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Merge branch 'dev' into mainPage

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This commit is contained in:
Lucas Oliveira 2023-06-05 07:43:31 -03:00
commit 56d0898861
33 changed files with 436 additions and 250 deletions
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4
src/backend/langflow/api/validate.py
View file

@ -9,7 +9,7 @@ from langflow.api.base import (
PromptValidationResponse,
validate_prompt,
)
from langflow.graph.node.types import VectorStoreNode
from langflow.graph.vertex.types import VectorStoreVertex
from langflow.interface.run import build_graph
from langflow.utils.logger import logger
from langflow.utils.validate import validate_code
@ -49,7 +49,7 @@ def post_validate_node(node_id: str, data: dict):
node = graph.get_node(node_id)
if node is None:
raise ValueError(f"Node {node_id} not found")
if not isinstance(node, VectorStoreNode):
if not isinstance(node, VectorStoreVertex):
node.build()
return json.dumps({"valid": True, "params": str(node._built_object_repr())})
except Exception as e:

5
src/backend/langflow/config.yaml
View file

@ -55,6 +55,8 @@ llms:
- LlamaCpp
- CTransformers
- Cohere
- Anthropic
- ChatAnthropic
memories:
- ConversationBufferMemory
- ConversationSummaryMemory
@ -79,7 +81,7 @@ tools:
- Calculator
- Serper Search
- Tool
- PythonFunction
- PythonFunctionTool
- JsonSpec
- News API
- TMDB API
@ -118,6 +120,7 @@ vectorstores:
- Chroma
- Qdrant
- Weaviate
- FAISS
wrappers:
- RequestsWrapper
# - ChatPromptTemplate

2
src/backend/langflow/custom/customs.py
View file

@ -4,7 +4,7 @@ from langflow.template import frontend_node
CUSTOM_NODES = {
"prompts": {"ZeroShotPrompt": frontend_node.prompts.ZeroShotPromptNode()},
"tools": {
"PythonFunction": frontend_node.tools.PythonFunctionNode(),
"PythonFunctionTool": frontend_node.tools.PythonFunctionToolNode(),
"Tool": frontend_node.tools.ToolNode(),
},
"agents": {

54
src/backend/langflow/graph/__init__.py
View file

@ -1,35 +1,35 @@
from langflow.graph.edge.base import Edge
from langflow.graph.graph.base import Graph
from langflow.graph.node.base import Node
from langflow.graph.node.types import (
AgentNode,
ChainNode,
DocumentLoaderNode,
EmbeddingNode,
LLMNode,
MemoryNode,
PromptNode,
TextSplitterNode,
ToolNode,
ToolkitNode,
VectorStoreNode,
WrapperNode,
from langflow.graph.vertex.base import Vertex
from langflow.graph.vertex.types import (
AgentVertex,
ChainVertex,
DocumentLoaderVertex,
EmbeddingVertex,
LLMVertex,
MemoryVertex,
PromptVertex,
TextSplitterVertex,
ToolVertex,
ToolkitVertex,
VectorStoreVertex,
WrapperVertex,
)
__all__ = [
"Graph",
"Node",
"Vertex",
"Edge",
"AgentNode",
"ChainNode",
"DocumentLoaderNode",
"EmbeddingNode",
"LLMNode",
"MemoryNode",
"PromptNode",
"TextSplitterNode",
"ToolNode",
"ToolkitNode",
"VectorStoreNode",
"WrapperNode",
"AgentVertex",
"ChainVertex",
"DocumentLoaderVertex",
"EmbeddingVertex",
"LLMVertex",
"MemoryVertex",
"PromptVertex",
"TextSplitterVertex",
"ToolVertex",
"ToolkitVertex",
"VectorStoreVertex",
"WrapperVertex",
]

10
src/backend/langflow/graph/edge/base.py
View file

@ -2,13 +2,13 @@ from langflow.utils.logger import logger
from typing import TYPE_CHECKING
if TYPE_CHECKING:
from langflow.graph.node.base import Node
from langflow.graph.vertex.base import Vertex
class Edge:
def __init__(self, source: "Node", target: "Node"):
self.source: "Node" = source
self.target: "Node" = target
def __init__(self, source: "Vertex", target: "Vertex"):
self.source: "Vertex" = source
self.target: "Vertex" = target
self.validate_edge()
def validate_edge(self) -> None:
@ -41,7 +41,7 @@ class Edge:
logger.debug(self.target_reqs)
if no_matched_type:
raise ValueError(
f"Edge between {self.source.node_type} and {self.target.node_type} "
f"Edge between {self.source.vertex_type} and {self.target.vertex_type} "
f"has no matched type"
)

