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actions/checkout@v3 + - name: Install poetry + run: pipx install poetry==$POETRY_VERSION + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v4 + with: + python-version: ${{ matrix.python-version }} + cache: "poetry" + - name: Install dependencies + run: poetry install + - name: Run unit tests + run: | + make test diff --git a/Makefile b/Makefile index 70407436b..328ac8580 100644 --- a/Makefile +++ b/Makefile @@ -42,14 +42,13 @@ build: dev: make install_frontend ifeq ($(build),1) - @echo 'Running docker compose up with build' - docker compose up --build + @echo 'Running docker compose up with build' + docker compose $(if $(debug),-f docker-compose.debug.yml) up --build else - @echo 'Running docker compose up without build' - docker compose up + @echo 'Running docker compose up without build' + docker compose $(if $(debug),-f docker-compose.debug.yml) up endif - publish: make build poetry publish diff --git a/dev.Dockerfile b/dev.Dockerfile index 0f559a0cf..7e439c69a 100644 --- a/dev.Dockerfile +++ b/dev.Dockerfile @@ -15,4 +15,4 @@ COPY ./ ./ # Install dependencies RUN poetry config virtualenvs.create false && poetry install --no-interaction --no-ansi -CMD ["uvicorn", "langflow.main:app", "--host", "0.0.0.0", "--port", "5003", "--reload"] \ No newline at end of file +CMD ["uvicorn", "langflow.main:app", "--host", "0.0.0.0", "--port", "5003", "--reload", "log-level", "debug"] \ No newline at end of file diff --git a/docker-compose.debug.yml b/docker-compose.debug.yml new file mode 100644 index 000000000..581bdc6da --- /dev/null +++ b/docker-compose.debug.yml @@ -0,0 +1,28 @@ +version: '3.4' + +services: + backend: + volumes: + - ./:/app + build: + context: ./ + dockerfile: ./dev.Dockerfile + command: ["sh", "-c", "pip install debugpy -t /tmp && python /tmp/debugpy --wait-for-client --listen 0.0.0.0:5678 -m uvicorn langflow.main:app --host 0.0.0.0 --port 7860 --reload"] + ports: + - 7860:7860 + - 5678:5678 + restart: on-failure 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host: str = "127.0.0.1", workers: int = 1, timeout: int = 60, port: int = 7860 + host: str = typer.Option("127.0.0.1", help="Host to bind the server to."), + workers: int = typer.Option(1, help="Number of worker processes."), + timeout: int = typer.Option(60, help="Worker timeout in seconds."), + port: int = typer.Option(7860, help="Port to listen on."), + config: str = typer.Option("config.yaml", help="Path to the configuration file."), + log_level: str = typer.Option("info", help="Logging level."), + log_file: Path = typer.Option("logs/langflow.log", help="Path to the log file."), ): + """ + Run the Langflow server. + """ + + configure(log_level=log_level, log_file=log_file) + update_settings(config) app = create_app() # get the directory of the current file path = Path(__file__).parent @@ -39,10 +59,10 @@ def serve( if platform.system() in ["Darwin", "Windows"]: # Run using uvicorn on MacOS and Windows # Windows doesn't support gunicorn - # MacOS requires a env variable to be set to use gunicorn + # MacOS requires an env variable to be set to use gunicorn import uvicorn - uvicorn.run(app, host=host, port=port, log_level="info") + uvicorn.run(app, host=host, port=port, log_level=log_level) else: from langflow.server import LangflowApplication @@ -50,7 +70,7 @@ def serve( def main(): - typer.run(serve) + app() if __name__ == "__main__": diff --git a/src/backend/langflow/api/base.py b/src/backend/langflow/api/base.py new file mode 100644 index 000000000..9096aff1b --- /dev/null +++ b/src/backend/langflow/api/base.py @@ -0,0 +1,27 @@ +from pydantic import BaseModel, validator + + +class Code(BaseModel): + code: str + + +class Prompt(BaseModel): + template: str + + +# Build ValidationResponse class for {"imports": {"errors": []}, "function": {"errors": []}} +class CodeValidationResponse(BaseModel): + imports: dict + function: dict + + @validator("imports") + def validate_imports(cls, v): + return v or {"errors": []} + + @validator("function") + def validate_function(cls, v): + return v or {"errors": []} + + +class PromptValidationResponse(BaseModel): + input_variables: list diff --git a/src/backend/langflow/api/endpoints.py b/src/backend/langflow/api/endpoints.py index 7214ec6a6..22f548156 100644 --- a/src/backend/langflow/api/endpoints.py +++ b/src/backend/langflow/api/endpoints.py @@ -1,12 +1,14 @@ +import logging from typing import Any, Dict from fastapi import APIRouter, HTTPException -from langflow.interface.run import process_data_graph +from langflow.interface.run import process_graph from langflow.interface.types import build_langchain_types_dict # build router router = APIRouter() +logger = logging.getLogger(__name__) @router.get("/all") @@ -17,6 +19,8 @@ def get_all(): @router.post("/predict") def get_load(data: Dict[str, Any]): try: - return process_data_graph(data) + return process_graph(data) except Exception as e: - return HTTPException(status_code=500, detail=str(e)) + # Log stack trace + logger.exception(e) + raise HTTPException(status_code=500, detail=str(e)) from e diff --git a/src/backend/langflow/api/list_endpoints.py b/src/backend/langflow/api/list_endpoints.py deleted file mode 100644 index 15946a2db..000000000 --- a/src/backend/langflow/api/list_endpoints.py +++ /dev/null @@ -1,58 +0,0 @@ -from fastapi import APIRouter - -from langflow.interface.listing import list_type - -# build router -router = APIRouter( - prefix="/list", - tags=["list"], -) - - -@router.get("/") -def read_items(): - """List all components""" - return [ - "chains", - "agents", - "prompts", - "llms", - "tools", - ] - - -@router.get("/chains") -def list_chains(): - """List all chain types""" - return list_type("chains") - - -@router.get("/agents") -def list_agents(): - """List all agent types""" - # return list(agents.loading.AGENT_TO_CLASS.keys()) - return list_type("agents") - - -@router.get("/prompts") -def list_prompts(): - """List all prompt types""" - return list_type("prompts") - - -@router.get("/llms") -def list_llms(): - """List all llm types""" - return list_type("llms") - - -@router.get("/memories") -def list_memories(): - """List all memory types""" - return list_type("memories") - - -@router.get("/tools") -def list_tools(): - """List all load tools""" - return list_type("tools") diff --git a/src/backend/langflow/api/signature.py b/src/backend/langflow/api/signature.py deleted file mode 100644 index 96b654dbe..000000000 --- a/src/backend/langflow/api/signature.py +++ /dev/null @@ -1,63 +0,0 @@ -from fastapi import APIRouter, HTTPException - -from langflow.interface.signature import get_signature - -# build router -router = APIRouter( - prefix="/signatures", - tags=["signatures"], -) - - -@router.get("/chain") -def get_chain(name: str): - """Get the signature of a chain.""" - try: - return get_signature(name, "chains") - except ValueError as exc: - raise HTTPException(status_code=404, detail="Chain not found") from exc - - -@router.get("/agent") -def get_agent(name: str): - """Get the signature of an agent.""" - try: - return get_signature(name, "agents") - except ValueError as exc: - raise HTTPException(status_code=404, detail="Agent not found") from exc - - -@router.get("/prompt") -def get_prompt(name: str): - """Get the signature of a prompt.""" - try: - return get_signature(name, "prompts") - except ValueError as exc: - raise HTTPException(status_code=404, detail="Prompt not found") from exc - - -@router.get("/llm") -def get_llm(name: str): - """Get the signature of an llm.""" - try: - return get_signature(name, "llms") - except ValueError as exc: - raise HTTPException(status_code=404, detail="LLM not found") from exc - - -@router.get("/memory") -def get_memory(name: str): - """Get the signature of a memory.""" - try: - return get_signature(name, "memories") - except ValueError as exc: - raise HTTPException(status_code=404, detail="Memory not found") from exc - - -@router.get("/tool") -def get_tool(name: str): - """Get the signature of a tool.""" - try: - return get_signature(name, "tools") - except ValueError as exc: - raise HTTPException(status_code=404, detail="Tool not found") from exc diff --git a/src/backend/langflow/api/validate.py b/src/backend/langflow/api/validate.py new file mode 100644 index 000000000..6dea45df0 --- /dev/null +++ b/src/backend/langflow/api/validate.py @@ -0,0 +1,36 @@ +from fastapi import APIRouter, HTTPException + +from langflow.api.base import ( + Code, + CodeValidationResponse, + Prompt, + PromptValidationResponse, +) +from langflow.graph.utils import extract_input_variables_from_prompt +from langflow.utils.logger import logger +from langflow.utils.validate import validate_code + +# build router +router = APIRouter(prefix="/validate", tags=["validate"]) + + +@router.post("/code", status_code=200, response_model=CodeValidationResponse) +def post_validate_code(code: Code): + try: + errors = validate_code(code.code) + return CodeValidationResponse( + imports=errors.get("imports", {}), + function=errors.get("function", {}), + ) + except Exception as e: + return HTTPException(status_code=500, detail=str(e)) + + +@router.post("/prompt", status_code=200, response_model=PromptValidationResponse) +def post_validate_prompt(prompt: Prompt): + try: + input_variables = extract_input_variables_from_prompt(prompt.template) + return PromptValidationResponse(input_variables=input_variables) + except Exception as e: + logger.exception(e) + return HTTPException(status_code=500, detail=str(e)) diff --git a/src/backend/langflow/cache/__init__.py b/src/backend/langflow/cache/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/src/backend/langflow/cache/utils.py b/src/backend/langflow/cache/utils.py new file mode 100644 index 000000000..3c416f4d7 --- /dev/null +++ b/src/backend/langflow/cache/utils.py @@ -0,0 +1,72 @@ +import contextlib +import hashlib +import json +import os +import tempfile +from pathlib import Path + +import dill # type: ignore + +PREFIX = "langflow_cache" + + +def clear_old_cache_files(max_cache_size: int = 3): + cache_dir = Path(tempfile.gettempdir()) + cache_files = list(cache_dir.glob(f"{PREFIX}_*.dill")) + + if len(cache_files) > max_cache_size: + cache_files_sorted_by_mtime = sorted( + cache_files, key=lambda x: x.stat().st_mtime, reverse=True + ) + + for cache_file in cache_files_sorted_by_mtime[max_cache_size:]: + with contextlib.suppress(OSError): + os.remove(cache_file) + + +def filter_json(json_data): + filtered_data = json_data.copy() + + # Remove 'viewport' and 'chatHistory' keys + if "viewport" in filtered_data: + del filtered_data["viewport"] + if "chatHistory" in filtered_data: + del filtered_data["chatHistory"] + + # Filter nodes + if "nodes" in filtered_data: + for node in filtered_data["nodes"]: + if "position" in node: + del node["position"] + if "positionAbsolute" in node: + del node["positionAbsolute"] + if "selected" in node: + del node["selected"] + if "dragging" in node: + del node["dragging"] + + return filtered_data + + +def compute_hash(graph_data): + graph_data = filter_json(graph_data) + + cleaned_graph_json = json.dumps(graph_data, sort_keys=True) + return hashlib.sha256(cleaned_graph_json.encode("utf-8")).hexdigest() + + +def save_cache(hash_val: str, chat_data, clean_old_cache_files: bool): + cache_path = Path(tempfile.gettempdir()) / f"{PREFIX}_{hash_val}.dill" + with cache_path.open("wb") as cache_file: + dill.dump(chat_data, cache_file) + + if clean_old_cache_files: + clear_old_cache_files() + + +def load_cache(hash_val): + cache_path = Path(tempfile.gettempdir()) / f"{PREFIX}_{hash_val}.dill" + if cache_path.exists(): + with cache_path.open("rb") as cache_file: + return dill.load(cache_file) + return None diff --git a/src/backend/langflow/config.yaml b/src/backend/langflow/config.yaml index 9b03aaeb1..a8775f561 100644 --- a/src/backend/langflow/config.yaml +++ b/src/backend/langflow/config.yaml @@ -1,27 +1,57 @@ chains: - LLMChain - LLMMathChain - - LLMChecker - # - ConversationChain + - LLMCheckerChain + - ConversationChain + - SeriesCharacterChain + - MidJourneyPromptChain + - TimeTravelGuideChain agents: - ZeroShotAgent + - JsonAgent + - CSVAgent + - initialize_agent prompts: - PromptTemplate - FewShotPromptTemplate + - ZeroShotPrompt + # Wait more tests + # - ChatPromptTemplate + # - SystemMessagePromptTemplate + # - HumanMessagePromptTemplate llms: - OpenAI - - OpenAIChat + - AzureOpenAI + - ChatOpenAI tools: - Search - PAL-MATH - Calculator - Serper Search + - Tool + - PythonFunction + - JsonSpec + +wrappers: + - RequestsWrapper + +toolkits: + - OpenAPIToolkit + - JsonToolkit memories: - # - ConversationBufferMemory + - ConversationBufferMemory + - ConversationSummaryMemory + - ConversationKGMemory + +embeddings: [] + +vectorstores: [] + +documentloaders: [] dev: false diff --git a/src/backend/langflow/custom/customs.py b/src/backend/langflow/custom/customs.py index cea81ac1a..22e833362 100644 --- a/src/backend/langflow/custom/customs.py +++ b/src/backend/langflow/custom/customs.py @@ -1,42 +1,17 @@ -from langchain.agents.mrkl import prompt +from langflow.template import nodes + +# These should always be instantiated +CUSTOM_NODES = { + "prompts": {"ZeroShotPrompt": nodes.ZeroShotPromptNode()}, + "tools": {"PythonFunction": nodes.PythonFunctionNode(), "Tool": nodes.ToolNode()}, + "agents": { + "JsonAgent": nodes.JsonAgentNode(), + "CSVAgent": nodes.CSVAgentNode(), + "initialize_agent": nodes.InitializeAgentNode(), + }, +} -def get_custom_prompts(): - """Get custom prompts.""" - - return { - "ZeroShotPrompt": { - "template": { - "_type": "zero_shot", - "prefix": { - "type": "str", - "required": False, - "placeholder": "", - "list": False, - "show": True, - "multiline": True, - "value": prompt.PREFIX, - }, - "suffix": { - "type": "str", - "required": True, - "placeholder": "", - "list": False, - "show": True, - "multiline": True, - "value": prompt.SUFFIX, - }, - "format_instructions": { - "type": "str", - "required": False, - "placeholder": "", - "list": False, - "show": True, - "multiline": True, - "value": prompt.FORMAT_INSTRUCTIONS, - }, - }, - "description": "Prompt template for Zero Shot Agent.", - "base_classes": ["BasePromptTemplate"], - } - } +def get_custom_nodes(node_type: str): + """Get custom nodes.""" + return CUSTOM_NODES.get(node_type, {}) diff --git a/src/backend/langflow/graph/__init__.py b/src/backend/langflow/graph/__init__.py new file mode 100644 index 000000000..097b7a695 --- /dev/null +++ b/src/backend/langflow/graph/__init__.py @@ -0,0 +1,4 @@ +from langflow.graph.base import Edge, Node +from langflow.graph.graph import Graph + +__all__ = ["Graph", "Node", "Edge"] diff --git a/src/backend/langflow/graph/base.py b/src/backend/langflow/graph/base.py new file mode 100644 index 000000000..fa93ed8ed --- /dev/null +++ b/src/backend/langflow/graph/base.py @@ -0,0 +1,244 @@ +# Description: Graph class for building a graph of nodes and edges +# Insights: +# - Defer prompts building to the last moment or when they have all the tools +# - Build each inner agent first, then build the outer agent + +import types +from copy import deepcopy +from typing import Any, Dict, List, Optional + +from langflow.graph.constants import DIRECT_TYPES +from langflow.graph.utils import load_file +from langflow.interface import loading +from langflow.interface.listing import ALL_TYPES_DICT +from langflow.utils.logger import logger + + +class Node: + def __init__(self, data: Dict, base_type: Optional[str] = None) -> None: + self.id: str = data["id"] + self._data = data + self.edges: List[Edge] = [] + self.base_type: Optional[str] = base_type + self._parse_data() + self._built_object = None + self._built = False + + def _parse_data(self) -> None: + self.data = self._data["data"] + self.output = self.data["node"]["base_classes"] + template_dicts = { + key: value + for key, value in self.data["node"]["template"].items() + if isinstance(value, dict) + } + + self.required_inputs = [ + template_dicts[key]["type"] + for key, value in template_dicts.items() + if value["required"] + ] + self.optional_inputs = [ + template_dicts[key]["type"] + for key, value in template_dicts.items() + if not value["required"] + ] + + template_dict = self.data["node"]["template"] + self.node_type = ( + self.data["type"] if "Tool" not in self.output else template_dict["_type"] + ) + if self.base_type is None: + for base_type, value in ALL_TYPES_DICT.items(): + if self.node_type in value: + self.base_type = base_type + break + + def _build_params(self): + # Some params are required, some are optional + # but most importantly, some params are python base classes + # like str and others are LangChain objects like LLMChain, BasePromptTemplate + # so we need to be able to distinguish between the two + + # The dicts with "type" == "str" are the ones that are python base classes + # and most likely have a "value" key + + # So for each key besides "_type" in the template dict, we have a dict + # with a "type" key. If the type is not "str", then we need to get the + # edge that connects to that node and get the Node with the required data + # and use that as the value for the param + # If the type is "str", then we need to get the value of the "value" key + # and use that as the value for the param + template_dict = { + key: value + for key, value in self.data["node"]["template"].items() + if isinstance(value, dict) + } + params = {} + for key, value in template_dict.items(): + if key == "_type": + continue + # If the type is not transformable to a python base class + # then we need to get the edge that connects to this node + if value.get("type") == "file": + # Load the type in value.get('suffixes') using + # what is inside value.get('content') + # value.get('value') is the file name + file_name = value.get("value") + content = value.get("content") + type_to_load = value.get("suffixes") + loaded_dict = load_file(file_name, content, type_to_load) + params[key] = loaded_dict + + # We should check if the type is in something not + # the opposite + elif value.get("type") not in DIRECT_TYPES: + # Get the edge that connects to this node + edges = [ + edge + for edge in self.edges + if edge.target == self and edge.matched_type in value["type"] + ] + + # Get the output of the node that the edge connects to + # if the value['list'] is True, then there will be more + # than one time setting to params[key] + # so we need to append to a list if it exists + # or create a new list if it doesn't + + if value["required"] and not edges: + # If a required parameter is not found, raise an error + raise ValueError( + f"Required input {key} for module {self.node_type} not found" + ) + elif value["list"]: + # If this is a list parameter, append all sources to a list + params[key] = [edge.source for edge in edges] + elif edges: + # If a single parameter is found, use its source + params[key] = edges[0].source + + elif value["required"] or value.get("value"): + params[key] = value["value"] + + # Add _type to params + self.params = params + + def _build(self): + # The params dict is used to build the module + # it contains values and keys that point to nodes which + # have their own params dict + # When build is called, we iterate through the params dict + # and if the value is a node, we call build on that node + # and use the output of that build as the value for the param + # if the value is not a node, then we use the value as the param + # and continue + # Another aspect is that the node_type is the class that we need to import + # and instantiate with these built params + logger.debug(f"Building {self.node_type}") + # Build each node in the params dict + for key, value in self.params.copy().items(): + # Check if Node or list of Nodes and not self + # to avoid recursion + if isinstance(value, Node): + if value == self: + del self.params[key] + continue + result = value.build() + # If the key is "func", then we need to use the run method + if key == "func" and not isinstance(result, types.FunctionType): + # func can be PythonFunction(code='\ndef upper_case(text: str) -> str:\n return text.upper()\n') + # so we need to check if there is an attribute called run + if hasattr(result, "run"): + result = result.run # type: ignore + elif hasattr(result, "get_function"): + result = result.get_function() # type: ignore + self.params[key] = result + elif isinstance(value, list) and all( + isinstance(node, Node) for node in value + ): + self.params[key] = [node.build() for node in value] # type: ignore + + # Get the class from LANGCHAIN_TYPES_DICT + # and instantiate it with the params + # and return the instance + + try: + self._built_object = loading.instantiate_class( + node_type=self.node_type, + base_type=self.base_type, + params=self.params, + ) + except Exception as exc: + raise ValueError( + f"Error building node {self.node_type}: {str(exc)}" + ) from exc + + if self._built_object is None: + raise ValueError(f"Node type {self.node_type} not found") + + self._built = True + + def build(self, force: bool = False) -> Any: + if not self._built or force: + self._build() + return deepcopy(self._built_object) + + def add_edge(self, edge: "Edge") -> None: + self.edges.append(edge) + + def __repr__(self) -> str: + return f"Node(id={self.id}, data={self.data})" + + def __eq__(self, __o: object) -> bool: + return self.id == __o.id if isinstance(__o, Node) else False + + def __hash__(self) -> int: + return id(self) + + +class Edge: + def __init__(self, source: "Node", target: "Node"): + self.source: "Node" = source + self.target: "Node" = target + self.validate_edge() + + def validate_edge(self) -> None: + # Validate that the outputs of the source node are valid inputs + # for the target node + self.source_types = self.source.output + self.target_reqs = self.target.required_inputs + self.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 + for target_req in self.target_reqs + if output in target_req + ), + None, + ) + no_matched_type = self.matched_type is None + if no_matched_type: + logger.debug(self.source_types) + logger.debug(self.target_reqs) + if no_matched_type: + raise ValueError( + f"Edge between {self.source.node_type} and {self.target.node_type} " + f"has no matched type" + ) + + def __repr__(self) -> str: + return ( + f"Edge(source={self.source.id}, target={self.target.id}, valid={self.valid}" + f", matched_type={self.matched_type})" + ) diff --git a/src/backend/langflow/graph/constants.py b/src/backend/langflow/graph/constants.py new file mode 100644 index 000000000..095843816 --- /dev/null +++ b/src/backend/langflow/graph/constants.py @@ -0,0 +1 @@ +DIRECT_TYPES = ["str", "bool", "code", "int", "float", "Any"] diff --git a/src/backend/langflow/graph/graph.py b/src/backend/langflow/graph/graph.py new file mode 100644 index 000000000..76cab071d --- /dev/null +++ b/src/backend/langflow/graph/graph.py @@ -0,0 +1,159 @@ +from typing import Dict, List, Union + +from langflow.graph.base import Edge, Node +from langflow.graph.nodes import ( + AgentNode, + ChainNode, + FileToolNode, + LLMNode, + MemoryNode, + PromptNode, + ToolkitNode, + ToolNode, + WrapperNode, +) +from langflow.interface.agents.base import agent_creator +from langflow.interface.chains.base import chain_creator +from langflow.interface.llms.base import llm_creator +from langflow.interface.prompts.base import prompt_creator +from langflow.interface.toolkits.base import toolkits_creator +from langflow.interface.tools.base import tool_creator +from langflow.interface.tools.constants import FILE_TOOLS +from langflow.interface.tools.util import get_tools_dict +from langflow.interface.wrappers.base import wrapper_creator +from langflow.interface.memories.base import memory_creator +from langflow.utils import payload + + +class Graph: + def __init__( + self, + nodes: List[Dict[str, Union[str, Dict[str, Union[str, List[str]]]]]], + edges: List[Dict[str, str]], + ) -> None: + self._nodes = nodes + self._edges = edges + self._build_graph() + + def _build_graph(self) -> None: + self.nodes = self._build_nodes() + self.edges = self._build_edges() + for edge in self.edges: + edge.source.add_edge(edge) + edge.target.add_edge(edge) + + # This is a hack to make sure that the LLM node is sent to + # the toolkit node + llm_node = None + for node in self.nodes: + node._build_params() + + if isinstance(node, LLMNode): + llm_node = node + + for node in self.nodes: + if isinstance(node, ToolkitNode): + node.params["llm"] = llm_node + # remove invalid nodes + self.nodes = [node for node in self.nodes if self._validate_node(node)] + + def _validate_node(self, node: Node) -> bool: + # All nodes that do not have edges are invalid + return len(node.edges) > 0 + + def get_node(self, node_id: str) -> Union[None, Node]: + return next((node for node in self.nodes if node.id == node_id), None) + + def get_nodes_with_target(self, node: Node) -> List[Node]: + connected_nodes: List[Node] = [ + edge.source for edge in self.edges if edge.target == node + ] + return connected_nodes + + def build(self) -> List[Node]: + # Get root node + root_node = payload.get_root_node(self) + if root_node is None: + raise ValueError("No root node found") + return root_node.build() + + def get_node_neighbors(self, node: Node) -> Dict[Node, int]: + neighbors: Dict[Node, int] = {} + for edge in self.edges: + if edge.source == node: + neighbor = edge.target + if neighbor not in neighbors: + neighbors[neighbor] = 0 + neighbors[neighbor] += 1 + elif edge.target == node: + neighbor = edge.source + if neighbor not in neighbors: + neighbors[neighbor] = 0 + neighbors[neighbor] += 1 + return neighbors + + def _build_edges(self) -> List[Edge]: + # Edge takes two nodes as arguments, so we need to build the nodes first + # and then build the edges + # if we can't find a node, we raise an error + + edges: List[Edge] = [] + for edge in self._edges: + source = self.get_node(edge["source"]) + target = self.get_node(edge["target"]) + if source is None: + raise ValueError(f"Source node {edge['source']} not found") + if target is None: + raise ValueError(f"Target node {edge['target']} not found") + edges.append(Edge(source, target)) + return edges + + def _build_nodes(self) -> List[Node]: + nodes: List[Node] = [] + for node in self._nodes: + node_data = node["data"] + node_type: str = node_data["type"] # type: ignore + node_lc_type: str = node_data["node"]["template"]["_type"] # type: ignore + + if node_type in prompt_creator.to_list(): + nodes.append(PromptNode(node)) + elif ( + node_type in agent_creator.to_list() + or node_lc_type in agent_creator.to_list() + ): + nodes.append(AgentNode(node)) + elif node_type in chain_creator.to_list(): + nodes.append(ChainNode(node)) + elif ( + node_type in tool_creator.to_list() + or node_lc_type in get_tools_dict().keys() + ): + if node_type in FILE_TOOLS: + nodes.append(FileToolNode(node)) + nodes.append(ToolNode(node)) + elif node_type in toolkits_creator.to_list(): + nodes.append(ToolkitNode(node)) + elif node_type in wrapper_creator.to_list(): + nodes.append(WrapperNode(node)) + elif ( + node_type in llm_creator.to_list() + or node_lc_type in llm_creator.to_list() + ): + nodes.append(LLMNode(node)) + elif ( + node_type in memory_creator.to_list() + or node_lc_type in memory_creator.to_list() + ): + nodes.append(MemoryNode(node)) + else: + nodes.append(Node(node)) + return nodes + + def get_children_by_node_type(self, node: Node, node_type: str) -> List[Node]: + children = [] + node_types = [node.data["type"]] + if "node" in node.data: + node_types += node.data["node"]["base_classes"] + if node_type in node_types: + children.append(node) + return children diff --git a/src/backend/langflow/graph/nodes.py b/src/backend/langflow/graph/nodes.py new file mode 100644 index 000000000..bf2f0bcec --- /dev/null +++ b/src/backend/langflow/graph/nodes.py @@ -0,0 +1,156 @@ +from copy import deepcopy +from typing import Any, Dict, List, Optional, Union + +from langflow.graph.base import Node +from langflow.graph.utils import extract_input_variables_from_prompt + + +class AgentNode(Node): + def __init__(self, data: Dict): + super().__init__(data, base_type="agents") + + self.tools: List[ToolNode] = [] + self.chains: List[ChainNode] = [] + + def _set_tools_and_chains(self) -> None: + for edge in self.edges: + source_node = edge.source + if isinstance(source_node, ToolNode): + self.tools.append(source_node) + elif isinstance(source_node, ChainNode): + self.chains.append(source_node) + + def build(self, force: bool = False) -> Any: + if not self._built or force: + self._set_tools_and_chains() + # First, build the tools + for tool_node in self.tools: + tool_node.build() + + # Next, build the chains and the rest + for chain_node in self.chains: + chain_node.build(tools=self.tools) + + self._build() + return deepcopy(self._built_object) + + +class ToolNode(Node): + def __init__(self, data: Dict): + super().__init__(data, base_type="tools") + + def build(self, force: bool = False) -> Any: + if not self._built or force: + self._build() + return deepcopy(self._built_object) + + +class PromptNode(Node): + def __init__(self, data: Dict): + super().