000000000 diff --git a/docs/docs/migration/experimental-components.mdx b/docs/docs/migration/experimental-components.mdx deleted file mode 100644 index e69de29bb..000000000 diff --git a/docs/docs/migration/flow-of-data.mdx b/docs/docs/migration/flow-of-data.mdx deleted file mode 100644 index e69de29bb..000000000 diff --git a/docs/docs/migration/global-variables.mdx b/docs/docs/migration/global-variables.mdx deleted file mode 100644 index 616fa3621..000000000 --- a/docs/docs/migration/global-variables.mdx +++ /dev/null @@ -1,118 +0,0 @@ -import ZoomableImage from "/src/theme/ZoomableImage.js"; -import Admonition from "@theme/Admonition"; - -# Global Variables - -## TLDR; - -- Global Variables are reusable variables that can be accessed from any Text field in your project. -- To create a Global Variable, click on the 🌐 button in a Text field and then **+ Add New Variable**. -- Define the **Name**, **Type**, and **Value** of the variable. -- Click on **Save Variable** to create the variable. -- All Credential Global Variables are encrypted and cannot be accessed by anyone but you. -- Set _`LANGFLOW_STORE_ENVIRONMENT_VARIABLES`_ to _`true`_ in your `.env` file to add all variables in _`LANGFLOW_VARIABLES_TO_GET_FROM_ENVIRONMENT`_ to your user's Global Variables. - -Global Variables are a really useful feature of Langflow. -They allow you to define reusable variables that can be accessed from any Text field in your project. - -The first thing you need to do is find a **Text field** in a Component, so let's talk about what a Text field is. - -## Text Fields - -Text fields are the fields in a Component where you can write text but that does not allow you to open a Text Area. - -The easiest way to find fields that are Text fields, though, is to look for fields that have a 🌐 button. - - - -## Creating a Global Variable - -To create a Global Variable, you need to click on the 🌐 button in a Text field and that will open a dropdown showing your currently available variables and at the end of it **+ Add New Variable**. - - - -Click on **+ Add New Variable** and a window will open where you can define your new Global Variable. - -In it, you can define the **Name** of the variable, the optional **Type** of the variable, and the **Value** of the variable. - -The **Name** is the name that you will use to refer to the variable in your Text fields. - -The **Type** is optional for now but will be used in the future to allow for more advanced features. - -The **Value** is the value that the variable will have. -{/* say that all variables are encrypted */} - - - All Credential Global Variables are encrypted and cannot be accessed by anyone - but you. - - - - -After you have defined your variable, click on **Save Variable** and your variable will be created. - -After that, once you click on the 🌐 button in a Text field, you will see your new variable in the dropdown. - -## Environment Variables - -If you set _`LANGFLOW_STORE_ENVIRONMENT_VARIABLES`_ to _`true`_ (which is the default value) in your `.env` file, all variables in _`LANGFLOW_VARIABLES_TO_GET_FROM_ENVIRONMENT`_ will be added to your user's Global Variables. - -All of these variables can be used in your project as any other Global Variable. - - - You can set _`LANGFLOW_STORE_ENVIRONMENT_VARIABLES`_ to _`false`_ in your - `.env` file to prevent this behavior. - - -You can also set _`LANGFLOW_VARIABLES_TO_GET_FROM_ENVIRONMENT`_ to a list of variables that you want to get from the environment. - -The default list at the moment is: - -- ANTHROPIC_API_KEY -- ASTRA_DB_API_ENDPOINT -- ASTRA_DB_APPLICATION_TOKEN -- AZURE_OPENAI_API_KEY -- AZURE_OPENAI_API_DEPLOYMENT_NAME -- AZURE_OPENAI_API_EMBEDDINGS_DEPLOYMENT_NAME -- AZURE_OPENAI_API_INSTANCE_NAME -- AZURE_OPENAI_API_VERSION -- COHERE_API_KEY -- GOOGLE_API_KEY -- GROQ_API_KEY -- HUGGINGFACEHUB_API_TOKEN -- OPENAI_API_KEY -- PINECONE_API_KEY -- SEARCHAPI_API_KEY -- SERPAPI_API_KEY -- UPSTASH_VECTOR_REST_URL -- UPSTASH_VECTOR_REST_TOKEN -- VECTARA_CUSTOMER_ID -- VECTARA_CORPUS_ID -- VECTARA_API_KEY - - - Set _`LANGFLOW_VARIABLES_TO_GET_FROM_ENVIRONMENT`_ as a comma-separated list - of variables (e.g. _`"VARIABLE1, VARIABLE2"`_) or as a JSON-encoded string - (e.g. _`'["VARIABLE1", "VARIABLE2"]'`_). - diff --git a/docs/docs/migration/inputs-and-outputs.mdx b/docs/docs/migration/inputs-and-outputs.mdx deleted file mode 100644 index 1e1745347..000000000 --- a/docs/docs/migration/inputs-and-outputs.mdx +++ /dev/null @@ -1,36 +0,0 @@ -# Inputs and Outputs - -TL;DR: Inputs and Outputs are a category of components that are used to define where data comes in and out of your flow. They also -dynamically change the Playground and can be renamed to make it easier to build and maintain your flows. - -## Introduction - -Langflow 1.0 introduces new categories of components called Inputs and Outputs. They are used to make it easier to understand and interact with your flows. - -Let's start with what they have in common: - -- Components in these categories connect to components that have Text or Record inputs or outputs. Some can connect to both but you have to pick what type of data you want to output or input. -- They can be renamed to help you identify them more easily in the Playground and while using the API. -- They dynamically change the Playground to make it easier to understand and interact with your flows. - -Native Langflow Components were created to be powerful tools that work around Langflow's features. They are designed to be easy to use and understand, and to help you build your flows faster. - -Let's dive into Inputs and Outputs. - -## Inputs - -Inputs are components that are used to define where data comes into your flow. They can be used to receive data from the user, from a database, or from any other source that can be converted to Text or Record. - -The difference between Chat Input and other Input components is the format of the output, the number of configurable fields, and the way they are displayed in the Playground. - -Chat Input components can output Text or Record. When you want to pass the sender name, or sender to the next component, you can use the Record output, and when you want to pass the message only you can use the Text output. This is useful when saving the message to a database or a memory system like Zep. - -You can find out more about it and the other Inputs [here](../components/inputs). - -## Outputs - -Outputs are components that are used to define where data comes out of your flow. They can be used to send data to the user, to the Playground, or to define how the data will be displayed in the Playground. - -The Chat Output works similarly to the Chat Input but does not have a field that allows for written input. It is used as an Output definition and can be used to send data to the user. - -You can find out more about it and the other Outputs [here](../components/outputs). diff --git a/docs/docs/migration/migrating-to-one-point-zero.mdx b/docs/docs/migration/migrating-to-one-point-zero.mdx index 827f0e118..973393606 100644 --- a/docs/docs/migration/migrating-to-one-point-zero.mdx +++ b/docs/docs/migration/migrating-to-one-point-zero.mdx @@ -41,7 +41,7 @@ We have a special channel in our Discord server dedicated to Langflow 1.0 migrat Langflow 1.0 introduces adds the concept of Inputs and Outputs to flows, allowing a clear definition of the data flow between components. Discover how to use Inputs and Outputs to pass data between components and create more dynamic flows. -[Learn more about Inputs and Outputs of Components](../migration/inputs-and-outputs) +[Learn more about Inputs and Outputs of Components](../components/inputs-and-outputs) ## To Compose or Not to Compose: the choice is yours @@ -71,7 +71,7 @@ Langflow 1.0 introduces many new native categories, including Inputs, Outputs, H With the introduction of Text and Record types connections between Components are more intuitive and easier to understand. This is the first step in a series of improvements to the way you interact with Langflow. Learn how to use Text, and Record and how they help you build better flows. -[Learn more about Text and Record](../migration/text-and-record) +[Learn more about Text and Record](../components/text-and-record) ## CustomComponent for All Components @@ -119,7 +119,7 @@ Things got a whole lot easier. You can now pass tweaks and inputs in the API by Global Variables can be used in any Text Field across your projects. Learn how to define and utilize Global Variables to streamline your workflow. -[Learn more about Global Variables](../migration/global-variables) +[Learn more about Global Variables](../administration/global-env.mdx) ## Experimental Components diff --git a/docs/docs/migration/multiple-flows.mdx b/docs/docs/migration/multiple-flows.mdx deleted file mode 100644 index e69de29bb..000000000 diff --git a/docs/docs/migration/new-categories-and-components.mdx b/docs/docs/migration/new-categories-and-components.mdx deleted file mode 100644 index e69de29bb..000000000 diff --git a/docs/docs/migration/passing-tweaks-and-inputs.mdx b/docs/docs/migration/passing-tweaks-and-inputs.mdx deleted file mode 100644 index e69de29bb..000000000 diff --git a/docs/docs/migration/possible-installation-issues.mdx b/docs/docs/migration/possible-installation-issues.mdx index 2590d0b8a..a012a1c09 100644 --- a/docs/docs/migration/possible-installation-issues.mdx +++ b/docs/docs/migration/possible-installation-issues.mdx @@ -25,11 +25,11 @@ ModuleNotFoundError: No module named 'langflow.__main__' There are two possible reasons for this error: 1. You've installed Langflow using _`pip install langflow`_ but you already had a previous version of Langflow installed in your system. - In this case, you might be running the wrong executable. - To solve this issue, run the correct executable by running _`python -m langflow run`_ instead of _`langflow run`_. - If that doesn't work, try uninstalling and reinstalling Langflow with _`python -m pip install langflow --pre -U`_. + In this case, you might be running the wrong executable. + To solve this issue, run the correct executable by running _`python -m langflow run`_ instead of _`langflow run`_. + If that doesn't work, try uninstalling and reinstalling Langflow with _`python -m pip install langflow --pre -U`_. 2. Some version conflicts might have occurred during the installation process. - Run _`python -m pip install langflow --pre -U --force-reinstall`_ to reinstall Langflow and its dependencies. + Run _`python -m pip install langflow --pre -U --force-reinstall`_ to reinstall Langflow and its dependencies. ## _`Something went wrong running migrations. Please, run 'langflow migration --fix'`_ @@ -45,4 +45,3 @@ There are two possible reasons for this error: This error can occur during Langflow upgrades when the new version can't override `langflow-pre.db` in `.cache/langflow/`. Clearing the cache removes this file but will also erase your settings. If you wish to retain your files, back them up before clearing the folder. - diff --git a/docs/docs/migration/renaming-and-editing-components.mdx b/docs/docs/migration/renaming-and-editing-components.mdx deleted file mode 100644 index e69de29bb..000000000 diff --git a/docs/docs/migration/sidebar-and-interaction-panel.mdx b/docs/docs/migration/sidebar-and-interaction-panel.mdx deleted file mode 100644 index e69de29bb..000000000 diff --git a/docs/docs/migration/state-management.mdx b/docs/docs/migration/state-management.mdx deleted file mode 100644 index e69de29bb..000000000 diff --git a/docs/docs/migration/supported-frameworks.mdx b/docs/docs/migration/supported-frameworks.mdx deleted file mode 100644 index e69de29bb..000000000 diff --git a/docs/docs/migration/text-and-record.mdx b/docs/docs/migration/text-and-record.mdx deleted file mode 100644 index cdfb26b6c..000000000 --- a/docs/docs/migration/text-and-record.mdx +++ /dev/null @@ -1,45 +0,0 @@ -# Text and Record - -In Langflow 1.0 we added two main input and output types: Text and Record. Text is a simple string input and output type, while Record is a structure very similar to a dictionary in Python. It is a key-value pair data structure. - -We've created a few components to help you work with these types. Let's see how a few of them work. - -### Records To Text - -This is a Component that takes in Records and outputs a Text. It does this using a template string and concatenating the values of the Record, one per line. - -If we have the following Records: - -```json -{ - "sender_name": "Alice", - "message": "Hello!" -} -{ - "sender_name": "John", - "message": "Hi!" -} -``` - -And the template string is: _`{sender_name}: {message}`_ - -``` -Alice: Hello! -John: Hi! -``` - -### Create Record - -This Component allows you to create a Record from a number of inputs. You can add as many key-value pairs as you want (as long as it is less than 15 😅). Once you've picked that number you'll need to write the name of the Key and can pass Text values from other components to it. - -### Documents To Records - -This Component takes in a [LangChain](https://langchain.com) Document and outputs a Record. It does this by extracting the _`page_content`_ and the _`metadata`_ from the Document and adding them to the Record as _`text`_ and _`data`_ respectively. - -## Why is this useful? - -The idea was to create a unified way to work with complex data in Langflow, and to make it easier to work with data that is not just a simple string. This way you can create more complex workflows and use the data in more ways. - -## What's next? - -We are planning to integrate an array of modalities to Langflow, such as images, audio, and video. This will allow you to create even more complex workflows and use cases. Stay tuned for more updates! 🚀 diff --git a/docs/docs/starter-projects/basic-prompting.mdx b/docs/docs/starter-projects/basic-prompting.mdx index 6fb7391e2..26b054bcc 100644 --- a/docs/docs/starter-projects/basic-prompting.mdx +++ b/docs/docs/starter-projects/basic-prompting.mdx @@ -14,12 +14,15 @@ This article demonstrates how to use Langflow's prompt tools to issue basic prom ## Prerequisites -* [Langflow installed and running](../getting-started/install-langflow.mdx) +- [Langflow installed and running](../getting-started/install-langflow.mdx) -* [OpenAI API key created](https://platform.openai.com) +- [OpenAI API key created](https://platform.openai.com) - Langflow v1.0 alpha is also available in HuggingFace Spaces. [Clone the space using this link](https://huggingface.co/spaces/Langflow/Langflow-Preview?duplicate=true) to create your own Langflow workspace in minutes. + Langflow v1.0 alpha is also available in HuggingFace Spaces. [Clone the space + using this + link](https://huggingface.co/spaces/Langflow/Langflow-Preview?duplicate=true) + to create your own Langflow workspace in minutes. ## Create the basic prompting project @@ -42,25 +45,21 @@ Examine the **Prompt** component. The **Template** field instructs the LLM to `A This should be interesting... 4. To create an environment variable for the **OpenAI** component, in the **OpenAI API Key** field, click the **Globe** button, and then click **Add New Variable**. - 1. In the **Variable Name** field, enter `openai_api_key`. - 2. In the **Value** field, paste your OpenAI API Key (`sk-...`). - 3. Click **Save Variable**. + 1. In the **Variable Name** field, enter `openai_api_key`. + 2. In the **Value** field, paste your OpenAI API Key (`sk-...`). + 3. Click **Save Variable**. ## Run the basic prompting flow 1. Click the **Run** button. -The **Interaction Panel** opens, where you can converse with your bot. + The **Interaction Panel** opens, where you can converse with your bot. 2. Type a message and press Enter. -The bot responds in a markedly piratical manner! + The bot responds in a markedly piratical manner! ## Modify the prompt for a different result 1. To modify your prompt results, in the **Prompt** template, click the **Template** field. -The **Edit Prompt** window opens. + The **Edit Prompt** window opens. 2. Change `Answer the user as if you were a pirate` to a different character, perhaps `Answer the user as if you were Harold Abelson.` 3. Run the basic prompting flow again. -The response will be markedly different. - - - - + The response will be markedly different. diff --git a/docs/docs/starter-projects/blog-writer.mdx b/docs/docs/starter-projects/blog-writer.mdx index 0e8047fd6..9380bf114 100644 --- a/docs/docs/starter-projects/blog-writer.mdx +++ b/docs/docs/starter-projects/blog-writer.mdx @@ -10,12 +10,15 @@ Build a blog writer with OpenAI that uses URLs for reference content. ## Prerequisites -* [Langflow installed and running](../getting-started/install-langflow.mdx) +- [Langflow installed and running](../getting-started/install-langflow.mdx) -* [OpenAI API key created](https://platform.openai.com) +- [OpenAI API key created](https://platform.openai.com) - Langflow v1.0 alpha is also available in HuggingFace Spaces. [Clone the space using this link](https://huggingface.co/spaces/Langflow/Langflow-Preview?duplicate=true) to create your own Langflow workspace in minutes. + Langflow v1.0 alpha is also available in HuggingFace Spaces. [Clone the space + using this + link](https://huggingface.co/spaces/Langflow/Langflow-Preview?duplicate=true) + to create your own Langflow workspace in minutes. ## Create the Blog Writer project @@ -36,6 +39,7 @@ Build a blog writer with OpenAI that uses URLs for reference content. This flow creates a one-shot prompt flow with **Prompt**, **OpenAI**, and **Chat Output** components, and augments the flow with reference content and instructions from the **URL** and **Instructions** components. The **Prompt** component's default **Template** field looks like this: + ```bash Reference 1: @@ -59,16 +63,16 @@ The `{instructions}` value is received from the **Value** field of the **Instruc The `reference_1` and `reference_2` values are received from the **URL** fields of the **URL** components. 4. To create an environment variable for the **OpenAI** component, in the **OpenAI API Key** field, click the **Globe** button, and then click **Add New Variable**. - 1. In the **Variable Name** field, enter `openai_api_key`. - 2. In the **Value** field, paste your OpenAI API Key (`sk-...`). - 3. Click **Save Variable**. + 1. In the **Variable Name** field, enter `openai_api_key`. + 2. In the **Value** field, paste your OpenAI API Key (`sk-...`). + 3. Click **Save Variable**. ## Run the Blog Writer flow 1. Click the **Run** button. -The **Interaction Panel** opens, where you can run your one-shot flow. + The **Interaction Panel** opens, where you can run your one-shot flow. 2. Click the **Lighting Bolt** icon to run your flow. 3. The **OpenAI** component constructs a blog post with the **URL** items as context. -The default **URL** values are for web pages at `promptingguide.ai`, so your blog post will be about prompting LLMs. + The default **URL** values are for web pages at `promptingguide.ai`, so your blog post will be about prompting LLMs. -To write about something different, change the values in the **URL** components, and see what the LLM constructs. \ No newline at end of file +To write about something different, change the values in the **URL** components, and see what the LLM constructs. diff --git a/docs/docs/starter-projects/document-qa.mdx b/docs/docs/starter-projects/document-qa.mdx index 5e5377355..ddbcd901a 100644 --- a/docs/docs/starter-projects/document-qa.mdx +++ b/docs/docs/starter-projects/document-qa.mdx @@ -10,12 +10,15 @@ Build a question-and-answer chatbot with a document loaded from local memory. ## Prerequisites -* [Langflow installed and running](../getting-started/install-langflow.mdx) +- [Langflow installed and running](../getting-started/install-langflow.mdx) -* [OpenAI API key created](https://platform.openai.com) +- [OpenAI API key created](https://platform.openai.com) - Langflow v1.0 alpha is also available in HuggingFace Spaces. [Clone the space using this link](https://huggingface.co/spaces/Langflow/Langflow-Preview?duplicate=true) to create your own Langflow workspace in minutes. + Langflow v1.0 alpha is also available in HuggingFace Spaces. [Clone the space + using this + link](https://huggingface.co/spaces/Langflow/Langflow-Preview?duplicate=true) + to create your own Langflow workspace in minutes. ## Create the Document QA project @@ -39,24 +42,27 @@ The **Prompt** component is instructed to answer questions based on the contents Including a file with the prompt gives the **OpenAI** component context it may not otherwise have access to. 4. To create an environment variable for the **OpenAI** component, in the **OpenAI API Key** field, click the **Globe** button, and then click **Add New Variable**. - 1. In the **Variable Name** field, enter `openai_api_key`. - 2. In the **Value** field, paste your OpenAI API Key (`sk-...`). - 3. Click **Save Variable**. + + 1. In the **Variable Name** field, enter `openai_api_key`. + 2. In the **Value** field, paste your OpenAI API Key (`sk-...`). + 3. Click **Save Variable**. 5. To select a document to load, in the **Files** component, click within the **Path** field. - 1. Select a local file, and then click **Open**. - 2. The file name appears in the field. - - The file must be of an extension type listed [here](https://github.com/langflow-ai/langflow/blob/dev/src/backend/base/langflow/base/data/utils.py#L13). - + 1. Select a local file, and then click **Open**. + 2. The file name appears in the field. + + The file must be of an extension type listed + [here](https://github.com/langflow-ai/langflow/blob/dev/src/backend/base/langflow/base/data/utils.py#L13). + ## Run the Document QA flow 1. Click the **Run** button. -The **Interaction Panel** opens, where you can converse with your bot. + The **Interaction Panel** opens, where you can converse with your bot. 2. Type a message and press Enter. -For this example, we loaded an error log `.txt` file and asked, "What went wrong?" -The bot responded: + For this example, we loaded an error log `.txt` file and asked, "What went wrong?" + The bot responded: + ``` The issue occurred during the execution of migrations in the application. Specifically, an error was raised by the Alembic library, indicating that new upgrade operations were detected that had not been accounted for in the existing migration scripts. The operation in question involved modifying the nullable property of a column (apikey, created_at) in the database, with details about the existing type (DATETIME()), existing server default, and other properties. ``` diff --git a/docs/docs/starter-projects/memory-chatbot.mdx b/docs/docs/starter-projects/memory-chatbot.mdx index 86c64d368..8e38ca3e0 100644 --- a/docs/docs/starter-projects/memory-chatbot.mdx +++ b/docs/docs/starter-projects/memory-chatbot.mdx @@ -10,12 +10,15 @@ This flow extends the [basic prompting flow](./basic-prompting.mdx) to include c ## Prerequisites -* [Langflow installed and running](../getting-started/install-langflow.mdx) +- [Langflow installed and running](../getting-started/install-langflow.mdx) -* [OpenAI API key created](https://platform.openai.com) +- [OpenAI API key created](https://platform.openai.com) - Langflow v1.0 alpha is also available in HuggingFace Spaces. [Clone the space using this link](https://huggingface.co/spaces/Langflow/Langflow-Preview?duplicate=true) to create your own Langflow workspace in minutes. + Langflow v1.0 alpha is also available in HuggingFace Spaces. [Clone the space + using this + link](https://huggingface.co/spaces/Langflow/Langflow-Preview?duplicate=true) + to create your own Langflow workspace in minutes. ## Create the memory chatbot project @@ -43,16 +46,16 @@ This chatbot is augmented with the **Chat Memory** component, which stores messa The **Chat History** component gives the **OpenAI** component a memory of previous questions. 4. To create an environment variable for the **OpenAI** component, in the **OpenAI API Key** field, click the **Globe** button, and then click **Add New Variable**. - 1. In the **Variable Name** field, enter `openai_api_key`. - 2. In the **Value** field, paste your OpenAI API Key (`sk-...`). - 3. Click **Save Variable**. + 1. In the **Variable Name** field, enter `openai_api_key`. + 2. In the **Value** field, paste your OpenAI API Key (`sk-...`). + 3. Click **Save Variable**. ## Run the memory chatbot flow 1. Click the **Run** button. -The **Interaction Panel** opens, where you can converse with your bot. + The **Interaction Panel** opens, where you can converse with your bot. 2. Type a message and press Enter. -The bot will respond according to the template in the **Prompt** component. + The bot will respond according to the template in the **Prompt** component. 3. Type more questions. In the **Outputs** log, your queries are logged in order. Up to 5 queries are stored by default. Try asking `What is the first subject I asked you about?` to see where the LLM's memory disappears. ## Modify the Session ID field to have multiple conversations @@ -65,11 +68,11 @@ You can demonstrate this by modifying the **Session ID** value to switch between 1. In the **Session ID** field of the **Chat Memory** and **Chat Input** components, change the **Session ID** value from `MySessionID` to `AnotherSessionID`. 2. Click the **Run** button to run your flow. -In the **Interaction Panel**, you will have a new conversation. (You may need to clear the cache with the **Eraser** button). + In the **Interaction Panel**, you will have a new conversation. (You may need to clear the cache with the **Eraser** button). 3. Type a few questions to your bot. 4. In the **Session ID** field of the **Chat Memory** and **Chat Input** components, change the **Session ID** value back to `MySessionID`. 5. Run your flow. -The **Outputs** log of the **Interaction Panel** displays the history from your initial chat with `MySessionID`. + The **Outputs** log of the **Interaction Panel** displays the history from your initial chat with `MySessionID`. ## Store Session ID as a Langflow variable @@ -79,4 +82,3 @@ To store **Session ID** as a Langflow variable, in the **Session ID** field, cli 2. In the **Value** field, enter a value like `1B5EBD79-6E9C-4533-B2C8-7E4FF29E983B`. 3. Click **Save Variable**. 4. Apply this variable to **Chat Input**. - diff --git a/docs/docs/starter-projects/vector-store-rag.mdx b/docs/docs/starter-projects/vector-store-rag.mdx index ddb0a1d46..d0054e6c4 100644 --- a/docs/docs/starter-projects/vector-store-rag.mdx +++ b/docs/docs/starter-projects/vector-store-rag.mdx @@ -17,16 +17,19 @@ We've chosen [Astra DB](https://astra.datastax.com/signup?utm_source=langflow-pr ## Prerequisites - Langflow v1.0 alpha is also available in HuggingFace Spaces. [Clone the space using this link](https://huggingface.co/spaces/Langflow/Langflow-Preview?duplicate=true) to create your own Langflow workspace in minutes. + Langflow v1.0 alpha is also available in HuggingFace Spaces. [Clone the space + using this + link](https://huggingface.co/spaces/Langflow/Langflow-Preview?duplicate=true) + to create your own Langflow workspace in minutes. -* [Langflow installed and running](../getting-started/install-langflow.mdx) +- [Langflow installed and running](../getting-started/install-langflow.mdx) -* [OpenAI API key](https://platform.openai.com) +- [OpenAI API key](https://platform.openai.com) -* [An Astra DB vector database created](https://docs.datastax.com/en/astra-db-serverless/get-started/quickstart.html) with: - * Application token (`AstraCS:WSnyFUhRxsrg
​`) - * API endpoint (`https://ASTRA_DB_ID-ASTRA_DB_REGION.apps.astra.datastax.com`) +- [An Astra DB vector database created](https://docs.datastax.com/en/astra-db-serverless/get-started/quickstart.html) with: + - Application token (`AstraCS:WSnyFUhRxsrg
​`) + - API endpoint (`https://ASTRA_DB_ID-ASTRA_DB_REGION.apps.astra.datastax.com`) ## Create the vector store RAG project @@ -49,38 +52,40 @@ The **ingestion** flow (bottom of the screen) populates the vector store with da It ingests data from a file (**File**), splits it into chunks (**Recursive Character Text Splitter**), indexes it in Astra DB (**Astra DB**), and computes embeddings for the chunks (**OpenAI Embeddings**). This forms a "brain" for the query flow. -The **query** flow (top of the screen) allows users to chat with the embedded vector store data. It's a little more complex: +The **query** flow (top of the screen) allows users to chat with the embedded vector store data. It's a little more complex: -* **Chat Input** component defines where to put the user input coming from the Playground. -* **OpenAI Embeddings** component generates embeddings from the user input. -* **Astra DB Search** component retrieves the most relevant Records from the Astra DB database. -* **Text Output** component turns the Records into Text by concatenating them and also displays it in the Playground. -* **Prompt** component takes in the user input and the retrieved Records as text and builds a prompt for the OpenAI model. -* **OpenAI** component generates a response to the prompt. -* **Chat Output** component displays the response in the Playground. +- **Chat Input** component defines where to put the user input coming from the Playground. +- **OpenAI Embeddings** component generates embeddings from the user input. +- **Astra DB Search** component retrieves the most relevant Records from the Astra DB database. +- **Text Output** component turns the Records into Text by concatenating them and also displays it in the Playground. +- **Prompt** component takes in the user input and the retrieved Records as text and builds a prompt for the OpenAI model. +- **OpenAI** component generates a response to the prompt. +- **Chat Output** component displays the response in the Playground. 4. To create an environment variable for the **OpenAI** component, in the **OpenAI API Key** field, click the **Globe** button, and then click **Add New Variable**. - 1. In the **Variable Name** field, enter `openai_api_key`. - 2. In the **Value** field, paste your OpenAI API Key (`sk-...`). - 3. Click **Save Variable**. -4. To create environment variables for the **Astra DB** and **Astra DB Search** components: - 1. In the **Token** field, click the **Globe** button, and then click **Add New Variable**. - 2. In the **Variable Name** field, enter `astra_token`. - 3. In the **Value** field, paste your Astra application token (`AstraCS:WSnyFUhRxsrg
​`). - 4. Click **Save Variable**. - 5. Repeat the above steps for the **API Endpoint** field, pasting your Astra API Endpoint instead (`https://ASTRA_DB_ID-ASTRA_DB_REGION.apps.astra.datastax.com`). - 6. Add the global variable to both the **Astra DB** and **Astra DB Search** components. + 1. In the **Variable Name** field, enter `openai_api_key`. + 2. In the **Value** field, paste your OpenAI API Key (`sk-...`). + 3. Click **Save Variable**. + +5. To create environment variables for the **Astra DB** and **Astra DB Search** components: + 1. In the **Token** field, click the **Globe** button, and then click **Add New Variable**. + 2. In the **Variable Name** field, enter `astra_token`. + 3. In the **Value** field, paste your Astra application token (`AstraCS:WSnyFUhRxsrg
​`). + 4. Click **Save Variable**. + 5. Repeat the above steps for the **API Endpoint** field, pasting your Astra API Endpoint instead (`https://ASTRA_DB_ID-ASTRA_DB_REGION.apps.astra.datastax.com`). + 6. Add the global variable to both the **Astra DB** and **Astra DB Search** components. ## Run the vector store RAG flow 1. Click the **Playground** button. -The **Playground** opens, where you can chat with your data. + The **Playground** opens, where you can chat with your data. 2. Type a message and press Enter. (Try something like "What topics do you know about?") 3. The bot will respond with a summary of the data you've embedded. For example, we embedded a PDF of an engine maintenance manual and asked, "How do I change the oil?" The bot responds: + ``` To change the oil in the engine, follow these steps: @@ -102,7 +107,3 @@ You should use a 3/8 inch wrench to remove the oil drain cap. ``` This is the size the engine manual lists as well. This confirms our flow works, because the query returns the unique knowledge we embedded from the Astra vector store. - - - - diff --git a/docs/docs/whats-new/a-new-chapter-langflow.mdx b/docs/docs/whats-new/a-new-chapter-langflow.mdx index 3ff74ffb2..bdc0f178b 100644 --- a/docs/docs/whats-new/a-new-chapter-langflow.mdx +++ b/docs/docs/whats-new/a-new-chapter-langflow.mdx @@ -41,7 +41,7 @@ By having a clear definition of Inputs and Outputs, we could build the experienc When building a project testing and debugging is crucial. The Playground is a tool that changes dynamically based on the Inputs and Outputs you defined in your project. For example, let's say you are building a simple RAG application. Generally, you have an Input, some references that come from a Vector Store Search, a Prompt and the answer. -Now, you could plug the output of your Prompt into a [Text Output](../components/outputs#Text-Output), rename that to "Prompt Result" and see the output of your Prompt in the Playground. +Now, you could plug the output of your Prompt into a [Text Output](../components/inputs-and-outputs), rename that to "Prompt Result" and see the output of your Prompt in the Playground. {/* Add image here of the described above */} diff --git a/docs/sidebars.js b/docs/sidebars.js index d3f4f2671..6a9b3580a 100644 --- a/docs/sidebars.js +++ b/docs/sidebars.js @@ -49,8 +49,8 @@ module.exports = { label: "Core Components", collapsed: false, items: [ - "components/inputs", - "components/outputs", + "components/inputs-and-outputs", + "components/text-and-record", "components/data", "components/models", "components/helpers", @@ -91,15 +91,12 @@ module.exports = { }, { type: "category", - label: "Migration Guides", + label: "Migration", collapsed: false, items: [ "migration/possible-installation-issues", "migration/migrating-to-one-point-zero", - "migration/inputs-and-outputs", - "migration/text-and-record", "migration/compatibility", - "migration/global-variables", ], }, { @@ -116,7 +113,10 @@ module.exports = { type: "category", label: "Deployment", collapsed: true, - items: ["deployment/gcp-deployment"], + items: ["deployment/docker", + "deployment/backend-only", + "deployment/gcp-deployment", + ], }, { type: "category", diff --git a/docs/static/data/AstraDB-RAG-Flows.json b/docs/static/data/AstraDB-RAG-Flows.json index 2b5ac607e..e57a15248 100644 --- a/docs/static/data/AstraDB-RAG-Flows.json +++ b/docs/static/data/AstraDB-RAG-Flows.json @@ -1,1102 +1,640 @@ { - "id": "51e2b78a-199b-4054-9f32-e288eef6924c", - "data": { - "nodes": [ - { - "id": "ChatInput-yxMKE", - "type": "genericNode", - "position": { - "x": 1195.5276981160775, - "y": 209.421875 - }, - "data": { - "type": "ChatInput", - "node": { - "template": { - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatInput(ChatComponent):\n display_name = \"Chat Input\"\n description = \"Get chat inputs from the Playground.\"\n icon = \"ChatInput\"\n\n def build_config(self):\n build_config = super().build_config()\n build_config[\"input_value\"] = {\n \"input_types\": [],\n \"display_name\": \"Message\",\n \"multiline\": True,\n }\n\n return build_config\n\n def build(\n self,\n sender: Optional[str] = \"User\",\n sender_name: Optional[str] = \"User\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n ) -> Union[Text, Record]:\n return super().build(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n )\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "input_value": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "input_value", - "display_name": "Message", - "advanced": false, - "input_types": [], - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "value": "what is a line" - }, - "return_record": { - "type": "bool", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "return_record", - "display_name": "Return Record", - "advanced": true, - "dynamic": false, - "info": "Return the message as a record containing the sender, sender_name, and session_id.", - "load_from_db": false, - "title_case": false - }, - "sender": { - "type": "str", - "required": false, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "value": "User", - "fileTypes": [], - "file_path": "", - "password": false, - "options": [ - "Machine", - "User" - ], - "name": "sender", - "display_name": "Sender Type", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "sender_name": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": "User", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "sender_name", - "display_name": "Sender Name", - "advanced": false, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "session_id": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "session_id", - "display_name": "Session ID", - "advanced": true, - "dynamic": false, - "info": "If provided, the message will be stored in the memory.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "_type": "CustomComponent" - }, - "description": "Get chat inputs from the Playground.", - "icon": "ChatInput", - "base_classes": [ - "Text", - "str", - "object", - "Record" - ], - "display_name": "Chat Input", - "documentation": "", - "custom_fields": { - "sender": null, - "sender_name": null, - "input_value": null, - "session_id": null, - "return_record": null - }, - "output_types": [ - "Text", - "Record" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": false - }, - "id": "ChatInput-yxMKE" - }, - "selected": false, - "width": 384, - "height": 383 + "id": "51e2b78a-199b-4054-9f32-e288eef6924c", + "data": { + "nodes": [ + { + "id": "ChatInput-yxMKE", + "type": "genericNode", + "position": { + "x": 1195.5276981160775, + "y": 209.421875 + }, + "data": { + "type": "ChatInput", + "node": { + "template": { + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatInput(ChatComponent):\n display_name = \"Chat Input\"\n description = \"Get chat inputs from the Playground.\"\n icon = \"ChatInput\"\n\n def build_config(self):\n build_config = super().build_config()\n build_config[\"input_value\"] = {\n \"input_types\": [],\n \"display_name\": \"Message\",\n \"multiline\": True,\n }\n\n return build_config\n\n def build(\n self,\n sender: Optional[str] = \"User\",\n sender_name: Optional[str] = \"User\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n ) -> Union[Text, Record]:\n return super().build(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n )\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "input_value": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "input_value", + "display_name": "Message", + "advanced": false, + "input_types": [], + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "value": "what is a line" + }, + "return_record": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "return_record", + "display_name": "Return Record", + "advanced": true, + "dynamic": false, + "info": "Return the message as a record containing the sender, sender_name, and session_id.", + "load_from_db": false, + "title_case": false + }, + "sender": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "value": "User", + "fileTypes": [], + "file_path": "", + "password": false, + "options": ["Machine", "User"], + "name": "sender", + "display_name": "Sender Type", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "sender_name": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": "User", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "sender_name", + "display_name": "Sender Name", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "session_id": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "session_id", + "display_name": "Session ID", + "advanced": true, + "dynamic": false, + "info": "If provided, the message will be stored in the memory.", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "_type": "CustomComponent" }, - { - "id": "TextOutput-BDknO", - "type": "genericNode", - "position": { - "x": 2322.600672827879, - "y": 604.9467307442569 - }, - "data": { - "type": "TextOutput", - "node": { - "template": { - "input_value": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": "", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "input_value", - "display_name": "Value", - "advanced": false, - "input_types": [ - "Record", - "Text" - ], - "dynamic": false, - "info": "Text or Record to be passed as output.", - "load_from_db": false, - "title_case": false - }, - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from typing import Optional\n\nfrom langflow.base.io.text import TextComponent\nfrom langflow.field_typing import Text\n\n\nclass TextOutput(TextComponent):\n display_name = \"Text Output\"\n description = \"Display a text output in the Playground.\"\n icon = \"type\"\n\n def build_config(self):\n return {\n \"input_value\": {\n \"display_name\": \"Value\",\n \"input_types\": [\"Record\", \"Text\"],\n \"info\": \"Text or Record to be passed as output.\",\n },\n \"record_template\": {\n \"display_name\": \"Record Template\",\n \"multiline\": True,\n \"info\": \"Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.\",\n \"advanced\": True,\n },\n }\n\n def build(self, input_value: Optional[Text] = \"\", record_template: str = \"\") -> Text:\n return super().build(input_value=input_value, record_template=record_template)\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "record_template": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "{text}", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "record_template", - "display_name": "Record Template", - "advanced": true, - "dynamic": false, - "info": "Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "_type": "CustomComponent" - }, - "description": "Display a text output in the Playground.", - "icon": "type", - "base_classes": [ - "object", - "Text", - "str" - ], - "display_name": "Extracted Chunks", - "documentation": "", - "custom_fields": { - "input_value": null, - "record_template": null - }, - "output_types": [ - "Text" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": false - }, - "id": "TextOutput-BDknO" - }, - "selected": false, - "width": 384, - "height": 289, - "positionAbsolute": { - "x": 2322.600672827879, - "y": 604.9467307442569 - }, - "dragging": false + "description": "Get chat inputs from the Playground.", + "icon": "ChatInput", + "base_classes": ["Text", "str", "object", "Record"], + "display_name": "Chat Input", + "documentation": "", + "custom_fields": { + "sender": null, + "sender_name": null, + "input_value": null, + "session_id": null, + "return_record": null }, - { - "id": "OpenAIEmbeddings-ZlOk1", - "type": "genericNode", - "position": { - "x": 1183.667250865064, - "y": 687.3171828430261 - }, - "data": { - "type": "OpenAIEmbeddings", - "node": { - "template": { - "allowed_special": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": [], - "fileTypes": [], - "file_path": "", - "password": false, - "name": "allowed_special", - "display_name": "Allowed Special", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "chunk_size": { - "type": "int", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": 1000, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "chunk_size", - "display_name": "Chunk Size", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "client": { - "type": "Any", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "client", - "display_name": "Client", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from typing import Any, Dict, List, Optional\n\nfrom langchain_openai.embeddings.base import OpenAIEmbeddings\n\nfrom langflow.field_typing import Embeddings, NestedDict\nfrom langflow.interface.custom.custom_component import CustomComponent\n\n\nclass OpenAIEmbeddingsComponent(CustomComponent):\n display_name = \"OpenAI Embeddings\"\n description = \"Generate embeddings using OpenAI models.\"\n\n def build_config(self):\n return {\n \"allowed_special\": {\n \"display_name\": \"Allowed Special\",\n \"advanced\": True,\n \"field_type\": \"str\",\n \"is_list\": True,\n },\n \"default_headers\": {\n \"display_name\": \"Default Headers\",\n \"advanced\": True,\n \"field_type\": \"dict\",\n },\n \"default_query\": {\n \"display_name\": \"Default Query\",\n \"advanced\": True,\n \"field_type\": \"NestedDict\",\n },\n \"disallowed_special\": {\n \"display_name\": \"Disallowed Special\",\n \"advanced\": True,\n \"field_type\": \"str\",\n \"is_list\": True,\n },\n \"chunk_size\": {\"display_name\": \"Chunk Size\", \"advanced\": True},\n \"client\": {\"display_name\": \"Client\", \"advanced\": True},\n \"deployment\": {\"display_name\": \"Deployment\", \"advanced\": True},\n \"embedding_ctx_length\": {\n \"display_name\": \"Embedding Context Length\",\n \"advanced\": True,\n },\n \"max_retries\": {\"display_name\": \"Max Retries\", \"advanced\": True},\n \"model\": {\n \"display_name\": \"Model\",\n \"advanced\": False,\n \"options\": [\n \"text-embedding-3-small\",\n \"text-embedding-3-large\",\n \"text-embedding-ada-002\",\n ],\n },\n \"model_kwargs\": {\"display_name\": \"Model Kwargs\", \"advanced\": True},\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"password\": True,\n \"advanced\": True,\n },\n \"openai_api_key\": {\"display_name\": \"OpenAI API Key\", \"password\": True},\n \"openai_api_type\": {\n \"display_name\": \"OpenAI API Type\",\n \"advanced\": True,\n \"password\": True,\n },\n \"openai_api_version\": {\n \"display_name\": \"OpenAI API Version\",\n \"advanced\": True,\n },\n \"openai_organization\": {\n \"display_name\": \"OpenAI Organization\",\n \"advanced\": True,\n },\n \"openai_proxy\": {\"display_name\": \"OpenAI Proxy\", \"advanced\": True},\n \"request_timeout\": {\"display_name\": \"Request Timeout\", \"advanced\": True},\n \"show_progress_bar\": {\n \"display_name\": \"Show Progress Bar\",\n \"advanced\": True,\n },\n \"skip_empty\": {\"display_name\": \"Skip Empty\", \"advanced\": True},\n \"tiktoken_model_name\": {\n \"display_name\": \"TikToken Model Name\",\n \"advanced\": True,\n },\n \"tiktoken_enable\": {\"display_name\": \"TikToken Enable\", \"advanced\": True},\n }\n\n def build(\n self,\n openai_api_key: str,\n default_headers: Optional[Dict[str, str]] = None,\n default_query: Optional[NestedDict] = {},\n allowed_special: List[str] = [],\n disallowed_special: List[str] = [\"all\"],\n chunk_size: int = 1000,\n client: Optional[Any] = None,\n deployment: str = \"text-embedding-ada-002\",\n embedding_ctx_length: int = 8191,\n max_retries: int = 6,\n model: str = \"text-embedding-ada-002\",\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n openai_api_type: Optional[str] = None,\n openai_api_version: Optional[str] = None,\n openai_organization: Optional[str] = None,\n openai_proxy: Optional[str] = None,\n request_timeout: Optional[float] = None,\n show_progress_bar: bool = False,\n skip_empty: bool = False,\n tiktoken_enable: bool = True,\n tiktoken_model_name: Optional[str] = None,\n ) -> Embeddings:\n # This is to avoid errors with Vector Stores (e.g Chroma)\n if disallowed_special == [\"all\"]:\n disallowed_special = \"all\" # type: ignore\n\n return OpenAIEmbeddings(\n tiktoken_enabled=tiktoken_enable,\n default_headers=default_headers,\n default_query=default_query,\n allowed_special=set(allowed_special),\n disallowed_special=\"all\",\n chunk_size=chunk_size,\n client=client,\n deployment=deployment,\n embedding_ctx_length=embedding_ctx_length,\n max_retries=max_retries,\n model=model,\n model_kwargs=model_kwargs,\n base_url=openai_api_base,\n api_key=openai_api_key,\n openai_api_type=openai_api_type,\n api_version=openai_api_version,\n organization=openai_organization,\n openai_proxy=openai_proxy,\n timeout=request_timeout,\n show_progress_bar=show_progress_bar,\n skip_empty=skip_empty,\n tiktoken_model_name=tiktoken_model_name,\n )\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "default_headers": { - "type": "dict", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "default_headers", - "display_name": "Default Headers", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "default_query": { - "type": "NestedDict", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": {}, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "default_query", - "display_name": "Default Query", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "deployment": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": "text-embedding-ada-002", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "deployment", - "display_name": "Deployment", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "disallowed_special": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": [ - "all" - ], - "fileTypes": [], - "file_path": "", - "password": false, - "name": "disallowed_special", - "display_name": "Disallowed Special", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "embedding_ctx_length": { - "type": "int", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": 8191, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "embedding_ctx_length", - "display_name": "Embedding Context Length", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "max_retries": { - "type": "int", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": 6, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "max_retries", - "display_name": "Max Retries", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "model": { - "type": "str", - "required": false, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "value": "text-embedding-ada-002", - "fileTypes": [], - "file_path": "", - "password": false, - "options": [ - "text-embedding-3-small", - "text-embedding-3-large", - "text-embedding-ada-002" - ], - "name": "model", - "display_name": "Model", - "advanced": false, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "model_kwargs": { - "type": "NestedDict", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": {}, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "model_kwargs", - "display_name": "Model Kwargs", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "openai_api_base": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": true, - "name": "openai_api_base", - "display_name": "OpenAI API Base", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "openai_api_key": { - "type": "str", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": true, - "name": "openai_api_key", - "display_name": "OpenAI API Key", - "advanced": false, - "dynamic": false, - "info": "", - "load_from_db": true, - "title_case": false, - "input_types": [ - "Text" - ], - "value": "OPENAI_API_KEY" - }, - "openai_api_type": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": true, - "name": "openai_api_type", - "display_name": "OpenAI API Type", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "openai_api_version": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "openai_api_version", - "display_name": "OpenAI API Version", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "openai_organization": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "openai_organization", - "display_name": "OpenAI Organization", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "openai_proxy": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "openai_proxy", - "display_name": "OpenAI Proxy", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "request_timeout": { - "type": "float", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "request_timeout", - "display_name": "Request Timeout", - "advanced": true, - "dynamic": false, - "info": "", - "rangeSpec": { - "step_type": "float", - "min": -1, - "max": 1, - "step": 0.1 - }, - "load_from_db": false, - "title_case": false - }, - "show_progress_bar": { - "type": "bool", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "show_progress_bar", - "display_name": "Show Progress Bar", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "skip_empty": { - "type": "bool", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "skip_empty", - "display_name": "Skip Empty", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "tiktoken_enable": { - "type": "bool", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": true, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "tiktoken_enable", - "display_name": "TikToken Enable", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "tiktoken_model_name": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "tiktoken_model_name", - "display_name": "TikToken Model Name", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "_type": "CustomComponent" - }, - "description": "Generate embeddings using OpenAI models.", - "base_classes": [ - "Embeddings" - ], - "display_name": "OpenAI Embeddings", - "documentation": "", - "custom_fields": { - "openai_api_key": null, - "default_headers": null, - "default_query": null, - "allowed_special": null, - "disallowed_special": null, - "chunk_size": null, - "client": null, - "deployment": null, - "embedding_ctx_length": null, - "max_retries": null, - "model": null, - "model_kwargs": null, - "openai_api_base": null, - "openai_api_type": null, - "openai_api_version": null, - "openai_organization": null, - "openai_proxy": null, - "request_timeout": null, - "show_progress_bar": null, - "skip_empty": null, - "tiktoken_enable": null, - "tiktoken_model_name": null - }, - "output_types": [ - "Embeddings" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": false - }, - "id": "OpenAIEmbeddings-ZlOk1" - }, - "selected": false, - "width": 384, - "height": 383, - "dragging": false + "output_types": ["Text", "Record"], + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false + }, + "id": "ChatInput-yxMKE" + }, + "selected": false, + "width": 384, + "height": 383 + }, + { + "id": "TextOutput-BDknO", + "type": "genericNode", + "position": { + "x": 2322.600672827879, + "y": 604.9467307442569 + }, + "data": { + "type": "TextOutput", + "node": { + "template": { + "input_value": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": "", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "input_value", + "display_name": "Value", + "advanced": false, + "input_types": ["Record", "Text"], + "dynamic": false, + "info": "Text or Record to be passed as output.", + "load_from_db": false, + "title_case": false + }, + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import Optional\n\nfrom langflow.base.io.text import TextComponent\nfrom langflow.field_typing import Text\n\n\nclass TextOutput(TextComponent):\n display_name = \"Text Output\"\n description = \"Display a text output in the Playground.\"\n icon = \"type\"\n\n def build_config(self):\n return {\n \"input_value\": {\n \"display_name\": \"Value\",\n \"input_types\": [\"Record\", \"Text\"],\n \"info\": \"Text or Record to be passed as output.\",\n },\n \"record_template\": {\n \"display_name\": \"Record Template\",\n \"multiline\": True,\n \"info\": \"Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.\",\n \"advanced\": True,\n },\n }\n\n def build(self, input_value: Optional[Text] = \"\", record_template: str = \"\") -> Text:\n return super().build(input_value=input_value, record_template=record_template)\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "record_template": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "{text}", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "record_template", + "display_name": "Record Template", + "advanced": true, + "dynamic": false, + "info": "Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "_type": "CustomComponent" }, - { - "id": "OpenAIModel-EjXlN", - "type": "genericNode", - "position": { - "x": 3410.117202077183, - "y": 431.2038048137648 - }, - "data": { - "type": "OpenAIModel", - "node": { - "template": { - "input_value": { - "type": "str", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "input_value", - "display_name": "Input", - "advanced": false, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\"},\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": True,\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": [\n \"gpt-4-turbo-preview\",\n \"gpt-3.5-turbo\",\n \"gpt-4-0125-preview\",\n \"gpt-4-1106-preview\",\n \"gpt-4-vision-preview\",\n \"gpt-3.5-turbo-0125\",\n \"gpt-3.5-turbo-1106\",\n ],\n \"value\": \"gpt-4-turbo-preview\",\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"openai_api_key\": {\n \"display_name\": \"OpenAI API Key\",\n \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n \"advanced\": False,\n \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float,\n model_name: str,\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n output = ChatOpenAI(\n max_tokens=max_tokens,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=openai_api_key,\n temperature=temperature,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "max_tokens": { - "type": "int", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": 256, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "max_tokens", - "display_name": "Max Tokens", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "model_kwargs": { - "type": "NestedDict", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": {}, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "model_kwargs", - "display_name": "Model Kwargs", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "model_name": { - "type": "str", - "required": true, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "value": "gpt-3.5-turbo", - "fileTypes": [], - "file_path": "", - "password": false, - "options": [ - "gpt-4-turbo-preview", - "gpt-3.5-turbo", - "gpt-4-0125-preview", - "gpt-4-1106-preview", - "gpt-4-vision-preview", - "gpt-3.5-turbo-0125", - "gpt-3.5-turbo-1106" - ], - "name": "model_name", - "display_name": "Model Name", - "advanced": false, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "openai_api_base": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "openai_api_base", - "display_name": "OpenAI API Base", - "advanced": true, - "dynamic": false, - "info": "The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\n\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "openai_api_key": { - "type": "str", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": true, - "name": "openai_api_key", - "display_name": "OpenAI API Key", - "advanced": false, - "dynamic": false, - "info": "The OpenAI API Key to use for the OpenAI model.", - "load_from_db": true, - "title_case": false, - "input_types": [ - "Text" - ], - "value": "OPENAI_API_KEY" - }, - "stream": { - "type": "bool", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "stream", - "display_name": "Stream", - "advanced": true, - "dynamic": false, - "info": "Stream the response from the model. Streaming works only in Chat.", - "load_from_db": false, - "title_case": false - }, - "system_message": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "system_message", - "display_name": "System Message", - "advanced": true, - "dynamic": false, - "info": "System message to pass to the model.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "temperature": { - "type": "float", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": 0.1, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "temperature", - "display_name": "Temperature", - "advanced": false, - "dynamic": false, - "info": "", - "rangeSpec": { - "step_type": "float", - "min": -1, - "max": 1, - "step": 0.1 - }, - "load_from_db": false, - "title_case": false - }, - "_type": "CustomComponent" - }, - "description": "Generates text using OpenAI LLMs.", - "icon": "OpenAI", - "base_classes": [ - "object", - "Text", - "str" - ], - "display_name": "OpenAI", - "documentation": "", - "custom_fields": { - "input_value": null, - "openai_api_key": null, - "temperature": null, - "model_name": null, - "max_tokens": null, - "model_kwargs": null, - "openai_api_base": null, - "stream": null, - "system_message": null - }, - "output_types": [ - "Text" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [ - "max_tokens", - "model_kwargs", - "model_name", - "openai_api_base", - "openai_api_key", - "temperature", - "input_value", - "system_message", - "stream" - ], - "beta": false - }, - "id": "OpenAIModel-EjXlN" - }, - "selected": true, - "width": 384, - "height": 563, - "positionAbsolute": { - "x": 3410.117202077183, - "y": 431.2038048137648 - }, - "dragging": false + "description": "Display a text output in the Playground.", + "icon": "type", + "base_classes": ["object", "Text", "str"], + "display_name": "Extracted Chunks", + "documentation": "", + "custom_fields": { + "input_value": null, + "record_template": null }, - { - "id": "Prompt-xeI6K", - "type": "genericNode", - "position": { - "x": 2969.0261961391298, - "y": 442.1613649809069 + "output_types": ["Text"], + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false + }, + "id": "TextOutput-BDknO" + }, + "selected": false, + "width": 384, + "height": 289, + "positionAbsolute": { + "x": 2322.600672827879, + "y": 604.9467307442569 + }, + "dragging": false + }, + { + "id": "OpenAIEmbeddings-ZlOk1", + "type": "genericNode", + "position": { + "x": 1183.667250865064, + "y": 687.3171828430261 + }, + "data": { + "type": "OpenAIEmbeddings", + "node": { + "template": { + "allowed_special": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": [], + "fileTypes": [], + "file_path": "", + "password": false, + "name": "allowed_special", + "display_name": "Allowed Special", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "chunk_size": { + "type": "int", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": 1000, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "chunk_size", + "display_name": "Chunk Size", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "client": { + "type": "Any", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "client", + "display_name": "Client", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import Any, Dict, List, Optional\n\nfrom langchain_openai.embeddings.base import OpenAIEmbeddings\n\nfrom langflow.field_typing import Embeddings, NestedDict\nfrom langflow.interface.custom.custom_component import CustomComponent\n\n\nclass OpenAIEmbeddingsComponent(CustomComponent):\n display_name = \"OpenAI Embeddings\"\n description = \"Generate embeddings using OpenAI models.\"\n\n def build_config(self):\n return {\n \"allowed_special\": {\n \"display_name\": \"Allowed Special\",\n \"advanced\": True,\n \"field_type\": \"str\",\n \"is_list\": True,\n },\n \"default_headers\": {\n \"display_name\": \"Default Headers\",\n \"advanced\": True,\n \"field_type\": \"dict\",\n },\n \"default_query\": {\n \"display_name\": \"Default Query\",\n \"advanced\": True,\n \"field_type\": \"NestedDict\",\n },\n \"disallowed_special\": {\n \"display_name\": \"Disallowed Special\",\n \"advanced\": True,\n \"field_type\": \"str\",\n \"is_list\": True,\n },\n \"chunk_size\": {\"display_name\": \"Chunk Size\", \"advanced\": True},\n \"client\": {\"display_name\": \"Client\", \"advanced\": True},\n \"deployment\": {\"display_name\": \"Deployment\", \"advanced\": True},\n \"embedding_ctx_length\": {\n \"display_name\": \"Embedding Context Length\",\n \"advanced\": True,\n },\n \"max_retries\": {\"display_name\": \"Max Retries\", \"advanced\": True},\n \"model\": {\n \"display_name\": \"Model\",\n \"advanced\": False,\n \"options\": [\n \"text-embedding-3-small\",\n \"text-embedding-3-large\",\n \"text-embedding-ada-002\",\n ],\n },\n \"model_kwargs\": {\"display_name\": \"Model Kwargs\", \"advanced\": True},\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"password\": True,\n \"advanced\": True,\n },\n \"openai_api_key\": {\"display_name\": \"OpenAI API Key\", \"password\": True},\n \"openai_api_type\": {\n \"display_name\": \"OpenAI API Type\",\n \"advanced\": True,\n \"password\": True,\n },\n \"openai_api_version\": {\n \"display_name\": \"OpenAI API Version\",\n \"advanced\": True,\n },\n \"openai_organization\": {\n \"display_name\": \"OpenAI Organization\",\n \"advanced\": True,\n },\n \"openai_proxy\": {\"display_name\": \"OpenAI Proxy\", \"advanced\": True},\n \"request_timeout\": {\"display_name\": \"Request Timeout\", \"advanced\": True},\n \"show_progress_bar\": {\n \"display_name\": \"Show Progress Bar\",\n \"advanced\": True,\n },\n \"skip_empty\": {\"display_name\": \"Skip Empty\", \"advanced\": True},\n \"tiktoken_model_name\": {\n \"display_name\": \"TikToken Model Name\",\n \"advanced\": True,\n },\n \"tiktoken_enable\": {\"display_name\": \"TikToken Enable\", \"advanced\": True},\n }\n\n def build(\n self,\n openai_api_key: str,\n default_headers: Optional[Dict[str, str]] = None,\n default_query: Optional[NestedDict] = {},\n allowed_special: List[str] = [],\n disallowed_special: List[str] = [\"all\"],\n chunk_size: int = 1000,\n client: Optional[Any] = None,\n deployment: str = \"text-embedding-ada-002\",\n embedding_ctx_length: int = 8191,\n max_retries: int = 6,\n model: str = \"text-embedding-ada-002\",\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n openai_api_type: Optional[str] = None,\n openai_api_version: Optional[str] = None,\n openai_organization: Optional[str] = None,\n openai_proxy: Optional[str] = None,\n request_timeout: Optional[float] = None,\n show_progress_bar: bool = False,\n skip_empty: bool = False,\n tiktoken_enable: bool = True,\n tiktoken_model_name: Optional[str] = None,\n ) -> Embeddings:\n # This is to avoid errors with Vector Stores (e.g Chroma)\n if disallowed_special == [\"all\"]:\n disallowed_special = \"all\" # type: ignore\n\n return OpenAIEmbeddings(\n tiktoken_enabled=tiktoken_enable,\n default_headers=default_headers,\n default_query=default_query,\n allowed_special=set(allowed_special),\n disallowed_special=\"all\",\n chunk_size=chunk_size,\n client=client,\n deployment=deployment,\n embedding_ctx_length=embedding_ctx_length,\n max_retries=max_retries,\n model=model,\n model_kwargs=model_kwargs,\n base_url=openai_api_base,\n api_key=openai_api_key,\n openai_api_type=openai_api_type,\n api_version=openai_api_version,\n organization=openai_organization,\n openai_proxy=openai_proxy,\n timeout=request_timeout,\n show_progress_bar=show_progress_bar,\n skip_empty=skip_empty,\n tiktoken_model_name=tiktoken_model_name,\n )\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "default_headers": { + "type": "dict", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "default_headers", + "display_name": "Default Headers", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "default_query": { + "type": "NestedDict", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": {}, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "default_query", + "display_name": "Default Query", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "deployment": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": "text-embedding-ada-002", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "deployment", + "display_name": "Deployment", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "disallowed_special": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": ["all"], + "fileTypes": [], + "file_path": "", + "password": false, + "name": "disallowed_special", + "display_name": "Disallowed Special", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "embedding_ctx_length": { + "type": "int", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": 8191, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "embedding_ctx_length", + "display_name": "Embedding Context Length", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "max_retries": { + "type": "int", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": 6, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "max_retries", + "display_name": "Max Retries", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "model": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "value": "text-embedding-ada-002", + "fileTypes": [], + "file_path": "", + "password": false, + "options": [ + "text-embedding-3-small", + "text-embedding-3-large", + "text-embedding-ada-002" + ], + "name": "model", + "display_name": "Model", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "model_kwargs": { + "type": "NestedDict", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": {}, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "model_kwargs", + "display_name": "Model Kwargs", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "openai_api_base": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": true, + "name": "openai_api_base", + "display_name": "OpenAI API Base", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "openai_api_key": { + "type": "str", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": true, + "name": "openai_api_key", + "display_name": "OpenAI API Key", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": true, + "title_case": false, + "input_types": ["Text"], + "value": "OPENAI_API_KEY" + }, + "openai_api_type": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": true, + "name": "openai_api_type", + "display_name": "OpenAI API Type", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "openai_api_version": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "openai_api_version", + "display_name": "OpenAI API Version", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "openai_organization": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "openai_organization", + "display_name": "OpenAI Organization", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "openai_proxy": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "openai_proxy", + "display_name": "OpenAI Proxy", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "request_timeout": { + "type": "float", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "request_timeout", + "display_name": "Request Timeout", + "advanced": true, + "dynamic": false, + "info": "", + "rangeSpec": { + "step_type": "float", + "min": -1, + "max": 1, + "step": 0.1 }, +<<<<<<< HEAD "data": { "type": "Prompt", "node": { @@ -1236,2168 +774,2516 @@ "y": 442.1613649809069 }, "dragging": false +======= + "load_from_db": false, + "title_case": false + }, + "show_progress_bar": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "show_progress_bar", + "display_name": "Show Progress Bar", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "skip_empty": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "skip_empty", + "display_name": "Skip Empty", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "tiktoken_enable": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": true, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "tiktoken_enable", + "display_name": "TikToken Enable", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "tiktoken_model_name": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "tiktoken_model_name", + "display_name": "TikToken Model Name", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "_type": "CustomComponent" +>>>>>>> origin/dev }, - { - "id": "ChatOutput-Q39I8", - "type": "genericNode", - "position": { - "x": 3887.2073667611485, - "y": 588.4801225794856 - }, - "data": { - "type": "ChatOutput", - "node": { - "template": { - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n def build(\n self,\n sender: Optional[str] = \"Machine\",\n sender_name: Optional[str] = \"AI\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n record_template: Optional[str] = \"{text}\",\n ) -> Union[Text, Record]:\n return super().build(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n record_template=record_template,\n )\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "input_value": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "input_value", - "display_name": "Message", - "advanced": false, - "input_types": [ - "Text" - ], - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "record_template": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "{text}", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "record_template", - "display_name": "Record Template", - "advanced": true, - "dynamic": false, - "info": "In case of Message being a Record, this template will be used to convert it to text.