56
src/backend/langflow/graph/graph/base.py
View file

@ -1,12 +1,12 @@
from typing import Dict, List, Type, Union
from langflow.graph.edge.base import Edge
from langflow.graph.graph.constants import NODE_TYPE_MAP
from langflow.graph.node.base import Node
from langflow.graph.node.types import (
FileToolNode,
LLMNode,
ToolkitNode,
from langflow.graph.graph.constants import VERTEX_TYPE_MAP
from langflow.graph.vertex.base import Vertex
from langflow.graph.vertex.types import (
FileToolVertex,
LLMVertex,
ToolkitVertex,
)
from langflow.interface.tools.constants import FILE_TOOLS
from langflow.utils import payload
@ -26,7 +26,7 @@ class Graph:
def _build_graph(self) -> None:
"""Builds the graph from the nodes and edges."""
self.nodes = self._build_nodes()
self.nodes = self._build_vertices()
self.edges = self._build_edges()
for edge in self.edges:
edge.source.add_edge(edge)
@ -43,12 +43,12 @@ class Graph:
llm_node = None
for node in self.nodes:
node._build_params()
if isinstance(node, LLMNode):
if isinstance(node, LLMVertex):
llm_node = node
if llm_node:
for node in self.nodes:
if isinstance(node, ToolkitNode):
if isinstance(node, ToolkitVertex):
node.params["llm"] = llm_node
def _remove_invalid_nodes(self) -> None:
@ -60,23 +60,23 @@ class Graph:
or (len(self.nodes) == 1 and len(self.edges) == 0)
]
def _validate_node(self, node: Node) -> bool:
def _validate_node(self, node: Vertex) -> bool:
"""Validates a node."""
# All nodes that do not have edges are invalid
return len(node.edges) > 0
def get_node(self, node_id: str) -> Union[None, Node]:
def get_node(self, node_id: str) -> Union[None, Vertex]:
"""Returns a node by id."""
return next((node for node in self.nodes if node.id == node_id), None)
def get_nodes_with_target(self, node: Node) -> List[Node]:
def get_nodes_with_target(self, node: Vertex) -> List[Vertex]:
"""Returns the nodes connected to a node."""
connected_nodes: List[Node] = [
connected_nodes: List[Vertex] = [
edge.source for edge in self.edges if edge.target == node
]
return connected_nodes
def build(self) -> List[Node]:
def build(self) -> List[Vertex]:
"""Builds the graph."""
# Get root node
root_node = payload.get_root_node(self)
@ -84,9 +84,9 @@ class Graph:
raise ValueError("No root node found")
return root_node.build()
def get_node_neighbors(self, node: Node) -> Dict[Node, int]:
def get_node_neighbors(self, node: Vertex) -> Dict[Vertex, int]:
"""Returns the neighbors of a node."""
neighbors: Dict[Node, int] = {}
neighbors: Dict[Vertex, int] = {}
for edge in self.edges:
if edge.source == node:
neighbor = edge.target
@ -117,28 +117,30 @@ class Graph:
edges.append(Edge(source, target))
return edges
def _get_node_class(self, node_type: str, node_lc_type: str) -> Type[Node]:
def _get_vertex_class(self, node_type: str, node_lc_type: str) -> Type[Vertex]:
"""Returns the node class based on the node type."""
if node_type in FILE_TOOLS:
return FileToolNode
if node_type in NODE_TYPE_MAP:
return NODE_TYPE_MAP[node_type]
return NODE_TYPE_MAP[node_lc_type] if node_lc_type in NODE_TYPE_MAP else Node
return FileToolVertex
if node_type in VERTEX_TYPE_MAP:
return VERTEX_TYPE_MAP[node_type]
return (
VERTEX_TYPE_MAP[node_lc_type] if node_lc_type in VERTEX_TYPE_MAP else Vertex
)
def _build_nodes(self) -> List[Node]:
"""Builds the nodes of the graph."""
nodes: List[Node] = []
def _build_vertices(self) -> List[Vertex]:
"""Builds the vertices of the graph."""
nodes: List[Vertex] = []
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
NodeClass = self._get_node_class(node_type, node_lc_type)
nodes.append(NodeClass(node))
VertexClass = self._get_vertex_class(node_type, node_lc_type)
nodes.append(VertexClass(node))
return nodes
def get_children_by_node_type(self, node: Node, node_type: str) -> List[Node]:
def get_children_by_node_type(self, node: Vertex, node_type: str) -> List[Vertex]:
"""Returns the children of a node based on the node type."""
children = []
node_types = [node.data["type"]]