__init__(data, base_type="prompts") + + def build( + self, + force: bool = False, + tools: Optional[Union[List[Node], List[ToolNode]]] = None, + ) -> Any: + if not self._built or force: + if "input_variables" not in self.params: + self.params["input_variables"] = [] + # Check if it is a ZeroShotPrompt and needs a tool + if "ShotPrompt" in self.node_type: + tools = ( + [tool_node.build() for tool_node in tools] + if tools is not None + else [] + ) + self.params["tools"] = tools + prompt_params = [ + key + for key, value in self.params.items() + if isinstance(value, str) and key != "format_instructions" + ] + else: + prompt_params = ["template"] + for param in prompt_params: + 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._build() + return deepcopy(self._built_object) + + +class ChainNode(Node): + def __init__(self, data: Dict): + super().__init__(data, base_type="chains") + + def build( + self, + force: bool = False, + tools: Optional[Union[List[Node], List[ToolNode]]] = None, + ) -> Any: + if not self._built or force: + # Check if the chain requires a PromptNode + for key, value in self.params.items(): + if isinstance(value, PromptNode): + # Build the PromptNode, passing the tools if available + self.params[key] = value.build(tools=tools, force=force) + + self._build() + return deepcopy(self._built_object) + + +class LLMNode(Node): + def __init__(self, data: Dict): + super().__init__(data, base_type="llms") + + def build(self, force: bool = False) -> Any: + if not self._built or force: + self._build() + return deepcopy(self._built_object) + + +class ToolkitNode(Node): + def __init__(self, data: Dict): + super().__init__(data, base_type="toolkits") + + def build(self, force: bool = False) -> Any: + if not self._built or force: + self._build() + + return deepcopy(self._built_object) + + +class FileToolNode(ToolNode): + def __init__(self, data: Dict): + super().__init__(data) + + def build(self, force: bool = False) -> Any: + if not self._built or force: + self._build() + return deepcopy(self._built_object) + + +class WrapperNode(Node): + def __init__(self, data: Dict): + super().__init__(data, base_type="wrappers") + + def build(self, force: bool = False) -> Any: + if not self._built or force: + if "headers" in self.params: + self.params["headers"] = eval(self.params["headers"]) + self._build() + return deepcopy(self._built_object) + + +class MemoryNode(Node): + def __init__(self, data: Dict): + super().__init__(data, base_type="memory") + + def build(self, force: bool = False) -> Any: + if not self._built or force: + self._build() + return deepcopy(self._built_object) diff --git a/src/backend/langflow/graph/utils.py b/src/backend/langflow/graph/utils.py new file mode 100644 index 000000000..3028ceeb0 --- /dev/null +++ b/src/backend/langflow/graph/utils.py @@ -0,0 +1,60 @@ +import base64 +import csv +import io +import json +import re +from typing import Any + +import yaml + + +def load_file(file_name, file_content, accepted_types) -> Any: + """Load a file from a string.""" + # Check if the file is accepted + if not any(file_name.endswith(suffix) for suffix in accepted_types): + raise ValueError(f"File {file_name} is not accepted") + # Get the suffix + suffix = file_name.split(".")[-1] + # file_content == 'data:application/x-yaml;base64,b3BlbmFwaTogIjMuMC4wIg...' + data = file_content.split(",")[1] + decoded_bytes = base64.b64decode(data) + + # Convert the bytes object to a string + decoded_string = decoded_bytes.decode("utf-8") + if suffix == "json": + # Return the json content + return json.loads(decoded_string) + elif suffix in ["yaml", "yml"]: + # Return the yaml content + loaded_yaml = yaml.load(decoded_string, Loader=yaml.FullLoader) + try: + from langchain.agents.agent_toolkits.openapi.spec import reduce_openapi_spec # type: ignore + + return reduce_openapi_spec(loaded_yaml) + except ImportError: + return loaded_yaml + + elif suffix == "csv": + # Load the csv content + csv_reader = csv.DictReader(io.StringIO(decoded_string)) + return list(csv_reader) + else: + raise ValueError(f"File {file_name} is not accepted") + + +def validate_prompt(prompt: str): + """Validate prompt.""" + if extract_input_variables_from_prompt(prompt): + return prompt + + return fix_prompt(prompt) + + +def fix_prompt(prompt: str): + """Fix prompt.""" + return prompt + " {input}" + + +def extract_input_variables_from_prompt(prompt: str) -> list[str]: + """Extract input variables from prompt.""" + return re.findall(r"{(.*?)}", prompt) diff --git a/src/backend/langflow/interface/agents/__init__.py b/src/backend/langflow/interface/agents/__init__.py new file mode 100644 index 000000000..df15bc39b --- /dev/null +++ b/src/backend/langflow/interface/agents/__init__.py @@ -0,0 +1,3 @@ +from langflow.interface.agents.base import AgentCreator + +__all__ = ["AgentCreator"] diff --git a/src/backend/langflow/interface/agents/base.py b/src/backend/langflow/interface/agents/base.py new file mode 100644 index 000000000..3774c3011 --- /dev/null +++ b/src/backend/langflow/interface/agents/base.py @@ -0,0 +1,49 @@ +from typing import Dict, List, Optional + +from langchain.agents import loading + +from langflow.custom.customs import get_custom_nodes +from langflow.interface.agents.custom import CUSTOM_AGENTS +from langflow.interface.base import LangChainTypeCreator +from langflow.settings import settings +from langflow.utils.util import build_template_from_class + + +class AgentCreator(LangChainTypeCreator): + type_name: str = "agents" + + @property + def type_to_loader_dict(self) -> Dict: + if self.type_dict is None: + self.type_dict = loading.AGENT_TO_CLASS + # Add JsonAgent to the list of agents + for name, agent in CUSTOM_AGENTS.items(): + # TODO: validate AgentType + self.type_dict[name] = agent # type: ignore + return self.type_dict + + def get_signature(self, name: str) -> Optional[Dict]: + try: + if name in get_custom_nodes(self.type_name).keys(): + return get_custom_nodes(self.type_name)[name] + return build_template_from_class( + name, self.type_to_loader_dict, add_function=True + ) + except ValueError as exc: + raise ValueError("Agent not found") from exc + + # Now this is a generator + def to_list(self) -> List[str]: + names = [] + for _, agent in self.type_to_loader_dict.items(): + agent_name = ( + agent.function_name() + if hasattr(agent, "function_name") + else agent.__name__ + ) + if agent_name in settings.agents or settings.dev: + names.append(agent_name) + return names + + +agent_creator = AgentCreator() diff --git a/src/backend/langflow/interface/agents/custom.py b/src/backend/langflow/interface/agents/custom.py new file mode 100644 index 000000000..4032eab19 --- /dev/null +++ b/src/backend/langflow/interface/agents/custom.py @@ -0,0 +1,131 @@ +from typing import Any, List, Optional + +from langchain import LLMChain +from langchain.agents import AgentExecutor, Tool, ZeroShotAgent, initialize_agent +from langchain.agents.agent_toolkits.json.prompt import JSON_PREFIX, JSON_SUFFIX +from langchain.agents.agent_toolkits.json.toolkit import JsonToolkit +from langchain.agents.agent_toolkits.pandas.prompt import PREFIX as PANDAS_PREFIX +from langchain.agents.agent_toolkits.pandas.prompt import SUFFIX as PANDAS_SUFFIX +from langchain.agents.mrkl.prompt import FORMAT_INSTRUCTIONS +from langchain.llms.base import BaseLLM +from langchain.memory.chat_memory import BaseChatMemory +from langchain.schema import BaseLanguageModel +from langchain.tools.python.tool import PythonAstREPLTool + + +class JsonAgent(AgentExecutor): + """Json agent""" + + @staticmethod + def function_name(): + return "JsonAgent" + + @classmethod + def initialize(cls, *args, **kwargs): + return cls.from_toolkit_and_llm(*args, **kwargs) + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + @classmethod + def from_toolkit_and_llm(cls, toolkit: JsonToolkit, llm: BaseLanguageModel): + tools = toolkit.get_tools() + tool_names = [tool.name for tool in tools] + prompt = ZeroShotAgent.create_prompt( + tools, + prefix=JSON_PREFIX, + suffix=JSON_SUFFIX, + format_instructions=FORMAT_INSTRUCTIONS, + input_variables=None, + ) + llm_chain = LLMChain( + llm=llm, + prompt=prompt, + ) + agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names) + return cls.from_agent_and_tools(agent=agent, tools=tools, verbose=True) + + def run(self, *args, **kwargs): + return super().run(*args, **kwargs) + + +class CSVAgent(AgentExecutor): + """CSV agent""" + + @staticmethod + def function_name(): + return "CSVAgent" + + @classmethod + def initialize(cls, *args, **kwargs): + return cls.from_toolkit_and_llm(*args, **kwargs) + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + @classmethod + def from_toolkit_and_llm( + cls, + path: dict, + llm: BaseLanguageModel, + pandas_kwargs: Optional[dict] = None, + **kwargs: Any + ): + import pandas as pd # type: ignore + + _kwargs = pandas_kwargs or {} + df = pd.DataFrame.from_dict(path, **_kwargs) + + tools = [PythonAstREPLTool(locals={"df": df})] # type: ignore + prompt = ZeroShotAgent.create_prompt( + tools, + prefix=PANDAS_PREFIX, + suffix=PANDAS_SUFFIX, + input_variables=["df", "input", "agent_scratchpad"], + ) + partial_prompt = prompt.partial(df=str(df.head())) + llm_chain = LLMChain( + llm=llm, + prompt=partial_prompt, + ) + tool_names = [tool.name for tool in tools] + agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names, **kwargs) + + return cls.from_agent_and_tools(agent=agent, tools=tools, verbose=True) + + def run(self, *args, **kwargs): + return super().run(*args, **kwargs) + + +class InitializeAgent(AgentExecutor): + """Implementation of initialize_agent function""" + + @staticmethod + def function_name(): + return "initialize_agent" + + @classmethod + def initialize( + cls, llm: BaseLLM, tools: List[Tool], agent: str, memory: BaseChatMemory + ): + return initialize_agent( + tools=tools, + llm=llm, + # LangChain now uses Enum for agent, but we still support string + agent=agent, # type: ignore + memory=memory, + return_intermediate_steps=True, + ) + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + def run(self, *args, **kwargs): + return super().run(*args, **kwargs) + + +CUSTOM_AGENTS = { + "JsonAgent": JsonAgent, + "CSVAgent": CSVAgent, + "initialize_agent": InitializeAgent, +} diff --git a/src/backend/langflow/interface/agents/prebuilt.py b/src/backend/langflow/interface/agents/prebuilt.py new file mode 100644 index 000000000..e20ec3bde --- /dev/null +++ b/src/backend/langflow/interface/agents/prebuilt.py @@ -0,0 +1,45 @@ +from langchain import LLMChain +from langchain.agents import AgentExecutor, ZeroShotAgent +from langchain.agents.agent_toolkits.json.prompt import JSON_PREFIX, JSON_SUFFIX +from langchain.agents.agent_toolkits.json.toolkit import JsonToolkit +from langchain.agents.mrkl.prompt import FORMAT_INSTRUCTIONS +from langchain.schema import BaseLanguageModel + + +class MalfoyAgent(AgentExecutor): + """Json agent""" + + prefix = "Malfoy: " + + @classmethod + def initialize(cls, *args, **kwargs): + return cls.from_toolkit_and_llm(*args, **kwargs) + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + @classmethod + def from_toolkit_and_llm(cls, toolkit: JsonToolkit, llm: BaseLanguageModel): + tools = toolkit.get_tools() + tool_names = [tool.name for tool in tools] + prompt = ZeroShotAgent.create_prompt( + tools, + prefix=JSON_PREFIX, + suffix=JSON_SUFFIX, + format_instructions=FORMAT_INSTRUCTIONS, + input_variables=None, + ) + llm_chain = LLMChain( + llm=llm, + prompt=prompt, + ) + agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names) + return cls.from_agent_and_tools(agent=agent, tools=tools, verbose=True) + + def run(self, *args, **kwargs): + return super().run(*args, **kwargs) + + +PREBUILT_AGENTS = { + "MalfoyAgent": MalfoyAgent, +} diff --git a/src/backend/langflow/interface/base.py b/src/backend/langflow/interface/base.py new file mode 100644 index 000000000..2694fd7a5 --- /dev/null +++ b/src/backend/langflow/interface/base.py @@ -0,0 +1,75 @@ +from abc import ABC, abstractmethod +from typing import Any, Dict, List, Optional, Type, Union + +from pydantic import BaseModel + +from langflow.template.base import FrontendNode, Template, TemplateField + +# Assuming necessary imports for Field, Template, and FrontendNode classes + + +class LangChainTypeCreator(BaseModel, ABC): + type_name: str + type_dict: Optional[Dict] = None + + @property + def frontend_node_class(self) -> Type[FrontendNode]: + """The class type of the FrontendNode created in frontend_node.""" + return FrontendNode + + @property + @abstractmethod + def type_to_loader_dict(self) -> Dict: + if self.type_dict is None: + raise NotImplementedError + return self.type_dict + + @abstractmethod + def get_signature(self, name: str) -> Union[Optional[Dict[Any, Any]], FrontendNode]: + pass + + @abstractmethod + def to_list(self) -> List[str]: + pass + + def to_dict(self) -> Dict: + result: Dict = {self.type_name: {}} + + for name in self.to_list(): + # frontend_node.to_dict() returns a dict with the following structure: + # {name: {template: {fields}, description: str}} + # so we should update the result dict + result[self.type_name].update(self.frontend_node(name).to_dict()) + + return result + + def frontend_node(self, name) -> FrontendNode: + signature = self.get_signature(name) + if signature is None: + raise ValueError(f"{name} not found") + if isinstance(signature, FrontendNode): + return signature + fields = [ + TemplateField( + name=key, + field_type=value["type"], + required=value.get("required", False), + placeholder=value.get("placeholder", ""), + is_list=value.get("list", False), + show=value.get("show", True), + multiline=value.get("multiline", False), + value=value.get("value", None), + suffixes=value.get("suffixes", []), + file_types=value.get("fileTypes", []), + content=value.get("content", None), + ) + for key, value in signature["template"].items() + if key != "_type" + ] + template = Template(type_name=name, fields=fields) + return self.frontend_node_class( + template=template, + description=signature.get("description", ""), + base_classes=signature["base_classes"], + name=name, + ) diff --git a/src/backend/langflow/interface/chains/__init__.py b/src/backend/langflow/interface/chains/__init__.py new file mode 100644 index 000000000..2e5570b3c --- /dev/null +++ b/src/backend/langflow/interface/chains/__init__.py @@ -0,0 +1,3 @@ +from langflow.interface.chains.base import ChainCreator + +__all__ = ["ChainCreator"] diff --git a/src/backend/langflow/interface/chains/base.py b/src/backend/langflow/interface/chains/base.py new file mode 100644 index 000000000..0a46d3474 --- /dev/null +++ b/src/backend/langflow/interface/chains/base.py @@ -0,0 +1,45 @@ +from typing import Dict, List, Optional + +from langflow.custom.customs import get_custom_nodes +from langflow.interface.base import LangChainTypeCreator +from langflow.interface.custom_lists import chain_type_to_cls_dict +from langflow.settings import settings +from langflow.utils.util import build_template_from_class + +# Assuming necessary imports for Field, Template, and FrontendNode classes + + +class ChainCreator(LangChainTypeCreator): + type_name: str = "chains" + + @property + def type_to_loader_dict(self) -> Dict: + if self.type_dict is None: + self.type_dict = chain_type_to_cls_dict + from langflow.interface.chains.custom import CUSTOM_CHAINS + + self.type_dict.update(CUSTOM_CHAINS) + # Filter according to settings.chains + self.type_dict = { + name: chain + for name, chain in self.type_dict.items() + if name in settings.chains or settings.dev + } + return self.type_dict + + def get_signature(self, name: str) -> Optional[Dict]: + try: + if name in get_custom_nodes(self.type_name).keys(): + return get_custom_nodes(self.type_name)[name] + return build_template_from_class(name, self.type_to_loader_dict) + except ValueError as exc: + raise ValueError("Chain not found") from exc + + def to_list(self) -> List[str]: + custom_chains = list(get_custom_nodes("chains").keys()) + default_chains = list(self.type_to_loader_dict.keys()) + + return default_chains + custom_chains + + +chain_creator = ChainCreator() diff --git a/src/backend/langflow/interface/chains/custom.py b/src/backend/langflow/interface/chains/custom.py new file mode 100644 index 000000000..98470d54b --- /dev/null +++ b/src/backend/langflow/interface/chains/custom.py @@ -0,0 +1,101 @@ +from typing import Dict, Optional, Type + +from langchain.chains import ConversationChain +from langchain.memory.buffer import ConversationBufferMemory +from langchain.schema import BaseMemory +from pydantic import Field, root_validator + +from langflow.graph.utils import extract_input_variables_from_prompt + +DEFAULT_SUFFIX = """" +Current conversation: +{history} +Human: {input} +{ai_prefix}""" + + +class BaseCustomChain(ConversationChain): + """BaseCustomChain is a chain you can use to have a conversation with a custom character.""" + + template: Optional[str] + + ai_prefix_key: Optional[str] + """Field to use as the ai_prefix. It needs to be set and has to be in the template""" + + @root_validator(pre=False) + def build_template(cls, values): + format_dict = {} + input_variables = extract_input_variables_from_prompt(values["template"]) + + if values.get("ai_prefix_key", None) is None: + values["ai_prefix_key"] = values["memory"].ai_prefix + + for key in input_variables: + new_value = values.get(key, f"{{{key}}}") + format_dict[key] = new_value + if key == values.get("ai_prefix_key", None): + values["memory"].ai_prefix = new_value + + values["template"] = values["template"].format(**format_dict) + + values["template"] = values["template"] + values["input_variables"] = extract_input_variables_from_prompt( + values["template"] + ) + values["prompt"].template = values["template"] + values["prompt"].input_variables = values["input_variables"] + return values + + +class SeriesCharacterChain(BaseCustomChain): + """SeriesCharacterChain is a chain you can use to have a conversation with a character from a series.""" + + character: str + series: str + template: Optional[ + str + ] = """I want you to act like {character} from {series}. +I want you to respond and answer like {character}. do not write any explanations. only answer like {character}. +You must know all of the knowledge of {character}. +Current conversation: +{history} +Human: {input} +{character}:""" + memory: BaseMemory = Field(default_factory=ConversationBufferMemory) + ai_prefix_key: Optional[str] = "character" + """Default memory store.""" + + +class MidJourneyPromptChain(BaseCustomChain): + """MidJourneyPromptChain is a chain you can use to generate new MidJourney prompts.""" + + template: Optional[ + str + ] = """I want you to act as a prompt generator for Midjourney's artificial intelligence program. + Your job is to provide detailed and creative descriptions that will inspire unique and interesting images from the AI. + Keep in mind that the AI is capable of understanding a wide range of language and can interpret abstract concepts, so feel free to be as imaginative and descriptive as possible. + For example, you could describe a scene from a futuristic city, or a surreal landscape filled with strange creatures. + The more detailed and imaginative your description, the more interesting the resulting image will be. Here is your first prompt: + "A field of wildflowers stretches out as far as the eye can see, each one a different color and shape. In the distance, a massive tree towers over the landscape, its branches reaching up to the sky like tentacles.\" + + Current conversation: + {history} + Human: {input} + AI:""" # noqa: E501 + + +class TimeTravelGuideChain(BaseCustomChain): + template: Optional[ + str + ] = """I want you to act as my time travel guide. You are helpful and creative. I will provide you with the historical period or future time I want to visit and you will suggest the best events, sights, or people to experience. Provide the suggestions and any necessary information. + Current conversation: + {history} + Human: {input} + AI:""" # noqa: E501 + + +CUSTOM_CHAINS: Dict[str, Type[ConversationChain]] = { + "SeriesCharacterChain": SeriesCharacterChain, + "MidJourneyPromptChain": MidJourneyPromptChain, + "TimeTravelGuideChain": TimeTravelGuideChain, +} diff --git a/src/backend/langflow/interface/custom/__init__.py b/src/backend/langflow/interface/custom/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/src/backend/langflow/interface/custom/types.py b/src/backend/langflow/interface/custom/types.py new file mode 100644 index 000000000..4c641f388 --- /dev/null +++ b/src/backend/langflow/interface/custom/types.py @@ -0,0 +1,37 @@ +from typing import Callable, Optional + +from pydantic import BaseModel, validator + +from langflow.utils import validate + + +class Function(BaseModel): + code: str + function: Optional[Callable] = None + imports: Optional[str] = None + + # Eval code and store the function + def __init__(self, **data): + super().__init__(**data) + + # Validate the function + @validator("code") + def validate_func(cls, v): + try: + validate.eval_function(v) + except Exception as e: + raise e + + return v + + def get_function(self): + """Get the function""" + function_name = validate.extract_function_name(self.code) + + return validate.create_function(self.code, function_name) + + +class PythonFunction(Function): + """Python function""" + + code: str diff --git a/src/backend/langflow/interface/custom_lists.py b/src/backend/langflow/interface/custom_lists.py index 3e8a800f9..746c58325 100644 --- a/src/backend/langflow/interface/custom_lists.py +++ b/src/backend/langflow/interface/custom_lists.py @@ -1,43 +1,71 @@ -## LLM from typing import Any -from langchain import llms -from langchain.llms.openai import OpenAIChat +## LLM +from langchain import ( + chains, + document_loaders, + embeddings, + llms, + memory, + requests, + vectorstores, +) +from langchain.agents import agent_toolkits +from langchain.chat_models import ChatOpenAI +from langflow.interface.importing.utils import import_class + +## LLM llm_type_to_cls_dict = llms.type_to_cls_dict -llm_type_to_cls_dict["openai-chat"] = OpenAIChat - - -## Memory - -# from langchain.memory.buffer_window import ConversationBufferWindowMemory -# from langchain.memory.chat_memory import ChatMessageHistory -# from langchain.memory.combined import CombinedMemory -# from langchain.memory.entity import ConversationEntityMemory -# from langchain.memory.kg import ConversationKGMemory -# from langchain.memory.readonly import ReadOnlySharedMemory -# from langchain.memory.simple import SimpleMemory -# from langchain.memory.summary import ConversationSummaryMemory -# from langchain.memory.summary_buffer import ConversationSummaryBufferMemory - -memory_type_to_cls_dict: dict[str, Any] = { - # "CombinedMemory": CombinedMemory, - # "ConversationBufferWindowMemory": ConversationBufferWindowMemory, - # "ConversationBufferMemory": ConversationBufferMemory, - # "SimpleMemory": SimpleMemory, - # "ConversationSummaryBufferMemory": ConversationSummaryBufferMemory, - # "ConversationKGMemory": ConversationKGMemory, - # "ConversationEntityMemory": ConversationEntityMemory, - # "ConversationSummaryMemory": ConversationSummaryMemory, - # "ChatMessageHistory": ChatMessageHistory, - # "ConversationStringBufferMemory": ConversationStringBufferMemory, - # "ReadOnlySharedMemory": ReadOnlySharedMemory, -} - +llm_type_to_cls_dict["openai-chat"] = ChatOpenAI # type: ignore ## Chain -# from langchain.chains.loading import type_to_loader_dict -# from langchain.chains.conversation.base import ConversationChain +chain_type_to_cls_dict: dict[str, Any] = { + chain_name: import_class(f"langchain.chains.{chain_name}") + for chain_name in chains.__all__ +} -# chain_type_to_cls_dict = type_to_loader_dict -# chain_type_to_cls_dict["conversation_chain"] = ConversationChain +toolkit_type_to_loader_dict: dict[str, Any] = { + toolkit_name: import_class(f"langchain.agents.agent_toolkits.{toolkit_name}") + # if toolkit_name is lower case it is a loader + for toolkit_name in agent_toolkits.__all__ + if toolkit_name.islower() +} + +toolkit_type_to_cls_dict: dict[str, Any] = { + toolkit_name: import_class(f"langchain.agents.agent_toolkits.{toolkit_name}") + # if toolkit_name is not lower case it is a class + for toolkit_name in agent_toolkits.__all__ + if not toolkit_name.islower() +} + +## Memories +memory_type_to_cls_dict: dict[str, Any] = { + memory_name: import_class(f"langchain.memory.{memory_name}") + for memory_name in memory.__all__ +} + +## Wrappers +wrapper_type_to_cls_dict: dict[str, Any] = { + wrapper.__name__: wrapper for wrapper in [requests.RequestsWrapper] +} + +## Embeddings +embedding_type_to_cls_dict: dict[str, Any] = { + embedding_name: import_class(f"langchain.embeddings.{embedding_name}") + for embedding_name in embeddings.__all__ +} + +## Vector Stores +vectorstores_type_to_cls_dict: dict[str, Any] = { + vectorstore_name: import_class(f"langchain.vectorstores.{vectorstore_name}") + for vectorstore_name in vectorstores.__all__ +} + +## Document Loaders +documentloaders_type_to_cls_dict: dict[str, Any] = { + documentloader_name: import_class( + f"langchain.document_loaders.{documentloader_name}" + ) + for documentloader_name in document_loaders.__all__ +} diff --git a/src/backend/langflow/interface/documentLoaders/base.py b/src/backend/langflow/interface/documentLoaders/base.py new file mode 100644 index 000000000..844820d7b --- /dev/null +++ b/src/backend/langflow/interface/documentLoaders/base.py @@ -0,0 +1,31 @@ +from typing import Dict, List, Optional + +from langflow.interface.base import LangChainTypeCreator +from langflow.interface.custom_lists import documentloaders_type_to_cls_dict +from langflow.settings import settings +from langflow.utils.util import build_template_from_class + + +class DocumentLoaderCreator(LangChainTypeCreator): + type_name: str = "documentloader" + + @property + def type_to_loader_dict(self) -> Dict: + return documentloaders_type_to_cls_dict + + def get_signature(self, name: str) -> Optional[Dict]: + """Get the signature of a document loader.""" + try: + return build_template_from_class(name, documentloaders_type_to_cls_dict) + except ValueError as exc: + raise ValueError(f"Documment Loader {name} not found") from exc + + def to_list(self) -> List[str]: + return [ + documentloader.__name__ + for documentloader in self.type_to_loader_dict.values() + if documentloader.__name__ in settings.documentloaders or settings.dev + ] + + +documentloader_creator = DocumentLoaderCreator() diff --git a/src/backend/langflow/interface/embeddings/base.py b/src/backend/langflow/interface/embeddings/base.py new file mode 100644 index 000000000..25038a8a5 --- /dev/null +++ b/src/backend/langflow/interface/embeddings/base.py @@ -0,0 +1,31 @@ +from typing import Dict, List, Optional + +from langflow.interface.base import LangChainTypeCreator +from langflow.interface.custom_lists import embedding_type_to_cls_dict +from langflow.settings import settings +from langflow.utils.util import build_template_from_class + + +class EmbeddingCreator(LangChainTypeCreator): + type_name: str = "embeddings" + + @property + def type_to_loader_dict(self) -> Dict: + return embedding_type_to_cls_dict + + def get_signature(self, name: str) -> Optional[Dict]: + """Get the signature of an embedding.""" + try: + return build_template_from_class(name, embedding_type_to_cls_dict) + except ValueError as exc: + raise ValueError(f"Embedding {name} not found") from exc + + def to_list(self) -> List[str]: + return [ + embedding.__name__ + for embedding in self.type_to_loader_dict.values() + if embedding.__name__ in settings.embeddings or settings.dev + ] + + +embedding_creator = EmbeddingCreator() diff --git a/src/backend/langflow/interface/importing/__init__.py b/src/backend/langflow/interface/importing/__init__.py new file mode 100644 index 000000000..317849f8e --- /dev/null +++ b/src/backend/langflow/interface/importing/__init__.py @@ -0,0 +1,7 @@ +from langflow.interface.importing.utils import import_by_type # noqa: F401 + +# This module is used to import any langchain class by name. + +ALL = [ + "import_by_type", +] diff --git a/src/backend/langflow/interface/importing/utils.py b/src/backend/langflow/interface/importing/utils.py new file mode 100644 index 000000000..45ad2ba06 --- /dev/null +++ b/src/backend/langflow/interface/importing/utils.py @@ -0,0 +1,115 @@ +# This module is used to import any langchain class by name. + +import importlib +from typing import Any, Type + +from langchain import PromptTemplate +from langchain.agents import Agent +from langchain.chains.base import Chain +from langchain.chat_models.base import BaseChatModel +from langchain.llms.base import BaseLLM +from langchain.tools import BaseTool + +from langflow.interface.tools.util import get_tool_by_name + + +def import_module(module_path: str) -> Any: + """Import module from module path""" + if "from" not in module_path: + # Import the module using the module path + return importlib.import_module(module_path) + # Split the module path into its components + _, module_path, _, object_name = module_path.split() + + # Import the module using the module path + module = importlib.import_module(module_path) + + return getattr(module, object_name) + + +def import_by_type(_type: str, name: str) -> Any: + """Import class by type and name""" + if _type is None: + raise ValueError(f"Type cannot be None. Check if {name} is in the config file.") + func_dict = { + "agents": import_agent, + "prompts": import_prompt, + "llms": {"llm": import_llm, "chat": import_chat_llm}, + "tools": import_tool, + "chains": import_chain, + "toolkits": import_toolkit, + "wrappers": import_wrapper, + "memory": import_memory, + } + if _type == "llms": + key = "chat" if "chat" in name.lower() else "llm" + loaded_func = func_dict[_type][key] # type: ignore + else: + loaded_func = func_dict[_type] + + return loaded_func(name) + + +def import_chat_llm(llm: str) -> BaseChatModel: + """Import chat llm from llm name""" + return import_class(f"langchain.chat_models.