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "return_record": { - "type": "bool", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "return_record", - "display_name": "Return Record", - "advanced": true, - "dynamic": false, - "info": "Return the message as a record containing the sender, sender_name, and session_id.", - "load_from_db": false, - "title_case": false - }, - "sender": { - "type": "str", - "required": false, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "value": "Machine", - "fileTypes": [], - "file_path": "", - "password": false, - "options": [ - "Machine", - "User" - ], - "name": "sender", - "display_name": "Sender Type", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "sender_name": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": "AI", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "sender_name", - "display_name": "Sender Name", - "advanced": false, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "session_id": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "session_id", - "display_name": "Session ID", - "advanced": true, - "dynamic": false, - "info": "If provided, the message will be stored in the memory.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "_type": "CustomComponent" - }, - "description": "Display a chat message in the Playground.", - "icon": "ChatOutput", - "base_classes": [ - "object", - "Text", - "Record", - "str" - ], - "display_name": "Chat Output", - "documentation": "", - "custom_fields": { - "sender": null, - "sender_name": null, - "input_value": null, - "session_id": null, - "return_record": null, - "record_template": null - }, - "output_types": [ - "Text", - "Record" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": false - }, - "id": "ChatOutput-Q39I8" - }, - "selected": false, - "width": 384, - "height": 383, - "positionAbsolute": { - "x": 3887.2073667611485, - "y": 588.4801225794856 - }, - "dragging": false + "description": "Generate embeddings using OpenAI models.", + "base_classes": ["Embeddings"], + "display_name": "OpenAI Embeddings", + "documentation": "", + "custom_fields": { + "openai_api_key": null, + "default_headers": null, + "default_query": null, + "allowed_special": null, + "disallowed_special": null, + "chunk_size": null, + "client": null, + "deployment": null, + "embedding_ctx_length": null, + "max_retries": null, + "model": null, + "model_kwargs": null, + "openai_api_base": null, + "openai_api_type": null, + "openai_api_version": null, + "openai_organization": null, + "openai_proxy": null, + "request_timeout": null, + "show_progress_bar": null, + "skip_empty": null, + "tiktoken_enable": null, + "tiktoken_model_name": null }, - { - "id": "File-t0a6a", - "type": "genericNode", - "position": { - "x": 2257.233450682836, - "y": 1747.5389618367233 + "output_types": ["Embeddings"], + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false + }, + "id": "OpenAIEmbeddings-ZlOk1" + }, + "selected": false, + "width": 384, + "height": 383, + "dragging": false + }, + { + "id": "OpenAIModel-EjXlN", + "type": "genericNode", + "position": { + "x": 3410.117202077183, + "y": 431.2038048137648 + }, + "data": { + "type": "OpenAIModel", + "node": { + "template": { + "input_value": { + "type": "str", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "input_value", + "display_name": "Input", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\"},\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": True,\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": [\n \"gpt-4-turbo-preview\",\n \"gpt-3.5-turbo\",\n \"gpt-4-0125-preview\",\n \"gpt-4-1106-preview\",\n \"gpt-4-vision-preview\",\n \"gpt-3.5-turbo-0125\",\n \"gpt-3.5-turbo-1106\",\n ],\n \"value\": \"gpt-4-turbo-preview\",\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"openai_api_key\": {\n \"display_name\": \"OpenAI API Key\",\n \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n \"advanced\": False,\n \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float,\n model_name: str,\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n output = ChatOpenAI(\n max_tokens=max_tokens,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=openai_api_key,\n temperature=temperature,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "max_tokens": { + "type": "int", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": 256, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "max_tokens", + "display_name": "Max Tokens", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "model_kwargs": { + "type": "NestedDict", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": {}, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "model_kwargs", + "display_name": "Model Kwargs", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "model_name": { + "type": "str", + "required": true, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "value": "gpt-3.5-turbo", + "fileTypes": [], + "file_path": "", + "password": false, + "options": [ + "gpt-4-turbo-preview", + "gpt-3.5-turbo", + "gpt-4-0125-preview", + "gpt-4-1106-preview", + "gpt-4-vision-preview", + "gpt-3.5-turbo-0125", + "gpt-3.5-turbo-1106" + ], + "name": "model_name", + "display_name": "Model Name", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "openai_api_base": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "openai_api_base", + "display_name": "OpenAI API Base", + "advanced": true, + "dynamic": false, + "info": "The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\n\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "openai_api_key": { + "type": "str", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": true, + "name": "openai_api_key", + "display_name": "OpenAI API Key", + "advanced": false, + "dynamic": false, + "info": "The OpenAI API Key to use for the OpenAI model.", + "load_from_db": true, + "title_case": false, + "input_types": ["Text"], + "value": "OPENAI_API_KEY" + }, + "stream": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "stream", + "display_name": "Stream", + "advanced": true, + "dynamic": false, + "info": "Stream the response from the model. Streaming works only in Chat.", + "load_from_db": false, + "title_case": false + }, + "system_message": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "system_message", + "display_name": "System Message", + "advanced": true, + "dynamic": false, + "info": "System message to pass to the model.", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "temperature": { + "type": "float", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": 0.1, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "temperature", + "display_name": "Temperature", + "advanced": false, + "dynamic": false, + "info": "", + "rangeSpec": { + "step_type": "float", + "min": -1, + "max": 1, + "step": 0.1 }, - "data": { - "type": "File", - "node": { - "template": { - "path": { - "type": "file", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [ - ".txt", - ".md", - ".mdx", - ".csv", - ".json", - ".yaml", - ".yml", - ".xml", - ".html", - ".htm", - ".pdf", - ".docx" - ], - "file_path": "51e2b78a-199b-4054-9f32-e288eef6924c/Langflow conversation.pdf", - "password": false, - "name": "path", - "display_name": "Path", - "advanced": false, - "dynamic": false, - "info": "Supported file types: txt, md, mdx, csv, json, yaml, yml, xml, html, htm, pdf, docx", - "load_from_db": false, - "title_case": false, - "value": "" - }, - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from pathlib import Path\nfrom typing import Any, Dict\n\nfrom langflow.base.data.utils import TEXT_FILE_TYPES, parse_text_file_to_record\nfrom langflow.interface.custom.custom_component import CustomComponent\nfrom langflow.schema import Record\n\n\nclass FileComponent(CustomComponent):\n display_name = \"File\"\n description = \"A generic file loader.\"\n icon = \"file-text\"\n\n def build_config(self) -> Dict[str, Any]:\n return {\n \"path\": {\n \"display_name\": \"Path\",\n \"field_type\": \"file\",\n \"file_types\": TEXT_FILE_TYPES,\n \"info\": f\"Supported file types: {', '.join(TEXT_FILE_TYPES)}\",\n },\n \"silent_errors\": {\n \"display_name\": \"Silent Errors\",\n \"advanced\": True,\n \"info\": \"If true, errors will not raise an exception.\",\n },\n }\n\n def load_file(self, path: str, silent_errors: bool = False) -> Record:\n resolved_path = self.resolve_path(path)\n path_obj = Path(resolved_path)\n extension = path_obj.suffix[1:].lower()\n if extension == \"doc\":\n raise ValueError(\"doc files are not supported. Please save as .docx\")\n if extension not in TEXT_FILE_TYPES:\n raise ValueError(f\"Unsupported file type: {extension}\")\n record = parse_text_file_to_record(resolved_path, silent_errors)\n self.status = record if record else \"No data\"\n return record or Record()\n\n def build(\n self,\n path: str,\n silent_errors: bool = False,\n ) -> Record:\n record = self.load_file(path, silent_errors)\n self.status = record\n return record\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "silent_errors": { - "type": "bool", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "silent_errors", - "display_name": "Silent Errors", - "advanced": true, - "dynamic": false, - "info": "If true, errors will not raise an exception.", - "load_from_db": false, - "title_case": false - }, - "_type": "CustomComponent" - }, - "description": "A generic file loader.", - "icon": "file-text", - "base_classes": [ - "Record" - ], - "display_name": "File", - "documentation": "", - "custom_fields": { - "path": null, - "silent_errors": null - }, - "output_types": [ - "Record" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": false - }, - "id": "File-t0a6a" - }, - "selected": false, - "width": 384, - "height": 281, - "positionAbsolute": { - "x": 2257.233450682836, - "y": 1747.5389618367233 - }, - "dragging": false + "load_from_db": false, + "title_case": false + }, + "_type": "CustomComponent" }, - { - "id": "RecursiveCharacterTextSplitter-tR9QM", - "type": "genericNode", - "position": { - "x": 2791.013514133929, - "y": 1462.9588953494142 - }, - "data": { - "type": "RecursiveCharacterTextSplitter", - "node": { - "template": { - "inputs": { - "type": "Document", - "required": true, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "inputs", - "display_name": "Input", - "advanced": false, - "input_types": [ - "Document", - "Record" - ], - "dynamic": false, - "info": "The texts to split.", - "load_from_db": false, - "title_case": false - }, - "chunk_overlap": { - "type": "int", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": 200, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "chunk_overlap", - "display_name": "Chunk Overlap", - "advanced": false, - "dynamic": false, - "info": "The amount of overlap between chunks.", - "load_from_db": false, - "title_case": false - }, - "chunk_size": { - "type": "int", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": 1000, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "chunk_size", - "display_name": "Chunk Size", - "advanced": false, - "dynamic": false, - "info": "The maximum length of each chunk.", - "load_from_db": false, - "title_case": false - }, - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from typing import Optional\n\nfrom langchain.text_splitter import RecursiveCharacterTextSplitter\nfrom langchain_core.documents import Document\n\nfrom langflow.interface.custom.custom_component import CustomComponent\nfrom langflow.schema import Record\nfrom langflow.utils.util import build_loader_repr_from_records, unescape_string\n\n\nclass RecursiveCharacterTextSplitterComponent(CustomComponent):\n display_name: str = \"Recursive Character Text Splitter\"\n description: str = \"Split text into chunks of a specified length.\"\n documentation: str = \"https://docs.langflow.org/components/text-splitters#recursivecharactertextsplitter\"\n\n def build_config(self):\n return {\n \"inputs\": {\n \"display_name\": \"Input\",\n \"info\": \"The texts to split.\",\n \"input_types\": [\"Document\", \"Record\"],\n },\n \"separators\": {\n \"display_name\": \"Separators\",\n \"info\": 'The characters to split on.\\nIf left empty defaults to [\"\\\\n\\\\n\", \"\\\\n\", \" \", \"\"].',\n \"is_list\": True,\n },\n \"chunk_size\": {\n \"display_name\": \"Chunk Size\",\n \"info\": \"The maximum length of each chunk.\",\n \"field_type\": \"int\",\n \"value\": 1000,\n },\n \"chunk_overlap\": {\n \"display_name\": \"Chunk Overlap\",\n \"info\": \"The amount of overlap between chunks.\",\n \"field_type\": \"int\",\n \"value\": 200,\n },\n \"code\": {\"show\": False},\n }\n\n def build(\n self,\n inputs: list[Document],\n separators: Optional[list[str]] = None,\n chunk_size: Optional[int] = 1000,\n chunk_overlap: Optional[int] = 200,\n ) -> list[Record]:\n \"\"\"\n Split text into chunks of a specified length.\n\n Args:\n separators (list[str]): The characters to split on.\n chunk_size (int): The maximum length of each chunk.\n chunk_overlap (int): The amount of overlap between chunks.\n length_function (function): The function to use to calculate the length of the text.\n\n Returns:\n list[str]: The chunks of text.\n \"\"\"\n\n if separators == \"\":\n separators = None\n elif separators:\n # check if the separators list has escaped characters\n # if there are escaped characters, unescape them\n separators = [unescape_string(x) for x in separators]\n\n # Make sure chunk_size and chunk_overlap are ints\n if isinstance(chunk_size, str):\n chunk_size = int(chunk_size)\n if isinstance(chunk_overlap, str):\n chunk_overlap = int(chunk_overlap)\n splitter = RecursiveCharacterTextSplitter(\n separators=separators,\n chunk_size=chunk_size,\n chunk_overlap=chunk_overlap,\n )\n documents = []\n for _input in inputs:\n if isinstance(_input, Record):\n documents.append(_input.to_lc_document())\n else:\n documents.append(_input)\n docs = splitter.split_documents(documents)\n records = self.to_records(docs)\n self.repr_value = build_loader_repr_from_records(records)\n return records\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "separators": { - "type": "str", - "required": false, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "separators", - "display_name": "Separators", - "advanced": false, - "dynamic": false, - "info": "The characters to split on.\nIf left empty defaults to [\"\\n\\n\", \"\\n\", \" \", \"\"].", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ], - "value": [ - "" - ] - }, - "_type": "CustomComponent" - }, - "description": "Split text into chunks of a specified length.", - "base_classes": [ - "Record" - ], - "display_name": "Recursive Character Text Splitter", - "documentation": "https://docs.langflow.org/components/text-splitters#recursivecharactertextsplitter", - "custom_fields": { - "inputs": null, - "separators": null, - "chunk_size": null, - "chunk_overlap": null - }, - "output_types": [ - "Record" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": false - }, - "id": "RecursiveCharacterTextSplitter-tR9QM" - }, - "selected": false, - "width": 384, - "height": 501, - "positionAbsolute": { - "x": 2791.013514133929, - "y": 1462.9588953494142 - }, - "dragging": false + "description": "Generates text using OpenAI LLMs.", + "icon": "OpenAI", + "base_classes": ["object", "Text", "str"], + "display_name": "OpenAI", + "documentation": "", + "custom_fields": { + "input_value": null, + "openai_api_key": null, + "temperature": null, + "model_name": null, + "max_tokens": null, + "model_kwargs": null, + "openai_api_base": null, + "stream": null, + "system_message": null }, - { - "id": "AstraDBSearch-41nRz", - "type": "genericNode", - "position": { - "x": 1723.976434815103, - "y": 277.03317407245913 - }, - "data": { - "type": "AstraDBSearch", - "node": { - "template": { - "embedding": { - "type": "Embeddings", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "embedding", - "display_name": "Embedding", - "advanced": false, - "dynamic": false, - "info": "Embedding to use", - "load_from_db": false, - "title_case": false - }, - "input_value": { - "type": "str", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "input_value", - "display_name": "Input Value", - "advanced": false, - "dynamic": false, - "info": "Input value to search", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "api_endpoint": { - "type": "str", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "api_endpoint", - "display_name": "API Endpoint", - "advanced": false, - "dynamic": false, - "info": "API endpoint URL for the Astra DB service.", - "load_from_db": true, - "title_case": false, - "input_types": [ - "Text" - ], - "value": "ASTRA_DB_API_ENDPOINT" - }, - "batch_size": { - "type": "int", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "batch_size", - "display_name": "Batch Size", - "advanced": true, - "dynamic": false, - "info": "Optional number of records to process in a single batch.", - "load_from_db": false, - "title_case": false - }, - "bulk_delete_concurrency": { - "type": "int", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "bulk_delete_concurrency", - "display_name": "Bulk Delete Concurrency", - "advanced": true, - "dynamic": false, - "info": "Optional concurrency level for bulk delete operations.", - "load_from_db": false, - "title_case": false - }, - "bulk_insert_batch_concurrency": { - "type": "int", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "bulk_insert_batch_concurrency", - "display_name": "Bulk Insert Batch Concurrency", - "advanced": true, - "dynamic": false, - "info": "Optional concurrency level for bulk insert operations.", - "load_from_db": false, - "title_case": false - }, - "bulk_insert_overwrite_concurrency": { - "type": "int", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "bulk_insert_overwrite_concurrency", - "display_name": "Bulk Insert Overwrite Concurrency", - "advanced": true, - "dynamic": false, - "info": "Optional concurrency level for bulk insert operations that overwrite existing records.", - "load_from_db": false, - "title_case": false - }, - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from typing import List, Optional\n\nfrom langflow.components.vectorstores.AstraDB import AstraDBVectorStoreComponent\nfrom langflow.components.vectorstores.base.model import LCVectorStoreComponent\nfrom langflow.field_typing import Embeddings, Text\nfrom langflow.schema import Record\n\n\nclass AstraDBSearchComponent(LCVectorStoreComponent):\n display_name = \"Astra DB Search\"\n description = \"Searches an existing Astra DB Vector Store.\"\n icon = \"AstraDB\"\n field_order = [\"token\", \"api_endpoint\", \"collection_name\", \"input_value\", \"embedding\"]\n\n def build_config(self):\n return {\n \"search_type\": {\n \"display_name\": \"Search Type\",\n \"options\": [\"Similarity\", \"MMR\"],\n },\n \"input_value\": {\n \"display_name\": \"Input Value\",\n \"info\": \"Input value to search\",\n },\n \"embedding\": {\"display_name\": \"Embedding\", \"info\": \"Embedding to use\"},\n \"collection_name\": {\n \"display_name\": \"Collection Name\",\n \"info\": \"The name of the collection within Astra DB where the vectors will be stored.\",\n },\n \"token\": {\n \"display_name\": \"Token\",\n \"info\": \"Authentication token for accessing Astra DB.\",\n \"password\": True,\n },\n \"api_endpoint\": {\n \"display_name\": \"API Endpoint\",\n \"info\": \"API endpoint URL for the Astra DB service.\",\n },\n \"namespace\": {\n \"display_name\": \"Namespace\",\n \"info\": \"Optional namespace within Astra DB to use for the collection.\",\n \"advanced\": True,\n },\n \"metric\": {\n \"display_name\": \"Metric\",\n \"info\": \"Optional distance metric for vector comparisons in the vector store.\",\n \"advanced\": True,\n },\n \"batch_size\": {\n \"display_name\": \"Batch Size\",\n \"info\": \"Optional number of records to process in a single batch.\",\n \"advanced\": True,\n },\n \"bulk_insert_batch_concurrency\": {\n \"display_name\": \"Bulk Insert Batch Concurrency\",\n \"info\": \"Optional concurrency level for bulk insert operations.\",\n \"advanced\": True,\n },\n \"bulk_insert_overwrite_concurrency\": {\n \"display_name\": \"Bulk Insert Overwrite Concurrency\",\n \"info\": \"Optional concurrency level for bulk insert operations that overwrite existing records.\",\n \"advanced\": True,\n },\n \"bulk_delete_concurrency\": {\n \"display_name\": \"Bulk Delete Concurrency\",\n \"info\": \"Optional concurrency level for bulk delete operations.\",\n \"advanced\": True,\n },\n \"setup_mode\": {\n \"display_name\": \"Setup Mode\",\n \"info\": \"Configuration mode for setting up the vector store, with options like \u201cSync\u201d, \u201cAsync\u201d, or \u201cOff\u201d.\",\n \"options\": [\"Sync\", \"Async\", \"Off\"],\n \"advanced\": True,\n },\n \"pre_delete_collection\": {\n \"display_name\": \"Pre Delete Collection\",\n \"info\": \"Boolean flag to determine whether to delete the collection before creating a new one.\",\n \"advanced\": True,\n },\n \"metadata_indexing_include\": {\n \"display_name\": \"Metadata Indexing Include\",\n \"info\": \"Optional list of metadata fields to include in the indexing.\",\n \"advanced\": True,\n },\n \"metadata_indexing_exclude\": {\n \"display_name\": \"Metadata Indexing Exclude\",\n \"info\": \"Optional list of metadata fields to exclude from the indexing.\",\n \"advanced\": True,\n },\n \"collection_indexing_policy\": {\n \"display_name\": \"Collection Indexing Policy\",\n \"info\": \"Optional dictionary defining the indexing policy for the collection.\",\n \"advanced\": True,\n },\n \"number_of_results\": {\n \"display_name\": \"Number of Results\",\n \"info\": \"Number of results to return.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n embedding: Embeddings,\n collection_name: str,\n input_value: Text,\n token: str,\n api_endpoint: str,\n search_type: str = \"Similarity\",\n number_of_results: int = 4,\n namespace: Optional[str] = None,\n metric: Optional[str] = None,\n batch_size: Optional[int] = None,\n bulk_insert_batch_concurrency: Optional[int] = None,\n bulk_insert_overwrite_concurrency: Optional[int] = None,\n bulk_delete_concurrency: Optional[int] = None,\n setup_mode: str = \"Sync\",\n pre_delete_collection: bool = False,\n metadata_indexing_include: Optional[List[str]] = None,\n metadata_indexing_exclude: Optional[List[str]] = None,\n collection_indexing_policy: Optional[dict] = None,\n ) -> List[Record]:\n vector_store = AstraDBVectorStoreComponent().build(\n embedding=embedding,\n collection_name=collection_name,\n token=token,\n api_endpoint=api_endpoint,\n namespace=namespace,\n metric=metric,\n batch_size=batch_size,\n bulk_insert_batch_concurrency=bulk_insert_batch_concurrency,\n bulk_insert_overwrite_concurrency=bulk_insert_overwrite_concurrency,\n bulk_delete_concurrency=bulk_delete_concurrency,\n setup_mode=setup_mode,\n pre_delete_collection=pre_delete_collection,\n metadata_indexing_include=metadata_indexing_include,\n metadata_indexing_exclude=metadata_indexing_exclude,\n collection_indexing_policy=collection_indexing_policy,\n )\n try:\n return self.search_with_vector_store(input_value, search_type, vector_store, k=number_of_results)\n except KeyError as e:\n if \"content\" in str(e):\n raise ValueError(\n \"You should ingest data through Langflow (or LangChain) to query it in Langflow. Your collection does not contain a field name 'content'.\"\n )\n else:\n raise e\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "collection_indexing_policy": { - "type": "dict", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "collection_indexing_policy", - "display_name": "Collection Indexing Policy", - "advanced": true, - "dynamic": false, - "info": "Optional dictionary defining the indexing policy for the collection.", - "load_from_db": false, - "title_case": false - }, - "collection_name": { - "type": "str", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "collection_name", - "display_name": "Collection Name", - "advanced": false, - "dynamic": false, - "info": "The name of the collection within Astra DB where the vectors will be stored.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ], - "value": "langflow" - }, - "metadata_indexing_exclude": { - "type": "str", - "required": false, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "metadata_indexing_exclude", - "display_name": "Metadata Indexing Exclude", - "advanced": true, - "dynamic": false, - "info": "Optional list of metadata fields to exclude from the indexing.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "metadata_indexing_include": { - "type": "str", - "required": false, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "metadata_indexing_include", - "display_name": "Metadata Indexing Include", - "advanced": true, - "dynamic": false, - "info": "Optional list of metadata fields to include in the indexing.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "metric": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "metric", - "display_name": "Metric", - "advanced": true, - "dynamic": false, - "info": "Optional distance metric for vector comparisons in the vector store.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "namespace": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "namespace", - "display_name": "Namespace", - "advanced": true, - "dynamic": false, - "info": "Optional namespace within Astra DB to use for the collection.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "number_of_results": { - "type": "int", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": 4, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "number_of_results", - "display_name": "Number of Results", - "advanced": true, - "dynamic": false, - "info": "Number of results to return.", - "load_from_db": false, - "title_case": false - }, - "pre_delete_collection": { - "type": "bool", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "pre_delete_collection", - "display_name": "Pre Delete Collection", - "advanced": true, - "dynamic": false, - "info": "Boolean flag to determine whether to delete the collection before creating a new one.", - "load_from_db": false, - "title_case": false - }, - "search_type": { - "type": "str", - "required": false, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "value": "Similarity", - "fileTypes": [], - "file_path": "", - "password": false, - "options": [ - "Similarity", - "MMR" - ], - "name": "search_type", - "display_name": "Search Type", - "advanced": false, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "setup_mode": { - "type": "str", - "required": false, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "value": "Sync", - "fileTypes": [], - "file_path": "", - "password": false, - "options": [ - "Sync", - "Async", - "Off" - ], - "name": "setup_mode", - "display_name": "Setup Mode", - "advanced": true, - "dynamic": false, - "info": "Configuration mode for setting up the vector store, with options like \u201cSync\u201d, \u201cAsync\u201d, or \u201cOff\u201d.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "token": { - "type": "str", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": true, - "name": "token", - "display_name": "Token", - "advanced": false, - "dynamic": false, - "info": "Authentication token for accessing Astra DB.", - "load_from_db": true, - "title_case": false, - "input_types": [ - "Text" - ], - "value": "ASTRA_DB_APPLICATION_TOKEN" - }, - "_type": "CustomComponent" - }, - "description": "Searches an existing Astra DB Vector Store.", - "icon": "AstraDB", - "base_classes": [ - "Record" - ], - "display_name": "Astra DB Search", - "documentation": "", - "custom_fields": { - "embedding": null, - "collection_name": null, - "input_value": null, - "token": null, - "api_endpoint": null, - "search_type": null, - "number_of_results": null, - "namespace": null, - "metric": null, - "batch_size": null, - "bulk_insert_batch_concurrency": null, - "bulk_insert_overwrite_concurrency": null, - "bulk_delete_concurrency": null, - "setup_mode": null, - "pre_delete_collection": null, - "metadata_indexing_include": null, - "metadata_indexing_exclude": null, - "collection_indexing_policy": null - }, - "output_types": [ - "Record" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [ - "token", - "api_endpoint", - "collection_name", - "input_value", - "embedding" - ], - "beta": false - }, - "id": "AstraDBSearch-41nRz" - }, - "selected": false, - "width": 384, - "height": 713, - "dragging": false, - "positionAbsolute": { - "x": 1723.976434815103, - "y": 277.03317407245913 - } + "output_types": ["Text"], + "field_formatters": {}, + "frozen": false, + "field_order": [ + "max_tokens", + "model_kwargs", + "model_name", + "openai_api_base", + "openai_api_key", + "temperature", + "input_value", + "system_message", + "stream" + ], + "beta": false + }, + "id": "OpenAIModel-EjXlN" + }, + "selected": true, + "width": 384, + "height": 563, + "positionAbsolute": { + "x": 3410.117202077183, + "y": 431.2038048137648 + }, + "dragging": false + }, + { + "id": "Prompt-xeI6K", + "type": "genericNode", + "position": { + "x": 2969.0261961391298, + "y": 442.1613649809069 + }, + "data": { + "type": "Prompt", + "node": { + "template": { + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.field_typing import Prompt, TemplateField, Text\nfrom langflow.interface.custom.custom_component import CustomComponent\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": TemplateField(display_name=\"Template\"),\n \"code\": TemplateField(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "template": { + "type": "prompt", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": "{context}\n\n---\n\nGiven the context above, answer the question as best as possible.\n\nQuestion: {question}\n\nAnswer: ", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "template", + "display_name": "Template", + "advanced": false, + "input_types": ["Text"], + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "_type": "CustomComponent", + "context": { + "field_type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "context", + "display_name": "context", + "advanced": false, + "input_types": [ + "Document", + "BaseOutputParser", + "Record", + "Text" + ], + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "type": "str" + }, + "question": { + "field_type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "question", + "display_name": "question", + "advanced": false, + "input_types": [ + "Document", + "BaseOutputParser", + "Record", + "Text" + ], + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "type": "str" + } }, - { - "id": "AstraDB-eUCSS", - "type": "genericNode", - "position": { - "x": 3372.04958055989, - "y": 1611.0742035495277 - }, - "data": { - "type": "AstraDB", - "node": { - "template": { - "embedding": { - "type": "Embeddings", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "embedding", - "display_name": "Embedding", - "advanced": false, - "dynamic": false, - "info": "Embedding to use", - "load_from_db": false, - "title_case": false - }, - "inputs": { - "type": "Record", - "required": false, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "inputs", - "display_name": "Inputs", - "advanced": false, - "dynamic": false, - "info": "Optional list of records to be processed and stored in the vector store.", - "load_from_db": false, - "title_case": false - }, - "api_endpoint": { - "type": "str", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "api_endpoint", - "display_name": "API Endpoint", - "advanced": false, - "dynamic": false, - "info": "API endpoint URL for the Astra DB service.", - "load_from_db": true, - "title_case": false, - "input_types": [ - "Text" - ], - "value": "ASTRA_DB_API_ENDPOINT" - }, - "batch_size": { - "type": "int", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "batch_size", - "display_name": "Batch Size", - "advanced": true, - "dynamic": false, - "info": "Optional number of records to process in a single batch.", - "load_from_db": false, - "title_case": false - }, - "bulk_delete_concurrency": { - "type": "int", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "bulk_delete_concurrency", - "display_name": "Bulk Delete Concurrency", - "advanced": true, - "dynamic": false, - "info": "Optional concurrency level for bulk delete operations.", - "load_from_db": false, - "title_case": false - }, - "bulk_insert_batch_concurrency": { - "type": "int", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "bulk_insert_batch_concurrency", - "display_name": "Bulk Insert Batch Concurrency", - "advanced": true, - "dynamic": false, - "info": "Optional concurrency level for bulk insert operations.", - "load_from_db": false, - "title_case": false - }, - "bulk_insert_overwrite_concurrency": { - "type": "int", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "bulk_insert_overwrite_concurrency", - "display_name": "Bulk Insert Overwrite Concurrency", - "advanced": true, - "dynamic": false, - "info": "Optional concurrency level for bulk insert operations that overwrite existing records.", - "load_from_db": false, - "title_case": false - }, - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from typing import List, Optional\n\nfrom langchain_astradb import AstraDBVectorStore\nfrom langchain_astradb.utils.astradb import SetupMode\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Embeddings, VectorStore\nfrom langflow.schema import Record\n\n\nclass AstraDBVectorStoreComponent(CustomComponent):\n display_name = \"Astra DB\"\n description = \"Builds or loads an Astra DB Vector Store.\"\n icon = \"AstraDB\"\n field_order = [\"token\", \"api_endpoint\", \"collection_name\", \"inputs\", \"embedding\"]\n\n def build_config(self):\n return {\n \"inputs\": {\n \"display_name\": \"Inputs\",\n \"info\": \"Optional list of records to be processed and stored in the vector store.\",\n },\n \"embedding\": {\"display_name\": \"Embedding\", \"info\": \"Embedding to use\"},\n \"collection_name\": {\n \"display_name\": \"Collection Name\",\n \"info\": \"The name of the collection within Astra DB where the vectors will be stored.\",\n },\n \"token\": {\n \"display_name\": \"Token\",\n \"info\": \"Authentication token for accessing Astra DB.\",\n \"password\": True,\n },\n \"api_endpoint\": {\n \"display_name\": \"API Endpoint\",\n \"info\": \"API endpoint URL for the Astra DB service.\",\n },\n \"namespace\": {\n \"display_name\": \"Namespace\",\n \"info\": \"Optional namespace within Astra DB to use for the collection.\",\n \"advanced\": True,\n },\n \"metric\": {\n \"display_name\": \"Metric\",\n \"info\": \"Optional distance metric for vector comparisons in the vector store.\",\n \"advanced\": True,\n },\n \"batch_size\": {\n \"display_name\": \"Batch Size\",\n \"info\": \"Optional number of records to process in a single batch.\",\n \"advanced\": True,\n },\n \"bulk_insert_batch_concurrency\": {\n \"display_name\": \"Bulk Insert Batch Concurrency\",\n \"info\": \"Optional concurrency level for bulk insert operations.\",\n \"advanced\": True,\n },\n \"bulk_insert_overwrite_concurrency\": {\n \"display_name\": \"Bulk Insert Overwrite Concurrency\",\n \"info\": \"Optional concurrency level for bulk insert operations that overwrite existing records.\",\n \"advanced\": True,\n },\n \"bulk_delete_concurrency\": {\n \"display_name\": \"Bulk Delete Concurrency\",\n \"info\": \"Optional concurrency level for bulk delete operations.\",\n \"advanced\": True,\n },\n \"setup_mode\": {\n \"display_name\": \"Setup Mode\",\n \"info\": \"Configuration mode for setting up the vector store, with options like \u201cSync\u201d, \u201cAsync\u201d, or \u201cOff\u201d.\",\n \"options\": [\"Sync\", \"Async\", \"Off\"],\n \"advanced\": True,\n },\n \"pre_delete_collection\": {\n \"display_name\": \"Pre Delete Collection\",\n \"info\": \"Boolean flag to determine whether to delete the collection before creating a new one.\",\n \"advanced\": True,\n },\n \"metadata_indexing_include\": {\n \"display_name\": \"Metadata Indexing Include\",\n \"info\": \"Optional list of metadata fields to include in the indexing.\",\n \"advanced\": True,\n },\n \"metadata_indexing_exclude\": {\n \"display_name\": \"Metadata Indexing Exclude\",\n \"info\": \"Optional list of metadata fields to exclude from the indexing.\",\n \"advanced\": True,\n },\n \"collection_indexing_policy\": {\n \"display_name\": \"Collection Indexing Policy\",\n \"info\": \"Optional dictionary defining the indexing policy for the collection.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n embedding: Embeddings,\n token: str,\n api_endpoint: str,\n collection_name: str,\n inputs: Optional[List[Record]] = None,\n namespace: Optional[str] = None,\n metric: Optional[str] = None,\n batch_size: Optional[int] = None,\n bulk_insert_batch_concurrency: Optional[int] = None,\n bulk_insert_overwrite_concurrency: Optional[int] = None,\n bulk_delete_concurrency: Optional[int] = None,\n setup_mode: str = \"Async\",\n pre_delete_collection: bool = False,\n metadata_indexing_include: Optional[List[str]] = None,\n metadata_indexing_exclude: Optional[List[str]] = None,\n collection_indexing_policy: Optional[dict] = None,\n ) -> VectorStore:\n try:\n setup_mode_value = SetupMode[setup_mode.upper()]\n except KeyError:\n raise ValueError(f\"Invalid setup mode: {setup_mode}\")\n if inputs:\n documents = [_input.to_lc_document() for _input in inputs]\n\n vector_store = AstraDBVectorStore.from_documents(\n documents=documents,\n embedding=embedding,\n collection_name=collection_name,\n token=token,\n api_endpoint=api_endpoint,\n namespace=namespace,\n metric=metric,\n batch_size=batch_size,\n bulk_insert_batch_concurrency=bulk_insert_batch_concurrency,\n bulk_insert_overwrite_concurrency=bulk_insert_overwrite_concurrency,\n bulk_delete_concurrency=bulk_delete_concurrency,\n setup_mode=setup_mode_value,\n pre_delete_collection=pre_delete_collection,\n metadata_indexing_include=metadata_indexing_include,\n metadata_indexing_exclude=metadata_indexing_exclude,\n collection_indexing_policy=collection_indexing_policy,\n )\n else:\n vector_store = AstraDBVectorStore(\n embedding=embedding,\n collection_name=collection_name,\n token=token,\n api_endpoint=api_endpoint,\n namespace=namespace,\n metric=metric,\n batch_size=batch_size,\n bulk_insert_batch_concurrency=bulk_insert_batch_concurrency,\n bulk_insert_overwrite_concurrency=bulk_insert_overwrite_concurrency,\n bulk_delete_concurrency=bulk_delete_concurrency,\n setup_mode=setup_mode_value,\n pre_delete_collection=pre_delete_collection,\n metadata_indexing_include=metadata_indexing_include,\n metadata_indexing_exclude=metadata_indexing_exclude,\n collection_indexing_policy=collection_indexing_policy,\n )\n\n return vector_store\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "collection_indexing_policy": { - "type": "dict", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "collection_indexing_policy", - "display_name": "Collection Indexing Policy", - "advanced": true, - "dynamic": false, - "info": "Optional dictionary defining the indexing policy for the collection.", - "load_from_db": false, - "title_case": false - }, - "collection_name": { - "type": "str", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "collection_name", - "display_name": "Collection Name", - "advanced": false, - "dynamic": false, - "info": "The name of the collection within Astra DB where the vectors will be stored.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ], - "value": "langflow" - }, - "metadata_indexing_exclude": { - "type": "str", - "required": false, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "metadata_indexing_exclude", - "display_name": "Metadata Indexing Exclude", - "advanced": true, - "dynamic": false, - "info": "Optional list of metadata fields to exclude from the indexing.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "metadata_indexing_include": { - "type": "str", - "required": false, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "metadata_indexing_include", - "display_name": "Metadata Indexing Include", - "advanced": true, - "dynamic": false, - "info": "Optional list of metadata fields to include in the indexing.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "metric": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "metric", - "display_name": "Metric", - "advanced": true, - "dynamic": false, - "info": "Optional distance metric for vector comparisons in the vector store.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "namespace": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "namespace", - "display_name": "Namespace", - "advanced": true, - "dynamic": false, - "info": "Optional namespace within Astra DB to use for the collection.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "pre_delete_collection": { - "type": "bool", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "pre_delete_collection", - "display_name": "Pre Delete Collection", - "advanced": true, - "dynamic": false, - "info": "Boolean flag to determine whether to delete the collection before creating a new one.", - "load_from_db": false, - "title_case": false - }, - "setup_mode": { - "type": "str", - "required": false, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "value": "Async", - "fileTypes": [], - "file_path": "", - "password": false, - "options": [ - "Sync", - "Async", - "Off" - ], - "name": "setup_mode", - "display_name": "Setup Mode", - "advanced": true, - "dynamic": false, - "info": "Configuration mode for setting up the vector store, with options like \u201cSync\u201d, \u201cAsync\u201d, or \u201cOff\u201d.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "token": { - "type": "str", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": true, - "name": "token", - "display_name": "Token", - "advanced": false, - "dynamic": false, - "info": "Authentication token for accessing Astra DB.", - "load_from_db": true, - "title_case": false, - "input_types": [ - "Text" - ], - "value": "ASTRA_DB_APPLICATION_TOKEN" - }, - "_type": "CustomComponent" - }, - "description": "Builds or loads an Astra DB Vector Store.", - "icon": "AstraDB", - "base_classes": [ - "VectorStore" - ], - "display_name": "Astra DB", - "documentation": "", - "custom_fields": { - "embedding": null, - "token": null, - "api_endpoint": null, - "collection_name": null, - "inputs": null, - "namespace": null, - "metric": null, - "batch_size": null, - "bulk_insert_batch_concurrency": null, - "bulk_insert_overwrite_concurrency": null, - "bulk_delete_concurrency": null, - "setup_mode": null, - "pre_delete_collection": null, - "metadata_indexing_include": null, - "metadata_indexing_exclude": null, - "collection_indexing_policy": null - }, - "output_types": [ - "VectorStore" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [ - "token", - "api_endpoint", - "collection_name", - "inputs", - "embedding" - ], - "beta": false - }, - "id": "AstraDB-eUCSS" - }, - "selected": false, - "width": 384, - "height": 573, - "positionAbsolute": { - "x": 3372.04958055989, - "y": 1611.0742035495277 - }, - "dragging": false + "description": "Create a prompt template with dynamic variables.", + "icon": "prompts", + "is_input": null, + "is_output": null, + "is_composition": null, + "base_classes": ["object", "Text", "str"], + "name": "", + "display_name": "Prompt", + "documentation": "", + "custom_fields": { + "template": ["context", "question"] }, - { - "id": "OpenAIEmbeddings-9TPjc", - "type": "genericNode", - "position": { - "x": 2814.0402191223047, - "y": 1955.9268168273086 - }, - "data": { - "type": "OpenAIEmbeddings", - "node": { - "template": { - "allowed_special": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": [], - "fileTypes": [], - "file_path": "", - "password": false, - "name": "allowed_special", - "display_name": "Allowed Special", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "chunk_size": { - "type": "int", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": 1000, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "chunk_size", - "display_name": "Chunk Size", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "client": { - "type": "Any", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "client", - "display_name": "Client", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from typing import Any, Dict, List, Optional\n\nfrom langchain_openai.embeddings.base import OpenAIEmbeddings\n\nfrom langflow.field_typing import Embeddings, NestedDict\nfrom langflow.interface.custom.custom_component import CustomComponent\n\n\nclass OpenAIEmbeddingsComponent(CustomComponent):\n display_name = \"OpenAI Embeddings\"\n description = \"Generate embeddings using OpenAI models.\"\n\n def build_config(self):\n return {\n \"allowed_special\": {\n \"display_name\": \"Allowed Special\",\n \"advanced\": True,\n \"field_type\": \"str\",\n \"is_list\": True,\n },\n \"default_headers\": {\n \"display_name\": \"Default Headers\",\n \"advanced\": True,\n \"field_type\": \"dict\",\n },\n \"default_query\": {\n \"display_name\": \"Default Query\",\n \"advanced\": True,\n \"field_type\": \"NestedDict\",\n },\n \"disallowed_special\": {\n \"display_name\": \"Disallowed Special\",\n \"advanced\": True,\n \"field_type\": \"str\",\n \"is_list\": True,\n },\n \"chunk_size\": {\"display_name\": \"Chunk Size\", \"advanced\": True},\n \"client\": {\"display_name\": \"Client\", \"advanced\": True},\n \"deployment\": {\"display_name\": \"Deployment\", \"advanced\": True},\n \"embedding_ctx_length\": {\n \"display_name\": \"Embedding Context Length\",\n \"advanced\": True,\n },\n \"max_retries\": {\"display_name\": \"Max Retries\", \"advanced\": True},\n \"model\": {\n \"display_name\": \"Model\",\n \"advanced\": False,\n \"options\": [\n \"text-embedding-3-small\",\n \"text-embedding-3-large\",\n \"text-embedding-ada-002\",\n ],\n },\n \"model_kwargs\": {\"display_name\": \"Model Kwargs\", \"advanced\": True},\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"password\": True,\n \"advanced\": True,\n },\n \"openai_api_key\": {\"display_name\": \"OpenAI API Key\", \"password\": True},\n \"openai_api_type\": {\n \"display_name\": \"OpenAI API Type\",\n \"advanced\": True,\n \"password\": True,\n },\n \"openai_api_version\": {\n \"display_name\": \"OpenAI API Version\",\n \"advanced\": True,\n },\n \"openai_organization\": {\n \"display_name\": \"OpenAI Organization\",\n \"advanced\": True,\n },\n \"openai_proxy\": {\"display_name\": \"OpenAI Proxy\", \"advanced\": True},\n \"request_timeout\": {\"display_name\": \"Request Timeout\", \"advanced\": True},\n \"show_progress_bar\": {\n \"display_name\": \"Show Progress Bar\",\n \"advanced\": True,\n },\n \"skip_empty\": {\"display_name\": \"Skip Empty\", \"advanced\": True},\n \"tiktoken_model_name\": {\n \"display_name\": \"TikToken Model Name\",\n \"advanced\": True,\n },\n \"tiktoken_enable\": {\"display_name\": \"TikToken Enable\", \"advanced\": True},\n }\n\n def build(\n self,\n openai_api_key: str,\n default_headers: Optional[Dict[str, str]] = None,\n default_query: Optional[NestedDict] = {},\n allowed_special: List[str] = [],\n disallowed_special: List[str] = [\"all\"],\n chunk_size: int = 1000,\n client: Optional[Any] = None,\n deployment: str = \"text-embedding-ada-002\",\n embedding_ctx_length: int = 8191,\n max_retries: int = 6,\n model: str = \"text-embedding-ada-002\",\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n openai_api_type: Optional[str] = None,\n openai_api_version: Optional[str] = None,\n openai_organization: Optional[str] = None,\n openai_proxy: Optional[str] = None,\n request_timeout: Optional[float] = None,\n show_progress_bar: bool = False,\n skip_empty: bool = False,\n tiktoken_enable: bool = True,\n tiktoken_model_name: Optional[str] = None,\n ) -> Embeddings:\n # This is to avoid errors with Vector Stores (e.g Chroma)\n if disallowed_special == [\"all\"]:\n disallowed_special = \"all\" # type: ignore\n\n return OpenAIEmbeddings(\n tiktoken_enabled=tiktoken_enable,\n default_headers=default_headers,\n default_query=default_query,\n allowed_special=set(allowed_special),\n disallowed_special=\"all\",\n chunk_size=chunk_size,\n client=client,\n deployment=deployment,\n embedding_ctx_length=embedding_ctx_length,\n max_retries=max_retries,\n model=model,\n model_kwargs=model_kwargs,\n base_url=openai_api_base,\n api_key=openai_api_key,\n openai_api_type=openai_api_type,\n api_version=openai_api_version,\n organization=openai_organization,\n openai_proxy=openai_proxy,\n timeout=request_timeout,\n show_progress_bar=show_progress_bar,\n skip_empty=skip_empty,\n tiktoken_model_name=tiktoken_model_name,\n )\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "default_headers": { - "type": "dict", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "default_headers", - "display_name": "Default Headers", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "default_query": { - "type": "NestedDict", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": {}, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "default_query", - "display_name": "Default Query", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "deployment": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": "text-embedding-ada-002", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "deployment", - "display_name": "Deployment", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "disallowed_special": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": [ - "all" - ], - "fileTypes": [], - "file_path": "", - "password": false, - "name": "disallowed_special", - "display_name": "Disallowed Special", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "embedding_ctx_length": { - "type": "int", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": 8191, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "embedding_ctx_length", - "display_name": "Embedding Context Length", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "max_retries": { - "type": "int", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": 6, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "max_retries", - "display_name": "Max Retries", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "model": { - "type": "str", - "required": false, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "value": "text-embedding-ada-002", - "fileTypes": [], - "file_path": "", - "password": false, - "options": [ - "text-embedding-3-small", - "text-embedding-3-large", - "text-embedding-ada-002" - ], - "name": "model", - "display_name": "Model", - "advanced": false, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "model_kwargs": { - "type": "NestedDict", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": {}, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "model_kwargs", - "display_name": "Model Kwargs", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "openai_api_base": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": true, - "name": "openai_api_base", - "display_name": "OpenAI API Base", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "openai_api_key": { - "type": "str", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": true, - "name": "openai_api_key", - "display_name": "OpenAI API Key", - "advanced": false, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ], - "value": "" - }, - "openai_api_type": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": true, - "name": "openai_api_type", - "display_name": "OpenAI API Type", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "openai_api_version": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "openai_api_version", - "display_name": "OpenAI API Version", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "openai_organization": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "openai_organization", - "display_name": "OpenAI Organization", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "openai_proxy": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "openai_proxy", - "display_name": "OpenAI Proxy", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "request_timeout": { - "type": "float", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "request_timeout", - "display_name": "Request Timeout", - "advanced": true, - "dynamic": false, - "info": "", - "rangeSpec": { - "step_type": "float", - "min": -1, - "max": 1, - "step": 0.1 - }, - "load_from_db": false, - "title_case": false - }, - "show_progress_bar": { - "type": "bool", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "show_progress_bar", - "display_name": "Show Progress Bar", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "skip_empty": { - "type": "bool", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "skip_empty", - "display_name": "Skip Empty", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "tiktoken_enable": { - "type": "bool", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": true, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "tiktoken_enable", - "display_name": "TikToken Enable", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "tiktoken_model_name": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "tiktoken_model_name", - "display_name": "TikToken Model Name", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "_type": "CustomComponent" - }, - "description": "Generate embeddings using OpenAI models.", - "base_classes": [ - "Embeddings" - ], - "display_name": "OpenAI Embeddings", - "documentation": "", - "custom_fields": { - "openai_api_key": null, - "default_headers": null, - "default_query": null, - "allowed_special": null, - "disallowed_special": null, - "chunk_size": null, - "client": null, - "deployment": null, - "embedding_ctx_length": null, - "max_retries": null, - "model": null, - "model_kwargs": null, - "openai_api_base": null, - "openai_api_type": null, - "openai_api_version": null, - "openai_organization": null, - "openai_proxy": null, - "request_timeout": null, - "show_progress_bar": null, - "skip_empty": null, - "tiktoken_enable": null, - "tiktoken_model_name": null - }, - "output_types": [ - "Embeddings" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": false - }, - "id": "OpenAIEmbeddings-9TPjc" - }, - "selected": false, - "width": 384, - "height": 383, - "positionAbsolute": { - "x": 2814.0402191223047, - "y": 1955.9268168273086 - }, - "dragging": false - } - ], - "edges": [ - { - "source": "TextOutput-BDknO", - "target": "Prompt-xeI6K", - "sourceHandle": "{\u0153baseClasses\u0153:[\u0153object\u0153,\u0153Text\u0153,\u0153str\u0153],\u0153dataType\u0153:\u0153TextOutput\u0153,\u0153id\u0153:\u0153TextOutput-BDknO\u0153}", - "targetHandle": "{\u0153fieldName\u0153:\u0153context\u0153,\u0153id\u0153:\u0153Prompt-xeI6K\u0153,\u0153inputTypes\u0153:[\u0153Document\u0153,\u0153BaseOutputParser\u0153,\u0153Record\u0153,\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}", - "id": "reactflow__edge-TextOutput-BDknO{\u0153baseClasses\u0153:[\u0153object\u0153,\u0153Text\u0153,\u0153str\u0153],\u0153dataType\u0153:\u0153TextOutput\u0153,\u0153id\u0153:\u0153TextOutput-BDknO\u0153}-Prompt-xeI6K{\u0153fieldName\u0153:\u0153context\u0153,\u0153id\u0153:\u0153Prompt-xeI6K\u0153,\u0153inputTypes\u0153:[\u0153Document\u0153,\u0153BaseOutputParser\u0153,\u0153Record\u0153,\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}", - "data": { - "targetHandle": { - "fieldName": "context", - "id": "Prompt-xeI6K", - "inputTypes": [ - "Document", - "BaseOutputParser", - "Record", - "Text" - ], - "type": "str" - }, - "sourceHandle": { - "baseClasses": [ - "object", - "Text", - "str" - ], - "dataType": "TextOutput", - "id": "TextOutput-BDknO" - } - }, - "style": { - "stroke": "#555" - }, - "className": "stroke-gray-900 stroke-connection", - "selected": false + "output_types": ["Text"], + "full_path": null, + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false, + "error": null + }, + "id": "Prompt-xeI6K", + "description": "Create a prompt template with dynamic variables.", + "display_name": "Prompt" + }, + "selected": false, + "width": 384, + "height": 477, + "positionAbsolute": { + "x": 2969.0261961391298, + "y": 442.1613649809069 + }, + "dragging": false + }, + { + "id": "ChatOutput-Q39I8", + "type": "genericNode", + "position": { + "x": 3887.2073667611485, + "y": 588.4801225794856 + }, + "data": { + "type": "ChatOutput", + "node": { + "template": { + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n def build(\n self,\n sender: Optional[str] = \"Machine\",\n sender_name: Optional[str] = \"AI\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n record_template: Optional[str] = \"{text}\",\n ) -> Union[Text, Record]:\n return super().build(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n record_template=record_template,\n )\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "input_value": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "input_value", + "display_name": "Message", + "advanced": false, + "input_types": ["Text"], + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "record_template": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "{text}", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "record_template", + "display_name": "Record Template", + "advanced": true, + "dynamic": false, + "info": "In case of Message being a Record, this template will be used to convert it to text.", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "return_record": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "return_record", + "display_name": "Return Record", + "advanced": true, + "dynamic": false, + "info": "Return the message as a record containing the sender, sender_name, and session_id.", + "load_from_db": false, + "title_case": false + }, + "sender": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "value": "Machine", + "fileTypes": [], + "file_path": "", + "password": false, + "options": ["Machine", "User"], + "name": "sender", + "display_name": "Sender Type", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "sender_name": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": "AI", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "sender_name", + "display_name": "Sender Name", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "session_id": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "session_id", + "display_name": "Session ID", + "advanced": true, + "dynamic": false, + "info": "If provided, the message will be stored in the memory.", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "_type": "CustomComponent" }, - { - "source": "ChatInput-yxMKE", - "target": "Prompt-xeI6K", - "sourceHandle": "{\u0153baseClasses\u0153:[\u0153Text\u0153,\u0153str\u0153,\u0153object\u0153,\u0153Record\u0153],\u0153dataType\u0153:\u0153ChatInput\u0153,\u0153id\u0153:\u0153ChatInput-yxMKE\u0153}", - "targetHandle": "{\u0153fieldName\u0153:\u0153question\u0153,\u0153id\u0153:\u0153Prompt-xeI6K\u0153,\u0153inputTypes\u0153:[\u0153Document\u0153,\u0153BaseOutputParser\u0153,\u0153Record\u0153,\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}", - "id": "reactflow__edge-ChatInput-yxMKE{\u0153baseClasses\u0153:[\u0153Text\u0153,\u0153str\u0153,\u0153object\u0153,\u0153Record\u0153],\u0153dataType\u0153:\u0153ChatInput\u0153,\u0153id\u0153:\u0153ChatInput-yxMKE\u0153}-Prompt-xeI6K{\u0153fieldName\u0153:\u0153question\u0153,\u0153id\u0153:\u0153Prompt-xeI6K\u0153,\u0153inputTypes\u0153:[\u0153Document\u0153,\u0153BaseOutputParser\u0153,\u0153Record\u0153,\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}", - "data": { - "targetHandle": { - "fieldName": "question", - "id": "Prompt-xeI6K", - "inputTypes": [ - "Document", - "BaseOutputParser", - "Record", - "Text" - ], - "type": "str" - }, - "sourceHandle": { - "baseClasses": [ - "Text", - "str", - "object", - "Record" - ], - "dataType": "ChatInput", - "id": "ChatInput-yxMKE" - } - }, - "style": { - "stroke": "#555" - }, - "className": "stroke-gray-900 stroke-connection", - "selected": false + "description": "Display a chat message in the Playground.", + "icon": "ChatOutput", + "base_classes": ["object", "Text", "Record", "str"], + "display_name": "Chat Output", + "documentation": "", + "custom_fields": { + "sender": null, + "sender_name": null, + "input_value": null, + "session_id": null, + "return_record": null, + "record_template": null }, - { - "source": "Prompt-xeI6K", - "target": "OpenAIModel-EjXlN", - "sourceHandle": "{\u0153baseClasses\u0153:[\u0153object\u0153,\u0153Text\u0153,\u0153str\u0153],\u0153dataType\u0153:\u0153Prompt\u0153,\u0153id\u0153:\u0153Prompt-xeI6K\u0153}", - "targetHandle": "{\u0153fieldName\u0153:\u0153input_value\u0153,\u0153id\u0153:\u0153OpenAIModel-EjXlN\u0153,\u0153inputTypes\u0153:[\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}", - "id": "reactflow__edge-Prompt-xeI6K{\u0153baseClasses\u0153:[\u0153object\u0153,\u0153Text\u0153,\u0153str\u0153],\u0153dataType\u0153:\u0153Prompt\u0153,\u0153id\u0153:\u0153Prompt-xeI6K\u0153}-OpenAIModel-EjXlN{\u0153fieldName\u0153:\u0153input_value\u0153,\u0153id\u0153:\u0153OpenAIModel-EjXlN\u0153,\u0153inputTypes\u0153:[\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}", - "data": { - "targetHandle": { - "fieldName": "input_value", - "id": "OpenAIModel-EjXlN", - "inputTypes": [ - "Text" - ], - "type": "str" - }, - "sourceHandle": { - "baseClasses": [ - "object", - "Text", - "str" - ], - "dataType": "Prompt", - "id": "Prompt-xeI6K" - } - }, - "style": { - "stroke": "#555" - }, - "className": "stroke-gray-900 stroke-connection", - "selected": false + "output_types": ["Text", "Record"], + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false + }, + "id": "ChatOutput-Q39I8" + }, + "selected": false, + "width": 384, + "height": 383, + "positionAbsolute": { + "x": 3887.2073667611485, + "y": 588.4801225794856 + }, + "dragging": false + }, + { + "id": "File-t0a6a", + "type": "genericNode", + "position": { + "x": 2257.233450682836, + "y": 1747.5389618367233 + }, + "data": { + "type": "File", + "node": { + "template": { + "path": { + "type": "file", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [ + ".txt", + ".md", + ".mdx", + ".csv", + ".json", + ".yaml", + ".yml", + ".xml", + ".html", + ".htm", + ".pdf", + ".docx" + ], + "file_path": "51e2b78a-199b-4054-9f32-e288eef6924c/Langflow conversation.pdf", + "password": false, + "name": "path", + "display_name": "Path", + "advanced": false, + "dynamic": false, + "info": "Supported file types: txt, md, mdx, csv, json, yaml, yml, xml, html, htm, pdf, docx", + "load_from_db": false, + "title_case": false, + "value": "" + }, + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from pathlib import Path\nfrom typing import Any, Dict\n\nfrom langflow.base.data.utils import TEXT_FILE_TYPES, parse_text_file_to_record\nfrom langflow.interface.custom.custom_component import CustomComponent\nfrom langflow.schema import Record\n\n\nclass FileComponent(CustomComponent):\n display_name = \"File\"\n description = \"A generic file loader.\"\n icon = \"file-text\"\n\n def build_config(self) -> Dict[str, Any]:\n return {\n \"path\": {\n \"display_name\": \"Path\",\n \"field_type\": \"file\",\n \"file_types\": TEXT_FILE_TYPES,\n \"info\": f\"Supported file types: {', '.join(TEXT_FILE_TYPES)}\",\n },\n \"silent_errors\": {\n \"display_name\": \"Silent Errors\",\n \"advanced\": True,\n \"info\": \"If true, errors will not raise an exception.\",\n },\n }\n\n def load_file(self, path: str, silent_errors: bool = False) -> Record:\n resolved_path = self.resolve_path(path)\n path_obj = Path(resolved_path)\n extension = path_obj.suffix[1:].lower()\n if extension == \"doc\":\n raise ValueError(\"doc files are not supported. Please save as .docx\")\n if extension not in TEXT_FILE_TYPES:\n raise ValueError(f\"Unsupported file type: {extension}\")\n record = parse_text_file_to_record(resolved_path, silent_errors)\n self.status = record if record else \"No data\"\n return record or Record()\n\n def build(\n self,\n path: str,\n silent_errors: bool = False,\n ) -> Record:\n record = self.load_file(path, silent_errors)\n self.status = record\n return record\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "silent_errors": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "silent_errors", + "display_name": "Silent Errors", + "advanced": true, + "dynamic": false, + "info": "If true, errors will not raise an exception.", + "load_from_db": false, + "title_case": false + }, + "_type": "CustomComponent" }, - { - "source": "OpenAIModel-EjXlN", - "target": "ChatOutput-Q39I8", - "sourceHandle": "{\u0153baseClasses\u0153:[\u0153object\u0153,\u0153Text\u0153,\u0153str\u0153],\u0153dataType\u0153:\u0153OpenAIModel\u0153,\u0153id\u0153:\u0153OpenAIModel-EjXlN\u0153}", - "targetHandle": "{\u0153fieldName\u0153:\u0153input_value\u0153,\u0153id\u0153:\u0153ChatOutput-Q39I8\u0153,\u0153inputTypes\u0153:[\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}", - "id": "reactflow__edge-OpenAIModel-EjXlN{\u0153baseClasses\u0153:[\u0153object\u0153,\u0153Text\u0153,\u0153str\u0153],\u0153dataType\u0153:\u0153OpenAIModel\u0153,\u0153id\u0153:\u0153OpenAIModel-EjXlN\u0153}-ChatOutput-Q39I8{\u0153fieldName\u0153:\u0153input_value\u0153,\u0153id\u0153:\u0153ChatOutput-Q39I8\u0153,\u0153inputTypes\u0153:[\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}", - "data": { - "targetHandle": { - "fieldName": "input_value", - "id": "ChatOutput-Q39I8", - "inputTypes": [ - "Text" - ], - "type": "str" - }, - "sourceHandle": { - "baseClasses": [ - "object", - "Text", - "str" - ], - "dataType": "OpenAIModel", - "id": "OpenAIModel-EjXlN" - } - }, - "style": { - "stroke": "#555" - }, - "className": "stroke-gray-900 stroke-connection", - "selected": false + "description": "A generic file loader.", + "icon": "file-text", + "base_classes": ["Record"], + "display_name": "File", + "documentation": "", + "custom_fields": { + "path": null, + "silent_errors": null }, - { - "source": "File-t0a6a", - "target": "RecursiveCharacterTextSplitter-tR9QM", - "sourceHandle": "{\u0153baseClasses\u0153:[\u0153Record\u0153],\u0153dataType\u0153:\u0153File\u0153,\u0153id\u0153:\u0153File-t0a6a\u0153}", - "targetHandle": "{\u0153fieldName\u0153:\u0153inputs\u0153,\u0153id\u0153:\u0153RecursiveCharacterTextSplitter-tR9QM\u0153,\u0153inputTypes\u0153:[\u0153Document\u0153,\u0153Record\u0153],\u0153type\u0153:\u0153Document\u0153}", - "id": "reactflow__edge-File-t0a6a{\u0153baseClasses\u0153:[\u0153Record\u0153],\u0153dataType\u0153:\u0153File\u0153,\u0153id\u0153:\u0153File-t0a6a\u0153}-RecursiveCharacterTextSplitter-tR9QM{\u0153fieldName\u0153:\u0153inputs\u0153,\u0153id\u0153:\u0153RecursiveCharacterTextSplitter-tR9QM\u0153,\u0153inputTypes\u0153:[\u0153Document\u0153,\u0153Record\u0153],\u0153type\u0153:\u0153Document\u0153}", - "data": { - "targetHandle": { - "fieldName": "inputs", - "id": "RecursiveCharacterTextSplitter-tR9QM", - "inputTypes": [ - "Document", - "Record" - ], - "type": "Document" - }, - "sourceHandle": { - "baseClasses": [ - "Record" - ], - "dataType": "File", - "id": "File-t0a6a" - } - }, - "style": { - "stroke": "#555" - }, - "className": "stroke-gray-900 stroke-connection", - "selected": false + "output_types": ["Record"], + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false + }, + "id": "File-t0a6a" + }, + "selected": false, + "width": 384, + "height": 281, + "positionAbsolute": { + "x": 2257.233450682836, + "y": 1747.5389618367233 + }, + "dragging": false + }, + { + "id": "RecursiveCharacterTextSplitter-tR9QM", + "type": "genericNode", + "position": { + "x": 2791.013514133929, + "y": 1462.9588953494142 + }, + "data": { + "type": "RecursiveCharacterTextSplitter", + "node": { + "template": { + "inputs": { + "type": "Document", + "required": true, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "inputs", + "display_name": "Input", + "advanced": false, + "input_types": ["Document", "Record"], + "dynamic": false, + "info": "The texts to split.", + "load_from_db": false, + "title_case": false + }, + "chunk_overlap": { + "type": "int", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": 200, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "chunk_overlap", + "display_name": "Chunk Overlap", + "advanced": false, + "dynamic": false, + "info": "The amount of overlap between chunks.", + "load_from_db": false, + "title_case": false + }, + "chunk_size": { + "type": "int", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": 1000, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "chunk_size", + "display_name": "Chunk Size", + "advanced": false, + "dynamic": false, + "info": "The maximum length of each chunk.", + "load_from_db": false, + "title_case": false + }, + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import Optional\n\nfrom langchain.text_splitter import RecursiveCharacterTextSplitter\nfrom langchain_core.documents import Document\n\nfrom langflow.interface.custom.custom_component import CustomComponent\nfrom langflow.schema import Record\nfrom langflow.utils.util import build_loader_repr_from_records, unescape_string\n\n\nclass RecursiveCharacterTextSplitterComponent(CustomComponent):\n display_name: str = \"Recursive Character Text Splitter\"\n description: str = \"Split text into chunks of a specified length.