54
src/backend/langflow/graph/graph/constants.py
View file

@ -1,17 +1,17 @@
from langflow.graph.node.base import Node
from langflow.graph.node.types import (
AgentNode,
ChainNode,
DocumentLoaderNode,
EmbeddingNode,
LLMNode,
MemoryNode,
PromptNode,
TextSplitterNode,
ToolNode,
ToolkitNode,
VectorStoreNode,
WrapperNode,
from langflow.graph.vertex.base import Vertex
from langflow.graph.vertex.types import (
AgentVertex,
ChainVertex,
DocumentLoaderVertex,
EmbeddingVertex,
LLMVertex,
MemoryVertex,
PromptVertex,
TextSplitterVertex,
ToolVertex,
ToolkitVertex,
VectorStoreVertex,
WrapperVertex,
)
from langflow.interface.agents.base import agent_creator
from langflow.interface.chains.base import chain_creator
@ -33,17 +33,17 @@ from typing import Dict, Type
DIRECT_TYPES = ["str", "bool", "code", "int", "float", "Any", "prompt"]
NODE_TYPE_MAP: Dict[str, Type[Node]] = {
**{t: PromptNode for t in prompt_creator.to_list()},
**{t: AgentNode for t in agent_creator.to_list()},
**{t: ChainNode for t in chain_creator.to_list()},
**{t: ToolNode for t in tool_creator.to_list()},
**{t: ToolkitNode for t in toolkits_creator.to_list()},
**{t: WrapperNode for t in wrapper_creator.to_list()},
**{t: LLMNode for t in llm_creator.to_list()},
**{t: MemoryNode for t in memory_creator.to_list()},
**{t: EmbeddingNode for t in embedding_creator.to_list()},
**{t: VectorStoreNode for t in vectorstore_creator.to_list()},
**{t: DocumentLoaderNode for t in documentloader_creator.to_list()},
**{t: TextSplitterNode for t in textsplitter_creator.to_list()},
VERTEX_TYPE_MAP: Dict[str, Type[Vertex]] = {
**{t: PromptVertex for t in prompt_creator.to_list()},
**{t: AgentVertex for t in agent_creator.to_list()},
**{t: ChainVertex for t in chain_creator.to_list()},
**{t: ToolVertex for t in tool_creator.to_list()},
**{t: ToolkitVertex for t in toolkits_creator.to_list()},
**{t: WrapperVertex for t in wrapper_creator.to_list()},
**{t: LLMVertex for t in llm_creator.to_list()},
**{t: MemoryVertex for t in memory_creator.to_list()},
**{t: EmbeddingVertex for t in embedding_creator.to_list()},
**{t: VectorStoreVertex for t in vectorstore_creator.to_list()},
**{t: DocumentLoaderVertex for t in documentloader_creator.to_list()},
**{t: TextSplitterVertex for t in textsplitter_creator.to_list()},
}

0
src/backend/langflow/graph/node/__init__.py → src/backend/langflow/graph/vertex/__init__.py
View file

26
src/backend/langflow/graph/node/base.py → src/backend/langflow/graph/vertex/base.py
View file

