mirror of
https://github.com/langchain-ai/langgraph.git
synced 2026-09-11 20:27:54 +02:00
replace function_call with tool_call
This commit is contained in:
midas8181919
@@ -5,7 +5,7 @@ from typing import Annotated, Sequence, TypedDict
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from langchain_core.agents import AgentAction
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from langchain_core.messages import BaseMessage, FunctionMessage
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from langchain_core.runnables import RunnableLambda
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from langchain_core.utils.function_calling import convert_to_openai_function
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from langchain_core.utils.function_calling import convert_to_openai_function, convert_to_openai_tool
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from langgraph.graph import END, StateGraph
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from langgraph.prebuilt.tool_executor import ToolExecutor
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@@ -124,3 +124,117 @@ def create_function_calling_executor(model, tools):
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# This compiles it into a LangChain Runnable,
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# meaning you can use it as you would any other runnable
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return workflow.compile()
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def create_tool_calling_executor(model, tools):
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if isinstance(tools, ToolExecutor):
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tool_executor = tools
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tool_classes = tools.tools
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else:
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tool_executor = ToolExecutor(tools)
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tool_classes = tools
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model = model.bind(functions=[convert_to_openai_tool(t) for t in tool_classes])
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# Define the function that determines whether to continue or not
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def should_continue(state):
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messages = state["messages"]
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last_message = messages[-1]
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# If there is no function call, then we finish
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if "tool_call" not in last_message.additional_kwargs:
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return "end"
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# Otherwise if there is, we continue
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else:
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return "continue"
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# Define the function that calls the model
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def call_model(state):
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messages = state["messages"]
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response = model.invoke(messages)
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# We return a list, because this will get added to the existing list
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return {"messages": [response]}
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async def acall_model(state):
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messages = state["messages"]
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response = await model.ainvoke(messages)
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# We return a list, because this will get added to the existing list
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return {"messages": [response]}
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# Define the function to execute tools
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def _get_action(state):
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messages = state["messages"]
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# Based on the continue condition
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# we know the last message involves a tool call
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last_message = messages[-1]
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# We construct an AgentAction from the tool_calls
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return AgentAction(
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tool=last_message.additional_kwargs["tool_calls"][0]["function"]["name"],
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tool_input=json.loads(
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last_message.additional_kwargs["tool_calls"][0]["function"]["arguments"]
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),
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log="",
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)
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def call_tool(state):
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action = _get_action(state)
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# We call the tool_executor and get back a response
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response = tool_executor.invoke(action)
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# We use the response to create a FunctionMessage
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function_message = FunctionMessage(content=str(response), name=action.tool)
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# We return a list, because this will get added to the existing list
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return {"messages": [function_message]}
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async def acall_tool(state):
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action = _get_action(state)
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# We call the tool_executor and get back a response
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response = await tool_executor.ainvoke(action)
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# We use the response to create a FunctionMessage
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function_message = FunctionMessage(content=str(response), name=action.tool)
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# We return a list, because this will get added to the existing list
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return {"messages": [function_message]}
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# We create the AgentState that we will pass around
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# This simply involves a list of messages
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# We want steps to return messages to append to the list
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# So we annotate the messages attribute with operator.add
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class AgentState(TypedDict):
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messages: Annotated[Sequence[BaseMessage], operator.add]
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# Define a new graph
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workflow = StateGraph(AgentState)
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# Define the two nodes we will cycle between
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workflow.add_node("agent", RunnableLambda(call_model, acall_model))
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workflow.add_node("action", RunnableLambda(call_tool, acall_tool))
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# Set the entrypoint as `agent`
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# This means that this node is the first one called
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workflow.set_entry_point("agent")
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# We now add a conditional edge
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workflow.add_conditional_edges(
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# First, we define the start node. We use `agent`.
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# This means these are the edges taken after the `agent` node is called.
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"agent",
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# Next, we pass in the function that will determine which node is called next.
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should_continue,
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# Finally we pass in a mapping.
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# The keys are strings, and the values are other nodes.
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# END is a special node marking that the graph should finish.
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# What will happen is we will call `should_continue`, and then the output of that
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# will be matched against the keys in this mapping.
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# Based on which one it matches, that node will then be called.
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{
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# If `tools`, then we call the tool node.
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"continue": "action",
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# Otherwise we finish.
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"end": END,
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},
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)
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# We now add a normal edge from `tools` to `agent`.
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# This means that after `tools` is called, `agent` node is called next.
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workflow.add_edge("action", "agent")
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# Finally, we compile it!