\"\n documentation: str = \"https://docs.langflow.org/components/text-splitters#recursivecharactertextsplitter\"\n\n def build_config(self):\n return {\n \"inputs\": {\n \"display_name\": \"Input\",\n \"info\": \"The texts to split.\",\n \"input_types\": [\"Document\", \"Record\"],\n },\n \"separators\": {\n \"display_name\": \"Separators\",\n \"info\": 'The characters to split on.\\nIf left empty defaults to [\"\\\\n\\\\n\", \"\\\\n\", \" \", \"\"].',\n \"is_list\": True,\n },\n \"chunk_size\": {\n \"display_name\": \"Chunk Size\",\n \"info\": \"The maximum length of each chunk.\",\n \"field_type\": \"int\",\n \"value\": 1000,\n },\n \"chunk_overlap\": {\n \"display_name\": \"Chunk Overlap\",\n \"info\": \"The amount of overlap between chunks.\",\n \"field_type\": \"int\",\n \"value\": 200,\n },\n \"code\": {\"show\": False},\n }\n\n def build(\n self,\n inputs: list[Document],\n separators: Optional[list[str]] = None,\n chunk_size: Optional[int] = 1000,\n chunk_overlap: Optional[int] = 200,\n ) -> list[Record]:\n \"\"\"\n Split text into chunks of a specified length.\n\n Args:\n separators (list[str]): The characters to split on.\n chunk_size (int): The maximum length of each chunk.\n chunk_overlap (int): The amount of overlap between chunks.\n length_function (function): The function to use to calculate the length of the text.\n\n Returns:\n list[str]: The chunks of text.\n \"\"\"\n\n if separators == \"\":\n separators = None\n elif separators:\n # check if the separators list has escaped characters\n # if there are escaped characters, unescape them\n separators = [unescape_string(x) for x in separators]\n\n # Make sure chunk_size and chunk_overlap are ints\n if isinstance(chunk_size, str):\n chunk_size = int(chunk_size)\n if isinstance(chunk_overlap, str):\n chunk_overlap = int(chunk_overlap)\n splitter = RecursiveCharacterTextSplitter(\n separators=separators,\n chunk_size=chunk_size,\n chunk_overlap=chunk_overlap,\n )\n documents = []\n for _input in inputs:\n if isinstance(_input, Record):\n documents.append(_input.to_lc_document())\n else:\n documents.append(_input)\n docs = splitter.split_documents(documents)\n records = self.to_records(docs)\n self.repr_value = build_loader_repr_from_records(records)\n return records\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "separators": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "separators", + "display_name": "Separators", + "advanced": false, + "dynamic": false, + "info": "The characters to split on.\nIf left empty defaults to [\"\\n\\n\", \"\\n\", \" \", \"\"].", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"], + "value": [""] + }, + "_type": "CustomComponent" }, - { - "source": "OpenAIEmbeddings-ZlOk1", - "sourceHandle": "{\u0153baseClasses\u0153:[\u0153Embeddings\u0153],\u0153dataType\u0153:\u0153OpenAIEmbeddings\u0153,\u0153id\u0153:\u0153OpenAIEmbeddings-ZlOk1\u0153}", - "target": "AstraDBSearch-41nRz", - "targetHandle": "{\u0153fieldName\u0153:\u0153embedding\u0153,\u0153id\u0153:\u0153AstraDBSearch-41nRz\u0153,\u0153inputTypes\u0153:null,\u0153type\u0153:\u0153Embeddings\u0153}", - "data": { - "targetHandle": { - "fieldName": "embedding", - "id": "AstraDBSearch-41nRz", - "inputTypes": null, - "type": "Embeddings" - }, - "sourceHandle": { - "baseClasses": [ - "Embeddings" - ], - "dataType": "OpenAIEmbeddings", - "id": "OpenAIEmbeddings-ZlOk1" - } - }, - "style": { - "stroke": "#555" - }, - "className": "stroke-gray-900 stroke-connection", - "id": "reactflow__edge-OpenAIEmbeddings-ZlOk1{\u0153baseClasses\u0153:[\u0153Embeddings\u0153],\u0153dataType\u0153:\u0153OpenAIEmbeddings\u0153,\u0153id\u0153:\u0153OpenAIEmbeddings-ZlOk1\u0153}-AstraDBSearch-41nRz{\u0153fieldName\u0153:\u0153embedding\u0153,\u0153id\u0153:\u0153AstraDBSearch-41nRz\u0153,\u0153inputTypes\u0153:null,\u0153type\u0153:\u0153Embeddings\u0153}" + "description": "Split text into chunks of a specified length.", + "base_classes": ["Record"], + "display_name": "Recursive Character Text Splitter", + "documentation": "https://docs.langflow.org/components/text-splitters#recursivecharactertextsplitter", + "custom_fields": { + "inputs": null, + "separators": null, + "chunk_size": null, + "chunk_overlap": null }, - { - "source": "ChatInput-yxMKE", - "sourceHandle": "{\u0153baseClasses\u0153:[\u0153Text\u0153,\u0153str\u0153,\u0153object\u0153,\u0153Record\u0153],\u0153dataType\u0153:\u0153ChatInput\u0153,\u0153id\u0153:\u0153ChatInput-yxMKE\u0153}", - "target": "AstraDBSearch-41nRz", - "targetHandle": "{\u0153fieldName\u0153:\u0153input_value\u0153,\u0153id\u0153:\u0153AstraDBSearch-41nRz\u0153,\u0153inputTypes\u0153:[\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}", - "data": { - "targetHandle": { - "fieldName": "input_value", - "id": "AstraDBSearch-41nRz", - "inputTypes": [ - "Text" - ], - "type": "str" - }, - "sourceHandle": { - "baseClasses": [ - "Text", - "str", - "object", - "Record" - ], - "dataType": "ChatInput", - "id": "ChatInput-yxMKE" - } - }, - "style": { - "stroke": "#555" - }, - "className": "stroke-gray-900 stroke-connection", - "id": "reactflow__edge-ChatInput-yxMKE{\u0153baseClasses\u0153:[\u0153Text\u0153,\u0153str\u0153,\u0153object\u0153,\u0153Record\u0153],\u0153dataType\u0153:\u0153ChatInput\u0153,\u0153id\u0153:\u0153ChatInput-yxMKE\u0153}-AstraDBSearch-41nRz{\u0153fieldName\u0153:\u0153input_value\u0153,\u0153id\u0153:\u0153AstraDBSearch-41nRz\u0153,\u0153inputTypes\u0153:[\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}" + "output_types": ["Record"], + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false + }, + "id": "RecursiveCharacterTextSplitter-tR9QM" + }, + "selected": false, + "width": 384, + "height": 501, + "positionAbsolute": { + "x": 2791.013514133929, + "y": 1462.9588953494142 + }, + "dragging": false + }, + { + "id": "AstraDBSearch-41nRz", + "type": "genericNode", + "position": { + "x": 1723.976434815103, + "y": 277.03317407245913 + }, + "data": { + "type": "AstraDBSearch", + "node": { + "template": { + "embedding": { + "type": "Embeddings", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "embedding", + "display_name": "Embedding", + "advanced": false, + "dynamic": false, + "info": "Embedding to use", + "load_from_db": false, + "title_case": false + }, + "input_value": { + "type": "str", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "input_value", + "display_name": "Input Value", + "advanced": false, + "dynamic": false, + "info": "Input value to search", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "api_endpoint": { + "type": "str", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "api_endpoint", + "display_name": "API Endpoint", + "advanced": false, + "dynamic": false, + "info": "API endpoint URL for the Astra DB service.", + "load_from_db": true, + "title_case": false, + "input_types": ["Text"], + "value": "ASTRA_DB_API_ENDPOINT" + }, + "batch_size": { + "type": "int", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "batch_size", + "display_name": "Batch Size", + "advanced": true, + "dynamic": false, + "info": "Optional number of records to process in a single batch.", + "load_from_db": false, + "title_case": false + }, + "bulk_delete_concurrency": { + "type": "int", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "bulk_delete_concurrency", + "display_name": "Bulk Delete Concurrency", + "advanced": true, + "dynamic": false, + "info": "Optional concurrency level for bulk delete operations.", + "load_from_db": false, + "title_case": false + }, + "bulk_insert_batch_concurrency": { + "type": "int", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "bulk_insert_batch_concurrency", + "display_name": "Bulk Insert Batch Concurrency", + "advanced": true, + "dynamic": false, + "info": "Optional concurrency level for bulk insert operations.", + "load_from_db": false, + "title_case": false + }, + "bulk_insert_overwrite_concurrency": { + "type": "int", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "bulk_insert_overwrite_concurrency", + "display_name": "Bulk Insert Overwrite Concurrency", + "advanced": true, + "dynamic": false, + "info": "Optional concurrency level for bulk insert operations that overwrite existing records.", + "load_from_db": false, + "title_case": false + }, + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import List, Optional\n\nfrom langflow.components.vectorstores.AstraDB import AstraDBVectorStoreComponent\nfrom langflow.components.vectorstores.base.model import LCVectorStoreComponent\nfrom langflow.field_typing import Embeddings, Text\nfrom langflow.schema import Record\n\n\nclass AstraDBSearchComponent(LCVectorStoreComponent):\n display_name = \"Astra DB Search\"\n description = \"Searches an existing Astra DB Vector Store.\"\n icon = \"AstraDB\"\n field_order = [\"token\", \"api_endpoint\", \"collection_name\", \"input_value\", \"embedding\"]\n\n def build_config(self):\n return {\n \"search_type\": {\n \"display_name\": \"Search Type\",\n \"options\": [\"Similarity\", \"MMR\"],\n },\n \"input_value\": {\n \"display_name\": \"Input Value\",\n \"info\": \"Input value to search\",\n },\n \"embedding\": {\"display_name\": \"Embedding\", \"info\": \"Embedding to use\"},\n \"collection_name\": {\n \"display_name\": \"Collection Name\",\n \"info\": \"The name of the collection within Astra DB where the vectors will be stored.\",\n },\n \"token\": {\n \"display_name\": \"Token\",\n \"info\": \"Authentication token for accessing Astra DB.\",\n \"password\": True,\n },\n \"api_endpoint\": {\n \"display_name\": \"API Endpoint\",\n \"info\": \"API endpoint URL for the Astra DB service.\",\n },\n \"namespace\": {\n \"display_name\": \"Namespace\",\n \"info\": \"Optional namespace within Astra DB to use for the collection.\",\n \"advanced\": True,\n },\n \"metric\": {\n \"display_name\": \"Metric\",\n \"info\": \"Optional distance metric for vector comparisons in the vector store.\",\n \"advanced\": True,\n },\n \"batch_size\": {\n \"display_name\": \"Batch Size\",\n \"info\": \"Optional number of records to process in a single batch.\",\n \"advanced\": True,\n },\n \"bulk_insert_batch_concurrency\": {\n \"display_name\": \"Bulk Insert Batch Concurrency\",\n \"info\": \"Optional concurrency level for bulk insert operations.\",\n \"advanced\": True,\n },\n \"bulk_insert_overwrite_concurrency\": {\n \"display_name\": \"Bulk Insert Overwrite Concurrency\",\n \"info\": \"Optional concurrency level for bulk insert operations that overwrite existing records.\",\n \"advanced\": True,\n },\n \"bulk_delete_concurrency\": {\n \"display_name\": \"Bulk Delete Concurrency\",\n \"info\": \"Optional concurrency level for bulk delete operations.\",\n \"advanced\": True,\n },\n \"setup_mode\": {\n \"display_name\": \"Setup Mode\",\n \"info\": \"Configuration mode for setting up the vector store, with options like \u201cSync\u201d, \u201cAsync\u201d, or \u201cOff\u201d.\",\n \"options\": [\"Sync\", \"Async\", \"Off\"],\n \"advanced\": True,\n },\n \"pre_delete_collection\": {\n \"display_name\": \"Pre Delete Collection\",\n \"info\": \"Boolean flag to determine whether to delete the collection before creating a new one.\",\n \"advanced\": True,\n },\n \"metadata_indexing_include\": {\n \"display_name\": \"Metadata Indexing Include\",\n \"info\": \"Optional list of metadata fields to include in the indexing.\",\n \"advanced\": True,\n },\n \"metadata_indexing_exclude\": {\n \"display_name\": \"Metadata Indexing Exclude\",\n \"info\": \"Optional list of metadata fields to exclude from the indexing.\",\n \"advanced\": True,\n },\n \"collection_indexing_policy\": {\n \"display_name\": \"Collection Indexing Policy\",\n \"info\": \"Optional dictionary defining the indexing policy for the collection.\",\n \"advanced\": True,\n },\n \"number_of_results\": {\n \"display_name\": \"Number of Results\",\n \"info\": \"Number of results to return.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n embedding: Embeddings,\n collection_name: str,\n input_value: Text,\n token: str,\n api_endpoint: str,\n search_type: str = \"Similarity\",\n number_of_results: int = 4,\n namespace: Optional[str] = None,\n metric: Optional[str] = None,\n batch_size: Optional[int] = None,\n bulk_insert_batch_concurrency: Optional[int] = None,\n bulk_insert_overwrite_concurrency: Optional[int] = None,\n bulk_delete_concurrency: Optional[int] = None,\n setup_mode: str = \"Sync\",\n pre_delete_collection: bool = False,\n metadata_indexing_include: Optional[List[str]] = None,\n metadata_indexing_exclude: Optional[List[str]] = None,\n collection_indexing_policy: Optional[dict] = None,\n ) -> List[Record]:\n vector_store = AstraDBVectorStoreComponent().build(\n embedding=embedding,\n collection_name=collection_name,\n token=token,\n api_endpoint=api_endpoint,\n namespace=namespace,\n metric=metric,\n batch_size=batch_size,\n bulk_insert_batch_concurrency=bulk_insert_batch_concurrency,\n bulk_insert_overwrite_concurrency=bulk_insert_overwrite_concurrency,\n bulk_delete_concurrency=bulk_delete_concurrency,\n setup_mode=setup_mode,\n pre_delete_collection=pre_delete_collection,\n metadata_indexing_include=metadata_indexing_include,\n metadata_indexing_exclude=metadata_indexing_exclude,\n collection_indexing_policy=collection_indexing_policy,\n )\n try:\n return self.search_with_vector_store(input_value, search_type, vector_store, k=number_of_results)\n except KeyError as e:\n if \"content\" in str(e):\n raise ValueError(\n \"You should ingest data through Langflow (or LangChain) to query it in Langflow. Your collection does not contain a field name 'content'.\"\n )\n else:\n raise e\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "collection_indexing_policy": { + "type": "dict", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "collection_indexing_policy", + "display_name": "Collection Indexing Policy", + "advanced": true, + "dynamic": false, + "info": "Optional dictionary defining the indexing policy for the collection.", + "load_from_db": false, + "title_case": false + }, + "collection_name": { + "type": "str", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "collection_name", + "display_name": "Collection Name", + "advanced": false, + "dynamic": false, + "info": "The name of the collection within Astra DB where the vectors will be stored.", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"], + "value": "langflow" + }, + "metadata_indexing_exclude": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "metadata_indexing_exclude", + "display_name": "Metadata Indexing Exclude", + "advanced": true, + "dynamic": false, + "info": "Optional list of metadata fields to exclude from the indexing.", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "metadata_indexing_include": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "metadata_indexing_include", + "display_name": "Metadata Indexing Include", + "advanced": true, + "dynamic": false, + "info": "Optional list of metadata fields to include in the indexing.", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "metric": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "metric", + "display_name": "Metric", + "advanced": true, + "dynamic": false, + "info": "Optional distance metric for vector comparisons in the vector store.", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "namespace": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "namespace", + "display_name": "Namespace", + "advanced": true, + "dynamic": false, + "info": "Optional namespace within Astra DB to use for the collection.", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "number_of_results": { + "type": "int", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": 4, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "number_of_results", + "display_name": "Number of Results", + "advanced": true, + "dynamic": false, + "info": "Number of results to return.", + "load_from_db": false, + "title_case": false + }, + "pre_delete_collection": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "pre_delete_collection", + "display_name": "Pre Delete Collection", + "advanced": true, + "dynamic": false, + "info": "Boolean flag to determine whether to delete the collection before creating a new one.", + "load_from_db": false, + "title_case": false + }, + "search_type": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "value": "Similarity", + "fileTypes": [], + "file_path": "", + "password": false, + "options": ["Similarity", "MMR"], + "name": "search_type", + "display_name": "Search Type", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "setup_mode": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "value": "Sync", + "fileTypes": [], + "file_path": "", + "password": false, + "options": ["Sync", "Async", "Off"], + "name": "setup_mode", + "display_name": "Setup Mode", + "advanced": true, + "dynamic": false, + "info": "Configuration mode for setting up the vector store, with options like \u201cSync\u201d, \u201cAsync\u201d, or \u201cOff\u201d.", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "token": { + "type": "str", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": true, + "name": "token", + "display_name": "Token", + "advanced": false, + "dynamic": false, + "info": "Authentication token for accessing Astra DB.", + "load_from_db": true, + "title_case": false, + "input_types": ["Text"], + "value": "ASTRA_DB_APPLICATION_TOKEN" + }, + "_type": "CustomComponent" }, - { - "source": "RecursiveCharacterTextSplitter-tR9QM", - "sourceHandle": "{\u0153baseClasses\u0153:[\u0153Record\u0153],\u0153dataType\u0153:\u0153RecursiveCharacterTextSplitter\u0153,\u0153id\u0153:\u0153RecursiveCharacterTextSplitter-tR9QM\u0153}", - "target": "AstraDB-eUCSS", - "targetHandle": "{\u0153fieldName\u0153:\u0153inputs\u0153,\u0153id\u0153:\u0153AstraDB-eUCSS\u0153,\u0153inputTypes\u0153:null,\u0153type\u0153:\u0153Record\u0153}", - "data": { - "targetHandle": { - "fieldName": "inputs", - "id": "AstraDB-eUCSS", - "inputTypes": null, - "type": "Record" - }, - "sourceHandle": { - "baseClasses": [ - "Record" - ], - "dataType": "RecursiveCharacterTextSplitter", - "id": "RecursiveCharacterTextSplitter-tR9QM" - } - }, - "style": { - "stroke": "#555" - }, - "className": "stroke-gray-900 stroke-connection", - "id": "reactflow__edge-RecursiveCharacterTextSplitter-tR9QM{\u0153baseClasses\u0153:[\u0153Record\u0153],\u0153dataType\u0153:\u0153RecursiveCharacterTextSplitter\u0153,\u0153id\u0153:\u0153RecursiveCharacterTextSplitter-tR9QM\u0153}-AstraDB-eUCSS{\u0153fieldName\u0153:\u0153inputs\u0153,\u0153id\u0153:\u0153AstraDB-eUCSS\u0153,\u0153inputTypes\u0153:null,\u0153type\u0153:\u0153Record\u0153}", - "selected": false + "description": "Searches an existing Astra DB Vector Store.", + "icon": "AstraDB", + "base_classes": ["Record"], + "display_name": "Astra DB Search", + "documentation": "", + "custom_fields": { + "embedding": null, + "collection_name": null, + "input_value": null, + "token": null, + "api_endpoint": null, + "search_type": null, + "number_of_results": null, + "namespace": null, + "metric": null, + "batch_size": null, + "bulk_insert_batch_concurrency": null, + "bulk_insert_overwrite_concurrency": null, + "bulk_delete_concurrency": null, + "setup_mode": null, + "pre_delete_collection": null, + "metadata_indexing_include": null, + "metadata_indexing_exclude": null, + "collection_indexing_policy": null }, - { - "source": "OpenAIEmbeddings-9TPjc", - "sourceHandle": "{\u0153baseClasses\u0153:[\u0153Embeddings\u0153],\u0153dataType\u0153:\u0153OpenAIEmbeddings\u0153,\u0153id\u0153:\u0153OpenAIEmbeddings-9TPjc\u0153}", - "target": "AstraDB-eUCSS", - "targetHandle": "{\u0153fieldName\u0153:\u0153embedding\u0153,\u0153id\u0153:\u0153AstraDB-eUCSS\u0153,\u0153inputTypes\u0153:null,\u0153type\u0153:\u0153Embeddings\u0153}", - "data": { - "targetHandle": { - "fieldName": "embedding", - "id": "AstraDB-eUCSS", - "inputTypes": null, - "type": "Embeddings" - }, - "sourceHandle": { - "baseClasses": [ - "Embeddings" - ], - "dataType": "OpenAIEmbeddings", - "id": "OpenAIEmbeddings-9TPjc" - } - }, - "style": { - "stroke": "#555" - }, - "className": "stroke-gray-900 stroke-connection", - "id": "reactflow__edge-OpenAIEmbeddings-9TPjc{\u0153baseClasses\u0153:[\u0153Embeddings\u0153],\u0153dataType\u0153:\u0153OpenAIEmbeddings\u0153,\u0153id\u0153:\u0153OpenAIEmbeddings-9TPjc\u0153}-AstraDB-eUCSS{\u0153fieldName\u0153:\u0153embedding\u0153,\u0153id\u0153:\u0153AstraDB-eUCSS\u0153,\u0153inputTypes\u0153:null,\u0153type\u0153:\u0153Embeddings\u0153}", - "selected": false - }, - { - "source": "AstraDBSearch-41nRz", - "sourceHandle": "{\u0153baseClasses\u0153:[\u0153Record\u0153],\u0153dataType\u0153:\u0153AstraDBSearch\u0153,\u0153id\u0153:\u0153AstraDBSearch-41nRz\u0153}", - "target": "TextOutput-BDknO", - "targetHandle": "{\u0153fieldName\u0153:\u0153input_value\u0153,\u0153id\u0153:\u0153TextOutput-BDknO\u0153,\u0153inputTypes\u0153:[\u0153Record\u0153,\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}", - "data": { - "targetHandle": { - "fieldName": "input_value", - "id": "TextOutput-BDknO", - "inputTypes": [ - "Record", - "Text" - ], - "type": "str" - }, - "sourceHandle": { - "baseClasses": [ - "Record" - ], - "dataType": "AstraDBSearch", - "id": "AstraDBSearch-41nRz" - } - }, - "style": { - "stroke": "#555" - }, - "className": "stroke-gray-900 stroke-connection", - "id": "reactflow__edge-AstraDBSearch-41nRz{\u0153baseClasses\u0153:[\u0153Record\u0153],\u0153dataType\u0153:\u0153AstraDBSearch\u0153,\u0153id\u0153:\u0153AstraDBSearch-41nRz\u0153}-TextOutput-BDknO{\u0153fieldName\u0153:\u0153input_value\u0153,\u0153id\u0153:\u0153TextOutput-BDknO\u0153,\u0153inputTypes\u0153:[\u0153Record\u0153,\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}" - } - ], - "viewport": { - "x": -259.6782520315529, - "y": 90.3428735006047, - "zoom": 0.2687057134854984 + "output_types": ["Record"], + "field_formatters": {}, + "frozen": false, + "field_order": [ + "token", + "api_endpoint", + "collection_name", + "input_value", + "embedding" + ], + "beta": false + }, + "id": "AstraDBSearch-41nRz" + }, + "selected": false, + "width": 384, + "height": 713, + "dragging": false, + "positionAbsolute": { + "x": 1723.976434815103, + "y": 277.03317407245913 } - }, - "description": "Visit https://pre-release.langflow.org/tutorials/rag-with-astradb for a detailed guide of this project.\nThis project give you both Ingestion and RAG in a single file. You'll need to visit https://astra.datastax.com/ to create an Astra DB instance, your Token and grab an API Endpoint.\nRunning this project requires you to add a file in the Files component, then define a Collection Name and click on the Play icon on the Astra DB component. \n\nAfter the ingestion ends you are ready to click on the Run button at the lower left corner and start asking questions about your data.", - "name": "Vector Store RAG", - "last_tested_version": "1.0.0a0", - "is_component": false + }, + { + "id": "AstraDB-eUCSS", + "type": "genericNode", + "position": { + "x": 3372.04958055989, + "y": 1611.0742035495277 + }, + "data": { + "type": "AstraDB", + "node": { + "template": { + "embedding": { + "type": "Embeddings", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "embedding", + "display_name": "Embedding", + "advanced": false, + "dynamic": false, + "info": "Embedding to use", + "load_from_db": false, + "title_case": false + }, + "inputs": { + "type": "Record", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "inputs", + "display_name": "Inputs", + "advanced": false, + "dynamic": false, + "info": "Optional list of records to be processed and stored in the vector store.", + "load_from_db": false, + "title_case": false + }, + "api_endpoint": { + "type": "str", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "api_endpoint", + "display_name": "API Endpoint", + "advanced": false, + "dynamic": false, + "info": "API endpoint URL for the Astra DB service.", + "load_from_db": true, + "title_case": false, + "input_types": ["Text"], + "value": "ASTRA_DB_API_ENDPOINT" + }, + "batch_size": { + "type": "int", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "batch_size", + "display_name": "Batch Size", + "advanced": true, + "dynamic": false, + "info": "Optional number of records to process in a single batch.", + "load_from_db": false, + "title_case": false + }, + "bulk_delete_concurrency": { + "type": "int", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "bulk_delete_concurrency", + "display_name": "Bulk Delete Concurrency", + "advanced": true, + "dynamic": false, + "info": "Optional concurrency level for bulk delete operations.", + "load_from_db": false, + "title_case": false + }, + "bulk_insert_batch_concurrency": { + "type": "int", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "bulk_insert_batch_concurrency", + "display_name": "Bulk Insert Batch Concurrency", + "advanced": true, + "dynamic": false, + "info": "Optional concurrency level for bulk insert operations.", + "load_from_db": false, + "title_case": false + }, + "bulk_insert_overwrite_concurrency": { + "type": "int", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "bulk_insert_overwrite_concurrency", + "display_name": "Bulk Insert Overwrite Concurrency", + "advanced": true, + "dynamic": false, + "info": "Optional concurrency level for bulk insert operations that overwrite existing records.", + "load_from_db": false, + "title_case": false + }, + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import List, Optional\n\nfrom langchain_astradb import AstraDBVectorStore\nfrom langchain_astradb.utils.astradb import SetupMode\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Embeddings, VectorStore\nfrom langflow.schema import Record\n\n\nclass AstraDBVectorStoreComponent(CustomComponent):\n display_name = \"Astra DB\"\n description = \"Builds or loads an Astra DB Vector Store.\"\n icon = \"AstraDB\"\n field_order = [\"token\", \"api_endpoint\", \"collection_name\", \"inputs\", \"embedding\"]\n\n def build_config(self):\n return {\n \"inputs\": {\n \"display_name\": \"Inputs\",\n \"info\": \"Optional list of records to be processed and stored in the vector store.\",\n },\n \"embedding\": {\"display_name\": \"Embedding\", \"info\": \"Embedding to use\"},\n \"collection_name\": {\n \"display_name\": \"Collection Name\",\n \"info\": \"The name of the collection within Astra DB where the vectors will be stored.\",\n },\n \"token\": {\n \"display_name\": \"Token\",\n \"info\": \"Authentication token for accessing Astra DB.\",\n \"password\": True,\n },\n \"api_endpoint\": {\n \"display_name\": \"API Endpoint\",\n \"info\": \"API endpoint URL for the Astra DB service.\",\n },\n \"namespace\": {\n \"display_name\": \"Namespace\",\n \"info\": \"Optional namespace within Astra DB to use for the collection.\",\n \"advanced\": True,\n },\n \"metric\": {\n \"display_name\": \"Metric\",\n \"info\": \"Optional distance metric for vector comparisons in the vector store.\",\n \"advanced\": True,\n },\n \"batch_size\": {\n \"display_name\": \"Batch Size\",\n \"info\": \"Optional number of records to process in a single batch.\",\n \"advanced\": True,\n },\n \"bulk_insert_batch_concurrency\": {\n \"display_name\": \"Bulk Insert Batch Concurrency\",\n \"info\": \"Optional concurrency level for bulk insert operations.\",\n \"advanced\": True,\n },\n \"bulk_insert_overwrite_concurrency\": {\n \"display_name\": \"Bulk Insert Overwrite Concurrency\",\n \"info\": \"Optional concurrency level for bulk insert operations that overwrite existing records.\",\n \"advanced\": True,\n },\n \"bulk_delete_concurrency\": {\n \"display_name\": \"Bulk Delete Concurrency\",\n \"info\": \"Optional concurrency level for bulk delete operations.\",\n \"advanced\": True,\n },\n \"setup_mode\": {\n \"display_name\": \"Setup Mode\",\n \"info\": \"Configuration mode for setting up the vector store, with options like \u201cSync\u201d, \u201cAsync\u201d, or \u201cOff\u201d.\",\n \"options\": [\"Sync\", \"Async\", \"Off\"],\n \"advanced\": True,\n },\n \"pre_delete_collection\": {\n \"display_name\": \"Pre Delete Collection\",\n \"info\": \"Boolean flag to determine whether to delete the collection before creating a new one.\",\n \"advanced\": True,\n },\n \"metadata_indexing_include\": {\n \"display_name\": \"Metadata Indexing Include\",\n \"info\": \"Optional list of metadata fields to include in the indexing.\",\n \"advanced\": True,\n },\n \"metadata_indexing_exclude\": {\n \"display_name\": \"Metadata Indexing Exclude\",\n \"info\": \"Optional list of metadata fields to exclude from the indexing.\",\n \"advanced\": True,\n },\n \"collection_indexing_policy\": {\n \"display_name\": \"Collection Indexing Policy\",\n \"info\": \"Optional dictionary defining the indexing policy for the collection.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n embedding: Embeddings,\n token: str,\n api_endpoint: str,\n collection_name: str,\n inputs: Optional[List[Record]] = None,\n namespace: Optional[str] = None,\n metric: Optional[str] = None,\n batch_size: Optional[int] = None,\n bulk_insert_batch_concurrency: Optional[int] = None,\n bulk_insert_overwrite_concurrency: Optional[int] = None,\n bulk_delete_concurrency: Optional[int] = None,\n setup_mode: str = \"Async\",\n pre_delete_collection: bool = False,\n metadata_indexing_include: Optional[List[str]] = None,\n metadata_indexing_exclude: Optional[List[str]] = None,\n collection_indexing_policy: Optional[dict] = None,\n ) -> VectorStore:\n try:\n setup_mode_value = SetupMode[setup_mode.upper()]\n except KeyError:\n raise ValueError(f\"Invalid setup mode: {setup_mode}\")\n if inputs:\n documents = [_input.to_lc_document() for _input in inputs]\n\n vector_store = AstraDBVectorStore.from_documents(\n documents=documents,\n embedding=embedding,\n collection_name=collection_name,\n token=token,\n api_endpoint=api_endpoint,\n namespace=namespace,\n metric=metric,\n batch_size=batch_size,\n bulk_insert_batch_concurrency=bulk_insert_batch_concurrency,\n bulk_insert_overwrite_concurrency=bulk_insert_overwrite_concurrency,\n bulk_delete_concurrency=bulk_delete_concurrency,\n setup_mode=setup_mode_value,\n pre_delete_collection=pre_delete_collection,\n metadata_indexing_include=metadata_indexing_include,\n metadata_indexing_exclude=metadata_indexing_exclude,\n collection_indexing_policy=collection_indexing_policy,\n )\n else:\n vector_store = AstraDBVectorStore(\n embedding=embedding,\n collection_name=collection_name,\n token=token,\n api_endpoint=api_endpoint,\n namespace=namespace,\n metric=metric,\n batch_size=batch_size,\n bulk_insert_batch_concurrency=bulk_insert_batch_concurrency,\n bulk_insert_overwrite_concurrency=bulk_insert_overwrite_concurrency,\n bulk_delete_concurrency=bulk_delete_concurrency,\n setup_mode=setup_mode_value,\n pre_delete_collection=pre_delete_collection,\n metadata_indexing_include=metadata_indexing_include,\n metadata_indexing_exclude=metadata_indexing_exclude,\n collection_indexing_policy=collection_indexing_policy,\n )\n\n return vector_store\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "collection_indexing_policy": { + "type": "dict", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "collection_indexing_policy", + "display_name": "Collection Indexing Policy", + "advanced": true, + "dynamic": false, + "info": "Optional dictionary defining the indexing policy for the collection.", + "load_from_db": false, + "title_case": false + }, + "collection_name": { + "type": "str", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "collection_name", + "display_name": "Collection Name", + "advanced": false, + "dynamic": false, + "info": "The name of the collection within Astra DB where the vectors will be stored.", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"], + "value": "langflow" + }, + "metadata_indexing_exclude": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "metadata_indexing_exclude", + "display_name": "Metadata Indexing Exclude", + "advanced": true, + "dynamic": false, + "info": "Optional list of metadata fields to exclude from the indexing.", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "metadata_indexing_include": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "metadata_indexing_include", + "display_name": "Metadata Indexing Include", + "advanced": true, + "dynamic": false, + "info": "Optional list of metadata fields to include in the indexing.", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "metric": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "metric", + "display_name": "Metric", + "advanced": true, + "dynamic": false, + "info": "Optional distance metric for vector comparisons in the vector store.", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "namespace": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "namespace", + "display_name": "Namespace", + "advanced": true, + "dynamic": false, + "info": "Optional namespace within Astra DB to use for the collection.", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "pre_delete_collection": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "pre_delete_collection", + "display_name": "Pre Delete Collection", + "advanced": true, + "dynamic": false, + "info": "Boolean flag to determine whether to delete the collection before creating a new one.", + "load_from_db": false, + "title_case": false + }, + "setup_mode": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "value": "Async", + "fileTypes": [], + "file_path": "", + "password": false, + "options": ["Sync", "Async", "Off"], + "name": "setup_mode", + "display_name": "Setup Mode", + "advanced": true, + "dynamic": false, + "info": "Configuration mode for setting up the vector store, with options like \u201cSync\u201d, \u201cAsync\u201d, or \u201cOff\u201d.", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "token": { + "type": "str", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": true, + "name": "token", + "display_name": "Token", + "advanced": false, + "dynamic": false, + "info": "Authentication token for accessing Astra DB.", + "load_from_db": true, + "title_case": false, + "input_types": ["Text"], + "value": "ASTRA_DB_APPLICATION_TOKEN" + }, + "_type": "CustomComponent" + }, + "description": "Builds or loads an Astra DB Vector Store.", + "icon": "AstraDB", + "base_classes": ["VectorStore"], + "display_name": "Astra DB", + "documentation": "", + "custom_fields": { + "embedding": null, + "token": null, + "api_endpoint": null, + "collection_name": null, + "inputs": null, + "namespace": null, + "metric": null, + "batch_size": null, + "bulk_insert_batch_concurrency": null, + "bulk_insert_overwrite_concurrency": null, + "bulk_delete_concurrency": null, + "setup_mode": null, + "pre_delete_collection": null, + "metadata_indexing_include": null, + "metadata_indexing_exclude": null, + "collection_indexing_policy": null + }, + "output_types": ["VectorStore"], + "field_formatters": {}, + "frozen": false, + "field_order": [ + "token", + "api_endpoint", + "collection_name", + "inputs", + "embedding" + ], + "beta": false + }, + "id": "AstraDB-eUCSS" + }, + "selected": false, + "width": 384, + "height": 573, + "positionAbsolute": { + "x": 3372.04958055989, + "y": 1611.0742035495277 + }, + "dragging": false + }, + { + "id": "OpenAIEmbeddings-9TPjc", + "type": "genericNode", + "position": { + "x": 2814.0402191223047, + "y": 1955.9268168273086 + }, + "data": { + "type": "OpenAIEmbeddings", + "node": { + "template": { + "allowed_special": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": [], + "fileTypes": [], + "file_path": "", + "password": false, + "name": "allowed_special", + "display_name": "Allowed Special", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "chunk_size": { + "type": "int", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": 1000, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "chunk_size", + "display_name": "Chunk Size", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "client": { + "type": "Any", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "client", + "display_name": "Client", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import Any, Dict, List, Optional\n\nfrom langchain_openai.embeddings.base import OpenAIEmbeddings\n\nfrom langflow.field_typing import Embeddings, NestedDict\nfrom langflow.interface.custom.custom_component import CustomComponent\n\n\nclass OpenAIEmbeddingsComponent(CustomComponent):\n display_name = \"OpenAI Embeddings\"\n description = \"Generate embeddings using OpenAI models.\"\n\n def build_config(self):\n return {\n \"allowed_special\": {\n \"display_name\": \"Allowed Special\",\n \"advanced\": True,\n \"field_type\": \"str\",\n \"is_list\": True,\n },\n \"default_headers\": {\n \"display_name\": \"Default Headers\",\n \"advanced\": True,\n \"field_type\": \"dict\",\n },\n \"default_query\": {\n \"display_name\": \"Default Query\",\n \"advanced\": True,\n \"field_type\": \"NestedDict\",\n },\n \"disallowed_special\": {\n \"display_name\": \"Disallowed Special\",\n \"advanced\": True,\n \"field_type\": \"str\",\n \"is_list\": True,\n },\n \"chunk_size\": {\"display_name\": \"Chunk Size\", \"advanced\": True},\n \"client\": {\"display_name\": \"Client\", \"advanced\": True},\n \"deployment\": {\"display_name\": \"Deployment\", \"advanced\": True},\n \"embedding_ctx_length\": {\n \"display_name\": \"Embedding Context Length\",\n \"advanced\": True,\n },\n \"max_retries\": {\"display_name\": \"Max Retries\", \"advanced\": True},\n \"model\": {\n \"display_name\": \"Model\",\n \"advanced\": False,\n \"options\": [\n \"text-embedding-3-small\",\n \"text-embedding-3-large\",\n \"text-embedding-ada-002\",\n ],\n },\n \"model_kwargs\": {\"display_name\": \"Model Kwargs\", \"advanced\": True},\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"password\": True,\n \"advanced\": True,\n },\n \"openai_api_key\": {\"display_name\": \"OpenAI API Key\", \"password\": True},\n \"openai_api_type\": {\n \"display_name\": \"OpenAI API Type\",\n \"advanced\": True,\n \"password\": True,\n },\n \"openai_api_version\": {\n \"display_name\": \"OpenAI API Version\",\n \"advanced\": True,\n },\n \"openai_organization\": {\n \"display_name\": \"OpenAI Organization\",\n \"advanced\": True,\n },\n \"openai_proxy\": {\"display_name\": \"OpenAI Proxy\", \"advanced\": True},\n \"request_timeout\": {\"display_name\": \"Request Timeout\", \"advanced\": True},\n \"show_progress_bar\": {\n \"display_name\": \"Show Progress Bar\",\n \"advanced\": True,\n },\n \"skip_empty\": {\"display_name\": \"Skip Empty\", \"advanced\": True},\n \"tiktoken_model_name\": {\n \"display_name\": \"TikToken Model Name\",\n \"advanced\": True,\n },\n \"tiktoken_enable\": {\"display_name\": \"TikToken Enable\", \"advanced\": True},\n }\n\n def build(\n self,\n openai_api_key: str,\n default_headers: Optional[Dict[str, str]] = None,\n default_query: Optional[NestedDict] = {},\n allowed_special: List[str] = [],\n disallowed_special: List[str] = [\"all\"],\n chunk_size: int = 1000,\n client: Optional[Any] = None,\n deployment: str = \"text-embedding-ada-002\",\n embedding_ctx_length: int = 8191,\n max_retries: int = 6,\n model: str = \"text-embedding-ada-002\",\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n openai_api_type: Optional[str] = None,\n openai_api_version: Optional[str] = None,\n openai_organization: Optional[str] = None,\n openai_proxy: Optional[str] = None,\n request_timeout: Optional[float] = None,\n show_progress_bar: bool = False,\n skip_empty: bool = False,\n tiktoken_enable: bool = True,\n tiktoken_model_name: Optional[str] = None,\n ) -> Embeddings:\n # This is to avoid errors with Vector Stores (e.g Chroma)\n if disallowed_special == [\"all\"]:\n disallowed_special = \"all\" # type: ignore\n\n return OpenAIEmbeddings(\n tiktoken_enabled=tiktoken_enable,\n default_headers=default_headers,\n default_query=default_query,\n allowed_special=set(allowed_special),\n disallowed_special=\"all\",\n chunk_size=chunk_size,\n client=client,\n deployment=deployment,\n embedding_ctx_length=embedding_ctx_length,\n max_retries=max_retries,\n model=model,\n model_kwargs=model_kwargs,\n base_url=openai_api_base,\n api_key=openai_api_key,\n openai_api_type=openai_api_type,\n api_version=openai_api_version,\n organization=openai_organization,\n openai_proxy=openai_proxy,\n timeout=request_timeout,\n show_progress_bar=show_progress_bar,\n skip_empty=skip_empty,\n tiktoken_model_name=tiktoken_model_name,\n )\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "default_headers": { + "type": "dict", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "default_headers", + "display_name": "Default Headers", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "default_query": { + "type": "NestedDict", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": {}, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "default_query", + "display_name": "Default Query", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "deployment": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": "text-embedding-ada-002", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "deployment", + "display_name": "Deployment", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "disallowed_special": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": ["all"], + "fileTypes": [], + "file_path": "", + "password": false, + "name": "disallowed_special", + "display_name": "Disallowed Special", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "embedding_ctx_length": { + "type": "int", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": 8191, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "embedding_ctx_length", + "display_name": "Embedding Context Length", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "max_retries": { + "type": "int", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": 6, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "max_retries", + "display_name": "Max Retries", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "model": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "value": "text-embedding-ada-002", + "fileTypes": [], + "file_path": "", + "password": false, + "options": [ + "text-embedding-3-small", + "text-embedding-3-large", + "text-embedding-ada-002" + ], + "name": "model", + "display_name": "Model", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "model_kwargs": { + "type": "NestedDict", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": {}, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "model_kwargs", + "display_name": "Model Kwargs", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "openai_api_base": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": true, + "name": "openai_api_base", + "display_name": "OpenAI API Base", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "openai_api_key": { + "type": "str", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": true, + "name": "openai_api_key", + "display_name": "OpenAI API Key", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"], + "value": "" + }, + "openai_api_type": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": true, + "name": "openai_api_type", + "display_name": "OpenAI API Type", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "openai_api_version": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "openai_api_version", + "display_name": "OpenAI API Version", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "openai_organization": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "openai_organization", + "display_name": "OpenAI Organization", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "openai_proxy": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "openai_proxy", + "display_name": "OpenAI Proxy", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "request_timeout": { + "type": "float", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "request_timeout", + "display_name": "Request Timeout", + "advanced": true, + "dynamic": false, + "info": "", + "rangeSpec": { + "step_type": "float", + "min": -1, + "max": 1, + "step": 0.1 + }, + "load_from_db": false, + "title_case": false + }, + "show_progress_bar": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "show_progress_bar", + "display_name": "Show Progress Bar", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "skip_empty": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "skip_empty", + "display_name": "Skip Empty", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "tiktoken_enable": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": true, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "tiktoken_enable", + "display_name": "TikToken Enable", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "tiktoken_model_name": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "tiktoken_model_name", + "display_name": "TikToken Model Name", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "_type": "CustomComponent" + }, + "description": "Generate embeddings using OpenAI models.", + "base_classes": ["Embeddings"], + "display_name": "OpenAI Embeddings", + "documentation": "", + "custom_fields": { + "openai_api_key": null, + "default_headers": null, + "default_query": null, + "allowed_special": null, + "disallowed_special": null, + "chunk_size": null, + "client": null, + "deployment": null, + "embedding_ctx_length": null, + "max_retries": null, + "model": null, + "model_kwargs": null, + "openai_api_base": null, + "openai_api_type": null, + "openai_api_version": null, + "openai_organization": null, + "openai_proxy": null, + "request_timeout": null, + "show_progress_bar": null, + "skip_empty": null, + "tiktoken_enable": null, + "tiktoken_model_name": null + }, + "output_types": ["Embeddings"], + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false + }, + "id": "OpenAIEmbeddings-9TPjc" + }, + "selected": false, + "width": 384, + "height": 383, + "positionAbsolute": { + "x": 2814.0402191223047, + "y": 1955.9268168273086 + }, + "dragging": false + } + ], + "edges": [ + { + "source": "TextOutput-BDknO", + "target": "Prompt-xeI6K", + "sourceHandle": "{\u0153baseClasses\u0153:[\u0153object\u0153,\u0153Text\u0153,\u0153str\u0153],\u0153dataType\u0153:\u0153TextOutput\u0153,\u0153id\u0153:\u0153TextOutput-BDknO\u0153}", + "targetHandle": "{\u0153fieldName\u0153:\u0153context\u0153,\u0153id\u0153:\u0153Prompt-xeI6K\u0153,\u0153inputTypes\u0153:[\u0153Document\u0153,\u0153BaseOutputParser\u0153,\u0153Record\u0153,\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}", + "id": "reactflow__edge-TextOutput-BDknO{\u0153baseClasses\u0153:[\u0153object\u0153,\u0153Text\u0153,\u0153str\u0153],\u0153dataType\u0153:\u0153TextOutput\u0153,\u0153id\u0153:\u0153TextOutput-BDknO\u0153}-Prompt-xeI6K{\u0153fieldName\u0153:\u0153context\u0153,\u0153id\u0153:\u0153Prompt-xeI6K\u0153,\u0153inputTypes\u0153:[\u0153Document\u0153,\u0153BaseOutputParser\u0153,\u0153Record\u0153,\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}", + "data": { + "targetHandle": { + "fieldName": "context", + "id": "Prompt-xeI6K", + "inputTypes": ["Document", "BaseOutputParser", "Record", "Text"], + "type": "str" + }, + "sourceHandle": { + "baseClasses": ["object", "Text", "str"], + "dataType": "TextOutput", + "id": "TextOutput-BDknO" + } + }, + "style": { + "stroke": "#555" + }, + "className": "stroke-gray-900 stroke-connection", + "selected": false + }, + { + "source": "ChatInput-yxMKE", + "target": "Prompt-xeI6K", + "sourceHandle": "{\u0153baseClasses\u0153:[\u0153Text\u0153,\u0153str\u0153,\u0153object\u0153,\u0153Record\u0153],\u0153dataType\u0153:\u0153ChatInput\u0153,\u0153id\u0153:\u0153ChatInput-yxMKE\u0153}", + "targetHandle": "{\u0153fieldName\u0153:\u0153question\u0153,\u0153id\u0153:\u0153Prompt-xeI6K\u0153,\u0153inputTypes\u0153:[\u0153Document\u0153,\u0153BaseOutputParser\u0153,\u0153Record\u0153,\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}", + "id": "reactflow__edge-ChatInput-yxMKE{\u0153baseClasses\u0153:[\u0153Text\u0153,\u0153str\u0153,\u0153object\u0153,\u0153Record\u0153],\u0153dataType\u0153:\u0153ChatInput\u0153,\u0153id\u0153:\u0153ChatInput-yxMKE\u0153}-Prompt-xeI6K{\u0153fieldName\u0153:\u0153question\u0153,\u0153id\u0153:\u0153Prompt-xeI6K\u0153,\u0153inputTypes\u0153:[\u0153Document\u0153,\u0153BaseOutputParser\u0153,\u0153Record\u0153,\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}", + "data": { + "targetHandle": { + "fieldName": "question", + "id": "Prompt-xeI6K", + "inputTypes": ["Document", "BaseOutputParser", "Record", "Text"], + "type": "str" + }, + "sourceHandle": { + "baseClasses": ["Text", "str", "object", "Record"], + "dataType": "ChatInput", + "id": "ChatInput-yxMKE" + } + }, + "style": { + "stroke": "#555" + }, + "className": "stroke-gray-900 stroke-connection", + "selected": false + }, + { + "source": "Prompt-xeI6K", + "target": "OpenAIModel-EjXlN", + "sourceHandle": "{\u0153baseClasses\u0153:[\u0153object\u0153,\u0153Text\u0153,\u0153str\u0153],\u0153dataType\u0153:\u0153Prompt\u0153,\u0153id\u0153:\u0153Prompt-xeI6K\u0153}", + "targetHandle": "{\u0153fieldName\u0153:\u0153input_value\u0153,\u0153id\u0153:\u0153OpenAIModel-EjXlN\u0153,\u0153inputTypes\u0153:[\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}", + "id": "reactflow__edge-Prompt-xeI6K{\u0153baseClasses\u0153:[\u0153object\u0153,\u0153Text\u0153,\u0153str\u0153],\u0153dataType\u0153:\u0153Prompt\u0153,\u0153id\u0153:\u0153Prompt-xeI6K\u0153}-OpenAIModel-EjXlN{\u0153fieldName\u0153:\u0153input_value\u0153,\u0153id\u0153:\u0153OpenAIModel-EjXlN\u0153,\u0153inputTypes\u0153:[\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}", + "data": { + "targetHandle": { + "fieldName": "input_value", + "id": "OpenAIModel-EjXlN", + "inputTypes": ["Text"], + "type": "str" + }, + "sourceHandle": { + "baseClasses": ["object", "Text", "str"], + "dataType": "Prompt", + "id": "Prompt-xeI6K" + } + }, + "style": { + "stroke": "#555" + }, + "className": "stroke-gray-900 stroke-connection", + "selected": false + }, + { + "source": "OpenAIModel-EjXlN", + "target": "ChatOutput-Q39I8", + "sourceHandle": "{\u0153baseClasses\u0153:[\u0153object\u0153,\u0153Text\u0153,\u0153str\u0153],\u0153dataType\u0153:\u0153OpenAIModel\u0153,\u0153id\u0153:\u0153OpenAIModel-EjXlN\u0153}", + "targetHandle": "{\u0153fieldName\u0153:\u0153input_value\u0153,\u0153id\u0153:\u0153ChatOutput-Q39I8\u0153,\u0153inputTypes\u0153:[\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}", + "id": "reactflow__edge-OpenAIModel-EjXlN{\u0153baseClasses\u0153:[\u0153object\u0153,\u0153Text\u0153,\u0153str\u0153],\u0153dataType\u0153:\u0153OpenAIModel\u0153,\u0153id\u0153:\u0153OpenAIModel-EjXlN\u0153}-ChatOutput-Q39I8{\u0153fieldName\u0153:\u0153input_value\u0153,\u0153id\u0153:\u0153ChatOutput-Q39I8\u0153,\u0153inputTypes\u0153:[\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}", + "data": { + "targetHandle": { + "fieldName": "input_value", + "id": "ChatOutput-Q39I8", + "inputTypes": ["Text"], + "type": "str" + }, + "sourceHandle": { + "baseClasses": ["object", "Text", "str"], + "dataType": "OpenAIModel", + "id": "OpenAIModel-EjXlN" + } + }, + "style": { + "stroke": "#555" + }, + "className": "stroke-gray-900 stroke-connection", + "selected": false + }, + { + "source": "File-t0a6a", + "target": "RecursiveCharacterTextSplitter-tR9QM", + "sourceHandle": "{\u0153baseClasses\u0153:[\u0153Record\u0153],\u0153dataType\u0153:\u0153File\u0153,\u0153id\u0153:\u0153File-t0a6a\u0153}", + "targetHandle": "{\u0153fieldName\u0153:\u0153inputs\u0153,\u0153id\u0153:\u0153RecursiveCharacterTextSplitter-tR9QM\u0153,\u0153inputTypes\u0153:[\u0153Document\u0153,\u0153Record\u0153],\u0153type\u0153:\u0153Document\u0153}", + "id": "reactflow__edge-File-t0a6a{\u0153baseClasses\u0153:[\u0153Record\u0153],\u0153dataType\u0153:\u0153File\u0153,\u0153id\u0153:\u0153File-t0a6a\u0153}-RecursiveCharacterTextSplitter-tR9QM{\u0153fieldName\u0153:\u0153inputs\u0153,\u0153id\u0153:\u0153RecursiveCharacterTextSplitter-tR9QM\u0153,\u0153inputTypes\u0153:[\u0153Document\u0153,\u0153Record\u0153],\u0153type\u0153:\u0153Document\u0153}", + "data": { + "targetHandle": { + "fieldName": "inputs", + "id": "RecursiveCharacterTextSplitter-tR9QM", + "inputTypes": ["Document", "Record"], + "type": "Document" + }, + "sourceHandle": { + "baseClasses": ["Record"], + "dataType": "File", + "id": "File-t0a6a" + } + }, + "style": { + "stroke": "#555" + }, + "className": "stroke-gray-900 stroke-connection", + "selected": false + }, + { + "source": "OpenAIEmbeddings-ZlOk1", + "sourceHandle": "{\u0153baseClasses\u0153:[\u0153Embeddings\u0153],\u0153dataType\u0153:\u0153OpenAIEmbeddings\u0153,\u0153id\u0153:\u0153OpenAIEmbeddings-ZlOk1\u0153}", + "target": "AstraDBSearch-41nRz", + "targetHandle": "{\u0153fieldName\u0153:\u0153embedding\u0153,\u0153id\u0153:\u0153AstraDBSearch-41nRz\u0153,\u0153inputTypes\u0153:null,\u0153type\u0153:\u0153Embeddings\u0153}", + "data": { + "targetHandle": { + "fieldName": "embedding", + "id": "AstraDBSearch-41nRz", + "inputTypes": null, + "type": "Embeddings" + }, + "sourceHandle": { + "baseClasses": ["Embeddings"], + "dataType": "OpenAIEmbeddings", + "id": "OpenAIEmbeddings-ZlOk1" + } + }, + "style": { + "stroke": "#555" + }, + "className": "stroke-gray-900 stroke-connection", + "id": "reactflow__edge-OpenAIEmbeddings-ZlOk1{\u0153baseClasses\u0153:[\u0153Embeddings\u0153],\u0153dataType\u0153:\u0153OpenAIEmbeddings\u0153,\u0153id\u0153:\u0153OpenAIEmbeddings-ZlOk1\u0153}-AstraDBSearch-41nRz{\u0153fieldName\u0153:\u0153embedding\u0153,\u0153id\u0153:\u0153AstraDBSearch-41nRz\u0153,\u0153inputTypes\u0153:null,\u0153type\u0153:\u0153Embeddings\u0153}" + }, + { + "source": "ChatInput-yxMKE", + "sourceHandle": "{\u0153baseClasses\u0153:[\u0153Text\u0153,\u0153str\u0153,\u0153object\u0153,\u0153Record\u0153],\u0153dataType\u0153:\u0153ChatInput\u0153,\u0153id\u0153:\u0153ChatInput-yxMKE\u0153}", + "target": "AstraDBSearch-41nRz", + "targetHandle": "{\u0153fieldName\u0153:\u0153input_value\u0153,\u0153id\u0153:\u0153AstraDBSearch-41nRz\u0153,\u0153inputTypes\u0153:[\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}", + "data": { + "targetHandle": { + "fieldName": "input_value", + "id": "AstraDBSearch-41nRz", + "inputTypes": ["Text"], + "type": "str" + }, + "sourceHandle": { + "baseClasses": ["Text", "str", "object", "Record"], + "dataType": "ChatInput", + "id": "ChatInput-yxMKE" + } + }, + "style": { + "stroke": "#555" + }, + "className": "stroke-gray-900 stroke-connection", + "id": "reactflow__edge-ChatInput-yxMKE{\u0153baseClasses\u0153:[\u0153Text\u0153,\u0153str\u0153,\u0153object\u0153,\u0153Record\u0153],\u0153dataType\u0153:\u0153ChatInput\u0153,\u0153id\u0153:\u0153ChatInput-yxMKE\u0153}-AstraDBSearch-41nRz{\u0153fieldName\u0153:\u0153input_value\u0153,\u0153id\u0153:\u0153AstraDBSearch-41nRz\u0153,\u0153inputTypes\u0153:[\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}" + }, + { + "source": "RecursiveCharacterTextSplitter-tR9QM", + "sourceHandle": "{\u0153baseClasses\u0153:[\u0153Record\u0153],\u0153dataType\u0153:\u0153RecursiveCharacterTextSplitter\u0153,\u0153id\u0153:\u0153RecursiveCharacterTextSplitter-tR9QM\u0153}", + "target": "AstraDB-eUCSS", + "targetHandle": "{\u0153fieldName\u0153:\u0153inputs\u0153,\u0153id\u0153:\u0153AstraDB-eUCSS\u0153,\u0153inputTypes\u0153:null,\u0153type\u0153:\u0153Record\u0153}", + "data": { + "targetHandle": { + "fieldName": "inputs", + "id": "AstraDB-eUCSS", + "inputTypes": null, + "type": "Record" + }, + "sourceHandle": { + "baseClasses": ["Record"], + "dataType": "RecursiveCharacterTextSplitter", + "id": "RecursiveCharacterTextSplitter-tR9QM" + } + }, + "style": { + "stroke": "#555" + }, + "className": "stroke-gray-900 stroke-connection", + "id": "reactflow__edge-RecursiveCharacterTextSplitter-tR9QM{\u0153baseClasses\u0153:[\u0153Record\u0153],\u0153dataType\u0153:\u0153RecursiveCharacterTextSplitter\u0153,\u0153id\u0153:\u0153RecursiveCharacterTextSplitter-tR9QM\u0153}-AstraDB-eUCSS{\u0153fieldName\u0153:\u0153inputs\u0153,\u0153id\u0153:\u0153AstraDB-eUCSS\u0153,\u0153inputTypes\u0153:null,\u0153type\u0153:\u0153Record\u0153}", + "selected": false + }, + { + "source": "OpenAIEmbeddings-9TPjc", + "sourceHandle": "{\u0153baseClasses\u0153:[\u0153Embeddings\u0153],\u0153dataType\u0153:\u0153OpenAIEmbeddings\u0153,\u0153id\u0153:\u0153OpenAIEmbeddings-9TPjc\u0153}", + "target": "AstraDB-eUCSS", + "targetHandle": "{\u0153fieldName\u0153:\u0153embedding\u0153,\u0153id\u0153:\u0153AstraDB-eUCSS\u0153,\u0153inputTypes\u0153:null,\u0153type\u0153:\u0153Embeddings\u0153}", + "data": { + "targetHandle": { + "fieldName": "embedding", + "id": "AstraDB-eUCSS", + "inputTypes": null, + "type": "Embeddings" + }, + "sourceHandle": { + "baseClasses": ["Embeddings"], + "dataType": "OpenAIEmbeddings", + "id": "OpenAIEmbeddings-9TPjc" + } + }, + "style": { + "stroke": "#555" + }, + "className": "stroke-gray-900 stroke-connection", + "id": "reactflow__edge-OpenAIEmbeddings-9TPjc{\u0153baseClasses\u0153:[\u0153Embeddings\u0153],\u0153dataType\u0153:\u0153OpenAIEmbeddings\u0153,\u0153id\u0153:\u0153OpenAIEmbeddings-9TPjc\u0153}-AstraDB-eUCSS{\u0153fieldName\u0153:\u0153embedding\u0153,\u0153id\u0153:\u0153AstraDB-eUCSS\u0153,\u0153inputTypes\u0153:null,\u0153type\u0153:\u0153Embeddings\u0153}", + "selected": false + }, + { + "source": "AstraDBSearch-41nRz", + "sourceHandle": "{\u0153baseClasses\u0153:[\u0153Record\u0153],\u0153dataType\u0153:\u0153AstraDBSearch\u0153,\u0153id\u0153:\u0153AstraDBSearch-41nRz\u0153}", + "target": "TextOutput-BDknO", + "targetHandle": "{\u0153fieldName\u0153:\u0153input_value\u0153,\u0153id\u0153:\u0153TextOutput-BDknO\u0153,\u0153inputTypes\u0153:[\u0153Record\u0153,\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}", + "data": { + "targetHandle": { + "fieldName": "input_value", + "id": "TextOutput-BDknO", + "inputTypes": ["Record", "Text"], + "type": "str" + }, + "sourceHandle": { + "baseClasses": ["Record"], + "dataType": "AstraDBSearch", + "id": "AstraDBSearch-41nRz" + } + }, + "style": { + "stroke": "#555" + }, + "className": "stroke-gray-900 stroke-connection", + "id": "reactflow__edge-AstraDBSearch-41nRz{\u0153baseClasses\u0153:[\u0153Record\u0153],\u0153dataType\u0153:\u0153AstraDBSearch\u0153,\u0153id\u0153:\u0153AstraDBSearch-41nRz\u0153}-TextOutput-BDknO{\u0153fieldName\u0153:\u0153input_value\u0153,\u0153id\u0153:\u0153TextOutput-BDknO\u0153,\u0153inputTypes\u0153:[\u0153Record\u0153,\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}" + } + ], + "viewport": { + "x": -259.6782520315529, + "y": 90.3428735006047, + "zoom": 0.2687057134854984 + } + }, + "description": "Visit https://pre-release.langflow.org/tutorials/rag-with-astradb for a detailed guide of this project.\nThis project give you both Ingestion and RAG in a single file. You'll need to visit https://astra.datastax.com/ to create an Astra DB instance, your Token and grab an API Endpoint.\nRunning this project requires you to add a file in the Files component, then define a Collection Name and click on the Play icon on the Astra DB component. \n\nAfter the ingestion ends you are ready to click on the Run button at the lower left corner and start asking questions about your data.", + "name": "Vector Store RAG", + "last_tested_version": "1.0.0a0", + "is_component": false } diff --git a/docs/static/json_files/Notion_Components_bundle.json b/docs/static/json_files/Notion_Components_bundle.json index 21181187c..5e632ad9c 100644 --- a/docs/static/json_files/Notion_Components_bundle.json +++ b/docs/static/json_files/Notion_Components_bundle.json @@ -1 +1,881 @@ -{"id":"7cd51434-9767-450f-8742-27857367f8c2","data":{"nodes":[{"id":"RecordsToText-Q69g5","type":"genericNode","position":{"x":-2671.5528488127866,"y":-963.4266471378126},"data":{"type":"RecordsToText","node":{"template":{"code":{"type":"code","required":true,"placeholder":"","list":false,"show":true,"multiline":true,"value":"import requests\r\nfrom typing import List\r\n\r\nfrom langflow import CustomComponent\r\nfrom langflow.schema import Record\r\n\r\n\r\nclass NotionUserList(CustomComponent):\r\n display_name = \"List Users [Notion]\"\r\n description = \"Retrieve users from Notion.\"\r\n documentation: str = \"https://docs.langflow.org/integrations/notion/list-users\"\r\n icon = \"NotionDirectoryLoader\"\r\n \r\n def build_config(self):\r\n return {\r\n \"notion_secret\": {\r\n \"display_name\": \"Notion Secret\",\r\n \"field_type\": \"str\",\r\n \"info\": \"The Notion integration token.\",\r\n \"password\": True,\r\n },\r\n }\r\n\r\n def build(\r\n self,\r\n notion_secret: str,\r\n ) -> List[Record]:\r\n url = \"https://api.notion.com/v1/users\"\r\n headers = {\r\n \"Authorization\": f\"Bearer {notion_secret}\",\r\n \"Notion-Version\": \"2022-06-28\",\r\n }\r\n\r\n response = requests.get(url, headers=headers)\r\n response.raise_for_status()\r\n\r\n data = response.json()\r\n results = data['results']\r\n\r\n records = []\r\n for user in results:\r\n id = user['id']\r\n type = user['type']\r\n name = user.get('name', '')\r\n avatar_url = user.get('avatar_url', '')\r\n\r\n record_data = {\r\n \"id\": id,\r\n \"type\": type,\r\n \"name\": name,\r\n \"avatar_url\": avatar_url,\r\n }\r\n\r\n output = \"User:\\n\"\r\n for key, value in record_data.items():\r\n output += f\"{key.replace('_', ' ').title()}: {value}\\n\"\r\n output += \"________________________\\n\"\r\n\r\n record = Record(text=output, data=record_data)\r\n records.append(record)\r\n\r\n self.status = \"\\n\".join(record.text for record in records)\r\n return records","fileTypes":[],"file_path":"","password":false,"name":"code","advanced":true,"dynamic":true,"info":"","load_from_db":false,"title_case":false},"notion_secret":{"type":"str","required":true,"placeholder":"","list":false,"show":true,"multiline":false,"fileTypes":[],"file_path":"","password":true,"name":"notion_secret","display_name":"Notion Secret","advanced":false,"dynamic":false,"info":"The Notion integration token.","load_from_db":false,"title_case":false,"input_types":["Text"],"value":""},"_type":"CustomComponent"},"description":"Retrieve users from Notion.","icon":"NotionDirectoryLoader","base_classes":["Record"],"display_name":"List Users [Notion] ","documentation":"https://docs.langflow.org/integrations/notion/list-users","custom_fields":{"notion_secret":null},"output_types":["Record"],"field_formatters":{},"frozen":false,"field_order":[],"beta":false},"id":"RecordsToText-Q69g5","description":"Retrieve users from Notion.","display_name":"List Users [Notion] "},"selected":false,"width":384,"height":289,"dragging":false,"positionAbsolute":{"x":-2671.5528488127866,"y":-963.4266471378126}},{"id":"CustomComponent-PU0K5","type":"genericNode","position":{"x":-3077.2269116193215,"y":-960.9450220159636},"data":{"type":"CustomComponent","node":{"template":{"code":{"type":"code","required":true,"placeholder":"","list":false,"show":true,"multiline":true,"value":"import json\r\nfrom typing import Optional\r\n\r\nimport requests\r\nfrom langflow.custom import CustomComponent\r\n\r\n\r\nclass NotionPageCreator(CustomComponent):\r\n display_name = \"Create Page [Notion]\"\r\n description = \"A component for creating Notion pages.\"\r\n documentation: str = \"https://docs.langflow.org/integrations/notion/page-create\"\r\n icon = \"NotionDirectoryLoader\"\r\n\r\n def build_config(self):\r\n return {\r\n \"database_id\": {\r\n \"display_name\": \"Database ID\",\r\n \"field_type\": \"str\",\r\n \"info\": \"The ID of the Notion database.\",\r\n },\r\n \"notion_secret\": {\r\n \"display_name\": \"Notion Secret\",\r\n \"field_type\": \"str\",\r\n \"info\": \"The Notion integration token.\",\r\n \"password\": True,\r\n },\r\n \"properties\": {\r\n \"display_name\": \"Properties\",\r\n \"field_type\": \"str\",\r\n \"info\": \"The properties of the new page. Depending on your database setup, this can change. E.G: {'Task name': {'id': 'title', 'type': 'title', 'title': [{'type': 'text', 'text': {'content': 'Send Notion Components to LF', 'link': null}}]}}\",\r\n },\r\n }\r\n\r\n def build(\r\n self,\r\n database_id: str,\r\n notion_secret: str,\r\n properties: str = '{\"Task name\": {\"id\": \"title\", \"type\": \"title\", \"title\": [{\"type\": \"text\", \"text\": {\"content\": \"Send Notion Components to LF\", \"link\": null}}]}}',\r\n ) -> str:\r\n if not database_id or not properties:\r\n raise ValueError(\"Invalid input. Please provide 'database_id' and 'properties'.\")\r\n\r\n headers = {\r\n \"Authorization\": f\"Bearer {notion_secret}\",\r\n \"Content-Type\": \"application/json\",\r\n \"Notion-Version\": \"2022-06-28\",\r\n }\r\n\r\n data = {\r\n \"parent\": {\"database_id\": database_id},\r\n \"properties\": json.loads(properties),\r\n }\r\n\r\n response = requests.post(\"https://api.notion.com/v1/pages\", headers=headers, json=data)\r\n\r\n if response.status_code == 200:\r\n page_id = response.json()[\"id\"]\r\n self.status = f\"Successfully created Notion page with ID: {page_id}\\n {str(response.json())}\"\r\n return response.json()\r\n else:\r\n error_message = f\"Failed to create Notion page. Status code: {response.status_code}, Error: {response.text}\"\r\n self.status = error_message\r\n raise Exception(error_message)","fileTypes":[],"file_path":"","password":false,"name":"code","advanced":true,"dynamic":true,"info":"","load_from_db":false,"title_case":false},"database_id":{"type":"str","required":true,"placeholder":"","list":false,"show":true,"multiline":false,"fileTypes":[],"file_path":"","password":false,"name":"database_id","display_name":"Database ID","advanced":false,"dynamic":false,"info":"The ID of the Notion database.","load_from_db":false,"title_case":false,"input_types":["Text"]},"notion_secret":{"type":"str","required":true,"placeholder":"","list":false,"show":true,"multiline":false,"fileTypes":[],"file_path":"","password":true,"name":"notion_secret","display_name":"Notion Secret","advanced":false,"dynamic":false,"info":"The Notion integration token.","load_from_db":false,"title_case":false,"input_types":["Text"],"value":""},"properties":{"type":"str","required":false,"placeholder":"","list":false,"show":true,"multiline":false,"value":"{\"Task name\": {\"id\": \"title\", \"type\": \"title\", \"title\": [{\"type\": \"text\", \"text\": {\"content\": \"Send Notion Components to LF\", \"link\": null}}]}}","fileTypes":[],"file_path":"","password":false,"name":"properties","display_name":"Properties","advanced":false,"dynamic":false,"info":"The properties of the new page. Depending on your database setup, this can change. E.G: {'Task name': {'id': 'title', 'type': 'title', 'title': [{'type': 'text', 'text': {'content': 'Send Notion Components to LF', 'link': null}}]}}","load_from_db":false,"title_case":false,"input_types":["Text"]},"_type":"CustomComponent"},"description":"A component for creating Notion pages.","icon":"NotionDirectoryLoader","base_classes":["object","str","Text"],"display_name":"Create Page [Notion] ","documentation":"https://docs.langflow.org/integrations/notion/page-create","custom_fields":{"database_id":null,"notion_secret":null,"properties":null},"output_types":["Text"],"field_formatters":{},"frozen":false,"field_order":[],"beta":false},"id":"CustomComponent-PU0K5","description":"A component for creating Notion pages.","display_name":"Create Page [Notion] "},"selected":false,"width":384,"height":477,"positionAbsolute":{"x":-3077.2269116193215,"y":-960.9450220159636},"dragging":false},{"id":"CustomComponent-YODla","type":"genericNode","position":{"x":-3485.297183150799,"y":-362.8525892356713},"data":{"type":"CustomComponent","node":{"template":{"code":{"type":"code","required":true,"placeholder":"","list":false,"show":true,"multiline":true,"value":"import requests\r\nfrom typing import Dict\r\n\r\nfrom langflow import CustomComponent\r\nfrom langflow.schema import Record\r\n\r\n\r\nclass NotionDatabaseProperties(CustomComponent):\r\n display_name = \"List Database Properties [Notion]\"\r\n description = \"Retrieve properties of a Notion database.