@ -1,5 +1,5 @@
from langflow.cache import base as cache_utils
from langflow.graph.node.constants import DIRECT_TYPES
from langflow.graph.vertex.constants import DIRECT_TYPES
from langflow.interface import loading
from langflow.interface.listing import ALL_TYPES_DICT
from langflow.utils.logger import logger
@ -17,7 +17,7 @@ if TYPE_CHECKING:
from langflow.graph.edge.base import Edge
class Node:
class Vertex:
def __init__(self, data: Dict, base_type: Optional[str] = None) -> None:
self.id: str = data["id"]
self._data = data
@ -48,12 +48,12 @@ class Node:
]
template_dict = self.data["node"]["template"]
self.node_type = (
self.vertex_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:
if self.vertex_type in value:
self.base_type = base_type
break
@ -113,7 +113,7 @@ class Node:
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"
f"Required input {key} for module {self.vertex_type} not found"
)
elif value["list"]:
# If this is a list parameter, append all sources to a list
@ -128,7 +128,7 @@ class Node:
# so we need to check if value has value
new_value = value.get("value")
if new_value is None:
warnings.warn(f"Value for {key} in {self.node_type} is None. ")
warnings.warn(f"Value for {key} in {self.vertex_type} is None. ")
if value.get("type") == "int":
with contextlib.suppress(TypeError, ValueError):
new_value = int(new_value) # type: ignore
@ -148,12 +148,12 @@ class Node:
# 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}")
logger.debug(f"Building {self.vertex_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 isinstance(value, Vertex):
if value == self:
del self.params[key]
continue
@ -177,7 +177,7 @@ class Node:
self.params[key] = result
elif isinstance(value, list) and all(
isinstance(node, Node) for node in value
isinstance(node, Vertex) for node in value
):
self.params[key] = []
for node in value:
@ -193,17 +193,17 @@ class Node:
try:
self._built_object = loading.instantiate_class(
node_type=self.node_type,
node_type=self.vertex_type,
base_type=self.base_type,
params=self.params,
)
except Exception as exc:
raise ValueError(
f"Error building node {self.node_type}: {str(exc)}"
f"Error building node {self.vertex_type}: {str(exc)}"
) from exc
if self._built_object is None:
raise ValueError(f"Node type {self.node_type} not found")
raise ValueError(f"Node type {self.vertex_type} not found")
self._built = True
@ -220,7 +220,7 @@ class Node:
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
return self.id == __o.id if isinstance(__o, Vertex) else False
def __hash__(self) -> int:
return id(self)

0
src/backend/langflow/graph/node/constants.py → src/backend/langflow/graph/vertex/constants.py
View file

65
src/backend/langflow/graph/node/types.py → src/backend/langflow/graph/vertex/types.py
View file

@ -1,22 +1,22 @@
from typing import Any, Dict, List, Optional, Union
from langflow.graph.node.base import Node
from langflow.graph.vertex.base import Vertex
from langflow.graph.utils import extract_input_variables_from_prompt
class AgentNode(Node):
class AgentVertex(Vertex):
def __init__(self, data: Dict):
super().__init__(data, base_type="agents")
self.tools: List[ToolNode] = []
self.chains: List[ChainNode] = []
self.tools: List[ToolVertex] = []
self.chains: List[ChainVertex] = []
def _set_tools_and_chains(self) -> None:
for edge in self.edges:
source_node = edge.source
if isinstance(source_node, ToolNode):
if isinstance(source_node, ToolVertex):
self.tools.append(source_node)
elif isinstance(source_node, ChainNode):
elif isinstance(source_node, ChainVertex):
self.chains.append(source_node)
def build(self, force: bool = False) -> Any:
@ -33,24 +33,28 @@ class AgentNode(Node):
self._build()
#! Cannot deepcopy VectorStore, VectorStoreRouter, or SQL agents
if self.node_type in ["VectorStoreAgent", "VectorStoreRouterAgent", "SQLAgent"]:
if self.vertex_type in [
"VectorStoreAgent",
"VectorStoreRouterAgent",
"SQLAgent",
]:
return self._built_object
return self._built_object
class ToolNode(Node):
class ToolVertex(Vertex):
def __init__(self, data: Dict):
super().__init__(data, base_type="tools")
class PromptNode(Node):
class PromptVertex(Vertex):
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,
tools: Optional[Union[List[Vertex], List[ToolVertex]]] = None,
) -> Any:
if not self._built or force:
if (
@ -59,7 +63,7 @@ class PromptNode(Node):
):
self.params["input_variables"] = []
# Check if it is a ZeroShotPrompt and needs a tool
if "ShotPrompt" in self.node_type:
if "ShotPrompt" in self.vertex_type:
tools = (
[tool_node.build() for tool_node in tools]
if tools is not None
@ -83,31 +87,31 @@ class PromptNode(Node):
return self._built_object
class ChainNode(Node):
class ChainVertex(Vertex):
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,
tools: Optional[Union[List[Vertex], List[ToolVertex]]] = 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):
if isinstance(value, PromptVertex):
# Build the PromptNode, passing the tools if available
self.params[key] = value.build(tools=tools, force=force)
self._build()
#! Cannot deepcopy SQLDatabaseChain
if self.node_type in ["SQLDatabaseChain"]:
if self.vertex_type in ["SQLDatabaseChain"]:
return self._built_object
return self._built_object
class LLMNode(Node):
class LLMVertex(Vertex):
built_node_type = None
class_built_object = None
@ -117,28 +121,28 @@ class LLMNode(Node):
def build(self, force: bool = False) -> Any:
# LLM is different because some models might take up too much memory
# or time to load. So we only load them when we need them.ß
if self.node_type == self.built_node_type:
if self.vertex_type == self.built_node_type:
return self.class_built_object
if not self._built or force:
self._build()
self.built_node_type = self.node_type
self.built_node_type = self.vertex_type
self.class_built_object = self._built_object
# Avoid deepcopying the LLM
# that are loaded from a file
return self._built_object
class ToolkitNode(Node):
class ToolkitVertex(Vertex):
def __init__(self, data: Dict):
super().__init__(data, base_type="toolkits")
class FileToolNode(ToolNode):
class FileToolVertex(ToolVertex):
def __init__(self, data: Dict):
super().__init__(data)
class WrapperNode(Node):
class WrapperVertex(Vertex):
def __init__(self, data: Dict):
super().__init__(data, base_type="wrappers")
@ -150,7 +154,7 @@ class WrapperNode(Node):
return self._built_object
class DocumentLoaderNode(Node):
class DocumentLoaderVertex(Vertex):
def __init__(self, data: Dict):
super().__init__(data, base_type="documentloaders")
@ -158,17 +162,17 @@ class DocumentLoaderNode(Node):
# This built_object is a list of documents. Maybe we should
# show how many documents are in the list?
if self._built_object:
return f"""{self.node_type}({len(self._built_object)} documents)
return f"""{self.vertex_type}({len(self._built_object)} documents)
Documents: {self._built_object[:3]}..."""
return f"{self.node_type}()"
return f"{self.vertex_type}()"
class EmbeddingNode(Node):
class EmbeddingVertex(Vertex):
def __init__(self, data: Dict):
super().__init__(data, base_type="embeddings")
class VectorStoreNode(Node):
class VectorStoreVertex(Vertex):
def __init__(self, data: Dict):
super().__init__(data, base_type="vectorstores")
@ -176,12 +180,12 @@ class VectorStoreNode(Node):
return "Vector stores can take time to build. It will build on the first query."
class MemoryNode(Node):
class MemoryVertex(Vertex):
def __init__(self, data: Dict):
super().__init__(data, base_type="memory")
class TextSplitterNode(Node):
class TextSplitterVertex(Vertex):
def __init__(self, data: Dict):
super().__init__(data, base_type="textsplitters")
@ -189,5 +193,6 @@ class TextSplitterNode(Node):
# This built_object is a list of documents. Maybe we should
# show how many documents are in the list?
if self._built_object:
return f"""{self.node_type}({len(self._built_object)} documents)\nDocuments: {self._built_object[:3]}..."""
return f"{self.node_type}()"
return f"""{self.vertex_type}({len(self._built_object)} documents)
\nDocuments: {self._built_object[:3]}..."""
return f"{self.vertex_type}()"