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# This compiles it into a LangChain Runnable,
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# meaning you can use it as you would any other runnable
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return workflow.compile()
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+360
-1
@@ -10,6 +10,7 @@ import pytest
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from langchain_core.runnables import RunnablePassthrough
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from pytest_mock import MockerFixture
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from langchain_core._api import deprecated
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from langgraph.channels.base import InvalidUpdateError
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from langgraph.channels.binop import BinaryOperatorAggregate
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from langgraph.channels.context import Context
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@@ -20,7 +21,7 @@ from langgraph.checkpoint.sqlite import SqliteSaver
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from langgraph.graph import END, Graph
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from langgraph.graph.message import MessageGraph
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from langgraph.graph.state import StateGraph
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from langgraph.prebuilt.chat_agent_executor import create_function_calling_executor
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from langgraph.prebuilt.chat_agent_executor import create_function_calling_executor, create_tool_calling_executor
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from langgraph.prebuilt.tool_executor import ToolExecutor
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from langgraph.pregel import Channel, GraphRecursionError, Pregel
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from langgraph.pregel.reserved import ReservedChannels
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@@ -1061,7 +1062,365 @@ def test_conditional_graph_state() -> None:
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},
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]
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def test_prebuilt_tool_chat() -> None:
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from langchain.chat_models.fake import FakeMessagesListChatModel
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from langchain_community.tools import tool
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from langchain_core.messages import AIMessage, FunctionMessage, HumanMessage
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class FakeFuntionChatModel(FakeMessagesListChatModel):
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def bind_functions(self, functions: list):
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return self
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@tool()
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def search_api(query: str) -> str:
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"""Searches the API for the query."""
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return f"result for {query}"
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tools = [search_api]
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app = create_tool_calling_executor(
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FakeFuntionChatModel(
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responses=[
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AIMessage(
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content="",
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additional_kwargs={
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"tool_calls": [{
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"id": "tool_call123",
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"type": "function",
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"function":{
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"name": "search_api",
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"arguments": json.dumps("query"),
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}
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}]
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},
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),
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AIMessage(
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content="",
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additional_kwargs={
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"tool_calls": [{
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"id": "tool_call234",
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"type": "function",
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"function":{
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"name": "search_api",
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"arguments": json.dumps("another"),
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}
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}]
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},
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),
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AIMessage(content="answer"),
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]
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),
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tools,
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)
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assert app.invoke(
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{"messages": [HumanMessage(content="what is weather in sf")]}
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) == {
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"messages": [
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HumanMessage(content="what is weather in sf"),
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AIMessage(
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content="",
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additional_kwargs={
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"tool_calls": [{
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"id": "tool_call123",
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"type": "function",
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"function":{
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"name": "search_api",
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"arguments": "query",
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}
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}]
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},
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),
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FunctionMessage(content="result for query", name="search_api"),
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AIMessage(
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content="",
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additional_kwargs={
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"tool_calls": [{
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"id": "tool_call234",
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"type": "function",
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"function":{
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"name": "search_api",
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"arguments": "another",
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}
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}]
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},
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),
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FunctionMessage(content="result for another", name="search_api"),
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AIMessage(content="answer"),
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]
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}
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assert [
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*app.stream({"messages": [HumanMessage(content="what is weather in sf")]})
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] == [
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{
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"agent": {
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"messages": [
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AIMessage(
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content="",
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additional_kwargs={
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"tool_calls": [{
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"id": "tool_call123",
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"type": "function",
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"function":{
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"name": "search_api",
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"arguments": "query",
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}
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}]
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},
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)
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]
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}
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},
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{
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"action": {
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"messages": [
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FunctionMessage(content="result for query", name="search_api")
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]
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}
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},
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{
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"agent": {
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"messages": [
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AIMessage(
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content="",
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additional_kwargs={
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"tool_calls": [{
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"id": "tool_call234",
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"type": "function",
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"function":{
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"name": "search_api",
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"arguments": "another",
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}
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}]
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},
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)
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]
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}
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},
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{
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"action": {
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"messages": [
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FunctionMessage(content="result for another", name="search_api")
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]
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}
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},
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{"agent": {"messages": [AIMessage(content="answer")]}},
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{
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"__end__": {
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"messages": [
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HumanMessage(content="what is weather in sf"),
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AIMessage(
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content="",
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additional_kwargs={
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"tool_calls": [{
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"id": "tool_call123",
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"type": "function",
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"function":{
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"name": "search_api",
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"arguments": "query",
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}
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}]
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},
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),
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FunctionMessage(content="result for query", name="search_api"),
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AIMessage(
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content="",
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additional_kwargs={
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"tool_calls": [{
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"id": "tool_call234",
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"type": "function",
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"function":{
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"name": "search_api",
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"arguments": "another",
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}
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}]
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},
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),
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FunctionMessage(content="result for another", name="search_api"),
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AIMessage(content="answer"),
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]
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}
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},
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]
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def test_message_graph() -> None:
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from langchain.chat_models.fake import FakeMessagesListChatModel
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from langchain_community.tools import tool
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from langchain_core.agents import AgentAction
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from langchain_core.messages import AIMessage, FunctionMessage, HumanMessage
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class FakeFuntionChatModel(FakeMessagesListChatModel):
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def bind_functions(self, functions: list):
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return self
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@tool()
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def search_api(query: str) -> str:
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"""Searches the API for the query."""