\"\r\n documentation: str = \"https://docs.langflow.org/integrations/notion/list-database-properties\"\r\n icon = \"NotionDirectoryLoader\"\r\n \r\n def build_config(self):\r\n return {\r\n \"database_id\": {\r\n \"display_name\": \"Database ID\",\r\n \"field_type\": \"str\",\r\n \"info\": \"The ID of the Notion database.\",\r\n },\r\n \"notion_secret\": {\r\n \"display_name\": \"Notion Secret\",\r\n \"field_type\": \"str\",\r\n \"info\": \"The Notion integration token.\",\r\n \"password\": True,\r\n },\r\n }\r\n\r\n def build(\r\n self,\r\n database_id: str,\r\n notion_secret: str,\r\n ) -> Record:\r\n url = f\"https://api.notion.com/v1/databases/{database_id}\"\r\n headers = {\r\n \"Authorization\": f\"Bearer {notion_secret}\",\r\n \"Notion-Version\": \"2022-06-28\", # Use the latest supported version\r\n }\r\n\r\n response = requests.get(url, headers=headers)\r\n response.raise_for_status()\r\n\r\n data = response.json()\r\n properties = data.get(\"properties\", {})\r\n\r\n record = Record(text=str(response.json()), data=properties)\r\n self.status = f\"Retrieved {len(properties)} properties from the Notion database.\\n {record.text}\"\r\n return record","fileTypes":[],"file_path":"","password":false,"name":"code","advanced":true,"dynamic":true,"info":"","load_from_db":false,"title_case":false},"database_id":{"type":"str","required":true,"placeholder":"","list":false,"show":true,"multiline":false,"fileTypes":[],"file_path":"","password":false,"name":"database_id","display_name":"Database ID","advanced":false,"dynamic":false,"info":"The ID of the Notion database.","load_from_db":true,"title_case":false,"input_types":["Text"],"value":"NOTION_NMSTX_DB_ID"},"notion_secret":{"type":"str","required":true,"placeholder":"","list":false,"show":true,"multiline":false,"fileTypes":[],"file_path":"","password":true,"name":"notion_secret","display_name":"Notion Secret","advanced":false,"dynamic":false,"info":"The Notion integration token.","load_from_db":true,"title_case":false,"input_types":["Text"],"value":""},"_type":"CustomComponent"},"description":"Retrieve properties of a Notion database.","icon":"NotionDirectoryLoader","base_classes":["Record"],"display_name":"List Database Properties [Notion] ","documentation":"https://docs.langflow.org/integrations/notion/list-database-properties","custom_fields":{"database_id":null,"notion_secret":null},"output_types":["Record"],"field_formatters":{},"frozen":false,"field_order":[],"beta":false},"id":"CustomComponent-YODla","description":"Retrieve properties of a Notion database.","display_name":"List Database Properties [Notion] "},"selected":true,"width":384,"height":383,"dragging":false,"positionAbsolute":{"x":-3485.297183150799,"y":-362.8525892356713}},{"id":"CustomComponent-wHlSz","type":"genericNode","position":{"x":-2668.7714642455403,"y":-657.2376228212606},"data":{"type":"CustomComponent","node":{"template":{"code":{"type":"code","required":true,"placeholder":"","list":false,"show":true,"multiline":true,"value":"import json\r\nimport requests\r\nfrom typing import Dict, Any\r\n\r\nfrom langflow import CustomComponent\r\nfrom langflow.schema import Record\r\n\r\n\r\nclass NotionPageUpdate(CustomComponent):\r\n display_name = \"Update Page Property [Notion]\"\r\n description = \"Update the properties of a Notion page.\"\r\n documentation: str = \"https://docs.langflow.org/integrations/notion/page-update\"\r\n icon = \"NotionDirectoryLoader\"\r\n\r\n def build_config(self):\r\n return {\r\n \"page_id\": {\r\n \"display_name\": \"Page ID\",\r\n \"field_type\": \"str\",\r\n \"info\": \"The ID of the Notion page to update.\",\r\n },\r\n \"properties\": {\r\n \"display_name\": \"Properties\",\r\n \"field_type\": \"str\",\r\n \"info\": \"The properties to update on the page (as a JSON string).\",\r\n \"multiline\": True,\r\n },\r\n \"notion_secret\": {\r\n \"display_name\": \"Notion Secret\",\r\n \"field_type\": \"str\",\r\n \"info\": \"The Notion integration token.\",\r\n \"password\": True,\r\n },\r\n }\r\n\r\n def build(\r\n self,\r\n page_id: str,\r\n properties: str,\r\n notion_secret: str,\r\n ) -> Record:\r\n url = f\"https://api.notion.com/v1/pages/{page_id}\"\r\n headers = {\r\n \"Authorization\": f\"Bearer {notion_secret}\",\r\n \"Content-Type\": \"application/json\",\r\n \"Notion-Version\": \"2022-06-28\", # Use the latest supported version\r\n }\r\n\r\n try:\r\n parsed_properties = json.loads(properties)\r\n except json.JSONDecodeError as e:\r\n raise ValueError(\"Invalid JSON format for properties\") from e\r\n\r\n data = {\r\n \"properties\": parsed_properties\r\n }\r\n\r\n response = requests.patch(url, headers=headers, json=data)\r\n response.raise_for_status()\r\n\r\n updated_page = response.json()\r\n\r\n output = \"Updated page properties:\\n\"\r\n for prop_name, prop_value in updated_page[\"properties\"].items():\r\n output += f\"{prop_name}: {prop_value}\\n\"\r\n\r\n self.status = output\r\n return Record(data=updated_page)","fileTypes":[],"file_path":"","password":false,"name":"code","advanced":true,"dynamic":true,"info":"","load_from_db":false,"title_case":false},"notion_secret":{"type":"str","required":true,"placeholder":"","list":false,"show":true,"multiline":false,"fileTypes":[],"file_path":"","password":true,"name":"notion_secret","display_name":"Notion Secret","advanced":false,"dynamic":false,"info":"The Notion integration token.","load_from_db":true,"title_case":false,"input_types":["Text"],"value":""},"page_id":{"type":"str","required":true,"placeholder":"","list":false,"show":true,"multiline":false,"fileTypes":[],"file_path":"","password":false,"name":"page_id","display_name":"Page ID","advanced":false,"dynamic":false,"info":"The ID of the Notion page to update.","load_from_db":false,"title_case":false,"input_types":["Text"]},"properties":{"type":"str","required":true,"placeholder":"","list":false,"show":true,"multiline":true,"fileTypes":[],"file_path":"","password":false,"name":"properties","display_name":"Properties","advanced":false,"dynamic":false,"info":"The properties to update on the page (as a JSON string).","load_from_db":false,"title_case":false,"input_types":["Text"],"value":"{ \"title\": [ { \"text\": { \"content\": \"Test Page\" } } ] }"},"_type":"CustomComponent"},"description":"Update the properties of a Notion page.","icon":"NotionDirectoryLoader","base_classes":["Record"],"display_name":"Update Page Property [Notion]","documentation":"https://docs.langflow.org/integrations/notion/page-update","custom_fields":{"page_id":null,"properties":null,"notion_secret":null},"output_types":["Record"],"field_formatters":{},"frozen":false,"field_order":[],"beta":false},"id":"CustomComponent-wHlSz","description":"Update the properties of a Notion page.","display_name":"Update Page Property [Notion]"},"selected":false,"width":384,"height":477,"dragging":false,"positionAbsolute":{"x":-2668.7714642455403,"y":-657.2376228212606}},{"id":"CustomComponent-oelYw","type":"genericNode","position":{"x":-2253.1007124701327,"y":-448.47240118604134},"data":{"type":"CustomComponent","node":{"template":{"code":{"type":"code","required":true,"placeholder":"","list":false,"show":true,"multiline":true,"value":"import requests\r\nfrom typing import Dict, Any\r\n\r\nfrom langflow import CustomComponent\r\nfrom langflow.schema import Record\r\n\r\n\r\nclass NotionPageContent(CustomComponent):\r\n display_name = \"Page Content Viewer [Notion]\"\r\n description = \"Retrieve the content of a Notion page as plain text.\"\r\n documentation: str = \"https://docs.langflow.org/integrations/notion/page-content-viewer\"\r\n icon = \"NotionDirectoryLoader\"\r\n\r\n def build_config(self):\r\n return {\r\n \"page_id\": {\r\n \"display_name\": \"Page ID\",\r\n \"field_type\": \"str\",\r\n \"info\": \"The ID of the Notion page to retrieve.\",\r\n },\r\n \"notion_secret\": {\r\n \"display_name\": \"Notion Secret\",\r\n \"field_type\": \"str\",\r\n \"info\": \"The Notion integration token.\",\r\n \"password\": True,\r\n },\r\n }\r\n\r\n def build(\r\n self,\r\n page_id: str,\r\n notion_secret: str,\r\n ) -> Record:\r\n blocks_url = f\"https://api.notion.com/v1/blocks/{page_id}/children?page_size=100\"\r\n headers = {\r\n \"Authorization\": f\"Bearer {notion_secret}\",\r\n \"Notion-Version\": \"2022-06-28\", # Use the latest supported version\r\n }\r\n\r\n # Retrieve the child blocks\r\n blocks_response = requests.get(blocks_url, headers=headers)\r\n blocks_response.raise_for_status()\r\n blocks_data = blocks_response.json()\r\n\r\n # Parse the blocks and extract the content as plain text\r\n content = self.parse_blocks(blocks_data[\"results\"])\r\n\r\n self.status = content\r\n return Record(data={\"content\": content}, text=content)\r\n\r\n def parse_blocks(self, blocks: list) -> str:\r\n content = \"\"\r\n for block in blocks:\r\n block_type = block[\"type\"]\r\n if block_type in [\"paragraph\", \"heading_1\", \"heading_2\", \"heading_3\", \"quote\"]:\r\n content += self.parse_rich_text(block[block_type][\"rich_text\"]) + \"\\n\\n\"\r\n elif block_type in [\"bulleted_list_item\", \"numbered_list_item\"]:\r\n content += self.parse_rich_text(block[block_type][\"rich_text\"]) + \"\\n\"\r\n elif block_type == \"to_do\":\r\n content += self.parse_rich_text(block[\"to_do\"][\"rich_text\"]) + \"\\n\"\r\n elif block_type == \"code\":\r\n content += self.parse_rich_text(block[\"code\"][\"rich_text\"]) + \"\\n\\n\"\r\n elif block_type == \"image\":\r\n content += f\"[Image: {block['image']['external']['url']}]\\n\\n\"\r\n elif block_type == \"divider\":\r\n content += \"---\\n\\n\"\r\n return content.strip()\r\n\r\n def parse_rich_text(self, rich_text: list) -> str:\r\n text = \"\"\r\n for segment in rich_text:\r\n text += segment[\"plain_text\"]\r\n return text","fileTypes":[],"file_path":"","password":false,"name":"code","advanced":true,"dynamic":true,"info":"","load_from_db":false,"title_case":false},"notion_secret":{"type":"str","required":true,"placeholder":"","list":false,"show":true,"multiline":false,"fileTypes":[],"file_path":"","password":true,"name":"notion_secret","display_name":"Notion Secret","advanced":false,"dynamic":false,"info":"The Notion integration token.","load_from_db":true,"title_case":false,"input_types":["Text"],"value":""},"page_id":{"type":"str","required":true,"placeholder":"","list":false,"show":true,"multiline":false,"fileTypes":[],"file_path":"","password":false,"name":"page_id","display_name":"Page ID","advanced":false,"dynamic":false,"info":"The ID of the Notion page to retrieve.","load_from_db":false,"title_case":false,"input_types":["Text"]},"_type":"CustomComponent"},"description":"Retrieve the content of a Notion page as plain text.","icon":"NotionDirectoryLoader","base_classes":["Record"],"display_name":"Page Content Viewer [Notion] ","documentation":"https://docs.langflow.org/integrations/notion/page-content-viewer","custom_fields":{"page_id":null,"notion_secret":null},"output_types":["Record"],"field_formatters":{},"frozen":false,"field_order":[],"beta":false},"id":"CustomComponent-oelYw","description":"Retrieve the content of a Notion page as plain text.","display_name":"Page Content Viewer [Notion] "},"selected":false,"width":384,"height":383,"positionAbsolute":{"x":-2253.1007124701327,"y":-448.47240118604134},"dragging":false},{"id":"CustomComponent-Pn52w","type":"genericNode","position":{"x":-3070.9222948695096,"y":-472.4537855763852},"data":{"type":"CustomComponent","node":{"template":{"code":{"type":"code","required":true,"placeholder":"","list":false,"show":true,"multiline":true,"value":"import requests\r\nimport json\r\nfrom typing import Dict, Any, List\r\nfrom langflow.custom import CustomComponent\r\nfrom langflow.schema import Record\r\n\r\nclass NotionListPages(CustomComponent):\r\n display_name = \"List Pages [Notion]\"\r\n description = (\r\n \"Query a Notion database with filtering and sorting. \"\r\n \"The input should be a JSON string containing the 'filter' and 'sorts' objects. \"\r\n \"Example input:\\n\"\r\n '{\"filter\": {\"property\": \"Status\", \"select\": {\"equals\": \"Done\"}}, \"sorts\": [{\"timestamp\": \"created_time\", \"direction\": \"descending\"}]}'\r\n )\r\n documentation: str = \"https://docs.langflow.org/integrations/notion/list-pages\"\r\n icon = \"NotionDirectoryLoader\"\r\n\r\n field_order = [\r\n \"notion_secret\",\r\n \"database_id\",\r\n \"query_payload\",\r\n ]\r\n\r\n def build_config(self):\r\n return {\r\n \"notion_secret\": {\r\n \"display_name\": \"Notion Secret\",\r\n \"field_type\": \"str\",\r\n \"info\": \"The Notion integration token.\",\r\n \"password\": True,\r\n },\r\n \"database_id\": {\r\n \"display_name\": \"Database ID\",\r\n \"field_type\": \"str\",\r\n \"info\": \"The ID of the Notion database to query.\",\r\n },\r\n \"query_payload\": {\r\n \"display_name\": \"Database query\",\r\n \"field_type\": \"str\",\r\n \"info\": \"A JSON string containing the filters that will be used for querying the database. EG: {'filter': {'property': 'Status', 'status': {'equals': 'In progress'}}}\",\r\n },\r\n }\r\n\r\n def build(\r\n self,\r\n notion_secret: str,\r\n database_id: str,\r\n query_payload: str = \"{}\",\r\n ) -> List[Record]:\r\n try:\r\n query_data = json.loads(query_payload)\r\n filter_obj = query_data.get(\"filter\")\r\n sorts = query_data.get(\"sorts\", [])\r\n\r\n url = f\"https://api.notion.com/v1/databases/{database_id}/query\"\r\n headers = {\r\n \"Authorization\": f\"Bearer {notion_secret}\",\r\n \"Content-Type\": \"application/json\",\r\n \"Notion-Version\": \"2022-06-28\",\r\n }\r\n\r\n data = {\r\n \"sorts\": sorts,\r\n }\r\n\r\n if filter_obj:\r\n data[\"filter\"] = filter_obj\r\n\r\n response = requests.post(url, headers=headers, json=data)\r\n response.raise_for_status()\r\n\r\n results = response.json()\r\n records = []\r\n combined_text = f\"Pages found: {len(results['results'])}\\n\\n\"\r\n for page in results['results']:\r\n page_data = {\r\n 'id': page['id'],\r\n 'url': page['url'],\r\n 'created_time': page['created_time'],\r\n 'last_edited_time': page['last_edited_time'],\r\n 'properties': page['properties'],\r\n }\r\n\r\n text = (\r\n f\"id: {page['id']}\\n\"\r\n f\"url: {page['url']}\\n\"\r\n f\"created_time: {page['created_time']}\\n\"\r\n f\"last_edited_time: {page['last_edited_time']}\\n\"\r\n f\"properties: {json.dumps(page['properties'], indent=2)}\\n\\n\"\r\n )\r\n\r\n combined_text += text\r\n records.append(Record(text=text, data=page_data))\r\n \r\n self.status = combined_text.strip()\r\n return records\r\n\r\n except Exception as e:\r\n self.status = f\"An error occurred: {str(e)}\"\r\n return [Record(text=self.status, data=[])]","fileTypes":[],"file_path":"","password":false,"name":"code","advanced":true,"dynamic":true,"info":"","load_from_db":false,"title_case":false},"database_id":{"type":"str","required":true,"placeholder":"","list":false,"show":true,"multiline":false,"fileTypes":[],"file_path":"","password":false,"name":"database_id","display_name":"Database ID","advanced":false,"dynamic":false,"info":"The ID of the Notion database to query.","load_from_db":true,"title_case":false,"input_types":["Text"],"value":"NOTION_NMSTX_DB_ID"},"notion_secret":{"type":"str","required":true,"placeholder":"","list":false,"show":true,"multiline":false,"fileTypes":[],"file_path":"","password":true,"name":"notion_secret","display_name":"Notion Secret","advanced":false,"dynamic":false,"info":"The Notion integration token.","load_from_db":true,"title_case":false,"input_types":["Text"],"value":""},"query_payload":{"type":"str","required":false,"placeholder":"","list":false,"show":true,"multiline":false,"value":{},"fileTypes":[],"file_path":"","password":false,"name":"query_payload","display_name":"Database query","advanced":false,"dynamic":false,"info":"A JSON string containing the filters that will be used for querying the database. EG: {'filter': {'property': 'Status', 'status': {'equals': 'In progress'}}}","load_from_db":false,"title_case":false,"input_types":["Text"]},"_type":"CustomComponent"},"description":"Query a Notion database with filtering and sorting. The input should be a JSON string containing the 'filter' and 'sorts' objects. Example input:\n{\"filter\": {\"property\": \"Status\", \"select\": {\"equals\": \"Done\"}}, \"sorts\": [{\"timestamp\": \"created_time\", \"direction\": \"descending\"}]}","icon":"NotionDirectoryLoader","base_classes":["Record"],"display_name":"List Pages [Notion] ","documentation":"https://docs.langflow.org/integrations/notion/list-pages","custom_fields":{"notion_secret":null,"database_id":null,"query_payload":null},"output_types":["Record"],"field_formatters":{},"frozen":false,"field_order":["notion_secret","database_id","query_payload"],"beta":false},"id":"CustomComponent-Pn52w","description":"Query a Notion database with filtering and sorting. The input should be a JSON string containing the 'filter' and 'sorts' objects. Example input:\n{\"filter\": {\"property\": \"Status\", \"select\": {\"equals\": \"Done\"}}, \"sorts\": [{\"timestamp\": \"created_time\", \"direction\": \"descending\"}]}","display_name":"List Pages [Notion] "},"selected":false,"width":384,"height":517,"positionAbsolute":{"x":-3070.9222948695096,"y":-472.4537855763852},"dragging":false},{"id":"CustomComponent-I8Dec","type":"genericNode","position":{"x":-2256.686402636563,"y":-963.4541117792749},"data":{"type":"CustomComponent","node":{"template":{"block_id":{"type":"str","required":true,"placeholder":"","list":false,"show":true,"multiline":false,"fileTypes":[],"file_path":"","password":false,"name":"block_id","display_name":"Page/Block ID","advanced":false,"dynamic":false,"info":"The ID of the page/block to add the content.","load_from_db":false,"title_case":false,"input_types":["Text"]},"code":{"type":"code","required":true,"placeholder":"","list":false,"show":true,"multiline":true,"value":"import json\r\nfrom typing import List, Dict, Any\r\nfrom markdown import markdown\r\nfrom bs4 import BeautifulSoup\r\nimport requests\r\n\r\nfrom langflow import CustomComponent\r\nfrom langflow.schema import Record\r\n\r\nclass AddContentToPage(CustomComponent):\r\n display_name = \"Add Content to Page [Notion]\"\r\n description = \"Convert markdown text to Notion blocks and append them to a Notion page.\"\r\n documentation: str = \"https://developers.notion.com/reference/patch-block-children\"\r\n icon = \"NotionDirectoryLoader\"\r\n\r\n def build_config(self):\r\n return {\r\n \"markdown_text\": {\r\n \"display_name\": \"Markdown Text\",\r\n \"field_type\": \"str\",\r\n \"info\": \"The markdown text to convert to Notion blocks.\",\r\n \"multiline\": True,\r\n },\r\n \"block_id\": {\r\n \"display_name\": \"Page/Block ID\",\r\n \"field_type\": \"str\",\r\n \"info\": \"The ID of the page/block to add the content.\",\r\n },\r\n \"notion_secret\": {\r\n \"display_name\": \"Notion Secret\",\r\n \"field_type\": \"str\",\r\n \"info\": \"The Notion integration token.\",\r\n \"password\": True,\r\n },\r\n }\r\n\r\n def build(self, markdown_text: str, block_id: str, notion_secret: str) -> Record:\r\n html_text = markdown(markdown_text)\r\n soup = BeautifulSoup(html_text, 'html.parser')\r\n blocks = self.process_node(soup)\r\n\r\n url = f\"https://api.notion.com/v1/blocks/{block_id}/children\"\r\n headers = {\r\n \"Authorization\": f\"Bearer {notion_secret}\",\r\n \"Content-Type\": \"application/json\",\r\n \"Notion-Version\": \"2022-06-28\",\r\n }\r\n\r\n data = {\r\n \"children\": blocks,\r\n }\r\n\r\n response = requests.patch(url, headers=headers, json=data)\r\n self.status = str(response.json())\r\n response.raise_for_status()\r\n\r\n result = response.json()\r\n self.status = f\"Appended {len(blocks)} blocks to page with ID: {block_id}\"\r\n return Record(data=result, text=json.dumps(result))\r\n\r\n def process_node(self, node):\r\n blocks = []\r\n if isinstance(node, str):\r\n text = node.strip()\r\n if text:\r\n if text.startswith('#'):\r\n heading_level = text.count('#', 0, 6)\r\n heading_text = text[heading_level:].strip()\r\n if heading_level == 1:\r\n blocks.append(self.create_block('heading_1', heading_text))\r\n elif heading_level == 2:\r\n blocks.append(self.create_block('heading_2', heading_text))\r\n elif heading_level == 3:\r\n blocks.append(self.create_block('heading_3', heading_text))\r\n else:\r\n blocks.append(self.create_block('paragraph', text))\r\n elif node.name == 'h1':\r\n blocks.append(self.create_block('heading_1', node.get_text(strip=True)))\r\n elif node.name == 'h2':\r\n blocks.append(self.create_block('heading_2', node.get_text(strip=True)))\r\n elif node.name == 'h3':\r\n blocks.append(self.create_block('heading_3', node.get_text(strip=True)))\r\n elif node.name == 'p':\r\n code_node = node.find('code')\r\n if code_node:\r\n code_text = code_node.get_text()\r\n language, code = self.extract_language_and_code(code_text)\r\n blocks.append(self.create_block('code', code, language=language))\r\n elif self.is_table(str(node)):\r\n blocks.extend(self.process_table(node))\r\n else:\r\n blocks.append(self.create_block('paragraph', node.get_text(strip=True)))\r\n elif node.name == 'ul':\r\n blocks.extend(self.process_list(node, 'bulleted_list_item'))\r\n elif node.name == 'ol':\r\n blocks.extend(self.process_list(node, 'numbered_list_item'))\r\n elif node.name == 'blockquote':\r\n blocks.append(self.create_block('quote', node.get_text(strip=True)))\r\n elif node.name == 'hr':\r\n blocks.append(self.create_block('divider', ''))\r\n elif node.name == 'img':\r\n blocks.append(self.create_block('image', '', image_url=node.get('src')))\r\n elif node.name == 'a':\r\n blocks.append(self.create_block('bookmark', node.get_text(strip=True), link_url=node.get('href')))\r\n elif node.name == 'table':\r\n blocks.extend(self.process_table(node))\r\n\r\n for child in node.children:\r\n if isinstance(child, str):\r\n continue\r\n blocks.extend(self.process_node(child))\r\n\r\n return blocks\r\n\r\n def extract_language_and_code(self, code_text):\r\n lines = code_text.split('\\n')\r\n language = lines[0].strip()\r\n code = '\\n'.join(lines[1:]).strip()\r\n return language, code\r\n\r\n def is_code_block(self, text):\r\n return text.startswith('```')\r\n\r\n def extract_code_block(self, text):\r\n lines = text.split('\\n')\r\n language = lines[0].strip('`').strip()\r\n code = '\\n'.join(lines[1:]).strip('`').strip()\r\n return language, code\r\n \r\n def is_table(self, text):\r\n rows = text.split('\\n')\r\n if len(rows) < 2:\r\n return False\r\n\r\n has_separator = False\r\n for i, row in enumerate(rows):\r\n if '|' in row:\r\n cells = [cell.strip() for cell in row.split('|')]\r\n cells = [cell for cell in cells if cell] # Remove empty cells\r\n if i == 1 and all(set(cell) <= set('-|') for cell in cells):\r\n has_separator = True\r\n elif not cells:\r\n return False\r\n\r\n return has_separator and len(rows) >= 3\r\n\r\n def process_list(self, node, list_type):\r\n blocks = []\r\n for item in node.find_all('li'):\r\n item_text = item.get_text(strip=True)\r\n checked = item_text.startswith('[x]')\r\n is_checklist = item_text.startswith('[ ]') or checked\r\n\r\n if is_checklist:\r\n item_text = item_text.replace('[x]', '').replace('[ ]', '').strip()\r\n blocks.append(self.create_block('to_do', item_text, checked=checked))\r\n else:\r\n blocks.append(self.create_block(list_type, item_text))\r\n return blocks\r\n\r\n def process_table(self, node):\r\n blocks = []\r\n header_row = node.find('thead').find('tr') if node.find('thead') else None\r\n body_rows = node.find('tbody').find_all('tr') if node.find('tbody') else []\r\n\r\n if header_row or body_rows:\r\n table_width = max(len(header_row.find_all(['th', 'td'])) if header_row else 0,\r\n max(len(row.find_all(['th', 'td'])) for row in body_rows))\r\n\r\n table_block = self.create_block('table', '', table_width=table_width, has_column_header=bool(header_row))\r\n blocks.append(table_block)\r\n\r\n if header_row:\r\n header_cells = [cell.get_text(strip=True) for cell in header_row.find_all(['th', 'td'])]\r\n header_row_block = self.create_block('table_row', header_cells)\r\n blocks.append(header_row_block)\r\n\r\n for row in body_rows:\r\n cells = [cell.get_text(strip=True) for cell in row.find_all(['th', 'td'])]\r\n row_block = self.create_block('table_row', cells)\r\n blocks.append(row_block)\r\n\r\n return blocks\r\n \r\n def create_block(self, block_type: str, content: str, **kwargs) -> Dict[str, Any]:\r\n block = {\r\n \"object\": \"block\",\r\n \"type\": block_type,\r\n block_type: {},\r\n }\r\n\r\n if block_type in [\"paragraph\", \"heading_1\", \"heading_2\", \"heading_3\", \"bulleted_list_item\", \"numbered_list_item\", \"quote\"]:\r\n block[block_type][\"rich_text\"] = [\r\n {\r\n \"type\": \"text\",\r\n \"text\": {\r\n \"content\": content,\r\n },\r\n }\r\n ]\r\n elif block_type == 'to_do':\r\n block[block_type][\"rich_text\"] = [\r\n {\r\n \"type\": \"text\",\r\n \"text\": {\r\n \"content\": content,\r\n },\r\n }\r\n ]\r\n block[block_type]['checked'] = kwargs.get('checked', False)\r\n elif block_type == 'code':\r\n block[block_type]['rich_text'] = [\r\n {\r\n \"type\": \"text\",\r\n \"text\": {\r\n \"content\": content,\r\n },\r\n }\r\n ]\r\n block[block_type]['language'] = kwargs.get('language', 'plain text')\r\n elif block_type == 'image':\r\n block[block_type] = {\r\n \"type\": \"external\",\r\n \"external\": {\r\n \"url\": kwargs.get('image_url', '')\r\n }\r\n }\r\n elif block_type == 'divider':\r\n pass\r\n elif block_type == 'bookmark':\r\n block[block_type]['url'] = kwargs.get('link_url', '')\r\n elif block_type == 'table':\r\n block[block_type]['table_width'] = kwargs.get('table_width', 0)\r\n block[block_type]['has_column_header'] = kwargs.get('has_column_header', False)\r\n block[block_type]['has_row_header'] = kwargs.get('has_row_header', False)\r\n elif block_type == 'table_row':\r\n block[block_type]['cells'] = [[{'type': 'text', 'text': {'content': cell}} for cell in content]]\r\n\r\n return block","fileTypes":[],"file_path":"","password":false,"name":"code","advanced":true,"dynamic":true,"info":"","load_from_db":false,"title_case":false},"markdown_text":{"type":"str","required":true,"placeholder":"","list":false,"show":true,"multiline":true,"fileTypes":[],"file_path":"","password":false,"name":"markdown_text","display_name":"Markdown Text","advanced":false,"dynamic":false,"info":"The markdown text to convert to Notion blocks.","load_from_db":false,"title_case":false,"input_types":["Text"],"value":"# Heading 1\n\n## Heading 2\n\n### Heading 3\n\nThis is a regular paragraph.\n\nHere's another paragraph with an image:\n![Image](https://example.com/image.jpg)\n\n## Checklist\n- [x] Completed task\n- [ ] Incomplete task\n- [x] Another completed task\n\n## Numbered List\n1. First item\n2. Second item\n3. Third item\n\n## Bulleted List\n- Item 1\n- Item 2\n- Item 3\n\n## Code Block\n```python\ndef hello_world():\n print(\"Hello, World!\")\n```\n\n## Quote\n> This is a blockquote.\n> It can span multiple lines.\n\n## Horizontal Rule\n---\n\n\n## Link\n[Notion API Documentation](https://developers.notion.com)\n\n"},"notion_secret":{"type":"str","required":true,"placeholder":"","list":false,"show":true,"multiline":false,"fileTypes":[],"file_path":"","password":true,"name":"notion_secret","display_name":"Notion Secret","advanced":false,"dynamic":false,"info":"The Notion integration token.","load_from_db":true,"title_case":false,"input_types":["Text"],"value":""},"_type":"CustomComponent"},"description":"Convert markdown text to Notion blocks and append them to a Notion page.","icon":"NotionDirectoryLoader","base_classes":["Record"],"display_name":"Add Content to Page [Notion] ","documentation":"https://developers.notion.com/reference/patch-block-children","custom_fields":{"markdown_text":null,"block_id":null,"notion_secret":null},"output_types":["Record"],"field_formatters":{},"frozen":false,"field_order":[],"beta":false,"official":false},"id":"CustomComponent-I8Dec"},"selected":false,"width":384,"height":497,"positionAbsolute":{"x":-2256.686402636563,"y":-963.4541117792749},"dragging":false},{"id":"CustomComponent-ZcsA9","type":"genericNode","position":{"x":-3488.029350341937,"y":-965.3756250644985},"data":{"type":"CustomComponent","node":{"template":{"code":{"type":"code","required":true,"placeholder":"","list":false,"show":true,"multiline":true,"value":"import requests\r\nfrom typing import Dict, Any, List\r\nfrom langflow.custom import CustomComponent\r\nfrom langflow.schema import Record\r\n\r\nclass NotionSearch(CustomComponent):\r\n display_name = \"Search Notion\"\r\n description = (\r\n \"Searches all pages and databases that have been shared with an integration.\"\r\n )\r\n documentation: str = \"https://docs.langflow.org/integrations/notion/search\"\r\n icon = \"NotionDirectoryLoader\"\r\n\r\n field_order = [\r\n \"notion_secret\",\r\n \"query\",\r\n \"filter_value\",\r\n \"sort_direction\",\r\n ]\r\n\r\n def build_config(self):\r\n return {\r\n \"notion_secret\": {\r\n \"display_name\": \"Notion Secret\",\r\n \"field_type\": \"str\",\r\n \"info\": \"The Notion integration token.\",\r\n \"password\": True,\r\n },\r\n \"query\": {\r\n \"display_name\": \"Search Query\",\r\n \"field_type\": \"str\",\r\n \"info\": \"The text that the API compares page and database titles against.\",\r\n },\r\n \"filter_value\": {\r\n \"display_name\": \"Filter Type\",\r\n \"field_type\": \"str\",\r\n \"info\": \"Limits the results to either only pages or only databases.\",\r\n \"options\": [\"page\", \"database\"],\r\n \"default_value\": \"page\",\r\n },\r\n \"sort_direction\": {\r\n \"display_name\": \"Sort Direction\",\r\n \"field_type\": \"str\",\r\n \"info\": \"The direction to sort the results.\",\r\n \"options\": [\"ascending\", \"descending\"],\r\n \"default_value\": \"descending\",\r\n },\r\n }\r\n\r\n def build(\r\n self,\r\n notion_secret: str,\r\n query: str = \"\",\r\n filter_value: str = \"page\",\r\n sort_direction: str = \"descending\",\r\n ) -> List[Record]:\r\n try:\r\n url = \"https://api.notion.com/v1/search\"\r\n headers = {\r\n \"Authorization\": f\"Bearer {notion_secret}\",\r\n \"Content-Type\": \"application/json\",\r\n \"Notion-Version\": \"2022-06-28\",\r\n }\r\n\r\n data = {\r\n \"query\": query,\r\n \"filter\": {\r\n \"value\": filter_value,\r\n \"property\": \"object\"\r\n },\r\n \"sort\":{\r\n \"direction\": sort_direction,\r\n \"timestamp\": \"last_edited_time\"\r\n }\r\n }\r\n\r\n response = requests.post(url, headers=headers, json=data)\r\n response.raise_for_status()\r\n\r\n results = response.json()\r\n records = []\r\n combined_text = f\"Results found: {len(results['results'])}\\n\\n\"\r\n for result in results['results']:\r\n result_data = {\r\n 'id': result['id'],\r\n 'type': result['object'],\r\n 'last_edited_time': result['last_edited_time'],\r\n }\r\n \r\n if result['object'] == 'page':\r\n result_data['title_or_url'] = result['url']\r\n text = f\"id: {result['id']}\\ntitle_or_url: {result['url']}\\n\"\r\n elif result['object'] == 'database':\r\n if 'title' in result and isinstance(result['title'], list) and len(result['title']) > 0:\r\n result_data['title_or_url'] = result['title'][0]['plain_text']\r\n text = f\"id: {result['id']}\\ntitle_or_url: {result['title'][0]['plain_text']}\\n\"\r\n else:\r\n result_data['title_or_url'] = \"N/A\"\r\n text = f\"id: {result['id']}\\ntitle_or_url: N/A\\n\"\r\n\r\n text += f\"type: {result['object']}\\nlast_edited_time: {result['last_edited_time']}\\n\\n\"\r\n combined_text += text\r\n records.append(Record(text=text, data=result_data))\r\n \r\n self.status = combined_text\r\n return records\r\n\r\n except Exception as e:\r\n self.status = f\"An error occurred: {str(e)}\"\r\n return [Record(text=self.status, data=[])]","fileTypes":[],"file_path":"","password":false,"name":"code","advanced":true,"dynamic":true,"info":"","load_from_db":false,"title_case":false},"filter_value":{"type":"str","required":false,"placeholder":"","list":true,"show":true,"multiline":false,"value":"database","fileTypes":[],"file_path":"","password":false,"options":["page","database"],"name":"filter_value","display_name":"Filter Type","advanced":false,"dynamic":false,"info":"Limits the results to either only pages or only databases.","load_from_db":false,"title_case":false,"input_types":["Text"]},"notion_secret":{"type":"str","required":true,"placeholder":"","list":false,"show":true,"multiline":false,"fileTypes":[],"file_path":"","password":true,"name":"notion_secret","display_name":"Notion Secret","advanced":false,"dynamic":false,"info":"The Notion integration token.","load_from_db":true,"title_case":false,"input_types":["Text"],"value":""},"query":{"type":"str","required":false,"placeholder":"","list":false,"show":true,"multiline":false,"value":"","fileTypes":[],"file_path":"","password":false,"name":"query","display_name":"Search Query","advanced":false,"dynamic":false,"info":"The text that the API compares page and database titles against.","load_from_db":false,"title_case":false,"input_types":["Text"]},"sort_direction":{"type":"str","required":false,"placeholder":"","list":true,"show":true,"multiline":false,"value":"descending","fileTypes":[],"file_path":"","password":false,"options":["ascending","descending"],"name":"sort_direction","display_name":"Sort Direction","advanced":false,"dynamic":false,"info":"The direction to sort the results.","load_from_db":false,"title_case":false,"input_types":["Text"]},"_type":"CustomComponent"},"description":"Searches all pages and databases that have been shared with an integration.","icon":"NotionDirectoryLoader","base_classes":["Record"],"display_name":"Search [Notion]","documentation":"https://docs.langflow.org/integrations/notion/search","custom_fields":{"notion_secret":null,"query":null,"filter_value":null,"sort_direction":null},"output_types":["Record"],"field_formatters":{},"frozen":false,"field_order":["notion_secret","query","filter_value","sort_direction"],"beta":false},"id":"CustomComponent-ZcsA9","description":"Searches all pages and databases that have been shared with an integration.","display_name":"Search [Notion]"},"selected":false,"width":384,"height":591,"positionAbsolute":{"x":-3488.029350341937,"y":-965.3756250644985},"dragging":false}],"edges":[],"viewport":{"x":2623.378922967084,"y":696.8541079344027,"zoom":0.5981384177708997}},"description":"A Bundle containing Notion components for Page and Database manipulation. You can list pages, users databases, update properties, create new pages and add content to Notion Pages.","name":"Notion - Components","last_tested_version":"1.0.0a36","is_component":false} \ No newline at end of file +{ + "id": "7cd51434-9767-450f-8742-27857367f8c2", + "data": { + "nodes": [ + { + "id": "RecordsToText-Q69g5", + "type": "genericNode", + "position": { "x": -2671.5528488127866, "y": -963.4266471378126 }, + "data": { + "type": "RecordsToText", + "node": { + "template": { + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "import requests\r\nfrom typing import List\r\n\r\nfrom langflow import CustomComponent\r\nfrom langflow.schema import Record\r\n\r\n\r\nclass NotionUserList(CustomComponent):\r\n display_name = \"List Users [Notion]\"\r\n description = \"Retrieve users from Notion.\"\r\n documentation: str = \"https://docs.langflow.org/integrations/notion/list-users\"\r\n icon = \"NotionDirectoryLoader\"\r\n \r\n def build_config(self):\r\n return {\r\n \"notion_secret\": {\r\n \"display_name\": \"Notion Secret\",\r\n \"field_type\": \"str\",\r\n \"info\": \"The Notion integration token.\",\r\n \"password\": True,\r\n },\r\n }\r\n\r\n def build(\r\n self,\r\n notion_secret: str,\r\n ) -> List[Record]:\r\n url = \"https://api.notion.com/v1/users\"\r\n headers = {\r\n \"Authorization\": f\"Bearer {notion_secret}\",\r\n \"Notion-Version\": \"2022-06-28\",\r\n }\r\n\r\n response = requests.get(url, headers=headers)\r\n response.raise_for_status()\r\n\r\n data = response.json()\r\n results = data['results']\r\n\r\n records = []\r\n for user in results:\r\n id = user['id']\r\n type = user['type']\r\n name = user.get('name', '')\r\n avatar_url = user.get('avatar_url', '')\r\n\r\n record_data = {\r\n \"id\": id,\r\n \"type\": type,\r\n \"name\": name,\r\n \"avatar_url\": avatar_url,\r\n }\r\n\r\n output = \"User:\\n\"\r\n for key, value in record_data.items():\r\n output += f\"{key.replace('_', ' ').title()}: {value}\\n\"\r\n output += \"________________________\\n\"\r\n\r\n record = Record(text=output, data=record_data)\r\n records.append(record)\r\n\r\n self.status = \"\\n\".join(record.text for record in records)\r\n return records", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "notion_secret": { + "type": "str", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": true, + "name": "notion_secret", + "display_name": "Notion Secret", + "advanced": false, + "dynamic": false, + "info": "The Notion integration token.", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"], + "value": "" + }, + "_type": "CustomComponent" + }, + "description": "Retrieve users from Notion.", + "icon": "NotionDirectoryLoader", + "base_classes": ["Record"], + "display_name": "List Users [Notion] ", + "documentation": "https://docs.langflow.org/integrations/notion/list-users", + "custom_fields": { "notion_secret": null }, + "output_types": ["Record"], + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false + }, + "id": "RecordsToText-Q69g5", + "description": "Retrieve users from Notion.", + "display_name": "List Users [Notion] " + }, + "selected": false, + "width": 384, + "height": 289, + "dragging": false, + "positionAbsolute": { + "x": -2671.5528488127866, + "y": -963.4266471378126 + } + }, + { + "id": "CustomComponent-PU0K5", + "type": "genericNode", + "position": { "x": -3077.2269116193215, "y": -960.9450220159636 }, + "data": { + "type": "CustomComponent", + "node": { + "template": { + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "import json\r\nfrom typing import Optional\r\n\r\nimport requests\r\nfrom langflow.custom import CustomComponent\r\n\r\n\r\nclass NotionPageCreator(CustomComponent):\r\n display_name = \"Create Page [Notion]\"\r\n description = \"A component for creating Notion pages.\"\r\n documentation: str = \"https://docs.langflow.org/integrations/notion/page-create\"\r\n icon = \"NotionDirectoryLoader\"\r\n\r\n def build_config(self):\r\n return {\r\n \"database_id\": {\r\n \"display_name\": \"Database ID\",\r\n \"field_type\": \"str\",\r\n \"info\": \"The ID of the Notion database.\",\r\n },\r\n \"notion_secret\": {\r\n \"display_name\": \"Notion Secret\",\r\n \"field_type\": \"str\",\r\n \"info\": \"The Notion integration token.\",\r\n \"password\": True,\r\n },\r\n \"properties\": {\r\n \"display_name\": \"Properties\",\r\n \"field_type\": \"str\",\r\n \"info\": \"The properties of the new page. Depending on your database setup, this can change. E.G: {'Task name': {'id': 'title', 'type': 'title', 'title': [{'type': 'text', 'text': {'content': 'Send Notion Components to LF', 'link': null}}]}}\",\r\n },\r\n }\r\n\r\n def build(\r\n self,\r\n database_id: str,\r\n notion_secret: str,\r\n properties: str = '{\"Task name\": {\"id\": \"title\", \"type\": \"title\", \"title\": [{\"type\": \"text\", \"text\": {\"content\": \"Send Notion Components to LF\", \"link\": null}}]}}',\r\n ) -> str:\r\n if not database_id or not properties:\r\n raise ValueError(\"Invalid input. Please provide 'database_id' and 'properties'.\")\r\n\r\n headers = {\r\n \"Authorization\": f\"Bearer {notion_secret}\",\r\n \"Content-Type\": \"application/json\",\r\n \"Notion-Version\": \"2022-06-28\",\r\n }\r\n\r\n data = {\r\n \"parent\": {\"database_id\": database_id},\r\n \"properties\": json.loads(properties),\r\n }\r\n\r\n response = requests.post(\"https://api.notion.com/v1/pages\", headers=headers, json=data)\r\n\r\n if response.status_code == 200:\r\n page_id = response.json()[\"id\"]\r\n self.status = f\"Successfully created Notion page with ID: {page_id}\\n {str(response.json())}\"\r\n return response.json()\r\n else:\r\n error_message = f\"Failed to create Notion page. Status code: {response.status_code}, Error: {response.text}\"\r\n self.status = error_message\r\n raise Exception(error_message)", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "database_id": { + "type": "str", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "database_id", + "display_name": "Database ID", + "advanced": false, + "dynamic": false, + "info": "The ID of the Notion database.", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "notion_secret": { + "type": "str", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": true, + "name": "notion_secret", + "display_name": "Notion Secret", + "advanced": false, + "dynamic": false, + "info": "The Notion integration token.", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"], + "value": "" + }, + "properties": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": "{\"Task name\": {\"id\": \"title\", \"type\": \"title\", \"title\": [{\"type\": \"text\", \"text\": {\"content\": \"Send Notion Components to LF\", \"link\": null}}]}}", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "properties", + "display_name": "Properties", + "advanced": false, + "dynamic": false, + "info": "The properties of the new page. Depending on your database setup, this can change. E.G: {'Task name': {'id': 'title', 'type': 'title', 'title': [{'type': 'text', 'text': {'content': 'Send Notion Components to LF', 'link': null}}]}}", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "_type": "CustomComponent" + }, + "description": "A component for creating Notion pages.", + "icon": "NotionDirectoryLoader", + "base_classes": ["object", "str", "Text"], + "display_name": "Create Page [Notion] ", + "documentation": "https://docs.langflow.org/integrations/notion/page-create", + "custom_fields": { + "database_id": null, + "notion_secret": null, + "properties": null + }, + "output_types": ["Text"], + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false + }, + "id": "CustomComponent-PU0K5", + "description": "A component for creating Notion pages.", + "display_name": "Create Page [Notion] " + }, + "selected": false, + "width": 384, + "height": 477, + "positionAbsolute": { + "x": -3077.2269116193215, + "y": -960.9450220159636 + }, + "dragging": false + }, + { + "id": "CustomComponent-YODla", + "type": "genericNode", + "position": { "x": -3485.297183150799, "y": -362.8525892356713 }, + "data": { + "type": "CustomComponent", + "node": { + "template": { + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "import requests\r\nfrom typing import Dict\r\n\r\nfrom langflow import CustomComponent\r\nfrom langflow.schema import Record\r\n\r\n\r\nclass NotionDatabaseProperties(CustomComponent):\r\n display_name = \"List Database Properties [Notion]\"\r\n description = \"Retrieve properties of a Notion database.\"\r\n documentation: str = \"https://docs.langflow.org/integrations/notion/list-database-properties\"\r\n icon = \"NotionDirectoryLoader\"\r\n \r\n def build_config(self):\r\n return {\r\n \"database_id\": {\r\n \"display_name\": \"Database ID\",\r\n \"field_type\": \"str\",\r\n \"info\": \"The ID of the Notion database.\",\r\n },\r\n \"notion_secret\": {\r\n \"display_name\": \"Notion Secret\",\r\n \"field_type\": \"str\",\r\n \"info\": \"The Notion integration token.\",\r\n \"password\": True,\r\n },\r\n }\r\n\r\n def build(\r\n self,\r\n database_id: str,\r\n notion_secret: str,\r\n ) -> Record:\r\n url = f\"https://api.notion.com/v1/databases/{database_id}\"\r\n headers = {\r\n \"Authorization\": f\"Bearer {notion_secret}\",\r\n \"Notion-Version\": \"2022-06-28\", # Use the latest supported version\r\n }\r\n\r\n response = requests.get(url, headers=headers)\r\n response.raise_for_status()\r\n\r\n data = response.json()\r\n properties = data.get(\"properties\", {})\r\n\r\n record = Record(text=str(response.json()), data=properties)\r\n self.status = f\"Retrieved {len(properties)} properties from the Notion database.\\n {record.text}\"\r\n return record", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "database_id": { + "type": "str", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "database_id", + "display_name": "Database ID", + "advanced": false, + "dynamic": false, + "info": "The ID of the Notion database.", + "load_from_db": true, + "title_case": false, + "input_types": ["Text"], + "value": "NOTION_NMSTX_DB_ID" + }, + "notion_secret": { + "type": "str", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": true, + "name": "notion_secret", + "display_name": "Notion Secret", + "advanced": false, + "dynamic": false, + "info": "The Notion integration token.", + "load_from_db": true, + "title_case": false, + "input_types": ["Text"], + "value": "" + }, + "_type": "CustomComponent" + }, + "description": "Retrieve properties of a Notion database.", + "icon": "NotionDirectoryLoader", + "base_classes": ["Record"], + "display_name": "List Database Properties [Notion] ", + "documentation": "https://docs.langflow.org/integrations/notion/list-database-properties", + "custom_fields": { "database_id": null, "notion_secret": null }, + "output_types": ["Record"], + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false + }, + "id": "CustomComponent-YODla", + "description": "Retrieve properties of a Notion database.", + "display_name": "List Database Properties [Notion] " + }, + "selected": true, + "width": 384, + "height": 383, + "dragging": false, + "positionAbsolute": { "x": -3485.297183150799, "y": -362.8525892356713 } + }, + { + "id": "CustomComponent-wHlSz", + "type": "genericNode", + "position": { "x": -2668.7714642455403, "y": -657.2376228212606 }, + "data": { + "type": "CustomComponent", + "node": { + "template": { + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "import json\r\nimport requests\r\nfrom typing import Dict, Any\r\n\r\nfrom langflow import CustomComponent\r\nfrom langflow.schema import Record\r\n\r\n\r\nclass NotionPageUpdate(CustomComponent):\r\n display_name = \"Update Page Property [Notion]\"\r\n description = \"Update the properties of a Notion page.\"\r\n documentation: str = \"https://docs.langflow.org/integrations/notion/page-update\"\r\n icon = \"NotionDirectoryLoader\"\r\n\r\n def build_config(self):\r\n return {\r\n \"page_id\": {\r\n \"display_name\": \"Page ID\",\r\n \"field_type\": \"str\",\r\n \"info\": \"The ID of the Notion page to update.\",\r\n },\r\n \"properties\": {\r\n \"display_name\": \"Properties\",\r\n \"field_type\": \"str\",\r\n \"info\": \"The properties to update on the page (as a JSON string).\",\r\n \"multiline\": True,\r\n },\r\n \"notion_secret\": {\r\n \"display_name\": \"Notion Secret\",\r\n \"field_type\": \"str\",\r\n \"info\": \"The Notion integration token.\",\r\n \"password\": True,\r\n },\r\n }\r\n\r\n def build(\r\n self,\r\n page_id: str,\r\n properties: str,\r\n notion_secret: str,\r\n ) -> Record:\r\n url = f\"https://api.notion.com/v1/pages/{page_id}\"\r\n headers = {\r\n \"Authorization\": f\"Bearer {notion_secret}\",\r\n \"Content-Type\": \"application/json\",\r\n \"Notion-Version\": \"2022-06-28\", # Use the latest supported version\r\n }\r\n\r\n try:\r\n parsed_properties = json.loads(properties)\r\n except json.JSONDecodeError as e:\r\n raise ValueError(\"Invalid JSON format for properties\") from e\r\n\r\n data = {\r\n \"properties\": parsed_properties\r\n }\r\n\r\n response = requests.patch(url, headers=headers, json=data)\r\n response.raise_for_status()\r\n\r\n updated_page = response.json()\r\n\r\n output = \"Updated page properties:\\n\"\r\n for prop_name, prop_value in updated_page[\"properties\"].items():\r\n output += f\"{prop_name}: {prop_value}\\n\"\r\n\r\n self.status = output\r\n return Record(data=updated_page)", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "notion_secret": { + "type": "str", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": true, + "name": "notion_secret", + "display_name": "Notion Secret", + "advanced": false, + "dynamic": false, + "info": "The Notion integration token.", + "load_from_db": true, + "title_case": false, + "input_types": ["Text"], + "value": "" + }, + "page_id": { + "type": "str", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "page_id", + "display_name": "Page ID", + "advanced": false, + "dynamic": false, + "info": "The ID of the Notion page to update.", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "properties": { + "type": "str", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "properties", + "display_name": "Properties", + "advanced": false, + "dynamic": false, + "info": "The properties to update on the page (as a JSON string).", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"], + "value": "{ \"title\": [ { \"text\": { \"content\": \"Test Page\" } } ] }" + }, + "_type": "CustomComponent" + }, + "description": "Update the properties of a Notion page.", + "icon": "NotionDirectoryLoader", + "base_classes": ["Record"], + "display_name": "Update Page Property [Notion]", + "documentation": "https://docs.langflow.org/integrations/notion/page-update", + "custom_fields": { + "page_id": null, + "properties": null, + "notion_secret": null + }, + "output_types": ["Record"], + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false + }, + "id": "CustomComponent-wHlSz", + "description": "Update the properties of a Notion page.", + "display_name": "Update Page Property [Notion]" + }, + "selected": false, + "width": 384, + "height": 477, + "dragging": false, + "positionAbsolute": { + "x": -2668.7714642455403, + "y": -657.2376228212606 + } + }, + { + "id": "CustomComponent-oelYw", + "type": "genericNode", + "position": { "x": -2253.1007124701327, "y": -448.47240118604134 }, + "data": { + "type": "CustomComponent", + "node": { + "template": { + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "import requests\r\nfrom typing import Dict, Any\r\n\r\nfrom langflow import CustomComponent\r\nfrom langflow.schema import Record\r\n\r\n\r\nclass NotionPageContent(CustomComponent):\r\n display_name = \"Page Content Viewer [Notion]\"\r\n description = \"Retrieve the content of a Notion page as plain text.\"\r\n documentation: str = \"https://docs.langflow.org/integrations/notion/page-content-viewer\"\r\n icon = \"NotionDirectoryLoader\"\r\n\r\n def build_config(self):\r\n return {\r\n \"page_id\": {\r\n \"display_name\": \"Page ID\",\r\n \"field_type\": \"str\",\r\n \"info\": \"The ID of the Notion page to retrieve.\",\r\n },\r\n \"notion_secret\": {\r\n \"display_name\": \"Notion Secret\",\r\n \"field_type\": \"str\",\r\n \"info\": \"The Notion integration token.\",\r\n \"password\": True,\r\n },\r\n }\r\n\r\n def build(\r\n self,\r\n page_id: str,\r\n notion_secret: str,\r\n ) -> Record:\r\n blocks_url = f\"https://api.notion.com/v1/blocks/{page_id}/children?page_size=100\"\r\n headers = {\r\n \"Authorization\": f\"Bearer {notion_secret}\",\r\n \"Notion-Version\": \"2022-06-28\", # Use the latest supported version\r\n }\r\n\r\n # Retrieve the child blocks\r\n blocks_response = requests.get(blocks_url, headers=headers)\r\n blocks_response.raise_for_status()\r\n blocks_data = blocks_response.json()\r\n\r\n # Parse the blocks and extract the content as plain text\r\n content = self.parse_blocks(blocks_data[\"results\"])\r\n\r\n self.status = content\r\n return Record(data={\"content\": content}, text=content)\r\n\r\n def parse_blocks(self, blocks: list) -> str:\r\n content = \"\"\r\n for block in blocks:\r\n block_type = block[\"type\"]\r\n if block_type in [\"paragraph\", \"heading_1\", \"heading_2\", \"heading_3\", \"quote\"]:\r\n content += self.parse_rich_text(block[block_type][\"rich_text\"]) + \"\\n\\n\"\r\n elif block_type in [\"bulleted_list_item\", \"numbered_list_item\"]:\r\n content += self.parse_rich_text(block[block_type][\"rich_text\"]) + \"\\n\"\r\n elif block_type == \"to_do\":\r\n content += self.parse_rich_text(block[\"to_do\"][\"rich_text\"]) + \"\\n\"\r\n elif block_type == \"code\":\r\n content += self.parse_rich_text(block[\"code\"][\"rich_text\"]) + \"\\n\\n\"\r\n elif block_type == \"image\":\r\n content += f\"[Image: {block['image']['external']['url']}]\\n\\n\"\r\n elif block_type == \"divider\":\r\n content += \"---\\n\\n\"\r\n return content.strip()\r\n\r\n def parse_rich_text(self, rich_text: list) -> str:\r\n text = \"\"\r\n for segment in rich_text:\r\n text += segment[\"plain_text\"]\r\n return text", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "notion_secret": { + "type": "str", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": true, + "name": "notion_secret", + "display_name": "Notion Secret", + "advanced": false, + "dynamic": false, + "info": "The Notion integration token.", + "load_from_db": true, + "title_case": false, + "input_types": ["Text"], + "value": "" + }, + "page_id": { + "type": "str", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "page_id", + "display_name": "Page ID", + "advanced": false, + "dynamic": false, + "info": "The ID of the Notion page to retrieve.", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "_type": "CustomComponent" + }, + "description": "Retrieve the content of a Notion page as plain text.", + "icon": "NotionDirectoryLoader", + "base_classes": ["Record"], + "display_name": "Page Content Viewer [Notion] ", + "documentation": "https://docs.langflow.org/integrations/notion/page-content-viewer", + "custom_fields": { "page_id": null, "notion_secret": null }, + "output_types": ["Record"], + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false + }, + "id": "CustomComponent-oelYw", + "description": "Retrieve the content of a Notion page as plain text.", + "display_name": "Page Content Viewer [Notion] " + }, + "selected": false, + "width": 384, + "height": 383, + "positionAbsolute": { + "x": -2253.1007124701327, + "y": -448.47240118604134 + }, + "dragging": false + }, + { + "id": "CustomComponent-Pn52w", + "type": "genericNode", + "position": { "x": -3070.9222948695096, "y": -472.4537855763852 }, + "data": { + "type": "CustomComponent", + "node": { + "template": { + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "import requests\r\nimport json\r\nfrom typing import Dict, Any, List\r\nfrom langflow.custom import CustomComponent\r\nfrom langflow.schema import Record\r\n\r\nclass NotionListPages(CustomComponent):\r\n display_name = \"List Pages [Notion]\"\r\n description = (\r\n \"Query a Notion database with filtering and sorting. \"\r\n \"The input should be a JSON string containing the 'filter' and 'sorts' objects. \"\r\n \"Example input:\\n\"\r\n '{\"filter\": {\"property\": \"Status\", \"select\": {\"equals\": \"Done\"}}, \"sorts\": [{\"timestamp\": \"created_time\", \"direction\": \"descending\"}]}'\r\n )\r\n documentation: str = \"https://docs.langflow.org/integrations/notion/list-pages\"\r\n icon = \"NotionDirectoryLoader\"\r\n\r\n field_order = [\r\n \"notion_secret\",\r\n \"database_id\",\r\n \"query_payload\",\r\n ]\r\n\r\n def build_config(self):\r\n return {\r\n \"notion_secret\": {\r\n \"display_name\": \"Notion Secret\",\r\n \"field_type\": \"str\",\r\n \"info\": \"The Notion integration token.\",\r\n \"password\": True,\r\n },\r\n \"database_id\": {\r\n \"display_name\": \"Database ID\",\r\n \"field_type\": \"str\",\r\n \"info\": \"The ID of the Notion database to query.\",\r\n },\r\n \"query_payload\": {\r\n \"display_name\": \"Database query\",\r\n \"field_type\": \"str\",\r\n \"info\": \"A JSON string containing the filters that will be used for querying the database. EG: {'filter': {'property': 'Status', 'status': {'equals': 'In progress'}}}\",\r\n },\r\n }\r\n\r\n def build(\r\n self,\r\n notion_secret: str,\r\n database_id: str,\r\n query_payload: str = \"{}\",\r\n ) -> List[Record]:\r\n try:\r\n query_data = json.loads(query_payload)\r\n filter_obj = query_data.get(\"filter\")\r\n sorts = query_data.get(\"sorts\", [])\r\n\r\n url = f\"https://api.notion.com/v1/databases/{database_id}/query\"\r\n headers = {\r\n \"Authorization\": f\"Bearer {notion_secret}\",\r\n \"Content-Type\": \"application/json\",\r\n \"Notion-Version\": \"2022-06-28\",\r\n }\r\n\r\n data = {\r\n \"sorts\": sorts,\r\n }\r\n\r\n if filter_obj:\r\n data[\"filter\"] = filter_obj\r\n\r\n response = requests.post(url, headers=headers, json=data)\r\n response.raise_for_status()\r\n\r\n results = response.json()\r\n records = []\r\n combined_text = f\"Pages found: {len(results['results'])}\\n\\n\"\r\n for page in results['results']:\r\n page_data = {\r\n 'id': page['id'],\r\n 'url': page['url'],\r\n 'created_time': page['created_time'],\r\n 'last_edited_time': page['last_edited_time'],\r\n 'properties': page['properties'],\r\n }\r\n\r\n text = (\r\n f\"id: {page['id']}\\n\"\r\n f\"url: {page['url']}\\n\"\r\n f\"created_time: {page['created_time']}\\n\"\r\n f\"last_edited_time: {page['last_edited_time']}\\n\"\r\n f\"properties: {json.dumps(page['properties'], indent=2)}\\n\\n\"\r\n )\r\n\r\n combined_text += text\r\n records.append(Record(text=text, data=page_data))\r\n \r\n self.status = combined_text.strip()\r\n return records\r\n\r\n except Exception as e:\r\n self.status = f\"An error occurred: {str(e)}\"\r\n return [Record(text=self.status, data=[])]", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "database_id": { + "type": "str", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "database_id", + "display_name": "Database ID", + "advanced": false, + "dynamic": false, + "info": "The ID of the Notion database to query.", + "load_from_db": true, + "title_case": false, + "input_types": ["Text"], + "value": "NOTION_NMSTX_DB_ID" + }, + "notion_secret": { + "type": "str", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": true, + "name": "notion_secret", + "display_name": "Notion Secret", + "advanced": false, + "dynamic": false, + "info": "The Notion integration token.", + "load_from_db": true, + "title_case": false, + "input_types": ["Text"], + "value": "" + }, + "query_payload": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": {}, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "query_payload", + "display_name": "Database query", + "advanced": false, + "dynamic": false, + "info": "A JSON string containing the filters that will be used for querying the database. EG: {'filter': {'property': 'Status', 'status': {'equals': 'In progress'}}}", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "_type": "CustomComponent" + }, + "description": "Query a Notion database with filtering and sorting. The input should be a JSON string containing the 'filter' and 'sorts' objects. Example input:\n{\"filter\": {\"property\": \"Status\", \"select\": {\"equals\": \"Done\"}}, \"sorts\": [{\"timestamp\": \"created_time\", \"direction\": \"descending\"}]}", + "icon": "NotionDirectoryLoader", + "base_classes": ["Record"], + "display_name": "List Pages [Notion] ", + "documentation": "https://docs.langflow.org/integrations/notion/list-pages", + "custom_fields": { + "notion_secret": null, + "database_id": null, + "query_payload": null + }, + "output_types": ["Record"], + "field_formatters": {}, + "frozen": false, + "field_order": ["notion_secret", "database_id", "query_payload"], + "beta": false + }, + "id": "CustomComponent-Pn52w", + "description": "Query a Notion database with filtering and sorting. The input should be a JSON string containing the 'filter' and 'sorts' objects. Example input:\n{\"filter\": {\"property\": \"Status\", \"select\": {\"equals\": \"Done\"}}, \"sorts\": [{\"timestamp\": \"created_time\", \"direction\": \"descending\"}]}", + "display_name": "List Pages [Notion] " + }, + "selected": false, + "width": 384, + "height": 517, + "positionAbsolute": { + "x": -3070.9222948695096, + "y": -472.4537855763852 + }, + "dragging": false + }, + { + "id": "CustomComponent-I8Dec", + "type": "genericNode", + "position": { "x": -2256.686402636563, "y": -963.4541117792749 }, + "data": { + "type": "CustomComponent", + "node": { + "template": { + "block_id": { + "type": "str", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "block_id", + "display_name": "Page/Block ID", + "advanced": false, + "dynamic": false, + "info": "The ID of the page/block to add the content.", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "import json\r\nfrom typing import List, Dict, Any\r\nfrom markdown import markdown\r\nfrom bs4 import BeautifulSoup\r\nimport requests\r\n\r\nfrom langflow import CustomComponent\r\nfrom langflow.schema import Record\r\n\r\nclass AddContentToPage(CustomComponent):\r\n display_name = \"Add Content to Page [Notion]\"\r\n description = \"Convert markdown text to Notion blocks and append them to a Notion page.\"\r\n documentation: str = \"https://developers.notion.com/reference/patch-block-children\"\r\n icon = \"NotionDirectoryLoader\"\r\n\r\n def build_config(self):\r\n return {\r\n \"markdown_text\": {\r\n \"display_name\": \"Markdown Text\",\r\n \"field_type\": \"str\",\r\n \"info\": \"The markdown text to convert to Notion blocks.\",\r\n \"multiline\": True,\r\n },\r\n \"block_id\": {\r\n \"display_name\": \"Page/Block ID\",\r\n \"field_type\": \"str\",\r\n \"info\": \"The ID of the page/block to add the content.\",\r\n },\r\n \"notion_secret\": {\r\n \"display_name\": \"Notion Secret\",\r\n \"field_type\": \"str\",\r\n \"info\": \"The Notion integration token.\",\r\n \"password\": True,\r\n },\r\n }\r\n\r\n def build(self, markdown_text: str, block_id: str, notion_secret: str) -> Record:\r\n html_text = markdown(markdown_text)\r\n soup = BeautifulSoup(html_text, 'html.parser')\r\n blocks = self.process_node(soup)\r\n\r\n url = f\"https://api.notion.com/v1/blocks/{block_id}/children\"\r\n headers = {\r\n \"Authorization\": f\"Bearer {notion_secret}\",\r\n \"Content-Type\": \"application/json\",\r\n \"Notion-Version\": \"2022-06-28\",\r\n }\r\n\r\n data = {\r\n \"children\": blocks,\r\n }\r\n\r\n response = requests.patch(url, headers=headers, json=data)\r\n self.status = str(response.json())\r\n response.raise_for_status()\r\n\r\n result = response.json()\r\n self.status = f\"Appended {len(blocks)} blocks to page with ID: {block_id}\"\r\n return Record(data=result, text=json.dumps(result))\r\n\r\n def process_node(self, node):\r\n blocks = []\r\n if isinstance(node, str):\r\n text = node.strip()\r\n if text:\r\n if text.startswith('#'):\r\n heading_level = text.count('#', 0, 6)\r\n heading_text = text[heading_level:].strip()\r\n if heading_level == 1:\r\n blocks.append(self.create_block('heading_1', heading_text))\r\n elif heading_level == 2:\r\n blocks.append(self.create_block('heading_2', heading_text))\r\n elif heading_level == 3:\r\n blocks.append(self.create_block('heading_3', heading_text))\r\n else:\r\n blocks.append(self.create_block('paragraph', text))\r\n elif node.name == 'h1':\r\n blocks.append(self.create_block('heading_1', node.get_text(strip=True)))\r\n elif node.name == 'h2':\r\n blocks.append(self.create_block('heading_2', node.get_text(strip=True)))\r\n elif node.name == 'h3':\r\n blocks.append(self.create_block('heading_3', node.get_text(strip=True)))\r\n elif node.name == 'p':\r\n code_node = node.find('code')\r\n if code_node:\r\n code_text = code_node.get_text()\r\n language, code = self.extract_language_and_code(code_text)\r\n blocks.append(self.create_block('code', code, language=language))\r\n elif self.is_table(str(node)):\r\n blocks.extend(self.process_table(node))\r\n else:\r\n blocks.append(self.create_block('paragraph', node.get_text(strip=True)))\r\n elif node.name == 'ul':\r\n blocks.extend(self.process_list(node, 'bulleted_list_item'))\r\n elif node.name == 'ol':\r\n blocks.extend(self.process_list(node, 'numbered_list_item'))\r\n elif node.name == 'blockquote':\r\n blocks.append(self.create_block('quote', node.get_text(strip=True)))\r\n elif node.name == 'hr':\r\n blocks.append(self.create_block('divider', ''))\r\n elif node.name == 'img':\r\n blocks.append(self.create_block('image', '', image_url=node.get('src')))\r\n elif node.name == 'a':\r\n blocks.append(self.create_block('bookmark', node.get_text(strip=True), link_url=node.get('href')))\r\n elif node.name == 'table':\r\n blocks.extend(self.process_table(node))\r\n\r\n for child in node.children:\r\n if isinstance(child, str):\r\n continue\r\n blocks.extend(self.process_node(child))\r\n\r\n return blocks\r\n\r\n def extract_language_and_code(self, code_text):\r\n lines = code_text.split('\\n')\r\n language = lines[0].strip()\r\n code = '\\n'.join(lines[1:]).strip()\r\n return language, code\r\n\r\n def is_code_block(self, text):\r\n return text.startswith('```')\r\n\r\n def extract_code_block(self, text):\r\n lines = text.split('\\n')\r\n language = lines[0].strip('`').strip()\r\n code = '\\n'.join(lines[1:]).strip('`').strip()\r\n return language, code\r\n \r\n def is_table(self, text):\r\n rows = text.split('\\n')\r\n if len(rows) < 2:\r\n return False\r\n\r\n has_separator = False\r\n for i, row in enumerate(rows):\r\n if '|' in row:\r\n cells = [cell.strip() for cell in row.split('|')]\r\n cells = [cell for cell in cells if cell] # Remove empty cells\r\n if i == 1 and all(set(cell) <= set('-|') for cell in cells):\r\n has_separator = True\r\n elif not cells:\r\n return False\r\n\r\n return has_separator and len(rows) >= 3\r\n\r\n def process_list(self, node, list_type):\r\n blocks = []\r\n for item in node.find_all('li'):\r\n item_text = item.get_text(strip=True)\r\n checked = item_text.startswith('[x]')\r\n is_checklist = item_text.startswith('[ ]') or checked\r\n\r\n if is_checklist:\r\n item_text = item_text.replace('[x]', '').replace('[ ]', '').strip()\r\n blocks.append(self.create_block('to_do', item_text, checked=checked))\r\n else:\r\n blocks.append(self.create_block(list_type, item_text))\r\n return blocks\r\n\r\n def process_table(self, node):\r\n blocks = []\r\n header_row = node.find('thead').find('tr') if node.find('thead') else None\r\n body_rows = node.find('tbody').find_all('tr') if node.find('tbody') else []\r\n\r\n if header_row or body_rows:\r\n table_width = max(len(header_row.find_all(['th', 'td'])) if header_row else 0,\r\n max(len(row.find_all(['th', 'td'])) for row in body_rows))\r\n\r\n table_block = self.create_block('table', '', table_width=table_width, has_column_header=bool(header_row))\r\n blocks.append(table_block)\r\n\r\n if header_row:\r\n header_cells = [cell.get_text(strip=True) for cell in header_row.find_all(['th', 'td'])]\r\n header_row_block = self.create_block('table_row', header_cells)\r\n blocks.append(header_row_block)\r\n\r\n for row in body_rows:\r\n cells = [cell.get_text(strip=True) for cell in row.find_all(['th', 'td'])]\r\n row_block = self.create_block('table_row', cells)\r\n blocks.append(row_block)\r\n\r\n return blocks\r\n \r\n def create_block(self, block_type: str, content: str, **kwargs) -> Dict[str, Any]:\r\n block = {\r\n \"object\": \"block\",\r\n \"type\": block_type,\r\n block_type: {},\r\n }\r\n\r\n if block_type in [\"paragraph\", \"heading_1\", \"heading_2\", \"heading_3\", \"bulleted_list_item\", \"numbered_list_item\", \"quote\"]:\r\n block[block_type][\"rich_text\"] = [\r\n {\r\n \"type\": \"text\",\r\n \"text\": {\r\n \"content\": content,\r\n },\r\n }\r\n ]\r\n elif block_type == 'to_do':\r\n block[block_type][\"rich_text\"] = [\r\n {\r\n \"type\": \"text\",\r\n \"text\": {\r\n \"content\": content,\r\n },\r\n }\r\n ]\r\n block[block_type]['checked'] = kwargs.get('checked', False)\r\n elif block_type == 'code':\r\n block[block_type]['rich_text'] = [\r\n {\r\n \"type\": \"text\",\r\n \"text\": {\r\n \"content\": content,\r\n },\r\n }\r\n ]\r\n block[block_type]['language'] = kwargs.get('language', 'plain text')\r\n elif block_type == 'image':\r\n block[block_type] = {\r\n \"type\": \"external\",\r\n \"external\": {\r\n \"url\": kwargs.get('image_url', '')\r\n }\r\n }\r\n elif block_type == 'divider':\r\n pass\r\n elif block_type == 'bookmark':\r\n block[block_type]['url'] = kwargs.get('link_url', '')\r\n elif block_type == 'table':\r\n block[block_type]['table_width'] = kwargs.get('table_width', 0)\r\n block[block_type]['has_column_header'] = kwargs.get('has_column_header', False)\r\n block[block_type]['has_row_header'] = kwargs.get('has_row_header', False)\r\n elif block_type == 'table_row':\r\n block[block_type]['cells'] = [[{'type': 'text', 'text': {'content': cell}} for cell in content]]\r\n\r\n return block", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "markdown_text": { + "type": "str", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "markdown_text", + "display_name": "Markdown Text", + "advanced": false, + "dynamic": false, + "info": "The markdown text to convert to Notion blocks.", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"], + "value": "# Heading 1\n\n## Heading 2\n\n### Heading 3\n\nThis is a regular paragraph.