2
src/backend/langflow/interface/custom_lists.py
View file

@ -11,12 +11,14 @@ from langchain import (
text_splitter,
)
from langchain.agents import agent_toolkits
from langchain.chat_models import ChatAnthropic
from langchain.chat_models import ChatOpenAI
from langflow.interface.importing.utils import import_class
## LLMs
llm_type_to_cls_dict = llms.type_to_cls_dict
llm_type_to_cls_dict["anthropic-chat"] = ChatAnthropic # type: ignore
llm_type_to_cls_dict["openai-chat"] = ChatOpenAI # type: ignore
## Chains

8
src/backend/langflow/interface/importing/utils.py
View file

@ -9,6 +9,7 @@ from langchain.base_language import BaseLanguageModel
from langchain.chains.base import Chain
from langchain.chat_models.base import BaseChatModel
from langchain.tools import BaseTool
from langflow.utils import validate
def import_module(module_path: str) -> Any:
@ -147,3 +148,10 @@ def import_utility(utility: str) -> Any:
if utility == "SQLDatabase":
return import_class(f"langchain.sql_database.{utility}")
return import_class(f"langchain.utilities.{utility}")
def get_function(code):
"""Get the function"""
function_name = validate.extract_function_name(code)
return validate.create_function(code, function_name)