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return f"result for {query}"
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tools = [search_api]
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model = FakeFuntionChatModel(
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responses=[
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AIMessage(
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content="",
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additional_kwargs={
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"function_call": {
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"name": "search_api",
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"arguments": json.dumps("query"),
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}
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},
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),
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AIMessage(
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content="",
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additional_kwargs={
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"function_call": {
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"name": "search_api",
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"arguments": json.dumps("another"),
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}
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},
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),
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AIMessage(content="answer"),
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]
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)
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tool_executor = ToolExecutor(tools)
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# Define the function that determines whether to continue or not
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def should_continue(messages):
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last_message = messages[-1]
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# If there is no function call, then we finish
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if "function_call" not in last_message.additional_kwargs:
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return "end"
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# Otherwise if there is, we continue
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else:
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return "continue"
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def call_tool(messages):
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# Based on the continue condition
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# we know the last message involves a function call
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last_message = messages[-1]
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# We construct an AgentAction from the function_call
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action = AgentAction(
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tool=last_message.additional_kwargs["function_call"]["name"],
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tool_input=json.loads(
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last_message.additional_kwargs["function_call"]["arguments"]
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),
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log="",
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)
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# We call the tool_executor and get back a response
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response = tool_executor.invoke(action)
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# We use the response to create a FunctionMessage
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return FunctionMessage(content=str(response), name=action.tool)
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# Define a new graph
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workflow = MessageGraph()
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# Define the two nodes we will cycle between
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workflow.add_node("agent", model)
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workflow.add_node("action", call_tool)
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# Set the entrypoint as `agent`
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# This means that this node is the first one called
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workflow.set_entry_point("agent")
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|
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# We now add a conditional edge
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workflow.add_conditional_edges(
|
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# First, we define the start node. We use `agent`.
|
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# This means these are the edges taken after the `agent` node is called.
|
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"agent",
|
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# Next, we pass in the function that will determine which node is called next.
|
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should_continue,
|
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# Finally we pass in a mapping.
|
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# The keys are strings, and the values are other nodes.
|
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# END is a special node marking that the graph should finish.
|
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# What will happen is we will call `should_continue`, and then the output of that
|
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# will be matched against the keys in this mapping.
|
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# Based on which one it matches, that node will then be called.
|
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{
|
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# If `tools`, then we call the tool node.
|
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"continue": "action",
|
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# Otherwise we finish.
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"end": END,
|
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},
|
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)
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|
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# We now add a normal edge from `tools` to `agent`.
|
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# This means that after `tools` is called, `agent` node is called next.
|
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workflow.add_edge("action", "agent")
|
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|
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# Finally, we compile it!
|
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# This compiles it into a LangChain Runnable,
|
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# meaning you can use it as you would any other runnable
|
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app = workflow.compile()
|
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|
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assert app.invoke(HumanMessage(content="what is weather in sf")) == [
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HumanMessage(content="what is weather in sf"),
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AIMessage(
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content="",
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additional_kwargs={
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"function_call": {"name": "search_api", "arguments": '"query"'}
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},
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),
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FunctionMessage(content="result for query", name="search_api"),
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AIMessage(
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content="",
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additional_kwargs={
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"function_call": {"name": "search_api", "arguments": '"another"'}
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},
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),
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FunctionMessage(content="result for another", name="search_api"),
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AIMessage(content="answer"),
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]
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assert [*app.stream([HumanMessage(content="what is weather in sf")])] == [
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{
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"agent": AIMessage(
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content="",
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additional_kwargs={
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"function_call": {"name": "search_api", "arguments": '"query"'}
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},
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)
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},
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{"action": FunctionMessage(content="result for query", name="search_api")},
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{
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"agent": AIMessage(
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content="",
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additional_kwargs={
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"function_call": {"name": "search_api", "arguments": '"another"'}
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},
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)
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},
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{"action": FunctionMessage(content="result for another", name="search_api")},
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{"agent": AIMessage(content="answer")},
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{
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"__end__": [
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HumanMessage(content="what is weather in sf"),
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AIMessage(
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content="",
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additional_kwargs={
|
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"function_call": {"name": "search_api", "arguments": '"query"'}
|
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},
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),
|
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FunctionMessage(content="result for query", name="search_api"),
|
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AIMessage(
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content="",
|
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additional_kwargs={
|
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"function_call": {
|
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"name": "search_api",
|
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"arguments": '"another"',
|
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}
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},
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),
|
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FunctionMessage(content="result for another", name="search_api"),
|
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AIMessage(content="answer"),
|
||||
]
|
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},
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]
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|
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@deprecated("*")
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def test_prebuilt_chat() -> None:
|
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from langchain.chat_models.fake import FakeMessagesListChatModel
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from langchain_community.tools import tool
|
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|
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Reference in New Issue
Block a user