\n\nHere's another paragraph with an image:\n![Image](https://example.com/image.jpg)\n\n## Checklist\n- [x] Completed task\n- [ ] Incomplete task\n- [x] Another completed task\n\n## Numbered List\n1. First item\n2. Second item\n3. Third item\n\n## Bulleted List\n- Item 1\n- Item 2\n- Item 3\n\n## Code Block\n```python\ndef hello_world():\n print(\"Hello, World!\")\n```\n\n## Quote\n> This is a blockquote.\n> It can span multiple lines.\n\n## Horizontal Rule\n---\n\n\n## Link\n[Notion API Documentation](https://developers.notion.com)\n\n" + }, + "notion_secret": { + "type": "str", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": true, + "name": "notion_secret", + "display_name": "Notion Secret", + "advanced": false, + "dynamic": false, + "info": "The Notion integration token.", + "load_from_db": true, + "title_case": false, + "input_types": ["Text"], + "value": "" + }, + "_type": "CustomComponent" + }, + "description": "Convert markdown text to Notion blocks and append them to a Notion page.", + "icon": "NotionDirectoryLoader", + "base_classes": ["Record"], + "display_name": "Add Content to Page [Notion] ", + "documentation": "https://developers.notion.com/reference/patch-block-children", + "custom_fields": { + "markdown_text": null, + "block_id": null, + "notion_secret": null + }, + "output_types": ["Record"], + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false, + "official": false + }, + "id": "CustomComponent-I8Dec" + }, + "selected": false, + "width": 384, + "height": 497, + "positionAbsolute": { + "x": -2256.686402636563, + "y": -963.4541117792749 + }, + "dragging": false + }, + { + "id": "CustomComponent-ZcsA9", + "type": "genericNode", + "position": { "x": -3488.029350341937, "y": -965.3756250644985 }, + "data": { + "type": "CustomComponent", + "node": { + "template": { + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "import requests\r\nfrom typing import Dict, Any, List\r\nfrom langflow.custom import CustomComponent\r\nfrom langflow.schema import Record\r\n\r\nclass NotionSearch(CustomComponent):\r\n display_name = \"Search Notion\"\r\n description = (\r\n \"Searches all pages and databases that have been shared with an integration.\"\r\n )\r\n documentation: str = \"https://docs.langflow.org/integrations/notion/search\"\r\n icon = \"NotionDirectoryLoader\"\r\n\r\n field_order = [\r\n \"notion_secret\",\r\n \"query\",\r\n \"filter_value\",\r\n \"sort_direction\",\r\n ]\r\n\r\n def build_config(self):\r\n return {\r\n \"notion_secret\": {\r\n \"display_name\": \"Notion Secret\",\r\n \"field_type\": \"str\",\r\n \"info\": \"The Notion integration token.\",\r\n \"password\": True,\r\n },\r\n \"query\": {\r\n \"display_name\": \"Search Query\",\r\n \"field_type\": \"str\",\r\n \"info\": \"The text that the API compares page and database titles against.\",\r\n },\r\n \"filter_value\": {\r\n \"display_name\": \"Filter Type\",\r\n \"field_type\": \"str\",\r\n \"info\": \"Limits the results to either only pages or only databases.\",\r\n \"options\": [\"page\", \"database\"],\r\n \"default_value\": \"page\",\r\n },\r\n \"sort_direction\": {\r\n \"display_name\": \"Sort Direction\",\r\n \"field_type\": \"str\",\r\n \"info\": \"The direction to sort the results.\",\r\n \"options\": [\"ascending\", \"descending\"],\r\n \"default_value\": \"descending\",\r\n },\r\n }\r\n\r\n def build(\r\n self,\r\n notion_secret: str,\r\n query: str = \"\",\r\n filter_value: str = \"page\",\r\n sort_direction: str = \"descending\",\r\n ) -> List[Record]:\r\n try:\r\n url = \"https://api.notion.com/v1/search\"\r\n headers = {\r\n \"Authorization\": f\"Bearer {notion_secret}\",\r\n \"Content-Type\": \"application/json\",\r\n \"Notion-Version\": \"2022-06-28\",\r\n }\r\n\r\n data = {\r\n \"query\": query,\r\n \"filter\": {\r\n \"value\": filter_value,\r\n \"property\": \"object\"\r\n },\r\n \"sort\":{\r\n \"direction\": sort_direction,\r\n \"timestamp\": \"last_edited_time\"\r\n }\r\n }\r\n\r\n response = requests.post(url, headers=headers, json=data)\r\n response.raise_for_status()\r\n\r\n results = response.json()\r\n records = []\r\n combined_text = f\"Results found: {len(results['results'])}\\n\\n\"\r\n for result in results['results']:\r\n result_data = {\r\n 'id': result['id'],\r\n 'type': result['object'],\r\n 'last_edited_time': result['last_edited_time'],\r\n }\r\n \r\n if result['object'] == 'page':\r\n result_data['title_or_url'] = result['url']\r\n text = f\"id: {result['id']}\\ntitle_or_url: {result['url']}\\n\"\r\n elif result['object'] == 'database':\r\n if 'title' in result and isinstance(result['title'], list) and len(result['title']) > 0:\r\n result_data['title_or_url'] = result['title'][0]['plain_text']\r\n text = f\"id: {result['id']}\\ntitle_or_url: {result['title'][0]['plain_text']}\\n\"\r\n else:\r\n result_data['title_or_url'] = \"N/A\"\r\n text = f\"id: {result['id']}\\ntitle_or_url: N/A\\n\"\r\n\r\n text += f\"type: {result['object']}\\nlast_edited_time: {result['last_edited_time']}\\n\\n\"\r\n combined_text += text\r\n records.append(Record(text=text, data=result_data))\r\n \r\n self.status = combined_text\r\n return records\r\n\r\n except Exception as e:\r\n self.status = f\"An error occurred: {str(e)}\"\r\n return [Record(text=self.status, data=[])]", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "filter_value": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "value": "database", + "fileTypes": [], + "file_path": "", + "password": false, + "options": ["page", "database"], + "name": "filter_value", + "display_name": "Filter Type", + "advanced": false, + "dynamic": false, + "info": "Limits the results to either only pages or only databases.", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "notion_secret": { + "type": "str", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": true, + "name": "notion_secret", + "display_name": "Notion Secret", + "advanced": false, + "dynamic": false, + "info": "The Notion integration token.", + "load_from_db": true, + "title_case": false, + "input_types": ["Text"], + "value": "" + }, + "query": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": "", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "query", + "display_name": "Search Query", + "advanced": false, + "dynamic": false, + "info": "The text that the API compares page and database titles against.", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "sort_direction": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "value": "descending", + "fileTypes": [], + "file_path": "", + "password": false, + "options": ["ascending", "descending"], + "name": "sort_direction", + "display_name": "Sort Direction", + "advanced": false, + "dynamic": false, + "info": "The direction to sort the results.", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "_type": "CustomComponent" + }, + "description": "Searches all pages and databases that have been shared with an integration.", + "icon": "NotionDirectoryLoader", + "base_classes": ["Record"], + "display_name": "Search [Notion]", + "documentation": "https://docs.langflow.org/integrations/notion/search", + "custom_fields": { + "notion_secret": null, + "query": null, + "filter_value": null, + "sort_direction": null + }, + "output_types": ["Record"], + "field_formatters": {}, + "frozen": false, + "field_order": [ + "notion_secret", + "query", + "filter_value", + "sort_direction" + ], + "beta": false + }, + "id": "CustomComponent-ZcsA9", + "description": "Searches all pages and databases that have been shared with an integration.", + "display_name": "Search [Notion]" + }, + "selected": false, + "width": 384, + "height": 591, + "positionAbsolute": { + "x": -3488.029350341937, + "y": -965.3756250644985 + }, + "dragging": false + } + ], + "edges": [], + "viewport": { + "x": 2623.378922967084, + "y": 696.8541079344027, + "zoom": 0.5981384177708997 + } + }, + "description": "A Bundle containing Notion components for Page and Database manipulation. You can list pages, users databases, update properties, create new pages and add content to Notion Pages.", + "name": "Notion - Components", + "last_tested_version": "1.0.0a36", + "is_component": false +} diff --git a/docs/static/logos/twitter.svg b/docs/static/logos/twitter.svg index 027488d3c..437e2bfdd 100644 --- a/docs/static/logos/twitter.svg +++ b/docs/static/logos/twitter.svg @@ -1,3 +1,3 @@ - - + + diff --git a/poetry.lock b/poetry.lock index 521a9e42b..624abc4f9 100644 --- a/poetry.lock +++ b/poetry.lock @@ -261,13 +261,13 @@ extras = ["pyaudio (>=0.2.13)"] [[package]] name = "astrapy" -version = "1.2.0" +version = "1.2.1" description = "AstraPy is a Pythonic SDK for DataStax Astra and its Data API" optional = false python-versions = "<4.0.0,>=3.8.0" files = [ - {file = "astrapy-1.2.0-py3-none-any.whl", hash = "sha256:5d65242771934c38ebe16f330e9e517968c1437846dabdbe7e48470f7b1782e8"}, - {file = "astrapy-1.2.0.tar.gz", hash = "sha256:6ce1b421d1ae21fe73373fa36048d8d56c775367886525504f01c48cbb742842"}, + {file = "astrapy-1.2.1-py3-none-any.whl", hash = "sha256:0d7ca1e6f18a6a4e9a41ffaf2aa4cc585d36de3e983b5c5ce0bbb30a1595e30b"}, + {file = "astrapy-1.2.1.tar.gz", hash = "sha256:c4ba88ef16ac1e990ccba322d376b6ea256513a3004a0894c14bfa2403f1d646"}, ] [package.dependencies] @@ -367,13 +367,13 @@ files = [ [[package]] name = "bce-python-sdk" -version = "0.9.11" +version = "0.9.14" description = "BCE SDK for python" optional = false python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,<4,>=2.7" files = [ - {file = "bce_python_sdk-0.9.11-py3-none-any.whl", hash = "sha256:3afb9717f6c0c5f5fe3104a8bea4c111bf2ab3fe87ae73b05492566bc2b5d11a"}, - {file = "bce_python_sdk-0.9.11.tar.gz", hash = "sha256:d9e977f059fef6466eebdbb34ad1e27b6f76ef90338807ab959693a78a761e7d"}, + {file = "bce_python_sdk-0.9.14-py3-none-any.whl", hash = "sha256:5704aa454151ee608b01ddda7531457433f9b4bb8afbd00706dd368f3b4339a1"}, + {file = "bce_python_sdk-0.9.14.tar.gz", hash = "sha256:7cbd182ec1e21034f10d3cdb812f3171d31908f1a783d6cf643039272942d8e8"}, ] [package.dependencies] @@ -471,17 +471,17 @@ files = [ [[package]] name = "boto3" -version = "1.34.119" +version = "1.34.121" description = "The AWS SDK for Python" optional = false python-versions = ">=3.8" files = [ - {file = "boto3-1.34.119-py3-none-any.whl", hash = "sha256:8f9c43c54b3dfaa36c4a0d7b42c417227a515bc7a2e163e62802780000a5a3e2"}, - {file = "boto3-1.34.119.tar.gz", hash = "sha256:cea2365a25b2b83a97e77f24ac6f922ef62e20636b42f9f6ee9f97188f9c1c03"}, + {file = "boto3-1.34.121-py3-none-any.whl", hash = "sha256:4e79e400d6d44b4eee5deda6ac0ecd08a3f5a30c45a0d30712795cdc4459fd79"}, + {file = "boto3-1.34.121.tar.gz", hash = "sha256:ec89f3e0b0dc959c418df29e14d3748c0b05ab7acf7c0b90c839e9f340a659fa"}, ] [package.dependencies] -botocore = ">=1.34.119,<1.35.0" +botocore = ">=1.34.121,<1.35.0" jmespath = ">=0.7.1,<2.0.0" s3transfer = ">=0.10.0,<0.11.0" @@ -490,13 +490,13 @@ crt = ["botocore[crt] (>=1.21.0,<2.0a0)"] [[package]] name = "botocore" -version = "1.34.119" +version = "1.34.121" description = "Low-level, data-driven core of boto 3." optional = false python-versions = ">=3.8" files = [ - {file = "botocore-1.34.119-py3-none-any.whl", hash = "sha256:4bdf7926a1290b2650d62899ceba65073dd2693e61c35f5cdeb3a286a0aaa27b"}, - {file = "botocore-1.34.119.tar.gz", hash = "sha256:b253f15b24b87b070e176af48e8ef146516090429d30a7d8b136a4c079b28008"}, + {file = "botocore-1.34.121-py3-none-any.whl", hash = "sha256:25b05c7646a9f240cde1c8f839552a43f27e71e15c42600275dea93e219f7dd9"}, + {file = "botocore-1.34.121.tar.gz", hash = "sha256:1a8f94b917c47dfd84a0b531ab607dc53570efb0d073d8686600f2d2be985323"}, ] [package.dependencies] @@ -505,7 +505,7 @@ python-dateutil = ">=2.1,<3.0.0" urllib3 = {version = ">=1.25.4,<2.2.0 || >2.2.0,<3", markers = "python_version >= \"3.10\""} [package.extras] -crt = ["awscrt (==0.20.9)"] +crt = ["awscrt (==0.20.11)"] [[package]] name = "brotli" @@ -698,13 +698,13 @@ graph = ["gremlinpython (==3.4.6)"] [[package]] name = "cassio" -version = "0.1.7" +version = "0.1.8" description = "A framework-agnostic Python library to seamlessly integrate Apache Cassandra(R) with ML/LLM/genAI workloads." optional = false python-versions = "<4.0,>=3.8" files = [ - {file = "cassio-0.1.7-py3-none-any.whl", hash = "sha256:08d1028a20d09bd207de0e17eaf7ae821b3c8e4788555e2d337aa440e0846d87"}, - {file = "cassio-0.1.7.tar.gz", hash = "sha256:44f705dff8a9a1c48527db2c9e968686358c960fa21ba940d9e66de00639ad78"}, + {file = "cassio-0.1.8-py3-none-any.whl", hash = "sha256:c09e7c884ba7227ff5277c86f3b0f31c523672ea407f56d093c7227e69c54d94"}, + {file = "cassio-0.1.8.tar.gz", hash = "sha256:4e09929506cb3dd6fad217e89846d0a1a59069afd24b82c72526ef6f2e9271af"}, ] [package.dependencies] @@ -1824,13 +1824,13 @@ develop = ["aiohttp", "furo", "httpx", "mock", "opentelemetry-api", "opentelemet [[package]] name = "elasticsearch" -version = "8.13.2" +version = "8.14.0" description = "Python client for Elasticsearch" optional = false python-versions = ">=3.7" files = [ - {file = "elasticsearch-8.13.2-py3-none-any.whl", hash = "sha256:7412ceae9c0e437a72854ab3123aa1f37110d1635cc645366988b8c0fee98598"}, - {file = "elasticsearch-8.13.2.tar.gz", hash = "sha256:d51c93431a459b2b7c6c919b6e92a2adc8ac712758de9aeeb16cd4997fc148ad"}, + {file = "elasticsearch-8.14.0-py3-none-any.whl", hash = "sha256:cef8ef70a81af027f3da74a4f7d9296b390c636903088439087b8262a468c130"}, + {file = "elasticsearch-8.14.0.tar.gz", hash = "sha256:aa2490029dd96f4015b333c1827aa21fd6c0a4d223b00dfb0fe933b8d09a511b"}, ] [package.dependencies] @@ -2588,13 +2588,13 @@ uritemplate = ">=3.0.1,<5" [[package]] name = "google-auth" -version = "2.29.0" +version = "2.30.0" description = "Google Authentication Library" optional = false python-versions = ">=3.7" files = [ - {file = "google-auth-2.29.0.tar.gz", hash = "sha256:672dff332d073227550ffc7457868ac4218d6c500b155fe6cc17d2b13602c360"}, - {file = "google_auth-2.29.0-py2.py3-none-any.whl", hash = "sha256:d452ad095688cd52bae0ad6fafe027f6a6d6f560e810fec20914e17a09526415"}, + {file = "google-auth-2.30.0.tar.gz", hash = "sha256:ab630a1320f6720909ad76a7dbdb6841cdf5c66b328d690027e4867bdfb16688"}, + {file = "google_auth-2.30.0-py2.py3-none-any.whl", hash = "sha256:8df7da660f62757388b8a7f249df13549b3373f24388cb5d2f1dd91cc18180b5"}, ] [package.dependencies] @@ -2626,13 +2626,13 @@ httplib2 = ">=0.19.0" [[package]] name = "google-cloud-aiplatform" -version = "1.53.0" +version = "1.54.0" description = "Vertex AI API client library" optional = false python-versions = ">=3.8" files = [ - {file = "google-cloud-aiplatform-1.53.0.tar.gz", hash = "sha256:574cfad8ac5fa5d57ef717f5335ce05636a5fa9b8aeea0f5c325b46b9448e6b1"}, - {file = "google_cloud_aiplatform-1.53.0-py2.py3-none-any.whl", hash = "sha256:9dfb1f110e6d4795b45afcfab79108fc5c8ed9aa4eaf899e433bc2ca1b76c778"}, + {file = "google-cloud-aiplatform-1.54.0.tar.gz", hash = "sha256:6f5187d35a32951028465804fbb42b478362bf41e2b634ddd22b150299f6e1d8"}, + {file = "google_cloud_aiplatform-1.54.0-py2.py3-none-any.whl", hash = "sha256:7b3ed849b9fb59a01bd6f44444ccbb7d18495b867a26f913542f6b2d4c3de252"}, ] [package.dependencies] @@ -2653,7 +2653,7 @@ autologging = ["mlflow (>=1.27.0,<=2.1.1)"] cloud-profiler = ["tensorboard-plugin-profile (>=2.4.0,<3.0.0dev)", "tensorflow (>=2.4.0,<3.0.0dev)", "werkzeug (>=2.0.0,<2.1.0dev)"] datasets = ["pyarrow (>=10.0.1)", "pyarrow (>=14.0.0)", "pyarrow (>=3.0.0,<8.0dev)"] endpoint = ["requests (>=2.28.1)"] -full = ["cloudpickle (<3.0)", "docker (>=5.0.3)", "explainable-ai-sdk (>=1.0.0)", "fastapi (>=0.71.0,<=0.109.1)", "google-cloud-bigquery", "google-cloud-bigquery-storage", "google-cloud-logging (<4.0)", "google-vizier (>=0.1.6)", "httpx (>=0.23.0,<0.25.0)", "immutabledict", "lit-nlp (==0.4.0)", "mlflow (>=1.27.0,<=2.1.1)", "nest-asyncio (>=1.0.0,<1.6.0)", "numpy (>=1.15.0)", "pandas (>=1.0.0)", "pandas (>=1.0.0,<2.2.0)", "pyarrow (>=10.0.1)", "pyarrow (>=14.0.0)", "pyarrow (>=3.0.0,<8.0dev)", "pyarrow (>=6.0.1)", "pydantic (<2)", "pyyaml (>=5.3.1,<7)", "ray[default] (>=2.4,<2.5.dev0 || >2.9.0,!=2.9.1,!=2.9.2,<=2.9.3)", "ray[default] (>=2.5,<=2.9.3)", "requests (>=2.28.1)", "starlette (>=0.17.1)", "tensorflow (>=2.3.0,<3.0.0dev)", "tensorflow (>=2.3.0,<3.0.0dev)", "urllib3 (>=1.21.1,<1.27)", "uvicorn[standard] (>=0.16.0)"] +full = ["cloudpickle (<3.0)", "docker (>=5.0.3)", "explainable-ai-sdk (>=1.0.0)", "fastapi (>=0.71.0,<=0.109.1)", "google-cloud-bigquery", "google-cloud-bigquery-storage", "google-cloud-logging (<4.0)", "google-vizier (>=0.1.6)", "httpx (>=0.23.0,<0.25.0)", "immutabledict", "lit-nlp (==0.4.0)", "mlflow (>=1.27.0,<=2.1.1)", "nest-asyncio (>=1.0.0,<1.6.0)", "numpy (>=1.15.0)", "pandas (>=1.0.0)", "pandas (>=1.0.0,<2.2.0)", "pyarrow (>=10.0.1)", "pyarrow (>=14.0.0)", "pyarrow (>=3.0.0,<8.0dev)", "pyarrow (>=6.0.1)", "pydantic (<2)", "pyyaml (>=5.3.1,<7)", "ray[default] (>=2.4,<2.5.dev0 || >2.9.0,!=2.9.1,!=2.9.2,<=2.9.3)", "ray[default] (>=2.5,<=2.9.3)", "requests (>=2.28.1)", "setuptools (<70.0.0)", "starlette (>=0.17.1)", "tensorflow (>=2.3.0,<3.0.0dev)", "tensorflow (>=2.3.0,<3.0.0dev)", "urllib3 (>=1.21.1,<1.27)", "uvicorn[standard] (>=0.16.0)"] langchain = ["langchain (>=0.1.16,<0.2)", "langchain-core (<0.2)", "langchain-google-vertexai (<2)"] langchain-testing = ["absl-py", "cloudpickle (>=2.2.1,<4.0)", "langchain (>=0.1.16,<0.2)", "langchain-core (<0.2)", "langchain-google-vertexai (<2)", "pydantic (>=2.6.3,<3)", "pytest-xdist"] lit = ["explainable-ai-sdk (>=1.0.0)", "lit-nlp (==0.4.0)", "pandas (>=1.0.0)", "tensorflow (>=2.3.0,<3.0.0dev)"] @@ -2663,11 +2663,11 @@ prediction = ["docker (>=5.0.3)", "fastapi (>=0.71.0,<=0.109.1)", "httpx (>=0.23 preview = ["cloudpickle (<3.0)", "google-cloud-logging (<4.0)"] private-endpoints = ["requests (>=2.28.1)", "urllib3 (>=1.21.1,<1.27)"] rapid-evaluation = ["nest-asyncio (>=1.0.0,<1.6.0)", "pandas (>=1.0.0,<2.2.0)"] -ray = ["google-cloud-bigquery", "google-cloud-bigquery-storage", "immutabledict", "pandas (>=1.0.0,<2.2.0)", "pyarrow (>=6.0.1)", "pydantic (<2)", "ray[default] (>=2.4,<2.5.dev0 || >2.9.0,!=2.9.1,!=2.9.2,<=2.9.3)", "ray[default] (>=2.5,<=2.9.3)"] -ray-testing = ["google-cloud-bigquery", "google-cloud-bigquery-storage", "immutabledict", "pandas (>=1.0.0,<2.2.0)", "pyarrow (>=6.0.1)", "pydantic (<2)", "pytest-xdist", "ray[default] (>=2.4,<2.5.dev0 || >2.9.0,!=2.9.1,!=2.9.2,<=2.9.3)", "ray[default] (>=2.5,<=2.9.3)", "ray[train] (>=2.4,<2.5.dev0 || >2.9.0,!=2.9.1,!=2.9.2,<=2.9.3)", "scikit-learn", "tensorflow", "torch (>=2.0.0,<2.1.0)", "xgboost", "xgboost-ray"] +ray = ["google-cloud-bigquery", "google-cloud-bigquery-storage", "immutabledict", "pandas (>=1.0.0,<2.2.0)", "pyarrow (>=6.0.1)", "pydantic (<2)", "ray[default] (>=2.4,<2.5.dev0 || >2.9.0,!=2.9.1,!=2.9.2,<=2.9.3)", "ray[default] (>=2.5,<=2.9.3)", "setuptools (<70.0.0)"] +ray-testing = ["google-cloud-bigquery", "google-cloud-bigquery-storage", "immutabledict", "pandas (>=1.0.0,<2.2.0)", "pyarrow (>=6.0.1)", "pydantic (<2)", "pytest-xdist", "ray[default] (>=2.4,<2.5.dev0 || >2.9.0,!=2.9.1,!=2.9.2,<=2.9.3)", "ray[default] (>=2.5,<=2.9.3)", "ray[train] (==2.9.3)", "scikit-learn", "setuptools (<70.0.0)", "tensorflow", "torch (>=2.0.0,<2.1.0)", "xgboost", "xgboost-ray"] reasoningengine = ["cloudpickle (>=2.2.1,<4.0)", "pydantic (>=2.6.3,<3)"] tensorboard = ["tensorflow (>=2.3.0,<3.0.0dev)"] -testing = ["bigframes", "cloudpickle (<3.0)", "docker (>=5.0.3)", "explainable-ai-sdk (>=1.0.0)", "fastapi (>=0.71.0,<=0.109.1)", "google-api-core (>=2.11,<3.0.0)", "google-cloud-bigquery", "google-cloud-bigquery-storage", "google-cloud-logging (<4.0)", "google-vizier (>=0.1.6)", "grpcio-testing", "httpx (>=0.23.0,<0.25.0)", "immutabledict", "ipython", "kfp (>=2.6.0,<3.0.0)", "lit-nlp (==0.4.0)", "mlflow (>=1.27.0,<=2.1.1)", "nest-asyncio (>=1.0.0,<1.6.0)", "numpy (>=1.15.0)", "pandas (>=1.0.0)", "pandas (>=1.0.0,<2.2.0)", "pyarrow (>=10.0.1)", "pyarrow (>=14.0.0)", "pyarrow (>=3.0.0,<8.0dev)", "pyarrow (>=6.0.1)", "pydantic (<2)", "pyfakefs", "pytest-asyncio", "pytest-xdist", "pyyaml (>=5.3.1,<7)", "ray[default] (>=2.4,<2.5.dev0 || >2.9.0,!=2.9.1,!=2.9.2,<=2.9.3)", "ray[default] (>=2.5,<=2.9.3)", "requests (>=2.28.1)", "requests-toolbelt (<1.0.0)", "scikit-learn", "starlette (>=0.17.1)", "tensorboard-plugin-profile (>=2.4.0,<3.0.0dev)", "tensorflow (==2.13.0)", "tensorflow (==2.16.1)", "tensorflow (>=2.3.0,<3.0.0dev)", "tensorflow (>=2.3.0,<3.0.0dev)", "tensorflow (>=2.4.0,<3.0.0dev)", "torch (>=2.0.0,<2.1.0)", "torch (>=2.2.0)", "urllib3 (>=1.21.1,<1.27)", "uvicorn[standard] (>=0.16.0)", "werkzeug (>=2.0.0,<2.1.0dev)", "xgboost"] +testing = ["bigframes", "cloudpickle (<3.0)", "docker (>=5.0.3)", "explainable-ai-sdk (>=1.0.0)", "fastapi (>=0.71.0,<=0.109.1)", "google-api-core (>=2.11,<3.0.0)", "google-cloud-bigquery", "google-cloud-bigquery-storage", "google-cloud-logging (<4.0)", "google-vizier (>=0.1.6)", "grpcio-testing", "httpx (>=0.23.0,<0.25.0)", "immutabledict", "ipython", "kfp (>=2.6.0,<3.0.0)", "lit-nlp (==0.4.0)", "mlflow (>=1.27.0,<=2.1.1)", "nest-asyncio (>=1.0.0,<1.6.0)", "numpy (>=1.15.0)", "pandas (>=1.0.0)", "pandas (>=1.0.0,<2.2.0)", "pyarrow (>=10.0.1)", "pyarrow (>=14.0.0)", "pyarrow (>=3.0.0,<8.0dev)", "pyarrow (>=6.0.1)", "pydantic (<2)", "pyfakefs", "pytest-asyncio", "pytest-xdist", "pyyaml (>=5.3.1,<7)", "ray[default] (>=2.4,<2.5.dev0 || >2.9.0,!=2.9.1,!=2.9.2,<=2.9.3)", "ray[default] (>=2.5,<=2.9.3)", "requests (>=2.28.1)", "requests-toolbelt (<1.0.0)", "scikit-learn", "setuptools (<70.0.0)", "starlette (>=0.17.1)", "tensorboard-plugin-profile (>=2.4.0,<3.0.0dev)", "tensorflow (==2.13.0)", "tensorflow (==2.16.1)", "tensorflow (>=2.3.0,<3.0.0dev)", "tensorflow (>=2.3.0,<3.0.0dev)", "tensorflow (>=2.4.0,<3.0.0dev)", "torch (>=2.0.0,<2.1.0)", "torch (>=2.2.0)", "urllib3 (>=1.21.1,<1.27)", "uvicorn[standard] (>=0.16.0)", "werkzeug (>=2.0.0,<2.1.0dev)", "xgboost"] vizier = ["google-vizier (>=0.1.6)"] xai = ["tensorflow (>=2.3.0,<3.0.0dev)"] @@ -2929,13 +2929,13 @@ pydantic = ">=1.10,<3" [[package]] name = "gprof2dot" -version = "2022.7.29" +version = "2024.6.6" description = "Generate a dot graph from the output of several profilers." optional = false -python-versions = ">=2.7" +python-versions = ">=3.8" files = [ - {file = "gprof2dot-2022.7.29-py2.py3-none-any.whl", hash = "sha256:f165b3851d3c52ee4915eb1bd6cca571e5759823c2cd0f71a79bda93c2dc85d6"}, - {file = "gprof2dot-2022.7.29.tar.gz", hash = "sha256:45b4d298bd36608fccf9511c3fd88a773f7a1abc04d6cd39445b11ba43133ec5"}, + {file = "gprof2dot-2024.6.6-py2.py3-none-any.whl", hash = "sha256:45b14ad7ce64e299c8f526881007b9eb2c6b75505d5613e96e66ee4d5ab33696"}, + {file = "gprof2dot-2024.6.6.tar.gz", hash = "sha256:fa1420c60025a9eb7734f65225b4da02a10fc6dd741b37fa129bc6b41951e5ab"}, ] [[package]] @@ -3478,100 +3478,105 @@ files = [ [[package]] name = "ijson" -version = "3.2.3" +version = "3.3.0" description = "Iterative JSON parser with standard Python iterator interfaces" optional = false python-versions = "*" files = [ - {file = "ijson-3.2.3-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:0a4ae076bf97b0430e4e16c9cb635a6b773904aec45ed8dcbc9b17211b8569ba"}, - {file = "ijson-3.2.3-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:cfced0a6ec85916eb8c8e22415b7267ae118eaff2a860c42d2cc1261711d0d31"}, - {file = "ijson-3.2.3-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:0b9d1141cfd1e6d6643aa0b4876730d0d28371815ce846d2e4e84a2d4f471cf3"}, - {file = "ijson-3.2.3-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9e0a27db6454edd6013d40a956d008361aac5bff375a9c04ab11fc8c214250b5"}, - {file = "ijson-3.2.3-cp310-cp310-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:3c0d526ccb335c3c13063c273637d8611f32970603dfb182177b232d01f14c23"}, - {file = "ijson-3.2.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:545a30b3659df2a3481593d30d60491d1594bc8005f99600e1bba647bb44cbb5"}, - {file = "ijson-3.2.3-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:9680e37a10fedb3eab24a4a7e749d8a73f26f1a4c901430e7aa81b5da15f7307"}, - {file = "ijson-3.2.3-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:2a80c0bb1053055d1599e44dc1396f713e8b3407000e6390add72d49633ff3bb"}, - {file = "ijson-3.2.3-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:f05ed49f434ce396ddcf99e9fd98245328e99f991283850c309f5e3182211a79"}, - {file = "ijson-3.2.3-cp310-cp310-win32.whl", hash = "sha256:b4eb2304573c9fdf448d3fa4a4fdcb727b93002b5c5c56c14a5ffbbc39f64ae4"}, - {file = "ijson-3.2.3-cp310-cp310-win_amd64.whl", hash = "sha256:923131f5153c70936e8bd2dd9dcfcff43c67a3d1c789e9c96724747423c173eb"}, - {file = "ijson-3.2.3-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:904f77dd3d87736ff668884fe5197a184748eb0c3e302ded61706501d0327465"}, - {file = "ijson-3.2.3-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:0974444c1f416e19de1e9f567a4560890095e71e81623c509feff642114c1e53"}, - {file = "ijson-3.2.3-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:c1a4b8eb69b6d7b4e94170aa991efad75ba156b05f0de2a6cd84f991def12ff9"}, - {file = "ijson-3.2.3-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d052417fd7ce2221114f8d3b58f05a83c1a2b6b99cafe0b86ac9ed5e2fc889df"}, - {file = "ijson-3.2.3-cp311-cp311-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:7b8064a85ec1b0beda7dd028e887f7112670d574db606f68006c72dd0bb0e0e2"}, - {file = "ijson-3.2.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:eaac293853f1342a8d2a45ac1f723c860f700860e7743fb97f7b76356df883a8"}, - {file = "ijson-3.2.3-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:6c32c18a934c1dc8917455b0ce478fd7a26c50c364bd52c5a4fb0fc6bb516af7"}, - {file = "ijson-3.2.3-cp311-cp311-musllinux_1_1_i686.whl", hash = "sha256:713a919e0220ac44dab12b5fed74f9130f3480e55e90f9d80f58de129ea24f83"}, - {file = "ijson-3.2.3-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:4a3a6a2fbbe7550ffe52d151cf76065e6b89cfb3e9d0463e49a7e322a25d0426"}, - {file = "ijson-3.2.3-cp311-cp311-win32.whl", hash = "sha256:6a4db2f7fb9acfb855c9ae1aae602e4648dd1f88804a0d5cfb78c3639bcf156c"}, - {file = "ijson-3.2.3-cp311-cp311-win_amd64.whl", hash = "sha256:ccd6be56335cbb845f3d3021b1766299c056c70c4c9165fb2fbe2d62258bae3f"}, - {file = "ijson-3.2.3-cp312-cp312-macosx_10_9_universal2.whl", hash = "sha256:055b71bbc37af5c3c5861afe789e15211d2d3d06ac51ee5a647adf4def19c0ea"}, - {file = "ijson-3.2.3-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:c075a547de32f265a5dd139ab2035900fef6653951628862e5cdce0d101af557"}, - {file = "ijson-3.2.3-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:457f8a5fc559478ac6b06b6d37ebacb4811f8c5156e997f0d87d708b0d8ab2ae"}, - {file = "ijson-3.2.3-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9788f0c915351f41f0e69ec2618b81ebfcf9f13d9d67c6d404c7f5afda3e4afb"}, - {file = "ijson-3.2.3-cp312-cp312-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:fa234ab7a6a33ed51494d9d2197fb96296f9217ecae57f5551a55589091e7853"}, - {file = "ijson-3.2.3-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:bdd0dc5da4f9dc6d12ab6e8e0c57d8b41d3c8f9ceed31a99dae7b2baf9ea769a"}, - {file = "ijson-3.2.3-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:c6beb80df19713e39e68dc5c337b5c76d36ccf69c30b79034634e5e4c14d6904"}, - {file = "ijson-3.2.3-cp312-cp312-musllinux_1_1_i686.whl", hash = "sha256:a2973ce57afb142d96f35a14e9cfec08308ef178a2c76b8b5e1e98f3960438bf"}, - {file = "ijson-3.2.3-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:105c314fd624e81ed20f925271ec506523b8dd236589ab6c0208b8707d652a0e"}, - {file = "ijson-3.2.3-cp312-cp312-win32.whl", hash = "sha256:ac44781de5e901ce8339352bb5594fcb3b94ced315a34dbe840b4cff3450e23b"}, - {file = "ijson-3.2.3-cp312-cp312-win_amd64.whl", hash = "sha256:0567e8c833825b119e74e10a7c29761dc65fcd155f5d4cb10f9d3b8916ef9912"}, - {file = "ijson-3.2.3-cp36-cp36m-macosx_10_9_x86_64.whl", hash = "sha256:eeb286639649fb6bed37997a5e30eefcacddac79476d24128348ec890b2a0ccb"}, - {file = "ijson-3.2.3-cp36-cp36m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:396338a655fb9af4ac59dd09c189885b51fa0eefc84d35408662031023c110d1"}, - {file = "ijson-3.2.3-cp36-cp36m-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:0e0243d166d11a2a47c17c7e885debf3b19ed136be2af1f5d1c34212850236ac"}, - {file = "ijson-3.2.3-cp36-cp36m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:85afdb3f3a5d0011584d4fa8e6dccc5936be51c27e84cd2882fe904ca3bd04c5"}, - {file = "ijson-3.2.3-cp36-cp36m-musllinux_1_1_aarch64.whl", hash = "sha256:4fc35d569eff3afa76bfecf533f818ecb9390105be257f3f83c03204661ace70"}, - {file = "ijson-3.2.3-cp36-cp36m-musllinux_1_1_i686.whl", hash = "sha256:455d7d3b7a6aacfb8ab1ebcaf697eedf5be66e044eac32508fccdc633d995f0e"}, - {file = "ijson-3.2.3-cp36-cp36m-musllinux_1_1_x86_64.whl", hash = "sha256:c63f3d57dbbac56cead05b12b81e8e1e259f14ce7f233a8cbe7fa0996733b628"}, - {file = "ijson-3.2.3-cp36-cp36m-win32.whl", hash = "sha256:a4d7fe3629de3ecb088bff6dfe25f77be3e8261ed53d5e244717e266f8544305"}, - {file = "ijson-3.2.3-cp36-cp36m-win_amd64.whl", hash = "sha256:96190d59f015b5a2af388a98446e411f58ecc6a93934e036daa75f75d02386a0"}, - {file = "ijson-3.2.3-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:35194e0b8a2bda12b4096e2e792efa5d4801a0abb950c48ade351d479cd22ba5"}, - {file = "ijson-3.2.3-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d1053fb5f0b010ee76ca515e6af36b50d26c1728ad46be12f1f147a835341083"}, - {file = "ijson-3.2.3-cp37-cp37m-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:211124cff9d9d139dd0dfced356f1472860352c055d2481459038b8205d7d742"}, - {file = "ijson-3.2.3-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:92dc4d48e9f6a271292d6079e9fcdce33c83d1acf11e6e12696fb05c5889fe74"}, - {file = "ijson-3.2.3-cp37-cp37m-musllinux_1_1_aarch64.whl", hash = "sha256:3dcc33ee56f92a77f48776014ddb47af67c33dda361e84371153c4f1ed4434e1"}, - {file = "ijson-3.2.3-cp37-cp37m-musllinux_1_1_i686.whl", hash = "sha256:98c6799925a5d1988da4cd68879b8eeab52c6e029acc45e03abb7921a4715c4b"}, - {file = "ijson-3.2.3-cp37-cp37m-musllinux_1_1_x86_64.whl", hash = "sha256:4252e48c95cd8ceefc2caade310559ab61c37d82dfa045928ed05328eb5b5f65"}, - {file = "ijson-3.2.3-cp37-cp37m-win32.whl", hash = "sha256:644f4f03349ff2731fd515afd1c91b9e439e90c9f8c28292251834154edbffca"}, - {file = "ijson-3.2.3-cp37-cp37m-win_amd64.whl", hash = "sha256:ba33c764afa9ecef62801ba7ac0319268a7526f50f7601370d9f8f04e77fc02b"}, - {file = "ijson-3.2.3-cp38-cp38-macosx_10_9_universal2.whl", hash = "sha256:4b2ec8c2a3f1742cbd5f36b65e192028e541b5fd8c7fd97c1fc0ca6c427c704a"}, - {file = "ijson-3.2.3-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:7dc357da4b4ebd8903e77dbcc3ce0555ee29ebe0747c3c7f56adda423df8ec89"}, - {file = "ijson-3.2.3-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:bcc51c84bb220ac330122468fe526a7777faa6464e3b04c15b476761beea424f"}, - {file = "ijson-3.2.3-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f8d54b624629f9903005c58d9321a036c72f5c212701bbb93d1a520ecd15e370"}, - {file = "ijson-3.2.3-cp38-cp38-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:d6ea7c7e3ec44742e867c72fd750c6a1e35b112f88a917615332c4476e718d40"}, - {file = "ijson-3.2.3-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:916acdc5e504f8b66c3e287ada5d4b39a3275fc1f2013c4b05d1ab9933671a6c"}, - {file = "ijson-3.2.3-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:81815b4184b85ce124bfc4c446d5f5e5e643fc119771c5916f035220ada29974"}, - {file = "ijson-3.2.3-cp38-cp38-musllinux_1_1_i686.whl", hash = "sha256:b49fd5fe1cd9c1c8caf6c59f82b08117dd6bea2ec45b641594e25948f48f4169"}, - {file = "ijson-3.2.3-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:86b3c91fdcb8ffb30556c9669930f02b7642de58ca2987845b04f0d7fe46d9a8"}, - {file = "ijson-3.2.3-cp38-cp38-win32.whl", hash = "sha256:a729b0c8fb935481afe3cf7e0dadd0da3a69cc7f145dbab8502e2f1e01d85a7c"}, - {file = "ijson-3.2.3-cp38-cp38-win_amd64.whl", hash = "sha256:d34e049992d8a46922f96483e96b32ac4c9cffd01a5c33a928e70a283710cd58"}, - {file = "ijson-3.2.3-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:9c2a12dcdb6fa28f333bf10b3a0f80ec70bc45280d8435be7e19696fab2bc706"}, - {file = "ijson-3.2.3-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:1844c5b57da21466f255a0aeddf89049e730d7f3dfc4d750f0e65c36e6a61a7c"}, - {file = "ijson-3.2.3-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:2ec3e5ff2515f1c40ef6a94983158e172f004cd643b9e4b5302017139b6c96e4"}, - {file = "ijson-3.2.3-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:46bafb1b9959872a1f946f8dd9c6f1a30a970fc05b7bfae8579da3f1f988e598"}, - {file = "ijson-3.2.3-cp39-cp39-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:ab4db9fee0138b60e31b3c02fff8a4c28d7b152040553b6a91b60354aebd4b02"}, - {file = "ijson-3.2.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f4bc87e69d1997c6a55fff5ee2af878720801ff6ab1fb3b7f94adda050651e37"}, - {file = "ijson-3.2.3-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:e9fd906f0c38e9f0bfd5365e1bed98d649f506721f76bb1a9baa5d7374f26f19"}, - {file = "ijson-3.2.3-cp39-cp39-musllinux_1_1_i686.whl", hash = "sha256:e84d27d1acb60d9102728d06b9650e5b7e5cb0631bd6e3dfadba8fb6a80d6c2f"}, - {file = "ijson-3.2.3-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:2cc04fc0a22bb945cd179f614845c8b5106c0b3939ee0d84ce67c7a61ac1a936"}, - {file = "ijson-3.2.3-cp39-cp39-win32.whl", hash = "sha256:e641814793a037175f7ec1b717ebb68f26d89d82cfd66f36e588f32d7e488d5f"}, - {file = "ijson-3.2.3-cp39-cp39-win_amd64.whl", hash = "sha256:6bd3e7e91d031f1e8cea7ce53f704ab74e61e505e8072467e092172422728b22"}, - {file = "ijson-3.2.3-pp37-pypy37_pp73-macosx_10_9_x86_64.whl", hash = "sha256:06f9707da06a19b01013f8c65bf67db523662a9b4a4ff027e946e66c261f17f0"}, - {file = "ijson-3.2.3-pp37-pypy37_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:be8495f7c13fa1f622a2c6b64e79ac63965b89caf664cc4e701c335c652d15f2"}, - {file = "ijson-3.2.3-pp37-pypy37_pp73-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:7596b42f38c3dcf9d434dddd50f46aeb28e96f891444c2b4b1266304a19a2c09"}, - {file = "ijson-3.2.3-pp37-pypy37_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:fbac4e9609a1086bbad075beb2ceec486a3b138604e12d2059a33ce2cba93051"}, - {file = "ijson-3.2.3-pp37-pypy37_pp73-win_amd64.whl", hash = "sha256:db2d6341f9cb538253e7fe23311d59252f124f47165221d3c06a7ed667ecd595"}, - {file = "ijson-3.2.3-pp38-pypy38_pp73-macosx_10_9_x86_64.whl", hash = "sha256:fa8b98be298efbb2588f883f9953113d8a0023ab39abe77fe734b71b46b1220a"}, - {file = "ijson-3.2.3-pp38-pypy38_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:674e585361c702fad050ab4c153fd168dc30f5980ef42b64400bc84d194e662d"}, - {file = "ijson-3.2.3-pp38-pypy38_pp73-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:fd12e42b9cb9c0166559a3ffa276b4f9fc9d5b4c304e5a13668642d34b48b634"}, - {file = "ijson-3.2.3-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d31e0d771d82def80cd4663a66de277c3b44ba82cd48f630526b52f74663c639"}, - {file = "ijson-3.2.3-pp38-pypy38_pp73-win_amd64.whl", hash = "sha256:7ce4c70c23521179d6da842bb9bc2e36bb9fad1e0187e35423ff0f282890c9ca"}, - {file = "ijson-3.2.3-pp39-pypy39_pp73-macosx_10_9_x86_64.whl", hash = "sha256:39f551a6fbeed4433c85269c7c8778e2aaea2501d7ebcb65b38f556030642c17"}, - {file = "ijson-3.2.3-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:3b14d322fec0de7af16f3ef920bf282f0dd747200b69e0b9628117f381b7775b"}, - {file = "ijson-3.2.3-pp39-pypy39_pp73-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:7851a341429b12d4527ca507097c959659baf5106c7074d15c17c387719ffbcd"}, - {file = "ijson-3.2.3-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:db3bf1b42191b5cc9b6441552fdcb3b583594cb6b19e90d1578b7cbcf80d0fae"}, - {file = "ijson-3.2.3-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:6f662dc44362a53af3084d3765bb01cd7b4734d1f484a6095cad4cb0cbfe5374"}, - {file = "ijson-3.2.3.tar.gz", hash = "sha256:10294e9bf89cb713da05bc4790bdff616610432db561964827074898e174f917"}, + {file = "ijson-3.3.0-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:7f7a5250599c366369fbf3bc4e176f5daa28eb6bc7d6130d02462ed335361675"}, + {file = "ijson-3.3.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:f87a7e52f79059f9c58f6886c262061065eb6f7554a587be7ed3aa63e6b71b34"}, + {file = "ijson-3.3.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:b73b493af9e947caed75d329676b1b801d673b17481962823a3e55fe529c8b8b"}, + {file = "ijson-3.3.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d5576415f3d76290b160aa093ff968f8bf6de7d681e16e463a0134106b506f49"}, + {file = "ijson-3.3.0-cp310-cp310-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:4e9ffe358d5fdd6b878a8a364e96e15ca7ca57b92a48f588378cef315a8b019e"}, + {file = "ijson-3.3.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8643c255a25824ddd0895c59f2319c019e13e949dc37162f876c41a283361527"}, + {file = "ijson-3.3.0-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:df3ab5e078cab19f7eaeef1d5f063103e1ebf8c26d059767b26a6a0ad8b250a3"}, + {file = "ijson-3.3.0-cp310-cp310-musllinux_1_2_i686.whl", hash = "sha256:3dc1fb02c6ed0bae1b4bf96971258bf88aea72051b6e4cebae97cff7090c0607"}, + {file = "ijson-3.3.0-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:e9afd97339fc5a20f0542c971f90f3ca97e73d3050cdc488d540b63fae45329a"}, + {file = "ijson-3.3.0-cp310-cp310-win32.whl", hash = "sha256:844c0d1c04c40fd1b60f148dc829d3f69b2de789d0ba239c35136efe9a386529"}, + {file = "ijson-3.3.0-cp310-cp310-win_amd64.whl", hash = "sha256:d654d045adafdcc6c100e8e911508a2eedbd2a1b5f93f930ba13ea67d7704ee9"}, + {file = "ijson-3.3.0-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:501dce8eaa537e728aa35810656aa00460a2547dcb60937c8139f36ec344d7fc"}, + {file = "ijson-3.3.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:658ba9cad0374d37b38c9893f4864f284cdcc7d32041f9808fba8c7bcaadf134"}, + {file = "ijson-3.3.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:2636cb8c0f1023ef16173f4b9a233bcdb1df11c400c603d5f299fac143ca8d70"}, + {file = "ijson-3.3.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:cd174b90db68c3bcca273e9391934a25d76929d727dc75224bf244446b28b03b"}, + {file = "ijson-3.3.0-cp311-cp311-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:97a9aea46e2a8371c4cf5386d881de833ed782901ac9f67ebcb63bb3b7d115af"}, + {file = "ijson-3.3.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c594c0abe69d9d6099f4ece17763d53072f65ba60b372d8ba6de8695ce6ee39e"}, + {file = "ijson-3.3.0-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:8e0ff16c224d9bfe4e9e6bd0395826096cda4a3ef51e6c301e1b61007ee2bd24"}, + {file = "ijson-3.3.0-cp311-cp311-musllinux_1_2_i686.whl", hash = "sha256:0015354011303175eae7e2ef5136414e91de2298e5a2e9580ed100b728c07e51"}, + {file = "ijson-3.3.0-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:034642558afa57351a0ffe6de89e63907c4cf6849070cc10a3b2542dccda1afe"}, + {file = "ijson-3.3.0-cp311-cp311-win32.whl", hash = "sha256:192e4b65495978b0bce0c78e859d14772e841724d3269fc1667dc6d2f53cc0ea"}, + {file = "ijson-3.3.0-cp311-cp311-win_amd64.whl", hash = "sha256:72e3488453754bdb45c878e31ce557ea87e1eb0f8b4fc610373da35e8074ce42"}, + {file = "ijson-3.3.0-cp312-cp312-macosx_10_9_universal2.whl", hash = "sha256:988e959f2f3d59ebd9c2962ae71b97c0df58323910d0b368cc190ad07429d1bb"}, + {file = "ijson-3.3.0-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:b2f73f0d0fce5300f23a1383d19b44d103bb113b57a69c36fd95b7c03099b181"}, + {file = "ijson-3.3.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:0ee57a28c6bf523d7cb0513096e4eb4dac16cd935695049de7608ec110c2b751"}, + {file = "ijson-3.3.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e0155a8f079c688c2ccaea05de1ad69877995c547ba3d3612c1c336edc12a3a5"}, + {file = "ijson-3.3.0-cp312-cp312-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:7ab00721304af1ae1afa4313ecfa1bf16b07f55ef91e4a5b93aeaa3e2bd7917c"}, + {file = "ijson-3.3.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:40ee3821ee90be0f0e95dcf9862d786a7439bd1113e370736bfdf197e9765bfb"}, + {file = "ijson-3.3.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:da3b6987a0bc3e6d0f721b42c7a0198ef897ae50579547b0345f7f02486898f5"}, + {file = "ijson-3.3.0-cp312-cp312-musllinux_1_2_i686.whl", hash = "sha256:63afea5f2d50d931feb20dcc50954e23cef4127606cc0ecf7a27128ed9f9a9e6"}, + {file = "ijson-3.3.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:b5c3e285e0735fd8c5a26d177eca8b52512cdd8687ca86ec77a0c66e9c510182"}, + {file = "ijson-3.3.0-cp312-cp312-win32.whl", hash = "sha256:907f3a8674e489abdcb0206723e5560a5cb1fa42470dcc637942d7b10f28b695"}, + {file = "ijson-3.3.0-cp312-cp312-win_amd64.whl", hash = "sha256:8f890d04ad33262d0c77ead53c85f13abfb82f2c8f078dfbf24b78f59534dfdd"}, + {file = "ijson-3.3.0-cp36-cp36m-macosx_10_9_x86_64.whl", hash = "sha256:b9d85a02e77ee8ea6d9e3fd5d515bcc3d798d9c1ea54817e5feb97a9bc5d52fe"}, + {file = "ijson-3.3.0-cp36-cp36m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e6576cdc36d5a09b0c1a3d81e13a45d41a6763188f9eaae2da2839e8a4240bce"}, + {file = "ijson-3.3.0-cp36-cp36m-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:e5589225c2da4bb732c9c370c5961c39a6db72cf69fb2a28868a5413ed7f39e6"}, + {file = "ijson-3.3.0-cp36-cp36m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ad04cf38164d983e85f9cba2804566c0160b47086dcca4cf059f7e26c5ace8ca"}, + {file = "ijson-3.3.0-cp36-cp36m-musllinux_1_2_aarch64.whl", hash = "sha256:a3b730ef664b2ef0e99dec01b6573b9b085c766400af363833e08ebc1e38eb2f"}, + {file = "ijson-3.3.0-cp36-cp36m-musllinux_1_2_i686.whl", hash = "sha256:4690e3af7b134298055993fcbea161598d23b6d3ede11b12dca6815d82d101d5"}, + {file = "ijson-3.3.0-cp36-cp36m-musllinux_1_2_x86_64.whl", hash = "sha256:aaa6bfc2180c31a45fac35d40e3312a3d09954638ce0b2e9424a88e24d262a13"}, + {file = "ijson-3.3.0-cp36-cp36m-win32.whl", hash = "sha256:44367090a5a876809eb24943f31e470ba372aaa0d7396b92b953dda953a95d14"}, + {file = "ijson-3.3.0-cp36-cp36m-win_amd64.whl", hash = "sha256:7e2b3e9ca957153557d06c50a26abaf0d0d6c0ddf462271854c968277a6b5372"}, + {file = "ijson-3.3.0-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:47c144117e5c0e2babb559bc8f3f76153863b8dd90b2d550c51dab5f4b84a87f"}, + {file = "ijson-3.3.0-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:29ce02af5fbf9ba6abb70765e66930aedf73311c7d840478f1ccecac53fefbf3"}, + {file = "ijson-3.3.0-cp37-cp37m-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:4ac6c3eeed25e3e2cb9b379b48196413e40ac4e2239d910bb33e4e7f6c137745"}, + {file = "ijson-3.3.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d92e339c69b585e7b1d857308ad3ca1636b899e4557897ccd91bb9e4a56c965b"}, + {file = "ijson-3.3.0-cp37-cp37m-musllinux_1_2_aarch64.whl", hash = "sha256:8c85447569041939111b8c7dbf6f8fa7a0eb5b2c4aebb3c3bec0fb50d7025121"}, + {file = "ijson-3.3.0-cp37-cp37m-musllinux_1_2_i686.whl", hash = "sha256:542c1e8fddf082159a5d759ee1412c73e944a9a2412077ed00b303ff796907dc"}, + {file = "ijson-3.3.0-cp37-cp37m-musllinux_1_2_x86_64.whl", hash = "sha256:30cfea40936afb33b57d24ceaf60d0a2e3d5c1f2335ba2623f21d560737cc730"}, + {file = "ijson-3.3.0-cp37-cp37m-win32.whl", hash = "sha256:6b661a959226ad0d255e49b77dba1d13782f028589a42dc3172398dd3814c797"}, + {file = "ijson-3.3.0-cp37-cp37m-win_amd64.whl", hash = "sha256:0b003501ee0301dbf07d1597482009295e16d647bb177ce52076c2d5e64113e0"}, + {file = "ijson-3.3.0-cp38-cp38-macosx_10_9_universal2.whl", hash = "sha256:3e8d8de44effe2dbd0d8f3eb9840344b2d5b4cc284a14eb8678aec31d1b6bea8"}, + {file = "ijson-3.3.0-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:9cd5c03c63ae06d4f876b9844c5898d0044c7940ff7460db9f4cd984ac7862b5"}, + {file = "ijson-3.3.0-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:04366e7e4a4078d410845e58a2987fd9c45e63df70773d7b6e87ceef771b51ee"}, + {file = "ijson-3.3.0-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:de7c1ddb80fa7a3ab045266dca169004b93f284756ad198306533b792774f10a"}, + {file = "ijson-3.3.0-cp38-cp38-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:8851584fb931cffc0caa395f6980525fd5116eab8f73ece9d95e6f9c2c326c4c"}, + {file = "ijson-3.3.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:bdcfc88347fd981e53c33d832ce4d3e981a0d696b712fbcb45dcc1a43fe65c65"}, + {file = "ijson-3.3.0-cp38-cp38-musllinux_1_2_aarch64.whl", hash = "sha256:3917b2b3d0dbbe3296505da52b3cb0befbaf76119b2edaff30bd448af20b5400"}, + {file = "ijson-3.3.0-cp38-cp38-musllinux_1_2_i686.whl", hash = "sha256:e10c14535abc7ddf3fd024aa36563cd8ab5d2bb6234a5d22c77c30e30fa4fb2b"}, + {file = "ijson-3.3.0-cp38-cp38-musllinux_1_2_x86_64.whl", hash = "sha256:3aba5c4f97f4e2ce854b5591a8b0711ca3b0c64d1b253b04ea7b004b0a197ef6"}, + {file = "ijson-3.3.0-cp38-cp38-win32.whl", hash = "sha256:b325f42e26659df1a0de66fdb5cde8dd48613da9c99c07d04e9fb9e254b7ee1c"}, + {file = "ijson-3.3.0-cp38-cp38-win_amd64.whl", hash = "sha256:ff835906f84451e143f31c4ce8ad73d83ef4476b944c2a2da91aec8b649570e1"}, + {file = "ijson-3.3.0-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:3c556f5553368dff690c11d0a1fb435d4ff1f84382d904ccc2dc53beb27ba62e"}, + {file = "ijson-3.3.0-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:e4396b55a364a03ff7e71a34828c3ed0c506814dd1f50e16ebed3fc447d5188e"}, + {file = "ijson-3.3.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:e6850ae33529d1e43791b30575070670070d5fe007c37f5d06aebc1dd152ab3f"}, + {file = "ijson-3.3.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:36aa56d68ea8def26778eb21576ae13f27b4a47263a7a2581ab2ef58b8de4451"}, + {file = "ijson-3.3.0-cp39-cp39-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:a7ec759c4a0fc820ad5dc6a58e9c391e7b16edcb618056baedbedbb9ea3b1524"}, + {file = "ijson-3.3.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b51bab2c4e545dde93cb6d6bb34bf63300b7cd06716f195dd92d9255df728331"}, + {file = "ijson-3.3.0-cp39-cp39-musllinux_1_2_aarch64.whl", hash = "sha256:92355f95a0e4da96d4c404aa3cff2ff033f9180a9515f813255e1526551298c1"}, + {file = "ijson-3.3.0-cp39-cp39-musllinux_1_2_i686.whl", hash = "sha256:8795e88adff5aa3c248c1edce932db003d37a623b5787669ccf205c422b91e4a"}, + {file = "ijson-3.3.0-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:8f83f553f4cde6d3d4eaf58ec11c939c94a0ec545c5b287461cafb184f4b3a14"}, + {file = "ijson-3.3.0-cp39-cp39-win32.whl", hash = "sha256:ead50635fb56577c07eff3e557dac39533e0fe603000684eea2af3ed1ad8f941"}, + {file = "ijson-3.3.0-cp39-cp39-win_amd64.whl", hash = "sha256:c8a9befb0c0369f0cf5c1b94178d0d78f66d9cebb9265b36be6e4f66236076b8"}, + {file = "ijson-3.3.0-pp310-pypy310_pp73-macosx_10_9_x86_64.whl", hash = "sha256:2af323a8aec8a50fa9effa6d640691a30a9f8c4925bd5364a1ca97f1ac6b9b5c"}, + {file = "ijson-3.3.0-pp310-pypy310_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f64f01795119880023ba3ce43072283a393f0b90f52b66cc0ea1a89aa64a9ccb"}, + {file = "ijson-3.3.0-pp310-pypy310_pp73-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:a716e05547a39b788deaf22725490855337fc36613288aa8ae1601dc8c525553"}, + {file = "ijson-3.3.0-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:473f5d921fadc135d1ad698e2697025045cd8ed7e5e842258295012d8a3bc702"}, + {file = "ijson-3.3.0-pp310-pypy310_pp73-win_amd64.whl", hash = "sha256:dd26b396bc3a1e85f4acebeadbf627fa6117b97f4c10b177d5779577c6607744"}, + {file = "ijson-3.3.0-pp37-pypy37_pp73-macosx_10_9_x86_64.whl", hash = "sha256:25fd49031cdf5fd5f1fd21cb45259a64dad30b67e64f745cc8926af1c8c243d3"}, + {file = "ijson-3.3.0-pp37-pypy37_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:4b72178b1e565d06ab19319965022b36ef41bcea7ea153b32ec31194bec032a2"}, + {file = "ijson-3.3.0-pp37-pypy37_pp73-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:7d0b6b637d05dbdb29d0bfac2ed8425bb369e7af5271b0cc7cf8b801cb7360c2"}, + {file = "ijson-3.3.0-pp37-pypy37_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:5378d0baa59ae422905c5f182ea0fd74fe7e52a23e3821067a7d58c8306b2191"}, + {file = "ijson-3.3.0-pp37-pypy37_pp73-win_amd64.whl", hash = "sha256:99f5c8ab048ee4233cc4f2b461b205cbe01194f6201018174ac269bf09995749"}, + {file = "ijson-3.3.0-pp38-pypy38_pp73-macosx_10_9_x86_64.whl", hash = "sha256:45ff05de889f3dc3d37a59d02096948ce470699f2368b32113954818b21aa74a"}, + {file = "ijson-3.3.0-pp38-pypy38_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1efb521090dd6cefa7aafd120581947b29af1713c902ff54336b7c7130f04c47"}, + {file = "ijson-3.3.0-pp38-pypy38_pp73-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:87c727691858fd3a1c085d9980d12395517fcbbf02c69fbb22dede8ee03422da"}, + {file = "ijson-3.3.0-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:0420c24e50389bc251b43c8ed379ab3e3ba065ac8262d98beb6735ab14844460"}, + {file = "ijson-3.3.0-pp38-pypy38_pp73-win_amd64.whl", hash = "sha256:8fdf3721a2aa7d96577970f5604bd81f426969c1822d467f07b3d844fa2fecc7"}, + {file = "ijson-3.3.0-pp39-pypy39_pp73-macosx_10_9_x86_64.whl", hash = "sha256:891f95c036df1bc95309951940f8eea8537f102fa65715cdc5aae20b8523813b"}, + {file = "ijson-3.3.0-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ed1336a2a6e5c427f419da0154e775834abcbc8ddd703004108121c6dd9eba9d"}, + {file = "ijson-3.3.0-pp39-pypy39_pp73-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:f0c819f83e4f7b7f7463b2dc10d626a8be0c85fbc7b3db0edc098c2b16ac968e"}, + {file = "ijson-3.3.0-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:33afc25057377a6a43c892de34d229a86f89ea6c4ca3dd3db0dcd17becae0dbb"}, + {file = "ijson-3.3.0-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:7914d0cf083471856e9bc2001102a20f08e82311dfc8cf1a91aa422f9414a0d6"}, + {file = "ijson-3.3.0.tar.gz", hash = "sha256:7f172e6ba1bee0d4c8f8ebd639577bfe429dee0f3f96775a067b8bae4492d8a0"}, ] [[package]] @@ -4026,13 +4031,13 @@ zookeeper = ["kazoo (>=2.8.0)"] [[package]] name = "kubernetes" -version = "29.0.0" +version = "30.1.0" description = "Kubernetes python client" optional = false python-versions = ">=3.6" files = [ - {file = "kubernetes-29.0.0-py2.py3-none-any.whl", hash = "sha256:ab8cb0e0576ccdfb71886366efb102c6a20f268d817be065ce7f9909c631e43e"}, - {file = "kubernetes-29.0.0.tar.gz", hash = "sha256:c4812e227ae74d07d53c88293e564e54b850452715a59a927e7e1bc6b9a60459"}, + {file = "kubernetes-30.1.0-py2.py3-none-any.whl", hash = "sha256:e212e8b7579031dd2e512168b617373bc1e03888d41ac4e04039240a292d478d"}, + {file = "kubernetes-30.1.0.tar.gz", hash = "sha256:41e4c77af9f28e7a6c314e3bd06a8c6229ddd787cad684e0ab9f69b498e98ebc"}, ] [package.dependencies] @@ -4052,13 +4057,13 @@ adal = ["adal (>=1.0.2)"] [[package]] name = "langchain" -version = "0.2.2" +version = "0.2.3" description = "Building applications with LLMs through composability" optional = false python-versions = "<4.0,>=3.8.1" files = [ - {file = "langchain-0.2.2-py3-none-any.whl", hash = "sha256:58ca0c47bcdd156da66f50a0a4fcedc49bf6950827f4a6b06c8c4842d55805f3"}, - {file = "langchain-0.2.2.tar.gz", hash = "sha256:9d61e50e9cdc2bea659bc5e6c03650ba048fda63a307490ae368e539f61a0d3a"}, + {file = "langchain-0.2.3-py3-none-any.whl", hash = "sha256:5dc33cd9c8008693d328b7cb698df69073acecc89ad9c2a95f243b3314f8d834"}, + {file = "langchain-0.2.3.tar.gz", hash = "sha256:81962cc72cce6515f7bd71e01542727870789bf8b666c6913d85559080c1a201"}, ] [package.dependencies] @@ -4074,20 +4079,6 @@ requests = ">=2,<3" SQLAlchemy = ">=1.4,<3" tenacity = ">=8.1.0,<9.0.0" -[package.extras] -azure = ["azure-ai-formrecognizer (>=3.2.1,<4.0.0)", "azure-ai-textanalytics (>=5.3.0,<6.0.0)", "azure-cognitiveservices-speech (>=1.28.0,<2.0.0)", "azure-core (>=1.26.4,<2.0.0)", "azure-cosmos (>=4.4.0b1,<5.0.0)", "azure-identity (>=1.12.0,<2.0.0)", "azure-search-documents (==11.4.0b8)", "openai (<2)"] -clarifai = ["clarifai (>=9.1.0)"] -cli = ["typer (>=0.9.0,<0.10.0)"] -cohere = ["cohere (>=4,<6)"] -docarray = ["docarray[hnswlib] (>=0.32.0,<0.33.0)"] -embeddings = ["sentence-transformers (>=2,<3)"] -extended-testing = ["aiosqlite (>=0.19.0,<0.20.0)", "aleph-alpha-client (>=2.15.0,<3.0.0)", "anthropic (>=0.3.11,<0.4.0)", "arxiv (>=1.4,<2.0)", "assemblyai (>=0.17.0,<0.18.0)", "atlassian-python-api (>=3.36.0,<4.0.0)", "beautifulsoup4 (>=4,<5)", "bibtexparser (>=1.4.0,<2.0.0)", "cassio (>=0.1.0,<0.2.0)", "chardet (>=5.1.0,<6.0.0)", "cohere (>=4,<6)", "couchbase (>=4.1.9,<5.0.0)", "dashvector (>=1.0.1,<2.0.0)", "databricks-vectorsearch (>=0.21,<0.22)", "datasets (>=2.15.0,<3.0.0)", "dgml-utils (>=0.3.0,<0.4.0)", "esprima (>=4.0.1,<5.0.0)", "faiss-cpu (>=1,<2)", "feedparser (>=6.0.10,<7.0.0)", "fireworks-ai (>=0.9.0,<0.10.0)", "geopandas (>=0.13.1,<0.14.0)", "gitpython (>=3.1.32,<4.0.0)", "google-cloud-documentai (>=2.20.1,<3.0.0)", "gql (>=3.4.1,<4.0.0)", "hologres-vector (>=0.0.6,<0.0.7)", "html2text (>=2020.1.16,<2021.0.0)", "javelin-sdk (>=0.1.8,<0.2.0)", "jinja2 (>=3,<4)", "jq (>=1.4.1,<2.0.0)", "jsonschema (>1)", "langchain-openai (>=0.1,<0.2)", "lxml (>=4.9.3,<6.0)", "markdownify (>=0.11.6,<0.12.0)", "motor (>=3.3.1,<4.0.0)", "msal (>=1.25.0,<2.0.0)", "mwparserfromhell (>=0.6.4,<0.7.0)", "mwxml (>=0.3.3,<0.4.0)", "newspaper3k (>=0.2.8,<0.3.0)", "numexpr (>=2.8.6,<3.0.0)", "openai (<2)", "openapi-pydantic (>=0.3.2,<0.4.0)", "pandas (>=2.0.1,<3.0.0)", "pdfminer-six (>=20221105,<20221106)", "pgvector (>=0.1.6,<0.2.0)", "praw (>=7.7.1,<8.0.0)", "psychicapi (>=0.8.0,<0.9.0)", "py-trello (>=0.19.0,<0.20.0)", "pymupdf (>=1.22.3,<2.0.0)", "pypdf (>=3.4.0,<4.0.0)", "pypdfium2 (>=4.10.0,<5.0.0)", "pyspark (>=3.4.0,<4.0.0)", "rank-bm25 (>=0.2.2,<0.3.0)", "rapidfuzz (>=3.1.1,<4.0.0)", "rapidocr-onnxruntime (>=1.3.2,<2.0.0)", "rdflib (==7.0.0)", "requests-toolbelt (>=1.0.0,<2.0.0)", "rspace_client (>=2.5.0,<3.0.0)", "scikit-learn (>=1.2.2,<2.0.0)", "sqlite-vss (>=0.1.2,<0.2.0)", "streamlit (>=1.18.0,<2.0.0)", "sympy (>=1.12,<2.0)", "telethon (>=1.28.5,<2.0.0)", "timescale-vector (>=0.0.1,<0.0.2)", "tqdm (>=4.48.0)", "upstash-redis (>=0.15.0,<0.16.0)", "xata (>=1.0.0a7,<2.0.0)", "xmltodict (>=0.13.0,<0.14.0)"] -javascript = ["esprima (>=4.0.1,<5.0.0)"] -llms = ["clarifai (>=9.1.0)", "cohere (>=4,<6)", "huggingface_hub (>=0,<1)", "manifest-ml (>=0.0.1,<0.0.2)", "nlpcloud (>=1,<2)", "openai (<2)", "openlm (>=0.0.5,<0.0.6)", "torch (>=1,<3)", "transformers (>=4,<5)"] -openai = ["openai (<2)", "tiktoken (>=0.7,<1.0)"] -qdrant = ["qdrant-client (>=1.3.1,<2.0.0)"] -text-helpers = ["chardet (>=5.1.0,<6.0.0)"] - [[package]] name = "langchain-anthropic" version = "0.1.15" @@ -4154,13 +4145,13 @@ langchain-core = ">=0.1.42,<0.3" [[package]] name = "langchain-community" -version = "0.2.2" +version = "0.2.4" description = "Community contributed LangChain integrations." optional = false python-versions = "<4.0,>=3.8.1" files = [ - {file = "langchain_community-0.2.2-py3-none-any.whl", hash = "sha256:470ee16e05f1acacb91a656b6d3c2cbf6fb6a8dcb00a13901cd1353cd29c2bb3"}, - {file = "langchain_community-0.2.2.tar.gz", hash = "sha256:fb09faf4640726a929932056dc55ff120e490aaf2e424fae8ddbb15605195447"}, + {file = "langchain_community-0.2.4-py3-none-any.whl", hash = "sha256:8582e9800f4837660dc297cccd2ee1ddc1d8c440d0fe8b64edb07620f0373b0e"}, + {file = "langchain_community-0.2.4.tar.gz", hash = "sha256:2bb6a1a36b8500a564d25d76469c02457b1a7c3afea6d4a609a47c06b993e3e4"}, ] [package.dependencies] @@ -4175,19 +4166,15 @@ requests = ">=2,<3" SQLAlchemy = ">=1.4,<3" tenacity = ">=8.1.0,<9.0.0" -[package.extras] -cli = ["typer (>=0.9.0,<0.10.0)"] -extended-testing = ["aiosqlite (>=0.19.0,<0.20.0)", "aleph-alpha-client (>=2.15.0,<3.0.0)", "anthropic (>=0.3.11,<0.4.0)", "arxiv (>=1.4,<2.0)", "assemblyai (>=0.17.0,<0.18.0)", "atlassian-python-api (>=3.36.0,<4.0.0)", "azure-ai-documentintelligence (>=1.0.0b1,<2.0.0)", "azure-identity (>=1.15.0,<2.0.0)", "azure-search-documents (==11.4.0)", "beautifulsoup4 (>=4,<5)", "bibtexparser (>=1.4.0,<2.0.0)", "cassio (>=0.1.6,<0.2.0)", "chardet (>=5.1.0,<6.0.0)", "cloudpathlib (>=0.18,<0.19)", "cloudpickle (>=2.0.0)", "cohere (>=4,<5)", "databricks-vectorsearch (>=0.21,<0.22)", "datasets (>=2.15.0,<3.0.0)", "dgml-utils (>=0.3.0,<0.4.0)", "elasticsearch (>=8.12.0,<9.0.0)", "esprima (>=4.0.1,<5.0.0)", "faiss-cpu (>=1,<2)", "feedparser (>=6.0.10,<7.0.0)", "fireworks-ai (>=0.9.0,<0.10.0)", "friendli-client (>=1.2.4,<2.0.0)", "geopandas (>=0.13.1,<0.14.0)", "gitpython (>=3.1.32,<4.0.0)", "google-cloud-documentai (>=2.20.1,<3.0.0)", "gql (>=3.4.1,<4.0.0)", "gradientai (>=1.4.0,<2.0.0)", "hdbcli (>=2.19.21,<3.0.0)", "hologres-vector (>=0.0.6,<0.0.7)", "html2text (>=2020.1.16,<2021.0.0)", "httpx (>=0.24.1,<0.25.0)", "httpx-sse (>=0.4.0,<0.5.0)", "javelin-sdk (>=0.1.8,<0.2.0)", "jinja2 (>=3,<4)", "jq (>=1.4.1,<2.0.0)", "jsonschema (>1)", "lxml (>=4.9.3,<6.0)", "markdownify (>=0.11.6,<0.12.0)", "motor (>=3.3.1,<4.0.0)", "msal (>=1.25.0,<2.0.0)", "mwparserfromhell (>=0.6.4,<0.7.0)", "mwxml (>=0.3.3,<0.4.0)", "newspaper3k (>=0.2.8,<0.3.0)", "numexpr (>=2.8.6,<3.0.0)", "nvidia-riva-client (>=2.14.0,<3.0.0)", "oci (>=2.119.1,<3.0.0)", "openai (<2)", "openapi-pydantic (>=0.3.2,<0.4.0)", "oracle-ads (>=2.9.1,<3.0.0)", "oracledb (>=2.2.0,<3.0.0)", "pandas (>=2.0.1,<3.0.0)", "pdfminer-six (>=20221105,<20221106)", "pgvector (>=0.1.6,<0.2.0)", "praw (>=7.7.1,<8.0.0)", "premai (>=0.3.25,<0.4.0)", "psychicapi (>=0.8.0,<0.9.0)", "py-trello (>=0.19.0,<0.20.0)", "pyjwt (>=2.8.0,<3.0.0)", "pymupdf (>=1.22.3,<2.0.0)", "pypdf (>=3.4.0,<4.0.0)", "pypdfium2 (>=4.10.0,<5.0.0)", "pyspark (>=3.4.0,<4.0.0)", "rank-bm25 (>=0.2.2,<0.3.0)", "rapidfuzz (>=3.1.1,<4.0.0)", "rapidocr-onnxruntime (>=1.3.2,<2.0.0)", "rdflib (==7.0.0)", "requests-toolbelt (>=1.0.0,<2.0.0)", "rspace_client (>=2.5.0,<3.0.0)", "scikit-learn (>=1.2.2,<2.0.0)", "simsimd (>=4.3.1,<5.0.0)", "sqlite-vss (>=0.1.2,<0.2.0)", "streamlit (>=1.18.0,<2.0.0)", "sympy (>=1.12,<2.0)", "telethon (>=1.28.5,<2.0.0)", "tidb-vector (>=0.0.3,<1.0.0)", "timescale-vector (>=0.0.1,<0.0.2)", "tqdm (>=4.48.0)", "tree-sitter (>=0.20.2,<0.21.0)", "tree-sitter-languages (>=1.8.0,<2.0.0)", "upstash-redis (>=0.15.0,<0.16.0)", "vdms (>=0.0.20,<0.0.21)", "xata (>=1.0.0a7,<2.0.0)", "xmltodict (>=0.13.0,<0.14.0)"] - [[package]] name = "langchain-core" -version = "0.2.4" +version = "0.2.5" description = "Building applications with LLMs through composability" optional = false python-versions = "<4.0,>=3.8.1" files = [ - {file = "langchain_core-0.2.4-py3-none-any.whl", hash = "sha256:5212f7ec78a525e88a178ed3aefe2fd7134b03fb92573dfbab9914f1d92d6ec5"}, - {file = "langchain_core-0.2.4.tar.gz", hash = "sha256:82bdcc546eb0341cefcf1f4ecb3e49836fff003903afddda2d1312bb8491ef81"}, + {file = "langchain_core-0.2.5-py3-none-any.whl", hash = "sha256:abe5138f22acff23a079ec538be5268bbf97cf023d51987a0dd474d2a16cae3e"}, + {file = "langchain_core-0.2.5.tar.gz", hash = "sha256:4a5c2f56b22396a63ef4790043660e393adbfa6832b978f023ca996a04b8e752"}, ] [package.dependencies] @@ -4198,9 +4185,6 @@ pydantic = ">=1,<3" PyYAML = ">=5.3" tenacity = ">=8.1.0,<9.0.0" -[package.extras] -extended-testing = ["jinja2 (>=3,<4)"] - [[package]] name = "langchain-experimental" version = "0.0.60" @@ -4221,37 +4205,37 @@ extended-testing = ["faker (>=19.3.1,<20.0.0)", "jinja2 (>=3,<4)", "pandas (>=2. [[package]] name = "langchain-google-genai" -version = "1.0.5" +version = "1.0.6" description = "An integration package connecting Google's genai package and LangChain" optional = false python-versions = "<4.0,>=3.9" files = [ - {file = "langchain_google_genai-1.0.5-py3-none-any.whl", hash = "sha256:06b1af072e14fe2d4f9257be4bf883ccd544896094f847c2b1ab09b123ba3b9e"}, - {file = "langchain_google_genai-1.0.5.tar.gz", hash = "sha256:5b515192755fd396a1b61b33d1b08c77fb9b53394cc25954f9d7e9a0f615de9b"}, + {file = "langchain_google_genai-1.0.6-py3-none-any.whl", hash = "sha256:65188b3c2867efda78e09c29371499ab0d25c6a111b175365fdae2b5be1502e6"}, + {file = "langchain_google_genai-1.0.6.tar.gz", hash = "sha256:7c964117fa385c490b323ee50ab46907229823d3678b80bfacc8fa0a237fb0b9"}, ] [package.dependencies] google-generativeai = ">=0.5.2,<0.6.0" -langchain-core = ">=0.2.0,<0.3" +langchain-core = ">=0.2.2,<0.3" [package.extras] images = ["pillow (>=10.1.0,<11.0.0)"] [[package]] name = "langchain-google-vertexai" -version = "1.0.4" +version = "1.0.5" description = "An integration package connecting Google VertexAI and LangChain" optional = false python-versions = "<4.0,>=3.8.1" files = [ - {file = "langchain_google_vertexai-1.0.4-py3-none-any.whl", hash = "sha256:f9d217df2d5cfafb2e551ddd5f1c43611222f542ee0df0cc3b5faed82e657ee3"}, - {file = "langchain_google_vertexai-1.0.4.tar.gz", hash = "sha256:bb2d2e93cc2896b9bdc96789c2df247f6392184dffc0c3dddc06889f2b530465"}, + {file = "langchain_google_vertexai-1.0.5-py3-none-any.whl", hash = "sha256:38f4a39bf35927d744d0883907c4d4a59eef059e9b36f28bb5c737c2aae6963b"}, + {file = "langchain_google_vertexai-1.0.5.tar.gz", hash = "sha256:50005dc12ff9d66bbbab9e1ab660574b1584eee3e7b5a647dc8a009a94f0c500"}, ] [package.dependencies] google-cloud-aiplatform = ">=1.47.0,<2.0.0" google-cloud-storage = ">=2.14.0,<3.0.0" -langchain-core = ">=0.1.42,<0.3" +langchain-core = ">=0.2.2,<0.3" [package.extras] anthropic = ["anthropic[vertexai] (>=0.23.0,<1)"] @@ -4354,7 +4338,7 @@ types-requests = ">=2.31.0.2,<3.0.0.0" [[package]] name = "langflow-base" -version = "0.0.56" +version = "0.0.60" description = "A Python package with a built-in web application" optional = false python-versions = ">=3.10,<3.13" @@ -4411,13 +4395,13 @@ url = "src/backend/base" [[package]] name = "langfuse" -version = "2.33.1" +version = "2.35.0" description = "A client library for accessing langfuse" optional = false python-versions = "<4.0,>=3.8.1" files = [ - {file = "langfuse-2.33.1-py3-none-any.whl", hash = "sha256:61ff3ff4b9c9c195028c981cba892106fdf90028e3950209a15f0ae06a378a36"}, - {file = "langfuse-2.33.1.tar.gz", hash = "sha256:444a870e8b13ad37df710931389ecd3bad9997e550edf3c3178b5a0bd7ada013"}, + {file = "langfuse-2.35.0-py3-none-any.whl", hash = "sha256:e9df2474a01f8e167b7b13674c554915415b27064e48ad207054475f7fa8f82d"}, + {file = "langfuse-2.35.0.tar.gz", hash = "sha256:b1d4b478233eefbc8a6fc63ca00ca82f6afecf2b0fdc1835ca65e751cf901577"}, ] [package.dependencies] @@ -4435,13 +4419,13 @@ openai = ["openai (>=0.27.8)"] [[package]] name = "langsmith" -version = "0.1.71" +version = "0.1.75" description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform." optional = false python-versions = "<4.0,>=3.8.1" files = [ - {file = "langsmith-0.1.71-py3-none-any.whl", hash = "sha256:a9979de2780442eb24eced31314e49f5ece6f807a0d70740b2c6c39217226794"}, - {file = "langsmith-0.1.71.tar.gz", hash = "sha256:bdb1037a08acf7c19b3969c085df09c1eecb65baca8400b3b76ae871e2c8a97e"}, + {file = "langsmith-0.1.75-py3-none-any.whl", hash = "sha256:d08b08dd6b3fa4da170377f95123d77122ef4c52999d10fff4ae08ff70d07aed"}, + {file = "langsmith-0.1.75.tar.gz", hash = "sha256:61274e144ea94c297dd78ce03e6dfae18459fe9bd8ab5094d61a0c4816561279"}, ] [package.dependencies] @@ -4451,13 +4435,13 @@ requests = ">=2,<3" [[package]] name = "litellm" -version = "1.40.2" +version = "1.40.4" description = "Library to easily interface with LLM API providers" optional = false python-versions = "!=2.7.*,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,!=3.7.