12
src/backend/langflow/interface/loading.py
View file

@ -21,12 +21,12 @@ from langchain.llms.loading import load_llm_from_config
from pydantic import ValidationError
from langflow.interface.agents.custom import CUSTOM_AGENTS
from langflow.interface.importing.utils import import_by_type
from langflow.interface.importing.utils import get_function, import_by_type
from langflow.interface.run import fix_memory_inputs
from langflow.interface.toolkits.base import toolkits_creator
from langflow.interface.types import get_type_list
from langflow.interface.utils import load_file_into_dict
from langflow.utils import util, validate
from langflow.utils import util
def instantiate_class(node_type: str, base_type: str, params: Dict) -> Any:
@ -100,11 +100,9 @@ def instantiate_tool(node_type, class_object, params):
if node_type == "JsonSpec":
params["dict_"] = load_file_into_dict(params.pop("path"))
return class_object(**params)
elif node_type == "PythonFunction":
function_string = params["code"]
if isinstance(function_string, str):
return validate.eval_function(function_string)
raise ValueError("Function should be a string")
elif node_type == "PythonFunctionTool":
params["func"] = get_function(params.get("code"))
return class_object(**params)
elif node_type.lower() == "tool":
return class_object(**params)
return class_object(**params)

3
src/backend/langflow/interface/tools/base.py
View file

@ -71,7 +71,8 @@ class ToolCreator(LangChainTypeCreator):
for tool, tool_fcn in ALL_TOOLS_NAMES.items():
tool_params = get_tool_params(tool_fcn)
tool_name = tool_params.get("name", tool)
tool_name = tool_params.get("name") or tool
if tool_name in settings.tools or settings.dev:
if tool_name == "JsonSpec":

4
src/backend/langflow/interface/tools/constants.py
View file

@ -9,10 +9,10 @@ from langchain.agents.load_tools import (
from langchain.tools.json.tool import JsonSpec
from langflow.interface.importing.utils import import_class
from langflow.interface.tools.custom import PythonFunction
from langflow.interface.tools.custom import PythonFunctionTool
FILE_TOOLS = {"JsonSpec": JsonSpec}
CUSTOM_TOOLS = {"Tool": Tool, "PythonFunction": PythonFunction}
CUSTOM_TOOLS = {"Tool": Tool, "PythonFunctionTool": PythonFunctionTool}
OTHER_TOOLS = {tool: import_class(f"langchain.tools.{tool}") for tool in tools.__all__}

22
src/backend/langflow/interface/tools/custom.py
View file

@ -1,13 +1,14 @@
from typing import Callable, Optional
from typing import Optional
from langflow.interface.importing.utils import get_function
from pydantic import BaseModel, validator
from langflow.utils import validate
from langchain.agents.tools import Tool
class Function(BaseModel):
code: str
function: Optional[Callable] = None
imports: Optional[str] = None
# Eval code and store the function
@ -24,14 +25,17 @@ class Function(BaseModel):
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):
class PythonFunctionTool(Function, Tool):
"""Python function"""
name: str = "Custom Tool"
description: str
code: str
def ___init__(self, name: str, description: str, code: str):
self.name = name
self.description = description
self.code = code
self.func = get_function(self.code)
super().__init__(name=name, description=description, func=self.func)

3
src/backend/langflow/template/frontend_node/base.py
View file

@ -125,6 +125,9 @@ class FrontendNode(BaseModel):
elif name == "ChatOpenAI" and key == "model_name":
field.options = constants.CHAT_OPENAI_MODELS
field.is_list = True
elif (name == "Anthropic" or name == "ChatAnthropic") and key == "model_name":
field.options = constants.ANTHROPIC_MODELS
field.is_list = True
if "api_key" in key and "OpenAI" in str(name):
field.display_name = "OpenAI API Key"
field.required = False

32
src/backend/langflow/template/frontend_node/tools.py
View file

@ -59,11 +59,33 @@ class ToolNode(FrontendNode):
return super().to_dict()
class PythonFunctionNode(FrontendNode):
name: str = "PythonFunction"
class PythonFunctionToolNode(FrontendNode):
name: str = "PythonFunctionTool"
template: Template = Template(
type_name="python_function",
type_name="PythonFunctionTool",
fields=[
TemplateField(
field_type="str",
required=True,
placeholder="",
is_list=False,
show=True,
multiline=False,
value="",
name="name",
advanced=False,
),
TemplateField(
field_type="str",
required=True,
placeholder="",
is_list=False,
show=True,
multiline=False,
value="",
name="description",
advanced=False,
),
TemplateField(
field_type="code",
required=True,
@ -73,11 +95,11 @@ class PythonFunctionNode(FrontendNode):
value=DEFAULT_PYTHON_FUNCTION,
name="code",
advanced=False,
)
),
],
)
description: str = "Python function to be executed."
base_classes: list[str] = ["function"]
base_classes: list[str] = ["Tool"]
def to_dict(self):
return super().to_dict()