{llm}") + + +def import_memory(memory: str) -> Any: + """Import memory from memory name""" + return import_module(f"from langchain.memory import {memory}") + + +def import_class(class_path: str) -> Any: + """Import class from class path""" + module_path, class_name = class_path.rsplit(".", 1) + module = import_module(module_path) + return getattr(module, class_name) + + +def import_prompt(prompt: str) -> Type[PromptTemplate]: + from langflow.interface.prompts.custom import CUSTOM_PROMPTS + + """Import prompt from prompt name""" + if prompt == "ZeroShotPrompt": + return import_class("langchain.prompts.PromptTemplate") + elif prompt in CUSTOM_PROMPTS: + return CUSTOM_PROMPTS[prompt] + return import_class(f"langchain.prompts.{prompt}") + + +def import_wrapper(wrapper: str) -> Any: + """Import wrapper from wrapper name""" + return import_module(f"from langchain.requests import {wrapper}") + + +def import_toolkit(toolkit: str) -> Any: + """Import toolkit from toolkit name""" + return import_module(f"from langchain.agents.agent_toolkits import {toolkit}") + + +def import_agent(agent: str) -> Agent: + """Import agent from agent name""" + # check for custom agent + + return import_class(f"langchain.agents.{agent}") + + +def import_llm(llm: str) -> BaseLLM: + """Import llm from llm name""" + return import_class(f"langchain.llms.{llm}") + + +def import_tool(tool: str) -> BaseTool: + """Import tool from tool name""" + + return get_tool_by_name(tool) + + +def import_chain(chain: str) -> Type[Chain]: + """Import chain from chain name""" + from langflow.interface.chains.custom import CUSTOM_CHAINS + + if chain in CUSTOM_CHAINS: + return CUSTOM_CHAINS[chain] + return import_class(f"langchain.chains.{chain}") diff --git a/src/backend/langflow/interface/listing.py b/src/backend/langflow/interface/listing.py index 21e763de4..b11b3cef9 100644 --- a/src/backend/langflow/interface/listing.py +++ b/src/backend/langflow/interface/listing.py @@ -1,82 +1,31 @@ -from langchain import agents, chains, prompts -from langchain.agents.load_tools import get_all_tool_names - -from langflow.custom import customs -from langflow.interface.custom_lists import ( - llm_type_to_cls_dict, - memory_type_to_cls_dict, -) -from langflow.settings import settings -from langflow.utils import util +from langflow.interface.agents.base import agent_creator +from langflow.interface.chains.base import chain_creator +from langflow.interface.llms.base import llm_creator +from langflow.interface.memories.base import memory_creator +from langflow.interface.prompts.base import prompt_creator +from langflow.interface.toolkits.base import toolkits_creator +from langflow.interface.tools.base import tool_creator +from langflow.interface.wrappers.base import wrapper_creator -def list_type(object_type: str): - """List all components""" +def get_type_dict(): return { - "chains": list_chain_types, - "agents": list_agents, - "prompts": list_prompts, - "llms": list_llms, - "memories": list_memories, - "tools": list_tools, - }.get(object_type, lambda: "Invalid type")() + "agents": agent_creator.to_list(), + "prompts": prompt_creator.to_list(), + "llms": llm_creator.to_list(), + "tools": tool_creator.to_list(), + "chains": chain_creator.to_list(), + "memory": memory_creator.to_list(), + "toolkits": toolkits_creator.to_list(), + "wrappers": wrapper_creator.to_list(), + } -def list_agents(): - """List all agent types""" - return [ - agent.__name__ - for agent in agents.loading.AGENT_TO_CLASS.values() - if agent.__name__ in settings.agents or settings.dev - ] +LANGCHAIN_TYPES_DICT = get_type_dict() +# Now we'll build a dict with Langchain types and ours -def list_prompts(): - """List all prompt types""" - custom_prompts = customs.get_custom_prompts() - library_prompts = [ - prompt.__annotations__["return"].__name__ - for prompt in prompts.loading.type_to_loader_dict.values() - if prompt.__annotations__["return"].__name__ in settings.prompts or settings.dev - ] - return library_prompts + list(custom_prompts.keys()) - - -def list_tools(): - """List all load tools""" - - tools = [] - - for tool in get_all_tool_names(): - tool_params = util.get_tool_params(util.get_tools_dict(tool)) - if tool_params and tool_params["name"] in settings.tools or settings.dev: - tools.append(tool_params["name"]) - - return tools - - -def list_llms(): - """List all llm types""" - return [ - llm.__name__ - for llm in llm_type_to_cls_dict.values() - if llm.__name__ in settings.llms or settings.dev - ] - - -def list_chain_types(): - """List all chain types""" - return [ - chain.__annotations__["return"].__name__ - for chain in chains.loading.type_to_loader_dict.values() - if chain.__annotations__["return"].__name__ in settings.chains or settings.dev - ] - - -def list_memories(): - """List all memory types""" - return [ - memory.__name__ - for memory in memory_type_to_cls_dict.values() - if memory.__name__ in settings.memories or settings.dev - ] +ALL_TYPES_DICT = { + **LANGCHAIN_TYPES_DICT, + "Custom": ["Custom Tool", "Python Function"], +} diff --git a/src/backend/langflow/interface/llms/__init__.py b/src/backend/langflow/interface/llms/__init__.py new file mode 100644 index 000000000..c5d7186fb --- /dev/null +++ b/src/backend/langflow/interface/llms/__init__.py @@ -0,0 +1,3 @@ +from langflow.interface.llms.base import LLMCreator + +__all__ = ["LLMCreator"] diff --git a/src/backend/langflow/interface/llms/base.py b/src/backend/langflow/interface/llms/base.py new file mode 100644 index 000000000..85f9035db --- /dev/null +++ b/src/backend/langflow/interface/llms/base.py @@ -0,0 +1,33 @@ +from typing import Dict, List, Optional + +from langflow.interface.base import LangChainTypeCreator +from langflow.interface.custom_lists import llm_type_to_cls_dict +from langflow.settings import settings +from langflow.utils.util import build_template_from_class + + +class LLMCreator(LangChainTypeCreator): + type_name: str = "llms" + + @property + def type_to_loader_dict(self) -> Dict: + if self.type_dict is None: + self.type_dict = llm_type_to_cls_dict + return self.type_dict + + def get_signature(self, name: str) -> Optional[Dict]: + """Get the signature of an llm.""" + try: + return build_template_from_class(name, llm_type_to_cls_dict) + except ValueError as exc: + raise ValueError("LLM not found") from exc + + def to_list(self) -> List[str]: + return [ + llm.__name__ + for llm in self.type_to_loader_dict.values() + if llm.__name__ in settings.llms or settings.dev + ] + + +llm_creator = LLMCreator() diff --git a/src/backend/langflow/interface/loading.py b/src/backend/langflow/interface/loading.py index 619264988..9a8d28d05 100644 --- a/src/backend/langflow/interface/loading.py +++ b/src/backend/langflow/interface/loading.py @@ -1,7 +1,10 @@ import json -from typing import Any, Dict, Optional +from typing import Any, Callable, Dict, Optional +from langchain.agents import ZeroShotAgent +from langchain.agents import agent as agent_module from langchain.agents.agent import AgentExecutor +from langchain.agents.agent_toolkits.base import BaseToolkit from langchain.agents.load_tools import ( _BASE_TOOLS, _EXTRA_LLM_TOOLS, @@ -15,29 +18,67 @@ from langchain.chains.loading import load_chain_from_config from langchain.llms.base import BaseLLM from langchain.llms.loading import load_llm_from_config +from langflow.interface.agents.custom import CUSTOM_AGENTS +from langflow.interface.importing.utils import import_by_type +from langflow.interface.toolkits.base import toolkits_creator from langflow.interface.types import get_type_list -from langflow.utils import payload, util +from langflow.utils import util, validate + + +def instantiate_class(node_type: str, base_type: str, params: Dict) -> Any: + """Instantiate class from module type and key, and params""" + if node_type in CUSTOM_AGENTS: + if custom_agent := CUSTOM_AGENTS.get(node_type): + return custom_agent.initialize(**params) # type: ignore + + class_object = import_by_type(_type=base_type, name=node_type) + + if base_type == "agents": + # We need to initialize it differently + return load_agent_executor(class_object, params) + elif node_type == "ZeroShotPrompt": + if "tools" not in params: + params["tools"] = [] + return ZeroShotAgent.create_prompt(**params) + + elif node_type == "PythonFunction": + # If the node_type is "PythonFunction" + # we need to get the function from the params + # which will be a str containing a python function + # and then we need to compile it and return the function + # as the instance + function_string = params["code"] + if isinstance(function_string, str): + return validate.eval_function(function_string) + raise ValueError("Function should be a string") + elif base_type == "toolkits": + loaded_toolkit = class_object(**params) + # Check if node_type has a loader + if toolkits_creator.has_create_function(node_type): + return load_toolkits_executor(node_type, loaded_toolkit, params) + return loaded_toolkit + else: + return class_object(**params) def load_flow_from_json(path: str): + # This is done to avoid circular imports + from langflow.graph import Graph + """Load flow from json file""" with open(path, "r") as f: flow_graph = json.load(f) data_graph = flow_graph["data"] - extracted_json = extract_json(data_graph) - return load_langchain_type_from_config(config=extracted_json) - - -def extract_json(data_graph): nodes = data_graph["nodes"] # Substitute ZeroShotPrompt with PromptTemplate - nodes = replace_zero_shot_prompt_with_prompt_template(nodes) + # nodes = replace_zero_shot_prompt_with_prompt_template(nodes) # Add input variables - nodes = payload.extract_input_variables(nodes) + # nodes = payload.extract_input_variables(nodes) + # Nodes, edges and root node edges = data_graph["edges"] - root = payload.get_root_node(nodes, edges) - return payload.build_json(root, nodes, edges) + graph = Graph(nodes, edges) + return graph.build() def replace_zero_shot_prompt_with_prompt_template(nodes): @@ -92,6 +133,25 @@ def load_agent_executor_from_config( ) +def load_agent_executor(agent_class: type[agent_module.Agent], params, **kwargs): + """Load agent executor from agent class, tools and chain""" + allowed_tools = params["allowed_tools"] + llm_chain = params["llm_chain"] + tool_names = [tool.name for tool in allowed_tools] + agent = agent_class(allowed_tools=tool_names, llm_chain=llm_chain) + return AgentExecutor.from_agent_and_tools( + agent=agent, + tools=allowed_tools, + **kwargs, + ) + + +def load_toolkits_executor(node_type: str, toolkit: BaseToolkit, params: dict): + create_function: Callable = toolkits_creator.get_create_function(node_type) + if llm := params.get("llm"): + return create_function(llm=llm, toolkit=toolkit) + + def load_tools_from_config(tool_list: list[dict]) -> list: """Load tools based on a config list. diff --git a/src/backend/langflow/interface/memories/__init__.py b/src/backend/langflow/interface/memories/__init__.py new file mode 100644 index 000000000..845eb29fe --- /dev/null +++ b/src/backend/langflow/interface/memories/__init__.py @@ -0,0 +1,3 @@ +from langflow.interface.memories.base import MemoryCreator + +__all__ = ["MemoryCreator"] diff --git a/src/backend/langflow/interface/memories/base.py b/src/backend/langflow/interface/memories/base.py new file mode 100644 index 000000000..7db99c0a1 --- /dev/null +++ b/src/backend/langflow/interface/memories/base.py @@ -0,0 +1,40 @@ +from typing import Dict, List, Optional, Type + +from langflow.interface.base import LangChainTypeCreator +from langflow.interface.custom_lists import memory_type_to_cls_dict +from langflow.settings import settings +from langflow.template.base import FrontendNode +from langflow.template.nodes import MemoryFrontendNode +from langflow.utils.util import build_template_from_class + + +class MemoryCreator(LangChainTypeCreator): + type_name: str = "memories" + + @property + def frontend_node_class(self) -> Type[FrontendNode]: + """The class type of the FrontendNode created in frontend_node.""" + return MemoryFrontendNode + + @property + def type_to_loader_dict(self) -> Dict: + if self.type_dict is None: + self.type_dict = memory_type_to_cls_dict + return self.type_dict + + def get_signature(self, name: str) -> Optional[Dict]: + """Get the signature of a memory.""" + try: + return build_template_from_class(name, memory_type_to_cls_dict) + except ValueError as exc: + raise ValueError("Memory not found") from exc + + def to_list(self) -> List[str]: + return [ + memory.__name__ + for memory in self.type_to_loader_dict.values() + if memory.__name__ in settings.memories or settings.dev + ] + + +memory_creator = MemoryCreator() diff --git a/src/backend/langflow/interface/prompts/__init__.py b/src/backend/langflow/interface/prompts/__init__.py new file mode 100644 index 000000000..2a81e8bf0 --- /dev/null +++ b/src/backend/langflow/interface/prompts/__init__.py @@ -0,0 +1,3 @@ +from langflow.interface.prompts.base import PromptCreator + +__all__ = ["PromptCreator"] diff --git a/src/backend/langflow/interface/prompts/base.py b/src/backend/langflow/interface/prompts/base.py new file mode 100644 index 000000000..73f842833 --- /dev/null +++ b/src/backend/langflow/interface/prompts/base.py @@ -0,0 +1,59 @@ +from typing import Dict, List, Optional, Type + +from langchain import prompts + +from langflow.custom.customs import get_custom_nodes +from langflow.interface.base import LangChainTypeCreator +from langflow.interface.importing.utils import import_class +from langflow.settings import settings +from langflow.template.nodes import PromptFrontendNode +from langflow.utils.util import build_template_from_class + + +class PromptCreator(LangChainTypeCreator): + type_name: str = "prompts" + + @property + def frontend_node_class(self) -> Type[PromptFrontendNode]: + return PromptFrontendNode + + @property + def type_to_loader_dict(self) -> Dict: + if self.type_dict is None: + self.type_dict = { + prompt_name: import_class(f"langchain.prompts.{prompt_name}") + # if prompt_name is not lower case it is a class + for prompt_name in prompts.__all__ + } + # Merge CUSTOM_PROMPTS into self.type_dict + from langflow.interface.prompts.custom import CUSTOM_PROMPTS + + self.type_dict.update(CUSTOM_PROMPTS) + # Now filter according to settings.prompts + self.type_dict = { + name: prompt + for name, prompt in self.type_dict.items() + if name in settings.prompts or settings.dev + } + return self.type_dict + + def get_signature(self, name: str) -> Optional[Dict]: + try: + if name in get_custom_nodes(self.type_name).keys(): + return get_custom_nodes(self.type_name)[name] + return build_template_from_class(name, self.type_to_loader_dict) + except ValueError as exc: + raise ValueError("Prompt not found") from exc + + def to_list(self) -> List[str]: + custom_prompts = get_custom_nodes("prompts") + # library_prompts = [ + # prompt.__annotations__["return"].__name__ + # for prompt in self.type_to_loader_dict.values() + # if prompt.__annotations__["return"].__name__ in settings.prompts + # or settings.dev + # ] + return list(self.type_to_loader_dict.keys()) + list(custom_prompts.keys()) + + +prompt_creator = PromptCreator() diff --git a/src/backend/langflow/interface/prompts/custom.py b/src/backend/langflow/interface/prompts/custom.py new file mode 100644 index 000000000..b1dbef370 --- /dev/null +++ b/src/backend/langflow/interface/prompts/custom.py @@ -0,0 +1,77 @@ +from typing import Dict, List, Optional, Type + +from langchain.prompts import PromptTemplate +from pydantic import root_validator + +from langflow.graph.utils import extract_input_variables_from_prompt + +# Steps to create a BaseCustomPrompt: +# 1. Create a prompt template that endes with: +# Current conversation: +# {history} +# Human: {input} +# {ai_prefix}: +# 2. Create a class that inherits from BaseCustomPrompt +# 3. Add the following class attributes: +# template: str = "" +# description: Optional[str] +# ai_prefix: Optional[str] = "{ai_prefix}" +# 3.1. The ai_prefix should be a value in input_variables +# SeriesCharacterPrompt is a working example +# If used in a LLMChain, with a Memory module, it will work as expected +# We should consider creating ConversationalChains that expose custom parameters +# That way it will be easier to create custom prompts + + +class BaseCustomPrompt(PromptTemplate): + template: str = "" + description: Optional[str] + ai_prefix: Optional[str] + + @root_validator(pre=False) + def build_template(cls, values): + format_dict = {} + ai_prefix_format_dict = {} + for key in values.get("input_variables", []): + new_value = values.get(key, f"{{{key}}}") + format_dict[key] = new_value + if key in values["ai_prefix"]: + ai_prefix_format_dict[key] = new_value + + values["ai_prefix"] = values["ai_prefix"].format(**ai_prefix_format_dict) + values["template"] = values["template"].format(**format_dict) + + values["template"] = values["template"] + values["input_variables"] = extract_input_variables_from_prompt( + values["template"] + ) + return values + + +class SeriesCharacterPrompt(BaseCustomPrompt): + # Add a very descriptive description for the prompt generator + description: Optional[ + str + ] = "A prompt that asks the AI to act like a character from a series." + character: str + series: str + template: str = """I want you to act like {character} from {series}. +I want you to respond and answer like {character}. do not write any explanations. only answer like {character}. +You must know all of the knowledge of {character}. + +Current conversation: +{history} +Human: {input} +{character}:""" + + ai_prefix: str = "{character}" + input_variables: List[str] = ["character", "series"] + + +CUSTOM_PROMPTS: Dict[str, Type[BaseCustomPrompt]] = { + "SeriesCharacterPrompt": SeriesCharacterPrompt +} + +if __name__ == "__main__": + prompt = SeriesCharacterPrompt(character="Harry Potter", series="Harry Potter") + print(prompt.template) diff --git a/src/backend/langflow/interface/run.py b/src/backend/langflow/interface/run.py index 0844e2073..c413dfc4f 100644 --- a/src/backend/langflow/interface/run.py +++ b/src/backend/langflow/interface/run.py @@ -1,47 +1,226 @@ import contextlib import io -import re from typing import Any, Dict +from langflow.cache.utils import compute_hash, load_cache, save_cache +from langflow.graph.graph import Graph from langflow.interface import loading +from langflow.utils.logger import logger -def process_data_graph(data_graph: Dict[str, Any]): +def load_langchain_object(data_graph, is_first_message=False): """ - Process data graph by extracting input variables and replacing ZeroShotPrompt + Load langchain object from cache if it exists, otherwise build it. + """ + computed_hash = compute_hash(data_graph) + if is_first_message: + langchain_object = build_langchain_object(data_graph) + else: + logger.debug("Loading langchain object from cache") + langchain_object = load_cache(computed_hash) + + return computed_hash, langchain_object + + +def build_langchain_object(data_graph): + """ + Build langchain object from data_graph. + """ + + logger.debug("Building langchain object") + nodes = data_graph["nodes"] + # Add input variables + # nodes = payload.extract_input_variables(nodes) + # Nodes, edges and root node + edges = data_graph["edges"] + graph = Graph(nodes, edges) + + return graph.build() + + +def process_graph(data_graph: Dict[str, Any]): + """ + Process graph by extracting input variables and replacing ZeroShotPrompt with PromptTemplate,then run the graph and return the result and thought. """ + # Load langchain object + logger.debug("Loading langchain object") + message = data_graph.pop("message", "") + is_first_message = len(data_graph.get("chatHistory", [])) == 0 + computed_hash, langchain_object = load_langchain_object( + data_graph, is_first_message + ) + logger.debug("Loaded langchain object") - extracted_json = loading.extract_json(data_graph) + if langchain_object is None: + # Raise user facing error + raise ValueError( + "There was an error loading the langchain_object. Please, check all the nodes and try again." + ) - message = data_graph["message"] + # Generate result and thought + logger.debug("Generating result and thought") + result, thought = get_result_and_thought_using_graph(langchain_object, message) + logger.debug("Generated result and thought") - # Process json - result, thought = get_result_and_thought(extracted_json, message) + # Save langchain_object to cache + # We have to save it here because if the + # memory is updated we need to keep the new values + logger.debug("Saving langchain object to cache") + save_cache(computed_hash, langchain_object, is_first_message) + logger.debug("Saved langchain object to cache") + return {"result": str(result), "thought": thought.strip()} - return { - "result": result, - "thought": re.sub( - r"\x1b\[([0-9,A-Z]{1,2}(;[0-9,A-Z]{1,2})?)?[m|K]", "", thought - ).strip(), + +def get_memory_key(langchain_object): + """ + Given a LangChain object, this function retrieves the current memory key from the object's memory attribute. + It then checks if the key exists in a dictionary of known memory keys and returns the corresponding key, + or None if the current key is not recognized. + """ + mem_key_dict = { + "chat_history": "history", + "history": "chat_history", } + memory_key = langchain_object.memory.memory_key + return mem_key_dict.get(memory_key) + + +def update_memory_keys(langchain_object, possible_new_mem_key): + """ + Given a LangChain object and a possible new memory key, this function updates the input and output keys in the + object's memory attribute to exclude the current memory key and the possible new key. It then sets the memory key + to the possible new key. + """ + input_key = [ + key + for key in langchain_object.input_keys + if key not in [langchain_object.memory.memory_key, possible_new_mem_key] + ][0] + + output_key = [ + key + for key in langchain_object.output_keys + if key not in [langchain_object.memory.memory_key, possible_new_mem_key] + ][0] + + langchain_object.memory.input_key = input_key + langchain_object.memory.output_key = output_key + langchain_object.memory.memory_key = possible_new_mem_key + + +def fix_memory_inputs(langchain_object): + """ + Given a LangChain object, this function checks if it has a memory attribute and if that memory key exists in the + object's input variables. If so, it does nothing. Otherwise, it gets a possible new memory key using the + get_memory_key function and updates the memory keys using the update_memory_keys function. + """ + if hasattr(langchain_object, "memory") and langchain_object.memory is not None: + try: + if langchain_object.memory.memory_key in langchain_object.input_variables: + return + except AttributeError: + if ( + langchain_object.memory.memory_key + in langchain_object.prompt.input_variables + ): + return + + possible_new_mem_key = get_memory_key(langchain_object) + if possible_new_mem_key is not None: + update_memory_keys(langchain_object, possible_new_mem_key) + + +def get_result_and_thought_using_graph(langchain_object, message: str): + """Get result and thought from extracted json""" + try: + if hasattr(langchain_object, "verbose"): + langchain_object.verbose = True + chat_input = None + memory_key = "" + if hasattr(langchain_object, "memory") and langchain_object.memory is not None: + memory_key = langchain_object.memory.memory_key + + for key in langchain_object.input_keys: + if key not in [memory_key, "chat_history"]: + chat_input = {key: message} + + if hasattr(langchain_object, "return_intermediate_steps"): + # https://github.com/hwchase17/langchain/issues/2068 + # Deactivating until we have a frontend solution + # to display intermediate steps + langchain_object.return_intermediate_steps = False + + fix_memory_inputs(langchain_object) + + with io.StringIO() as output_buffer, contextlib.redirect_stdout(output_buffer): + try: + output = langchain_object(chat_input) + except ValueError as exc: + # make the error message more informative + logger.debug(f"Error: {str(exc)}") + output = langchain_object.run(chat_input) + + intermediate_steps = ( + output.get("intermediate_steps", []) if isinstance(output, dict) else [] + ) + + result = ( + output.get(langchain_object.output_keys[0]) + if isinstance(output, dict) + else output + ) + if intermediate_steps: + thought = format_intermediate_steps(intermediate_steps) + else: + thought = output_buffer.getvalue() + + except Exception as exc: + raise ValueError(f"Error: {str(exc)}") from exc + return result, thought def get_result_and_thought(extracted_json: Dict[str, Any], message: str): """Get result and thought from extracted json""" try: - loaded_langchain = loading.load_langchain_type_from_config( + langchain_object = loading.load_langchain_type_from_config( config=extracted_json ) with io.StringIO() as output_buffer, contextlib.redirect_stdout(output_buffer): - result = loaded_langchain(message) - result = ( - result.get(loaded_langchain.output_keys[0]) - if isinstance(result, dict) - else result + output = langchain_object(message) + intermediate_steps = ( + output.get("intermediate_steps", []) if isinstance(output, dict) else [] ) - thought = output_buffer.getvalue() + result = ( + output.get(langchain_object.output_keys[0]) + if isinstance(output, dict) + else output + ) + + if intermediate_steps: + thought = format_intermediate_steps(intermediate_steps) + else: + thought = output_buffer.getvalue() + except Exception as e: result = f"Error: {str(e)}" thought = "" return result, thought + + +def format_intermediate_steps(intermediate_steps): + formatted_chain = "> Entering new AgentExecutor chain...\n" + for step in intermediate_steps: + action = step[0] + observation = step[1] + + formatted_chain += ( + f" {action.log}\nAction: {action.tool}\nAction Input: {action.tool_input}\n" + ) + formatted_chain += f"Observation: {observation}\n" + + final_answer = f"Final Answer: {observation}\n" + formatted_chain += f"Thought: I now know the final answer\n{final_answer}\n" + formatted_chain += "> Finished chain.\n" + + return formatted_chain diff --git a/src/backend/langflow/interface/signature.py b/src/backend/langflow/interface/signature.py deleted file mode 100644 index 69e83fad6..000000000 --- a/src/backend/langflow/interface/signature.py +++ /dev/null @@ -1,133 +0,0 @@ -from typing import Any, Dict # noqa: F401 - -from langchain import agents, chains, prompts -from langchain.agents.load_tools import ( - _BASE_TOOLS, - _EXTRA_LLM_TOOLS, - _EXTRA_OPTIONAL_TOOLS, - _LLM_TOOLS, - get_all_tool_names, -) - -from langflow.custom import customs -from langflow.interface.custom_lists import ( - llm_type_to_cls_dict, - memory_type_to_cls_dict, -) -from langflow.utils import util - - -def get_signature(name: str, object_type: str): - """Get the signature of an object.""" - return { - "chains": get_chain_signature, - "agents": get_agent_signature, - "prompts": get_prompt_signature, - "llms": get_llm_signature, - "memories": get_memory_signature, - "tools": get_tool_signature, - }.get(object_type, lambda name: f"Invalid type: {name}")(name) - - -def get_chain_signature(name: str): - """Get the chain type by signature.""" - try: - return util.build_template_from_function( - name, chains.loading.type_to_loader_dict - ) - except ValueError as exc: - raise ValueError("Chain not found") from exc - - -def get_agent_signature(name: str): - """Get the signature of an agent.""" - try: - return util.build_template_from_class(name, agents.loading.AGENT_TO_CLASS) - except ValueError as exc: - raise ValueError("Agent not found") from exc - - -def get_prompt_signature(name: str): - """Get the signature of a prompt.""" - try: - if name in customs.get_custom_prompts().keys(): - return customs.get_custom_prompts()[name] - return util.build_template_from_function( - name, prompts.loading.type_to_loader_dict - ) - except ValueError as exc: - raise ValueError("Prompt not found") from exc - - -def get_llm_signature(name: str): - """Get the signature of an llm.""" - try: - return util.build_template_from_class(name, llm_type_to_cls_dict) - except ValueError as exc: - raise ValueError("LLM not found") from exc - - -def get_memory_signature(name: str): - """Get the signature of a memory.""" - try: - return util.build_template_from_class(name, memory_type_to_cls_dict) - except ValueError as exc: - raise ValueError("Memory not found") from exc - - -def get_tool_signature(name: str): - """Get the signature of a tool.""" - - all_tools = {} - for tool in get_all_tool_names(): - if tool_params := util.get_tool_params(util.get_tools_dict(tool)): - all_tools[tool_params["name"]] = tool - - # Raise error if name is not in tools - if name not in all_tools.keys(): - raise ValueError("Tool not found") - - type_dict = { - "str": { - "type": "str", - "required": True, - "list": False, - "show": True, - "placeholder": "", - "value": "", - }, - "llm": {"type": "BaseLLM", "required": True, "list": False, "show": True}, - } - - tool_type = all_tools[name] - - if tool_type in _BASE_TOOLS: - params = [] - elif tool_type in _LLM_TOOLS: - params = ["llm"] - elif tool_type in _EXTRA_LLM_TOOLS: - _, extra_keys = _EXTRA_LLM_TOOLS[tool_type] - params = ["llm"] + extra_keys - elif tool_type in _EXTRA_OPTIONAL_TOOLS: - _, extra_keys = _EXTRA_OPTIONAL_TOOLS[tool_type] - params = extra_keys - else: - params = [] - - template = { - param: (type_dict[param].copy() if param == "llm" else type_dict["str"].copy()) - for param in params - } - - # Remove required from aiosession - if "aiosession" in template.keys(): - template["aiosession"]["required"] = False - template["aiosession"]["show"] = False - - template["_type"] = tool_type # type: ignore - - return { - "template": template, - **util.get_tool_params(util.get_tools_dict(tool_type)), - "base_classes": ["Tool"], - } diff --git a/src/backend/langflow/interface/toolkits/__init__.py b/src/backend/langflow/interface/toolkits/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/src/backend/langflow/interface/toolkits/base.py b/src/backend/langflow/interface/toolkits/base.py new file mode 100644 index 000000000..5346ae4b5 --- /dev/null +++ b/src/backend/langflow/interface/toolkits/base.py @@ -0,0 +1,65 @@ +from typing import Callable, Dict, List, Optional + +from langchain.agents import agent_toolkits + +from langflow.interface.base import LangChainTypeCreator +from langflow.interface.importing.utils import import_class, import_module +from langflow.settings import settings +from langflow.utils.util import build_template_from_class + + +class ToolkitCreator(LangChainTypeCreator): + type_name: str = "toolkits" + all_types: List[str] = agent_toolkits.