*,>=3.8" files = [ - {file = "litellm-1.40.2-py3-none-any.whl", hash = "sha256:56ee777eed30ee9acb86e74401d090dcac4adb57b5c8a8714f791b0c97a34afc"}, - {file = "litellm-1.40.2.tar.gz", hash = "sha256:1f5dc4eab7100962c3a2985c7d8c13070ff5793b341540d19b98a2bd85955cb0"}, + {file = "litellm-1.40.4-py3-none-any.whl", hash = "sha256:b3b8e4401f717c3a18595446bfdb80fc6bb74974aac4eae537fb7b3be37fbf9e"}, + {file = "litellm-1.40.4.tar.gz", hash = "sha256:3edaa1189742afd7c7df2b122f77373d47154a8fb6df6187ff5875e188baa3e1"}, ] [package.dependencies] @@ -4828,13 +4812,13 @@ files = [ [[package]] name = "marshmallow" -version = "3.21.2" +version = "3.21.3" description = "A lightweight library for converting complex datatypes to and from native Python datatypes." optional = false python-versions = ">=3.8" files = [ - {file = "marshmallow-3.21.2-py3-none-any.whl", hash = "sha256:70b54a6282f4704d12c0a41599682c5c5450e843b9ec406308653b47c59648a1"}, - {file = "marshmallow-3.21.2.tar.gz", hash = "sha256:82408deadd8b33d56338d2182d455db632c6313aa2af61916672146bb32edc56"}, + {file = "marshmallow-3.21.3-py3-none-any.whl", hash = "sha256:86ce7fb914aa865001a4b2092c4c2872d13bc347f3d42673272cabfdbad386f1"}, + {file = "marshmallow-3.21.3.tar.gz", hash = "sha256:4f57c5e050a54d66361e826f94fba213eb10b67b2fdb02c3e0343ce207ba1662"}, ] [package.dependencies] @@ -5615,13 +5599,13 @@ sympy = "*" [[package]] name = "openai" -version = "1.31.0" +version = "1.32.0" description = "The official Python library for the openai API" optional = false python-versions = ">=3.7.1" files = [ - {file = "openai-1.31.0-py3-none-any.whl", hash = "sha256:82044ee3122113f2a468a1f308a8882324d09556ba5348687c535d3655ee331c"}, - {file = "openai-1.31.0.tar.gz", hash = "sha256:54ae0625b005d6a3b895db2b8438dae1059cffff0cd262a26e9015c13a29ab06"}, + {file = "openai-1.32.0-py3-none-any.whl", hash = "sha256:953d57669f309002044fd2f678aba9f07a43256d74b3b00cd04afb5b185568ea"}, + {file = "openai-1.32.0.tar.gz", hash = "sha256:a6df15a7ab9344b1bc2bc8d83639f68b7a7e2453c0f5e50c1666547eee86f0bd"}, ] [package.dependencies] @@ -6869,13 +6853,13 @@ typing-extensions = ">=4.6.0,<4.7.0 || >4.7.0" [[package]] name = "pydantic-settings" -version = "2.3.0" +version = "2.3.1" description = "Settings management using Pydantic" optional = false python-versions = ">=3.8" files = [ - {file = "pydantic_settings-2.3.0-py3-none-any.whl", hash = "sha256:26eeed27370a9c5e3f64e4a7d6602573cbedf05ed940f1d5b11c3f178427af7a"}, - {file = "pydantic_settings-2.3.0.tar.gz", hash = "sha256:78db28855a71503cfe47f39500a1dece523c640afd5280edb5c5c9c9cfa534c9"}, + {file = "pydantic_settings-2.3.1-py3-none-any.whl", hash = "sha256:acb2c213140dfff9669f4fe9f8180d43914f51626db28ab2db7308a576cce51a"}, + {file = "pydantic_settings-2.3.1.tar.gz", hash = "sha256:e34bbd649803a6bb3e2f0f58fb0edff1f0c7f556849fda106cc21bcce12c30ab"}, ] [package.dependencies] @@ -7607,13 +7591,13 @@ websockets = ">=11,<13" [[package]] name = "redis" -version = "5.0.4" +version = "5.0.5" description = "Python client for Redis database and key-value store" optional = true python-versions = ">=3.7" files = [ - {file = "redis-5.0.4-py3-none-any.whl", hash = "sha256:7adc2835c7a9b5033b7ad8f8918d09b7344188228809c98df07af226d39dec91"}, - {file = "redis-5.0.4.tar.gz", hash = "sha256:ec31f2ed9675cc54c21ba854cfe0462e6faf1d83c8ce5944709db8a4700b9c61"}, + {file = "redis-5.0.5-py3-none-any.whl", hash = "sha256:30b47d4ebb6b7a0b9b40c1275a19b87bb6f46b3bed82a89012cf56dea4024ada"}, + {file = "redis-5.0.5.tar.gz", hash = "sha256:3417688621acf6ee368dec4a04dd95881be24efd34c79f00d31f62bb528800ae"}, ] [package.dependencies] @@ -7798,28 +7782,28 @@ pyasn1 = ">=0.1.3" [[package]] name = "ruff" -version = "0.4.7" +version = "0.4.8" description = "An extremely fast Python linter and code formatter, written in Rust." optional = false python-versions = ">=3.7" files = [ - {file = "ruff-0.4.7-py3-none-macosx_10_12_x86_64.whl", hash = "sha256:e089371c67892a73b6bb1525608e89a2aca1b77b5440acf7a71dda5dac958f9e"}, - {file = "ruff-0.4.7-py3-none-macosx_11_0_arm64.whl", hash = "sha256:10f973d521d910e5f9c72ab27e409e839089f955be8a4c8826601a6323a89753"}, - {file = "ruff-0.4.7-py3-none-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:59c3d110970001dfa494bcd95478e62286c751126dfb15c3c46e7915fc49694f"}, - {file = "ruff-0.4.7-py3-none-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:fa9773c6c00f4958f73b317bc0fd125295110c3776089f6ef318f4b775f0abe4"}, - {file = "ruff-0.4.7-py3-none-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:07fc80bbb61e42b3b23b10fda6a2a0f5a067f810180a3760c5ef1b456c21b9db"}, - {file = "ruff-0.4.7-py3-none-manylinux_2_17_ppc64.manylinux2014_ppc64.whl", hash = "sha256:fa4dafe3fe66d90e2e2b63fa1591dd6e3f090ca2128daa0be33db894e6c18648"}, - {file = "ruff-0.4.7-py3-none-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:a7c0083febdec17571455903b184a10026603a1de078428ba155e7ce9358c5f6"}, - {file = "ruff-0.4.7-py3-none-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:ad1b20e66a44057c326168437d680a2166c177c939346b19c0d6b08a62a37589"}, - {file = "ruff-0.4.7-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:cbf5d818553add7511c38b05532d94a407f499d1a76ebb0cad0374e32bc67202"}, - {file = "ruff-0.4.7-py3-none-musllinux_1_2_aarch64.whl", hash = "sha256:50e9651578b629baec3d1513b2534de0ac7ed7753e1382272b8d609997e27e83"}, - {file = "ruff-0.4.7-py3-none-musllinux_1_2_armv7l.whl", hash = "sha256:8874a9df7766cb956b218a0a239e0a5d23d9e843e4da1e113ae1d27ee420877a"}, - {file = "ruff-0.4.7-py3-none-musllinux_1_2_i686.whl", hash = "sha256:b9de9a6e49f7d529decd09381c0860c3f82fa0b0ea00ea78409b785d2308a567"}, - {file = "ruff-0.4.7-py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:13a1768b0691619822ae6d446132dbdfd568b700ecd3652b20d4e8bc1e498f78"}, - {file = "ruff-0.4.7-py3-none-win32.whl", hash = "sha256:769e5a51df61e07e887b81e6f039e7ed3573316ab7dd9f635c5afaa310e4030e"}, - {file = "ruff-0.4.7-py3-none-win_amd64.whl", hash = "sha256:9e3ab684ad403a9ed1226894c32c3ab9c2e0718440f6f50c7c5829932bc9e054"}, - {file = "ruff-0.4.7-py3-none-win_arm64.whl", hash = "sha256:10f2204b9a613988e3484194c2c9e96a22079206b22b787605c255f130db5ed7"}, - {file = "ruff-0.4.7.tar.gz", hash = "sha256:2331d2b051dc77a289a653fcc6a42cce357087c5975738157cd966590b18b5e1"}, + {file = "ruff-0.4.8-py3-none-macosx_10_12_x86_64.whl", hash = "sha256:7663a6d78f6adb0eab270fa9cf1ff2d28618ca3a652b60f2a234d92b9ec89066"}, + {file = "ruff-0.4.8-py3-none-macosx_11_0_arm64.whl", hash = "sha256:eeceb78da8afb6de0ddada93112869852d04f1cd0f6b80fe464fd4e35c330913"}, + {file = "ruff-0.4.8-py3-none-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:aad360893e92486662ef3be0a339c5ca3c1b109e0134fcd37d534d4be9fb8de3"}, + {file = "ruff-0.4.8-py3-none-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:284c2e3f3396fb05f5f803c9fffb53ebbe09a3ebe7dda2929ed8d73ded736deb"}, + {file = "ruff-0.4.8-py3-none-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:a7354f921e3fbe04d2a62d46707e569f9315e1a613307f7311a935743c51a764"}, + {file = "ruff-0.4.8-py3-none-manylinux_2_17_ppc64.manylinux2014_ppc64.whl", hash = "sha256:72584676164e15a68a15778fd1b17c28a519e7a0622161eb2debdcdabdc71883"}, + {file = "ruff-0.4.8-py3-none-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:9678d5c9b43315f323af2233a04d747409d1e3aa6789620083a82d1066a35199"}, + {file = "ruff-0.4.8-py3-none-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:704977a658131651a22b5ebeb28b717ef42ac6ee3b11e91dc87b633b5d83142b"}, + {file = "ruff-0.4.8-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d05f8d6f0c3cce5026cecd83b7a143dcad503045857bc49662f736437380ad45"}, + {file = "ruff-0.4.8-py3-none-musllinux_1_2_aarch64.whl", hash = "sha256:6ea874950daca5697309d976c9afba830d3bf0ed66887481d6bca1673fc5b66a"}, + {file = "ruff-0.4.8-py3-none-musllinux_1_2_armv7l.whl", hash = "sha256:fc95aac2943ddf360376be9aa3107c8cf9640083940a8c5bd824be692d2216dc"}, + {file = "ruff-0.4.8-py3-none-musllinux_1_2_i686.whl", hash = "sha256:384154a1c3f4bf537bac69f33720957ee49ac8d484bfc91720cc94172026ceed"}, + {file = "ruff-0.4.8-py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:e9d5ce97cacc99878aa0d084c626a15cd21e6b3d53fd6f9112b7fc485918e1fa"}, + {file = "ruff-0.4.8-py3-none-win32.whl", hash = "sha256:6d795d7639212c2dfd01991259460101c22aabf420d9b943f153ab9d9706e6a9"}, + {file = "ruff-0.4.8-py3-none-win_amd64.whl", hash = "sha256:e14a3a095d07560a9d6769a72f781d73259655919d9b396c650fc98a8157555d"}, + {file = "ruff-0.4.8-py3-none-win_arm64.whl", hash = "sha256:14019a06dbe29b608f6b7cbcec300e3170a8d86efaddb7b23405cb7f7dcaf780"}, + {file = "ruff-0.4.8.tar.gz", hash = "sha256:16d717b1d57b2e2fd68bd0bf80fb43931b79d05a7131aa477d66fc40fbd86268"}, ] [[package]] @@ -8356,13 +8340,13 @@ full = ["httpx (>=0.22.0)", "itsdangerous", "jinja2", "python-multipart (>=0.0.7 [[package]] name = "storage3" -version = "0.7.5" +version = "0.7.6" description = "Supabase Storage client for Python." optional = false python-versions = "<4.0,>=3.8" files = [ - {file = "storage3-0.7.5-py3-none-any.whl", hash = "sha256:a2d9fdacafdcbcdb6776a54987a7d84c3e3195a5e4782955c4ccfb36cb021f14"}, - {file = "storage3-0.7.5.tar.gz", hash = "sha256:ffe43f3877898b43a94024e68c2aaf4cebb3ad73dbbbd67747041d1d70bbf032"}, + {file = "storage3-0.7.6-py3-none-any.whl", hash = "sha256:d8c23bf87b3a88cafb03761b7f936e4e49daca67741d571513edf746e0f8ba72"}, + {file = "storage3-0.7.6.tar.gz", hash = "sha256:0b7781cea7fe6382e6b9349b84395808c5f4203dfcac31478304eedc2f81acf6"}, ] [package.dependencies] @@ -8424,13 +8408,13 @@ supafunc = ">=0.3.1,<0.5.0" [[package]] name = "supafunc" -version = "0.4.5" +version = "0.4.6" description = "Library for Supabase Functions" optional = false python-versions = "<4.0,>=3.8" files = [ - {file = "supafunc-0.4.5-py3-none-any.whl", hash = "sha256:2208045f8f5c797924666f6a332efad75ad368f8030b2e4ceb9d2bf63f329373"}, - {file = "supafunc-0.4.5.tar.gz", hash = "sha256:a6466d78bdcaa58b7f0303793643103baae8106a87acd5d01e196179a9d0d024"}, + {file = "supafunc-0.4.6-py3-none-any.whl", hash = "sha256:f7ca7b244365e171da7055a64edb462c2ec449cdaa210fc418cfccd132f4cf98"}, + {file = "supafunc-0.4.6.tar.gz", hash = "sha256:92db51f8f8568d1430285219c9c0072e44207409c416622d7387f609e31928a6"}, ] [package.dependencies] @@ -8696,31 +8680,31 @@ files = [ [[package]] name = "torch" -version = "2.3.0" +version = "2.3.1" description = "Tensors and Dynamic neural networks in Python with strong GPU acceleration" optional = true python-versions = ">=3.8.0" files = [ - {file = "torch-2.3.0-cp310-cp310-manylinux1_x86_64.whl", hash = "sha256:d8ea5a465dbfd8501f33c937d1f693176c9aef9d1c1b0ca1d44ed7b0a18c52ac"}, - {file = "torch-2.3.0-cp310-cp310-manylinux2014_aarch64.whl", hash = "sha256:09c81c5859a5b819956c6925a405ef1cdda393c9d8a01ce3851453f699d3358c"}, - {file = "torch-2.3.0-cp310-cp310-win_amd64.whl", hash = "sha256:1bf023aa20902586f614f7682fedfa463e773e26c58820b74158a72470259459"}, - {file = "torch-2.3.0-cp310-none-macosx_11_0_arm64.whl", hash = "sha256:758ef938de87a2653bba74b91f703458c15569f1562bf4b6c63c62d9c5a0c1f5"}, - {file = "torch-2.3.0-cp311-cp311-manylinux1_x86_64.whl", hash = "sha256:493d54ee2f9df100b5ce1d18c96dbb8d14908721f76351e908c9d2622773a788"}, - {file = "torch-2.3.0-cp311-cp311-manylinux2014_aarch64.whl", hash = "sha256:bce43af735c3da16cc14c7de2be7ad038e2fbf75654c2e274e575c6c05772ace"}, - {file = "torch-2.3.0-cp311-cp311-win_amd64.whl", hash = "sha256:729804e97b7cf19ae9ab4181f91f5e612af07956f35c8b2c8e9d9f3596a8e877"}, - {file = "torch-2.3.0-cp311-none-macosx_11_0_arm64.whl", hash = "sha256:d24e328226d8e2af7cf80fcb1d2f1d108e0de32777fab4aaa2b37b9765d8be73"}, - {file = "torch-2.3.0-cp312-cp312-manylinux1_x86_64.whl", hash = "sha256:b0de2bdc0486ea7b14fc47ff805172df44e421a7318b7c4d92ef589a75d27410"}, - {file = "torch-2.3.0-cp312-cp312-manylinux2014_aarch64.whl", hash = "sha256:a306c87a3eead1ed47457822c01dfbd459fe2920f2d38cbdf90de18f23f72542"}, - {file = "torch-2.3.0-cp312-cp312-win_amd64.whl", hash = "sha256:f9b98bf1a3c8af2d4c41f0bf1433920900896c446d1ddc128290ff146d1eb4bd"}, - {file = "torch-2.3.0-cp312-none-macosx_11_0_arm64.whl", hash = "sha256:dca986214267b34065a79000cee54232e62b41dff1ec2cab9abc3fc8b3dee0ad"}, - {file = "torch-2.3.0-cp38-cp38-manylinux1_x86_64.whl", hash = "sha256:20572f426965dd8a04e92a473d7e445fa579e09943cc0354f3e6fef6130ce061"}, - {file = "torch-2.3.0-cp38-cp38-manylinux2014_aarch64.whl", hash = "sha256:e65ba85ae292909cde0dde6369826d51165a3fc8823dc1854cd9432d7f79b932"}, - {file = "torch-2.3.0-cp38-cp38-win_amd64.whl", hash = "sha256:5515503a193781fd1b3f5c474e89c9dfa2faaa782b2795cc4a7ab7e67de923f6"}, - {file = "torch-2.3.0-cp38-none-macosx_11_0_arm64.whl", hash = "sha256:6ae9f64b09516baa4ef890af0672dc981c20b1f0d829ce115d4420a247e88fba"}, - {file = "torch-2.3.0-cp39-cp39-manylinux1_x86_64.whl", hash = "sha256:cd0dc498b961ab19cb3f8dbf0c6c50e244f2f37dbfa05754ab44ea057c944ef9"}, - {file = "torch-2.3.0-cp39-cp39-manylinux2014_aarch64.whl", hash = "sha256:e05f836559251e4096f3786ee99f4a8cbe67bc7fbedba8ad5e799681e47c5e80"}, - {file = "torch-2.3.0-cp39-cp39-win_amd64.whl", hash = "sha256:4fb27b35dbb32303c2927da86e27b54a92209ddfb7234afb1949ea2b3effffea"}, - {file = "torch-2.3.0-cp39-none-macosx_11_0_arm64.whl", hash = "sha256:760f8bedff506ce9e6e103498f9b1e9e15809e008368594c3a66bf74a8a51380"}, + {file = "torch-2.3.1-cp310-cp310-manylinux1_x86_64.whl", hash = "sha256:605a25b23944be5ab7c3467e843580e1d888b8066e5aaf17ff7bf9cc30001cc3"}, + {file = "torch-2.3.1-cp310-cp310-manylinux2014_aarch64.whl", hash = "sha256:f2357eb0965583a0954d6f9ad005bba0091f956aef879822274b1bcdb11bd308"}, + {file = "torch-2.3.1-cp310-cp310-win_amd64.whl", hash = "sha256:32b05fe0d1ada7f69c9f86c14ff69b0ef1957a5a54199bacba63d22d8fab720b"}, + {file = "torch-2.3.1-cp310-none-macosx_11_0_arm64.whl", hash = "sha256:7c09a94362778428484bcf995f6004b04952106aee0ef45ff0b4bab484f5498d"}, + {file = "torch-2.3.1-cp311-cp311-manylinux1_x86_64.whl", hash = "sha256:b2ec81b61bb094ea4a9dee1cd3f7b76a44555375719ad29f05c0ca8ef596ad39"}, + {file = "torch-2.3.1-cp311-cp311-manylinux2014_aarch64.whl", hash = "sha256:490cc3d917d1fe0bd027057dfe9941dc1d6d8e3cae76140f5dd9a7e5bc7130ab"}, + {file = "torch-2.3.1-cp311-cp311-win_amd64.whl", hash = "sha256:5802530783bd465fe66c2df99123c9a54be06da118fbd785a25ab0a88123758a"}, + {file = "torch-2.3.1-cp311-none-macosx_11_0_arm64.whl", hash = "sha256:a7dd4ed388ad1f3d502bf09453d5fe596c7b121de7e0cfaca1e2017782e9bbac"}, + {file = "torch-2.3.1-cp312-cp312-manylinux1_x86_64.whl", hash = "sha256:a486c0b1976a118805fc7c9641d02df7afbb0c21e6b555d3bb985c9f9601b61a"}, + {file = "torch-2.3.1-cp312-cp312-manylinux2014_aarch64.whl", hash = "sha256:224259821fe3e4c6f7edf1528e4fe4ac779c77addaa74215eb0b63a5c474d66c"}, + {file = "torch-2.3.1-cp312-cp312-win_amd64.whl", hash = "sha256:e5fdccbf6f1334b2203a61a0e03821d5845f1421defe311dabeae2fc8fbeac2d"}, + {file = "torch-2.3.1-cp312-none-macosx_11_0_arm64.whl", hash = "sha256:3c333dc2ebc189561514eda06e81df22bf8fb64e2384746b2cb9f04f96d1d4c8"}, + {file = "torch-2.3.1-cp38-cp38-manylinux1_x86_64.whl", hash = "sha256:07e9ba746832b8d069cacb45f312cadd8ad02b81ea527ec9766c0e7404bb3feb"}, + {file = "torch-2.3.1-cp38-cp38-manylinux2014_aarch64.whl", hash = "sha256:462d1c07dbf6bb5d9d2f3316fee73a24f3d12cd8dacf681ad46ef6418f7f6626"}, + {file = "torch-2.3.1-cp38-cp38-win_amd64.whl", hash = "sha256:ff60bf7ce3de1d43ad3f6969983f321a31f0a45df3690921720bcad6a8596cc4"}, + {file = "torch-2.3.1-cp38-none-macosx_11_0_arm64.whl", hash = "sha256:bee0bd33dc58aa8fc8a7527876e9b9a0e812ad08122054a5bff2ce5abf005b10"}, + {file = "torch-2.3.1-cp39-cp39-manylinux1_x86_64.whl", hash = "sha256:aaa872abde9a3d4f91580f6396d54888620f4a0b92e3976a6034759df4b961ad"}, + {file = "torch-2.3.1-cp39-cp39-manylinux2014_aarch64.whl", hash = "sha256:3d7a7f7ef21a7520510553dc3938b0c57c116a7daee20736a9e25cbc0e832bdc"}, + {file = "torch-2.3.1-cp39-cp39-win_amd64.whl", hash = "sha256:4777f6cefa0c2b5fa87223c213e7b6f417cf254a45e5829be4ccd1b2a4ee1011"}, + {file = "torch-2.3.1-cp39-none-macosx_11_0_arm64.whl", hash = "sha256:2bb5af780c55be68fe100feb0528d2edebace1d55cb2e351de735809ba7391eb"}, ] [package.dependencies] @@ -8741,7 +8725,7 @@ nvidia-cusparse-cu12 = {version = "12.1.0.106", markers = "platform_system == \" nvidia-nccl-cu12 = {version = "2.20.5", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""} nvidia-nvtx-cu12 = {version = "12.1.105", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""} sympy = "*" -triton = {version = "2.3.0", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and python_version < \"3.12\""} +triton = {version = "2.3.1", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and python_version < \"3.12\""} typing-extensions = ">=4.8.0" [package.extras] @@ -8750,22 +8734,22 @@ optree = ["optree (>=0.9.1)"] [[package]] name = "tornado" -version = "6.4" +version = "6.4.1" description = "Tornado is a Python web framework and asynchronous networking library, originally developed at FriendFeed." optional = false -python-versions = ">= 3.8" +python-versions = ">=3.8" files = [ - {file = "tornado-6.4-cp38-abi3-macosx_10_9_universal2.whl", hash = "sha256:02ccefc7d8211e5a7f9e8bc3f9e5b0ad6262ba2fbb683a6443ecc804e5224ce0"}, - {file = "tornado-6.4-cp38-abi3-macosx_10_9_x86_64.whl", hash = "sha256:27787de946a9cffd63ce5814c33f734c627a87072ec7eed71f7fc4417bb16263"}, - {file = "tornado-6.4-cp38-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f7894c581ecdcf91666a0912f18ce5e757213999e183ebfc2c3fdbf4d5bd764e"}, - {file = "tornado-6.4-cp38-abi3-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:e43bc2e5370a6a8e413e1e1cd0c91bedc5bd62a74a532371042a18ef19e10579"}, - {file = "tornado-6.4-cp38-abi3-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f0251554cdd50b4b44362f73ad5ba7126fc5b2c2895cc62b14a1c2d7ea32f212"}, - {file = "tornado-6.4-cp38-abi3-musllinux_1_1_aarch64.whl", hash = "sha256:fd03192e287fbd0899dd8f81c6fb9cbbc69194d2074b38f384cb6fa72b80e9c2"}, - {file = "tornado-6.4-cp38-abi3-musllinux_1_1_i686.whl", hash = "sha256:88b84956273fbd73420e6d4b8d5ccbe913c65d31351b4c004ae362eba06e1f78"}, - {file = "tornado-6.4-cp38-abi3-musllinux_1_1_x86_64.whl", hash = "sha256:71ddfc23a0e03ef2df1c1397d859868d158c8276a0603b96cf86892bff58149f"}, - {file = "tornado-6.4-cp38-abi3-win32.whl", hash = "sha256:6f8a6c77900f5ae93d8b4ae1196472d0ccc2775cc1dfdc9e7727889145c45052"}, - {file = "tornado-6.4-cp38-abi3-win_amd64.whl", hash = "sha256:10aeaa8006333433da48dec9fe417877f8bcc21f48dda8d661ae79da357b2a63"}, - {file = "tornado-6.4.tar.gz", hash = "sha256:72291fa6e6bc84e626589f1c29d90a5a6d593ef5ae68052ee2ef000dfd273dee"}, + {file = "tornado-6.4.1-cp38-abi3-macosx_10_9_universal2.whl", hash = "sha256:163b0aafc8e23d8cdc3c9dfb24c5368af84a81e3364745ccb4427669bf84aec8"}, + {file = "tornado-6.4.1-cp38-abi3-macosx_10_9_x86_64.whl", hash = "sha256:6d5ce3437e18a2b66fbadb183c1d3364fb03f2be71299e7d10dbeeb69f4b2a14"}, + {file = "tornado-6.4.1-cp38-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e2e20b9113cd7293f164dc46fffb13535266e713cdb87bd2d15ddb336e96cfc4"}, + {file = "tornado-6.4.1-cp38-abi3-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:8ae50a504a740365267b2a8d1a90c9fbc86b780a39170feca9bcc1787ff80842"}, + {file = "tornado-6.4.1-cp38-abi3-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:613bf4ddf5c7a95509218b149b555621497a6cc0d46ac341b30bd9ec19eac7f3"}, + {file = "tornado-6.4.1-cp38-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:25486eb223babe3eed4b8aecbac33b37e3dd6d776bc730ca14e1bf93888b979f"}, + {file = "tornado-6.4.1-cp38-abi3-musllinux_1_2_i686.whl", hash = "sha256:454db8a7ecfcf2ff6042dde58404164d969b6f5d58b926da15e6b23817950fc4"}, + {file = "tornado-6.4.1-cp38-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:a02a08cc7a9314b006f653ce40483b9b3c12cda222d6a46d4ac63bb6c9057698"}, + {file = "tornado-6.4.1-cp38-abi3-win32.whl", hash = "sha256:d9a566c40b89757c9aa8e6f032bcdb8ca8795d7c1a9762910c722b1635c9de4d"}, + {file = "tornado-6.4.1-cp38-abi3-win_amd64.whl", hash = "sha256:b24b8982ed444378d7f21d563f4180a2de31ced9d8d84443907a0a64da2072e7"}, + {file = "tornado-6.4.1.tar.gz", hash = "sha256:92d3ab53183d8c50f8204a51e6f91d18a15d5ef261e84d452800d4ff6fc504e9"}, ] [[package]] @@ -8873,17 +8857,17 @@ vision = ["Pillow (>=10.0.1,<=15.0)"] [[package]] name = "triton" -version = "2.3.0" +version = "2.3.1" description = "A language and compiler for custom Deep Learning operations" optional = true python-versions = "*" files = [ - {file = "triton-2.3.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:5ce4b8ff70c48e47274c66f269cce8861cf1dc347ceeb7a67414ca151b1822d8"}, - {file = "triton-2.3.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3c3d9607f85103afdb279938fc1dd2a66e4f5999a58eb48a346bd42738f986dd"}, - {file = "triton-2.3.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:218d742e67480d9581bafb73ed598416cc8a56f6316152e5562ee65e33de01c0"}, - {file = "triton-2.3.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:381ec6b3dac06922d3e4099cfc943ef032893b25415de295e82b1a82b0359d2c"}, - {file = "triton-2.3.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:038e06a09c06a164fef9c48de3af1e13a63dc1ba3c792871e61a8e79720ea440"}, - {file = "triton-2.3.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6d8f636e0341ac348899a47a057c3daea99ea7db31528a225a3ba4ded28ccc65"}, + {file = "triton-2.3.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3c84595cbe5e546b1b290d2a58b1494df5a2ef066dd890655e5b8a8a92205c33"}, + {file = "triton-2.3.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c9d64ae33bcb3a7a18081e3a746e8cf87ca8623ca13d2c362413ce7a486f893e"}, + {file = "triton-2.3.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:eaf80e8761a9e3498aa92e7bf83a085b31959c61f5e8ac14eedd018df6fccd10"}, + {file = "triton-2.3.1-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b13bf35a2b659af7159bf78e92798dc62d877aa991de723937329e2d382f1991"}, + {file = "triton-2.3.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:63381e35ded3304704ea867ffde3b7cfc42c16a55b3062d41e017ef510433d66"}, + {file = "triton-2.3.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1d968264523c7a07911c8fb51b4e0d1b920204dae71491b1fe7b01b62a31e124"}, ] [package.dependencies] @@ -10097,4 +10081,4 @@ local = ["ctransformers", "llama-cpp-python", "sentence-transformers"] [metadata] lock-version = "2.0" python-versions = ">=3.10,<3.13" -content-hash = "8fd6622a9bdd88dcac9ab9ca8136b6b7c7939f0abeb6cd3f4ee754cc38527648" +content-hash = "2ba268be17a69253c9631ec721ece465a85a22949c2df7c712b7aa12d1a002fa" diff --git a/pyproject.toml b/pyproject.toml index dd3057bff..e707957cb 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "langflow" -version = "1.0.0a45" +version = "1.0.0a49" description = "A Python package with a built-in web application" authors = ["Langflow "] maintainers = [ @@ -66,7 +66,7 @@ qianfan = "0.3.5" pgvector = "^0.2.3" pyautogen = "^0.2.0" langchain-google-genai = "^1.0.1" -langchain-cohere = "^0.1.0rc1" +langchain-cohere = "^0.1.5" elasticsearch = "^8.12.0" pytube = "^15.0.0" dspy-ai = "^2.4.0" diff --git a/scripts/factory_restart_space.py b/scripts/factory_restart_space.py index e9972e8cb..07a25d0de 100644 --- a/scripts/factory_restart_space.py +++ b/scripts/factory_restart_space.py @@ -1,4 +1,4 @@ -import os +import argparse from huggingface_hub import HfApi, list_models from rich import print @@ -6,11 +6,27 @@ from rich import print # Use root method models = list_models() +args = argparse.ArgumentParser(description="Restart a space in the Hugging Face Hub.") +args.add_argument("--space", type=str, help="The space to restart.") +args.add_argument("--token", type=str, help="The Hugging Face API token.") + +parsed_args = args.parse_args() + +space = parsed_args.space + +if not space: + print("Please provide a space to restart.") + exit() + +if not parsed_args.token: + print("Please provide an API token.") + exit() + # Or configure a HfApi client hf_api = HfApi( endpoint="https://huggingface.co", # Can be a Private Hub endpoint. - token=os.getenv("HUGGINFACE_API_TOKEN"), + token=parsed_args.token, ) -space_runtime = hf_api.restart_space("Langflow/Langflow-Preview", factory_reboot=True) +space_runtime = hf_api.restart_space(space, factory_reboot=True) print(space_runtime) diff --git a/scripts/gcp/GCP_DEPLOYMENT.md b/scripts/gcp/GCP_DEPLOYMENT.md index 9f17e550b..a848d3d2b 100644 --- a/scripts/gcp/GCP_DEPLOYMENT.md +++ b/scripts/gcp/GCP_DEPLOYMENT.md @@ -20,8 +20,7 @@ When running as a [spot (preemptible) instance](https://cloud.google.com/compute ## Pricing (approximate) -> For a more accurate breakdown of costs, please use the [**GCP Pricing Calculator**](https://cloud.google.com/products/calculator) ->
+> For a more accurate breakdown of costs, please use the [**GCP Pricing Calculator**](https://cloud.google.com/products/calculator) >
| Component | Regular Cost (Hourly) | Regular Cost (Monthly) | Spot/Preemptible Cost (Hourly) | Spot/Preemptible Cost (Monthly) | Notes | | ------------------ | --------------------- | ---------------------- | ------------------------------ | ------------------------------- | -------------------------------------------------------------------------- | diff --git a/src/backend/base/langflow/__main__.py b/src/backend/base/langflow/__main__.py index 4162629dd..343188336 100644 --- a/src/backend/base/langflow/__main__.py +++ b/src/backend/base/langflow/__main__.py @@ -121,7 +121,7 @@ def run( ), ): """ - Run the Langflow. + Run Langflow. """ configure(log_level=log_level, log_file=log_file) diff --git a/src/backend/base/langflow/api/v1/flows.py b/src/backend/base/langflow/api/v1/flows.py index 36030a12d..c1ccf68db 100644 --- a/src/backend/base/langflow/api/v1/flows.py +++ b/src/backend/base/langflow/api/v1/flows.py @@ -9,7 +9,7 @@ from loguru import logger from sqlmodel import Session, col, select from langflow.api.utils import remove_api_keys, validate_is_component -from langflow.api.v1.schemas import FlowListCreate, FlowListIds, FlowListRead +from langflow.api.v1.schemas import FlowListCreate, FlowListRead from langflow.initial_setup.setup import STARTER_FOLDER_NAME from langflow.services.auth.utils import get_current_active_user from langflow.services.database.models.flow import Flow, FlowCreate, FlowRead, FlowUpdate @@ -258,9 +258,9 @@ async def download_file( return FlowListRead(flows=flows) -@router.post("/multiple_delete/") +@router.delete("/") async def delete_multiple_flows( - flow_ids: FlowListIds, user: User = Depends(get_current_active_user), db: Session = Depends(get_session) + flow_ids: List[UUID], user: User = Depends(get_current_active_user), db: Session = Depends(get_session) ): """ Delete multiple flows by their IDs. @@ -274,9 +274,7 @@ async def delete_multiple_flows( """ try: - deleted_flows = db.exec( - select(Flow).where(col(Flow.id).in_(flow_ids.flow_ids)).where(Flow.user_id == user.id) - ).all() + deleted_flows = db.exec(select(Flow).where(col(Flow.id).in_(flow_ids)).where(Flow.user_id == user.id)).all() for flow in deleted_flows: db.delete(flow) db.commit() diff --git a/src/backend/base/langflow/api/v1/monitor.py b/src/backend/base/langflow/api/v1/monitor.py index 05fee6f03..ffd01b470 100644 --- a/src/backend/base/langflow/api/v1/monitor.py +++ b/src/backend/base/langflow/api/v1/monitor.py @@ -1,9 +1,9 @@ from typing import List, Optional - from fastapi import APIRouter, Depends, HTTPException, Query from langflow.services.deps import get_monitor_service from langflow.services.monitor.schema import ( + MessageModelRequest, MessageModelResponse, TransactionModelResponse, VertexBuildMapModel, @@ -66,6 +66,44 @@ async def get_messages( raise HTTPException(status_code=500, detail=str(e)) +@router.delete("/messages", status_code=204) +async def delete_messages( + message_ids: List[int], + monitor_service: MonitorService = Depends(get_monitor_service), +): + try: + monitor_service.delete_messages(message_ids=message_ids) + except Exception as e: + raise HTTPException(status_code=500, detail=str(e)) + + +@router.post("/messages/{message_id}", response_model=MessageModelResponse) +async def update_message( + message_id: str, + message: MessageModelRequest, + monitor_service: MonitorService = Depends(get_monitor_service), +): + try: + message_dict = message.model_dump(exclude_none=True) + message_dict.pop("index", None) + monitor_service.update_message(message_id=message_id, **message_dict) + return MessageModelResponse(index=message_id, **message_dict) + + except Exception as e: + raise HTTPException(status_code=500, detail=str(e)) + + +@router.delete("/messages/session/{session_id}", status_code=204) +async def delete_messages_session( + session_id: str, + monitor_service: MonitorService = Depends(get_monitor_service), +): + try: + monitor_service.delete_messages_session(session_id=session_id) + except Exception as e: + raise HTTPException(status_code=500, detail=str(e)) + + @router.get("/transactions", response_model=List[TransactionModelResponse]) async def get_transactions( source: Optional[str] = Query(None), diff --git a/src/backend/base/langflow/base/curl/parse.py b/src/backend/base/langflow/base/curl/parse.py index c86638306..892abdde2 100644 --- a/src/backend/base/langflow/base/curl/parse.py +++ b/src/backend/base/langflow/base/curl/parse.py @@ -15,10 +15,25 @@ import shlex from collections import OrderedDict, namedtuple from http.cookies import SimpleCookie -from uncurl.api import parser # type: ignore - -parser.add_argument("-x", "--proxy", default={}) -parser.add_argument("-U", "--proxy-user", default="") +ParsedArgs = namedtuple( + "ParsedContext", + [ + "command", + "url", + "data", + "data_binary", + "method", + "headers", + "compressed", + "insecure", + "user", + "include", + "silent", + "proxy", + "proxy_user", + "cookies", + ], +) ParsedContext = namedtuple("ParsedContext", ["method", "url", "data", "headers", "cookies", "verify", "auth", "proxy"]) @@ -27,24 +42,89 @@ def normalize_newlines(multiline_text): return multiline_text.replace(" \\\n", " ") +def parse_curl_command(curl_command): + tokens = shlex.split(normalize_newlines(curl_command)) + tokens = [token for token in tokens if token and token != " "] + if "curl" not in tokens[0]: + raise ValueError("Invalid curl command") + args_template = { + "command": None, + "url": None, + "data": None, + "data_binary": None, + "method": "get", + "headers": [], + "compressed": False, + "insecure": False, + "user": (), + "include": False, + "silent": False, + "proxy": None, + "proxy_user": None, + "cookies": {}, + } + args = args_template.copy() + + i = 0 + while i < len(tokens): + token = tokens[i] + if token == "-X": + i += 1 + args["method"] = tokens[i].lower() + elif token in ("-d", "--data"): + i += 1 + args["data"] = tokens[i] + args["method"] = "post" + elif token in ("-b", "--data-binary", "--data-raw"): + i += 1 + args["data_binary"] = tokens[i] + args["method"] = "post" + elif token in ("-H", "--header"): + i += 1 + args["headers"].append(tokens[i]) + elif token == "--compressed": + args["compressed"] = True + elif token in ("-k", "--insecure"): + args["insecure"] = True + elif token in ("-u", "--user"): + i += 1 + args["user"] = tuple(tokens[i].split(":")) + elif token in ("-I", "--include"): + args["include"] = True + elif token in ("-s", "--silent"): + args["silent"] = True + elif token in ("-x", "--proxy"): + i += 1 + args["proxy"] = tokens[i] + elif token in ("-U", "--proxy-user"): + i += 1 + args["proxy_user"] = tokens[i] + elif not token.startswith("-"): + if args["command"] is None: + args["command"] = token + else: + args["url"] = token + i += 1 + + return ParsedArgs(**args) + + def parse_context(curl_command): method = "get" - tokens = shlex.split(normalize_newlines(curl_command)) - tokens = [token for token in tokens if token and token != " "] - parsed_args = parser.parse_args(tokens) + parsed_args: ParsedArgs = parse_curl_command(curl_command) post_data = parsed_args.data or parsed_args.data_binary if post_data: method = "post" - if parsed_args.X: - method = parsed_args.X.lower() + if parsed_args.method: + method = parsed_args.method.lower() cookie_dict = OrderedDict() quoted_headers = OrderedDict() - for curl_header in parsed_args.header: + for curl_header in parsed_args.headers: if curl_header.startswith(":"): occurrence = [m.start() for m in re.finditer(":", curl_header)] header_key, header_value = curl_header[: occurrence[1]], curl_header[occurrence[1] + 1 :] diff --git a/src/backend/base/langflow/base/data/utils.py b/src/backend/base/langflow/base/data/utils.py index 2aaf3b23d..c72c9b5b8 100644 --- a/src/backend/base/langflow/base/data/utils.py +++ b/src/backend/base/langflow/base/data/utils.py @@ -92,7 +92,7 @@ def read_text_file(file_path: str) -> str: with open(file_path, "rb") as f: raw_data = f.read() result = chardet.detect(raw_data) - encoding = result['encoding'] + encoding = result["encoding"] with open(file_path, "r", encoding=encoding) as f: return f.read() diff --git a/src/backend/base/langflow/components/vectorsearch/__init__.py b/src/backend/base/langflow/components/vectorsearch/__init__.py index 83ce34b26..e69de29bb 100644 --- a/src/backend/base/langflow/components/vectorsearch/__init__.py +++ b/src/backend/base/langflow/components/vectorsearch/__init__.py @@ -1,27 +0,0 @@ -from .AstraDBSearch import AstraDBSearchComponent -from .ChromaSearch import ChromaSearchComponent -from .FAISSSearch import FAISSSearchComponent -from .MongoDBAtlasVectorSearch import MongoDBAtlasSearchComponent -from .PineconeSearch import PineconeSearchComponent -from .QdrantSearch import QdrantSearchComponent -from .RedisSearch import RedisSearchComponent -from .SupabaseVectorStoreSearch import SupabaseSearchComponent -from .VectaraSearch import VectaraSearchComponent -from .WeaviateSearch import WeaviateSearchVectorStore -from .pgvectorSearch import PGVectorSearchComponent -from .Couchbase import CouchbaseSearchComponent # type: ignore - -__all__ = [ - "AstraDBSearchComponent", - "ChromaSearchComponent", - "CouchbaseSearchComponent", - "FAISSSearchComponent", - "MongoDBAtlasSearchComponent", - "PineconeSearchComponent", - "QdrantSearchComponent", - "RedisSearchComponent", - "SupabaseSearchComponent", - "VectaraSearchComponent", - "WeaviateSearchVectorStore", - "PGVectorSearchComponent", -] diff --git a/src/backend/base/langflow/components/vectorstores/__init__.py b/src/backend/base/langflow/components/vectorstores/__init__.py index d38b0a735..e69de29bb 100644 --- a/src/backend/base/langflow/components/vectorstores/__init__.py +++ b/src/backend/base/langflow/components/vectorstores/__init__.py @@ -1,28 +0,0 @@ -from .AstraDB import AstraDBVectorStoreComponent -from .Chroma import ChromaComponent -from .FAISS import FAISSComponent -from .MongoDBAtlasVector import MongoDBAtlasComponent -from .Pinecone import PineconeComponent -from .Qdrant import QdrantComponent -from .Redis import RedisComponent -from .SupabaseVectorStore import SupabaseComponent -from .Vectara import VectaraComponent -from .Weaviate import WeaviateVectorStoreComponent -from .pgvector import PGVectorComponent -from .Couchbase import CouchbaseComponent - -__all__ = [ - "AstraDBVectorStoreComponent", - "ChromaComponent", - "CouchbaseComponent", - "FAISSComponent", - "MongoDBAtlasComponent", - "PineconeComponent", - "QdrantComponent", - "RedisComponent", - "SupabaseComponent", - "VectaraComponent", - "WeaviateVectorStoreComponent", - "base", - "PGVectorComponent", -] diff --git a/src/backend/base/langflow/custom/code_parser/code_parser.py b/src/backend/base/langflow/custom/code_parser/code_parser.py index 17fe12896..705e779f4 100644 --- a/src/backend/base/langflow/custom/code_parser/code_parser.py +++ b/src/backend/base/langflow/custom/code_parser/code_parser.py @@ -297,7 +297,7 @@ class CodeParser: bases = self.execute_and_inspect_classes(self.code) except Exception as e: # If the code cannot be executed, return an empty list - logger.exception(e) + logger.debug(e) bases = [] raise e return bases diff --git a/src/backend/base/langflow/custom/directory_reader/directory_reader.py b/src/backend/base/langflow/custom/directory_reader/directory_reader.py index 679ecdf94..31fbd4165 100644 --- a/src/backend/base/langflow/custom/directory_reader/directory_reader.py +++ b/src/backend/base/langflow/custom/directory_reader/directory_reader.py @@ -79,7 +79,8 @@ class DirectoryReader: component_tuple = (*build_component(component), component) components.append(component_tuple) except Exception as e: - logger.error(f"Error while loading component { component['name']}: {e}") + logger.debug(f"Error while loading component { component['name']}") + logger.debug(e) continue items.append({"name": menu["name"], "path": menu["path"], "components": components}) filtered = [menu for menu in items if menu["components"]] @@ -265,8 +266,7 @@ class DirectoryReader: if validation_result: try: output_types = self.get_output_types_from_code(result_content) - except Exception as exc: - logger.exception(f"Error while getting output types from code: {str(exc)}") + except Exception: output_types = [component_name_camelcase] else: output_types = [component_name_camelcase] diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting (Hello, world!).json b/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting (Hello, world!).json index c3014eab4..3d81a80c7 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting (Hello, world!).json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting (Hello, world!).json @@ -45,9 +45,15 @@ "name": "template", "display_name": "Template", "advanced": false, +<<<<<<< HEAD "input_types": [ "Text" ], +======= + "input_types": [ + "Text" + ], +>>>>>>> origin/dev "dynamic": false, "info": "", "load_from_db": false, @@ -86,15 +92,24 @@ "is_input": null, "is_output": null, "is_composition": null, +<<<<<<< HEAD "base_classes": [ "object", "str", "Text" ], +======= + "base_classes": [ + "object", + "str", + "Text" + ], +>>>>>>> origin/dev "name": "", "display_name": "Prompt", "documentation": "", "custom_fields": { +<<<<<<< HEAD "template": [ "user_input" ] @@ -102,808 +117,1136 @@ "output_types": [ "Text" ], +======= + "template": [ + "user_input" + ] + }, + "output_types": [ + "Text" + ], +>>>>>>> origin/dev "full_path": null, - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": false, + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false, +<<<<<<< HEAD "error": null, - "outputs": [ - { - "types": [ - "Text" - ], - "selected": null, - "display_name": null, - "name": "Text", - "method": null - } - ] - }, - "id": "Prompt-uxBqP", - "description": "Create a prompt template with dynamic variables.", - "display_name": "Prompt" + "outputs": [ + { + "types": [ + "Text" + ], + "selected": null, + "display_name": null, + "name": "Text", + "method": null + } + ] +======= + "error": null +>>>>>>> origin/dev }, - "selected": true, - "width": 384, - "height": 383, - "dragging": false, - "positionAbsolute": { - "x": 53.588791333410654, - "y": -107.07318910019967 - } + "id": "Prompt-uxBqP", + "description": "Create a prompt template with dynamic variables.", + "display_name": "Prompt" }, - { - "id": "OpenAIModel-k39HS", - "type": "genericNode", - "position": { - "x": 634.8148772766217, - "y": 27.035057029045305 - }, - "data": { - "type": "OpenAIModel", - "node": { - "template": { - "input_value": { - "type": "str", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "input_value", - "display_name": "Input", - "advanced": false, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, + "selected": true, + "width": 384, + "height": 383, + "dragging": false, + "positionAbsolute": { + "x": 53.588791333410654, + "y": -107.07318910019967 + } + }, + { + "id": "OpenAIModel-k39HS", + "type": "genericNode", + "position": { + "x": 634.8148772766217, + "y": 27.035057029045305 + }, + "data": { + "type": "OpenAIModel", + "node": { + "template": { + "input_value": { + "type": "str", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "input_value", + "display_name": "Input", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, +<<<<<<< HEAD "input_types": [ - "Text" - ] - }, - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\"},\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": True,\n \"info\": \"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": MODEL_NAMES,\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"openai_api_key\": {\n \"display_name\": \"OpenAI API Key\",\n \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n \"advanced\": False,\n \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float = 0.1,\n model_name: str = \"gpt-4o\",\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n output = ChatOpenAI(\n max_tokens=max_tokens or None,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "max_tokens": { - "type": "int", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": 256, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "max_tokens", - "display_name": "Max Tokens", - "advanced": true, - "dynamic": false, - "info": "The maximum number of tokens to generate. Set to 0 for unlimited tokens.", - "load_from_db": false, - "title_case": false - }, - "model_kwargs": { - "type": "NestedDict", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": {}, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "model_kwargs", - "display_name": "Model Kwargs", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "model_name": { - "type": "str", - "required": false, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "value": "gpt-3.5-turbo", - "fileTypes": [], - "file_path": "", - "password": false, - "options": [ - "gpt-4o", - "gpt-4-turbo", - "gpt-4-turbo-preview", - "gpt-3.5-turbo", - "gpt-3.5-turbo-0125" - ], - "name": "model_name", - "display_name": "Model Name", - "advanced": false, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, + "Text" + ] +======= "input_types": [ - "Text" - ] - }, - "openai_api_base": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "openai_api_base", - "display_name": "OpenAI API Base", - "advanced": true, - "dynamic": false, - "info": "The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\n\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.", - "load_from_db": false, - "title_case": false, + "Text" + ] +>>>>>>> origin/dev + }, + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\"},\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": True,\n \"info\": \"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": MODEL_NAMES,\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"openai_api_key\": {\n \"display_name\": \"OpenAI API Key\",\n \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n \"advanced\": False,\n \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float = 0.1,\n model_name: str = \"gpt-4o\",\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n output = ChatOpenAI(\n max_tokens=max_tokens or None,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "max_tokens": { + "type": "int", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": 256, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "max_tokens", + "display_name": "Max Tokens", + "advanced": true, + "dynamic": false, + "info": "The maximum number of tokens to generate. Set to 0 for unlimited tokens.", + "load_from_db": false, + "title_case": false + }, + "model_kwargs": { + "type": "NestedDict", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": {}, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "model_kwargs", + "display_name": "Model Kwargs", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "model_name": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "value": "gpt-3.5-turbo", + "fileTypes": [], + "file_path": "", + "password": false, + "options": [ + "gpt-4o", + "gpt-4-turbo", + "gpt-4-turbo-preview", + "gpt-3.5-turbo", + "gpt-3.5-turbo-0125" + ], + "name": "model_name", + "display_name": "Model Name", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, +<<<<<<< HEAD "input_types": [ - "Text" - ] - }, - "openai_api_key": { - "type": "str", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": true, - "name": "openai_api_key", - "display_name": "OpenAI API Key", - "advanced": false, - "dynamic": false, - "info": "The OpenAI API Key to use for the OpenAI model.", - "load_from_db": true, - "title_case": false, + "Text" + ] +======= "input_types": [ - "Text" - ], + "Text" + ] +>>>>>>> origin/dev + }, + "openai_api_base": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "openai_api_base", + "display_name": "OpenAI API Base", + "advanced": true, + "dynamic": false, + "info": "The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\n\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.", + "load_from_db": false, + "title_case": false, +<<<<<<< HEAD + "input_types": [ + "Text" + ] +======= + "input_types": [ + "Text" + ] +>>>>>>> origin/dev + }, + "openai_api_key": { + "type": "str", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": true, + "name": "openai_api_key", + "display_name": "OpenAI API Key", + "advanced": false, + "dynamic": false, + "info": "The OpenAI API Key to use for the OpenAI model.", + "load_from_db": true, + "title_case": false, +<<<<<<< HEAD + "input_types": [ + "Text" + ], +======= + "input_types": [ + "Text" + ], +>>>>>>> origin/dev "value": "OPENAI_API_KEY" - }, - "stream": { - "type": "bool", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": true, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "stream", - "display_name": "Stream", - "advanced": true, - "dynamic": false, - "info": "Stream the response from the model. Streaming works only in Chat.", - "load_from_db": false, - "title_case": false - }, - "system_message": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "system_message", - "display_name": "System Message", - "advanced": true, - "dynamic": false, - "info": "System message to pass to the model.", - "load_from_db": false, - "title_case": false, + }, + "stream": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": true, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "stream", + "display_name": "Stream", + "advanced": true, + "dynamic": false, + "info": "Stream the response from the model. Streaming works only in Chat.", + "load_from_db": false, + "title_case": false + }, + "system_message": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "system_message", + "display_name": "System Message", + "advanced": true, + "dynamic": false, + "info": "System message to pass to the model.", + "load_from_db": false, + "title_case": false, +<<<<<<< HEAD "input_types": [ - "Text" - ] - }, - "temperature": { - "type": "float", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": 0.1, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "temperature", - "display_name": "Temperature", - "advanced": false, - "dynamic": false, - "info": "", - "rangeSpec": { - "step_type": "float", - "min": -1, - "max": 1, - "step": 0.1 - }, - "load_from_db": false, - "title_case": false - }, - "_type": "CustomComponent" + "Text" + ] +======= + "input_types": [ + "Text" + ] +>>>>>>> origin/dev }, - "description": "Generates text using OpenAI LLMs.", - "icon": "OpenAI", - "base_classes": [ - "object", - "Text", - "str" - ], - "display_name": "OpenAI", - "documentation": "", - "custom_fields": { - "input_value": null, - "openai_api_key": null, - "temperature": null, - "model_name": null, - "max_tokens": null, - "model_kwargs": null, - "openai_api_base": null, - "stream": null, - "system_message": null + "temperature": { + "type": "float", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": 0.1, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "temperature", + "display_name": "Temperature", + "advanced": false, + "dynamic": false, + "info": "", + "rangeSpec": { + "step_type": "float", + "min": -1, + "max": 1, + "step": 0.1 + }, + "load_from_db": false, + "title_case": false }, - "output_types": [ - "Text" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [ - "max_tokens", - "model_kwargs", - "model_name", - "openai_api_base", - "openai_api_key", - "temperature", - "input_value", - "system_message", - "stream" - ], - "beta": false, - "outputs": [ - { - "types": [ - "Text" - ], - "selected": null, - "display_name": null, - "name": "Text", - "method": null - } - ] + "_type": "CustomComponent" }, - "id": "OpenAIModel-k39HS", "description": "Generates text using OpenAI LLMs.", - "display_name": "OpenAI" + "icon": "OpenAI", +<<<<<<< HEAD + "base_classes": [ + "object", + "Text", + "str" + ], +======= + "base_classes": [ + "object", + "Text", + "str" + ], +>>>>>>> origin/dev + "display_name": "OpenAI", + "documentation": "", + "custom_fields": { + "input_value": null, + "openai_api_key": null, + "temperature": null, + "model_name": null, + "max_tokens": null, + "model_kwargs": null, + "openai_api_base": null, + "stream": null, + "system_message": null + }, +<<<<<<< HEAD + "output_types": [ + "Text" + ], +======= + "output_types": [ + "Text" + ], +>>>>>>> origin/dev + "field_formatters": {}, + "frozen": false, + "field_order": [ + "max_tokens", + "model_kwargs", + "model_name", + "openai_api_base", + "openai_api_key", + "temperature", + "input_value", + "system_message", + "stream" + ], +<<<<<<< HEAD + "beta": false, + "outputs": [ + { + "types": [ + "Text" + ], + "selected": null, + "display_name": null, + "name": "Text", + "method": null + } + ] +======= + "beta": false +>>>>>>> origin/dev }, - "selected": false, - "width": 384, - "height": 563, - "positionAbsolute": { - "x": 634.8148772766217, - "y": 27.035057029045305 - }, - "dragging": false + "id": "OpenAIModel-k39HS", + "description": "Generates text using OpenAI LLMs.", + "display_name": "OpenAI" }, - { - "id": "ChatOutput-njtka", - "type": "genericNode", - "position": { - "x": 1193.250417197867, - "y": 71.88476890163852 - }, - "data": { - "type": "ChatOutput", - "node": { - "template": { - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, + "selected": false, + "width": 384, + "height": 563, + "positionAbsolute": { + "x": 634.8148772766217, + "y": 27.035057029045305 + }, + "dragging": false + }, + { + "id": "ChatOutput-njtka", + "type": "genericNode", + "position": { + "x": 1193.250417197867, + "y": 71.88476890163852 + }, + "data": { + "type": "ChatOutput", + "node": { + "template": { + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, +<<<<<<< HEAD "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\nfrom langflow.template import Input, Output\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n inputs = [\n Input(\n name=\"input_value\", type=str, display_name=\"Message\", multiline=True, info=\"Message to be passed as output.\"\n ),\n Input(\n name=\"sender\",\n type=str,\n display_name=\"Sender Type\",\n options=[\"Machine\", \"User\"],\n value=\"Machine\",\n advanced=True,\n info=\"Type of sender.\",\n ),\n Input(name=\"sender_name\", type=str, display_name=\"Sender Name\", info=\"Name of the sender.\", value=\"AI\"),\n Input(\n name=\"session_id\", type=str, display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True\n ),\n Input(\n name=\"record_template\",\n type=str,\n display_name=\"Record Template\",\n value=\"{text}\",\n advanced=True,\n info=\"Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.\",\n ),\n ]\n outputs = [\n Output(display_name=\"Message\", name=\"message\", method=\"text_response\"),\n Output(display_name=\"Record\", name=\"record\", method=\"record_response\"),\n ]\n\n def text_response(self) -> Text:\n result = self.input_value\n if self.session_id and isinstance(result, (Record, str)):\n self.store_message(result, self.session_id, self.sender, self.sender_name)\n return result\n\n def record_response(self) -> Record:\n record = Record(\n data={\n \"message\": self.input_value,\n \"sender\": self.sender,\n \"sender_name\": self.sender_name,\n \"session_id\": self.session_id,\n \"template\": self.record_template or \"\",\n }\n )\n if self.session_id and isinstance(record, (Record, str)):\n self.store_message(record, self.session_id, self.sender, self.sender_name)\n return record\n", +======= + "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n def build(\n self,\n sender: Optional[str] = \"Machine\",\n sender_name: Optional[str] = \"AI\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n record_template: Optional[str] = \"{text}\",\n ) -> Union[Text, Record]:\n return super().build_with_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n record_template=record_template or \"\",\n )\n", +>>>>>>> origin/dev "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "input_value": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "input_value": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, +<<<<<<< HEAD "value": "", +======= +>>>>>>> origin/dev "fileTypes": [], - "file_path": "", - "password": false, - "name": "input_value", - "display_name": "Message", - "advanced": false, + "file_path": "", + "password": false, + "name": "input_value", + "display_name": "Message", + "advanced": false, +<<<<<<< HEAD "dynamic": false, - "info": "Message to be passed as output.", - "load_from_db": false, - "title_case": false, + "info": "Message to be passed as output.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] +======= "input_types": [ - "Text" - ] - }, - "record_template": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, + "Text" + ], + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false +>>>>>>> origin/dev + }, + "record_template": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, +<<<<<<< HEAD "multiline": false, - "value": "", + "value": "", +======= + "multiline": true, + "value": "{text}", +>>>>>>> origin/dev "fileTypes": [], - "file_path": "", - "password": false, - "name": "record_template", - "display_name": "Record Template", - "advanced": true, - "dynamic": false, + "file_path": "", + "password": false, + "name": "record_template", + "display_name": "Record Template", + "advanced": true, + "dynamic": false, +<<<<<<< HEAD "info": "Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "sender": { - "type": "str", - "required": false, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "value": "Machine", - "fileTypes": [], - "file_path": "", - "password": false, + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] +======= + "info": "In case of Message being a Record, this template will be used to convert it to text.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "return_record": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "return_record", + "display_name": "Return Record", + "advanced": true, + "dynamic": false, + "info": "Return the message as a record containing the sender, sender_name, and session_id.", + "load_from_db": false, + "title_case": false +>>>>>>> origin/dev + }, + "sender": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "value": "Machine", + "fileTypes": [], + "file_path": "", + "password": false, +<<<<<<< HEAD "options": [ - "Machine", - "User" - ], + "Machine", + "User" + ], +======= + "options": [ + "Machine", + "User" + ], +>>>>>>> origin/dev "name": "sender", - "display_name": "Sender Type", - "advanced": true, - "dynamic": false, + "display_name": "Sender Type", + "advanced": true, + "dynamic": false, +<<<<<<< HEAD "info": "Type of sender.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "sender_name": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": "AI", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "sender_name", - "display_name": "Sender Name", - "advanced": false, - "dynamic": false, - "info": "Name of the sender.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "session_id": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": "", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "session_id", - "display_name": "Session ID", - "advanced": true, - "dynamic": false, - "info": "Session ID for the message.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "_type": "Component" - }, - "description": "Display a chat message in the Playground.", - "icon": "ChatOutput", - "base_classes": [ - "Record", - "Text", - "str", - "object" - ], - "display_name": "Chat Output", - "documentation": "", - "custom_fields": { - "sender": null, - "sender_name": null, - "input_value": null, - "session_id": null, - "return_record": null, - "record_template": null - }, - "output_types": [ - "Text", - "Record" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": false, - "outputs": [ - { - "types": [ - "Text" - ], - "selected": "Text", - "display_name": "Message", - "name": "message", - "method": "text_response" - }, - { - "types": [ - "Record" - ], - "selected": "Record", - "display_name": "Record", - "name": "record", - "method": "record_response" - } - ] - }, - "id": "ChatOutput-njtka" - }, - "selected": false, - "width": 384, - "height": 383, - "positionAbsolute": { - "x": 1193.250417197867, - "y": 71.88476890163852 - }, - "dragging": false - }, - { - "id": "ChatInput-P3fgL", - "type": "genericNode", - "position": { - "x": -495.2223093083827, - "y": -232.56998443685862 - }, - "data": { - "type": "ChatInput", - "node": { - "template": { - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\nfrom langflow.template import Input, Output\n\n\nclass ChatInput(ChatComponent):\n display_name = \"Chat Input\"\n description = \"Get chat inputs from the Playground.\"\n icon = \"ChatInput\"\n\n inputs = [\n Input(\n name=\"input_value\",\n type=str,\n display_name=\"Message\",\n multiline=True,\n input_types=[],\n info=\"Message to be passed as input.\",\n ),\n Input(\n name=\"sender\",\n type=str,\n display_name=\"Sender Type\",\n options=[\"Machine\", \"User\"],\n value=\"User\",\n info=\"Type of sender.\",\n advanced=True,\n ),\n Input(name=\"sender_name\", type=str, display_name=\"Sender Name\", info=\"Name of the sender.\", value=\"User\"),\n Input(\n name=\"session_id\", type=str, display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True\n ),\n ]\n outputs = [\n Output(display_name=\"Message\", name=\"message\", method=\"text_response\"),\n Output(display_name=\"Record\", name=\"record\", method=\"record_response\"),\n ]\n\n def text_response(self) -> Text:\n result = self.input_value\n if self.session_id and isinstance(result, (Record, str)):\n self.store_message(result, self.session_id, self.sender, self.sender_name)\n return result\n\n def record_response(self) -> Record:\n record = Record(\n data={\n \"text\": self.input_value,\n \"sender\": self.sender,\n \"sender_name\": self.sender_name,\n \"session_id\": self.session_id,\n },\n )\n if self.session_id and isinstance(record, (Record, str)):\n self.store_message(record, self.session_id, self.sender, self.sender_name)\n return record\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] +======= "info": "", - "load_from_db": false, - "title_case": false - }, - "input_value": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "Hello, world!", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "input_value", - "display_name": "Message", - "advanced": false, - "input_types": [], - "dynamic": false, - "info": "Message to be passed as input.", - "load_from_db": false, - "title_case": false - }, - "sender": { - "type": "str", - "required": false, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "value": "User", - "fileTypes": [], - "file_path": "", - "password": false, - "options": [ - "Machine", - "User" - ], - "name": "sender", - "display_name": "Sender Type", - "advanced": true, - "dynamic": false, - "info": "Type of sender.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "sender_name": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": "User", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "sender_name", - "display_name": "Sender Name", - "advanced": false, - "dynamic": false, + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] +>>>>>>> origin/dev + }, + "sender_name": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": "AI", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "sender_name", + "display_name": "Sender Name", + "advanced": false, + "dynamic": false, +<<<<<<< HEAD "info": "Name of the sender.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "session_id": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] +======= + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] +>>>>>>> origin/dev + }, + "session_id": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, +<<<<<<< HEAD "value": "", +======= +>>>>>>> origin/dev "fileTypes": [], - "file_path": "", - "password": false, - "name": "session_id", - "display_name": "Session ID", - "advanced": true, - "dynamic": false, + "file_path": "", + "password": false, + "name": "session_id", + "display_name": "Session ID", + "advanced": true, + "dynamic": false, +<<<<<<< HEAD "info": "Session ID for the message.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "_type": "Component" + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] }, - "description": "Get chat inputs from the Playground.", - "icon": "ChatInput", - "base_classes": [ - "object", - "Record", - "str", - "Text" - ], - "display_name": "Chat Input", - "documentation": "", - "custom_fields": { - "sender": null, - "sender_name": null, - "input_value": null, - "session_id": null, - "return_record": null - }, - "output_types": [ - "Text", - "Record" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": false, - "outputs": [ - { - "types": [ - "Text" - ], - "selected": "Text", - "display_name": "Message", - "name": "message", - "method": "text_response" - }, - { - "types": [ - "Record" - ], - "selected": "Record", - "display_name": "Record", - "name": "record", - "method": "record_response" - } - ] + "_type": "Component" }, - "id": "ChatInput-P3fgL" + "description": "Display a chat message in the Playground.", + "icon": "ChatOutput", + "base_classes": [ + "Record", + "Text", + "str", + "object" + ], +======= + "info": "If provided, the message will be stored in the memory.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] }, - "selected": false, - "width": 384, - "height": 375, - "positionAbsolute": { - "x": -495.2223093083827, - "y": -232.56998443685862 - }, - "dragging": false - } - ], - "edges": [ - { - "source": "OpenAIModel-k39HS", - "sourceHandle": "{\"dataType\": \"OpenAIModel\", \"id\": \"OpenAIModel-k39HS\", \"output_types\": [\"Text\"], \"name\": \"Text\"}", - "target": "ChatOutput-njtka", - "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-njtkaœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}", - "data": { - "targetHandle": { - "fieldName": "input_value", - "id": "ChatOutput-njtka", - "inputTypes": [ - "Text" - ], - "type": "str" - }, - "sourceHandle": { - "dataType": "OpenAIModel", - "id": "OpenAIModel-k39HS", - "output_types": [ - "Text" - ], - "name": "Text" - } - }, - "style": { - "stroke": "#555" - }, - "className": "stroke-gray-900 stroke-connection", - "id": "reactflow__edge-OpenAIModel-k39HS{œbaseClassesœ:[œobjectœ,œTextœ,œstrœ],œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-k39HSœ}-ChatOutput-njtka{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-njtkaœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}" + "_type": "CustomComponent" }, - { - "source": "Prompt-uxBqP", - "sourceHandle": "{\"dataType\": \"Prompt\", \"id\": \"Prompt-uxBqP\", \"output_types\": [\"Text\"], \"name\": \"Text\"}", - "target": "OpenAIModel-k39HS", - "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-k39HSœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}", - "data": { - "targetHandle": { - "fieldName": "input_value", - "id": "OpenAIModel-k39HS", - "inputTypes": [ - "Text" - ], - "type": "str" - }, - "sourceHandle": { - "dataType": "Prompt", - "id": "Prompt-uxBqP", - "output_types": [ - "Text" - ], - "name": "Text" - } - }, - "style": { - "stroke": "#555" - }, - "className": "stroke-gray-900 stroke-connection", - "id": "reactflow__edge-Prompt-uxBqP{œbaseClassesœ:[œobjectœ,œstrœ,œTextœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-uxBqPœ}-OpenAIModel-k39HS{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-k39HSœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}" + "description": "Display a chat message in the Playground.", + "icon": "ChatOutput", + "base_classes": [ + "Record", + "Text", + "str", + "object" + ], +>>>>>>> origin/dev + "display_name": "Chat Output", + "documentation": "", + "custom_fields": { + "sender": null, + "sender_name": null, + "input_value": null, + "session_id": null, + "return_record": null, + "record_template": null }, - { - "source": "ChatInput-P3fgL", - "sourceHandle": "{\"dataType\": \"ChatInput\", \"id\": \"ChatInput-P3fgL\", \"output_types\": [\"Text\"], \"name\": \"message\"}", - "target": "Prompt-uxBqP", - "targetHandle": "{œfieldNameœ:œuser_inputœ,œidœ:œPrompt-uxBqPœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}", - "data": { - "targetHandle": { - "fieldName": "user_input", - "id": "Prompt-uxBqP", - "inputTypes": [ - "Document", - "BaseOutputParser", - "Record", - "Text" - ], - "type": "str" - }, - "sourceHandle": { - "dataType": "ChatInput", - "id": "ChatInput-P3fgL", +<<<<<<< HEAD "output_types": [ - "Text" - ], - "name": "message" - } + "Text", + "Record" + ], + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false, + "outputs": [ + { + "types": [ + "Text" + ], + "selected": "Text", + "display_name": "Message", + "name": "message", + "method": "text_response" }, - "style": { - "stroke": "#555" - }, - "className": "stroke-gray-900 stroke-connection", - "id": "reactflow__edge-ChatInput-P3fgL{œbaseClassesœ:[œobjectœ,œRecordœ,œstrœ,œTextœ],œdataTypeœ:œChatInputœ,œidœ:œChatInput-P3fgLœ}-Prompt-uxBqP{œfieldNameœ:œuser_inputœ,œidœ:œPrompt-uxBqPœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}" - } - ], - "viewport": { - "x": 260.58251815500563, - "y": 318.2261172111936, - "zoom": 0.43514115784696294 - } + { + "types": [ + "Record" + ], + "selected": "Record", + "display_name": "Record", + "name": "record", + "method": "record_response" + } + ] +======= + "output_types": [ + "Text", + "Record" + ], + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false +>>>>>>> origin/dev + }, + "id": "ChatOutput-njtka" }, - "description": "This flow will get you experimenting with the basics of the UI, the Chat and the Prompt component. \n\nTry changing the Template in it to see how the model behaves. \nYou can change it to this and a Text Input into the `type_of_person` variable : \"Answer the user as if you were a pirate.\n\nUser: {user_input}\n\nAnswer: \" ", - "name": "Basic Prompting (Hello, World)", - "last_tested_version": "1.0.0a4", - "is_component": false -} \ No newline at end of file + "selected": false, + "width": 384, + "height": 383, + "positionAbsolute": { + "x": 1193.250417197867, + "y": 71.88476890163852 + }, + "dragging": false +}, +{ + "id": "ChatInput-P3fgL", + "type": "genericNode", + "position": { + "x": -495.2223093083827, + "y": -232.56998443685862 + }, + "data": { + "type": "ChatInput", + "node": { + "template": { + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, +<<<<<<< HEAD + "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\nfrom langflow.template import Input, Output\n\n\nclass ChatInput(ChatComponent):\n display_name = \"Chat Input\"\n description = \"Get chat inputs from the Playground.\"\n icon = \"ChatInput\"\n\n inputs = [\n Input(\n name=\"input_value\",\n type=str,\n display_name=\"Message\",\n multiline=True,\n input_types=[],\n info=\"Message to be passed as input.\",\n ),\n Input(\n name=\"sender\",\n type=str,\n display_name=\"Sender Type\",\n options=[\"Machine\", \"User\"],\n value=\"User\",\n info=\"Type of sender.\",\n advanced=True,\n ),\n Input(name=\"sender_name\", type=str, display_name=\"Sender Name\", info=\"Name of the sender.\", value=\"User\"),\n Input(\n name=\"session_id\", type=str, display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True\n ),\n ]\n outputs = [\n Output(display_name=\"Message\", name=\"message\", method=\"text_response\"),\n Output(display_name=\"Record\", name=\"record\", method=\"record_response\"),\n ]\n\n def text_response(self) -> Text:\n result = self.input_value\n if self.session_id and isinstance(result, (Record, str)):\n self.store_message(result, self.session_id, self.sender, self.sender_name)\n return result\n\n def record_response(self) -> Record:\n record = Record(\n data={\n \"text\": self.input_value,\n \"sender\": self.sender,\n \"sender_name\": self.sender_name,\n \"session_id\": self.session_id,\n },\n )\n if self.session_id and isinstance(record, (Record, str)):\n self.store_message(record, self.session_id, self.sender, self.sender_name)\n return record\n", +======= + "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatInput(ChatComponent):\n display_name = \"Chat Input\"\n description = \"Get chat inputs from the Playground.\"\n icon = \"ChatInput\"\n\n def build_config(self):\n build_config = super().build_config()\n build_config[\"input_value\"] = {\n \"input_types\": [],\n \"display_name\": \"Message\",\n \"multiline\": True,\n }\n\n return build_config\n\n def build(\n self,\n sender: Optional[str] = \"User\",\n sender_name: Optional[str] = \"User\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n ) -> Union[Text, Record]:\n return super().build_no_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n )\n", +>>>>>>> origin/dev + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "input_value": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, +<<<<<<< HEAD + "value": "Hello, world!", +======= +>>>>>>> origin/dev + "fileTypes": [], + "file_path": "", + "password": false, + "name": "input_value", + "display_name": "Message", + "advanced": false, + "input_types": [], + "dynamic": false, +<<<<<<< HEAD + "info": "Message to be passed as input.", +======= + "info": "", + "load_from_db": false, + "title_case": false, + "value": "hi" + }, + "return_record": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "return_record", + "display_name": "Return Record", + "advanced": true, + "dynamic": false, + "info": "Return the message as a record containing the sender, sender_name, and session_id.", +>>>>>>> origin/dev + "load_from_db": false, + "title_case": false + }, + "sender": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "value": "User", + "fileTypes": [], + "file_path": "", + "password": false, +<<<<<<< HEAD + "options": [ + "Machine", + "User" + ], +======= + "options": [ + "Machine", + "User" + ], +>>>>>>> origin/dev + "name": "sender", + "display_name": "Sender Type", + "advanced": true, + "dynamic": false, +<<<<<<< HEAD + "info": "Type of sender.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] +======= + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] +>>>>>>> origin/dev + }, + "sender_name": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": "User", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "sender_name", + "display_name": "Sender Name", + "advanced": false, + "dynamic": false, +<<<<<<< HEAD + "info": "Name of the sender.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] +======= + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] +>>>>>>> origin/dev + }, + "session_id": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, +<<<<<<< HEAD + "value": "", +======= +>>>>>>> origin/dev + "fileTypes": [], + "file_path": "", + "password": false, + "name": "session_id", + "display_name": "Session ID", + "advanced": true, + "dynamic": false, +<<<<<<< HEAD + "info": "Session ID for the message.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "_type": "Component" + }, + "description": "Get chat inputs from the Playground.", + "icon": "ChatInput", + "base_classes": [ + "object", + "Record", + "str", + "Text" + ], +======= + "info": "If provided, the message will be stored in the memory.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "_type": "CustomComponent" + }, + "description": "Get chat inputs from the Playground.", + "icon": "ChatInput", + "base_classes": [ + "object", + "Record", + "str", + "Text" + ], +>>>>>>> origin/dev + "display_name": "Chat Input", + "documentation": "", + "custom_fields": { + "sender": null, + "sender_name": null, + "input_value": null, + "session_id": null, + "return_record": null + }, +<<<<<<< HEAD + "output_types": [ + "Text", + "Record" + ], + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false, + "outputs": [ + { + "types": [ + "Text" + ], + "selected": "Text", + "display_name": "Message", + "name": "message", + "method": "text_response" + }, + { + "types": [ + "Record" + ], + "selected": "Record", + "display_name": "Record", + "name": "record", + "method": "record_response" + } + ] +======= + "output_types": [ + "Text", + "Record" + ], + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false +>>>>>>> origin/dev +}, +"id": "ChatInput-P3fgL" +}, +"selected": false, +"width": 384, +"height": 375, +"positionAbsolute": { +"x": -495.2223093083827, +"y": -232.56998443685862 +}, +"dragging": false +} +], +"edges": [ +{ +"source": "OpenAIModel-k39HS", +<<<<<<< HEAD + "sourceHandle": "{\"dataType\": \"OpenAIModel\", \"id\": \"OpenAIModel-k39HS\", \"output_types\": [\"Text\"], \"name\": \"Text\"}", +======= + "sourceHandle": "{œbaseClassesœ:[œobjectœ,œTextœ,œstrœ],œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-k39HSœ}", +>>>>>>> origin/dev + "target": "ChatOutput-njtka", +"targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-njtkaœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}", +"data": { +"targetHandle": { + "fieldName": "input_value", + "id": "ChatOutput-njtka", +<<<<<<< HEAD + "inputTypes": [ + "Text" + ], + "type": "str" +}, +"sourceHandle": { + "dataType": "OpenAIModel", + "id": "OpenAIModel-k39HS", + "output_types": [ + "Text" + ], + "name": "Text" +======= + "inputTypes": [ + "Text" + ], + "type": "str" +}, +"sourceHandle": { + "baseClasses": [ + "object", + "Text", + "str" + ], + "dataType": "OpenAIModel", + "id": "OpenAIModel-k39HS" +>>>>>>> origin/dev +} +}, +"style": { +"stroke": "#555" +}, +"className": "stroke-gray-900 stroke-connection", +"id": "reactflow__edge-OpenAIModel-k39HS{œbaseClassesœ:[œobjectœ,œTextœ,œstrœ],œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-k39HSœ}-ChatOutput-njtka{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-njtkaœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}" +}, +{ +"source": "Prompt-uxBqP", +<<<<<<< HEAD + "sourceHandle": "{\"dataType\": \"Prompt\", \"id\": \"Prompt-uxBqP\", \"output_types\": [\"Text\"], \"name\": \"Text\"}", +======= + "sourceHandle": "{œbaseClassesœ:[œobjectœ,œstrœ,œTextœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-uxBqPœ}", +>>>>>>> origin/dev + "target": "OpenAIModel-k39HS", +"targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-k39HSœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}", +"data": { +"targetHandle": { + "fieldName": "input_value", + "id": "OpenAIModel-k39HS", +<<<<<<< HEAD + "inputTypes": [ + "Text" + ], + "type": "str" +}, +"sourceHandle": { + "dataType": "Prompt", + "id": "Prompt-uxBqP", + "output_types": [ + "Text" + ], + "name": "Text" +======= + "inputTypes": [ + "Text" + ], + "type": "str" +}, +"sourceHandle": { + "baseClasses": [ + "object", + "str", + "Text" + ], + "dataType": "Prompt", + "id": "Prompt-uxBqP" +>>>>>>> origin/dev +} +}, +"style": { +"stroke": "#555" +}, +"className": "stroke-gray-900 stroke-connection", +"id": "reactflow__edge-Prompt-uxBqP{œbaseClassesœ:[œobjectœ,œstrœ,œTextœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-uxBqPœ}-OpenAIModel-k39HS{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-k39HSœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}" +}, +{ +"source": "ChatInput-P3fgL", +<<<<<<< HEAD + "sourceHandle": "{\"dataType\": \"ChatInput\", \"id\": \"ChatInput-P3fgL\", \"output_types\": [\"Text\"], \"name\": \"message\"}", +======= + "sourceHandle": "{œbaseClassesœ:[œobjectœ,œRecordœ,œstrœ,œTextœ],œdataTypeœ:œChatInputœ,œidœ:œChatInput-P3fgLœ}", +>>>>>>> origin/dev + "target": "Prompt-uxBqP", +"targetHandle": "{œfieldNameœ:œuser_inputœ,œidœ:œPrompt-uxBqPœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}", +"data": { +"targetHandle": { + "fieldName": "user_input", + "id": "Prompt-uxBqP", +<<<<<<< HEAD + "inputTypes": [ + "Document", + "BaseOutputParser", + "Record", + "Text" + ], + "type": "str" +}, +"sourceHandle": { + "dataType": "ChatInput", + "id": "ChatInput-P3fgL", + "output_types": [ + "Text" + ], + "name": "message" +======= + "inputTypes": [ + "Document", + "BaseOutputParser", + "Record", + "Text" + ], + "type": "str" +}, +"sourceHandle": { + "baseClasses": [ + "object", + "Record", + "str", + "Text" + ], + "dataType": "ChatInput", + "id": "ChatInput-P3fgL" +>>>>>>> origin/dev +} +}, +"style": { +"stroke": "#555" +}, +"className": "stroke-gray-900 stroke-connection", +"id": "reactflow__edge-ChatInput-P3fgL{œbaseClassesœ:[œobjectœ,œRecordœ,œstrœ,œTextœ],œdataTypeœ:œChatInputœ,œidœ:œChatInput-P3fgLœ}-Prompt-uxBqP{œfieldNameœ:œuser_inputœ,œidœ:œPrompt-uxBqPœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}" +} +], +"viewport": { +"x": 260.58251815500563, +"y": 318.2261172111936, +"zoom": 0.43514115784696294 +} +}, +"description": "This flow will get you experimenting with the basics of the UI, the Chat and the Prompt component. \n\nTry changing the Template in it to see how the model behaves. \nYou can change it to this and a Text Input into the `type_of_person` variable : \"Answer the user as if you were a pirate.