14
src/backend/langflow/utils/constants.py
View file

@ -7,6 +7,20 @@ OPENAI_MODELS = [
]
CHAT_OPENAI_MODELS = ["gpt-3.5-turbo", "gpt-4", "gpt-4-32k"]
ANTHROPIC_MODELS = [
"claude-v1", # largest model, ideal for a wide range of more complex tasks.
"claude-v1-100k", # An enhanced version of claude-v1 with a 100,000 token (roughly 75,000 word) context window.
"claude-instant-v1", # A smaller model with far lower latency, sampling at roughly 40 words/sec!
"claude-instant-v1-100k", # Like claude-instant-v1 with a 100,000 token context window but retains its performance.
# Specific sub-versions of the above models:
"claude-v1.3", # Vs claude-v1.2: better instruction-following, code, and non-English dialogue and writing.
"claude-v1.3-100k", # An enhanced version of claude-v1.3 with a 100,000 token (roughly 75,000 word) context window.
"claude-v1.2", # Vs claude-v1.1: small adv in general helpfulness, instruction following, coding, and other tasks.
"claude-v1.0", # An earlier version of claude-v1.
"claude-instant-v1.1", # Latest version of claude-instant-v1. Better than claude-instant-v1.0 at most tasks.
"claude-instant-v1.1-100k", # Version of claude-instant-v1.1 with a 100K token context window.
"claude-instant-v1.0", # An earlier version of claude-instant-v1.
]
DEFAULT_PYTHON_FUNCTION = """
def python_function(text: str) -> str:

4
src/backend/langflow/utils/util.py
View file

@ -302,7 +302,9 @@ def format_dict(d, name: Optional[str] = None):
elif name == "ChatOpenAI" and key == "model_name":
value["options"] = constants.CHAT_OPENAI_MODELS
value["list"] = True
elif (name == "Anthropic" or name == "ChatAnthropic") and key == "model_name":
value["options"] = constants.ANTHROPIC_MODELS
value["list"] = True
return d

9
src/frontend/src/icons/Anthropic/anthropic.svg Normal file
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@ -0,0 +1,9 @@
<?xml version="1.0" encoding="utf-8"?>
<svg version="1.1" xmlns="http://www.w3.org/2000/svg" viewBox="-170.333 113.047 600 67.4" width="600" height="67.4">
<defs>
<style type="text/css">
.st0{fill:#1F1F1E;}
</style>
</defs>
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</svg>
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11
src/frontend/src/icons/Anthropic/anthropic_box.svg Normal file
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@ -0,0 +1,11 @@
<?xml version="1.0" encoding="utf-8"?>
<svg version="1.1" xmlns="http://www.w3.org/2000/svg" viewBox="-126.9 247.9 207.161 212.728" width="207.161" height="212.728">
<defs>
<style type="text/css">
.st0{fill:#1F1F1E;}
</style>
</defs>
<path class="st0" d="M 19.9 260.5 L 41.8 260.5 L 41.8 313.1 L 55.8 313.1 L 55.8 260.5 L 77.7 260.5 L 77.7 247.9 L 19.9 247.9 L 19.9 260.5 Z M -4.7 293.5 L -34 247.9 L -49.8 247.9 L -49.8 313 L -36.3 313 L -36.3 267.4 L -7 313.1 L 8.8 313.1 L 8.8 248 L -4.7 248 L -4.7 293.5 Z M -100.9 247.9 L -126.9 313 L -112.4 313 L -107.1 299.3 L -79.9 299.3 L -74.6 313 L -60.1 313 L -86.1 247.9 L -100.9 247.9 Z M -102.4 287.3 L -93.5 264.4 L -84.6 287.3 L -102.4 287.3 Z"></path>
<path class="st0" d="M 38.246 437.628 C 35.846 443.928 30.946 447.628 24.346 447.628 C 13.346 447.628 6.646 439.828 6.646 427.028 C 6.646 414.128 13.346 406.228 24.346 406.228 C 30.946 406.228 35.746 409.828 38.246 416.228 L 53.046 416.228 C 49.446 402.228 38.546 393.228 24.346 393.228 C 5.546 393.228 -7.754 407.228 -7.754 427.028 C -7.754 446.628 5.546 460.628 24.346 460.628 C 38.546 460.628 49.446 451.528 53.146 437.628 L 38.246 437.628 Z M -50.654 394.328 L -24.654 459.428 L -10.454 459.428 L -36.454 394.328 L -50.654 394.328 Z M -80.154 394.328 L -111.954 394.328 L -111.954 459.428 L -97.954 459.428 L -97.954 435.828 L -80.054 435.828 C -65.254 435.828 -56.254 428.028 -56.254 415.028 C -56.254 402.128 -65.354 394.328 -80.154 394.328 Z M -80.754 423.328 L -97.954 423.328 L -97.954 406.928 L -80.754 406.928 C -73.854 406.928 -70.254 409.728 -70.254 415.128 C -70.254 420.528 -73.854 423.328 -80.754 423.328 Z"></path>
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</svg>
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9
src/frontend/src/icons/Anthropic/index.tsx Normal file
View file

