5. langgraph中的react agent使用 (从零构建一个react agent)

1. 定义 Agent 状态

首先，我们需要定义 Agent 的状态，这包括 Agent 所持有的消息。

from typing import (
    Annotated,
    Sequence,
    TypedDict,
)
from langchain_core.messages import BaseMessage
from langgraph.graph.message import add_messages

class AgentState(TypedDict):
    
    messages: Annotated[Sequence[BaseMessage], add_messages]

2. 初始化模型和工具

接下来，我们初始化一个 ChatOpenAI 模型，并定义一个工具 get_weather。

from langchain_openai import ChatOpenAI
from langchain_core.tools import tool

model = ChatOpenAI(
    temperature=0,
    model="glm-4-plus",
    openai_api_key="your_api_key",
    openai_api_base="https://open.bigmodel.cn/api/paas/v4/"
)

@tool
def get_weather(location: str):
    """Call to get the weather from a specific location."""
    # This is a placeholder for the actual implementation
    # Don't let the LLM know this though 😊
    if any([city in location.lower() for city in ["sf", "san francisco"]]):
        return "It's sunny in San Francisco, but you better look out if you're a Gemini 😈."
    else:
        return f"I am not sure what the weather is in {location}"

tools = [get_weather]

model = model.bind_tools(tools)

3. 定义工具节点和模型调用节点

我们需要定义工具节点和模型调用节点，以便在 Agent 工作流中使用。

import json
from langchain_core.messages import ToolMessage, SystemMessage
from langchain_core.runnables import RunnableConfig

tools_by_name = {tool.name: tool for tool in tools}

def tool_node(state: AgentState):
    outputs = []
    for tool_call in state["messages"][-1].tool_calls:
        tool_result = tools_by_name[tool_call["name"]].invoke(tool_call["args"])
        outputs.append(
            ToolMessage(
                content=json.dumps(tool_result),
                name=tool_call["name"],
                tool_call_id=tool_call["id"],
            )
        )
    return {"messages": outputs}

def call_model(
    state: AgentState,
    config: RunnableConfig,
):
 
    system_prompt = SystemMessage(
        "You are a helpful AI assistant, please respond to the users query to the best of your ability!"
    )
    response = model.invoke([system_prompt] + state["messages"], config)

    return {"messages": [response]}

def should_continue(state: AgentState):
    messages = state["messages"]
    last_message = messages[-1]
    # If there is no function call, then we finish
    if not last_message.tool_calls:
        return "end"
    # Otherwise if there is, we continue
    else:
        return "continue"

4. 构建工作流

使用 StateGraph 构建工作流，定义节点和边。

from langgraph.graph import StateGraph, END

workflow = StateGraph(AgentState)

workflow.add_node("agent", call_model)
workflow.add_node("tools", tool_node)

workflow.set_entry_point("agent")

workflow.add_conditional_edges(
    "agent",
    should_continue,
    {
        "continue": "tools",
        "end": END,
    },
)

workflow.add_edge("tools", "agent")

graph = workflow.compile()

from IPython.display import Image, display

try:
    display(Image(graph.get_graph().draw_mermaid_png()))
except Exception:
    pass

5. 运行工作流

最后，我们定义一个辅助函数来格式化输出，并运行工作流。

# Helper function for formatting the stream nicely
def print_stream(stream):
    for s in stream:
        message = s["messages"][-1]
        if isinstance(message, tuple):
            print(message)
        else:
            message.pretty_print()

inputs = {"messages": [("user", "what is the weather in sf")]}
print_stream(graph.stream(inputs, stream_mode="values"))

输出结果如下：

================================[1m Human Message [0m=================================
what is the weather in sf
================================[1m Ai Message [0m==================================
Tool Calls:
  get_weather (call_9208187575599553774)
 Call ID: call_9208187575599553774
  Args:
    location: San Francisco
================================[1m Tool Message [0m=================================
Name: get_weather

"It's sunny in San Francisco, but you better look out if you're a Gemini 😈."
================================[1m Ai Message [0m==================================

It's sunny in San Francisco, but you better look out if you're a Gemini 😈.

参考链接：https://langchain-ai.github.io/langgraph/how-tos/react-agent-from-scratch/

原文地址：https://blog.csdn.net/qq_41472205/article/details/143810892

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