\n\nUser: {user_input}\n\nAnswer: \" ", +"name": "Basic Prompting (Hello, World)", +"last_tested_version": "1.0.0a4", +"is_component": false +<<<<<<< HEAD +} +======= +} +>>>>>>> origin/dev diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Blog Writter.json b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Blog Writter.json index 36b512f55..534cb60da 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Blog Writter.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Blog Writter.json @@ -20,7 +20,11 @@ "list": false, "show": true, "multiline": true, +<<<<<<< HEAD "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Input, Prompt, Text\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": Input(display_name=\"Template\"),\n \"code\": Input(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n", +======= + "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Prompt, TemplateField, Text\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": TemplateField(display_name=\"Template\"),\n \"code\": TemplateField(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n", +>>>>>>> origin/dev "fileTypes": [], "file_path": "", "password": false, @@ -45,9 +49,13 @@ "name": "template", "display_name": "Template", "advanced": false, +<<<<<<< HEAD "input_types": [ "Text" ], +======= + "input_types": ["Text"], +>>>>>>> origin/dev "dynamic": false, "info": "", "load_from_db": false, @@ -138,15 +146,20 @@ "is_input": null, "is_output": null, "is_composition": null, +<<<<<<< HEAD "base_classes": [ "object", "Text", "str" ], +======= + "base_classes": ["object", "Text", "str"], +>>>>>>> origin/dev "name": "", "display_name": "Prompt", "documentation": "", "custom_fields": { +<<<<<<< HEAD "template": [ "reference_1", "reference_2", @@ -156,11 +169,17 @@ "output_types": [ "Text" ], +======= + "template": ["reference_1", "reference_2", "instructions"] + }, + "output_types": ["Text"], +>>>>>>> origin/dev "full_path": null, "field_formatters": {}, "frozen": false, "field_order": [], "beta": false, +<<<<<<< HEAD "error": null, "outputs": [ { @@ -173,6 +192,9 @@ "method": null } ] +======= + "error": null +>>>>>>> origin/dev }, "id": "Prompt-Rse03", "description": "Create a prompt template with dynamic variables.", @@ -233,9 +255,13 @@ "info": "", "load_from_db": false, "title_case": false, +<<<<<<< HEAD "input_types": [ "Text" ], +======= + "input_types": ["Text"], +>>>>>>> origin/dev "value": [ "https://www.promptingguide.ai/techniques/prompt_chaining" ] @@ -244,14 +270,19 @@ }, "description": "Fetch content from one or more URLs.", "icon": "layout-template", +<<<<<<< HEAD "base_classes": [ "Record" ], +======= + "base_classes": ["Record"], +>>>>>>> origin/dev "display_name": "URL", "documentation": "", "custom_fields": { "urls": null }, +<<<<<<< HEAD "output_types": [ "Record" ], @@ -270,6 +301,13 @@ "method": null } ] +======= + "output_types": ["Record"], + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false +>>>>>>> origin/dev }, "id": "URL-HYPkR" }, @@ -300,7 +338,11 @@ "list": false, "show": true, "multiline": true, +<<<<<<< HEAD "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\nfrom langflow.template import Input, Output\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n inputs = [\n Input(\n name=\"input_value\", type=str, display_name=\"Message\", multiline=True, info=\"Message to be passed as output.\"\n ),\n Input(\n name=\"sender\",\n type=str,\n display_name=\"Sender Type\",\n options=[\"Machine\", \"User\"],\n value=\"Machine\",\n advanced=True,\n info=\"Type of sender.\",\n ),\n Input(name=\"sender_name\", type=str, display_name=\"Sender Name\", info=\"Name of the sender.\", value=\"AI\"),\n Input(\n name=\"session_id\", type=str, display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True\n ),\n Input(\n name=\"record_template\",\n type=str,\n display_name=\"Record Template\",\n value=\"{text}\",\n advanced=True,\n info=\"Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.\",\n ),\n ]\n outputs = [\n Output(display_name=\"Message\", name=\"message\", method=\"text_response\"),\n Output(display_name=\"Record\", name=\"record\", method=\"record_response\"),\n ]\n\n def text_response(self) -> Text:\n result = self.input_value\n if self.session_id and isinstance(result, (Record, str)):\n self.store_message(result, self.session_id, self.sender, self.sender_name)\n return result\n\n def record_response(self) -> Record:\n record = Record(\n data={\n \"message\": self.input_value,\n \"sender\": self.sender,\n \"sender_name\": self.sender_name,\n \"session_id\": self.session_id,\n \"template\": self.record_template or \"\",\n }\n )\n if self.session_id and isinstance(record, (Record, str)):\n self.store_message(record, self.session_id, self.sender, self.sender_name)\n return record\n", +======= + "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n def build(\n self,\n sender: Optional[str] = \"Machine\",\n sender_name: Optional[str] = \"AI\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n record_template: Optional[str] = \"{text}\",\n ) -> Union[Text, Record]:\n return super().build_with_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n record_template=record_template or \"\",\n )\n", +>>>>>>> origin/dev "fileTypes": [], "file_path": "", "password": false, @@ -318,13 +360,17 @@ "list": false, "show": true, "multiline": true, +<<<<<<< HEAD "value": "", +======= +>>>>>>> origin/dev "fileTypes": [], "file_path": "", "password": false, "name": "input_value", "display_name": "Message", "advanced": false, +<<<<<<< HEAD "dynamic": false, "info": "Message to be passed as output.", "load_from_db": false, @@ -332,6 +378,13 @@ "input_types": [ "Text" ] +======= + "input_types": ["Text"], + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false +>>>>>>> origin/dev }, "record_template": { "type": "str", @@ -339,8 +392,13 @@ "placeholder": "", "list": false, "show": true, +<<<<<<< HEAD "multiline": false, "value": "", +======= + "multiline": true, + "value": "{text}", +>>>>>>> origin/dev "fileTypes": [], "file_path": "", "password": false, @@ -348,12 +406,38 @@ "display_name": "Record Template", "advanced": true, "dynamic": false, +<<<<<<< HEAD "info": "Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.", "load_from_db": false, "title_case": false, "input_types": [ "Text" ] +======= + "info": "In case of Message being a Record, this template will be used to convert it to text.", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "return_record": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "return_record", + "display_name": "Return Record", + "advanced": true, + "dynamic": false, + "info": "Return the message as a record containing the sender, sender_name, and session_id.", + "load_from_db": false, + "title_case": false +>>>>>>> origin/dev }, "sender": { "type": "str", @@ -362,6 +446,7 @@ "list": true, "show": true, "multiline": false, +<<<<<<< HEAD "value": "", "fileTypes": [], "file_path": "", @@ -370,16 +455,30 @@ "Machine", "User" ], +======= + "value": "Machine", + "fileTypes": [], + "file_path": "", + "password": false, + "options": ["Machine", "User"], +>>>>>>> origin/dev "name": "sender", "display_name": "Sender Type", "advanced": true, "dynamic": false, +<<<<<<< HEAD "info": "Type of sender.", "load_from_db": false, "title_case": false, "input_types": [ "Text" ] +======= + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] +>>>>>>> origin/dev }, "sender_name": { "type": "str", @@ -388,7 +487,11 @@ "list": false, "show": true, "multiline": false, +<<<<<<< HEAD "value": "", +======= + "value": "AI", +>>>>>>> origin/dev "fileTypes": [], "file_path": "", "password": false, @@ -396,12 +499,19 @@ "display_name": "Sender Name", "advanced": false, "dynamic": false, +<<<<<<< HEAD "info": "Name of the sender.", "load_from_db": false, "title_case": false, "input_types": [ "Text" ] +======= + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] +>>>>>>> origin/dev }, "session_id": { "type": "str", @@ -410,7 +520,10 @@ "list": false, "show": true, "multiline": false, +<<<<<<< HEAD "value": "", +======= +>>>>>>> origin/dev "fileTypes": [], "file_path": "", "password": false, @@ -418,6 +531,7 @@ "display_name": "Session ID", "advanced": true, "dynamic": false, +<<<<<<< HEAD "info": "Session ID for the message.", "load_from_db": false, "title_case": false, @@ -435,6 +549,18 @@ "object", "str" ], +======= + "info": "If provided, the message will be stored in the memory.", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "_type": "CustomComponent" + }, + "description": "Display a chat message in the Playground.", + "icon": "ChatOutput", + "base_classes": ["Text", "Record", "object", "str"], +>>>>>>> origin/dev "display_name": "Chat Output", "documentation": "", "custom_fields": { @@ -445,6 +571,7 @@ "return_record": null, "record_template": null }, +<<<<<<< HEAD "output_types": [ "Text", "Record" @@ -473,6 +600,13 @@ "method": "record_response" } ] +======= + "output_types": ["Text", "Record"], + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false +>>>>>>> origin/dev }, "id": "ChatOutput-JPlxl" }, @@ -508,9 +642,13 @@ "info": "", "load_from_db": false, "title_case": false, +<<<<<<< HEAD "input_types": [ "Text" ] +======= + "input_types": ["Text"] +>>>>>>> origin/dev }, "code": { "type": "code", @@ -593,9 +731,13 @@ "info": "", "load_from_db": false, "title_case": false, +<<<<<<< HEAD "input_types": [ "Text" ] +======= + "input_types": ["Text"] +>>>>>>> origin/dev }, "openai_api_base": { "type": "str", @@ -614,9 +756,13 @@ "info": "The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\n\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.", "load_from_db": false, "title_case": false, +<<<<<<< HEAD "input_types": [ "Text" ] +======= + "input_types": ["Text"] +>>>>>>> origin/dev }, "openai_api_key": { "type": "str", @@ -635,9 +781,13 @@ "info": "The OpenAI API Key to use for the OpenAI model.", "load_from_db": false, "title_case": false, +<<<<<<< HEAD "input_types": [ "Text" ], +======= + "input_types": ["Text"], +>>>>>>> origin/dev "value": "OPENAI_API_KEY" }, "stream": { @@ -676,9 +826,13 @@ "info": "System message to pass to the model.", "load_from_db": false, "title_case": false, +<<<<<<< HEAD "input_types": [ "Text" ] +======= + "input_types": ["Text"] +>>>>>>> origin/dev }, "temperature": { "type": "float", @@ -709,11 +863,15 @@ }, "description": "Generates text using OpenAI LLMs.", "icon": "OpenAI", +<<<<<<< HEAD "base_classes": [ "str", "Text", "object" ], +======= + "base_classes": ["str", "Text", "object"], +>>>>>>> origin/dev "display_name": "OpenAI", "documentation": "", "custom_fields": { @@ -727,9 +885,13 @@ "stream": null, "system_message": null }, +<<<<<<< HEAD "output_types": [ "Text" ], +======= + "output_types": ["Text"], +>>>>>>> origin/dev "field_formatters": {}, "frozen": false, "field_order": [ @@ -743,6 +905,7 @@ "system_message", "stream" ], +<<<<<<< HEAD "beta": false, "outputs": [ { @@ -755,6 +918,9 @@ "method": null } ] +======= + "beta": false +>>>>>>> origin/dev }, "id": "OpenAIModel-gi29P" }, @@ -813,25 +979,35 @@ "info": "", "load_from_db": false, "title_case": false, +<<<<<<< HEAD "input_types": [ "Text" ], "value": [ "https://www.promptingguide.ai/introduction/basics" ] +======= + "input_types": ["Text"], + "value": ["https://www.promptingguide.ai/introduction/basics"] +>>>>>>> origin/dev }, "_type": "CustomComponent" }, "description": "Fetch content from one or more URLs.", "icon": "layout-template", +<<<<<<< HEAD "base_classes": [ "Record" ], +======= + "base_classes": ["Record"], +>>>>>>> origin/dev "display_name": "URL", "documentation": "", "custom_fields": { "urls": null }, +<<<<<<< HEAD "output_types": [ "Record" ], @@ -850,6 +1026,13 @@ "method": null } ] +======= + "output_types": ["Record"], + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false +>>>>>>> origin/dev }, "id": "URL-2cX90" }, @@ -905,10 +1088,14 @@ "name": "input_value", "display_name": "Value", "advanced": false, +<<<<<<< HEAD "input_types": [ "Record", "Text" ], +======= + "input_types": ["Record", "Text"], +>>>>>>> origin/dev "dynamic": false, "info": "Text or Record to be passed as input.", "load_from_db": false, @@ -932,25 +1119,34 @@ "info": "Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.", "load_from_db": false, "title_case": false, +<<<<<<< HEAD "input_types": [ "Text" ] +======= + "input_types": ["Text"] +>>>>>>> origin/dev }, "_type": "CustomComponent" }, "description": "Get text inputs from the Playground.", "icon": "type", +<<<<<<< HEAD "base_classes": [ "object", "Text", "str" ], +======= + "base_classes": ["object", "Text", "str"], +>>>>>>> origin/dev "display_name": "Instructions", "documentation": "", "custom_fields": { "input_value": null, "record_template": null }, +<<<<<<< HEAD "output_types": [ "Text" ], @@ -967,6 +1163,13 @@ "name": "Text" } ] +======= + "output_types": ["Text"], + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false +>>>>>>> origin/dev }, "id": "TextInput-og8Or" }, @@ -984,13 +1187,18 @@ { "source": "URL-HYPkR", "target": "Prompt-Rse03", +<<<<<<< HEAD "sourceHandle": "{\"dataType\": \"URL\", \"id\": \"URL-HYPkR\", \"output_types\": [\"Record\"], \"name\": \"Record\"}", +======= + "sourceHandle": "{œbaseClassesœ:[œRecordœ],œdataTypeœ:œURLœ,œidœ:œURL-HYPkRœ}", +>>>>>>> origin/dev "targetHandle": "{œfieldNameœ:œreference_2œ,œidœ:œPrompt-Rse03œ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}", "id": "reactflow__edge-URL-HYPkR{œbaseClassesœ:[œRecordœ],œdataTypeœ:œURLœ,œidœ:œURL-HYPkRœ}-Prompt-Rse03{œfieldNameœ:œreference_2œ,œidœ:œPrompt-Rse03œ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}", "data": { "targetHandle": { "fieldName": "reference_2", "id": "Prompt-Rse03", +<<<<<<< HEAD "inputTypes": [ "Document", "BaseOutputParser", @@ -1006,6 +1214,15 @@ "Record" ], "name": "Record" +======= + "inputTypes": ["Document", "BaseOutputParser", "Record", "Text"], + "type": "str" + }, + "sourceHandle": { + "baseClasses": ["Record"], + "dataType": "URL", + "id": "URL-HYPkR" +>>>>>>> origin/dev } }, "style": { @@ -1016,13 +1233,18 @@ }, { "source": "OpenAIModel-gi29P", +<<<<<<< HEAD "sourceHandle": "{\"dataType\": \"OpenAIModel\", \"id\": \"OpenAIModel-gi29P\", \"output_types\": [\"Text\"], \"name\": \"Text\"}", +======= + "sourceHandle": "{œbaseClassesœ:[œstrœ,œTextœ,œobjectœ],œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-gi29Pœ}", +>>>>>>> origin/dev "target": "ChatOutput-JPlxl", "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-JPlxlœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}", "data": { "targetHandle": { "fieldName": "input_value", "id": "ChatOutput-JPlxl", +<<<<<<< HEAD "inputTypes": [ "Text" ], @@ -1035,6 +1257,15 @@ "Text" ], "name": "Text" +======= + "inputTypes": ["Text"], + "type": "str" + }, + "sourceHandle": { + "baseClasses": ["str", "Text", "object"], + "dataType": "OpenAIModel", + "id": "OpenAIModel-gi29P" +>>>>>>> origin/dev } }, "style": { @@ -1045,13 +1276,18 @@ }, { "source": "URL-2cX90", +<<<<<<< HEAD "sourceHandle": "{\"dataType\": \"URL\", \"id\": \"URL-2cX90\", \"output_types\": [\"Record\"], \"name\": \"Record\"}", +======= + "sourceHandle": "{œbaseClassesœ:[œRecordœ],œdataTypeœ:œURLœ,œidœ:œURL-2cX90œ}", +>>>>>>> origin/dev "target": "Prompt-Rse03", "targetHandle": "{œfieldNameœ:œreference_1œ,œidœ:œPrompt-Rse03œ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}", "data": { "targetHandle": { "fieldName": "reference_1", "id": "Prompt-Rse03", +<<<<<<< HEAD "inputTypes": [ "Document", "BaseOutputParser", @@ -1067,6 +1303,15 @@ "Record" ], "name": "Record" +======= + "inputTypes": ["Document", "BaseOutputParser", "Record", "Text"], + "type": "str" + }, + "sourceHandle": { + "baseClasses": ["Record"], + "dataType": "URL", + "id": "URL-2cX90" +>>>>>>> origin/dev } }, "style": { @@ -1077,13 +1322,18 @@ }, { "source": "TextInput-og8Or", +<<<<<<< HEAD "sourceHandle": "{\"dataType\": \"TextInput\", \"id\": \"TextInput-og8Or\", \"output_types\": [\"Text\"], \"name\": \"Text\"}", +======= + "sourceHandle": "{œbaseClassesœ:[œobjectœ,œTextœ,œstrœ],œdataTypeœ:œTextInputœ,œidœ:œTextInput-og8Orœ}", +>>>>>>> origin/dev "target": "Prompt-Rse03", "targetHandle": "{œfieldNameœ:œinstructionsœ,œidœ:œPrompt-Rse03œ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}", "data": { "targetHandle": { "fieldName": "instructions", "id": "Prompt-Rse03", +<<<<<<< HEAD "inputTypes": [ "Document", "BaseOutputParser", @@ -1099,6 +1349,15 @@ "Text" ], "name": "Text" +======= + "inputTypes": ["Document", "BaseOutputParser", "Record", "Text"], + "type": "str" + }, + "sourceHandle": { + "baseClasses": ["object", "Text", "str"], + "dataType": "TextInput", + "id": "TextInput-og8Or" +>>>>>>> origin/dev } }, "style": { @@ -1109,13 +1368,18 @@ }, { "source": "Prompt-Rse03", +<<<<<<< HEAD "sourceHandle": "{\"dataType\": \"Prompt\", \"id\": \"Prompt-Rse03\", \"output_types\": [\"Text\"], \"name\": \"Text\"}", +======= + "sourceHandle": "{œbaseClassesœ:[œobjectœ,œTextœ,œstrœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-Rse03œ}", +>>>>>>> origin/dev "target": "OpenAIModel-gi29P", "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-gi29Pœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}", "data": { "targetHandle": { "fieldName": "input_value", "id": "OpenAIModel-gi29P", +<<<<<<< HEAD "inputTypes": [ "Text" ], @@ -1128,6 +1392,15 @@ "Text" ], "name": "Text" +======= + "inputTypes": ["Text"], + "type": "str" + }, + "sourceHandle": { + "baseClasses": ["object", "Text", "str"], + "dataType": "Prompt", + "id": "Prompt-Rse03" +>>>>>>> origin/dev } }, "style": { @@ -1148,4 +1421,8 @@ "name": "Blog Writer", "last_tested_version": "1.0.0a0", "is_component": false -} \ No newline at end of file +<<<<<<< HEAD +} +======= +} +>>>>>>> origin/dev diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Document QA.json b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Document QA.json index df07a7a58..3b2cf65af 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Document QA.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Document QA.json @@ -20,7 +20,11 @@ "list": false, "show": true, "multiline": true, +<<<<<<< HEAD "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Input, Prompt, Text\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": Input(display_name=\"Template\"),\n \"code\": Input(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n", +======= + "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Prompt, TemplateField, Text\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": TemplateField(display_name=\"Template\"),\n \"code\": TemplateField(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n", +>>>>>>> origin/dev "fileTypes": [], "file_path": "", "password": false, @@ -45,9 +49,13 @@ "name": "template", "display_name": "Template", "advanced": false, +<<<<<<< HEAD "input_types": [ "Text" ], +======= + "input_types": ["Text"], +>>>>>>> origin/dev "dynamic": false, "info": "", "load_from_db": false, @@ -112,15 +120,20 @@ "is_input": null, "is_output": null, "is_composition": null, +<<<<<<< HEAD "base_classes": [ "object", "str", "Text" ], +======= + "base_classes": ["object", "str", "Text"], +>>>>>>> origin/dev "name": "", "display_name": "Prompt", "documentation": "", "custom_fields": { +<<<<<<< HEAD "template": [ "Document", "Question" @@ -129,11 +142,17 @@ "output_types": [ "Text" ], +======= + "template": ["Document", "Question"] + }, + "output_types": ["Text"], +>>>>>>> origin/dev "full_path": null, "field_formatters": {}, "frozen": false, "field_order": [], "beta": false, +<<<<<<< HEAD "error": null, "outputs": [ { @@ -146,6 +165,9 @@ "method": null } ] +======= + "error": null +>>>>>>> origin/dev }, "id": "Prompt-tHwPf", "description": "A component for creating prompt templates using dynamic variables.", @@ -242,15 +264,20 @@ "_type": "CustomComponent" }, "description": "A generic file loader.", +<<<<<<< HEAD "base_classes": [ "Record" ], +======= + "base_classes": ["Record"], +>>>>>>> origin/dev "display_name": "Files", "documentation": "", "custom_fields": { "path": null, "silent_errors": null }, +<<<<<<< HEAD "output_types": [ "Record" ], @@ -267,6 +294,13 @@ "name": "Record" } ] +======= + "output_types": ["Record"], + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false +>>>>>>> origin/dev }, "id": "File-6TEsD" }, @@ -297,7 +331,11 @@ "list": false, "show": true, "multiline": true, +<<<<<<< HEAD "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\nfrom langflow.template import Input, Output\n\n\nclass ChatInput(ChatComponent):\n display_name = \"Chat Input\"\n description = \"Get chat inputs from the Playground.\"\n icon = \"ChatInput\"\n\n inputs = [\n Input(\n name=\"input_value\",\n type=str,\n display_name=\"Message\",\n multiline=True,\n input_types=[],\n info=\"Message to be passed as input.\",\n ),\n Input(\n name=\"sender\",\n type=str,\n display_name=\"Sender Type\",\n options=[\"Machine\", \"User\"],\n value=\"User\",\n info=\"Type of sender.\",\n advanced=True,\n ),\n Input(name=\"sender_name\", type=str, display_name=\"Sender Name\", info=\"Name of the sender.\", value=\"User\"),\n Input(\n name=\"session_id\", type=str, display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True\n ),\n ]\n outputs = [\n Output(display_name=\"Message\", name=\"message\", method=\"text_response\"),\n Output(display_name=\"Record\", name=\"record\", method=\"record_response\"),\n ]\n\n def text_response(self) -> Text:\n result = self.input_value\n if self.session_id and isinstance(result, (Record, str)):\n self.store_message(result, self.session_id, self.sender, self.sender_name)\n return result\n\n def record_response(self) -> Record:\n record = Record(\n data={\n \"text\": self.input_value,\n \"sender\": self.sender,\n \"sender_name\": self.sender_name,\n \"session_id\": self.session_id,\n },\n )\n if self.session_id and isinstance(record, (Record, str)):\n self.store_message(record, self.session_id, self.sender, self.sender_name)\n return record\n", +======= + "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatInput(ChatComponent):\n display_name = \"Chat Input\"\n description = \"Get chat inputs from the Playground.\"\n icon = \"ChatInput\"\n\n def build_config(self):\n build_config = super().build_config()\n build_config[\"input_value\"] = {\n \"input_types\": [],\n \"display_name\": \"Message\",\n \"multiline\": True,\n }\n\n return build_config\n\n def build(\n self,\n sender: Optional[str] = \"User\",\n sender_name: Optional[str] = \"User\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n ) -> Union[Text, Record]:\n return super().build_no_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n )\n", +>>>>>>> origin/dev "fileTypes": [], "file_path": "", "password": false, @@ -315,7 +353,10 @@ "list": false, "show": true, "multiline": true, +<<<<<<< HEAD "value": "", +======= +>>>>>>> origin/dev "fileTypes": [], "file_path": "", "password": false, @@ -324,7 +365,31 @@ "advanced": false, "input_types": [], "dynamic": false, +<<<<<<< HEAD "info": "Message to be passed as input.", +======= + "info": "", + "load_from_db": false, + "title_case": false, + "value": "" + }, + "return_record": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "return_record", + "display_name": "Return Record", + "advanced": true, + "dynamic": false, + "info": "Return the message as a record containing the sender, sender_name, and session_id.", +>>>>>>> origin/dev "load_from_db": false, "title_case": false }, @@ -335,6 +400,7 @@ "list": true, "show": true, "multiline": false, +<<<<<<< HEAD "value": "", "fileTypes": [], "file_path": "", @@ -343,16 +409,30 @@ "Machine", "User" ], +======= + "value": "User", + "fileTypes": [], + "file_path": "", + "password": false, + "options": ["Machine", "User"], +>>>>>>> origin/dev "name": "sender", "display_name": "Sender Type", "advanced": true, "dynamic": false, +<<<<<<< HEAD "info": "Type of sender.", "load_from_db": false, "title_case": false, "input_types": [ "Text" ] +======= + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] +>>>>>>> origin/dev }, "sender_name": { "type": "str", @@ -361,7 +441,11 @@ "list": false, "show": true, "multiline": false, +<<<<<<< HEAD "value": "", +======= + "value": "User", +>>>>>>> origin/dev "fileTypes": [], "file_path": "", "password": false, @@ -369,12 +453,19 @@ "display_name": "Sender Name", "advanced": false, "dynamic": false, +<<<<<<< HEAD "info": "Name of the sender.", "load_from_db": false, "title_case": false, "input_types": [ "Text" ] +======= + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] +>>>>>>> origin/dev }, "session_id": { "type": "str", @@ -383,7 +474,10 @@ "list": false, "show": true, "multiline": false, +<<<<<<< HEAD "value": "", +======= +>>>>>>> origin/dev "fileTypes": [], "file_path": "", "password": false, @@ -391,6 +485,7 @@ "display_name": "Session ID", "advanced": true, "dynamic": false, +<<<<<<< HEAD "info": "Session ID for the message.", "load_from_db": false, "title_case": false, @@ -408,6 +503,18 @@ "Text", "object" ], +======= + "info": "If provided, the message will be stored in the memory.", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "_type": "CustomComponent" + }, + "description": "Get chat inputs from the Playground.", + "icon": "ChatInput", + "base_classes": ["str", "Record", "Text", "object"], +>>>>>>> origin/dev "display_name": "Chat Input", "documentation": "", "custom_fields": { @@ -417,6 +524,7 @@ "session_id": null, "return_record": null }, +<<<<<<< HEAD "output_types": [ "Text", "Record" @@ -445,6 +553,13 @@ "method": "record_response" } ] +======= + "output_types": ["Text", "Record"], + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false +>>>>>>> origin/dev }, "id": "ChatInput-MsSJ9" }, @@ -475,7 +590,11 @@ "list": false, "show": true, "multiline": true, +<<<<<<< HEAD "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\nfrom langflow.template import Input, Output\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n inputs = [\n Input(\n name=\"input_value\", type=str, display_name=\"Message\", multiline=True, info=\"Message to be passed as output.\"\n ),\n Input(\n name=\"sender\",\n type=str,\n display_name=\"Sender Type\",\n options=[\"Machine\", \"User\"],\n value=\"Machine\",\n advanced=True,\n info=\"Type of sender.\",\n ),\n Input(name=\"sender_name\", type=str, display_name=\"Sender Name\", info=\"Name of the sender.\", value=\"AI\"),\n Input(\n name=\"session_id\", type=str, display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True\n ),\n Input(\n name=\"record_template\",\n type=str,\n display_name=\"Record Template\",\n value=\"{text}\",\n advanced=True,\n info=\"Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.\",\n ),\n ]\n outputs = [\n Output(display_name=\"Message\", name=\"message\", method=\"text_response\"),\n Output(display_name=\"Record\", name=\"record\", method=\"record_response\"),\n ]\n\n def text_response(self) -> Text:\n result = self.input_value\n if self.session_id and isinstance(result, (Record, str)):\n self.store_message(result, self.session_id, self.sender, self.sender_name)\n return result\n\n def record_response(self) -> Record:\n record = Record(\n data={\n \"message\": self.input_value,\n \"sender\": self.sender,\n \"sender_name\": self.sender_name,\n \"session_id\": self.session_id,\n \"template\": self.record_template or \"\",\n }\n )\n if self.session_id and isinstance(record, (Record, str)):\n self.store_message(record, self.session_id, self.sender, self.sender_name)\n return record\n", +======= + "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n def build(\n self,\n sender: Optional[str] = \"Machine\",\n sender_name: Optional[str] = \"AI\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n record_template: Optional[str] = \"{text}\",\n ) -> Union[Text, Record]:\n return super().build_with_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n record_template=record_template or \"\",\n )\n", +>>>>>>> origin/dev "fileTypes": [], "file_path": "", "password": false, @@ -493,13 +612,17 @@ "list": false, "show": true, "multiline": true, +<<<<<<< HEAD "value": "", +======= +>>>>>>> origin/dev "fileTypes": [], "file_path": "", "password": false, "name": "input_value", "display_name": "Message", "advanced": false, +<<<<<<< HEAD "dynamic": false, "info": "Message to be passed as output.", "load_from_db": false, @@ -510,11 +633,22 @@ }, "record_template": { "type": "str", +======= + "input_types": ["Text"], + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "return_record": { + "type": "bool", +>>>>>>> origin/dev "required": false, "placeholder": "", "list": false, "show": true, "multiline": false, +<<<<<<< HEAD "value": "", "fileTypes": [], "file_path": "", @@ -529,6 +663,19 @@ "input_types": [ "Text" ] +======= + "value": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "return_record", + "display_name": "Return Record", + "advanced": true, + "dynamic": false, + "info": "Return the message as a record containing the sender, sender_name, and session_id.", + "load_from_db": false, + "title_case": false +>>>>>>> origin/dev }, "sender": { "type": "str", @@ -537,6 +684,7 @@ "list": true, "show": true, "multiline": false, +<<<<<<< HEAD "value": "", "fileTypes": [], "file_path": "", @@ -545,16 +693,30 @@ "Machine", "User" ], +======= + "value": "Machine", + "fileTypes": [], + "file_path": "", + "password": false, + "options": ["Machine", "User"], +>>>>>>> origin/dev "name": "sender", "display_name": "Sender Type", "advanced": true, "dynamic": false, +<<<<<<< HEAD "info": "Type of sender.", "load_from_db": false, "title_case": false, "input_types": [ "Text" ] +======= + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] +>>>>>>> origin/dev }, "sender_name": { "type": "str", @@ -563,7 +725,11 @@ "list": false, "show": true, "multiline": false, +<<<<<<< HEAD "value": "", +======= + "value": "AI", +>>>>>>> origin/dev "fileTypes": [], "file_path": "", "password": false, @@ -571,12 +737,19 @@ "display_name": "Sender Name", "advanced": false, "dynamic": false, +<<<<<<< HEAD "info": "Name of the sender.", "load_from_db": false, "title_case": false, "input_types": [ "Text" ] +======= + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] +>>>>>>> origin/dev }, "session_id": { "type": "str", @@ -585,7 +758,10 @@ "list": false, "show": true, "multiline": false, +<<<<<<< HEAD "value": "", +======= +>>>>>>> origin/dev "fileTypes": [], "file_path": "", "password": false, @@ -593,6 +769,7 @@ "display_name": "Session ID", "advanced": true, "dynamic": false, +<<<<<<< HEAD "info": "Session ID for the message.", "load_from_db": false, "title_case": false, @@ -610,6 +787,18 @@ "Text", "object" ], +======= + "info": "If provided, the message will be stored in the memory.", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "_type": "CustomComponent" + }, + "description": "Display a chat message in the Playground.", + "icon": "ChatOutput", + "base_classes": ["str", "Record", "Text", "object"], +>>>>>>> origin/dev "display_name": "Chat Output", "documentation": "", "custom_fields": { @@ -619,6 +808,7 @@ "session_id": null, "return_record": null }, +<<<<<<< HEAD "output_types": [ "Text", "Record" @@ -647,6 +837,13 @@ "method": "record_response" } ] +======= + "output_types": ["Text", "Record"], + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false +>>>>>>> origin/dev }, "id": "ChatOutput-F5Awj" }, @@ -687,9 +884,13 @@ "info": "", "load_from_db": false, "title_case": false, +<<<<<<< HEAD "input_types": [ "Text" ] +======= + "input_types": ["Text"] +>>>>>>> origin/dev }, "code": { "type": "code", @@ -772,9 +973,13 @@ "info": "", "load_from_db": false, "title_case": false, +<<<<<<< HEAD "input_types": [ "Text" ] +======= + "input_types": ["Text"] +>>>>>>> origin/dev }, "openai_api_base": { "type": "str", @@ -793,9 +998,13 @@ "info": "The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\n\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.", "load_from_db": false, "title_case": false, +<<<<<<< HEAD "input_types": [ "Text" ] +======= + "input_types": ["Text"] +>>>>>>> origin/dev }, "openai_api_key": { "type": "str", @@ -814,9 +1023,13 @@ "info": "The OpenAI API Key to use for the OpenAI model.", "load_from_db": false, "title_case": false, +<<<<<<< HEAD "input_types": [ "Text" ], +======= + "input_types": ["Text"], +>>>>>>> origin/dev "value": "OPENAI_API_KEY" }, "stream": { @@ -832,7 +1045,11 @@ "password": false, "name": "stream", "display_name": "Stream", +<<<<<<< HEAD "advanced": true, +======= + "advanced": false, +>>>>>>> origin/dev "dynamic": false, "info": "Stream the response from the model. Streaming works only in Chat.", "load_from_db": false, @@ -855,9 +1072,13 @@ "info": "System message to pass to the model.", "load_from_db": false, "title_case": false, +<<<<<<< HEAD "input_types": [ "Text" ] +======= + "input_types": ["Text"] +>>>>>>> origin/dev }, "temperature": { "type": "float", @@ -888,11 +1109,15 @@ }, "description": "Generates text using OpenAI LLMs.", "icon": "OpenAI", +<<<<<<< HEAD "base_classes": [ "object", "str", "Text" ], +======= + "base_classes": ["object", "str", "Text"], +>>>>>>> origin/dev "display_name": "OpenAI", "documentation": "", "custom_fields": { @@ -906,9 +1131,13 @@ "stream": null, "system_message": null }, +<<<<<<< HEAD "output_types": [ "Text" ], +======= + "output_types": ["Text"], +>>>>>>> origin/dev "field_formatters": {}, "frozen": false, "field_order": [ @@ -922,6 +1151,7 @@ "system_message", "stream" ], +<<<<<<< HEAD "beta": false, "outputs": [ { @@ -934,6 +1164,9 @@ "method": null } ] +======= + "beta": false +>>>>>>> origin/dev }, "id": "OpenAIModel-Bt067" }, @@ -950,13 +1183,18 @@ "edges": [ { "source": "ChatInput-MsSJ9", +<<<<<<< HEAD "sourceHandle": "{\"dataType\": \"ChatInput\", \"id\": \"ChatInput-MsSJ9\", \"output_types\": [\"Text\"], \"name\": \"message\"}", +======= + "sourceHandle": "{œbaseClassesœ:[œstrœ,œRecordœ,œTextœ,œobjectœ],œdataTypeœ:œChatInputœ,œidœ:œChatInput-MsSJ9œ}", +>>>>>>> origin/dev "target": "Prompt-tHwPf", "targetHandle": "{œfieldNameœ:œQuestionœ,œidœ:œPrompt-tHwPfœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}", "data": { "targetHandle": { "fieldName": "Question", "id": "Prompt-tHwPf", +<<<<<<< HEAD "inputTypes": [ "Document", "BaseOutputParser", @@ -972,6 +1210,15 @@ "Text" ], "name": "message" +======= + "inputTypes": ["Document", "BaseOutputParser", "Record", "Text"], + "type": "str" + }, + "sourceHandle": { + "baseClasses": ["str", "Record", "Text", "object"], + "dataType": "ChatInput", + "id": "ChatInput-MsSJ9" +>>>>>>> origin/dev } }, "style": { @@ -982,13 +1229,18 @@ }, { "source": "File-6TEsD", +<<<<<<< HEAD "sourceHandle": "{\"dataType\": \"File\", \"id\": \"File-6TEsD\", \"output_types\": [\"Record\"], \"name\": \"Record\"}", +======= + "sourceHandle": "{œbaseClassesœ:[œRecordœ],œdataTypeœ:œFileœ,œidœ:œFile-6TEsDœ}", +>>>>>>> origin/dev "target": "Prompt-tHwPf", "targetHandle": "{œfieldNameœ:œDocumentœ,œidœ:œPrompt-tHwPfœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}", "data": { "targetHandle": { "fieldName": "Document", "id": "Prompt-tHwPf", +<<<<<<< HEAD "inputTypes": [ "Document", "BaseOutputParser", @@ -1004,6 +1256,15 @@ "Record" ], "name": "Record" +======= + "inputTypes": ["Document", "BaseOutputParser", "Record", "Text"], + "type": "str" + }, + "sourceHandle": { + "baseClasses": ["Record"], + "dataType": "File", + "id": "File-6TEsD" +>>>>>>> origin/dev } }, "style": { @@ -1014,13 +1275,18 @@ }, { "source": "Prompt-tHwPf", +<<<<<<< HEAD "sourceHandle": "{\"dataType\": \"Prompt\", \"id\": \"Prompt-tHwPf\", \"output_types\": [\"Text\"], \"name\": \"Text\"}", +======= + "sourceHandle": "{œbaseClassesœ:[œobjectœ,œstrœ,œTextœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-tHwPfœ}", +>>>>>>> origin/dev "target": "OpenAIModel-Bt067", "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-Bt067œ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}", "data": { "targetHandle": { "fieldName": "input_value", "id": "OpenAIModel-Bt067", +<<<<<<< HEAD "inputTypes": [ "Text" ], @@ -1033,6 +1299,15 @@ "Text" ], "name": "Text" +======= + "inputTypes": ["Text"], + "type": "str" + }, + "sourceHandle": { + "baseClasses": ["object", "str", "Text"], + "dataType": "Prompt", + "id": "Prompt-tHwPf" +>>>>>>> origin/dev } }, "style": { @@ -1043,13 +1318,18 @@ }, { "source": "OpenAIModel-Bt067", +<<<<<<< HEAD "sourceHandle": "{\"dataType\": \"OpenAIModel\", \"id\": \"OpenAIModel-Bt067\", \"output_types\": [\"Text\"], \"name\": \"Text\"}", +======= + "sourceHandle": "{œbaseClassesœ:[œobjectœ,œstrœ,œTextœ],œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-Bt067œ}", +>>>>>>> origin/dev "target": "ChatOutput-F5Awj", "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-F5Awjœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}", "data": { "targetHandle": { "fieldName": "input_value", "id": "ChatOutput-F5Awj", +<<<<<<< HEAD "inputTypes": [ "Text" ], @@ -1062,6 +1342,15 @@ "Text" ], "name": "Text" +======= + "inputTypes": ["Text"], + "type": "str" + }, + "sourceHandle": { + "baseClasses": ["object", "str", "Text"], + "dataType": "OpenAIModel", + "id": "OpenAIModel-Bt067" +>>>>>>> origin/dev } }, "style": { @@ -1081,4 +1370,8 @@ "name": "Document QA", "last_tested_version": "1.0.0a0", "is_component": false -} \ No newline at end of file +<<<<<<< HEAD +} +======= +} +>>>>>>> origin/dev diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Memory Conversation.json b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Memory Conversation.json index 8ad3c73d1..bbc425c1e 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Memory Conversation.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Memory Conversation.json @@ -22,7 +22,11 @@ "list": false, "show": true, "multiline": true, +<<<<<<< HEAD "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\nfrom langflow.template import Input, Output\n\n\nclass ChatInput(ChatComponent):\n display_name = \"Chat Input\"\n description = \"Get chat inputs from the Playground.\"\n icon = \"ChatInput\"\n\n inputs = [\n Input(\n name=\"input_value\",\n type=str,\n display_name=\"Message\",\n multiline=True,\n input_types=[],\n info=\"Message to be passed as input.\",\n ),\n Input(\n name=\"sender\",\n type=str,\n display_name=\"Sender Type\",\n options=[\"Machine\", \"User\"],\n value=\"User\",\n info=\"Type of sender.\",\n advanced=True,\n ),\n Input(name=\"sender_name\", type=str, display_name=\"Sender Name\", info=\"Name of the sender.\", value=\"User\"),\n Input(\n name=\"session_id\", type=str, display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True\n ),\n ]\n outputs = [\n Output(display_name=\"Message\", name=\"message\", method=\"text_response\"),\n Output(display_name=\"Record\", name=\"record\", method=\"record_response\"),\n ]\n\n def text_response(self) -> Text:\n result = self.input_value\n if self.session_id and isinstance(result, (Record, str)):\n self.store_message(result, self.session_id, self.sender, self.sender_name)\n return result\n\n def record_response(self) -> Record:\n record = Record(\n data={\n \"text\": self.input_value,\n \"sender\": self.sender,\n \"sender_name\": self.sender_name,\n \"session_id\": self.session_id,\n },\n )\n if self.session_id and isinstance(record, (Record, str)):\n self.store_message(record, self.session_id, self.sender, self.sender_name)\n return record\n", +======= + "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatInput(ChatComponent):\n display_name = \"Chat Input\"\n description = \"Get chat inputs from the Playground.\"\n icon = \"ChatInput\"\n\n def build_config(self):\n build_config = super().build_config()\n build_config[\"input_value\"] = {\n \"input_types\": [],\n \"display_name\": \"Message\",\n \"multiline\": True,\n }\n\n return build_config\n\n def build(\n self,\n sender: Optional[str] = \"User\",\n sender_name: Optional[str] = \"User\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n ) -> Union[Text, Record]:\n return super().build_no_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n )\n", +>>>>>>> origin/dev "fileTypes": [], "file_path": "", "password": false, @@ -40,7 +44,10 @@ "list": false, "show": true, "multiline": true, +<<<<<<< HEAD "value": "", +======= +>>>>>>> origin/dev "fileTypes": [], "file_path": "", "password": false, @@ -49,7 +56,31 @@ "advanced": false, "input_types": [], "dynamic": false, +<<<<<<< HEAD "info": "Message to be passed as input.", +======= + "info": "", + "load_from_db": false, + "title_case": false, + "value": "" + }, + "return_record": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "return_record", + "display_name": "Return Record", + "advanced": true, + "dynamic": false, + "info": "Return the message as a record containing the sender, sender_name, and session_id.", +>>>>>>> origin/dev "load_from_db": false, "title_case": false }, @@ -60,6 +91,7 @@ "list": true, "show": true, "multiline": false, +<<<<<<< HEAD "value": "", "fileTypes": [], "file_path": "", @@ -68,16 +100,30 @@ "Machine", "User" ], +======= + "value": "User", + "fileTypes": [], + "file_path": "", + "password": false, + "options": ["Machine", "User"], +>>>>>>> origin/dev "name": "sender", "display_name": "Sender Type", "advanced": true, "dynamic": false, +<<<<<<< HEAD "info": "Type of sender.", "load_from_db": false, "title_case": false, "input_types": [ "Text" ] +======= + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] +>>>>>>> origin/dev }, "sender_name": { "type": "str", @@ -86,7 +132,11 @@ "list": false, "show": true, "multiline": false, +<<<<<<< HEAD "value": "", +======= + "value": "User", +>>>>>>> origin/dev "fileTypes": [], "file_path": "", "password": false, @@ -94,12 +144,19 @@ "display_name": "Sender Name", "advanced": false, "dynamic": false, +<<<<<<< HEAD "info": "Name of the sender.", "load_from_db": false, "title_case": false, "input_types": [ "Text" ] +======= + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] +>>>>>>> origin/dev }, "session_id": { "type": "str", @@ -108,12 +165,16 @@ "list": false, "show": true, "multiline": false, +<<<<<<< HEAD "value": "", +======= +>>>>>>> origin/dev "fileTypes": [], "file_path": "", "password": false, "name": "session_id", "display_name": "Session ID", +<<<<<<< HEAD "advanced": true, "dynamic": false, "info": "Session ID for the message.", @@ -133,6 +194,21 @@ "Record", "str" ], +======= + "advanced": false, + "dynamic": false, + "info": "If provided, the message will be stored in the memory.", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"], + "value": "MySessionID" + }, + "_type": "CustomComponent" + }, + "description": "Get chat inputs from the Playground.", + "icon": "ChatInput", + "base_classes": ["Text", "object", "Record", "str"], +>>>>>>> origin/dev "display_name": "Chat Input", "documentation": "", "custom_fields": { @@ -142,6 +218,7 @@ "session_id": null, "return_record": null }, +<<<<<<< HEAD "output_types": [ "Text", "Record" @@ -170,6 +247,13 @@ "method": "record_response" } ] +======= + "output_types": ["Text", "Record"], + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false +>>>>>>> origin/dev }, "id": "ChatInput-t7F8v" }, @@ -200,7 +284,11 @@ "list": false, "show": true, "multiline": true, +<<<<<<< HEAD "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\nfrom langflow.template import Input, Output\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n inputs = [\n Input(\n name=\"input_value\", type=str, display_name=\"Message\", multiline=True, info=\"Message to be passed as output.\"\n ),\n Input(\n name=\"sender\",\n type=str,\n display_name=\"Sender Type\",\n options=[\"Machine\", \"User\"],\n value=\"Machine\",\n advanced=True,\n info=\"Type of sender.\",\n ),\n Input(name=\"sender_name\", type=str, display_name=\"Sender Name\", info=\"Name of the sender.\", value=\"AI\"),\n Input(\n name=\"session_id\", type=str, display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True\n ),\n Input(\n name=\"record_template\",\n type=str,\n display_name=\"Record Template\",\n value=\"{text}\",\n advanced=True,\n info=\"Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.\",\n ),\n ]\n outputs = [\n Output(display_name=\"Message\", name=\"message\", method=\"text_response\"),\n Output(display_name=\"Record\", name=\"record\", method=\"record_response\"),\n ]\n\n def text_response(self) -> Text:\n result = self.input_value\n if self.session_id and isinstance(result, (Record, str)):\n self.store_message(result, self.session_id, self.sender, self.sender_name)\n return result\n\n def record_response(self) -> Record:\n record = Record(\n data={\n \"message\": self.input_value,\n \"sender\": self.sender,\n \"sender_name\": self.sender_name,\n \"session_id\": self.session_id,\n \"template\": self.record_template or \"\",\n }\n )\n if self.session_id and isinstance(record, (Record, str)):\n self.store_message(record, self.session_id, self.sender, self.sender_name)\n return record\n", +======= + "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n def build(\n self,\n sender: Optional[str] = \"Machine\",\n sender_name: Optional[str] = \"AI\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n record_template: Optional[str] = \"{text}\",\n ) -> Union[Text, Record]:\n return super().build_with_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n record_template=record_template or \"\",\n )\n", +>>>>>>> origin/dev "fileTypes": [], "file_path": "", "password": false, @@ -218,13 +306,17 @@ "list": false, "show": true, "multiline": true, +<<<<<<< HEAD "value": "", +======= +>>>>>>> origin/dev "fileTypes": [], "file_path": "", "password": false, "name": "input_value", "display_name": "Message", "advanced": false, +<<<<<<< HEAD "dynamic": false, "info": "Message to be passed as output.", "load_from_db": false, @@ -235,11 +327,22 @@ }, "record_template": { "type": "str", +======= + "input_types": ["Text"], + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "return_record": { + "type": "bool", +>>>>>>> origin/dev "required": false, "placeholder": "", "list": false, "show": true, "multiline": false, +<<<<<<< HEAD "value": "", "fileTypes": [], "file_path": "", @@ -254,6 +357,19 @@ "input_types": [ "Text" ] +======= + "value": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "return_record", + "display_name": "Return Record", + "advanced": true, + "dynamic": false, + "info": "Return the message as a record containing the sender, sender_name, and session_id.", + "load_from_db": false, + "title_case": false +>>>>>>> origin/dev }, "sender": { "type": "str", @@ -262,6 +378,7 @@ "list": true, "show": true, "multiline": false, +<<<<<<< HEAD "value": "", "fileTypes": [], "file_path": "", @@ -270,16 +387,30 @@ "Machine", "User" ], +======= + "value": "Machine", + "fileTypes": [], + "file_path": "", + "password": false, + "options": ["Machine", "User"], +>>>>>>> origin/dev "name": "sender", "display_name": "Sender Type", "advanced": true, "dynamic": false, +<<<<<<< HEAD "info": "Type of sender.", "load_from_db": false, "title_case": false, "input_types": [ "Text" ] +======= + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] +>>>>>>> origin/dev }, "sender_name": { "type": "str", @@ -288,7 +419,11 @@ "list": false, "show": true, "multiline": false, +<<<<<<< HEAD "value": "", +======= + "value": "AI", +>>>>>>> origin/dev "fileTypes": [], "file_path": "", "password": false, @@ -296,12 +431,19 @@ "display_name": "Sender Name", "advanced": false, "dynamic": false, +<<<<<<< HEAD "info": "Name of the sender.", "load_from_db": false, "title_case": false, "input_types": [ "Text" ] +======= + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] +>>>>>>> origin/dev }, "session_id": { "type": "str", @@ -310,12 +452,16 @@ "list": false, "show": true, "multiline": false, +<<<<<<< HEAD "value": "", +======= +>>>>>>> origin/dev "fileTypes": [], "file_path": "", "password": false, "name": "session_id", "display_name": "Session ID", +<<<<<<< HEAD "advanced": true, "dynamic": false, "info": "Session ID for the message.", @@ -335,6 +481,21 @@ "Record", "str" ], +======= + "advanced": false, + "dynamic": false, + "info": "If provided, the message will be stored in the memory.", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"], + "value": "MySessionID" + }, + "_type": "CustomComponent" + }, + "description": "Display a chat message in the Playground.", + "icon": "ChatOutput", + "base_classes": ["Text", "object", "Record", "str"], +>>>>>>> origin/dev "display_name": "Chat Output", "documentation": "", "custom_fields": { @@ -344,6 +505,7 @@ "session_id": null, "return_record": null }, +<<<<<<< HEAD "output_types": [ "Text", "Record" @@ -372,6 +534,13 @@ "method": "record_response" } ] +======= + "output_types": ["Text", "Record"], + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false +>>>>>>> origin/dev }, "id": "ChatOutput-P1jEe" }, @@ -443,10 +612,14 @@ "fileTypes": [], "file_path": "", "password": false, +<<<<<<< HEAD "options": [ "Ascending", "Descending" ], +======= + "options": ["Ascending", "Descending"], +>>>>>>> origin/dev "name": "order", "display_name": "Order", "advanced": true, @@ -454,9 +627,13 @@ "info": "Order of the messages.", "load_from_db": false, "title_case": false, +<<<<<<< HEAD "input_types": [ "Text" ] +======= + "input_types": ["Text"] +>>>>>>> origin/dev }, "record_template": { "type": "str", @@ -476,9 +653,13 @@ "info": "Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.", "load_from_db": false, "title_case": false, +<<<<<<< HEAD "input_types": [ "Text" ] +======= + "input_types": ["Text"] +>>>>>>> origin/dev }, "sender": { "type": "str", @@ -491,11 +672,15 @@ "fileTypes": [], "file_path": "", "password": false, +<<<<<<< HEAD "options": [ "Machine", "User", "Machine and User" ], +======= + "options": ["Machine", "User", "Machine and User"], +>>>>>>> origin/dev "name": "sender", "display_name": "Sender Type", "advanced": false, @@ -503,9 +688,13 @@ "info": "", "load_from_db": false, "title_case": false, +<<<<<<< HEAD "input_types": [ "Text" ] +======= + "input_types": ["Text"] +>>>>>>> origin/dev }, "sender_name": { "type": "str", @@ -524,9 +713,13 @@ "info": "", "load_from_db": false, "title_case": false, +<<<<<<< HEAD "input_types": [ "Text" ] +======= + "input_types": ["Text"] +>>>>>>> origin/dev }, "session_id": { "type": "str", @@ -541,9 +734,13 @@ "name": "session_id", "display_name": "Session ID", "advanced": false, +<<<<<<< HEAD "input_types": [ "Text" ], +======= + "input_types": ["Text"], +>>>>>>> origin/dev "dynamic": false, "info": "Session ID of the chat history.", "load_from_db": false, @@ -554,11 +751,15 @@ }, "description": "Retrieves stored chat messages given a specific Session ID.", "icon": "history", +<<<<<<< HEAD "base_classes": [ "str", "Text", "object" ], +======= + "base_classes": ["str", "Text", "object"], +>>>>>>> origin/dev "display_name": "Chat Memory", "documentation": "", "custom_fields": { @@ -569,6 +770,7 @@ "order": null, "record_template": null }, +<<<<<<< HEAD "output_types": [ "Text" ], @@ -587,6 +789,13 @@ "method": null } ] +======= + "output_types": ["Text"], + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": true +>>>>>>> origin/dev }, "id": "MemoryComponent-cdA1J", "description": "Retrieves stored chat messages given a specific Session ID.", @@ -619,7 +828,11 @@ "list": false, "show": true, "multiline": true, +<<<<<<< HEAD "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Input, Prompt, Text\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": Input(display_name=\"Template\"),\n \"code\": Input(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n", +======= + "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Prompt, TemplateField, Text\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": TemplateField(display_name=\"Template\"),\n \"code\": TemplateField(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n", +>>>>>>> origin/dev "fileTypes": [], "file_path": "", "password": false, @@ -644,9 +857,13 @@ "name": "template", "display_name": "Template", "advanced": false, +<<<<<<< HEAD "input_types": [ "Text" ], +======= + "input_types": ["Text"], +>>>>>>> origin/dev "dynamic": false, "info": "", "load_from_db": false, @@ -711,15 +928,20 @@ "is_input": null, "is_output": null, "is_composition": null, +<<<<<<< HEAD "base_classes": [ "Text", "str", "object" ], +======= + "base_classes": ["Text", "str", "object"], +>>>>>>> origin/dev "name": "", "display_name": "Prompt", "documentation": "", "custom_fields": { +<<<<<<< HEAD "template": [ "context", "user_message" @@ -728,11 +950,17 @@ "output_types": [ "Text" ], +======= + "template": ["context", "user_message"] + }, + "output_types": ["Text"], +>>>>>>> origin/dev "full_path": null, "field_formatters": {}, "frozen": false, "field_order": [], "beta": false, +<<<<<<< HEAD "error": null, "outputs": [ { @@ -745,6 +973,9 @@ "method": null } ] +======= + "error": null +>>>>>>> origin/dev }, "id": "Prompt-ODkUx", "description": "A component for creating prompt templates using dynamic variables.", @@ -787,9 +1018,13 @@ "info": "", "load_from_db": false, "title_case": false, +<<<<<<< HEAD "input_types": [ "Text" ] +======= + "input_types": ["Text"] +>>>>>>> origin/dev }, "code": { "type": "code", @@ -872,9 +1107,13 @@ "info": "", "load_from_db": false, "title_case": false, +<<<<<<< HEAD "input_types": [ "Text" ] +======= + "input_types": ["Text"] +>>>>>>> origin/dev }, "openai_api_base": { "type": "str", @@ -893,9 +1132,13 @@ "info": "The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\n\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.", "load_from_db": false, "title_case": false, +<<<<<<< HEAD "input_types": [ "Text" ] +======= + "input_types": ["Text"] +>>>>>>> origin/dev }, "openai_api_key": { "type": "str", @@ -914,9 +1157,13 @@ "info": "The OpenAI API Key to use for the OpenAI model.", "load_from_db": false, "title_case": false, +<<<<<<< HEAD "input_types": [ "Text" ], +======= + "input_types": ["Text"], +>>>>>>> origin/dev "value": "OPENAI_API_KEY" }, "stream": { @@ -955,9 +1202,13 @@ "info": "System message to pass to the model.", "load_from_db": false, "title_case": false, +<<<<<<< HEAD "input_types": [ "Text" ] +======= + "input_types": ["Text"] +>>>>>>> origin/dev }, "temperature": { "type": "float", @@ -988,11 +1239,15 @@ }, "description": "Generates text using OpenAI LLMs.", "icon": "OpenAI", +<<<<<<< HEAD "base_classes": [ "str", "object", "Text" ], +======= + "base_classes": ["str", "object", "Text"], +>>>>>>> origin/dev "display_name": "OpenAI", "documentation": "", "custom_fields": { @@ -1006,9 +1261,13 @@ "stream": null, "system_message": null }, +<<<<<<< HEAD "output_types": [ "Text" ], +======= + "output_types": ["Text"], +>>>>>>> origin/dev "field_formatters": {}, "frozen": false, "field_order": [ @@ -1022,6 +1281,7 @@ "system_message", "stream" ], +<<<<<<< HEAD "beta": false, "outputs": [ { @@ -1034,6 +1294,9 @@ "method": null } ] +======= + "beta": false +>>>>>>> origin/dev }, "id": "OpenAIModel-9RykF" }, @@ -1071,10 +1334,14 @@ "name": "input_value", "display_name": "Value", "advanced": false, +<<<<<<< HEAD "input_types": [ "Record", "Text" ], +======= + "input_types": ["Record", "Text"], +>>>>>>> origin/dev "dynamic": false, "info": "Text or Record to be passed as output.", "load_from_db": false, @@ -1116,28 +1383,40 @@ "info": "Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.", "load_from_db": false, "title_case": false, +<<<<<<< HEAD "input_types": [ "Text" ] +======= + "input_types": ["Text"] +>>>>>>> origin/dev }, "_type": "CustomComponent" }, "description": "Display a text output in the Playground.", "icon": "type", +<<<<<<< HEAD "base_classes": [ "str", "object", "Text" ], +======= + "base_classes": ["str", "object", "Text"], +>>>>>>> origin/dev "display_name": "Inspect Memory", "documentation": "", "custom_fields": { "input_value": null, "record_template": null }, +<<<<<<< HEAD "output_types": [ "Text" ], +======= + "output_types": ["Text"], +>>>>>>> origin/dev "field_formatters": {}, "frozen": false, "field_order": [], @@ -1158,12 +1437,17 @@ "edges": [ { "source": "MemoryComponent-cdA1J", +<<<<<<< HEAD "sourceHandle": "{\"dataType\": \"MemoryComponent\", \"id\": \"MemoryComponent-cdA1J\", \"output_types\": [\"Text\"], \"name\": \"Text\"}", +======= + "sourceHandle": "{œbaseClassesœ:[œstrœ,œTextœ,œobjectœ],œdataTypeœ:œMemoryComponentœ,œidœ:œMemoryComponent-cdA1Jœ}", +>>>>>>> origin/dev "target": "Prompt-ODkUx", "targetHandle": "{œfieldNameœ:œcontextœ,œidœ:œPrompt-ODkUxœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}", "data": { "targetHandle": { "fieldName": "context", +<<<<<<< HEAD "id": "Prompt-ODkUx", "inputTypes": [ "Document", @@ -1180,6 +1464,16 @@ "Text" ], "name": "Text" +======= + "type": "str", + "id": "Prompt-ODkUx", + "inputTypes": ["Document", "BaseOutputParser", "Record", "Text"] + }, + "sourceHandle": { + "baseClasses": ["str", "Text", "object"], + "dataType": "MemoryComponent", + "id": "MemoryComponent-cdA1J" +>>>>>>> origin/dev } }, "style": { @@ -1191,12 +1485,17 @@ }, { "source": "ChatInput-t7F8v", +<<<<<<< HEAD "sourceHandle": "{\"dataType\": \"ChatInput\", \"id\": \"ChatInput-t7F8v\", \"output_types\": [\"Text\"], \"name\": \"message\"}", +======= + "sourceHandle": "{œbaseClassesœ:[œTextœ,œobjectœ,œRecordœ,œstrœ],œdataTypeœ:œChatInputœ,œidœ:œChatInput-t7F8vœ}", +>>>>>>> origin/dev "target": "Prompt-ODkUx", "targetHandle": "{œfieldNameœ:œuser_messageœ,œidœ:œPrompt-ODkUxœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}", "data": { "targetHandle": { "fieldName": "user_message", +<<<<<<< HEAD "id": "Prompt-ODkUx", "inputTypes": [ "Document", @@ -1213,6 +1512,16 @@ "Text" ], "name": "message" +======= + "type": "str", + "id": "Prompt-ODkUx", + "inputTypes": ["Document", "BaseOutputParser", "Record", "Text"] + }, + "sourceHandle": { + "baseClasses": ["Text", "object", "Record", "str"], + "dataType": "ChatInput", + "id": "ChatInput-t7F8v" +>>>>>>> origin/dev } }, "style": { @@ -1224,13 +1533,18 @@ }, { "source": "Prompt-ODkUx", +<<<<<<< HEAD "sourceHandle": "{\"dataType\": \"Prompt\", \"id\": \"Prompt-ODkUx\", \"output_types\": [\"Text\"], \"name\": \"Text\"}", +======= + "sourceHandle": "{œbaseClassesœ:[œTextœ,œstrœ,œobjectœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-ODkUxœ}", +>>>>>>> origin/dev "target": "OpenAIModel-9RykF", "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-9RykFœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}", "data": { "targetHandle": { "fieldName": "input_value", "id": "OpenAIModel-9RykF", +<<<<<<< HEAD "inputTypes": [ "Text" ], @@ -1243,6 +1557,15 @@ "Text" ], "name": "Text" +======= + "inputTypes": ["Text"], + "type": "str" + }, + "sourceHandle": { + "baseClasses": ["Text", "str", "object"], + "dataType": "Prompt", + "id": "Prompt-ODkUx" +>>>>>>> origin/dev } }, "style": { @@ -1253,13 +1576,18 @@ }, { "source": "OpenAIModel-9RykF", +<<<<<<< HEAD "sourceHandle": "{\"dataType\": \"OpenAIModel\", \"id\": \"OpenAIModel-9RykF\", \"output_types\": [\"Text\"], \"name\": \"Text\"}", +======= + "sourceHandle": "{œbaseClassesœ:[œstrœ,œobjectœ,œTextœ],œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-9RykFœ}", +>>>>>>> origin/dev "target": "ChatOutput-P1jEe", "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-P1jEeœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}", "data": { "targetHandle": { "fieldName": "input_value", "id": "ChatOutput-P1jEe", +<<<<<<< HEAD "inputTypes": [ "Text" ], @@ -1272,6 +1600,15 @@ "Text" ], "name": "Text" +======= + "inputTypes": ["Text"], + "type": "str" + }, + "sourceHandle": { + "baseClasses": ["str", "object", "Text"], + "dataType": "OpenAIModel", + "id": "OpenAIModel-9RykF" +>>>>>>> origin/dev } }, "style": { @@ -1282,13 +1619,18 @@ }, { "source": "MemoryComponent-cdA1J", +<<<<<<< HEAD "sourceHandle": "{\"dataType\": \"MemoryComponent\", \"id\": \"MemoryComponent-cdA1J\", \"output_types\": [\"Text\"], \"name\": \"Text\"}", +======= + "sourceHandle": "{œbaseClassesœ:[œstrœ,œTextœ,œobjectœ],œdataTypeœ:œMemoryComponentœ,œidœ:œMemoryComponent-cdA1Jœ}", +>>>>>>> origin/dev "target": "TextOutput-vrs6T", "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œTextOutput-vrs6Tœ,œinputTypesœ:[œRecordœ,œTextœ],œtypeœ:œstrœ}", "data": { "targetHandle": { "fieldName": "input_value", "id": "TextOutput-vrs6T", +<<<<<<< HEAD "inputTypes": [ "Record", "Text" @@ -1302,6 +1644,15 @@ "Text" ], "name": "Text" +======= + "inputTypes": ["Record", "Text"], + "type": "str" + }, + "sourceHandle": { + "baseClasses": ["str", "Text", "object"], + "dataType": "MemoryComponent", + "id": "MemoryComponent-cdA1J" +>>>>>>> origin/dev } }, "style": { @@ -1321,4 +1672,8 @@ "name": "Memory Chatbot", "last_tested_version": "1.0.0a0", "is_component": false -} \ No newline at end of file +<<<<<<< HEAD +} +======= +} +>>>>>>> origin/dev diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Prompt Chaining.json b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Prompt Chaining.json index 3234512c8..81f2bfd18 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Prompt Chaining.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Prompt Chaining.json @@ -20,7 +20,11 @@ "list": false, "show": true, "multiline": true, +<<<<<<< HEAD "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Input, Prompt, Text\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": Input(display_name=\"Template\"),\n \"code\": Input(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n", +======= + "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Prompt, TemplateField, Text\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": TemplateField(display_name=\"Template\"),\n \"code\": TemplateField(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n", +>>>>>>> origin/dev "fileTypes": [], "file_path": "", "password": false, @@ -45,9 +49,13 @@ "name": "template", "display_name": "Template", "advanced": false, +<<<<<<< HEAD "input_types": [ "Text" ], +======= + "input_types": ["Text"], +>>>>>>> origin/dev "dynamic": false, "info": "", "load_from_db": false, @@ -86,15 +94,20 @@ "is_input": null, "is_output": null, "is_composition": null, +<<<<<<< HEAD "base_classes": [ "object", "str", "Text" ], +======= + "base_classes": ["object", "str", "Text"], +>>>>>>> origin/dev "name": "", "display_name": "Prompt", "documentation": "", "custom_fields": { +<<<<<<< HEAD "template": [ "document" ] @@ -102,11 +115,17 @@ "output_types": [ "Text" ], +======= + "template": ["document"] + }, + "output_types": ["Text"], +>>>>>>> origin/dev "full_path": null, "field_formatters": {}, "frozen": false, "field_order": [], "beta": false, +<<<<<<< HEAD "error": null, "outputs": [ { @@ -119,6 +138,9 @@ "method": null } ] +======= + "error": null +>>>>>>> origin/dev }, "id": "Prompt-amqBu", "description": "Create a prompt template with dynamic variables.", @@ -151,7 +173,11 @@ "list": false, "show": true, "multiline": true, +<<<<<<< HEAD "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Input, Prompt, Text\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": Input(display_name=\"Template\"),\n \"code\": Input(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n", +======= + "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Prompt, TemplateField, Text\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": TemplateField(display_name=\"Template\"),\n \"code\": TemplateField(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n", +>>>>>>> origin/dev "fileTypes": [], "file_path": "", "password": false, @@ -176,9 +202,13 @@ "name": "template", "display_name": "Template", "advanced": false, +<<<<<<< HEAD "input_types": [ "Text" ], +======= + "input_types": ["Text"], +>>>>>>> origin/dev "dynamic": false, "info": "", "load_from_db": false, @@ -217,15 +247,20 @@ "is_input": null, "is_output": null, "is_composition": null, +<<<<<<< HEAD "base_classes": [ "object", "str", "Text" ], +======= + "base_classes": ["object", "str", "Text"], +>>>>>>> origin/dev "name": "", "display_name": "Prompt", "documentation": "", "custom_fields": { +<<<<<<< HEAD "template": [ "summary" ] @@ -233,11 +268,17 @@ "output_types": [ "Text" ], +======= + "template": ["summary"] + }, + "output_types": ["Text"], +>>>>>>> origin/dev "full_path": null, "field_formatters": {}, "frozen": false, "field_order": [], "beta": false, +<<<<<<< HEAD "error": null, "outputs": [ { @@ -250,6 +291,9 @@ "method": null } ] +======= + "error": null +>>>>>>> origin/dev }, "id": "Prompt-gTNiz", "description": "Create a prompt template with dynamic variables.", @@ -278,7 +322,11 @@ "list": false, "show": true, "multiline": true, +<<<<<<< HEAD "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\nfrom langflow.template import Input, Output\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n inputs = [\n Input(\n name=\"input_value\", type=str, display_name=\"Message\", multiline=True, info=\"Message to be passed as output.\"\n ),\n Input(\n name=\"sender\",\n type=str,\n display_name=\"Sender Type\",\n options=[\"Machine\", \"User\"],\n value=\"Machine\",\n advanced=True,\n info=\"Type of sender.\",\n ),\n Input(name=\"sender_name\", type=str, display_name=\"Sender Name\", info=\"Name of the sender.\", value=\"AI\"),\n Input(\n name=\"session_id\", type=str, display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True\n ),\n Input(\n name=\"record_template\",\n type=str,\n display_name=\"Record Template\",\n value=\"{text}\",\n advanced=True,\n info=\"Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.