__all__ + create_functions: Dict = { + "JsonToolkit": [], + "SQLDatabaseToolkit": [], + "OpenAPIToolkit": ["create_openapi_agent"], + "VectorStoreToolkit": [ + "create_vectorstore_agent", + "create_vectorstore_router_agent", + "VectorStoreInfo", + ], + "ZapierToolkit": [], + "PandasToolkit": ["create_pandas_dataframe_agent"], + "CSVToolkit": ["create_csv_agent"], + } + + @property + def type_to_loader_dict(self) -> Dict: + if self.type_dict is None: + self.type_dict = { + toolkit_name: import_class( + f"langchain.agents.agent_toolkits.{toolkit_name}" + ) + # if toolkit_name is not lower case it is a class + for toolkit_name in agent_toolkits.__all__ + if not toolkit_name.islower() and toolkit_name in settings.toolkits + } + + return self.type_dict + + def get_signature(self, name: str) -> Optional[Dict]: + try: + return build_template_from_class(name, self.type_to_loader_dict) + except ValueError as exc: + raise ValueError("Prompt not found") from exc + + def to_list(self) -> List[str]: + return list(self.type_to_loader_dict.keys()) + + def get_create_function(self, name: str) -> Callable: + if loader_name := self.create_functions.get(name, None): + # import loader + return import_module( + f"from langchain.agents.agent_toolkits import {loader_name[0]}" + ) + else: + raise ValueError("Loader not found") + + def has_create_function(self, name: str) -> bool: + # check if the function list is not empty + return bool(self.create_functions.get(name, None)) + + +toolkits_creator = ToolkitCreator() diff --git a/src/backend/langflow/interface/tools/__init__.py b/src/backend/langflow/interface/tools/__init__.py new file mode 100644 index 000000000..148892e90 --- /dev/null +++ b/src/backend/langflow/interface/tools/__init__.py @@ -0,0 +1,3 @@ +from langflow.interface.tools.base import ToolCreator + +__all__ = ["ToolCreator"] diff --git a/src/backend/langflow/interface/tools/base.py b/src/backend/langflow/interface/tools/base.py new file mode 100644 index 000000000..4989a7d4b --- /dev/null +++ b/src/backend/langflow/interface/tools/base.py @@ -0,0 +1,150 @@ +from typing import Dict, List, Optional + +from langchain.agents.load_tools import ( + _BASE_TOOLS, + _EXTRA_LLM_TOOLS, + _EXTRA_OPTIONAL_TOOLS, + _LLM_TOOLS, +) + +from langflow.custom import customs +from langflow.interface.base import LangChainTypeCreator +from langflow.interface.tools.constants import ( + CUSTOM_TOOLS, + FILE_TOOLS, +) +from langflow.interface.tools.util import ( + get_tool_by_name, + get_tool_params, + get_tools_dict, +) +from langflow.settings import settings +from langflow.template.base import Template, TemplateField +from langflow.utils import util + +TOOL_INPUTS = { + "str": TemplateField( + field_type="str", + required=True, + is_list=False, + show=True, + placeholder="", + value="", + ), + "llm": TemplateField(field_type="BaseLLM", required=True, is_list=False, show=True), + "func": TemplateField( + field_type="function", + required=True, + is_list=False, + show=True, + multiline=True, + ), + "code": TemplateField( + field_type="str", + required=True, + is_list=False, + show=True, + value="", + multiline=True, + ), + "dict_": TemplateField( + field_type="file", + required=True, + is_list=False, + show=True, + value="", + ), +} + + +class ToolCreator(LangChainTypeCreator): + type_name: str = "tools" + tools_dict: Optional[Dict] = None + + @property + def type_to_loader_dict(self) -> Dict: + if self.tools_dict is None: + self.tools_dict = get_tools_dict() + return self.tools_dict + + def get_signature(self, name: str) -> Optional[Dict]: + """Get the signature of a tool.""" + + base_classes = ["Tool"] + all_tools = {} + for tool in self.type_to_loader_dict.keys(): + if tool_params := get_tool_params(get_tool_by_name(tool)): + tool_name = tool_params.get("name") or str(tool) + all_tools[tool_name] = {"type": tool, "params": tool_params} + + # Raise error if name is not in tools + if name not in all_tools.keys(): + raise ValueError("Tool not found") + + tool_type: str = all_tools[name]["type"] # type: ignore + + if tool_type in _BASE_TOOLS: + params = [] + elif tool_type in _LLM_TOOLS: + params = ["llm"] + elif tool_type in _EXTRA_LLM_TOOLS: + _, extra_keys = _EXTRA_LLM_TOOLS[tool_type] + params = ["llm"] + extra_keys + elif tool_type in _EXTRA_OPTIONAL_TOOLS: + _, extra_keys = _EXTRA_OPTIONAL_TOOLS[tool_type] + params = extra_keys + elif tool_type == "Tool": + params = ["name", "description", "func"] + elif tool_type in CUSTOM_TOOLS: + # Get custom tool params + params = all_tools[name]["params"] # type: ignore + base_classes = ["function"] + if node := customs.get_custom_nodes("tools").get(tool_type): + return node + elif tool_type in FILE_TOOLS: + params = all_tools[name]["params"] # type: ignore + base_classes += [name] + + else: + params = [] + + # Copy the field and add the name + fields = [] + for param in params: + field = TOOL_INPUTS.get(param, TOOL_INPUTS["str"]).copy() + field.name = param + if param == "aiosession": + field.show = False + field.required = False + fields.append(field) + + template = Template(fields=fields, type_name=tool_type) + + tool_params = all_tools[name]["params"] + return { + "template": util.format_dict(template.to_dict()), + **tool_params, + "base_classes": base_classes, + } + + def to_list(self) -> List[str]: + """List all load tools""" + + tools = [] + + for tool, fcn in get_tools_dict().items(): + tool_params = get_tool_params(fcn) + + if tool_params and not tool_params.get("name"): + tool_params["name"] = tool + + if tool_params and ( + tool_params.get("name") in settings.tools + or (tool_params.get("name") and settings.dev) + ): + tools.append(tool_params["name"]) + + return tools + + +tool_creator = ToolCreator() diff --git a/src/backend/langflow/interface/tools/constants.py b/src/backend/langflow/interface/tools/constants.py new file mode 100644 index 000000000..89a6bad01 --- /dev/null +++ b/src/backend/langflow/interface/tools/constants.py @@ -0,0 +1,21 @@ +from langchain.agents import Tool +from langchain.agents.load_tools import ( + _BASE_TOOLS, + _EXTRA_LLM_TOOLS, + _EXTRA_OPTIONAL_TOOLS, + _LLM_TOOLS, +) +from langchain.tools.json.tool import JsonSpec + +from langflow.interface.custom.types import PythonFunction + +FILE_TOOLS = {"JsonSpec": JsonSpec} +CUSTOM_TOOLS = {"Tool": Tool, "PythonFunction": PythonFunction} +ALL_TOOLS_NAMES = { + **_BASE_TOOLS, + **_LLM_TOOLS, # type: ignore + **{k: v[0] for k, v in _EXTRA_LLM_TOOLS.items()}, # type: ignore + **{k: v[0] for k, v in _EXTRA_OPTIONAL_TOOLS.items()}, + **CUSTOM_TOOLS, + **FILE_TOOLS, # type: ignore +} diff --git a/src/backend/langflow/interface/tools/util.py b/src/backend/langflow/interface/tools/util.py new file mode 100644 index 000000000..8f8673b6b --- /dev/null +++ b/src/backend/langflow/interface/tools/util.py @@ -0,0 +1,118 @@ +import ast +import inspect +from typing import Dict, Union + +from langchain.agents.tools import Tool + +from langflow.interface.tools.constants import ALL_TOOLS_NAMES + + +def get_tools_dict(): + """Get the tools dictionary.""" + + all_tools = {} + + for tool, fcn in ALL_TOOLS_NAMES.items(): + if tool_params := get_tool_params(fcn): + tool_name = tool_params.get("name") or str(tool) + all_tools[tool_name] = fcn + + return all_tools + + +def get_tool_by_name(name: str): + """Get a tool from the tools dictionary.""" + tools = get_tools_dict() + if name not in tools: + raise ValueError(f"{name} not found.") + return tools[name] + + +def get_func_tool_params(func, **kwargs) -> Union[Dict, None]: + tree = ast.parse(inspect.getsource(func)) + + # Iterate over the statements in the abstract syntax tree + for node in ast.walk(tree): + # Find the first return statement + if isinstance(node, ast.Return): + tool = node.value + if isinstance(tool, ast.Call): + if isinstance(tool.func, ast.Name) and tool.func.id == "Tool": + if tool.keywords: + tool_params = {} + for keyword in tool.keywords: + if keyword.arg == "name": + tool_params["name"] = ast.literal_eval(keyword.value) + elif keyword.arg == "description": + tool_params["description"] = ast.literal_eval( + keyword.value + ) + + return tool_params + return { + "name": ast.literal_eval(tool.args[0]), + "description": ast.literal_eval(tool.args[2]), + } + # + else: + # get the class object from the return statement + try: + class_obj = eval( + compile(ast.Expression(tool), "", "eval") + ) + except Exception: + return None + + return { + "name": getattr(class_obj, "name"), + "description": getattr(class_obj, "description"), + } + # Return None if no return statement was found + return None + + +def get_class_tool_params(cls, **kwargs) -> Union[Dict, None]: + tree = ast.parse(inspect.getsource(cls)) + + tool_params = {} + + # Iterate over the statements in the abstract syntax tree + for node in ast.walk(tree): + if isinstance(node, ast.ClassDef): + # Find the class definition and look for methods + for stmt in node.body: + if isinstance(stmt, ast.FunctionDef) and stmt.name == "__init__": + # There is no assignment statements in the __init__ method + # So we need to get the params from the function definition + for arg in stmt.args.args: + if arg.arg == "name": + # It should be the name of the class + tool_params[arg.arg] = cls.__name__ + elif arg.arg == "self": + continue + # If there is not default value, set it to an empty string + else: + try: + annotation = ast.literal_eval(arg.annotation) # type: ignore + tool_params[arg.arg] = annotation + except ValueError: + tool_params[arg.arg] = "" + # Get the attribute name and the annotation + elif cls != Tool and isinstance(stmt, ast.AnnAssign): + # Get the attribute name and the annotation + tool_params[stmt.target.id] = "" # type: ignore + + return tool_params + + +def get_tool_params(tool, **kwargs) -> Dict: + # Parse the function code into an abstract syntax tree + # Define if it is a function or a class + if inspect.isfunction(tool): + return get_func_tool_params(tool, **kwargs) or {} + elif inspect.isclass(tool): + # Get the parameters necessary to + # instantiate the class + return get_class_tool_params(tool, **kwargs) or {} + else: + raise ValueError("Tool must be a function or class.") diff --git a/src/backend/langflow/interface/types.py b/src/backend/langflow/interface/types.py index 57f3a2578..bf3cea372 100644 --- a/src/backend/langflow/interface/types.py +++ b/src/backend/langflow/interface/types.py @@ -1,12 +1,21 @@ -from langflow.interface.listing import list_type -from langflow.interface.signature import get_signature +from langflow.interface.agents.base import agent_creator +from langflow.interface.chains.base import chain_creator +from langflow.interface.documentLoaders.base import documentloader_creator +from langflow.interface.embeddings.base import embedding_creator +from langflow.interface.llms.base import llm_creator +from langflow.interface.memories.base import memory_creator +from langflow.interface.prompts.base import prompt_creator +from langflow.interface.toolkits.base import toolkits_creator +from langflow.interface.tools.base import tool_creator +from langflow.interface.vectorStore.base import vectorstore_creator +from langflow.interface.wrappers.base import wrapper_creator def get_type_list(): """Get a list of all langchain types""" all_types = build_langchain_types_dict() - all_types.pop("tools") + # all_types.pop("tools") for key, value in all_types.items(): all_types[key] = [item["template"]["_type"] for item in value.values()] @@ -14,23 +23,28 @@ def get_type_list(): return all_types -def build_langchain_types_dict(): +def build_langchain_types_dict(): # sourcery skip: dict-assign-update-to-union """Build a dictionary of all langchain types""" - return { - "chains": { - chain: get_signature(chain, "chains") for chain in list_type("chains") - }, - "agents": { - agent: get_signature(agent, "agents") for agent in list_type("agents") - }, - "prompts": { - prompt: get_signature(prompt, "prompts") for prompt in list_type("prompts") - }, - "llms": {llm: get_signature(llm, "llms") for llm in list_type("llms")}, - "memories": { - memory: get_signature(memory, "memories") - for memory in list_type("memories") - }, - "tools": {tool: get_signature(tool, "tools") for tool in list_type("tools")}, - } + all_types = {} + + creators = [ + chain_creator, + agent_creator, + prompt_creator, + llm_creator, + memory_creator, + tool_creator, + toolkits_creator, + wrapper_creator, + embedding_creator, + vectorstore_creator, + documentloader_creator, + ] + + all_types = {} + for creator in creators: + created_types = creator.to_dict() + if created_types[creator.type_name].values(): + all_types.update(created_types) + return all_types diff --git a/src/backend/langflow/interface/vectorStore/base.py b/src/backend/langflow/interface/vectorStore/base.py new file mode 100644 index 000000000..6c2baa86d --- /dev/null +++ b/src/backend/langflow/interface/vectorStore/base.py @@ -0,0 +1,31 @@ +from typing import Dict, List, Optional + +from langflow.interface.base import LangChainTypeCreator +from langflow.interface.custom_lists import vectorstores_type_to_cls_dict +from langflow.settings import settings +from langflow.utils.util import build_template_from_class + + +class VectorstoreCreator(LangChainTypeCreator): + type_name: str = "vectorstore" + + @property + def type_to_loader_dict(self) -> Dict: + return vectorstores_type_to_cls_dict + + def get_signature(self, name: str) -> Optional[Dict]: + """Get the signature of an embedding.""" + try: + return build_template_from_class(name, vectorstores_type_to_cls_dict) + except ValueError as exc: + raise ValueError(f"Vector Store {name} not found") from exc + + def to_list(self) -> List[str]: + return [ + vectorstore + for vectorstore in self.type_to_loader_dict.keys() + if vectorstore in settings.vectorstores or settings.dev + ] + + +vectorstore_creator = VectorstoreCreator() diff --git a/src/backend/langflow/interface/wrappers/__init__.py b/src/backend/langflow/interface/wrappers/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/src/backend/langflow/interface/wrappers/base.py b/src/backend/langflow/interface/wrappers/base.py new file mode 100644 index 000000000..8d0df8141 --- /dev/null +++ b/src/backend/langflow/interface/wrappers/base.py @@ -0,0 +1,30 @@ +from typing import Dict, List, Optional + +from langchain import requests + +from langflow.interface.base import LangChainTypeCreator +from langflow.utils.util import build_template_from_class + + +class WrapperCreator(LangChainTypeCreator): + type_name: str = "wrappers" + + @property + def type_to_loader_dict(self) -> Dict: + if self.type_dict is None: + self.type_dict = { + wrapper.__name__: wrapper for wrapper in [requests.TextRequestsWrapper] + } + return self.type_dict + + def get_signature(self, name: str) -> Optional[Dict]: + try: + return build_template_from_class(name, self.type_to_loader_dict) + except ValueError as exc: + raise ValueError("Wrapper not found") from exc + + def to_list(self) -> List[str]: + return list(self.type_to_loader_dict.keys()) + + +wrapper_creator = WrapperCreator() diff --git a/src/backend/langflow/main.py b/src/backend/langflow/main.py index a2a02465e..176e46236 100644 --- a/src/backend/langflow/main.py +++ b/src/backend/langflow/main.py @@ -2,8 +2,7 @@ from fastapi import FastAPI from fastapi.middleware.cors import CORSMiddleware from langflow.api.endpoints import router as endpoints_router -from langflow.api.list_endpoints import router as list_router -from langflow.api.signature import router as signatures_router +from langflow.api.validate import router as validate_router def create_app(): @@ -23,8 +22,7 @@ def create_app(): ) app.include_router(endpoints_router) - app.include_router(list_router) - app.include_router(signatures_router) + app.include_router(validate_router) return app diff --git a/src/backend/langflow/settings.py b/src/backend/langflow/settings.py index f4dd4ae30..c0fd66c58 100644 --- a/src/backend/langflow/settings.py +++ b/src/backend/langflow/settings.py @@ -1,29 +1,47 @@ import os -from typing import List, Optional +from typing import List import yaml -from pydantic import BaseSettings, Field, root_validator +from pydantic import BaseSettings, root_validator class Settings(BaseSettings): - chains: Optional[List[str]] = Field(...) - agents: Optional[List[str]] = Field(...) - prompts: Optional[List[str]] = Field(...) - llms: Optional[List[str]] = Field(...) - tools: Optional[List[str]] = Field(...) - memories: Optional[List[str]] = Field(...) - dev: bool = Field(...) + chains: List[str] = [] + agents: List[str] = [] + prompts: List[str] = [] + llms: List[str] = [] + tools: List[str] = [] + memories: List[str] = [] + embeddings: List[str] = [] + vectorstores: List[str] = [] + documentloaders: List[str] = [] + wrappers: List[str] = [] + toolkits: List[str] = [] + dev: bool = False class Config: validate_assignment = True + extra = "ignore" - @root_validator + @root_validator(allow_reuse=True) def validate_lists(cls, values): for key, value in values.items(): if key != "dev" and not value: values[key] = [] return values + def update_from_yaml(self, file_path: str): + new_settings = load_settings_from_yaml(file_path) + self.chains = new_settings.chains or [] + self.agents = new_settings.agents or [] + self.prompts = new_settings.prompts or [] + self.llms = new_settings.llms or [] + self.tools = new_settings.tools or [] + self.memories = new_settings.memories or [] + self.wrappers = new_settings.wrappers or [] + self.toolkits = new_settings.toolkits or [] + self.dev = new_settings.dev or False + def save_settings_to_yaml(settings: Settings, file_path: str): with open(file_path, "w") as f: @@ -41,9 +59,8 @@ def load_settings_from_yaml(file_path: str) -> Settings: with open(file_path, "r") as f: settings_dict = yaml.safe_load(f) - a = Settings.parse_obj(settings_dict) - return a + return Settings(**settings_dict) settings = load_settings_from_yaml("config.yaml") diff --git a/src/backend/langflow/template/__init__.py b/src/backend/langflow/template/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/src/backend/langflow/template/base.py b/src/backend/langflow/template/base.py new file mode 100644 index 000000000..4c053b19d --- /dev/null +++ b/src/backend/langflow/template/base.py @@ -0,0 +1,228 @@ +from abc import ABC +from typing import Any, Callable, Dict, Optional, Union + +from pydantic import BaseModel + +from langflow.template.constants import FORCE_SHOW_FIELDS +from langflow.utils import constants + + +class TemplateFieldCreator(BaseModel, ABC): + field_type: str = "str" + required: bool = False + placeholder: str = "" + is_list: bool = False + show: bool = True + multiline: bool = False + value: Any = None + suffixes: list[str] = [] + fileTypes: list[str] = [] + file_types: list[str] = [] + content: Union[str, None] = None + password: bool = False + options: list[str] = [] + name: str = "" + display_name: Optional[str] = None + + def to_dict(self): + result = self.dict() + # Remove key if it is None + for key in list(result.keys()): + if result[key] is None or result[key] == []: + del result[key] + result["type"] = result.pop("field_type") + result["list"] = result.pop("is_list") + + if result.get("file_types"): + result["fileTypes"] = result.pop("file_types") + + if self.field_type == "file": + result["content"] = self.content + return result + + def process_field( + self, key: str, value: Dict[str, Any], name: Optional[str] = None + ) -> None: + _type = value["type"] + + # Remove 'Optional' wrapper + if "Optional" in _type: + _type = _type.replace("Optional[", "")[:-1] + + # Check for list type + if "List" in _type: + _type = _type.replace("List[", "")[:-1] + self.is_list = True + + # Replace 'Mapping' with 'dict' + if "Mapping" in _type: + _type = _type.replace("Mapping", "dict") + + # Change type from str to Tool + self.field_type = "Tool" if key in {"allowed_tools"} else self.field_type + + self.field_type = "int" if key in {"max_value_length"} else self.field_type + + # Show or not field + self.show = bool( + (self.required and key not in ["input_variables"]) + or key in FORCE_SHOW_FIELDS + or "api_key" in key + ) + + # Add password field + self.password = any( + text in key.lower() for text in {"password", "token", "api", "key"} + ) + + # Add multline + self.multiline = key in { + "suffix", + "prefix", + "template", + "examples", + "code", + "headers", + } + + # Replace dict type with str + if "dict" in self.field_type.lower(): + self.field_type = "code" + + if key == "dict_": + self.field_type = "file" + self.suffixes = [".json", ".yaml", ".yml"] + self.file_types = ["json", "yaml", "yml"] + + # Replace default value with actual value + if "default" in value: + self.value = value["default"] + + if key == "headers": + self.value = """{'Authorization': + 'Bearer '}""" + + # Add options to openai + if name == "OpenAI" and key == "model_name": + self.options = constants.OPENAI_MODELS + self.is_list = True + elif name == "ChatOpenAI" and key == "model_name": + self.options = constants.CHAT_OPENAI_MODELS + self.is_list = True + + +class TemplateField(TemplateFieldCreator): + pass + + +class Template(BaseModel): + type_name: str + fields: list[TemplateField] + + def process_fields( + self, + name: Optional[str] = None, + format_field_func: Union[Callable, None] = None, + ): + if format_field_func: + for field in self.fields: + format_field_func(field, name) + + def to_dict(self, format_field_func=None): + self.process_fields(self.type_name, format_field_func) + result = {field.name: field.to_dict() for field in self.fields} + result["_type"] = self.type_name # type: ignore + return result + + +class FrontendNode(BaseModel): + template: Template + description: str + base_classes: list + name: str = "" + + def to_dict(self): + return { + self.name: { + "template": self.template.to_dict(self.format_field), + "description": self.description, + "base_classes": self.base_classes, + } + } + + @staticmethod + def format_field(field: TemplateField, name: Optional[str] = None) -> None: + key = field.name + value = field.to_dict() + _type = value["type"] + + # Remove 'Optional' wrapper + if "Optional" in _type: + _type = _type.replace("Optional[", "")[:-1] + + # Check for list type + if "List" in _type: + _type = _type.replace("List[", "")[:-1] + field.is_list = True + + # Replace 'Mapping' with 'dict' + if "Mapping" in _type: + _type = _type.replace("Mapping", "dict") + + # Change type from str to Tool + field.field_type = "Tool" if key in {"allowed_tools"} else field.field_type + + field.field_type = "int" if key in {"max_value_length"} else field.field_type + + # Show or not field + field.show = bool( + (field.required and key not in ["input_variables"]) + or key in FORCE_SHOW_FIELDS + or "api_key" in key + ) + + # Add password field + field.password = any( + text in key.lower() for text in {"password", "token", "api", "key"} + ) + + # Add multline + field.multiline = key in { + "suffix", + "prefix", + "template", + "examples", + "code", + "headers", + } + + # Replace dict type with str + if "dict" in field.field_type.lower(): + field.field_type = "code" + + if key == "dict_": + field.field_type = "file" + field.suffixes = [".json", ".yaml", ".yml"] + field.file_types = ["json", "yaml", "yml"] + + # Replace default value with actual value + if "default" in value: + field.value = value["default"] + + if key == "headers": + field.value = """{'Authorization': + 'Bearer '}""" + + # Add options to openai + if name == "OpenAI" and key == "model_name": + field.options = constants.OPENAI_MODELS + field.is_list = True + elif name == "ChatOpenAI": + if key == "model_name": + field.options = constants.CHAT_OPENAI_MODELS + field.is_list = True + if "api_key" in key and "OpenAI" in str(name): + field.display_name = "OpenAI API Key" + field.required = True + if field.value is None: + field.value = "" diff --git a/src/backend/langflow/template/constants.py b/src/backend/langflow/template/constants.py new file mode 100644 index 000000000..ae08d3691 --- /dev/null +++ b/src/backend/langflow/template/constants.py @@ -0,0 +1,32 @@ +FORCE_SHOW_FIELDS = [ + "allowed_tools", + "memory", + "prefix", + "examples", + "temperature", + "model_name", + "headers", + "max_value_length", + "max_tokens", +] + +DEFAULT_PROMPT = """ +I want you to act as a naming consultant for new companies. + +Here are some examples of good company names: + +- search engine, Google +- social media, Facebook +- video sharing, YouTube + +The name should be short, catchy and easy to remember. + +What is a good name for a company that makes {product}? +""" + +SYSTEM_PROMPT = """ +You are a helpful assistant that talks casually about life in general. +You are a good listener and you can talk about anything. +""" + +HUMAN_PROMPT = "{input}" diff --git a/src/backend/langflow/template/fields.py b/src/backend/langflow/template/fields.py new file mode 100644 index 000000000..e69de29bb diff --git a/src/backend/langflow/template/nodes.py b/src/backend/langflow/template/nodes.py new file mode 100644 index 000000000..acfc5c2d2 --- /dev/null +++ b/src/backend/langflow/template/nodes.py @@ -0,0 +1,282 @@ +from typing import Optional + +from langchain.agents import loading +from langchain.agents.mrkl import prompt + +from langflow.template.base import FrontendNode, Template, TemplateField +from langflow.template.constants import DEFAULT_PROMPT, HUMAN_PROMPT, SYSTEM_PROMPT +from langflow.utils.constants import DEFAULT_PYTHON_FUNCTION + +NON_CHAT_AGENTS = { + agent_type: agent_class + for agent_type, agent_class in loading.AGENT_TO_CLASS.items() + if "chat" not in agent_type.value +} + + +class BasePromptFrontendNode(FrontendNode): + name: str + template: Template + description: str + base_classes: list[str] + + def to_dict(self): + return super().to_dict() + + +class ZeroShotPromptNode(BasePromptFrontendNode): + name: str = "ZeroShotPrompt" + template: Template = Template( + type_name="zero_shot", + fields=[ + TemplateField( + field_type="str", + required=False, + placeholder="", + is_list=False, + show=True, + multiline=True, + value=prompt.PREFIX, + name="prefix", + ), + TemplateField( + field_type="str", + required=True, + placeholder="", + is_list=False, + show=True, + multiline=True, + value=prompt.SUFFIX, + name="suffix", + ), + TemplateField( + field_type="str", + required=False, + placeholder="", + is_list=False, + show=True, + multiline=True, + value=prompt.FORMAT_INSTRUCTIONS, + name="format_instructions", + ), + ], + ) + description: str = "Prompt template for Zero Shot Agent." + base_classes: list[str] = ["BasePromptTemplate"] + + def to_dict(self): + return super().to_dict() + + +class PromptTemplateNode(FrontendNode): + name: str = "PromptTemplate" + template: Template + description: str + base_classes: list[str] = ["BasePromptTemplate"] + + def to_dict(self): + return super().to_dict() + + +class PythonFunctionNode(FrontendNode): + name: str = "PythonFunction" + template: Template = Template( + type_name="python_function", + fields=[ + TemplateField( + field_type="code", + required=True, + placeholder="", + is_list=False, + show=True, + value=DEFAULT_PYTHON_FUNCTION, + name="code", + ) + ], + ) + description: str = "Python function to be executed." + base_classes: list[str] = ["function"] + + def to_dict(self): + return super().to_dict() + + +class ToolNode(FrontendNode): + name: str = "Tool" + template: Template = Template( + type_name="tool", + fields=[ + TemplateField( + field_type="str", + required=True, + placeholder="", + is_list=False, + show=True, + multiline=True, + value="", + name="name", + ), + TemplateField( + field_type="str", + required=True, + placeholder="", + is_list=False, + show=True, + multiline=True, + value="", + name="description", + ), + TemplateField( + field_type="str", + required=True, + placeholder="", + is_list=False, + show=True, + multiline=True, + value="", + name="func", + ), + ], + ) + description: str = "Tool to be used in the flow." + base_classes: list[str] = ["BaseTool"] + + def to_dict(self): + return super().to_dict() + + +class JsonAgentNode(FrontendNode): + name: str = "JsonAgent" + template: Template = Template( + type_name="json_agent", + fields=[ + TemplateField( + field_type="BaseToolkit", + required=True, + show=True, + name="toolkit", + ), + TemplateField( + field_type="BaseLanguageModel", + required=True, + show=True, + name="llm", + ), + ], + ) + description: str = """Construct a json agent from an LLM and tools.""" + base_classes: list[str] = ["AgentExecutor"] + + def to_dict(self): + return super().to_dict() + + +class InitializeAgentNode(FrontendNode): + name: str = "initialize_agent" + template: Template = Template( + type_name="initailize_agent", + fields=[ + TemplateField( + field_type="str", + required=True, + is_list=True, + show=True, + multiline=False, + options=list(NON_CHAT_AGENTS.keys()), + value=list(NON_CHAT_AGENTS.keys())[0], + name="agent", + ), + TemplateField( + field_type="BaseChatMemory", + required=False, + show=True, + name="memory", + ), + TemplateField( + field_type="Tool", + required=False, + show=True, + name="tools", + is_list=True, + ), + TemplateField( + field_type="BaseLanguageModel", + required=True, + show=True, + name="llm", + ), + ], + ) + description: str = """Construct a json agent from an LLM and tools.""" + base_classes: list[str] = ["AgentExecutor"] + + def to_dict(self): + return super().to_dict() + + @staticmethod + def format_field(field: TemplateField, name: Optional[str] = None) -> None: + # do nothing and don't return anything + pass + + +class CSVAgentNode(FrontendNode): + name: str = "CSVAgent" + template: Template = Template( + type_name="csv_agent", + fields=[ + TemplateField( + field_type="file", + required=True, + show=True, + name="path", + value="", + suffixes=[".csv"], + fileTypes=["csv"], + ), + TemplateField( + field_type="BaseLanguageModel", + required=True, + show=True, + name="llm", + ), + ], + ) + description: str = """Construct a json agent from a CSV and tools.""" + base_classes: list[str] = ["AgentExecutor"] + + def to_dict(self): + return super().to_dict() + + +class PromptFrontendNode(FrontendNode): + @staticmethod + def format_field(field: TemplateField, name: Optional[str] = None) -> None: + # if field.field_type == "StringPromptTemplate" + # change it to str + if field.field_type == "StringPromptTemplate" and "Message" in str(name): + field.field_type = "str" + field.multiline = True + field.value = HUMAN_PROMPT if "Human" in field.name else SYSTEM_PROMPT + if field.name == "template" and field.value == "": + field.value = DEFAULT_PROMPT + + if ( + "Union" in field.field_type + and "BaseMessagePromptTemplate" in field.field_type + ): + field.field_type = "BaseMessagePromptTemplate" + + +class MemoryFrontendNode(FrontendNode): + @staticmethod + def format_field(field: TemplateField, name: Optional[str] = None) -> None: + FrontendNode.format_field(field, name) + + if not isinstance(field.value, str): + field.value = None + if field.name == "k": + field.required = True + field.show = True + field.field_type = "int" + field.value = 10 + field.display_name = "Memory Size" diff --git a/src/backend/langflow/utils/constants.py b/src/backend/langflow/utils/constants.py index 73a50ce40..2d101ab98 100644 --- a/src/backend/langflow/utils/constants.py +++ b/src/backend/langflow/utils/constants.py @@ -6,3 +6,10 @@ OPENAI_MODELS = [ "text-ada-001", ] CHAT_OPENAI_MODELS = ["gpt-3.5-turbo", "gpt-4", "gpt-4-32k"] + + +DEFAULT_PYTHON_FUNCTION = """ +def python_function(text: str) -> str: + \"\"\"This is a default python function that returns the input text\"\"\" + return text +""" diff --git a/src/backend/langflow/utils/logger.py b/src/backend/langflow/utils/logger.py new file mode 100644 index 000000000..b70a451d4 --- /dev/null +++ b/src/backend/langflow/utils/logger.py @@ -0,0 +1,30 @@ +import logging +from pathlib import Path + +from rich.logging import RichHandler + +logger = logging.getLogger("langflow") + + +def configure(log_level: str = "INFO", log_file: Path = None): # type: ignore + log_format = "%(asctime)s - %(levelname)s - %(message)s" + log_level_value = getattr(logging, log_level.upper(), logging.INFO) + + logging.basicConfig( + level=log_level_value, + format=log_format, + datefmt="[%X]", + handlers=[RichHandler(rich_tracebacks=True)], + ) + + if log_file: + log_file = Path(log_file) + log_file.parent.mkdir(parents=True, exist_ok=True) + + file_handler = logging.FileHandler(log_file) + file_handler.setFormatter(logging.Formatter(log_format)) + logger.addHandler(file_handler) + + logger.info(f"Logger set up with log level: {log_level_value}({log_level})") + if log_file: + logger.info(f"Log file: {log_file}") diff --git a/src/backend/langflow/utils/payload.py b/src/backend/langflow/utils/payload.py index e7d38139e..380ec800c 100644 --- a/src/backend/langflow/utils/payload.py +++ b/src/backend/langflow/utils/payload.py @@ -1,5 +1,6 @@ import contextlib import re +from typing import Dict def extract_input_variables(nodes): @@ -27,48 +28,59 @@ def extract_input_variables(nodes): return nodes -def get_root_node(nodes, edges): +def get_root_node(graph): """ Returns the root node of the template. """ - incoming_edges = {edge["source"] for edge in edges} - return next((node for node in nodes if node["id"] not in incoming_edges), None) + incoming_edges = {edge.source for edge in graph.edges} + return next((node for node in graph.nodes if node not in incoming_edges), None) -def build_json(root, nodes, edges): - """ - Builds a json from the nodes and edges - """ - edge_ids = [edge["source"] for edge in edges if edge["target"] == root["id"]] - local_nodes = [node for node in nodes if node["id"] in edge_ids] +def build_json(root, graph) -> Dict: + if "node" not in root.data: + # If the root node has no "node" key, then it has only one child, + # which is the target of the single outgoing edge + edge = root.edges[0] + local_nodes = [edge.target] + else: + # Otherwise, find all children whose type matches the type + # specified in the template + node_type = root.node_type + local_nodes = graph.get_nodes_with_target(root) - if "node" not in root["data"]: - return build_json(local_nodes[0], nodes, edges) - - final_dict = root["data"]["node"]["template"].copy() + if len(local_nodes) == 1: + return build_json(local_nodes[0], graph) + # Build a dictionary from the template + template = root.data["node"]["template"] + final_dict = template.copy() for key, value in final_dict.items(): if key == "_type": continue - module_type = value["type"] + node_type = value["type"] if "value" in value and value["value"] is not None: + # If the value is specified, use it value = value["value"] - elif "dict" in module_type: + elif "dict" in node_type: + # If the value is a dictionary, create an empty dictionary value = {} else: + # Otherwise, recursively build the child nodes children = [] - for c in local_nodes: - module_types = [c["data"]["type"]] - if "node" in c["data"]: - module_types += c["data"]["node"]["base_classes"] - if module_type in module_types: - children.append(c) + for local_node in local_nodes: + node_children = graph.get_children_by_node_type(local_node, node_type) + children.extend(node_children) if value["required"] and not children: - raise ValueError(f"No child with type {module_type} found") - values = [build_json(child, nodes, edges) for child in children] - value = list(values) if value["list"] else next(iter(values), None) + raise ValueError(f"No child with type {node_type} found") + values = [build_json(child, graph) for child in children] + value = ( + list(values) + if value["list"] + else next(iter(values), None) # type: ignore + ) final_dict[key] = value + return final_dict diff --git a/src/backend/langflow/utils/util.py b/src/backend/langflow/utils/util.py index 4710114ed..1ca73746b 100644 --- a/src/backend/langflow/utils/util.py +++ b/src/backend/langflow/utils/util.py @@ -1,20 +1,58 @@ -import ast import importlib import inspect import re from typing import Dict, Optional -from langchain.agents.load_tools import ( - _BASE_TOOLS, - _EXTRA_LLM_TOOLS, - _EXTRA_OPTIONAL_TOOLS, - _LLM_TOOLS, -) - +from langflow.template.constants import FORCE_SHOW_FIELDS from langflow.utils import constants -def build_template_from_function(name: str, type_to_loader_dict: Dict): +def build_template_from_parameters( + name: str, type_to_loader_dict: Dict, add_function: bool = False +): + # Retrieve the function that matches the provided name + func = None + for _, v in type_to_loader_dict.items(): + if v.__name__ == name: + func = v + break + + if func is None: + raise ValueError(f"{name} not found") + + # Process parameters + parameters = func.__annotations__ + variables = {} + for param_name, param_type in parameters.items(): + if param_name in ["return", "kwargs"]: + continue + + variables[param_name] = { + "type": param_type.__name__, + "default": parameters[param_name].__repr_args__()[0][1], + # Op + "placeholder": "", + } + + # Get the base classes of the return type + return_type = parameters.get("return") + base_classes = get_base_classes(return_type) if return_type else [] + if add_function: + base_classes.append("function") + + # Get the function's docstring + docs = inspect.getdoc(func) or "" + + return { + "template": format_dict(variables, name), + "description": docs["Description"], # type: ignore + "base_classes": base_classes, + } + + +def build_template_from_function( + name: str, type_to_loader_dict: Dict, add_function: bool = False +): classes = [ item.__annotations__["return"].__name__ for item in type_to_loader_dict.values() ] @@ -29,51 +67,6 @@ def build_template_from_function(name: str, type_to_loader_dict: Dict): docs = get_class_doc(_class) - variables = {"_type": _type} - for class_field_items, value in _class.__fields__.items(): - if class_field_items in ["callback_manager", "requests_wrapper"]: - continue - variables[class_field_items] = {} - for name_, value_ in value.__repr_args__(): - if name_ == "default_factory": - try: - variables[class_field_items][ - "default" - ] = get_default_factory( - module=_class.__base__.__module__, function=value_ - ) - except Exception: - variables[class_field_items]["default"] = None - elif name_ not in ["name"]: - variables[class_field_items][name_] = value_ - - variables[class_field_items]["placeholder"] = ( - docs["Attributes"][class_field_items] - if class_field_items in docs["Attributes"] - else "" - ) - - return { - "template": format_dict(variables, name), - "description": docs["Description"], - "base_classes": get_base_classes(_class), - } - - -def build_template_from_class(name: str, type_to_cls_dict: Dict): - classes = [item.__name__ for item in type_to_cls_dict.values()] - - # Raise error if name is not in chains - if name not in classes: - raise ValueError(f"{name} not found.") - - for _type, v in type_to_cls_dict.items(): - if v.__name__ == name: - _class = v - - # Get the docstring - docs = get_class_doc(_class) - variables = {"_type": _type} for class_field_items, value in _class.__fields__.items(): if class_field_items in ["callback_manager"]: @@ -97,26 +90,93 @@ def build_template_from_class(name: str, type_to_cls_dict: Dict): if class_field_items in docs["Attributes"] else "" ) + # Adding function to base classes to allow + # the output to be a function + base_classes = get_base_classes(_class) + if add_function: + base_classes.append("function") return { "template": format_dict(variables, name), "description": docs["Description"], - "base_classes": get_base_classes(_class), + "base_classes": base_classes, + } + + +def build_template_from_class( + name: str, type_to_cls_dict: Dict, add_function: bool = False +): + classes = [item.__name__ for item in type_to_cls_dict.values()] + + # Raise error if name is not in chains + if name not in classes: + raise ValueError(f"{name} not found.") + + for _type, v in type_to_cls_dict.items(): + if v.__name__ == name: + _class = v + + # Get the docstring + docs = get_class_doc(_class) + + variables = {"_type": _type} + + if "__fields__" in _class.__dict__: + for class_field_items, value in _class.__fields__.items(): + if class_field_items in ["callback_manager"]: + continue + variables[class_field_items] = {} + for name_, value_ in value.__repr_args__(): + if name_ == "default_factory": + try: + variables[class_field_items][ + "default" + ] = get_default_factory( + module=_class.__base__.__module__, function=value_ + ) + except Exception: + variables[class_field_items]["default"] = None + elif name_ not in ["name"]: + variables[class_field_items][name_] = value_ + + variables[class_field_items]["placeholder"] = ( + docs["Attributes"][class_field_items] + if class_field_items in docs["Attributes"] + else "" + ) + base_classes = get_base_classes(_class) + # Adding function to base classes to allow + # the output to be a function + if add_function: + base_classes.append("function") + return { + "template": format_dict(variables, name), + "description": docs["Description"], + "base_classes": base_classes, } def get_base_classes(cls): - bases = cls.__bases__ - if not bases: - return [] - else: + """Get the base classes of a class. + These are used to determine the output of the nodes. + """ + if bases := cls.__bases__: result = [] for base in bases: if any(type in base.__module__ for type in ["pydantic", "abc"]): continue result.append(base.__name__) - result.extend(get_base_classes(base)) - return result + base_classes = get_base_classes(base) + # check if the base_classes are in the result + # if not, add them + for base_class in base_classes: + if base_class not in result: + result.append(base_class) + else: + result = [cls.__name__] + if not result: + result = [cls.__name__] + return list(set(result + [cls.__name__])) def get_default_factory(module: str, function: str): @@ -128,60 +188,6 @@ def get_default_factory(module: str, function: str): return None -def get_tools_dict(name: Optional[str] = None): - """Get the tools dictionary.""" - tools = { - **_BASE_TOOLS, - **_LLM_TOOLS, # type: ignore - **{k: v[0] for k, v in _EXTRA_LLM_TOOLS.items()}, # type: ignore - **{k: v[0] for k, v in _EXTRA_OPTIONAL_TOOLS.items()}, - } - return tools[name] if name else tools - - -def get_tool_params(func, **kwargs): - # Parse the function code into an abstract syntax tree - tree = ast.parse(inspect.getsource(func)) - - # Iterate over the statements in the abstract syntax tree - for node in ast.walk(tree): - # Find the first return statement - if isinstance(node, ast.Return): - tool = node.value - if isinstance(tool, ast.Call): - if tool.func.id == "Tool": - if tool.keywords: - tool_params = {} - for keyword in tool.keywords: - if keyword.arg == "name": - tool_params["name"] = ast.literal_eval(keyword.value) - elif keyword.arg == "description": - tool_params["description"] = ast.literal_eval( - keyword.value - ) - return tool_params - return { - "name": ast.literal_eval(tool.args[0]), - "description": ast.literal_eval(tool.args[2]), - } - else: - # get the class object from the return statement - try: - class_obj = eval( - compile(ast.Expression(tool), "", "eval") - ) - except Exception: - return None - - return { - "name": getattr(class_obj, "name"), - "description": getattr(class_obj, "description"), - } - - # Return None if no return statement was found - return None - - def get_class_doc(class_name): """ Extracts information from the docstring of a given class. @@ -272,41 +278,58 @@ def format_dict(d, name: Optional[str] = None): _type = _type.replace("Mapping", "dict") # Change type from str to Tool - value["type"] = "Tool" if key == "allowed_tools" else _type + value["type"] = "Tool" if key in ["allowed_tools"] else _type + + value["type"] = "int" if key in ["max_value_length"] else value["type"] # Show or not field value["show"] = bool( (value["required"] and key not in ["input_variables"]) - or key - in [ - "allowed_tools", - "memory", - "prefix", - "examples", - "temperature", - "model_name", - ] + or key in FORCE_SHOW_FIELDS or "api_key" in key ) # Add password field value["password"] = any( - text in key for text in ["password", "token", "api", "key"] + text in key.lower() for text in ["password", "token", "api", "key"] ) # Add multline - value["multiline"] = key in ["suffix", "prefix", "template", "examples"] + value["multiline"] = key in [ + "suffix", + "prefix", + "template", + "examples", + "code", + "headers", + ] + + # Replace dict type with str + if "dict" in value["type"].lower(): + value["type"] = "code" + + if key == "dict_": + value["type"] = "file" + value["suffixes"] = [".json", ".yaml", ".yml"] + value["fileTypes"] = ["json", "yaml", "yml"] # Replace default value with actual value if "default" in value: value["value"] = value["default"] value.pop("default") + if key == "headers": + value[ + "value" + ] = """{'Authorization': + 'Bearer '}""" # Add options to openai if name == "OpenAI" and key == "model_name": value["options"] = constants.OPENAI_MODELS - elif name == "OpenAIChat" and key == "model_name": + value["list"] = True + elif name == "ChatOpenAI" and key == "model_name": value["options"] = constants.CHAT_OPENAI_MODELS + value["list"] = True return d diff --git a/src/backend/langflow/utils/validate.py b/src/backend/langflow/utils/validate.py new file mode 100644 index 000000000..af5ddea3c --- /dev/null +++ b/src/backend/langflow/utils/validate.py @@ -0,0 +1,168 @@ +import ast +import importlib +import types +from typing import Dict + + +def add_type_ignores(): + if not hasattr(ast, "TypeIgnore"): + + class TypeIgnore(ast.AST): + _fields = () + + ast.TypeIgnore = TypeIgnore + + +def validate_code(code): + # Initialize the errors dictionary + errors = {"imports": {"errors": []}, "function": {"errors": []}} + + # Parse the code string into an abstract syntax tree (AST) + try: + tree = ast.parse(code) + except Exception as e: + errors["function"]["errors"].append(str(e)) + return errors + + # Add a dummy type_ignores field to the AST + add_type_ignores() + tree.type_ignores = [] + + # Evaluate the import statements + for node in tree.body: + if isinstance(node, ast.Import): + for alias in node.names: + try: + importlib.import_module(alias.name) + except ModuleNotFoundError as e: + errors["imports"]["errors"].append(str(e)) + + # Evaluate the function definition + for node in tree.body: + if isinstance(node, ast.FunctionDef): + code_obj = compile( + ast.Module(body=[node], type_ignores=[]), "", "exec" + ) + try: + exec(code_obj) + except Exception as e: + errors["function"]["errors"].append(str(e)) + + # Return the errors dictionary + return errors + + +def eval_function(function_string: str): + # Create an empty dictionary to serve as a separate namespace + namespace: Dict = {} + + # Execute the code string in the new namespace + exec(function_string, namespace) + function_object = next( + (obj for name, obj in namespace.items() if isinstance(obj, types.FunctionType)), + None, + ) + if function_object is None: + raise ValueError("Function string does not contain a function") + return function_object + + +def execute_function(code, function_name, *args, **kwargs): + add_type_ignores() + + module = ast.parse(code) + exec_globals = globals().copy() + + for node in module.body: + if isinstance(node, ast.Import): + for alias in node.names: + try: + exec( + f"{alias.asname or alias.name} = importlib.import_module('{alias.name}')", + exec_globals, + locals(), + ) + exec_globals[alias.asname or alias.name] = importlib.import_module( + alias.name + ) + except ModuleNotFoundError as e: + raise ModuleNotFoundError( + f"Module {alias.name} not found. Please install it and try again." + ) from e + + function_code = next( + node + for node in module.body + if isinstance(node, ast.FunctionDef) and node.name == function_name + ) + function_code.parent = None + code_obj = compile( + ast.Module(body=[function_code], type_ignores=[]), "", "exec" + ) + try: + exec(code_obj, exec_globals, locals()) + except Exception as exc: + raise ValueError("Function string does not contain a function") from exc + + # Add the function to the exec_globals dictionary + exec_globals[function_name] = locals()[function_name] + + return exec_globals[function_name](*args, **kwargs) + + +def create_function(code, function_name): + if not hasattr(ast, "TypeIgnore"): + + class TypeIgnore(ast.AST): + _fields = () + + ast.TypeIgnore = TypeIgnore + + module = ast.parse(code) + exec_globals = globals().copy() + + for node in module.body: + if isinstance(node, ast.Import): + for alias in node.names: + try: + exec_globals[alias.asname or alias.name] = importlib.import_module( + alias.name + ) + except ModuleNotFoundError as e: + raise ModuleNotFoundError( + f"Module {alias.name} not found. Please install it and try again." + ) from e + + function_code = next( + node + for node in module.body + if isinstance(node, ast.FunctionDef) and node.name == function_name + ) + function_code.parent = None + code_obj = compile( + ast.Module(body=[function_code], type_ignores=[]), "", "exec" + ) + try: + exec(code_obj, exec_globals, locals()) + except Exception: + pass + + exec_globals[function_name] = locals()[function_name] + + # Return a function that imports necessary modules and calls the target function + def wrapped_function(*args, **kwargs): + for module_name, module in exec_globals.items(): + if isinstance(module, type(importlib)): + globals()[module_name] = module + + return exec_globals[function_name](*args, **kwargs) + + return wrapped_function + + +def extract_function_name(code): + module = ast.parse(code) + for node in module.body: + if isinstance(node, ast.FunctionDef): + return node.name + raise ValueError("No function definition found in the code string") diff --git a/src/frontend/package-lock.json b/src/frontend/package-lock.json index f0a40a452..df61239c5 100644 --- a/src/frontend/package-lock.json +++ b/src/frontend/package-lock.json @@ -21,9 +21,12 @@ "@types/node": "^16.18.12", "@types/react": "^18.0.27", "@types/react-dom": "^18.0.10", + "ace-builds": "^1.16.0", + "ansi-to-html": "^0.7.2", "axios": "^1.3.2", "lodash": "^4.17.21", "react": "^18.2.0", + "react-ace": "^10.1.0", "react-cookie": "^4.1.1", "react-dom": "^18.2.0", "react-error-boundary": "^4.0.2", @@ -5098,6 +5101,11 @@ "node": ">= 0.6" } }, + "node_modules/ace-builds": { + "version": "1.16.0", + "resolved": "https://registry.npmjs.org/ace-builds/-/ace-builds-1.16.0.tgz", + "integrity": "sha512-EriMhoxdfhh0zKm7icSt8EXekODAOVsYh9fpnlru9ALwf0Iw7J7bpuqLjhi3QRxvVKR7P0teQdJwTvjVMcYHuw==" + }, "node_modules/acorn": { "version": "8.8.2", "resolved": "https://registry.npmjs.org/acorn/-/acorn-8.8.2.tgz", @@ -5308,6 +5316,20 @@ "node": ">=4" } }, + "node_modules/ansi-to-html": { + "version": "0.7.2", + "resolved": "https://registry.npmjs.org/ansi-to-html/-/ansi-to-html-0.7.2.tgz", + "integrity": "sha512-v6MqmEpNlxF+POuyhKkidusCHWWkaLcGRURzivcU3I9tv7k4JVhFcnukrM5Rlk2rUywdZuzYAZ+kbZqWCnfN3g==", + "dependencies": { + "entities": "^2.2.0" + }, + "bin": { + "ansi-to-html": "bin/ansi-to-html" + }, + "engines": { + "node": ">=8.0.0" + } + }, "node_modules/anymatch": { "version": "3.1.3", "resolved": "https://registry.npmjs.org/anymatch/-/anymatch-3.1.3.tgz", @@ -7205,6 +7227,11 @@ "resolved": "https://registry.npmjs.org/didyoumean/-/didyoumean-1.2.2.tgz", "integrity": "sha512-gxtyfqMg7GKyhQmb056K7M3xszy/myH8w+B4RT+QXBQsvAOdc3XymqDDPHx1BgPgsdAA5SIifona89YtRATDzw==" }, + "node_modules/diff-match-patch": { + "version": "1.0.5", + "resolved": "https://registry.npmjs.org/diff-match-patch/-/diff-match-patch-1.0.5.tgz", + "integrity": "sha512-IayShXAgj/QMXgB0IWmKx+rOPuGMhqm5w6jvFxmVenXKIzRqTAAsbBPT3kWQeGANj3jGgvcvv4yK6SxqYmikgw==" + }, "node_modules/diff-sequences": { "version": "27.5.1", "resolved": "https://registry.npmjs.org/diff-sequences/-/diff-sequences-27.5.1.tgz", @@ -12409,6 +12436,16 @@ "resolved": "https://registry.npmjs.org/lodash.debounce/-/lodash.debounce-4.0.8.tgz", "integrity": "sha512-FT1yDzDYEoYWhnSGnpE/4Kj1fLZkDFyqRb7fNt6FdYOSxlUWAtp42Eh6Wb0rGIv/m9Bgo7x4GhQbm5Ys4SG5ow==" }, + "node_modules/lodash.get": { + "version": "4.4.2", + "resolved": "https://registry.npmjs.org/lodash.get/-/lodash.get-4.4.2.tgz", + "integrity": "sha512-z+Uw/vLuy6gQe8cfaFWD7p0wVv8fJl3mbzXh33RS+0oW2wvUqiRXiQ69gLWSLpgB5/6sU+r6BlQR0MBILadqTQ==" + }, + "node_modules/lodash.isequal": { + "version": "4.5.0", + "resolved": "https://registry.npmjs.org/lodash.isequal/-/lodash.isequal-4.5.0.tgz", + "integrity": "sha512-pDo3lu8Jhfjqls6GkMgpahsF9kCyayhgykjyLMNFTKWrpVdAQtYyB4muAMWozBB4ig/dtWAmsMxLEI8wuz+DYQ==" + }, "node_modules/lodash.memoize": { "version": "4.1.2", "resolved": "https://registry.npmjs.org/lodash.memoize/-/lodash.memoize-4.1.2.tgz", @@ -14803,6 +14840,22 @@ "node": ">=0.10.0" } }, + "node_modules/react-ace": { + "version": "10.1.0", + "resolved": "https://registry.npmjs.org/react-ace/-/react-ace-10.1.0.tgz", + "integrity": "sha512-VkvUjZNhdYTuKOKQpMIZi7uzZZVgzCjM7cLYu6F64V0mejY8a2XTyPUIMszC6A4trbeMIHbK5fYFcT/wkP/8VA==", + "dependencies": { + "ace-builds": "^1.4.14", + "diff-match-patch": "^1.0.5", + "lodash.get": "^4.4.2", + "lodash.isequal": "^4.5.0", + "prop-types": "^15.7.2" + }, + "peerDependencies": { + "react": "^0.13.0 || ^0.14.0 || ^15.0.1 || ^16.0.0 || ^17.0.0 || ^18.0.0", + "react-dom": "^0.13.0 || ^0.14.0 || ^15.0.1 || ^16.0.0 || ^17.0.0 || ^18.0.0" + } + }, "node_modules/react-app-polyfill": { "version": "3.0.0", "resolved": "https://registry.npmjs.org/react-app-polyfill/-/react-app-polyfill-3.0.0.tgz", diff --git a/src/frontend/package.json b/src/frontend/package.json index 312e52051..b669569e3 100644 --- a/src/frontend/package.json +++ b/src/frontend/package.json @@ -16,9 +16,12 @@ "@types/node": "^16.18.12", "@types/react": "^18.0.27", "@types/react-dom": "^18.0.10", + "ace-builds": "^1.16.0", + "ansi-to-html": "^0.7.2", "axios": "^1.3.2", "lodash": "^4.17.21", "react": "^18.2.0", + "react-ace": "^10.1.0", "react-cookie": "^4.1.1", "react-dom": "^18.2.0", "react-error-boundary": "^4.0.2", @@ -57,4 +60,4 @@ ] }, "proxy": "http://backend:7860" -} +} \ No newline at end of file diff --git a/src/frontend/public/favicon.ico b/src/frontend/public/favicon.ico new file mode 100644 index 000000000..0612ba9b3 Binary files /dev/null and b/src/frontend/public/favicon.ico differ diff --git a/src/frontend/public/index.html b/src/frontend/public/index.html index 57757fb21..b2e9c4b82 100644 --- a/src/frontend/public/index.html +++ b/src/frontend/public/index.html @@ -4,7 +4,8 @@ - LangFLow + + LangFlow diff --git a/src/frontend/src/CustomNodes/GenericNode/components/parameterComponent/index.tsx b/src/frontend/src/CustomNodes/GenericNode/components/parameterComponent/index.tsx index c1a6d5f5f..6bf038066 100644 --- a/src/frontend/src/CustomNodes/GenericNode/components/parameterComponent/index.tsx +++ b/src/frontend/src/CustomNodes/GenericNode/components/parameterComponent/index.tsx @@ -10,6 +10,10 @@ import { typesContext } from "../../../../contexts/typesContext"; import { ParameterComponentType } from "../../../../types/components"; import FloatComponent from "../../../../components/floatComponent"; import Dropdown from "../../../../components/dropdownComponent"; +import CodeAreaComponent from "../../../../components/codeAreaComponent"; +import InputFileComponent from "../../../../components/inputFileComponent"; +import { TabsContext } from "../../../../contexts/tabsContext"; +import IntComponent from "../../../../components/intComponent"; export default function ParameterComponent({ left, @@ -42,6 +46,7 @@ export default function ParameterComponent({ const { reactFlowInstance } = useContext(typesContext); let disabled = reactFlowInstance?.getEdges().some((e) => e.targetHandle === id) ?? false; + const { save } = useContext(TabsContext); return (
<> -
+
{title} {required ? " *" : ""}
- {left && (type === "str" || type === "bool" || type === "float") ? ( + {left && + (type === "str" || + type === "bool" || + type === "float" || + type === "code" || + type === "file" || + type === "int") ? ( <> ) : ( @@ -91,6 +102,7 @@ export default function ParameterComponent({ } onChange={(t: string[]) => { data.node.template[name].value = t; + save(); }} /> ) : data.node.template[name].multiline ? ( @@ -99,15 +111,17 @@ export default function ParameterComponent({ value={data.node.template[name].value ?? ""} onChange={(t: string) => { data.node.template[name].value = t; + save(); }} /> ) : ( { data.node.template[name].value = t; + save(); }} /> )} @@ -120,6 +134,7 @@ export default function ParameterComponent({ setEnabled={(t) => { data.node.template[name].value = t; setEnabled(t); + save(); }} />
