mirror of
https://github.com/langchain-ai/langgraph.git
synced 2026-09-13 21:27:52 +02:00
finish test
This commit is contained in:
midas8181919
+414
-2
@@ -17,6 +17,7 @@ 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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@@ -26,7 +27,7 @@ from langgraph.checkpoint.aiosqlite import AsyncSqliteSaver
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from langgraph.checkpoint.memory import MemorySaver
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from langgraph.graph import END, Graph, StateGraph
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from langgraph.graph.message import MessageGraph
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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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@@ -1111,6 +1112,417 @@ async def test_conditional_graph_state() -> None:
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]
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async 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_function_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 await app.ainvoke(
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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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c
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async for c in app.astream(
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{"messages": [HumanMessage(content="what is weather in sf")]}
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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_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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async def test_message_tool_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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"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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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 "tool_calls" 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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async 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["tool_calls"][0]["fcuntion"]["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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# 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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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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# 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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app = workflow.compile()
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assert await app.ainvoke(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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"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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assert [
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c async for c in app.astream([HumanMessage(content="what is weather in sf")])
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] == [
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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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"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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{"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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"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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{"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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"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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@deprecated("*")
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async 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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@@ -1258,7 +1670,7 @@ async def test_prebuilt_chat() -> None:
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},
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]
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@deprecated("*")
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async 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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