\",\n ),\n ]\n outputs = [\n Output(display_name=\"Message\", name=\"message\", method=\"text_response\"),\n Output(display_name=\"Record\", name=\"record\", method=\"record_response\"),\n ]\n\n def text_response(self) -> Text:\n result = self.input_value\n if self.session_id and isinstance(result, (Record, str)):\n self.store_message(result, self.session_id, self.sender, self.sender_name)\n return result\n\n def record_response(self) -> Record:\n record = Record(\n data={\n \"message\": self.input_value,\n \"sender\": self.sender,\n \"sender_name\": self.sender_name,\n \"session_id\": self.session_id,\n \"template\": self.record_template or \"\",\n }\n )\n if self.session_id and isinstance(record, (Record, str)):\n self.store_message(record, self.session_id, self.sender, self.sender_name)\n return record\n", +======= + "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n def build(\n self,\n sender: Optional[str] = \"Machine\",\n sender_name: Optional[str] = \"AI\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n record_template: Optional[str] = \"{text}\",\n ) -> Union[Text, Record]:\n return super().build_with_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n record_template=record_template or \"\",\n )\n", +>>>>>>> origin/dev "fileTypes": [], "file_path": "", "password": false, @@ -296,13 +344,17 @@ "list": false, "show": true, "multiline": true, +<<<<<<< HEAD "value": "", +======= +>>>>>>> origin/dev "fileTypes": [], "file_path": "", "password": false, "name": "input_value", "display_name": "Message", "advanced": false, +<<<<<<< HEAD "dynamic": false, "info": "Message to be passed as output.", "load_from_db": false, @@ -310,6 +362,13 @@ "input_types": [ "Text" ] +======= + "input_types": ["Text"], + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false +>>>>>>> origin/dev }, "record_template": { "type": "str", @@ -317,8 +376,13 @@ "placeholder": "", "list": false, "show": true, +<<<<<<< HEAD "multiline": false, "value": "", +======= + "multiline": true, + "value": "{text}", +>>>>>>> origin/dev "fileTypes": [], "file_path": "", "password": false, @@ -326,12 +390,38 @@ "display_name": "Record Template", "advanced": true, "dynamic": false, +<<<<<<< HEAD "info": "Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.", "load_from_db": false, "title_case": false, "input_types": [ "Text" ] +======= + "info": "In case of Message being a Record, this template will be used to convert it to text.", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "return_record": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "return_record", + "display_name": "Return Record", + "advanced": true, + "dynamic": false, + "info": "Return the message as a record containing the sender, sender_name, and session_id.", + "load_from_db": false, + "title_case": false +>>>>>>> origin/dev }, "sender": { "type": "str", @@ -340,6 +430,7 @@ "list": true, "show": true, "multiline": false, +<<<<<<< HEAD "value": "", "fileTypes": [], "file_path": "", @@ -348,16 +439,30 @@ "Machine", "User" ], +======= + "value": "Machine", + "fileTypes": [], + "file_path": "", + "password": false, + "options": ["Machine", "User"], +>>>>>>> origin/dev "name": "sender", "display_name": "Sender Type", "advanced": true, "dynamic": false, +<<<<<<< HEAD "info": "Type of sender.", "load_from_db": false, "title_case": false, "input_types": [ "Text" ] +======= + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] +>>>>>>> origin/dev }, "sender_name": { "type": "str", @@ -366,7 +471,11 @@ "list": false, "show": true, "multiline": false, +<<<<<<< HEAD "value": "", +======= + "value": "Summarizer", +>>>>>>> origin/dev "fileTypes": [], "file_path": "", "password": false, @@ -374,12 +483,19 @@ "display_name": "Sender Name", "advanced": false, "dynamic": false, +<<<<<<< HEAD "info": "Name of the sender.", "load_from_db": false, "title_case": false, "input_types": [ "Text" ] +======= + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] +>>>>>>> origin/dev }, "session_id": { "type": "str", @@ -388,7 +504,10 @@ "list": false, "show": true, "multiline": false, +<<<<<<< HEAD "value": "", +======= +>>>>>>> origin/dev "fileTypes": [], "file_path": "", "password": false, @@ -396,6 +515,7 @@ "display_name": "Session ID", "advanced": true, "dynamic": false, +<<<<<<< HEAD "info": "Session ID for the message.", "load_from_db": false, "title_case": false, @@ -413,6 +533,18 @@ "Text", "str" ], +======= + "info": "If provided, the message will be stored in the memory.", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "_type": "CustomComponent" + }, + "description": "Display a chat message in the Playground.", + "icon": "ChatOutput", + "base_classes": ["object", "Record", "Text", "str"], +>>>>>>> origin/dev "display_name": "Chat Output", "documentation": "", "custom_fields": { @@ -423,6 +555,7 @@ "return_record": null, "record_template": null }, +<<<<<<< HEAD "output_types": [ "Text", "Record" @@ -451,6 +584,13 @@ "method": "record_response" } ] +======= + "output_types": ["Text", "Record"], + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false +>>>>>>> origin/dev }, "id": "ChatOutput-EJkG3" }, @@ -477,7 +617,11 @@ "list": false, "show": true, "multiline": true, +<<<<<<< HEAD "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\nfrom langflow.template import Input, Output\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n inputs = [\n Input(\n name=\"input_value\", type=str, display_name=\"Message\", multiline=True, info=\"Message to be passed as output.\"\n ),\n Input(\n name=\"sender\",\n type=str,\n display_name=\"Sender Type\",\n options=[\"Machine\", \"User\"],\n value=\"Machine\",\n advanced=True,\n info=\"Type of sender.\",\n ),\n Input(name=\"sender_name\", type=str, display_name=\"Sender Name\", info=\"Name of the sender.\", value=\"AI\"),\n Input(\n name=\"session_id\", type=str, display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True\n ),\n Input(\n name=\"record_template\",\n type=str,\n display_name=\"Record Template\",\n value=\"{text}\",\n advanced=True,\n info=\"Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.\",\n ),\n ]\n outputs = [\n Output(display_name=\"Message\", name=\"message\", method=\"text_response\"),\n Output(display_name=\"Record\", name=\"record\", method=\"record_response\"),\n ]\n\n def text_response(self) -> Text:\n result = self.input_value\n if self.session_id and isinstance(result, (Record, str)):\n self.store_message(result, self.session_id, self.sender, self.sender_name)\n return result\n\n def record_response(self) -> Record:\n record = Record(\n data={\n \"message\": self.input_value,\n \"sender\": self.sender,\n \"sender_name\": self.sender_name,\n \"session_id\": self.session_id,\n \"template\": self.record_template or \"\",\n }\n )\n if self.session_id and isinstance(record, (Record, str)):\n self.store_message(record, self.session_id, self.sender, self.sender_name)\n return record\n", +======= + "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n def build(\n self,\n sender: Optional[str] = \"Machine\",\n sender_name: Optional[str] = \"AI\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n record_template: Optional[str] = \"{text}\",\n ) -> Union[Text, Record]:\n return super().build_with_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n record_template=record_template or \"\",\n )\n", +>>>>>>> origin/dev "fileTypes": [], "file_path": "", "password": false, @@ -495,13 +639,17 @@ "list": false, "show": true, "multiline": true, +<<<<<<< HEAD "value": "", +======= +>>>>>>> origin/dev "fileTypes": [], "file_path": "", "password": false, "name": "input_value", "display_name": "Message", "advanced": false, +<<<<<<< HEAD "dynamic": false, "info": "Message to be passed as output.", "load_from_db": false, @@ -509,6 +657,13 @@ "input_types": [ "Text" ] +======= + "input_types": ["Text"], + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false +>>>>>>> origin/dev }, "record_template": { "type": "str", @@ -516,8 +671,13 @@ "placeholder": "", "list": false, "show": true, +<<<<<<< HEAD "multiline": false, "value": "", +======= + "multiline": true, + "value": "{text}", +>>>>>>> origin/dev "fileTypes": [], "file_path": "", "password": false, @@ -525,12 +685,38 @@ "display_name": "Record Template", "advanced": true, "dynamic": false, +<<<<<<< HEAD "info": "Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.", "load_from_db": false, "title_case": false, "input_types": [ "Text" ] +======= + "info": "In case of Message being a Record, this template will be used to convert it to text.", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "return_record": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "return_record", + "display_name": "Return Record", + "advanced": true, + "dynamic": false, + "info": "Return the message as a record containing the sender, sender_name, and session_id.", + "load_from_db": false, + "title_case": false +>>>>>>> origin/dev }, "sender": { "type": "str", @@ -539,6 +725,7 @@ "list": true, "show": true, "multiline": false, +<<<<<<< HEAD "value": "", "fileTypes": [], "file_path": "", @@ -547,16 +734,30 @@ "Machine", "User" ], +======= + "value": "Machine", + "fileTypes": [], + "file_path": "", + "password": false, + "options": ["Machine", "User"], +>>>>>>> origin/dev "name": "sender", "display_name": "Sender Type", "advanced": true, "dynamic": false, +<<<<<<< HEAD "info": "Type of sender.", "load_from_db": false, "title_case": false, "input_types": [ "Text" ] +======= + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] +>>>>>>> origin/dev }, "sender_name": { "type": "str", @@ -565,7 +766,11 @@ "list": false, "show": true, "multiline": false, +<<<<<<< HEAD "value": "", +======= + "value": "Question Generator", +>>>>>>> origin/dev "fileTypes": [], "file_path": "", "password": false, @@ -573,12 +778,19 @@ "display_name": "Sender Name", "advanced": false, "dynamic": false, +<<<<<<< HEAD "info": "Name of the sender.", "load_from_db": false, "title_case": false, "input_types": [ "Text" ] +======= + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] +>>>>>>> origin/dev }, "session_id": { "type": "str", @@ -587,7 +799,10 @@ "list": false, "show": true, "multiline": false, +<<<<<<< HEAD "value": "", +======= +>>>>>>> origin/dev "fileTypes": [], "file_path": "", "password": false, @@ -595,6 +810,7 @@ "display_name": "Session ID", "advanced": true, "dynamic": false, +<<<<<<< HEAD "info": "Session ID for the message.", "load_from_db": false, "title_case": false, @@ -612,6 +828,18 @@ "Text", "str" ], +======= + "info": "If provided, the message will be stored in the memory.", + "load_from_db": false, + "title_case": false, + "input_types": ["Text"] + }, + "_type": "CustomComponent" + }, + "description": "Display a chat message in the Playground.", + "icon": "ChatOutput", + "base_classes": ["object", "Record", "Text", "str"], +>>>>>>> origin/dev "display_name": "Chat Output", "documentation": "", "custom_fields": { @@ -622,6 +850,7 @@ "return_record": null, "record_template": null }, +<<<<<<< HEAD "output_types": [ "Text", "Record" @@ -650,6 +879,13 @@ "method": "record_response" } ] +======= + "output_types": ["Text", "Record"], + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false +>>>>>>> origin/dev }, "id": "ChatOutput-DNmvg" }, @@ -675,7 +911,11 @@ "list": false, "show": true, "multiline": true, +<<<<<<< HEAD "value": "from langflow.base.io.text import TextComponent\nfrom langflow.field_typing import Text\nfrom langflow.template import Input, Output\n\n\nclass TextInput(TextComponent):\n display_name = \"Text Input\"\n description = \"Get text inputs from the Playground.\"\n icon = \"type\"\n\n inputs = [\n Input(\n name=\"input_value\",\n type=str,\n display_name=\"Value\",\n info=\"Text or Record to be passed as input.\",\n input_types=[\"Record\", \"Text\"],\n ),\n Input(\n name=\"record_template\",\n type=str,\n display_name=\"Record Template\",\n multiline=True,\n info=\"Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.\",\n advanced=True,\n ),\n ]\n outputs = [\n Output(display_name=\"Text\", name=\"text\", method=\"text_response\"),\n ]\n\n def text_response(self) -> Text:\n return self.build(input_value=self.input_value, record_template=self.record_template)\n", +======= + "value": "from typing import Optional\n\nfrom langflow.base.io.text import TextComponent\nfrom langflow.field_typing import Text\n\n\nclass TextInput(TextComponent):\n display_name = \"Text Input\"\n description = \"Get text inputs from the Playground.\"\n icon = \"type\"\n\n def build_config(self):\n return {\n \"input_value\": {\n \"display_name\": \"Value\",\n \"input_types\": [\"Record\", \"Text\"],\n \"info\": \"Text or Record to be passed as input.\",\n },\n \"record_template\": {\n \"display_name\": \"Record Template\",\n \"multiline\": True,\n \"info\": \"Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Optional[Text] = \"\",\n record_template: Optional[str] = \"\",\n ) -> Text:\n return super().build(input_value=input_value, record_template=record_template)\n", +>>>>>>> origin/dev "fileTypes": [], "file_path": "", "password": false, @@ -693,17 +933,25 @@ "list": false, "show": true, "multiline": false, +<<<<<<< HEAD "value": "", +======= + "value": "Revolutionary Nano-Battery Technology Unveiled In a groundbreaking announcement yesterday, researchers from the fictional Tech Innovations Institute revealed the development of a new nano-battery technology that promises to revolutionize energy storage. The new battery, dubbed the \"EnerGCell\", uses advanced nanomaterials to achieve unprecedented efficiency and storage capacities. According to lead researcher Dr. Ada Byron, the EnerGCell can store up to ten times more energy than the best lithium-ion batteries available today, while charging in just a fraction of the time. \"We're talking about charging your electric vehicle in just five minutes for a range of over 1,000 miles,\" Dr. Byron stated during the press conference. The technology behind the EnerGCell involves a complex arrangement of nanostructured electrodes that allow for rapid ion transfer and extremely high energy density. This breakthrough was achieved after a decade of research into nanomaterials and their applications in energy storage. The implications of this technology are vast, promising to accelerate the adoption of renewable energy by making it more practical and affordable to store wind and solar power. It could also lead to significant advancements in electric vehicles, mobile devices, and any other technology that relies on batteries. Despite the excitement, some experts are calling for patience, noting that the EnerGCell is still in its early stages of development and may take several years before it's commercially available. However, the potential impact of such a technology on the environment and the global economy is undeniable. Tech Innovations Institute plans to continue refining the EnerGCell and begin pilot projects with select partners in the coming year. If successful, this nano-battery technology could indeed be the breakthrough needed to usher in a new era of clean energy and technology.", +>>>>>>> origin/dev "fileTypes": [], "file_path": "", "password": false, "name": "input_value", "display_name": "Value", "advanced": false, +<<<<<<< HEAD "input_types": [ "Record", "Text" ], +======= + "input_types": ["Record", "Text"], +>>>>>>> origin/dev "dynamic": false, "info": "Text or Record to be passed as input.", "load_from_db": false, @@ -727,6 +975,7 @@ "info": "Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.", "load_from_db": false, "title_case": false, +<<<<<<< HEAD "input_types": [ "Text" ] @@ -740,12 +989,22 @@ "Text", "object" ], +======= + "input_types": ["Text"] + }, + "_type": "CustomComponent" + }, + "description": "Get text inputs from the Playground.", + "icon": "type", + "base_classes": ["str", "Text", "object"], +>>>>>>> origin/dev "display_name": "Text Input", "documentation": "", "custom_fields": { "input_value": null, "record_template": null }, +<<<<<<< HEAD "output_types": [ "Text" ], @@ -764,6 +1023,13 @@ "method": "text_response" } ] +======= + "output_types": ["Text"], + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false +>>>>>>> origin/dev }, "id": "TextInput-sptaH" }, @@ -801,10 +1067,14 @@ "name": "input_value", "display_name": "Value", "advanced": false, +<<<<<<< HEAD "input_types": [ "Record", "Text" ], +======= + "input_types": ["Record", "Text"], +>>>>>>> origin/dev "dynamic": false, "info": "Text or Record to be passed as output.", "load_from_db": false, @@ -846,28 +1116,40 @@ "info": "Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.", "load_from_db": false, "title_case": false, +<<<<<<< HEAD "input_types": [ "Text" ] +======= + "input_types": ["Text"] +>>>>>>> origin/dev }, "_type": "CustomComponent" }, "description": "Display a text output in the Playground.", "icon": "type", +<<<<<<< HEAD "base_classes": [ "str", "Text", "object" ], +======= + "base_classes": ["str", "Text", "object"], +>>>>>>> origin/dev "display_name": "First Prompt", "documentation": "", "custom_fields": { "input_value": null, "record_template": null }, +<<<<<<< HEAD "output_types": [ "Text" ], +======= + "output_types": ["Text"], +>>>>>>> origin/dev "field_formatters": {}, "frozen": false, "field_order": [], @@ -912,9 +1194,13 @@ "info": "", "load_from_db": false, "title_case": false, +<<<<<<< HEAD "input_types": [ "Text" ] +======= + "input_types": ["Text"] +>>>>>>> origin/dev }, "code": { "type": "code", @@ -997,9 +1283,13 @@ "info": "", "load_from_db": false, "title_case": false, +<<<<<<< HEAD "input_types": [ "Text" ] +======= + "input_types": ["Text"] +>>>>>>> origin/dev }, "openai_api_base": { "type": "str", @@ -1018,9 +1308,13 @@ "info": "The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\n\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.", "load_from_db": false, "title_case": false, +<<<<<<< HEAD "input_types": [ "Text" ] +======= + "input_types": ["Text"] +>>>>>>> origin/dev }, "openai_api_key": { "type": "str", @@ -1039,9 +1333,13 @@ "info": "The OpenAI API Key to use for the OpenAI model.", "load_from_db": false, "title_case": false, +<<<<<<< HEAD "input_types": [ "Text" ], +======= + "input_types": ["Text"], +>>>>>>> origin/dev "value": "OPENAI_API_KEY" }, "stream": { @@ -1080,9 +1378,13 @@ "info": "System message to pass to the model.", "load_from_db": false, "title_case": false, +<<<<<<< HEAD "input_types": [ "Text" ] +======= + "input_types": ["Text"] +>>>>>>> origin/dev }, "temperature": { "type": "float", @@ -1113,11 +1415,15 @@ }, "description": "Generates text using OpenAI LLMs.", "icon": "OpenAI", +<<<<<<< HEAD "base_classes": [ "str", "Text", "object" ], +======= + "base_classes": ["str", "Text", "object"], +>>>>>>> origin/dev "display_name": "OpenAI", "documentation": "", "custom_fields": { @@ -1131,9 +1437,13 @@ "stream": null, "system_message": null }, +<<<<<<< HEAD "output_types": [ "Text" ], +======= + "output_types": ["Text"], +>>>>>>> origin/dev "field_formatters": {}, "frozen": false, "field_order": [ @@ -1147,6 +1457,7 @@ "system_message", "stream" ], +<<<<<<< HEAD "beta": false, "outputs": [ { @@ -1159,6 +1470,9 @@ "method": null } ] +======= + "beta": false +>>>>>>> origin/dev }, "id": "OpenAIModel-uYXZJ" }, @@ -1196,10 +1510,14 @@ "name": "input_value", "display_name": "Value", "advanced": false, +<<<<<<< HEAD "input_types": [ "Record", "Text" ], +======= + "input_types": ["Record", "Text"], +>>>>>>> origin/dev "dynamic": false, "info": "Text or Record to be passed as output.", "load_from_db": false, @@ -1241,28 +1559,40 @@ "info": "Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.", "load_from_db": false, "title_case": false, +<<<<<<< HEAD "input_types": [ "Text" ] +======= + "input_types": ["Text"] +>>>>>>> origin/dev }, "_type": "CustomComponent" }, "description": "Display a text output in the Playground.", "icon": "type", +<<<<<<< HEAD "base_classes": [ "str", "Text", "object" ], +======= + "base_classes": ["str", "Text", "object"], +>>>>>>> origin/dev "display_name": "Second Prompt", "documentation": "", "custom_fields": { "input_value": null, "record_template": null }, +<<<<<<< HEAD "output_types": [ "Text" ], +======= + "output_types": ["Text"], +>>>>>>> origin/dev "field_formatters": {}, "frozen": false, "field_order": [], @@ -1307,9 +1637,13 @@ "info": "", "load_from_db": false, "title_case": false, +<<<<<<< HEAD "input_types": [ "Text" ] +======= + "input_types": ["Text"] +>>>>>>> origin/dev }, "code": { "type": "code", @@ -1392,9 +1726,13 @@ "info": "", "load_from_db": false, "title_case": false, +<<<<<<< HEAD "input_types": [ "Text" ] +======= + "input_types": ["Text"] +>>>>>>> origin/dev }, "openai_api_base": { "type": "str", @@ -1413,9 +1751,13 @@ "info": "The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\n\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.", "load_from_db": false, "title_case": false, +<<<<<<< HEAD "input_types": [ "Text" ] +======= + "input_types": ["Text"] +>>>>>>> origin/dev }, "openai_api_key": { "type": "str", @@ -1434,9 +1776,13 @@ "info": "The OpenAI API Key to use for the OpenAI model.", "load_from_db": false, "title_case": false, +<<<<<<< HEAD "input_types": [ "Text" ], +======= + "input_types": ["Text"], +>>>>>>> origin/dev "value": "" }, "stream": { @@ -1475,9 +1821,13 @@ "info": "System message to pass to the model.", "load_from_db": false, "title_case": false, +<<<<<<< HEAD "input_types": [ "Text" ] +======= + "input_types": ["Text"] +>>>>>>> origin/dev }, "temperature": { "type": "float", @@ -1508,11 +1858,15 @@ }, "description": "Generates text using OpenAI LLMs.", "icon": "OpenAI", +<<<<<<< HEAD "base_classes": [ "str", "Text", "object" ], +======= + "base_classes": ["str", "Text", "object"], +>>>>>>> origin/dev "display_name": "OpenAI", "documentation": "", "custom_fields": { @@ -1526,9 +1880,13 @@ "stream": null, "system_message": null }, +<<<<<<< HEAD "output_types": [ "Text" ], +======= + "output_types": ["Text"], +>>>>>>> origin/dev "field_formatters": {}, "frozen": false, "field_order": [ @@ -1542,6 +1900,7 @@ "system_message", "stream" ], +<<<<<<< HEAD "beta": false, "outputs": [ { @@ -1554,6 +1913,9 @@ "method": null } ] +======= + "beta": false +>>>>>>> origin/dev }, "id": "OpenAIModel-XawYB" }, @@ -1570,13 +1932,18 @@ "edges": [ { "source": "TextInput-sptaH", +<<<<<<< HEAD "sourceHandle": "{\"dataType\": \"TextInput\", \"id\": \"TextInput-sptaH\", \"output_types\": [\"Text\"], \"name\": \"text\"}", +======= + "sourceHandle": "{œbaseClassesœ:[œstrœ,œTextœ,œobjectœ],œdataTypeœ:œTextInputœ,œidœ:œTextInput-sptaHœ}", +>>>>>>> origin/dev "target": "Prompt-amqBu", "targetHandle": "{œfieldNameœ:œdocumentœ,œidœ:œPrompt-amqBuœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}", "data": { "targetHandle": { "fieldName": "document", "id": "Prompt-amqBu", +<<<<<<< HEAD "inputTypes": [ "Document", "BaseOutputParser", @@ -1592,6 +1959,15 @@ "Text" ], "name": "text" +======= + "inputTypes": ["Document", "BaseOutputParser", "Record", "Text"], + "type": "str" + }, + "sourceHandle": { + "baseClasses": ["str", "Text", "object"], + "dataType": "TextInput", + "id": "TextInput-sptaH" +>>>>>>> origin/dev } }, "style": { @@ -1602,13 +1978,18 @@ }, { "source": "Prompt-amqBu", +<<<<<<< HEAD "sourceHandle": "{\"dataType\": \"Prompt\", \"id\": \"Prompt-amqBu\", \"output_types\": [\"Text\"], \"name\": \"Text\"}", +======= + "sourceHandle": "{œbaseClassesœ:[œobjectœ,œstrœ,œTextœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-amqBuœ}", +>>>>>>> origin/dev "target": "TextOutput-2MS4a", "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œTextOutput-2MS4aœ,œinputTypesœ:[œRecordœ,œTextœ],œtypeœ:œstrœ}", "data": { "targetHandle": { "fieldName": "input_value", "id": "TextOutput-2MS4a", +<<<<<<< HEAD "inputTypes": [ "Record", "Text" @@ -1622,6 +2003,15 @@ "Text" ], "name": "Text" +======= + "inputTypes": ["Record", "Text"], + "type": "str" + }, + "sourceHandle": { + "baseClasses": ["object", "str", "Text"], + "dataType": "Prompt", + "id": "Prompt-amqBu" +>>>>>>> origin/dev } }, "style": { @@ -1632,13 +2022,18 @@ }, { "source": "Prompt-amqBu", +<<<<<<< HEAD "sourceHandle": "{\"dataType\": \"Prompt\", \"id\": \"Prompt-amqBu\", \"output_types\": [\"Text\"], \"name\": \"Text\"}", +======= + "sourceHandle": "{œbaseClassesœ:[œobjectœ,œstrœ,œTextœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-amqBuœ}", +>>>>>>> origin/dev "target": "OpenAIModel-uYXZJ", "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-uYXZJœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}", "data": { "targetHandle": { "fieldName": "input_value", "id": "OpenAIModel-uYXZJ", +<<<<<<< HEAD "inputTypes": [ "Text" ], @@ -1651,6 +2046,15 @@ "Text" ], "name": "Text" +======= + "inputTypes": ["Text"], + "type": "str" + }, + "sourceHandle": { + "baseClasses": ["object", "str", "Text"], + "dataType": "Prompt", + "id": "Prompt-amqBu" +>>>>>>> origin/dev } }, "style": { @@ -1661,13 +2065,18 @@ }, { "source": "OpenAIModel-uYXZJ", +<<<<<<< HEAD "sourceHandle": "{\"dataType\": \"OpenAIModel\", \"id\": \"OpenAIModel-uYXZJ\", \"output_types\": [\"Text\"], \"name\": \"Text\"}", +======= + "sourceHandle": "{œbaseClassesœ:[œstrœ,œTextœ,œobjectœ],œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-uYXZJœ}", +>>>>>>> origin/dev "target": "Prompt-gTNiz", "targetHandle": "{œfieldNameœ:œsummaryœ,œidœ:œPrompt-gTNizœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}", "data": { "targetHandle": { "fieldName": "summary", "id": "Prompt-gTNiz", +<<<<<<< HEAD "inputTypes": [ "Document", "BaseOutputParser", @@ -1683,6 +2092,15 @@ "Text" ], "name": "Text" +======= + "inputTypes": ["Document", "BaseOutputParser", "Record", "Text"], + "type": "str" + }, + "sourceHandle": { + "baseClasses": ["str", "Text", "object"], + "dataType": "OpenAIModel", + "id": "OpenAIModel-uYXZJ" +>>>>>>> origin/dev } }, "style": { @@ -1693,13 +2111,18 @@ }, { "source": "OpenAIModel-uYXZJ", +<<<<<<< HEAD "sourceHandle": "{\"dataType\": \"OpenAIModel\", \"id\": \"OpenAIModel-uYXZJ\", \"output_types\": [\"Text\"], \"name\": \"Text\"}", +======= + "sourceHandle": "{œbaseClassesœ:[œstrœ,œTextœ,œobjectœ],œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-uYXZJœ}", +>>>>>>> origin/dev "target": "ChatOutput-EJkG3", "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-EJkG3œ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}", "data": { "targetHandle": { "fieldName": "input_value", "id": "ChatOutput-EJkG3", +<<<<<<< HEAD "inputTypes": [ "Text" ], @@ -1712,6 +2135,15 @@ "Text" ], "name": "Text" +======= + "inputTypes": ["Text"], + "type": "str" + }, + "sourceHandle": { + "baseClasses": ["str", "Text", "object"], + "dataType": "OpenAIModel", + "id": "OpenAIModel-uYXZJ" +>>>>>>> origin/dev } }, "style": { @@ -1722,13 +2154,18 @@ }, { "source": "Prompt-gTNiz", +<<<<<<< HEAD "sourceHandle": "{\"dataType\": \"Prompt\", \"id\": \"Prompt-gTNiz\", \"output_types\": [\"Text\"], \"name\": \"Text\"}", +======= + "sourceHandle": "{œbaseClassesœ:[œobjectœ,œstrœ,œTextœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-gTNizœ}", +>>>>>>> origin/dev "target": "TextOutput-MUDOR", "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œTextOutput-MUDORœ,œinputTypesœ:[œRecordœ,œTextœ],œtypeœ:œstrœ}", "data": { "targetHandle": { "fieldName": "input_value", "id": "TextOutput-MUDOR", +<<<<<<< HEAD "inputTypes": [ "Record", "Text" @@ -1742,6 +2179,15 @@ "Text" ], "name": "Text" +======= + "inputTypes": ["Record", "Text"], + "type": "str" + }, + "sourceHandle": { + "baseClasses": ["object", "str", "Text"], + "dataType": "Prompt", + "id": "Prompt-gTNiz" +>>>>>>> origin/dev } }, "style": { @@ -1752,13 +2198,18 @@ }, { "source": "Prompt-gTNiz", +<<<<<<< HEAD "sourceHandle": "{\"dataType\": \"Prompt\", \"id\": \"Prompt-gTNiz\", \"output_types\": [\"Text\"], \"name\": \"Text\"}", +======= + "sourceHandle": "{œbaseClassesœ:[œobjectœ,œstrœ,œTextœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-gTNizœ}", +>>>>>>> origin/dev "target": "OpenAIModel-XawYB", "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-XawYBœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}", "data": { "targetHandle": { "fieldName": "input_value", "id": "OpenAIModel-XawYB", +<<<<<<< HEAD "inputTypes": [ "Text" ], @@ -1771,6 +2222,15 @@ "Text" ], "name": "Text" +======= + "inputTypes": ["Text"], + "type": "str" + }, + "sourceHandle": { + "baseClasses": ["object", "str", "Text"], + "dataType": "Prompt", + "id": "Prompt-gTNiz" +>>>>>>> origin/dev } }, "style": { @@ -1781,13 +2241,18 @@ }, { "source": "OpenAIModel-XawYB", +<<<<<<< HEAD "sourceHandle": "{\"dataType\": \"OpenAIModel\", \"id\": \"OpenAIModel-XawYB\", \"output_types\": [\"Text\"], \"name\": \"Text\"}", +======= + "sourceHandle": "{œbaseClassesœ:[œstrœ,œTextœ,œobjectœ],œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-XawYBœ}", +>>>>>>> origin/dev "target": "ChatOutput-DNmvg", "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-DNmvgœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}", "data": { "targetHandle": { "fieldName": "input_value", "id": "ChatOutput-DNmvg", +<<<<<<< HEAD "inputTypes": [ "Text" ], @@ -1800,6 +2265,15 @@ "Text" ], "name": "Text" +======= + "inputTypes": ["Text"], + "type": "str" + }, + "sourceHandle": { + "baseClasses": ["str", "Text", "object"], + "dataType": "OpenAIModel", + "id": "OpenAIModel-XawYB" +>>>>>>> origin/dev } }, "style": { @@ -1819,4 +2293,8 @@ "name": "Prompt Chaining", "last_tested_version": "1.0.0a0", "is_component": false -} \ No newline at end of file +<<<<<<< HEAD +} +======= +} +>>>>>>> origin/dev diff --git a/src/backend/base/langflow/load/__init__.py b/src/backend/base/langflow/load/__init__.py index 2002e8bb1..59dbdf6e0 100644 --- a/src/backend/base/langflow/load/__init__.py +++ b/src/backend/base/langflow/load/__init__.py @@ -1,3 +1,4 @@ -from .load import load_flow_from_json, run_flow_from_json # noqa: F401 +from .load import load_flow_from_json, run_flow_from_json +from .utils import upload_file, get_flow -__all__ = ["load_flow_from_json", "run_flow_from_json"] +__all__ = ["load_flow_from_json", "run_flow_from_json", "upload_file", "get_flow"] diff --git a/src/backend/base/langflow/load/utils.py b/src/backend/base/langflow/load/utils.py new file mode 100644 index 000000000..9c2918e91 --- /dev/null +++ b/src/backend/base/langflow/load/utils.py @@ -0,0 +1,89 @@ +import httpx + +from langflow.services.database.models.flow.model import FlowBase + + +def upload(file_path, host, flow_id): + """ + Upload a file to Langflow and return the file path. + + Args: + file_path (str): The path to the file to be uploaded. + host (str): The host URL of Langflow. + flow_id (UUID): The ID of the flow to which the file belongs. + + Returns: + dict: A dictionary containing the file path. + + Raises: + Exception: If an error occurs during the upload process. + """ + try: + url = f"{host}/api/v1/upload/{flow_id}" + response = httpx.post(url, files={"file": open(file_path, "rb")}) + if response.status_code == 200: + return response.json() + else: + raise Exception(f"Error uploading file: {response.status_code}") + except Exception as e: + raise Exception(f"Error uploading file: {e}") + + +def upload_file(file_path, host, flow_id, components, tweaks={}): + """ + Upload a file to Langflow and return the file path. + + Args: + file_path (str): The path to the file to be uploaded. + host (str): The host URL of Langflow. + port (int): The port number of Langflow. + flow_id (UUID): The ID of the flow to which the file belongs. + components (str): List of component IDs or names that need the file. + tweaks (dict): A dictionary of tweaks to be applied to the file. + + Returns: + dict: A dictionary containing the file path and any tweaks that were applied. + + Raises: + Exception: If an error occurs during the upload process. + """ + try: + response = upload(file_path, host, flow_id) + if response["file_path"]: + for component in components: + if isinstance(component, str): + tweaks[component] = {"file_path": response["file_path"]} + else: + raise ValueError(f"Component ID or name must be a string. Got {type(component)}") + return tweaks + else: + raise ValueError("Error uploading file") + except Exception as e: + raise ValueError(f"Error uploading file: {e}") + + +def get_flow(url: str, flow_id: str): + """Get the details of a flow from Langflow. + + Args: + url (str): The host URL of Langflow. + port (int): The port number of Langflow. + flow_id (UUID): The ID of the flow to retrieve. + + Returns: + dict: A dictionary containing the details of the flow. + + Raises: + Exception: If an error occurs during the retrieval process. + """ + try: + flow_url = f"{url}/api/v1/flows/{flow_id}" + response = httpx.get(flow_url) + if response.status_code == 200: + json_response = response.json() + flow = FlowBase(**json_response).model_dump() + return flow + else: + raise Exception(f"Error retrieving flow: {response.status_code}") + except Exception as e: + raise Exception(f"Error retrieving flow: {e}") diff --git a/src/backend/base/langflow/processing/process.py b/src/backend/base/langflow/processing/process.py index aeff0f1a4..1b54d3f08 100644 --- a/src/backend/base/langflow/processing/process.py +++ b/src/backend/base/langflow/processing/process.py @@ -59,7 +59,7 @@ async def run_graph_internal( outputs or [], stream=stream, session_id=session_id_str or "", - fallback_to_env_vars=fallback_to_env_vars + fallback_to_env_vars=fallback_to_env_vars, ) if session_id_str and session_service: await session_service.update_session(session_id_str, (graph, artifacts)) diff --git a/src/backend/base/langflow/services/database/models/api_key/model.py b/src/backend/base/langflow/services/database/models/api_key/model.py index cb216d9ae..157b08b32 100644 --- a/src/backend/base/langflow/services/database/models/api_key/model.py +++ b/src/backend/base/langflow/services/database/models/api_key/model.py @@ -55,6 +55,7 @@ class ApiKeyRead(ApiKeyBase): id: UUID api_key: str = Field(schema_extra={"validate_default": True}) user_id: UUID = Field() + created_at: datetime = Field() @field_validator("api_key") @classmethod diff --git a/src/backend/base/langflow/services/database/models/flow/model.py b/src/backend/base/langflow/services/database/models/flow/model.py index 4de1e0bc8..7727c7b86 100644 --- a/src/backend/base/langflow/services/database/models/flow/model.py +++ b/src/backend/base/langflow/services/database/models/flow/model.py @@ -29,7 +29,6 @@ class FlowBase(SQLModel): is_component: Optional[bool] = Field(default=False, nullable=True) updated_at: Optional[datetime] = Field(default_factory=lambda: datetime.now(timezone.utc), nullable=True) webhook: Optional[bool] = Field(default=False, nullable=True, description="Can be used on the webhook endpoint") - folder_id: Optional[UUID] = Field(default=None, nullable=True) endpoint_name: Optional[str] = Field(default=None, nullable=True, index=True) @field_validator("endpoint_name") diff --git a/src/backend/base/langflow/services/monitor/schema.py b/src/backend/base/langflow/services/monitor/schema.py index e7bc7a963..b3a9ce5c6 100644 --- a/src/backend/base/langflow/services/monitor/schema.py +++ b/src/backend/base/langflow/services/monitor/schema.py @@ -122,6 +122,13 @@ class MessageModelResponse(MessageModel): return v +class MessageModelRequest(MessageModel): + message: str = Field(default="") + sender: str = Field(default="") + sender_name: str = Field(default="") + session_id: str = Field(default="") + + class VertexBuildModel(BaseModel): index: Optional[int] = Field(default=None, alias="index", exclude=True) id: Optional[str] = Field(default=None, alias="id") diff --git a/src/backend/base/langflow/services/monitor/service.py b/src/backend/base/langflow/services/monitor/service.py index 9fce7dd59..02bc59cc2 100644 --- a/src/backend/base/langflow/services/monitor/service.py +++ b/src/backend/base/langflow/services/monitor/service.py @@ -32,6 +32,10 @@ class MonitorService(Service): except Exception as e: logger.exception(f"Error initializing monitor service: {e}") + def exec_query(self, query: str): + with duckdb.connect(str(self.db_path)) as conn: + return conn.execute(query).df() + def to_df(self, table_name): return self.load_table_as_dataframe(table_name) @@ -69,7 +73,7 @@ class MonitorService(Service): valid: Optional[bool] = None, order_by: Optional[str] = "timestamp", ): - query = "SELECT index,flow_id, valid, params, data, artifacts, timestamp FROM vertex_builds" + query = "SELECT id, index,flow_id, valid, params, data, artifacts, timestamp FROM vertex_builds" conditions = [] if flow_id: conditions.append(f"flow_id = '{flow_id}'") @@ -88,6 +92,8 @@ class MonitorService(Service): with duckdb.connect(str(self.db_path)) as conn: df = conn.execute(query).df() + print(query) + return df.to_dict(orient="records") def delete_vertex_builds(self, flow_id: Optional[str] = None): @@ -98,11 +104,22 @@ class MonitorService(Service): with duckdb.connect(str(self.db_path)) as conn: conn.execute(query) - def delete_messages(self, session_id: str): + def delete_messages_session(self, session_id: str): query = f"DELETE FROM messages WHERE session_id = '{session_id}'" - with duckdb.connect(str(self.db_path)) as conn: - conn.execute(query) + return self.exec_query(query) + + def delete_messages(self, message_ids: list[int]): + query = f"DELETE FROM messages WHERE index IN ({','.join(map(str, message_ids))})" + + return self.exec_query(query) + + def update_message(self, message_id: int, **kwargs): + query = ( + f"""UPDATE messages SET {', '.join(f"{k} = '{v}'" for k, v in kwargs.items())} WHERE index = {message_id}""" + ) + + return self.exec_query(query) def add_message(self, message: MessageModel): self.add_row("messages", message) diff --git a/src/backend/base/langflow/services/settings/base.py b/src/backend/base/langflow/services/settings/base.py index 259e10170..4f50cb756 100644 --- a/src/backend/base/langflow/services/settings/base.py +++ b/src/backend/base/langflow/services/settings/base.py @@ -78,7 +78,6 @@ class Settings(BaseSettings): langchain_cache: str = "InMemoryCache" load_flows_path: Optional[str] = None - # Redis redis_host: str = "localhost" redis_port: int = 6379 diff --git a/src/backend/base/poetry.lock b/src/backend/base/poetry.lock index d54969e68..75fbdcd6a 100644 --- a/src/backend/base/poetry.lock +++ b/src/backend/base/poetry.lock @@ -1159,13 +1159,13 @@ files = [ [[package]] name = "langchain" -version = "0.2.2" +version = "0.2.3" description = "Building applications with LLMs through composability" optional = false python-versions = "<4.0,>=3.8.1" files = [ - {file = "langchain-0.2.2-py3-none-any.whl", hash = "sha256:58ca0c47bcdd156da66f50a0a4fcedc49bf6950827f4a6b06c8c4842d55805f3"}, - {file = "langchain-0.2.2.tar.gz", hash = "sha256:9d61e50e9cdc2bea659bc5e6c03650ba048fda63a307490ae368e539f61a0d3a"}, + {file = "langchain-0.2.3-py3-none-any.whl", hash = "sha256:5dc33cd9c8008693d328b7cb698df69073acecc89ad9c2a95f243b3314f8d834"}, + {file = "langchain-0.2.3.tar.gz", hash = "sha256:81962cc72cce6515f7bd71e01542727870789bf8b666c6913d85559080c1a201"}, ] [package.dependencies] @@ -1181,29 +1181,15 @@ requests = ">=2,<3" SQLAlchemy = ">=1.4,<3" tenacity = ">=8.1.0,<9.0.0" -[package.extras] -azure = ["azure-ai-formrecognizer (>=3.2.1,<4.0.0)", "azure-ai-textanalytics (>=5.3.0,<6.0.0)", "azure-cognitiveservices-speech (>=1.28.0,<2.0.0)", "azure-core (>=1.26.4,<2.0.0)", "azure-cosmos (>=4.4.0b1,<5.0.0)", "azure-identity (>=1.12.0,<2.0.0)", "azure-search-documents (==11.4.0b8)", "openai (<2)"] -clarifai = ["clarifai (>=9.1.0)"] -cli = ["typer (>=0.9.0,<0.10.0)"] -cohere = ["cohere (>=4,<6)"] -docarray = ["docarray[hnswlib] (>=0.32.0,<0.33.0)"] -embeddings = ["sentence-transformers (>=2,<3)"] -extended-testing = ["aiosqlite (>=0.19.0,<0.20.0)", "aleph-alpha-client (>=2.15.0,<3.0.0)", "anthropic (>=0.3.11,<0.4.0)", "arxiv (>=1.4,<2.0)", "assemblyai (>=0.17.0,<0.18.0)", "atlassian-python-api (>=3.36.0,<4.0.0)", "beautifulsoup4 (>=4,<5)", "bibtexparser (>=1.4.0,<2.0.0)", "cassio (>=0.1.0,<0.2.0)", "chardet (>=5.1.0,<6.0.0)", "cohere (>=4,<6)", "couchbase (>=4.1.9,<5.0.0)", "dashvector (>=1.0.1,<2.0.0)", "databricks-vectorsearch (>=0.21,<0.22)", "datasets (>=2.15.0,<3.0.0)", "dgml-utils (>=0.3.0,<0.4.0)", "esprima (>=4.0.1,<5.0.0)", "faiss-cpu (>=1,<2)", "feedparser (>=6.0.10,<7.0.0)", "fireworks-ai (>=0.9.0,<0.10.0)", "geopandas (>=0.13.1,<0.14.0)", "gitpython (>=3.1.32,<4.0.0)", "google-cloud-documentai (>=2.20.1,<3.0.0)", "gql (>=3.4.1,<4.0.0)", "hologres-vector (>=0.0.6,<0.0.7)", "html2text (>=2020.1.16,<2021.0.0)", "javelin-sdk (>=0.1.8,<0.2.0)", "jinja2 (>=3,<4)", "jq (>=1.4.1,<2.0.0)", "jsonschema (>1)", "langchain-openai (>=0.1,<0.2)", "lxml (>=4.9.3,<6.0)", "markdownify (>=0.11.6,<0.12.0)", "motor (>=3.3.1,<4.0.0)", "msal (>=1.25.0,<2.0.0)", "mwparserfromhell (>=0.6.4,<0.7.0)", "mwxml (>=0.3.3,<0.4.0)", "newspaper3k (>=0.2.8,<0.3.0)", "numexpr (>=2.8.6,<3.0.0)", "openai (<2)", "openapi-pydantic (>=0.3.2,<0.4.0)", "pandas (>=2.0.1,<3.0.0)", "pdfminer-six (>=20221105,<20221106)", "pgvector (>=0.1.6,<0.2.0)", "praw (>=7.7.1,<8.0.0)", "psychicapi (>=0.8.0,<0.9.0)", "py-trello (>=0.19.0,<0.20.0)", "pymupdf (>=1.22.3,<2.0.0)", "pypdf (>=3.4.0,<4.0.0)", "pypdfium2 (>=4.10.0,<5.0.0)", "pyspark (>=3.4.0,<4.0.0)", "rank-bm25 (>=0.2.2,<0.3.0)", "rapidfuzz (>=3.1.1,<4.0.0)", "rapidocr-onnxruntime (>=1.3.2,<2.0.0)", "rdflib (==7.0.0)", "requests-toolbelt (>=1.0.0,<2.0.0)", "rspace_client (>=2.5.0,<3.0.0)", "scikit-learn (>=1.2.2,<2.0.0)", "sqlite-vss (>=0.1.2,<0.2.0)", "streamlit (>=1.18.0,<2.0.0)", "sympy (>=1.12,<2.0)", "telethon (>=1.28.5,<2.0.0)", "timescale-vector (>=0.0.1,<0.0.2)", "tqdm (>=4.48.0)", "upstash-redis (>=0.15.0,<0.16.0)", "xata (>=1.0.0a7,<2.0.0)", "xmltodict (>=0.13.0,<0.14.0)"] -javascript = ["esprima (>=4.0.1,<5.0.0)"] -llms = ["clarifai (>=9.1.0)", "cohere (>=4,<6)", "huggingface_hub (>=0,<1)", "manifest-ml (>=0.0.1,<0.0.2)", "nlpcloud (>=1,<2)", "openai (<2)", "openlm (>=0.0.5,<0.0.6)", "torch (>=1,<3)", "transformers (>=4,<5)"] -openai = ["openai (<2)", "tiktoken (>=0.7,<1.0)"] -qdrant = ["qdrant-client (>=1.3.1,<2.0.0)"] -text-helpers = ["chardet (>=5.1.0,<6.0.0)"] - [[package]] name = "langchain-community" -version = "0.2.2" +version = "0.2.4" description = "Community contributed LangChain integrations." optional = false python-versions = "<4.0,>=3.8.1" files = [ - {file = "langchain_community-0.2.2-py3-none-any.whl", hash = "sha256:470ee16e05f1acacb91a656b6d3c2cbf6fb6a8dcb00a13901cd1353cd29c2bb3"}, - {file = "langchain_community-0.2.2.tar.gz", hash = "sha256:fb09faf4640726a929932056dc55ff120e490aaf2e424fae8ddbb15605195447"}, + {file = "langchain_community-0.2.4-py3-none-any.whl", hash = "sha256:8582e9800f4837660dc297cccd2ee1ddc1d8c440d0fe8b64edb07620f0373b0e"}, + {file = "langchain_community-0.2.4.tar.gz", hash = "sha256:2bb6a1a36b8500a564d25d76469c02457b1a7c3afea6d4a609a47c06b993e3e4"}, ] [package.dependencies] @@ -1218,19 +1204,15 @@ requests = ">=2,<3" SQLAlchemy = ">=1.4,<3" tenacity = ">=8.1.0,<9.0.0" -[package.extras] -cli = ["typer (>=0.9.0,<0.10.0)"] -extended-testing = ["aiosqlite (>=0.19.0,<0.20.0)", "aleph-alpha-client (>=2.15.0,<3.0.0)", "anthropic (>=0.3.11,<0.4.0)", "arxiv (>=1.4,<2.0)", "assemblyai (>=0.17.0,<0.18.0)", "atlassian-python-api (>=3.36.0,<4.0.0)", "azure-ai-documentintelligence (>=1.0.0b1,<2.0.0)", "azure-identity (>=1.15.0,<2.0.0)", "azure-search-documents (==11.4.0)", "beautifulsoup4 (>=4,<5)", "bibtexparser (>=1.4.0,<2.0.0)", "cassio (>=0.1.6,<0.2.0)", "chardet (>=5.1.0,<6.0.0)", "cloudpathlib (>=0.18,<0.19)", "cloudpickle (>=2.0.0)", "cohere (>=4,<5)", "databricks-vectorsearch (>=0.21,<0.22)", "datasets (>=2.15.0,<3.0.0)", "dgml-utils (>=0.3.0,<0.4.0)", "elasticsearch (>=8.12.0,<9.0.0)", "esprima (>=4.0.1,<5.0.0)", "faiss-cpu (>=1,<2)", "feedparser (>=6.0.10,<7.0.0)", "fireworks-ai (>=0.9.0,<0.10.0)", "friendli-client (>=1.2.4,<2.0.0)", "geopandas (>=0.13.1,<0.14.0)", "gitpython (>=3.1.32,<4.0.0)", "google-cloud-documentai (>=2.20.1,<3.0.0)", "gql (>=3.4.1,<4.0.0)", "gradientai (>=1.4.0,<2.0.0)", "hdbcli (>=2.19.21,<3.0.0)", "hologres-vector (>=0.0.6,<0.0.7)", "html2text (>=2020.1.16,<2021.0.0)", "httpx (>=0.24.1,<0.25.0)", "httpx-sse (>=0.4.0,<0.5.0)", "javelin-sdk (>=0.1.8,<0.2.0)", "jinja2 (>=3,<4)", "jq (>=1.4.1,<2.0.0)", "jsonschema (>1)", "lxml (>=4.9.3,<6.0)", "markdownify (>=0.11.6,<0.12.0)", "motor (>=3.3.1,<4.0.0)", "msal (>=1.25.0,<2.0.0)", "mwparserfromhell (>=0.6.4,<0.7.0)", "mwxml (>=0.3.3,<0.4.0)", "newspaper3k (>=0.2.8,<0.3.0)", "numexpr (>=2.8.6,<3.0.0)", "nvidia-riva-client (>=2.14.0,<3.0.0)", "oci (>=2.119.1,<3.0.0)", "openai (<2)", "openapi-pydantic (>=0.3.2,<0.4.0)", "oracle-ads (>=2.9.1,<3.0.0)", "oracledb (>=2.2.0,<3.0.0)", "pandas (>=2.0.1,<3.0.0)", "pdfminer-six (>=20221105,<20221106)", "pgvector (>=0.1.6,<0.2.0)", "praw (>=7.7.1,<8.0.0)", "premai (>=0.3.25,<0.4.0)", "psychicapi (>=0.8.0,<0.9.0)", "py-trello (>=0.19.0,<0.20.0)", "pyjwt (>=2.8.0,<3.0.0)", "pymupdf (>=1.22.3,<2.0.0)", "pypdf (>=3.4.0,<4.0.0)", "pypdfium2 (>=4.10.0,<5.0.0)", "pyspark (>=3.4.0,<4.0.0)", "rank-bm25 (>=0.2.2,<0.3.0)", "rapidfuzz (>=3.1.1,<4.0.0)", "rapidocr-onnxruntime (>=1.3.2,<2.0.0)", "rdflib (==7.0.0)", "requests-toolbelt (>=1.0.0,<2.0.0)", "rspace_client (>=2.5.0,<3.0.0)", "scikit-learn (>=1.2.2,<2.0.0)", "simsimd (>=4.3.1,<5.0.0)", "sqlite-vss (>=0.1.2,<0.2.0)", "streamlit (>=1.18.0,<2.0.0)", "sympy (>=1.12,<2.0)", "telethon (>=1.28.5,<2.0.0)", "tidb-vector (>=0.0.3,<1.0.0)", "timescale-vector (>=0.0.1,<0.0.2)", "tqdm (>=4.48.0)", "tree-sitter (>=0.20.2,<0.21.0)", "tree-sitter-languages (>=1.8.0,<2.0.0)", "upstash-redis (>=0.15.0,<0.16.0)", "vdms (>=0.0.20,<0.0.21)", "xata (>=1.0.0a7,<2.0.0)", "xmltodict (>=0.13.0,<0.14.0)"] - [[package]] name = "langchain-core" -version = "0.2.4" +version = "0.2.5" description = "Building applications with LLMs through composability" optional = false python-versions = "<4.0,>=3.8.1" files = [ - {file = "langchain_core-0.2.4-py3-none-any.whl", hash = "sha256:5212f7ec78a525e88a178ed3aefe2fd7134b03fb92573dfbab9914f1d92d6ec5"}, - {file = "langchain_core-0.2.4.tar.gz", hash = "sha256:82bdcc546eb0341cefcf1f4ecb3e49836fff003903afddda2d1312bb8491ef81"}, + {file = "langchain_core-0.2.5-py3-none-any.whl", hash = "sha256:abe5138f22acff23a079ec538be5268bbf97cf023d51987a0dd474d2a16cae3e"}, + {file = "langchain_core-0.2.5.tar.gz", hash = "sha256:4a5c2f56b22396a63ef4790043660e393adbfa6832b978f023ca996a04b8e752"}, ] [package.dependencies] @@ -1241,9 +1223,6 @@ pydantic = ">=1,<3" PyYAML = ">=5.3" tenacity = ">=8.1.0,<9.0.0" -[package.extras] -extended-testing = ["jinja2 (>=3,<4)"] - [[package]] name = "langchain-experimental" version = "0.0.60" @@ -1296,13 +1275,13 @@ types-requests = ">=2.31.0.2,<3.0.0.0" [[package]] name = "langsmith" -version = "0.1.71" +version = "0.1.75" description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform." optional = false python-versions = "<4.0,>=3.8.1" files = [ - {file = "langsmith-0.1.71-py3-none-any.whl", hash = "sha256:a9979de2780442eb24eced31314e49f5ece6f807a0d70740b2c6c39217226794"}, - {file = "langsmith-0.1.71.tar.gz", hash = "sha256:bdb1037a08acf7c19b3969c085df09c1eecb65baca8400b3b76ae871e2c8a97e"}, + {file = "langsmith-0.1.75-py3-none-any.whl", hash = "sha256:d08b08dd6b3fa4da170377f95123d77122ef4c52999d10fff4ae08ff70d07aed"}, + {file = "langsmith-0.1.75.tar.gz", hash = "sha256:61274e144ea94c297dd78ce03e6dfae18459fe9bd8ab5094d61a0c4816561279"}, ] [package.dependencies] @@ -1600,13 +1579,13 @@ files = [ [[package]] name = "marshmallow" -version = "3.21.2" +version = "3.21.3" description = "A lightweight library for converting complex datatypes to and from native Python datatypes." optional = false python-versions = ">=3.8" files = [ - {file = "marshmallow-3.21.2-py3-none-any.whl", hash = "sha256:70b54a6282f4704d12c0a41599682c5c5450e843b9ec406308653b47c59648a1"}, - {file = "marshmallow-3.21.2.tar.gz", hash = "sha256:82408deadd8b33d56338d2182d455db632c6313aa2af61916672146bb32edc56"}, + {file = "marshmallow-3.21.3-py3-none-any.whl", hash = "sha256:86ce7fb914aa865001a4b2092c4c2872d13bc347f3d42673272cabfdbad386f1"}, + {file = "marshmallow-3.21.3.tar.gz", hash = "sha256:4f57c5e050a54d66361e826f94fba213eb10b67b2fdb02c3e0343ce207ba1662"}, ] [package.dependencies] @@ -2214,13 +2193,13 @@ typing-extensions = ">=4.6.0,<4.7.0 || >4.7.0" [[package]] name = "pydantic-settings" -version = "2.3.0" +version = "2.3.1" description = "Settings management using Pydantic" optional = false python-versions = ">=3.8" files = [ - {file = "pydantic_settings-2.3.0-py3-none-any.whl", hash = "sha256:26eeed27370a9c5e3f64e4a7d6602573cbedf05ed940f1d5b11c3f178427af7a"}, - {file = "pydantic_settings-2.3.0.tar.gz", hash = "sha256:78db28855a71503cfe47f39500a1dece523c640afd5280edb5c5c9c9cfa534c9"}, + {file = "pydantic_settings-2.3.1-py3-none-any.whl", hash = "sha256:acb2c213140dfff9669f4fe9f8180d43914f51626db28ab2db7308a576cce51a"}, + {file = "pydantic_settings-2.3.1.tar.gz", hash = "sha256:e34bbd649803a6bb3e2f0f58fb0edff1f0c7f556849fda106cc21bcce12c30ab"}, ] [package.dependencies] diff --git a/src/backend/base/pyproject.toml b/src/backend/base/pyproject.toml index 6cf0a4b19..b175fbcf5 100644 --- a/src/backend/base/pyproject.toml +++ b/src/backend/base/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "langflow-base" -version = "0.0.56" +version = "0.0.60" description = "A Python package with a built-in web application" authors = ["Langflow "] maintainers = [ diff --git a/src/frontend/package-lock.json b/src/frontend/package-lock.json index 363415ccf..7aa230dd7 100644 --- a/src/frontend/package-lock.json +++ b/src/frontend/package-lock.json @@ -26,6 +26,7 @@ "@radix-ui/react-slot": "^1.0.2", "@radix-ui/react-switch": "^1.0.3", "@radix-ui/react-tabs": "^1.0.4", + "@radix-ui/react-toggle": "^1.0.3", "@radix-ui/react-tooltip": "^1.0.6", "@tabler/icons-react": "^2.32.0", "@tailwindcss/forms": "^0.5.6", @@ -42,6 +43,7 @@ "cmdk": "^1.0.0", "dompurify": "^3.0.5", "dotenv": "^16.4.5", + "emoji-regex": "^10.3.0", "esbuild": "^0.17.19", "file-saver": "^2.0.5", "framer-motion": "^11.0.6", @@ -50,6 +52,7 @@ "million": "^3.0.6", "moment": "^2.29.4", "openseadragon": "^4.1.1", + "p-debounce": "^4.0.0", "playwright": "^1.42.0", "react": "^18.2.21", "react-ace": "^10.1.0", @@ -2761,6 +2764,31 @@ } } }, + "node_modules/@radix-ui/react-toggle": { + "version": "1.0.3", + "resolved": "https://registry.npmjs.org/@radix-ui/react-toggle/-/react-toggle-1.0.3.tgz", + "integrity": "sha512-Pkqg3+Bc98ftZGsl60CLANXQBBQ4W3mTFS9EJvNxKMZ7magklKV69/id1mlAlOFDDfHvlCms0fx8fA4CMKDJHg==", + "dependencies": { + "@babel/runtime": "^7.13.10", + "@radix-ui/primitive": "1.0.1", + "@radix-ui/react-primitive": "1.0.3", + "@radix-ui/react-use-controllable-state": "1.0.1" + }, + "peerDependencies": { + "@types/react": "*", + "@types/react-dom": "*", + "react": "^16.8 || ^17.0 || ^18.0", + "react-dom": "^16.8 || ^17.0 || ^18.0" + }, + "peerDependenciesMeta": { + "@types/react": { + "optional": true + }, + "@types/react-dom": { + "optional": true + } + } + }, "node_modules/@radix-ui/react-tooltip": { "version": "1.0.7", "resolved": "https://registry.npmjs.org/@radix-ui/react-tooltip/-/react-tooltip-1.0.7.tgz", @@ -5965,9 +5993,9 @@ "integrity": "sha512-C6q/xcUJf/2yODRxAVCfIk4j3y3LMsD0ehiE2RQNV2cxc8XU62gR6vvYh3+etSUzlgTfil+qDHI1vubpdf0TOA==" }, "node_modules/emoji-regex": { - "version": "8.0.0", - "resolved": "https://registry.npmjs.org/emoji-regex/-/emoji-regex-8.0.0.tgz", - "integrity": "sha512-MSjYzcWNOA0ewAHpz0MxpYFvwg6yjy1NG3xteoqz644VCo/RPgnr1/GGt+ic3iJTzQ8Eu3TdM14SawnVUmGE6A==" + "version": "10.3.0", + "resolved": "https://registry.npmjs.org/emoji-regex/-/emoji-regex-10.3.0.tgz", + "integrity": "sha512-QpLs9D9v9kArv4lfDEgg1X/gN5XLnf/A6l9cs8SPZLRZR3ZkY9+kwIQTxm+fsSej5UMYGE8fdoaZVIBlqG0XTw==" }, "node_modules/end-of-stream": { "version": "1.4.4", @@ -10017,6 +10045,15 @@ "node": ">=8" } }, + "node_modules/p-debounce": { + "version": "4.0.0", + "resolved": "https://registry.npmjs.org/p-debounce/-/p-debounce-4.0.0.tgz", + "integrity": "sha512-4Ispi9I9qYGO4lueiLDhe4q4iK5ERK8reLsuzH6BPaXn53EGaua8H66PXIFGrW897hwjXp+pVLrm/DLxN0RF0A==", + "license": "MIT", + "engines": { + "node": ">=12" + } + }, "node_modules/p-finally": { "version": "1.0.0", "resolved": "https://registry.npmjs.org/p-finally/-/p-finally-1.0.0.tgz", @@ -12172,6 +12209,16 @@ "node": ">=8" } }, + "node_modules/string-width-cjs/node_modules/emoji-regex": { + "version": "8.0.0", + "resolved": "https://registry.npmjs.org/emoji-regex/-/emoji-regex-8.0.0.tgz", + "integrity": "sha512-MSjYzcWNOA0ewAHpz0MxpYFvwg6yjy1NG3xteoqz644VCo/RPgnr1/GGt+ic3iJTzQ8Eu3TdM14SawnVUmGE6A==" + }, + "node_modules/string-width/node_modules/emoji-regex": { + "version": "8.0.0", + "resolved": "https://registry.npmjs.org/emoji-regex/-/emoji-regex-8.0.0.tgz", + "integrity": "sha512-MSjYzcWNOA0ewAHpz0MxpYFvwg6yjy1NG3xteoqz644VCo/RPgnr1/GGt+ic3iJTzQ8Eu3TdM14SawnVUmGE6A==" + }, "node_modules/strip-ansi": { "version": "6.0.1", "resolved": "https://registry.npmjs.org/strip-ansi/-/strip-ansi-6.0.1.tgz", diff --git a/src/frontend/package.json b/src/frontend/package.json index 053a246ee..c9219b5e5 100644 --- a/src/frontend/package.json +++ b/src/frontend/package.json @@ -21,6 +21,7 @@ "@radix-ui/react-slot": "^1.0.2", "@radix-ui/react-switch": "^1.0.3", "@radix-ui/react-tabs": "^1.0.4", + "@radix-ui/react-toggle": "^1.0.3", "@radix-ui/react-tooltip": "^1.0.6", "@tabler/icons-react": "^2.32.0", "@tailwindcss/forms": "^0.5.6", @@ -37,6 +38,7 @@ "cmdk": "^1.0.0", "dompurify": "^3.0.5", "dotenv": "^16.4.5", + "emoji-regex": "^10.3.0", "esbuild": "^0.17.19", "file-saver": "^2.0.5", "framer-motion": "^11.0.6", @@ -45,6 +47,7 @@ "million": "^3.0.6", "moment": "^2.29.4", "openseadragon": "^4.1.1", + "p-debounce": "^4.0.0", "playwright": "^1.42.0", "react": "^18.2.21", "react-ace": "^10.1.0", diff --git a/src/frontend/playwright.config.ts b/src/frontend/playwright.config.ts index 9535e0a15..eeb9497ae 100644 --- a/src/frontend/playwright.config.ts +++ b/src/frontend/playwright.config.ts @@ -15,7 +15,7 @@ dotenv.config({ path: path.resolve(__dirname, "../../.env") }); export default defineConfig({ testDir: "./tests", /* Run tests in files in parallel */ - fullyParallel: true, + fullyParallel: false, /* Fail the build on CI if you accidentally left test.only in the source code. */ forbidOnly: !!process.env.CI, /* Retry on CI only */ @@ -52,18 +52,18 @@ export default defineConfig({ }, }, - { - name: "firefox", - use: { - ...devices["Desktop Firefox"], - launchOptions: { - firefoxUserPrefs: { - "dom.events.asyncClipboard.readText": true, - "dom.events.testing.asyncClipboard": true, - }, - }, - }, - }, + // { + // name: "firefox", + // use: { + // ...devices["Desktop Firefox"], + // launchOptions: { + // firefoxUserPrefs: { + // "dom.events.asyncClipboard.readText": true, + // "dom.events.testing.asyncClipboard": true, + // }, + // }, + // }, + // }, ], webServer: [ { diff --git a/src/frontend/src/App.css b/src/frontend/src/App.css index a4ff01961..809959757 100644 --- a/src/frontend/src/App.css +++ b/src/frontend/src/App.css @@ -164,3 +164,13 @@ body { .ag-body-vertical-scroll-viewport::-webkit-scrollbar-thumb:hover { background-color: #bbb; } + +/* This CSS is to not apply the border for the column having 'no-border' class */ +.no-border.ag-cell:focus { + border: none !important; + outline: none; +} +.no-border.ag-cell { + border: none !important; + outline: none; +} diff --git a/src/frontend/src/App.tsx b/src/frontend/src/App.tsx index 3d144f194..36f2ad9f9 100644 --- a/src/frontend/src/App.tsx +++ b/src/frontend/src/App.tsx @@ -1,4 +1,3 @@ -import axios from "axios"; import { useContext, useEffect, useState } from "react"; import { ErrorBoundary } from "react-error-boundary"; import { useNavigate } from "react-router-dom"; @@ -222,12 +221,19 @@ export default function App() { id={alert.id} removeAlert={removeAlert} /> + ) : alert.type === "notice" ? ( + ) : ( - alert.type === "notice" && ( - @@ -236,20 +242,6 @@ export default function App() { ))} -
- {tempNotificationList.map((alert) => ( -
- {alert.type === "success" && ( - - )} -
- ))} -
); diff --git a/src/frontend/src/alerts/alertDropDown/index.tsx b/src/frontend/src/alerts/alertDropDown/index.tsx index 3577a5de6..6eff32fe2 100644 --- a/src/frontend/src/alerts/alertDropDown/index.tsx +++ b/src/frontend/src/alerts/alertDropDown/index.tsx @@ -36,7 +36,7 @@ export default function AlertDropdown({ }} > {children} - +
Notifications
diff --git a/src/frontend/src/alerts/error/index.tsx b/src/frontend/src/alerts/error/index.tsx index ec23c103e..b70a5ae45 100644 --- a/src/frontend/src/alerts/error/index.tsx +++ b/src/frontend/src/alerts/error/index.tsx @@ -40,7 +40,7 @@ export default function ErrorAlert({ removeAlert(id); }, 500); }} - className="error-build-message nocopy nopan nodelete nodrag noundo" + className="error-build-message nocopy nowheel nopan nodelete nodrag noundo" >
@@ -51,13 +51,15 @@ export default function ErrorAlert({ />
-

{title}

+

{title}

{list?.length !== 0 && list?.some((item) => item !== null && item !== undefined) ? (
    {list.map((item, index) => ( -
  • {item}
    • +
  • + {item} +
    • ))}
diff --git a/src/frontend/src/alerts/notice/index.tsx b/src/frontend/src/alerts/notice/index.tsx index faaa4db6a..dcb034691 100644 --- a/src/frontend/src/alerts/notice/index.tsx +++ b/src/frontend/src/alerts/notice/index.tsx @@ -36,7 +36,7 @@ export default function NoticeAlert({ setShow(false); removeAlert(id); }} - className="nocopy nopan nodelete nodrag noundo mt-6 w-96 rounded-md bg-info-background p-4 shadow-xl" + className="nocopy nowheel nopan nodelete nodrag noundo mt-6 w-96 rounded-md bg-info-background p-4 shadow-xl" >
@@ -47,7 +47,7 @@ export default function NoticeAlert({ />
-

+

{title}

diff --git a/src/frontend/src/alerts/success/index.tsx b/src/frontend/src/alerts/success/index.tsx index ec6abf589..270ae5515 100644 --- a/src/frontend/src/alerts/success/index.tsx +++ b/src/frontend/src/alerts/success/index.tsx @@ -34,7 +34,7 @@ export default function SuccessAlert({ setShow(false); removeAlert(id); }} - className="success-alert nocopy nopan nodelete nodrag noundo" + className="success-alert nocopy nowheel nopan nodelete nodrag noundo" >

@@ -45,7 +45,7 @@ export default function SuccessAlert({ />
-

{title}

+

{title}

diff --git a/src/frontend/src/components/ImageViewer/index.tsx b/src/frontend/src/components/ImageViewer/index.tsx index 8433962a7..dc7f41ad7 100644 --- a/src/frontend/src/components/ImageViewer/index.tsx +++ b/src/frontend/src/components/ImageViewer/index.tsx @@ -31,14 +31,14 @@ export default function ImageViewer({ image }) { const fullPageButton = document.getElementById("full-page-button"); zoomInButton!.addEventListener("click", () => - viewer.viewport.zoomBy(1.2), + viewer.viewport.zoomBy(1.2) ); zoomOutButton!.addEventListener("click", () => - viewer.viewport.zoomBy(0.8), + viewer.viewport.zoomBy(0.8) ); homeButton!.addEventListener("click", () => viewer.viewport.goHome()); fullPageButton!.addEventListener("click", () => - viewer.setFullScreen(true), + viewer.setFullScreen(true) ); // Optionally, you can set additional viewer options here @@ -47,16 +47,16 @@ export default function ImageViewer({ image }) { return () => { viewer.destroy(); zoomInButton!.removeEventListener("click", () => - viewer.viewport.zoomBy(1.2), + viewer.viewport.zoomBy(1.2) ); zoomOutButton!.removeEventListener("click", () => - viewer.viewport.zoomBy(0.8), + viewer.viewport.zoomBy(0.8) ); homeButton!.removeEventListener("click", () => - viewer.viewport.goHome(), + viewer.viewport.goHome() ); fullPageButton!.removeEventListener("click", () => - viewer.setFullScreen(true), + viewer.setFullScreen(true) ); }; } diff --git a/src/frontend/src/components/accordionComponent/index.tsx b/src/frontend/src/components/accordionComponent/index.tsx index fdcf8b96c..43a0aef79 100644 --- a/src/frontend/src/components/accordionComponent/index.tsx +++ b/src/frontend/src/components/accordionComponent/index.tsx @@ -6,16 +6,18 @@ import { AccordionTrigger, } from "../../components/ui/accordion"; import { AccordionComponentType } from "../../types/components"; +import { cn } from "../../utils/utils"; export default function AccordionComponent({ trigger, children, + disabled, open = [], keyValue, sideBar, }: AccordionComponentType): JSX.Element { const [value, setValue] = useState( - open.length === 0 ? "" : getOpenAccordion(), + open.length === 0 ? "" : getOpenAccordion() ); function getOpenAccordion(): string { @@ -29,7 +31,9 @@ export default function AccordionComponent({ } function handleClick(): void { - value === "" ? setValue(keyValue!) : setValue(""); + if (!disabled) { + value === "" ? setValue(keyValue!) : setValue(""); + } } return ( @@ -38,16 +42,18 @@ export default function AccordionComponent({ type="single" className="w-full" value={value} - onValueChange={setValue} + onValueChange={!disabled ? setValue : () => {}} > { handleClick(); }} - className={ - sideBar ? "w-full bg-muted px-[0.75rem] py-[0.5rem]" : "ml-3" - } + disabled={disabled} + className={cn( + sideBar ? "w-full bg-muted px-[0.75rem] py-[0.5rem]" : "ml-3", + disabled ? "cursor-not-allowed" : "cursor-pointer" + )} > {trigger} diff --git a/src/frontend/src/components/addNewVariableButtonComponent/addNewVariableButton.tsx b/src/frontend/src/components/addNewVariableButtonComponent/addNewVariableButton.tsx index 5da7d1461..61dada650 100644 --- a/src/frontend/src/components/addNewVariableButtonComponent/addNewVariableButton.tsx +++ b/src/frontend/src/components/addNewVariableButtonComponent/addNewVariableButton.tsx @@ -7,7 +7,6 @@ import { useTypesStore } from "../../stores/typesStore"; import { ResponseErrorDetailAPI } from "../../types/api"; import ForwardedIconComponent from "../genericIconComponent"; import InputComponent from "../inputComponent"; -import { Button } from "../ui/button"; import { Input } from "../ui/input"; import { Label } from "../ui/label"; import { Textarea } from "../ui/textarea"; @@ -24,19 +23,19 @@ export default function AddNewVariableButton({ children }): JSX.Element { const setErrorData = useAlertStore((state) => state.setErrorData); const componentFields = useTypesStore((state) => state.ComponentFields); const unavaliableFields = new Set( - Object.keys(useGlobalVariablesStore((state) => state.unavaliableFields)), + Object.keys(useGlobalVariablesStore((state) => state.unavaliableFields)) ); const availableFields = () => { const fields = Array.from(componentFields).filter( - (field) => !unavaliableFields.has(field), + (field) => !unavaliableFields.has(field) ); return sortByName(fields); }; const addGlobalVariable = useGlobalVariablesStore( - (state) => state.addGlobalVariable, + (state) => state.addGlobalVariable ); function handleSaveVariable() { @@ -65,12 +64,17 @@ export default function AddNewVariableButton({ children }): JSX.Element { let responseError = error as ResponseErrorDetailAPI; setErrorData({ title: "Error creating variable", - list: [responseError.response.data.detail ?? "Unknown error"], + list: [responseError?.response?.data?.detail ?? "Unknown error"], }); }); } return ( - +
- - - + ); } diff --git a/src/frontend/src/components/cardComponent/index.tsx b/src/frontend/src/components/cardComponent/index.tsx index 09b8ff833..a5d671aa0 100644 --- a/src/frontend/src/components/cardComponent/index.tsx +++ b/src/frontend/src/components/cardComponent/index.tsx @@ -27,8 +27,8 @@ import { import { Checkbox } from "../ui/checkbox"; import { FormControl, FormField } from "../ui/form"; import Loading from "../ui/loading"; -import { convertTestName } from "./utils/convert-test-name"; import DragCardComponent from "./components/dragCardComponent"; +import { convertTestName } from "./utils/convert-test-name"; export default function CollectionCardComponent({ data, @@ -60,11 +60,11 @@ export default function CollectionCardComponent({ const [loading, setLoading] = useState(false); const [loadingLike, setLoadingLike] = useState(false); const [liked_by_user, setLiked_by_user] = useState( - data?.liked_by_user ?? false, + data?.liked_by_user ?? false ); const [likes_count, setLikes_count] = useState(data?.liked_by_count ?? 0); const [downloads_count, setDownloads_count] = useState( - data?.downloads_count ?? 0, + data?.downloads_count ?? 0 ); const currentFlow = useFlowsManagerStore((state) => state.currentFlow); const setCurrentFlow = useFlowsManagerStore((state) => state.setCurrentFlow); @@ -75,12 +75,12 @@ export default function CollectionCardComponent({ const [openPlayground, setOpenPlayground] = useState(false); const [openDelete, setOpenDelete] = useState(false); const setCurrentFlowId = useFlowsManagerStore( - (state) => state.setCurrentFlowId, + (state) => state.setCurrentFlowId ); const [loadingPlayground, setLoadingPlayground] = useState(false); const selectedFlowsComponentsCards = useFlowsManagerStore( - (state) => state.selectedFlowsComponentsCards, + (state) => state.selectedFlowsComponentsCards ); const name = data.is_component ? "Component" : "Flow"; @@ -220,7 +220,7 @@ export default function CollectionCardComponent({ "group relative flex min-h-[11rem] flex-col justify-between overflow-hidden transition-all hover:bg-muted/50 hover:shadow-md hover:dark:bg-[#ffffff10]", disabled ? "pointer-events-none opacity-50" : "", onClick ? "cursor-pointer" : "", - isSelectedCard ? "border border-selected" : "", + isSelectedCard ? "border border-selected" : "" )} onClick={onClick} > @@ -233,7 +233,7 @@ export default function CollectionCardComponent({ "visible flex-shrink-0", data.is_component ? "mx-0.5 h-6 w-6 text-component-icon" - : "h-7 w-7 flex-shrink-0 text-flow-icon", + : "h-7 w-7 flex-shrink-0 text-flow-icon" )} name={data.is_component ? "ToyBrick" : "Group"} /> @@ -428,7 +428,7 @@ export default function CollectionCardComponent({ name="Trash2" className={cn( "h-5 w-5", - !authorized ? " text-ring" : "", + !authorized ? " text-ring" : "" )} /> @@ -463,7 +463,7 @@ export default function CollectionCardComponent({ liked_by_user ? "fill-destructive stroke-destructive" : "", - !authorized ? " text-ring" : "", + !authorized ? " text-ring" : "" )} /> @@ -501,7 +501,7 @@ export default function CollectionCardComponent({ } className={cn( loading ? "h-5 w-5 animate-spin" : "h-5 w-5", - !authorized ? " text-ring" : "", + !authorized ? " text-ring" : "" )} /> diff --git a/src/frontend/src/components/cardsWrapComponent/index.tsx b/src/frontend/src/components/cardsWrapComponent/index.tsx index c7ca01588..0de3f1a2f 100644 --- a/src/frontend/src/components/cardsWrapComponent/index.tsx +++ b/src/frontend/src/components/cardsWrapComponent/index.tsx @@ -65,7 +65,7 @@ export default function CardsWrapComponent({ "h-full w-full", isDragging ? "mb-36 flex flex-col items-center justify-center gap-4 text-2xl font-light" - : "", + : "" )} > {isDragging ? ( diff --git a/src/frontend/src/components/chatComponent/index.tsx b/src/frontend/src/components/chatComponent/index.tsx index 81dade485..1fa82e9dd 100644 --- a/src/frontend/src/components/chatComponent/index.tsx +++ b/src/frontend/src/components/chatComponent/index.tsx @@ -50,7 +50,7 @@ export default function FlowToolbar(): JSX.Element { "relative inline-flex h-full w-full items-center justify-center gap-[4px] bg-muted px-5 py-3 text-sm font-semibold text-foreground transition-all duration-150 ease-in-out hover:bg-background hover:bg-hover ", !hasApiKey || !validApiKey || !hasStore ? " button-disable text-muted-foreground " - : "", + : "" )} > Share ), - [hasApiKey, validApiKey, currentFlow, hasStore], + [hasApiKey, validApiKey, currentFlow, hasStore] ); return ( @@ -118,7 +118,7 @@ export default function FlowToolbar(): JSX.Element {
{data?.map((node: any, i) => ( @@ -275,8 +275,8 @@ export default function CodeTabsComponent({ .show && LANGFLOW_SUPPORTED_TYPES.has( node.data.node.template[templateField] - .type, - ), + .type + ) ) .map((templateField, indx) => { return ( @@ -334,7 +334,7 @@ export default function CodeTabsComponent({ target, node.data.node.template[ templateField - ], + ] ); }} /> @@ -380,7 +380,7 @@ export default function CodeTabsComponent({ target, node.data.node.template[ templateField - ], + ] ); }} /> @@ -433,7 +433,7 @@ export default function CodeTabsComponent({ target, node.data.node.template[ templateField - ], + ] ); }} /> @@ -470,7 +470,7 @@ export default function CodeTabsComponent({ e, node.data.node.template[ templateField - ], + ] ); }} size="small" @@ -501,7 +501,7 @@ export default function CodeTabsComponent({ ].fileTypes } onFileChange={( - value: any, + value: any ) => { node.data.node.template[ templateField @@ -554,7 +554,7 @@ export default function CodeTabsComponent({ target, node.data.node.template[ templateField - ], + ] ); }} /> @@ -594,7 +594,7 @@ export default function CodeTabsComponent({ target, node.data.node.template[ templateField - ], + ] ); }} value={ @@ -656,7 +656,7 @@ export default function CodeTabsComponent({ target, node.data.node.template[ templateField - ], + ] ); }} /> @@ -702,7 +702,7 @@ export default function CodeTabsComponent({ target, node.data.node.template[ templateField - ], + ] ); }} /> @@ -748,7 +748,7 @@ export default function CodeTabsComponent({ target, node.data.node.template[ templateField - ], + ] ); }} /> @@ -780,8 +780,8 @@ export default function CodeTabsComponent({ ].value, type( node, - templateField, - ), + templateField + ) ) } duplicateKey={ @@ -790,15 +790,15 @@ export default function CodeTabsComponent({ onChange={(target) => { const valueToNumbers = convertValuesToNumbers( - target, + target ); node.data.node!.template[ templateField ].value = valueToNumbers; setErrorDuplicateKey( hasDuplicateKeys( - valueToNumbers, - ), + valueToNumbers + ) ); setData((old) => { let newInputList = @@ -815,7 +815,7 @@ export default function CodeTabsComponent({ target, node.data.node.template[ templateField - ], + ] ); }} isList={ @@ -863,7 +863,7 @@ export default function CodeTabsComponent({ target, node.data.node.template[ templateField - ], + ] ); }} /> diff --git a/src/frontend/src/components/csvOutputComponent/index.tsx b/src/frontend/src/components/csvOutputComponent/index.tsx index a98d9c028..a25cfb679 100644 --- a/src/frontend/src/components/csvOutputComponent/index.tsx +++ b/src/frontend/src/components/csvOutputComponent/index.tsx @@ -67,7 +67,7 @@ function CsvOutputComponent({ if (file) { const { rowData: data, colDefs: columns } = convertCSVToData( file, - separator, + separator ); setRowData(data); setColDefs(columns); diff --git a/src/frontend/src/components/dropdownComponent/index.tsx b/src/frontend/src/components/dropdownComponent/index.tsx index bff138681..0fd981603 100644 --- a/src/frontend/src/components/dropdownComponent/index.tsx +++ b/src/frontend/src/components/dropdownComponent/index.tsx @@ -33,9 +33,8 @@ export default function Dropdown({ const refButton = useRef(null); - const PopoverContentDropdown = children - ? PopoverContent - : PopoverContentWithoutPortal; + const PopoverContentDropdown = + children || editNode ? PopoverContent : PopoverContentWithoutPortal; return ( <> diff --git a/src/frontend/src/components/editFlowSettingsComponent/index.tsx b/src/frontend/src/components/editFlowSettingsComponent/index.tsx index d8a6fc43e..3dd813965 100644 --- a/src/frontend/src/components/editFlowSettingsComponent/index.tsx +++ b/src/frontend/src/components/editFlowSettingsComponent/index.tsx @@ -109,7 +109,7 @@ export const EditFlowSettings: React.FC = ({ {setEndpointName && (