@@ -129,6 +144,7 @@ export default function ParameterComponent({ value={data.node.template[name].value ?? ""} onChange={(t) => { data.node.template[name].value = t; + save(); }} /> ) : left === true && @@ -136,9 +152,41 @@ export default function ParameterComponent({ data.node.template[name].options ? ( data.node.template[name].value=newValue} - value={data.node.template[name].value??"chose an option"} + onSelect={(newValue) => (data.node.template[name].value = newValue)} + value={data.node.template[name].value ?? "Choose an option"} > + ) : left === true && type === "code" ? ( + { + data.node.template[name].value = t; + save(); + }} + /> + ) : left === true && type === "file" ? ( + { + data.node.template[name].value = t; + }} + fileTypes={data.node.template[name].fileTypes} + suffixes={data.node.template[name].suffixes} + onFileChange={(t: string) => { + data.node.template[name].content = t; + save(); + }} + > + ) : left === true && type === "int" ? ( + { + data.node.template[name].value = t; + save(); + }} + /> ) : ( <> )} diff --git a/src/frontend/src/CustomNodes/GenericNode/index.tsx b/src/frontend/src/CustomNodes/GenericNode/index.tsx index ddb9524ee..ff13af901 100644 --- a/src/frontend/src/CustomNodes/GenericNode/index.tsx +++ b/src/frontend/src/CustomNodes/GenericNode/index.tsx @@ -23,7 +23,6 @@ export default function GenericNode({ const { types, deleteNode } = useContext(typesContext); const Icon = nodeIcons[types[data.type]]; if (!Icon) { - console.log(data); if (showError.current) { setErrorData({ title: data.type @@ -32,9 +31,9 @@ export default function GenericNode({ }); showError.current = false; } + deleteNode(data.id); return; } - return (
(
{idx === 0 ? ( -
- Inputs: +
+ !key.startsWith("_") && data.node.template[key].show + ).length === 0 + ? "hidden" + : "" + )} + > + Inputs
) : ( <> @@ -85,7 +94,13 @@ export default function GenericNode({ nodeColors[types[data.node.template[t].type]] ?? nodeColors.unknown } - title={snakeToNormalCase(t)} + title={ + data.node.template[t].display_name + ? data.node.template[t].display_name + : data.node.template[t].name + ? snakeToNormalCase(data.node.template[t].name) + : snakeToNormalCase(t) + } name={t} tooltipTitle={ "Type: " + @@ -103,7 +118,7 @@ export default function GenericNode({
))}
- Output: + Output
diff --git a/src/frontend/src/components/chatComponent/chatMessage/index.tsx b/src/frontend/src/components/chatComponent/chatMessage/index.tsx index e26a8c05d..aa2a0d080 100644 --- a/src/frontend/src/components/chatComponent/chatMessage/index.tsx +++ b/src/frontend/src/components/chatComponent/chatMessage/index.tsx @@ -2,6 +2,8 @@ import { ChatBubbleLeftEllipsisIcon, ChatBubbleOvalLeftEllipsisIcon, PlusSmallIc import { useState } from "react"; import { ChatMessageType } from "../../../types/chat"; import { nodeColors } from "../../../utils"; +var Convert = require('ansi-to-html'); +var convert = new Convert({newline:true}); export default function ChatMessage({ chat }: { chat: ChatMessageType }) { const [hidden, setHidden] = useState(true); @@ -27,7 +29,7 @@ export default function ChatMessage({ chat }: { chat: ChatMessageType }) { style={{ backgroundColor: nodeColors["thought"] }} className=" text-start inline-block w-full pb-3 pt-3 px-5 cursor-pointer" dangerouslySetInnerHTML={{ - __html: chat.thought.replace(/\n/g, "
"), + __html: convert.toHtml(chat.thought) }} >
)} diff --git a/src/frontend/src/components/chatComponent/index.tsx b/src/frontend/src/components/chatComponent/index.tsx index 037c9cdaf..59cc54b4c 100644 --- a/src/frontend/src/components/chatComponent/index.tsx +++ b/src/frontend/src/components/chatComponent/index.tsx @@ -5,8 +5,14 @@ import { PaperAirplaneIcon, XMarkIcon, } from "@heroicons/react/24/outline"; -import { MouseEventHandler, useContext, useEffect, useRef, useState } from "react"; -import { sendAll } from "../../controllers/NodesServices"; +import { + MouseEventHandler, + useContext, + useEffect, + useRef, + useState, +} from "react"; +import { sendAll } from "../../controllers/API"; import { alertContext } from "../../contexts/alertContext"; import { classNames, nodeColors } from "../../utils"; import { TabsContext } from "../../contexts/tabsContext"; @@ -16,7 +22,8 @@ import ChatMessage from "./chatMessage"; const _ = require("lodash"); export default function Chat({ flow, reactFlowInstance }: ChatType) { - const { updateFlow,lockChat,setLockChat,flows,tabIndex } = useContext(TabsContext); + const { updateFlow, lockChat, setLockChat, flows, tabIndex } = + useContext(TabsContext); const [saveChat, setSaveChat] = useState(false); const [open, setOpen] = useState(true); const [chatValue, setChatValue] = useState(""); @@ -25,14 +32,14 @@ export default function Chat({ flow, reactFlowInstance }: ChatType) { const addChatHistory = ( message: string, isSend: boolean, - thought?: string, + thought?: string ) => { let tabsChange = false; setChatHistory((old) => { let newChat = _.cloneDeep(old); - if(JSON.stringify(flow.chat) !==JSON.stringify(old)){ - tabsChange = true - return old + if (JSON.stringify(flow.chat) !== JSON.stringify(old)) { + tabsChange = true; + return old; } if (thought) { newChat.push({ message, isSend, thought }); @@ -41,12 +48,17 @@ export default function Chat({ flow, reactFlowInstance }: ChatType) { } return newChat; }); - if(tabsChange){ - if(thought){ - updateFlow({..._.cloneDeep(flow),chat:[...flow.chat,{isSend,message,thought}]}) - } - else{ - updateFlow({..._.cloneDeep(flow),chat:[...flow.chat,{isSend,message}]}) + if (tabsChange) { + if (thought) { + updateFlow({ + ..._.cloneDeep(flow), + chat: [...flow.chat, { isSend, message, thought }], + }); + } else { + updateFlow({ + ..._.cloneDeep(flow), + chat: [...flow.chat, { isSend, message }], + }); } } setSaveChat((chat) => !chat); @@ -63,25 +75,24 @@ export default function Chat({ flow, reactFlowInstance }: ChatType) { }, [chatHistory]); function validateNodes() { if ( - reactFlowInstance - .getNodes() - .some( - (n) => - n.data.node && - Object.keys(n.data.node.template).some( - (t: any) => - n.data.node.template[t].required && - n.data.node.template[t].value === "" && - n.data.node.template[t].required && - !reactFlowInstance - .getEdges() - .some( - (e) => - e.sourceHandle.split("|")[1] === t && - e.sourceHandle.split("|")[2] === n.id - ) - ) - ) + reactFlowInstance.getNodes().some( + (n) => + n.data.node && + Object.keys(n.data.node.template).some((t: any) => { + return ( + n.data.node.template[t].required && + (!n.data.node.template[t].value || + n.data.node.template[t].value === "") && + !reactFlowInstance + .getEdges() + .some( + (e) => + e.targetHandle.split("|")[1] === t && + e.targetHandle.split("|")[2] === n.id + ) + ); + }) + ) ) { return false; } @@ -97,19 +108,38 @@ export default function Chat({ flow, reactFlowInstance }: ChatType) { setChatValue(""); addChatHistory(message, true); - sendAll({ ...reactFlowInstance.toObject(), message, chatHistory,name:flow.name,description:flow.description}) + sendAll({ + ...reactFlowInstance.toObject(), + message, + chatHistory, + name: flow.name, + description: flow.description, + }) .then((r) => { addChatHistory(r.data.result, false, r.data.thought); setLockChat(false); }) .catch((error) => { - setErrorData({ title: error.message ?? "unknow error" }); + setErrorData({ + title: error.message ?? "Unknown Error", + list: [error.response.data.detail], + }); setLockChat(false); + let lastMessage; + setChatHistory((chatHistory) => { + let newChat = chatHistory; + + lastMessage = newChat.pop().message; + return newChat; + }); + setChatValue(lastMessage); }); } else { setErrorData({ title: "Error sending message", - list: [ "Oops! Looks like you missed some required information. Please fill in all the required fields before continuing."], + list: [ + "Oops! Looks like you missed some required information. Please fill in all the required fields before continuing.", + ], }); } } else { @@ -120,8 +150,8 @@ export default function Chat({ flow, reactFlowInstance }: ChatType) { } } function clearChat() { - setChatHistory([]) - updateFlow({ ..._.cloneDeep(flow), chat: []}); + setChatHistory([]); + updateFlow({ ..._.cloneDeep(flow), chat: [] }); } return ( @@ -151,9 +181,10 @@ export default function Chat({ flow, reactFlowInstance }: ChatType) { /> Chat
- +
+ + ); +} diff --git a/src/frontend/src/components/dropdownComponent/index.tsx b/src/frontend/src/components/dropdownComponent/index.tsx index d1a0f47b8..1bd163310 100644 --- a/src/frontend/src/components/dropdownComponent/index.tsx +++ b/src/frontend/src/components/dropdownComponent/index.tsx @@ -5,7 +5,7 @@ import { DropDownComponentType } from "../../types/components"; import { classNames } from "../../utils"; export default function Dropdown({value, options, onSelect}:DropDownComponentType) { - let [internalValue,setInternalValue] = useState(value??"choose an option") + let [internalValue,setInternalValue] = useState(value===""||!value?"Choose an option":value) return ( <> { diff --git a/src/frontend/src/components/inputFileComponent/index.tsx b/src/frontend/src/components/inputFileComponent/index.tsx new file mode 100644 index 000000000..36d13e918 --- /dev/null +++ b/src/frontend/src/components/inputFileComponent/index.tsx @@ -0,0 +1,85 @@ +import { DocumentMagnifyingGlassIcon } from "@heroicons/react/24/outline"; +import { useContext, useEffect, useState } from "react"; +import { alertContext } from "../../contexts/alertContext"; +import { FileComponentType } from "../../types/components"; + +export default function InputFileComponent({ + value, + onChange, + disabled, + suffixes, + fileTypes, + onFileChange +}: FileComponentType) { + const [myValue, setMyValue] = useState(value); + const { setErrorData } = useContext(alertContext); + useEffect(() => { + if (disabled) { + setMyValue(""); + onChange(""); + onFileChange("") + } + }, [disabled, onChange]); + + function attachFile(fileReadEvent: ProgressEvent) { + fileReadEvent.preventDefault(); + const file = fileReadEvent.target.result; + onFileChange(file as string) + } + + function checkFileType(fileName:string):boolean{ + for (let index = 0; index < suffixes.length; index++) { + if(fileName.endsWith(suffixes[index])){ + return true + } + } + return false + } + + const handleButtonClick = () => { + const input = document.createElement("input"); + input.type = "file"; + input.accept = suffixes.join(","); + input.style.display = "none"; + input.multiple = false; + input.onchange = (e: Event) => { + const file = (e.target as HTMLInputElement).files?.[0]; + const fileData = new FileReader(); + fileData.onload = attachFile; + if (file && checkFileType(file.name)) { + fileData.readAsDataURL(file); + setMyValue(file.name); + onChange(file.name); + } else { + setErrorData({ + title: + "Please select a valid file. Only files this files are allowed:", + list: fileTypes, + }); + } + }; + input.click(); + }; + + return ( +
+
+ + {myValue !== "" ? myValue : "No file"} + + +
+
+ ); +} diff --git a/src/frontend/src/components/intComponent/index.tsx b/src/frontend/src/components/intComponent/index.tsx new file mode 100644 index 000000000..520a78c0d --- /dev/null +++ b/src/frontend/src/components/intComponent/index.tsx @@ -0,0 +1,38 @@ +import { useEffect, useState } from "react"; +import { FloatComponentType } from "../../types/components"; + +export default function IntComponent({ + value, + onChange, + disabled, +}: FloatComponentType) { + const [myValue, setMyValue] = useState(value ?? ""); + useEffect(() => { + if (disabled) { + setMyValue(""); + onChange(""); + } + }, [disabled, onChange]); + return ( +
+ { + if (event.key !== 'Backspace' && event.key !== 'Enter' && event.key !== 'Delete' && event.key !== 'ArrowLeft' && event.key !== 'ArrowRight' && !/^[-]?\d*$/.test(event.key)) { + event.preventDefault(); + } + }} + type="number" + value={myValue} + className={ + "block w-full form-input dark:bg-gray-900 arrow-hide dark:border-gray-600 rounded-md border-gray-300 shadow-sm focus:border-indigo-500 focus:ring-indigo-500 sm:text-sm" + + (disabled ? " bg-gray-200 dark:bg-gray-700" : "") + } + placeholder="Type a integer number" + onChange={(e) => { + setMyValue(e.target.value); + onChange(e.target.value); + }} + /> +
+ ); +} diff --git a/src/frontend/src/components/textAreaComponent/index.tsx b/src/frontend/src/components/textAreaComponent/index.tsx index 144c0c66a..df61e1ae1 100644 --- a/src/frontend/src/components/textAreaComponent/index.tsx +++ b/src/frontend/src/components/textAreaComponent/index.tsx @@ -1,6 +1,7 @@ import { ArrowTopRightOnSquareIcon } from "@heroicons/react/24/outline"; import { useContext, useEffect, useState } from "react"; import { PopUpContext } from "../../contexts/popUpContext"; +import CodeAreaModal from "../../modals/codeAreaModal"; import TextAreaModal from "../../modals/textAreaModal"; import { TextAreaComponentType } from "../../types/components"; @@ -9,8 +10,8 @@ export default function TextAreaComponent({ value, onChange, disabled }:TextArea const { openPopUp } = useContext(PopUpContext); useEffect(() => { if (disabled) { - setMyValue([""]); - onChange([""]); + setMyValue(""); + onChange(""); } }, [disabled, onChange]); return ( diff --git a/src/frontend/src/contexts/tabsContext.tsx b/src/frontend/src/contexts/tabsContext.tsx index 10c0b943e..a11f0339d 100644 --- a/src/frontend/src/contexts/tabsContext.tsx +++ b/src/frontend/src/contexts/tabsContext.tsx @@ -5,6 +5,7 @@ import { normalCaseToSnakeCase } from "../utils"; import { alertContext } from "./alertContext"; const TabsContextInitialValue: TabsContextType = { + save:()=>{}, tabIndex: 0, setTabIndex: (index: number) => {}, flows: [], @@ -16,7 +17,7 @@ const TabsContextInitialValue: TabsContextType = { uploadFlow: () => {}, lockChat: false, setLockChat:(prevState:boolean)=>{}, - hardReset:()=>{} + hardReset:()=>{}, }; export const TabsContext = createContext( @@ -35,15 +36,20 @@ export function TabsProvider({ children }: { children: ReactNode }) { newNodeId.current = newNodeId.current + 1; return newNodeId.current; } + function save(){ + if (flows.length !== 0) + window.localStorage.setItem( + "tabsData", + JSON.stringify({ tabIndex, flows, id, nodeId: newNodeId.current }) + ); + } useEffect(() => { //save tabs locally - if (flows.length !== 0) - window.localStorage.setItem( - "tabsData", - JSON.stringify({ tabIndex, flows, id, nodeId: newNodeId.current }) - ); + save() }, [flows, id, tabIndex, newNodeId]); + + useEffect(() => { //get tabs locally saved let cookie = window.localStorage.getItem("tabsData"); @@ -177,6 +183,7 @@ export function TabsProvider({ children }: { children: ReactNode }) { return ( > { export async function sendAll(data:sendAllProps) { return await axios.post(`/predict`, data); +} + +export async function checkCode(code:string):Promise>{ + + return await axios.post('/validate/code',{code}) } \ No newline at end of file diff --git a/src/frontend/src/modals/codeAreaModal/index.tsx b/src/frontend/src/modals/codeAreaModal/index.tsx new file mode 100644 index 000000000..72b40ed40 --- /dev/null +++ b/src/frontend/src/modals/codeAreaModal/index.tsx @@ -0,0 +1,178 @@ +import { Dialog, Transition } from "@headlessui/react"; +import { XMarkIcon, CommandLineIcon } from "@heroicons/react/24/outline"; +import { Fragment, useContext, useRef, useState } from "react"; +import { PopUpContext } from "../../contexts/popUpContext"; +import AceEditor from "react-ace"; +import "ace-builds/src-noconflict/mode-python"; +import "ace-builds/src-noconflict/theme-github"; +import "ace-builds/src-noconflict/theme-twilight"; +import "ace-builds/src-noconflict/ext-language_tools"; +import "ace-builds/webpack-resolver"; +import { darkContext } from "../../contexts/darkContext"; +import { checkCode } from "../../controllers/API"; +import { alertContext } from "../../contexts/alertContext"; +export default function CodeAreaModal({ + value, + setValue, +}: { + setValue: (value: string) => void; + value: string; +}) { + const [open, setOpen] = useState(true); + const [code, setCode] = useState(value); + const { dark } = useContext(darkContext); + const { setErrorData, setSuccessData } = useContext(alertContext); + const { closePopUp } = useContext(PopUpContext); + const ref = useRef(); + function setModalOpen(x: boolean) { + setOpen(x); + if (x === false) { + setTimeout(() => { + closePopUp(); + }, 300); + } + } + return ( + + + +
+ + +
+
+ + +
+ +
+
+
+
+
+
+ + Edit Code + +
+
+
+
+
+ {/* need to insert code editor */} + { + setCode(value); + }} + className="h-full w-full rounded-lg" + /> +
+
+
+
+ +
+
+ + +
+
+
+ + ); +} diff --git a/src/frontend/src/modals/exportModal/index.tsx b/src/frontend/src/modals/exportModal/index.tsx index c1da32946..aa48f6ce1 100644 --- a/src/frontend/src/modals/exportModal/index.tsx +++ b/src/frontend/src/modals/exportModal/index.tsx @@ -26,6 +26,7 @@ export default function ExportModal() { } } const [checked,setChecked] = useState(true) + const [name,setName] = useState(flows[tabIndex].name) return ( {title}
{title}
-
{deactivate? "cooming soon":description}
+
{deactivate? "Coming soon":description}
diff --git a/src/frontend/src/pages/FlowPage/components/extraSidebarComponent/index.tsx b/src/frontend/src/pages/FlowPage/components/extraSidebarComponent/index.tsx index e489432de..0eb3876f1 100644 --- a/src/frontend/src/pages/FlowPage/components/extraSidebarComponent/index.tsx +++ b/src/frontend/src/pages/FlowPage/components/extraSidebarComponent/index.tsx @@ -1,86 +1,93 @@ import { Bars2Icon } from "@heroicons/react/24/outline"; import DisclosureComponent from "../DisclosureComponent"; -import { - nodeColors, - nodeIcons, - nodeNames, -} from "../../../../utils"; +import { nodeColors, nodeIcons, nodeNames } from "../../../../utils"; import { useContext, useEffect, useState } from "react"; -import { getAll } from "../../../../controllers/NodesServices"; +import { getAll } from "../../../../controllers/API"; import { typesContext } from "../../../../contexts/typesContext"; -import { APIClassType, APIKindType, APIObjectType } from "../../../../types/api"; +import { + APIClassType, + APIKindType, + APIObjectType, +} from "../../../../types/api"; export default function ExtraSidebar() { - const [data, setData] = useState({}); - const { setTypes} = useContext(typesContext); + const [data, setData] = useState({}); + const { setTypes } = useContext(typesContext); - useEffect(() => { - async function getTypes():Promise{ - - // Make an asynchronous API call to retrieve all data. - let result = await getAll(); - - // Update the state of the component with the retrieved data. - setData(result.data); - - // Set the types by reducing over the keys of the result data and updating the accumulator. - setTypes( - Object.keys(result.data).reduce( - (acc, curr) => { - Object.keys(result.data[curr]).forEach((c:keyof APIKindType) => { - acc[c] = curr; - // Add the base classes to the accumulator as well. - result.data[curr][c].base_classes?.forEach((b) => { - acc[b] = curr; - }); - }); - return acc; - },{} - ) - ); - } - // Call the getTypes function. - getTypes(); - }, [setTypes]); + useEffect(() => { + async function getTypes(): Promise { + // Make an asynchronous API call to retrieve all data. + let result = await getAll(); + // Update the state of the component with the retrieved data. + setData(result.data); - function onDragStart(event: React.DragEvent, data:{type:string,node?:APIClassType}) { - //start drag event - event.dataTransfer.effectAllowed = "move"; - event.dataTransfer.setData("json", JSON.stringify(data)); - } + // Set the types by reducing over the keys of the result data and updating the accumulator. + setTypes( + Object.keys(result.data).reduce((acc, curr) => { + Object.keys(result.data[curr]).forEach((c: keyof APIKindType) => { + acc[c] = curr; + // Add the base classes to the accumulator as well. + result.data[curr][c].base_classes?.forEach((b) => { + acc[b] = curr; + }); + }); + return acc; + }, {}) + ); + } + // Call the getTypes function. + getTypes(); + }, [setTypes]); - return ( -
- {Object.keys(data).map((d:keyof APIObjectType, i) => ( - -
- {Object.keys(data[d]).map((t: string, k) => ( -
-
- onDragStart(event, { - type: t, - node: data[d][t], - }) - } - > -
- {t} - -
-
-
- ))} -
- - ))} -
- ); + function onDragStart( + event: React.DragEvent, + data: { type: string; node?: APIClassType } + ) { + //start drag event + event.dataTransfer.effectAllowed = "move"; + event.dataTransfer.setData("json", JSON.stringify(data)); + } + + return ( +
+ {Object.keys(data).map((d: keyof APIObjectType, i) => ( + +
+ {Object.keys(data[d]).map((t: string, k) => ( +
+
+ onDragStart(event, { + type: t, + node: data[d][t], + }) + } + > +
+ + {t} + + +
+
+
+ ))} + {Object.keys(data[d]).length===0 &&
Coming soon
} +
+ + ))} +
+ ); } diff --git a/src/frontend/src/types/api/index.ts b/src/frontend/src/types/api/index.ts index f85605ce4..e7ab4e0da 100644 --- a/src/frontend/src/types/api/index.ts +++ b/src/frontend/src/types/api/index.ts @@ -15,4 +15,5 @@ export type sendAllProps={ message:string; chatHistory:{message:string,isSend:boolean}[], -}; \ No newline at end of file +}; +export type errorsTypeAPI={function:{errors:Array},imports:{errors:Array}} \ No newline at end of file diff --git a/src/frontend/src/types/components/index.ts b/src/frontend/src/types/components/index.ts index 99a5dabe9..9ba96d8c0 100644 --- a/src/frontend/src/types/components/index.ts +++ b/src/frontend/src/types/components/index.ts @@ -36,7 +36,16 @@ export type InputListComponentType = { export type TextAreaComponentType = { disabled: boolean; onChange: (value: string[] | string) => void; - value: string[] | string; + value: string; +}; + +export type FileComponentType = { + disabled: boolean; + onChange: (value: string[] | string) => void; + value: string; + suffixes:Array; + fileTypes:Array; + onFileChange:(value: string) => void; }; export type DisclosureComponentType = { diff --git a/src/frontend/src/types/tabs/index.ts b/src/frontend/src/types/tabs/index.ts index e872a3f58..625073fb3 100644 --- a/src/frontend/src/types/tabs/index.ts +++ b/src/frontend/src/types/tabs/index.ts @@ -1,6 +1,7 @@ import { FlowType } from "../flow"; export type TabsContextType = { + save:()=>void; tabIndex: number; setTabIndex: (index: number) => void; flows: Array; diff --git a/src/frontend/src/utils.ts b/src/frontend/src/utils.ts index 796cd6de4..795f5f058 100644 --- a/src/frontend/src/utils.ts +++ b/src/frontend/src/utils.ts @@ -5,9 +5,10 @@ import { LightBulbIcon, CommandLineIcon, WrenchScrewdriverIcon, + WrenchIcon, ComputerDesktopIcon, Bars3CenterLeftIcon, - PaperClipIcon, + GiftIcon, QuestionMarkCircleIcon, } from "@heroicons/react/24/outline"; import { Connection, Edge, Node, ReactFlowInstance } from "reactflow"; @@ -88,6 +89,8 @@ export const nodeNames:{[char: string]: string} = { advanced: "Advanced", chat: "Chat", docloaders:"Document Loader", + toolkits:"Toolkits", + wrappers:"Wrappers", unknown:"Unknown" }; @@ -97,10 +100,12 @@ export const nodeIcons:{[char: string]: React.ForwardRefExoticComponent'}", + "password": false, + "name": "headers", + "type": "code", + "list": false + }, + "aiosession": { + "required": false, + "placeholder": "", + "show": false, + "multiline": false, + "password": false, + "name": "aiosession", + "type": "ClientSession", + "list": false + }, + "_type": "TextRequestsWrapper" + }, + "description": "Lightweight wrapper around requests library.", + "base_classes": [ + "TextRequestsWrapper" + ] + }, + "id": "dndnode_34", + "value": null + }, + "positionAbsolute": { + "x": 301.30887344475104, + "y": 532.9920887988924 + } + }, + { + "width": 384, + "height": 407, + "id": "dndnode_35", + "type": "genericNode", + "position": { + "x": -754.691126555249, + "y": -37.00791120110762 + }, + "data": { + "type": "JsonSpec", + "node": { + "template": { + "dict_": { + "required": true, + "placeholder": "", + "show": true, + "multiline": false, + "value": "api-with-examples.yaml", + "suffixes": [ + ".json", + ".yaml", + ".yml" + ], + "password": false, + "name": "dict_", + "type": "file", + "list": false, + "fileTypes": [ + "json", + "yaml", + "yml" + ], + "content": "data:application/x-yaml;base64,openapi: "3.0.0"
info:
  title: Simple API overview
  version: 2.0.0
paths:
  /:
    get:
      operationId: listVersionsv2
      summary: List API versions
      responses:
        '200':
          description: |-
            200 response
          content:
            application/json:
              examples: 
                foo:
                  value:
                    {
                      "versions": [
                        {
                            "status": "CURRENT",
                            "updated": "2011-01-21T11:33:21Z",
                            "id": "v2.0",
                            "links": [
                                {
                                    "href": "http://127.0.0.1:8774/v2/",
                                    "rel": "self"
                                }
                            ]
                        },
                        {
                            "status": "EXPERIMENTAL",
                            "updated": "2013-07-23T11:33:21Z",
                            "id": "v3.0",
                            "links": [
                                {
                                    "href": "http://127.0.0.1:8774/v3/",
                                    "rel": "self"
                                }
                            ]
                        }
                      ]
                    }
        '300':
          description: |-
            300 response
          content:
            application/json: 
              examples: 
                foo:
                  value: |
                   {
                    "versions": [
                          {
                            "status": "CURRENT",
                            "updated": "2011-01-21T11:33:21Z",
                            "id": "v2.0",
                            "links": [
                                {
                                    "href": "http://127.0.0.1:8774/v2/",
                                    "rel": "self"
                                }
                            ]
                        },
                        {
                            "status": "EXPERIMENTAL",
                            "updated": "2013-07-23T11:33:21Z",
                            "id": "v3.0",
                            "links": [
                                {
                                    "href": "http://127.0.0.1:8774/v3/",
                                    "rel": "self"
                                }
                            ]
                        }
                    ]
                   }
  /v2:
    get:
      operationId: getVersionDetailsv2
      summary: Show API version details
      responses:
        '200':
          description: |-
            200 response
          content:
            application/json: 
              examples:
                foo:
                  value:
                    {
                      "version": {
                        "status": "CURRENT",
                        "updated": "2011-01-21T11:33:21Z",
                        "media-types": [
                          {
                              "base": "application/xml",
                              "type": "application/vnd.openstack.compute+xml;version=2"
                          },
                          {
                              "base": "application/json",
                              "type": "application/vnd.openstack.compute+json;version=2"
                          }
                        ],
                        "id": "v2.0",
                        "links": [
                          {
                              "href": "http://127.0.0.1:8774/v2/",
                              "rel": "self"
                          },
                          {
                              "href": "http://docs.openstack.org/api/openstack-compute/2/os-compute-devguide-2.pdf",
                              "type": "application/pdf",
                              "rel": "describedby"
                          },
                          {
                              "href": "http://docs.openstack.org/api/openstack-compute/2/wadl/os-compute-2.wadl",
                              "type": "application/vnd.sun.wadl+xml",
                              "rel": "describedby"
                          },
                          {
                            "href": "http://docs.openstack.org/api/openstack-compute/2/wadl/os-compute-2.wadl",
                            "type": "application/vnd.sun.wadl+xml",
                            "rel": "describedby"
                          }
                        ]
                      }
                    }
        '203':
          description: |-
            203 response
          content:
            application/json: 
              examples:
                foo:
                  value:
                    {
                      "version": {
                        "status": "CURRENT",
                        "updated": "2011-01-21T11:33:21Z",
                        "media-types": [
                          {
                              "base": "application/xml",
                              "type": "application/vnd.openstack.compute+xml;version=2"
                          },
                          {
                              "base": "application/json",
                              "type": "application/vnd.openstack.compute+json;version=2"
                          }
                        ],
                        "id": "v2.0",
                        "links": [
                          {
                              "href": "http://23.253.228.211:8774/v2/",
                              "rel": "self"
                          },
                          {
                              "href": "http://docs.openstack.org/api/openstack-compute/2/os-compute-devguide-2.pdf",