@ -0,0 +1,9 @@
import React, { forwardRef } from "react";
import { ReactComponent as AnthropicSVG } from "./anthropic_box.svg";
export const AnthropicIcon = forwardRef<
SVGSVGElement,
React.PropsWithChildren<{}>
>((props, ref) => {
return <AnthropicSVG ref={ref} {...props} />;
});

5
src/frontend/src/pages/FlowPage/components/DisclosureComponent/index.tsx
View file

@ -5,6 +5,7 @@ import { DisclosureComponentType } from "../../../../types/components";
export default function DisclosureComponent({
button: { title, Icon, buttons = [] },
children,
openDisc,
}: DisclosureComponentType) {
return (
<Disclosure as="div" key={title}>
@ -27,14 +28,14 @@ export default function DisclosureComponent({
<div>
<ChevronRightIcon
className={`${
open ? "rotate-90 transform" : ""
open || openDisc ? "rotate-90 transform" : ""
} h-4 w-4 text-gray-800 dark:text-white`}
/>
</div>
</div>
</Disclosure.Button>
</div>
<Disclosure.Panel as="div" className="-mt-px">
<Disclosure.Panel as="div" className="-mt-px" static={openDisc}>
{children}
</Disclosure.Panel>
</>

3
src/frontend/src/pages/FlowPage/components/extraSidebarComponent/index.tsx
View file

@ -15,6 +15,7 @@ import { MagnifyingGlassIcon } from "@heroicons/react/24/outline";
export default function ExtraSidebar() {
const { data } = useContext(typesContext);
const [dataFilter, setFilterData] = useState(data);
const [search, setSearch] = useState("");
function onDragStart(
event: React.DragEvent<any>,
@ -58,6 +59,7 @@ export default function ExtraSidebar() {
className="dark:text-white focus:outline-none block w-full rounded-md py-1.5 ps-3 pr-9 text-gray-900 shadow-sm ring-1 ring-inset ring-gray-300 placeholder:text-gray-400 sm:text-sm sm:leading-6 dark:ring-0 dark:bg-[#2d3747] dark:focus:outline-none"
onChange={(e) => {
handleSearchInput(e.target.value);
setSearch(e.target.value);
}}
/>
<div className="absolute inset-y-0 right-0 flex py-1.5 pr-3 items-center">
@ -71,6 +73,7 @@ export default function ExtraSidebar() {
.map((d: keyof APIObjectType, i) =>
Object.keys(dataFilter[d]).length > 0 ? (
<DisclosureComponent
openDisc={search.length == 0 ? false : true}
key={i}
button={{
title: nodeNames[d] ?? nodeNames.unknown,

1
src/frontend/src/types/components/index.ts
View file

@ -56,6 +56,7 @@ export type FileComponentType = {
export type DisclosureComponentType = {
children: ReactNode;
openDisc: boolean;
button: {
title: string;
Icon: ForwardRefExoticComponent<React.SVGProps<SVGSVGElement>>;

3
src/frontend/src/utils.ts
View file

@ -21,6 +21,7 @@ import { FlowType, NodeType } from "./types/flow";
import { APITemplateType, TemplateVariableType } from "./types/api";
import _ from "lodash";
import { ChromaIcon } from "./icons/ChromaIcon";
import { AnthropicIcon } from "./icons/Anthropic";
import { AirbyteIcon } from "./icons/Airbyte";
import { AzIcon } from "./icons/AzLogo";
import { BingIcon } from "./icons/Bing";
@ -155,6 +156,8 @@ export const nodeIcons: {
AirbyteJSONLoader: AirbyteIcon,
// SerpAPIWrapper: SerperIcon,
// AZLyricsLoader: AzIcon,
Anthropic: AnthropicIcon,
ChatAnthropic: AnthropicIcon,
BingSearchAPIWrapper: BingIcon,
BingSearchRun: BingIcon,
Cohere: CohereIcon,