                              "type": "application/pdf",
                              "rel": "describedby"
                          },
                          {
                              "href": "http://docs.openstack.org/api/openstack-compute/2/wadl/os-compute-2.wadl",
                              "type": "application/vnd.sun.wadl+xml",
                              "rel": "describedby"
                          }
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                      }
                    }
" + }, + "max_value_length": { + "required": false, + "placeholder": "", + "show": true, + "multiline": false, + "value": "4000", + "password": false, + "name": "max_value_length", + "type": "int", + "list": false + }, + "_type": "JsonSpec" + }, + "description": "", + "base_classes": [ + "Tool", + "JsonSpec" + ] + }, + "id": "dndnode_35", + "value": null + }, + "selected": false, + "dragging": false, + "positionAbsolute": { + "x": -754.691126555249, + "y": -37.00791120110762 + } + }, + { + "width": 384, + "height": 563, + "id": "dndnode_36", + "type": "genericNode", + "position": { + "x": -310.69112655524896, + "y": 514.9920887988924 + }, + "data": { + "type": "ChatOpenAI", + "node": { + "template": { + "cache": { + "required": false, + "placeholder": "", + "show": false, + "multiline": false, + "password": false, + "name": "cache", + "type": "bool", + "list": false + }, + "verbose": { + "required": false, + "placeholder": "", + "show": false, + "multiline": false, + "value": false, + "password": false, + "name": "verbose", + "type": "bool", + "list": false + }, + "client": { + "required": false, + "placeholder": "", + "show": false, + "multiline": false, + "password": false, + "name": "client", + "type": "Any", + "list": false + }, + "model_name": { + "required": false, + "placeholder": "", + "show": true, + "multiline": false, + "value": "gpt-3.5-turbo", + "password": false, + "name": "model_name", + "type": "str", + "list": false + }, + "model_kwargs": { + "required": false, + "placeholder": "", + "show": false, + "multiline": false, + "password": false, + "name": "model_kwargs", + "type": "code", + "list": false + }, + "openai_api_key": { + "required": false, + "placeholder": "", + "show": true, + "multiline": false, + "password": false, + "name": "openai_api_key", + "type": "str", + "list": false, + "value": "sk-" + }, + "max_retries": { + "required": false, + "placeholder": "", + "show": false, + "multiline": false, + "value": 6, + "password": false, + "name": "max_retries", + "type": "int", + "list": false + }, + "prefix_messages": { + "required": false, + "placeholder": "", + "show": false, + "multiline": false, + "password": false, + "name": "prefix_messages", + "type": "Any", + "list": true + }, + "streaming": { + "required": false, + "placeholder": "", + "show": false, + "multiline": false, + "value": false, + "password": false, + "name": "streaming", + "type": "bool", + "list": false + }, + "_type": "ChatOpenAI" + }, + "description": "Wrapper around OpenAI Chat large language models.To use, you should have the ``openai`` python package installed, and theenvironment variable ``OPENAI_API_KEY`` set with your API key.Any parameters that are valid to be passed to the openai.create call can be passedin, even if not explicitly saved on this class.", + "base_classes": [ + "BaseLanguageModel", + "BaseLLM" + ] + }, + "id": "dndnode_36", + "value": null + }, + "selected": false, + "dragging": false, + "positionAbsolute": { + "x": -310.69112655524896, + "y": 514.9920887988924 + } + } + ], + "edges": [ + { + "source": "dndnode_19", + "sourceHandle": "JsonToolkit|dndnode_19|BaseToolkit", + "target": "dndnode_33", + "targetHandle": "BaseToolkit|toolkit|dndnode_33", + "className": "animate-pulse", + "id": "reactflow__edge-dndnode_19JsonToolkit|dndnode_19|BaseToolkit-dndnode_33BaseToolkit|toolkit|dndnode_33", + "selected": false + }, + { + "source": "dndnode_33", + "sourceHandle": "JsonAgent|dndnode_33|AgentExecutor", + "target": "dndnode_32", + "targetHandle": "AgentExecutor|json_agent|dndnode_32", + "className": "animate-pulse", + "id": "reactflow__edge-dndnode_33JsonAgent|dndnode_33|AgentExecutor-dndnode_32AgentExecutor|json_agent|dndnode_32", + "selected": false + }, + { + "source": "dndnode_34", + "sourceHandle": "TextRequestsWrapper|dndnode_34|TextRequestsWrapper", + "target": "dndnode_32", + "targetHandle": "TextRequestsWrapper|requests_wrapper|dndnode_32", + "className": "animate-pulse", + "id": "reactflow__edge-dndnode_34RequestsWrapper|dndnode_34|TextRequestsWrapper-dndnode_32RequestsWrapper|requests_wrapper|dndnode_32", + "selected": false + }, + { + "source": "dndnode_35", + "sourceHandle": "JsonSpec|dndnode_35|Tool|JsonSpec", + "target": "dndnode_19", + "targetHandle": "JsonSpec|spec|dndnode_19", + "className": "animate-pulse", + "id": "reactflow__edge-dndnode_35JsonSpec|dndnode_35|Tool|JsonSpec-dndnode_19JsonSpec|spec|dndnode_19", + "selected": false + }, + { + "source": "dndnode_36", + "sourceHandle": "ChatOpenAI|dndnode_36|BaseLanguageModel|BaseLLM", + "target": "dndnode_33", + "targetHandle": "BaseLanguageModel|llm|dndnode_33", + "className": "animate-pulse", + "id": "reactflow__edge-dndnode_36OpenAIChat|dndnode_36|BaseLanguageModel|BaseLLM-dndnode_33BaseLanguageModel|llm|dndnode_33" + } + ], + "viewport": { + "x": 0, + "y": 0, + "zoom": 1 + } + }, + "chat": [ + { + "message": "test", + "isSend": true + } + ] +} \ No newline at end of file diff --git a/tests/data/example_flow.json b/tests/data/basic_example.json similarity index 71% rename from tests/data/example_flow.json rename to tests/data/basic_example.json index de3893b00..dabe16786 100644 --- a/tests/data/example_flow.json +++ b/tests/data/basic_example.json @@ -1,15 +1,16 @@ { - "name": "New Flow 9", - "id": "1", + "name": "New Flow ", + "id": "0", "data": { - "nodes": [{ + "nodes": [ + { "width": 384, "height": 391, - "id": "dndnode_61", + "id": "dndnode_1", "type": "genericNode", "position": { - "x": 764, - "y": 382 + "x": 644, + "y": 348 }, "data": { "type": "LLMChain", @@ -17,7 +18,7 @@ "template": { "_type": "llm_chain", "memory": { - "type": "Memory", + "type": "BaseMemory", "required": false, "placeholder": "", "list": false, @@ -34,7 +35,7 @@ "show": false, "password": false, "multiline": false, - "value": true + "value": false }, "prompt": { "type": "BasePromptTemplate", @@ -46,7 +47,7 @@ "multiline": false }, "llm": { - "type": "BaseLLM", + "type": "BaseLanguageModel", "required": true, "placeholder": "", "list": false, @@ -70,24 +71,22 @@ "Chain" ] }, - "id": "dndnode_61", + "id": "dndnode_1", "value": null }, - "selected": false, "positionAbsolute": { - "x": 764, - "y": 382 - }, - "dragging": false + "x": 644, + "y": 348 + } }, { "width": 384, "height": 351, - "id": "dndnode_62", + "id": "dndnode_4", "type": "genericNode", "position": { - "x": 1488, - "y": 550 + "x": 1236, + "y": 39.59999999999957 }, "data": { "type": "ZeroShotAgent", @@ -131,22 +130,22 @@ "Agent" ] }, - "id": "dndnode_62", + "id": "dndnode_4", "value": null }, "positionAbsolute": { - "x": 1488, - "y": 550 + "x": 1236, + "y": 39.59999999999957 } }, { "width": 384, "height": 529, - "id": "dndnode_63", + "id": "dndnode_5", "type": "genericNode", "position": { - "x": 206, - "y": 210 + "x": 96, + "y": -299.2000000000003 }, "data": { "type": "ZeroShotPrompt", @@ -186,24 +185,24 @@ "BasePromptTemplate" ] }, - "id": "dndnode_63", + "id": "dndnode_5", "value": null }, "selected": false, "positionAbsolute": { - "x": 206, - "y": 210 + "x": 96, + "y": -299.2000000000003 }, "dragging": false }, { "width": 384, - "height": 501, - "id": "dndnode_64", + "height": 477, + "id": "dndnode_8", "type": "genericNode", "position": { - "x": 210, - "y": 818 + "x": 75, + "y": 328 }, "data": { "type": "OpenAI", @@ -228,7 +227,7 @@ "show": false, "password": false, "multiline": false, - "value": true + "value": null }, "client": { "type": "Any", @@ -250,7 +249,6 @@ "multiline": false, "value": "text-davinci-003", "options": [ - "gpt-3.5-turbo", "text-davinci-003", "text-davinci-002" ] @@ -343,7 +341,7 @@ "show": true, "password": true, "multiline": false, - "value": "---" + "value": "sk-" }, "batch_size": { "type": "int", @@ -399,27 +397,28 @@ "description": "Generic OpenAI class that uses model name.", "base_classes": [ "BaseOpenAI", - "BaseLLM" + "BaseLLM", + "BaseLanguageModel" ] }, - "id": "dndnode_64", + "id": "dndnode_8", "value": null }, "selected": false, "positionAbsolute": { - "x": 210, - "y": 818 + "x": 75, + "y": 328 }, "dragging": false }, { "width": 384, "height": 397, - "id": "dndnode_65", + "id": "dndnode_9", "type": "genericNode", "position": { - "x": 776, - "y": 922 + "x": 643, + "y": 824 }, "data": { "type": "Serper Search", @@ -427,13 +426,13 @@ "template": { "serper_api_key": { "type": "str", - "required": false, + "required": true, "list": false, "show": true, "placeholder": "", "value": "---" }, - "_type": "google-serper" + "_type": "Serper Search" }, "name": "Serper Search", "description": "A low-cost Google Search API. Useful for when you need to answer questions about current events. Input should be a search query.", @@ -441,101 +440,66 @@ "Tool" ] }, - "id": "dndnode_65", + "id": "dndnode_9", "value": null }, - "selected": false, + "selected": true, "positionAbsolute": { - "x": 776, - "y": 922 + "x": 643, + "y": 824 }, "dragging": false } ], - "edges": [{ - "source": "dndnode_63", - "sourceHandle": "ZeroShotPrompt|dndnode_63|BasePromptTemplate", - "target": "dndnode_61", - "targetHandle": "BasePromptTemplate|prompt|dndnode_61", + "edges": [ + { + "source": "dndnode_5", + "sourceHandle": "ZeroShotPrompt|dndnode_5|BasePromptTemplate", + "target": "dndnode_1", + "targetHandle": "BasePromptTemplate|prompt|dndnode_1", "className": "animate-pulse", - "id": "reactflow__edge-dndnode_63ZeroShotPrompt|dndnode_63|BasePromptTemplate-dndnode_61BasePromptTemplate|prompt|dndnode_61" + "id": "reactflow__edge-dndnode_5ZeroShotPrompt|dndnode_5|BasePromptTemplate-dndnode_1BasePromptTemplate|prompt|dndnode_1" }, { - "source": "dndnode_64", - "sourceHandle": "OpenAI|dndnode_64|BaseOpenAI,|BaseLLM", - "target": "dndnode_61", - "targetHandle": "BaseLLM|llm|dndnode_61", + "source": "dndnode_1", + "sourceHandle": "LLMChain|dndnode_1|Chain", + "target": "dndnode_4", + "targetHandle": "LLMChain|llm_chain|dndnode_4", "className": "animate-pulse", - "id": "reactflow__edge-dndnode_64OpenAI|dndnode_64|BaseOpenAI,|BaseLLM-dndnode_61BaseLLM|llm|dndnode_61" + "id": "reactflow__edge-dndnode_1LLMChain|dndnode_1|Chain-dndnode_4LLMChain|llm_chain|dndnode_4" }, { - "source": "dndnode_65", - "sourceHandle": "Serper Search|dndnode_65|Tool", - "target": "dndnode_62", - "targetHandle": "Tool|allowed_tools|dndnode_62", + "source": "dndnode_8", + "sourceHandle": "OpenAI|dndnode_8|BaseOpenAI|BaseLLM|BaseLanguageModel", + "target": "dndnode_1", + "targetHandle": "BaseLanguageModel|llm|dndnode_1", "className": "animate-pulse", - "id": "reactflow__edge-dndnode_65Serper Search|dndnode_65|Tool-dndnode_62Tool|allowed_tools|dndnode_62" + "id": "reactflow__edge-dndnode_8OpenAI|dndnode_8|BaseOpenAI|BaseLLM|BaseLanguageModel-dndnode_1BaseLanguageModel|llm|dndnode_1" }, { - "source": "dndnode_61", - "sourceHandle": "LLMChain|dndnode_61|Chain", - "target": "dndnode_62", - "targetHandle": "LLMChain|llm_chain|dndnode_62", + "source": "dndnode_9", + "sourceHandle": "Serper Search|dndnode_9|Tool", + "target": "dndnode_4", + "targetHandle": "Tool|allowed_tools|dndnode_4", "className": "animate-pulse", - "id": "reactflow__edge-dndnode_61LLMChain|dndnode_61|Chain-dndnode_62LLMChain|llm_chain|dndnode_62" + "id": "reactflow__edge-dndnode_9Serper Search|dndnode_9|Tool-dndnode_4Tool|allowed_tools|dndnode_4" } ], "viewport": { - "x": -103, - "y": -135, - "zoom": 1 + "x": 22.5, + "y": 42, + "zoom": 0.5 } }, - "chat": [{ - "message": "What is the new llm Prismer?", + "chat": [ + { + "message": "Langflow. What is it?", "isSend": true }, { - "message": "What is the prismer llm?", - "isSend": true - }, - { - "message": "What is the prismer llm?", - "isSend": true - }, - { - "message": "What is the prismer llm?", - "isSend": true - }, - { - "message": "What is the prismer llm?", - "isSend": true - }, - { - "message": "What is the prismer llm?", - "isSend": true - }, - { - "message": "What is the prismer llm?", - "isSend": true - }, - { - "message": "What is the prismer llm?", - "isSend": true - }, - { - "message": "Prismer LLM is a vision-language model with an ensemble of experts.", + "message": "Langflow is a GUI for LangChain, designed with react-flow to provide an effortless way to experiment and prototype flows with drag-and-drop components and a chrome extension powered by GPT-3.", "isSend": false, - "thought": "Thought: I should research what this is\nAction: Serper Search\nAction Input: \"prismer llm\"\nObservation: Announcing Prismer, my team's latest vision-language AI, empowered by domain-expert models in depth, surface normal, segmentation, etc. My team's work, Prismer, is a representative example. We use a textual LM as the backbone, and plug in many visual domain experts through a neural adapter ... “Prismer is able to achieve superior data efficiency compared to ... New OpenAI post about future of ChatGPT's (and its other LLM) alignment. Prismer: A Vision-Language Model with An Ensemble of Experts ... UDAPDR: Unsupervised Domain Adaptation via LLM Prompting and Distillation of Rerankers. Emory Law's LLM program offers graduates of US and foreign law schools the opportunity to pursue the master of laws degree from one of the top nationally ranked ... Missing: prismer | Must include:prismer. Prismer: A Vision-Language Model with An Ensemble of Experts ... github: github.com/NVlabs/prismer ... into LLM embedding space viously to their being sworn, the prisoner, Michael. Solomon, standing at the bar, challenged two ... a« wa« brought before- them.\" The prisoner ?llM'eow*. RWKV is a RNN with transformer-level LLM performance. ... The implementation of \"Prismer: A Vision-Language Model with An Ensemble of Experts\". Announcing Prismer, our NVIDIA team's latest vision-language #ai empowered by domain-expert models in depth, surface normal, segmentation, etc. No paywall.\nThought:\n\n> Finished chain." - }, - { - "message": "What is the prismer llm?", - "isSend": true - }, - { - "message": "Prismer is an AI vision-language model with an ensemble of experts developed by a NVIDIA team. It is capable of achieving superior data efficiency compared to other models and can be directly trained like a GPT.", - "isSend": false, - "thought": "Thought: I need to search for the term\nAction: Serper Search\nAction Input: \"prismer llm\"\nObservation: Announcing Prismer, my team's latest vision-language AI, empowered by domain-expert models in depth, surface normal, segmentation, etc. My team's work, Prismer, is a representative example. We use a textual LM as the backbone, and plug in many visual domain experts through a neural adapter ... “Prismer is able to achieve superior data efficiency compared to ... New OpenAI post about future of ChatGPT's (and its other LLM) alignment. Prismer: A Vision-Language Model with An Ensemble of Experts ... UDAPDR: Unsupervised Domain Adaptation via LLM Prompting and Distillation of Rerankers. A very insightful post about the potential issues with LLM chatbots for general ... Announcing Prismer, our NVIDIA team's latest vision-language #ai ... As technology continues to transform our economy and culture, businesses need a new breed of lawyers who understand the legal and commercial aspects of new ... Missing: prismer | Must include:prismer. RWKV is a RNN with transformer-level LLM performance. ... The implementation of \"Prismer: A Vision-Language Model with An Ensemble of Experts\". ... Bendix vorad collision warning system, Vegglampe med prismer, Ok magazine ... Alabama llm, Colt 25 caliber magazine, Poppie clinch, Mcds upper school, ... RWKV is an RNN with transformer-level LLM performance. It can be directly trained like a GPT (parallelizable). So it's combining the best of RNN ...\nThought:\n\n> Finished chain." + "thought": "> Entering new AgentExecutor chain...\n I need to research what Langflow is.\nAction: Serper Search\nAction Input: \"What is Langflow?\"\nObservation: Researchers introduce LangFlow, a graphical user interface (GUI) for LangChain that simplifies testing and creation of smart applications. The drag-and-drop feature provides a quick and effortless way to experiment and prototype, and the built-in chat interface enables real-time ... LangFlow is a GUI for LangChain, designed with react-flow to provide an effortless way to experiment and prototype flows with drag-and-drop ... LangFlow is a GUI for LangChain, designed with react-flow to provide an effortless way to experiment and prototype flows with drag-and-drop components and a ... an open-source chrome extension powered by GPT-3 to get the explanation of a piece of code on any site (Stackoverflow, Github, etc). ChatGPT correctly identified Mona Lisa but failed to guess the cartoon character Michael was thinking of, suggesting Bugs Bunny holding a ... Introducing LangFlow! An open-source UI for. @LangChainAI. that enables seamless combination of multiple deep learning models to perform ... See student reviews, school photos, Chinese Mandarin courses, and housing options available at Langflow Education Centre (Macau, Macau) - Reviews - Language ... Longfellow Elementary students play on the playground during recess. Longfellow Elementary students pause for a photo during class.\nThought: Langflow is a GUI for LangChain, designed with react-flow to provide an effortless way to experiment and prototype flows with drag-and-drop components and a chrome extension powered by GPT-3.\nFinal Answer: Langflow is a GUI for LangChain, designed with react-flow to provide an effortless way to experiment and prototype flows with drag-and-drop components and a chrome extension powered by GPT-3.\n\n> Finished chain." } ] } \ No newline at end of file diff --git a/tests/data/complex_example.json b/tests/data/complex_example.json new file mode 100644 index 000000000..8d46a97d6 --- /dev/null +++ b/tests/data/complex_example.json @@ -0,0 +1,723 @@ +{ + "name": "New Flow", + "id": "0", + "data": { + "nodes": [ + { + "width": 384, + "height": 351, + "id": "dndnode_3", + "type": "genericNode", + "position": { + "x": 612.9299322834961, + "y": 194.75070242078417 + }, + "data": { + "type": "ZeroShotAgent", + "node": { + "template": { + "_type": "zero-shot-react-description", + "llm_chain": { + "type": "LLMChain", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "password": false, + "multiline": false + }, + "allowed_tools": { + "type": "Tool", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "password": false, + "multiline": false, + "value": null + }, + "return_values": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": false, + "password": false, + "multiline": false, + "value": [ + "output" + ] + } + }, + "description": "Agent for the MRKL chain.", + "base_classes": [ + "Agent", + "function" + ] + }, + "id": "dndnode_3", + "value": null + }, + "selected": false, + "positionAbsolute": { + "x": 612.9299322834961, + "y": 194.75070242078417 + }, + "dragging": false + }, + { + "width": 384, + "height": 463, + "id": "dndnode_27", + "type": "genericNode", + "position": { + "x": 86.29922452047686, + "y": 39.132143332238115 + }, + "data": { + "type": "Tool", + "node": { + "template": { + "name": { + "type": "str", + "required": true, + "list": false, + "show": true, + "placeholder": "", + "value": "Uppercase", + "password": false, + "multiline": false + }, + "description": { + "type": "str", + "required": true, + "list": false, + "show": true, + "placeholder": "", + "value": "Returns an uppercase version of the text passed.", + "password": false, + "multiline": false + }, + "func": { + "type": "function", + "required": true, + "list": false, + "show": true, + "value": "", + "multiline": false, + "password": false + }, + "_type": "Tool" + }, + "name": "Tool", + "func": "", + "description": "", + "base_classes": [ + "Tool" + ] + }, + "id": "dndnode_27", + "value": null + }, + "selected": false, + "positionAbsolute": { + "x": 86.29922452047686, + "y": 39.132143332238115 + }, + "dragging": false + }, + { + "width": 384, + "height": 463, + "id": "dndnode_28", + "type": "genericNode", + "position": { + "x": 1134.4549802672202, + "y": 287.9885910233929 + }, + "data": { + "type": "Tool", + "node": { + "template": { + "name": { + "type": "str", + "required": true, + "list": false, + "show": true, + "placeholder": "", + "value": "", + "password": false, + "multiline": false + }, + "description": { + "type": "str", + "required": true, + "list": false, + "show": true, + "placeholder": "", + "value": "", + "password": false, + "multiline": false + }, + "func": { + "type": "function", + "required": true, + "list": false, + "show": true, + "value": "", + "multiline": false, + "password": false + }, + "_type": "Tool" + }, + "name": "Tool", + "func": "", + "description": "", + "base_classes": [ + "Tool" + ] + }, + "id": "dndnode_28", + "value": null + }, + "positionAbsolute": { + "x": 1134.4549802672202, + "y": 287.9885910233929 + }, + "selected": false, + "dragging": false + }, + { + "width": 384, + "height": 357, + "id": "dndnode_40", + "type": "genericNode", + "position": { + "x": -366.4341715850213, + "y": 136.29836646158452 + }, + "data": { + "type": "PythonFunction", + "node": { + "template": { + "code": { + "required": true, + "placeholder": "", + "show": true, + "multiline": true, + "value": "\ndef upper_case(text: str) -> str:\n return text.upper()\n", + "name": "code", + "type": "str", + "list": false + }, + "_type": "python_function" + }, + "description": "Python function to be executed.", + "base_classes": [ + "function" + ] + }, + "id": "dndnode_40", + "value": null + }, + "selected": false, + "positionAbsolute": { + "x": -366.4341715850213, + "y": 136.29836646158452 + }, + "dragging": false + }, + { + "width": 384, + "height": 351, + "id": "dndnode_41", + "type": "genericNode", + "position": { + "x": 1642.7653281427417, + "y": 69.01105573790835 + }, + "data": { + "type": "ZeroShotAgent", + "node": { + "template": { + "_type": "zero-shot-react-description", + "llm_chain": { + "type": "LLMChain", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "password": false, + "multiline": false + }, + "allowed_tools": { + "type": "Tool", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "password": false, + "multiline": false, + "value": null + }, + "return_values": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": false, + "password": false, + "multiline": false, + "value": [ + "output" + ] + } + }, + "description": "Agent for the MRKL chain.", + "base_classes": [ + "Agent", + "function" + ] + }, + "id": "dndnode_41", + "value": null + }, + "selected": false, + "positionAbsolute": { + "x": 1642.7653281427417, + "y": 69.01105573790835 + }, + "dragging": false + }, + { + "width": 384, + "height": 529, + "id": "dndnode_42", + "type": "genericNode", + "position": { + "x": -379.23467185725826, + "y": -551.3889442620921 + }, + "data": { + "type": "ZeroShotPrompt", + "node": { + "template": { + "prefix": { + "required": false, + "placeholder": "", + "show": true, + "multiline": true, + "value": "Answer the following questions as best you can. You have access to the following tools:", + "name": "prefix", + "type": "str", + "list": false + }, + "suffix": { + "required": true, + "placeholder": "", + "show": true, + "multiline": true, + "value": "Begin!\n\nQuestion: {input}\nThought:{agent_scratchpad}", + "name": "suffix", + "type": "str", + "list": false + }, + "format_instructions": { + "required": false, + "placeholder": "", + "show": true, + "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... 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pytest.BASIC_EXAMPLE_PATH + elif _type == "complex": + path = pytest.COMPLEX_EXAMPLE_PATH + elif _type == "openapi": + path = pytest.OPENAPI_EXAMPLE_PATH + + with open(path, "r") as f: + flow_graph = json.load(f) + return flow_graph["data"] + + +@pytest.fixture +def basic_data_graph(): + return get_graph() + + +@pytest.fixture +def complex_data_graph(): + return get_graph("complex") + + +@pytest.fixture +def openapi_data_graph(): + return get_graph("openapi") + + +def langchain_objects_are_equal(obj1, obj2): + return str(obj1) == str(obj2) + + +def test_cache_creation(basic_data_graph): + # Compute hash for the input data_graph + # Call process_graph function to build and cache the langchain_object + is_first_message = True + computed_hash, langchain_object = load_langchain_object( + basic_data_graph, is_first_message=is_first_message + ) + save_cache(computed_hash, langchain_object, is_first_message) + # Check if the cache file exists + cache_file = Path(tempfile.gettempdir()) / f"{PREFIX}_{computed_hash}.dill" + + assert cache_file.exists() + + +def test_cache_reuse(basic_data_graph): + # Call process_graph function to build and cache the langchain_object + result1 = load_langchain_object(basic_data_graph) + + # Call process_graph function again to use the cached langchain_object + result2 = load_langchain_object(basic_data_graph) + + # Compare the results to ensure the same langchain_object was used + assert langchain_objects_are_equal(result1, result2) diff --git a/tests/test_creators.py b/tests/test_creators.py new file mode 100644 index 000000000..5453b57eb --- /dev/null +++ b/tests/test_creators.py @@ -0,0 +1,50 @@ +from typing import Dict, List + +import pytest +from langflow.interface.agents.base import AgentCreator +from langflow.interface.base import LangChainTypeCreator + + +@pytest.fixture +def sample_lang_chain_type_creator() -> LangChainTypeCreator: + class SampleLangChainTypeCreator(LangChainTypeCreator): + type_name: str = "test_type" + + def type_to_loader_dict(self) -> Dict: # type: ignore + return {"test_type": "TestClass"} + + def to_list(self) -> List[str]: + return ["node1", "node2"] + + def get_signature(self, name: str) -> Dict: + return { + "template": {"test_field": {"type": "str"}}, + "description": "test description", + "base_classes": ["base_class1", "base_class2"], + } + + return SampleLangChainTypeCreator() + + +@pytest.fixture +def sample_agent_creator() -> AgentCreator: + return AgentCreator() + + +def test_lang_chain_type_creator_to_dict( + sample_lang_chain_type_creator: LangChainTypeCreator, +): + type_dict = sample_lang_chain_type_creator.to_dict() + assert len(type_dict) == 1 + assert "test_type" in type_dict + assert "node1" in type_dict["test_type"] + assert "node2" in type_dict["test_type"] + assert "template" in type_dict["test_type"]["node1"] + assert "description" in type_dict["test_type"]["node1"] + assert "base_classes" in type_dict["test_type"]["node1"] + + +def test_agent_creator_type_to_loader_dict(sample_agent_creator: AgentCreator): + type_to_loader_dict = sample_agent_creator.type_to_loader_dict + assert len(type_to_loader_dict) > 0 + assert "JsonAgent" diff --git a/tests/test_custom_types.py b/tests/test_custom_types.py new file mode 100644 index 000000000..f42421bd1 --- /dev/null +++ b/tests/test_custom_types.py @@ -0,0 +1,16 @@ +# Test this: +import pytest +from langflow.interface.custom.types import PythonFunction +from langflow.utils import constants + + +def test_python_function(): + """Test Python function""" + func = PythonFunction(code=constants.DEFAULT_PYTHON_FUNCTION) + assert func.get_function()("text") == "text" + # the tool decorator should raise an error if + # the function is not str -> str + + # This raises ValidationError + with pytest.raises(SyntaxError): + func = PythonFunction(code=pytest.CODE_WITH_SYNTAX_ERROR) diff --git a/tests/test_endpoints.py b/tests/test_endpoints.py new file mode 100644 index 000000000..83f6c62b1 --- /dev/null +++ b/tests/test_endpoints.py @@ -0,0 +1,123 @@ +import pytest +from fastapi.testclient import TestClient +from langflow.interface.tools.constants import CUSTOM_TOOLS + + +def test_get_all(client: TestClient): + response = client.get("/all") + assert response.status_code == 200 + json_response = response.json() + # We need to test the custom nodes + assert "ZeroShotPrompt" in json_response["prompts"] + # All CUSTOM_TOOLS(dict) should be in the response + assert all(tool in json_response["tools"] for tool in CUSTOM_TOOLS.keys()) + + +def test_post_validate_code(client: TestClient): + # Test case with a valid import and function + code1 = """ +import math + +def square(x): + return x ** 2 +""" + response1 = client.post("/validate/code", json={"code": code1}) + assert response1.status_code == 200 + assert response1.json() == {"imports": {"errors": []}, "function": {"errors": []}} + + # Test case with an invalid import and valid function + code2 = """ +import non_existent_module + +def square(x): + return x ** 2 +""" + response2 = client.post("/validate/code", json={"code": code2}) + assert response2.status_code == 200 + assert response2.json() == { + "imports": {"errors": ["No module named 'non_existent_module'"]}, + "function": {"errors": []}, + } + + # Test case with a valid import and invalid function syntax + code3 = """ +import math + +def square(x) + return x ** 2 +""" + response3 = client.post("/validate/code", json={"code": code3}) + assert response3.status_code == 200 + assert response3.json() == { + "imports": {"errors": []}, + "function": {"errors": ["expected ':' (, line 4)"]}, + } + + # Test case with invalid JSON payload + response4 = client.post("/validate/code", json={"invalid_key": code1}) + assert response4.status_code == 422 + + # Test case with an empty code string + response5 = client.post("/validate/code", json={"code": ""}) + assert response5.status_code == 200 + assert response5.json() == {"imports": {"errors": []}, "function": {"errors": []}} + + # Test case with a syntax error in the code + code6 = """ +import math + +def square(x) + return x ** 2 +""" + response6 = client.post("/validate/code", json={"code": code6}) + assert response6.status_code == 200 + assert response6.json() == { + "imports": {"errors": []}, + "function": {"errors": ["expected ':' (, line 4)"]}, + } + + +VALID_PROMPT = """ +I want you to act as a naming consultant for new companies. + +Here are some examples of good company names: + +- search engine, Google +- social media, Facebook +- video sharing, YouTube + +The name should be short, catchy and easy to remember. + +What is a good name for a company that makes {product}? +""" + +INVALID_PROMPT = "This is an invalid prompt without any input variable." + + +def test_valid_prompt(client: TestClient): + response = client.post("/validate/prompt", json={"template": VALID_PROMPT}) + assert response.status_code == 200 + assert response.json() == {"input_variables": ["product"]} + + +def test_invalid_prompt(client: TestClient): + response = client.post("/validate/prompt", json={"template": INVALID_PROMPT}) + assert response.status_code == 200 + assert response.json() == {"input_variables": []} + + +@pytest.mark.parametrize( + "prompt,expected_input_variables", + [ + ("{color} is my favorite color.", ["color"]), + ("The weather is {weather} today.", ["weather"]), + ("This prompt has no variables.", []), + ("{a}, {b}, and {c} are variables.", ["a", "b", "c"]), + ], +) +def test_various_prompts(client, prompt, expected_input_variables): + response = client.post("/validate/prompt", json={"template": prompt}) + assert response.status_code == 200 + assert response.json() == { + "input_variables": expected_input_variables, + } diff --git a/tests/test_frontend_nodes.py b/tests/test_frontend_nodes.py new file mode 100644 index 000000000..673bdaec0 --- /dev/null +++ b/tests/test_frontend_nodes.py @@ -0,0 +1,57 @@ +import pytest +from langflow.template.base import FrontendNode, Template, TemplateField + + +@pytest.fixture +def sample_template_field() -> TemplateField: + return TemplateField(name="test_field", field_type="str") + + +@pytest.fixture +def sample_template(sample_template_field: TemplateField) -> Template: + return Template(type_name="test_template", fields=[sample_template_field]) + + +@pytest.fixture +def sample_frontend_node(sample_template: Template) -> FrontendNode: + return FrontendNode( + template=sample_template, + description="test description", + base_classes=["base_class1", "base_class2"], + name="test_frontend_node", + ) + + +def test_template_field_defaults(sample_template_field: TemplateField): + assert sample_template_field.field_type == "str" + assert sample_template_field.required is False + assert sample_template_field.placeholder == "" + assert sample_template_field.is_list is False + assert sample_template_field.show is True + assert sample_template_field.multiline is False + assert sample_template_field.value is None + assert sample_template_field.suffixes == [] + assert sample_template_field.file_types == [] + assert sample_template_field.content is None + assert sample_template_field.password is False + assert sample_template_field.name == "test_field" + + +def test_template_to_dict( + sample_template: Template, sample_template_field: TemplateField +): + template_dict = sample_template.to_dict() + assert template_dict["_type"] == "test_template" + assert len(template_dict) == 2 # _type and test_field + assert "test_field" in template_dict + assert "type" in template_dict["test_field"] + assert "required" in template_dict["test_field"] + + +def test_frontend_node_to_dict(sample_frontend_node: FrontendNode): + node_dict = sample_frontend_node.to_dict() + assert len(node_dict) == 1 + assert "test_frontend_node" in node_dict + assert "description" in node_dict["test_frontend_node"] + assert "template" in node_dict["test_frontend_node"] + assert "base_classes" in node_dict["test_frontend_node"] diff --git a/tests/test_graph.py b/tests/test_graph.py new file mode 100644 index 000000000..bbdacc7cb --- /dev/null +++ b/tests/test_graph.py @@ -0,0 +1,420 @@ +import json + +import pytest +from langchain.agents import AgentExecutor +from langflow.graph import Edge, Graph, Node +from langflow.graph.nodes import ( + AgentNode, + ChainNode, + FileToolNode, + LLMNode, + PromptNode, + ToolkitNode, + ToolNode, + WrapperNode, +) +from langflow.utils.payload import build_json, get_root_node + +# Test cases for the graph module + +# now we have three types of graph: +# BASIC_EXAMPLE_PATH, COMPLEX_EXAMPLE_PATH, OPENAPI_EXAMPLE_PATH + + +def get_graph(_type="basic"): + """Get a graph from a json file""" + if _type == "basic": + path = pytest.BASIC_EXAMPLE_PATH + elif _type == "complex": + path = pytest.COMPLEX_EXAMPLE_PATH + elif _type == "openapi": + path = pytest.OPENAPI_EXAMPLE_PATH + + with open(path, "r") as f: + flow_graph = json.load(f) + data_graph = flow_graph["data"] + nodes = data_graph["nodes"] + edges = data_graph["edges"] + return Graph(nodes, edges) + + +@pytest.fixture +def basic_graph(): + return get_graph() + + +@pytest.fixture +def complex_graph(): + return get_graph("complex") + + +@pytest.fixture +def openapi_graph(): + return get_graph("openapi") + + +def get_node_by_type(graph, node_type): + """Get a node by type""" + return next((node for node in graph.nodes if isinstance(node, node_type)), None) + + +def test_graph_structure(basic_graph): + assert isinstance(basic_graph, Graph) + assert len(basic_graph.nodes) > 0 + assert len(basic_graph.edges) > 0 + for node in basic_graph.nodes: + assert isinstance(node, Node) + for edge in basic_graph.edges: + assert isinstance(edge, Edge) + assert edge.source in basic_graph.nodes + assert edge.target in basic_graph.nodes + + +def test_circular_dependencies(basic_graph): + assert isinstance(basic_graph, Graph) + + def check_circular(node, visited): + visited.add(node) + neighbors = basic_graph.get_nodes_with_target(node) + for neighbor in neighbors: + if neighbor in visited: + return True + if check_circular(neighbor, visited.copy()): + return True + return False + + for node in basic_graph.nodes: + assert not check_circular(node, set()) + + +def test_invalid_node_types(): + graph_data = { + "nodes": [ + { + "id": "1", + "data": { + "node": { + "base_classes": ["BaseClass"], + "template": { + "_type": "InvalidNodeType", + }, + }, + }, + }, + ], + "edges": [], + } + with pytest.raises(Exception): + Graph(graph_data["nodes"], graph_data["edges"]) + + +def test_get_nodes_with_target(basic_graph): + """Test getting connected nodes""" + assert isinstance(basic_graph, Graph) + # Get root node + root = get_root_node(basic_graph) + assert root is not None + connected_nodes = basic_graph.get_nodes_with_target(root) + assert connected_nodes is not None + + +def test_get_node_neighbors_basic(basic_graph): + """Test getting node neighbors""" + + assert isinstance(basic_graph, Graph) + # Get root node + root = get_root_node(basic_graph) + assert root is not None + neighbors = basic_graph.get_node_neighbors(root) + assert neighbors is not None + assert isinstance(neighbors, dict) + # Root Node is an Agent, it requires an LLMChain and tools + # We need to check if there is a Chain in the one of the neighbors' + # data attribute in the type key + assert any( + "Chain" in neighbor.data["type"] for neighbor, val in neighbors.items() if val + ) + # assert Serper Search is in the neighbors + assert any( + "Serper" in neighbor.data["type"] for neighbor, val in neighbors.items() if val + ) + # Now on to the Chain's neighbors + chain = next( + neighbor + for neighbor, val in neighbors.items() + if "Chain" in neighbor.data["type"] and val + ) + chain_neighbors = basic_graph.get_node_neighbors(chain) + assert chain_neighbors is not None + assert isinstance(chain_neighbors, dict) + # Check if there is a LLM in the chain's neighbors + assert any( + "OpenAI" in neighbor.data["type"] + for neighbor, val in chain_neighbors.items() + if val + ) + # Chain should have a Prompt as a neighbor + assert any( + "Prompt" in neighbor.data["type"] + for neighbor, val in chain_neighbors.items() + if val + ) + + +def test_get_node_neighbors_complex(complex_graph): + """Test getting node neighbors""" + assert isinstance(complex_graph, Graph) + # Get root node + root = get_root_node(complex_graph) + assert root is not None + neighbors = complex_graph.get_nodes_with_target(root) + assert neighbors is not None + # Neighbors should be a list of nodes + assert isinstance(neighbors, list) + # Root Node is an Agent, it requires an LLMChain and tools + # We need to check if there is a Chain in the one of the neighbors' + assert any("Chain" in neighbor.data["type"] for neighbor in neighbors) + # assert Tool is in the neighbors + assert any("Tool" in neighbor.data["type"] for neighbor in neighbors) + # Now on to the Chain's neighbors + chain = next(neighbor for neighbor in neighbors if "Chain" in neighbor.data["type"]) + chain_neighbors = complex_graph.get_nodes_with_target(chain) + assert chain_neighbors is not None + # Check if there is a LLM in the chain's neighbors + assert any("OpenAI" in neighbor.data["type"] for neighbor in chain_neighbors) + # Chain should have a Prompt as a neighbor + assert any("Prompt" in neighbor.data["type"] for neighbor in chain_neighbors) + # Now on to the Tool's neighbors + tool = next(neighbor for neighbor in neighbors if "Tool" in neighbor.data["type"]) + tool_neighbors = complex_graph.get_nodes_with_target(tool) + assert tool_neighbors is not None + # Check if there is an Agent in the tool's neighbors + assert any("Agent" in neighbor.data["type"] for neighbor in tool_neighbors) + # This Agent has a Tool that has a PythonFunction as func + agent = next( + neighbor for neighbor in tool_neighbors if "Agent" in neighbor.data["type"] + ) + agent_neighbors = complex_graph.get_nodes_with_target(agent) + assert agent_neighbors is not None + # Check if there is a Tool in the agent's neighbors + assert any("Tool" in neighbor.data["type"] for neighbor in agent_neighbors) + # This Tool has a PythonFunction as func + tool = next( + neighbor for neighbor in agent_neighbors if "Tool" in neighbor.data["type"] + ) + tool_neighbors = complex_graph.get_nodes_with_target(tool) + assert tool_neighbors is not None + # Check if there is a PythonFunction in the tool's neighbors + assert any("PythonFunction" in neighbor.data["type"] for neighbor in tool_neighbors) + + +def test_get_node(basic_graph): + """Test getting a single node""" + node_id = basic_graph.nodes[0].id + node = basic_graph.get_node(node_id) + assert isinstance(node, Node) + assert node.id == node_id + + +def test_build_nodes(basic_graph): + """Test building nodes""" + + assert len(basic_graph.nodes) == len(basic_graph._nodes) + for node in basic_graph.nodes: + assert isinstance(node, Node) + + +def test_build_edges(basic_graph): + """Test building edges""" + assert len(basic_graph.edges) == len(basic_graph._edges) + for edge in basic_graph.edges: + assert isinstance(edge, Edge) + assert isinstance(edge.source, Node) + assert isinstance(edge.target, Node) + + +def test_get_root_node(basic_graph, complex_graph): + """Test getting root node""" + assert isinstance(basic_graph, Graph) + root = get_root_node(basic_graph) + assert root is not None + assert isinstance(root, Node) + assert root.data["type"] == "ZeroShotAgent" + # For complex example, the root node is a ZeroShotAgent too + assert isinstance(complex_graph, Graph) + root = get_root_node(complex_graph) + assert root is not None + assert isinstance(root, Node) + assert root.data["type"] == "ZeroShotAgent" + + +def test_build_json(basic_graph): + """Test building JSON from graph""" + assert isinstance(basic_graph, Graph) + root = get_root_node(basic_graph) + json_data = build_json(root, basic_graph) + assert isinstance(json_data, dict) + assert json_data["_type"] == "zero-shot-react-description" + assert isinstance(json_data["llm_chain"], dict) + assert json_data["llm_chain"]["_type"] == "llm_chain" + assert json_data["llm_chain"]["memory"] is None + assert json_data["llm_chain"]["verbose"] is False + assert isinstance(json_data["llm_chain"]["prompt"], dict) + assert isinstance(json_data["llm_chain"]["llm"], dict) + assert json_data["llm_chain"]["output_key"] == "text" + assert isinstance(json_data["allowed_tools"], list) + assert all(isinstance(tool, dict) for tool in json_data["allowed_tools"]) + assert isinstance(json_data["return_values"], list) + assert all(isinstance(val, str) for val in json_data["return_values"]) + + +def test_validate_edges(basic_graph): + """Test validating edges""" + + assert isinstance(basic_graph, Graph) + # all edges should be valid + assert all(edge.valid for edge in basic_graph.edges) + + +def test_matched_type(basic_graph): + """Test matched type attribute in Edge""" + assert isinstance(basic_graph, Graph) + # all edges should be valid + assert all(edge.valid for edge in basic_graph.edges) + # all edges should have a matched_type attribute + assert all(hasattr(edge, "matched_type") for edge in basic_graph.edges) + # The matched_type attribute should be in the source_types attr + assert all(edge.matched_type in edge.source_types for edge in basic_graph.edges) + + +def test_build_params(basic_graph): + """Test building params""" + + assert isinstance(basic_graph, Graph) + # all edges should be valid + assert all(edge.valid for edge in basic_graph.edges) + # all edges should have a matched_type attribute + assert all(hasattr(edge, "matched_type") for edge in basic_graph.edges) + # The matched_type attribute should be in the source_types attr + assert all(edge.matched_type in edge.source_types for edge in basic_graph.edges) + # Get the root node + root = get_root_node(basic_graph) + # Root node is a ZeroShotAgent + # which requires an llm_chain, allowed_tools and return_values + assert isinstance(root.params, dict) + assert "llm_chain" in root.params + assert "allowed_tools" in root.params + assert "return_values" in root.params + # The llm_chain should be a Node + assert isinstance(root.params["llm_chain"], Node) + # The allowed_tools should be a list of Nodes + assert isinstance(root.params["allowed_tools"], list) + assert all(isinstance(tool, Node) for tool in root.params["allowed_tools"]) + # The return_values is of type str so it should be a list of strings + assert isinstance(root.params["return_values"], list) + assert all(isinstance(val, str) for val in root.params["return_values"]) + # The llm_chain should have a prompt and llm + llm_chain_node = root.params["llm_chain"] + assert isinstance(llm_chain_node.params, dict) + assert "prompt" in llm_chain_node.params + assert "llm" in llm_chain_node.params + # The prompt should be a Node + assert isinstance(llm_chain_node.params["prompt"], Node) + # The llm should be a Node + assert isinstance(llm_chain_node.params["llm"], Node) + # The prompt should have format_insctructions, suffix, prefix + prompt_node = llm_chain_node.params["prompt"] + assert isinstance(prompt_node.params, dict) + assert "format_instructions" in prompt_node.params + assert "suffix" in prompt_node.params + assert "prefix" in prompt_node.params + # All of them should be of type str + assert isinstance(prompt_node.params["format_instructions"], str) + assert isinstance(prompt_node.params["suffix"], str) + assert isinstance(prompt_node.params["prefix"], str) + # The llm should have a model + llm_node = llm_chain_node.params["llm"] + assert isinstance(llm_node.params, dict) + assert "model_name" in llm_node.params + # The model should be a str + assert isinstance(llm_node.params["model_name"], str) + + +def test_build(basic_graph, complex_graph, openapi_graph): + """Test Node's build method""" + assert_agent_was_built(basic_graph) + assert_agent_was_built(complex_graph) + assert_agent_was_built(openapi_graph) + + +def assert_agent_was_built(graph): + """Assert that the agent was built""" + assert isinstance(graph, Graph) + # Now we test the build method + # Build the Agent + result = graph.build() + # The agent should be a AgentExecutor + assert isinstance(result, AgentExecutor) + + +def test_agent_node_build(basic_graph): + agent_node = get_node_by_type(basic_graph, AgentNode) + assert agent_node is not None + built_object = agent_node.build() + assert built_object is not None + + +def test_tool_node_build(basic_graph): + tool_node = get_node_by_type(basic_graph, ToolNode) + assert tool_node is not None + built_object = tool_node.build() + assert built_object is not None + # Add any further assertions specific to the ToolNode's build() method + + +def test_chain_node_build(complex_graph): + chain_node = get_node_by_type(complex_graph, ChainNode) + assert chain_node is not None + built_object = chain_node.build() + assert built_object is not None + # Add any further assertions specific to the ChainNode's build() method + + +def test_prompt_node_build(complex_graph): + prompt_node = get_node_by_type(complex_graph, PromptNode) + assert prompt_node is not None + built_object = prompt_node.build() + assert built_object is not None + # Add any further assertions specific to the PromptNode's build() method + + +def test_llm_node_build(basic_graph): + llm_node = get_node_by_type(basic_graph, LLMNode) + assert llm_node is not None + built_object = llm_node.build() + assert built_object is not None + # Add any further assertions specific to the LLMNode's build() method + + +def test_toolkit_node_build(openapi_graph): + toolkit_node = get_node_by_type(openapi_graph, ToolkitNode) + assert toolkit_node is not None + built_object = toolkit_node.build() + assert built_object is not None + # Add any further assertions specific to the ToolkitNode's build() method + + +def test_file_tool_node_build(openapi_graph): + file_tool_node = get_node_by_type(openapi_graph, FileToolNode) + assert file_tool_node is not None + built_object = file_tool_node.build() + assert built_object is not None + # Add any further assertions specific to the FileToolNode's build() method + + +def test_wrapper_node_build(openapi_graph): + wrapper_node = get_node_by_type(openapi_graph, WrapperNode) + assert wrapper_node is not None + built_object = wrapper_node.build() + assert built_object is not None + # Add any further assertions specific to the WrapperNode's build() method diff --git a/tests/test_loading.py b/tests/test_loading.py index 9478e9be1..444c85fd9 100644 --- a/tests/test_loading.py +++ b/tests/test_loading.py @@ -1,11 +1,27 @@ -from pathlib import Path +import json +import pytest +from langchain.agents import AgentExecutor from langflow import load_flow_from_json +from langflow.graph import Graph +from langflow.utils.payload import get_root_node def test_load_flow_from_json(): """Test loading a flow from a json file""" - path = Path(__file__).parent.absolute() - path = f"{path}/data/example_flow.json" - loaded = load_flow_from_json(path) + loaded = load_flow_from_json(pytest.BASIC_EXAMPLE_PATH) assert loaded is not None + assert isinstance(loaded, AgentExecutor) + + +def test_get_root_node(): + with open(pytest.BASIC_EXAMPLE_PATH, "r") as f: + flow_graph = json.load(f) + data_graph = flow_graph["data"] + nodes = data_graph["nodes"] + edges = data_graph["edges"] + graph = Graph(nodes, edges) + root = get_root_node(graph) + assert root is not None + assert hasattr(root, "id") + assert hasattr(root, "data") diff --git a/tests/test_template.py b/tests/test_template.py new file mode 100644 index 000000000..b5a424dab --- /dev/null +++ b/tests/test_template.py @@ -0,0 +1,291 @@ +import importlib +from typing import Dict, List, Optional + +import pytest +from langflow.utils.constants import CHAT_OPENAI_MODELS, OPENAI_MODELS +from langflow.utils.util import ( + build_template_from_class, + build_template_from_function, + format_dict, + get_base_classes, + get_class_doc, + get_default_factory, +) +from pydantic import BaseModel + + +# Dummy classes for testing purposes +class Parent(BaseModel): + """Parent Class""" + + parent_field: str + + +class Child(Parent): + """Child Class""" + + child_field: int + + +class ExampleClass1(BaseModel): + """Example class 1.""" + + def __init__(self, data: Optional[List[int]] = None): + self.data = data or [1, 2, 3] + + +class ExampleClass2(BaseModel): + """Example class 2.""" + + def __init__(self, data: Optional[Dict[str, int]] = None): + self.data = data or {"a": 1, "b": 2, "c": 3} + + +def example_loader_1() -> ExampleClass1: + """Example loader function 1.""" + return ExampleClass1() + + +def example_loader_2() -> ExampleClass2: + """Example loader function 2.""" + return ExampleClass2() + + +def test_build_template_from_function(): + type_to_loader_dict = { + "example1": example_loader_1, + "example2": example_loader_2, + } + + # Test with valid name + result = build_template_from_function("ExampleClass1", type_to_loader_dict) + + assert "template" in result + assert "description" in result + assert "base_classes" in result + + # Test with add_function=True + result_with_function = build_template_from_function( + "ExampleClass1", type_to_loader_dict, add_function=True + ) + assert "function" in result_with_function["base_classes"] + + # Test with invalid name + with pytest.raises(ValueError, match=r".* not found"): + build_template_from_function("NonExistent", type_to_loader_dict) + + +# Test build_template_from_class +def test_build_template_from_class(): + type_to_cls_dict: Dict[str, type] = {"parent": Parent, "child": Child} + + # Test valid input + result = build_template_from_class("Child", type_to_cls_dict) + assert "template" in result + assert "description" in result + assert "base_classes" in result + assert "Child" in result["base_classes"] + assert "Parent" in result["base_classes"] + assert result["description"] == "Child Class" + + # Test invalid input + with pytest.raises(ValueError, match="InvalidClass not found."): + build_template_from_class("InvalidClass", type_to_cls_dict) + + +# Test format_dict +def test_format_dict(): + # Test 1: Optional type removal + input_dict = { + "field1": {"type": "Optional[str]", "required": False}, + } + expected_output = { + "field1": { + "type": "str", + "required": False, + "list": False, + "show": False, + "password": False, + "multiline": False, + }, + } + assert format_dict(input_dict) == expected_output + + # Test 2: List type processing + input_dict = { + "field1": {"type": "List[str]", "required": False}, + } + expected_output = { + "field1": { + "type": "str", + "required": False, + "list": True, + "show": False, + "password": False, + "multiline": False, + }, + } + assert format_dict(input_dict) == expected_output + + # Test 3: Mapping type replacement + input_dict = { + "field1": {"type": "Mapping[str, int]", "required": False}, + } + expected_output = { + "field1": { + "type": "code", # Mapping type is replaced with dict which is replaced with code + "required": False, + "list": False, + "show": False, + "password": False, + "multiline": False, + }, + } + assert format_dict(input_dict) == expected_output + + # Test 4: Replace default value with actual value + input_dict = { + "field1": {"type": "str", "required": False, "default": "test"}, + } + expected_output = { + "field1": { + "type": "str", + "required": False, + "list": False, + "show": False, + "password": False, + "multiline": False, + "value": "test", + }, + } + assert format_dict(input_dict) == expected_output + + # Test 5: Add password field + input_dict = { + "field1": {"type": "str", "required": False}, + "api_key": {"type": "str", "required": False}, + } + expected_output = { + "field1": { + "type": "str", + "required": False, + "list": False, + "show": False, + "password": False, + "multiline": False, + }, + "api_key": { + "type": "str", + "required": False, + "list": False, + "show": True, + "password": True, + "multiline": False, + }, + } + assert format_dict(input_dict) == expected_output + + # Test 6: Add multiline + input_dict = { + "field1": {"type": "str", "required": False}, + "prefix": {"type": "str", "required": False}, + } + expected_output = { + "field1": { + "type": "str", + "required": False, + "list": False, + "show": False, + "password": False, + "multiline": False, + }, + "prefix": { + "type": "str", + "required": False, + "list": False, + "show": True, + "password": False, + "multiline": True, + }, + } + assert format_dict(input_dict) == expected_output + + # Test 7: Check class name-specific cases (OpenAI, ChatOpenAI) + input_dict = { + "model_name": {"type": "str", "required": False}, + } + expected_output_openai = { + "model_name": { + "type": "str", + "required": False, + "list": True, + "show": True, + "password": False, + "multiline": False, + "options": OPENAI_MODELS, + }, + } + expected_output_openai_chat = { + "model_name": { + "type": "str", + "required": False, + "list": True, + "show": True, + "password": False, + "multiline": False, + "options": CHAT_OPENAI_MODELS, + }, + } + assert format_dict(input_dict, "OpenAI") == expected_output_openai + assert format_dict(input_dict, "ChatOpenAI") == expected_output_openai_chat + + # Test 8: Replace dict type with str + input_dict = { + "field1": {"type": "Dict[str, int]", "required": False}, + } + expected_output = { + "field1": { + "type": "code", + "required": False, + "list": False, + "show": False, + "password": False, + "multiline": False, + }, + } + assert format_dict(input_dict) == expected_output + + +# Test get_base_classes +def test_get_base_classes(): + base_classes_parent = get_base_classes(Parent) + base_classes_child = get_base_classes(Child) + + assert "Parent" in base_classes_parent + assert "Child" in base_classes_child + assert "Parent" in base_classes_child + + +# Test get_default_factory +def test_get_default_factory(): + module_name = "langflow.utils.util" + function_repr = "" + + def dummy_function(): + return "default_value" + + # Add dummy_function to your_module + setattr(importlib.import_module(module_name), "dummy_function", dummy_function) + + default_value = get_default_factory(module_name, function_repr) + + assert default_value == "default_value" + + +# Test get_class_doc +def test_get_class_doc(): + class_doc_parent = get_class_doc(Parent) + class_doc_child = get_class_doc(Child) + + assert class_doc_parent["Description"] == "Parent Class" + assert class_doc_child["Description"] == "Child Class" diff --git a/tests/test_validate_code.py b/tests/test_validate_code.py new file mode 100644 index 000000000..cacf3669c --- /dev/null +++ b/tests/test_validate_code.py @@ -0,0 +1,107 @@ +from unittest import mock + +import pytest +from langflow.utils.validate import ( + create_function, + execute_function, + extract_function_name, + validate_code, +) +from requests.exceptions import MissingSchema + + +def test_validate_code(): + # Test case with a valid import and function + code1 = """ +import math + +def square(x): + return x ** 2 +""" + errors1 = validate_code(code1) + assert errors1 == {"imports": {"errors": []}, "function": {"errors": []}} + + # Test case with an invalid import and valid function + code2 = """ +import non_existent_module + +def square(x): + return x ** 2 +""" + errors2 = validate_code(code2) + assert errors2 == { + "imports": {"errors": ["No module named 'non_existent_module'"]}, + "function": {"errors": []}, + } + + # Test case with a valid import and invalid function syntax + code3 = """ +import math + +def square(x) + return x ** 2 +""" + errors3 = validate_code(code3) + assert errors3 == { + "imports": {"errors": []}, + "function": {"errors": ["expected ':' (, line 4)"]}, + } + + +def test_execute_function_success(): + code = """ +import math + +def my_function(x): + return math.sin(x) + 1 + """ + result = execute_function(code, "my_function", 0.5) + assert result == 1.479425538604203 + + +def test_execute_function_missing_module(): + code = """ +import some_missing_module + +def my_function(x): + return some_missing_module.some_function(x) + """ + with pytest.raises(ModuleNotFoundError): + execute_function(code, "my_function", 0.5) + + +def test_execute_function_missing_function(): + code = """ +import math + +def my_function(x): + return math.some_missing_function(x) + """ + with pytest.raises(AttributeError): + execute_function(code, "my_function", 0.5) + + +def test_execute_function_missing_schema(): + code = """ +import requests + +def my_function(x): + return requests.get(x).text + """ + with mock.patch("requests.get", side_effect=MissingSchema): + with pytest.raises(MissingSchema): + execute_function(code, "my_function", "invalid_url") + + +def test_create_function(): + code = """ +import math + +def my_function(x): + return math.sin(x) + 1 +""" + + function_name = extract_function_name(code) + function = create_function(code, function_name) + result = function(0.5) + assert result == 1.479425538604203