103 lines
2.9 KiB
Python
103 lines
2.9 KiB
Python
import os
|
||
from config import OPENAI_API_KEY,LANGCHAIN_API_KEY
|
||
from langchain_core.messages import HumanMessage
|
||
from langchain.tools import tool
|
||
from langchain_openai import ChatOpenAI
|
||
from langgraph.graph import StateGraph, MessagesState, END,START
|
||
from langgraph.prebuilt import ToolNode
|
||
from langchain_core.messages import SystemMessage
|
||
from langgraph.checkpoint.memory import MemorySaver
|
||
|
||
os.environ["LANGCHAIN_TRACING_V2"] = "true" # 总开关，决定启用追踪功能
|
||
os.environ["LANGCHAIN_PROJECT"] = "demo02" # 自定义项目名
|
||
os.environ["LANGCHAIN_API_KEY"] = LANGCHAIN_API_KEY
|
||
|
||
# LLM配置
|
||
llm = ChatOpenAI(
|
||
model="deepseek-chat",
|
||
api_key=OPENAI_API_KEY,
|
||
base_url="https://api.deepseek.com"
|
||
)
|
||
|
||
# Prompt配置
|
||
sys_prompt = "你是一个强大的助手，能查天气，也能回答一般问题。请使用中文回答。"
|
||
|
||
# 工具定义
|
||
@tool
|
||
def get_weather(loaction):
|
||
"""模拟获取天气"""
|
||
return f'{loaction}当前天气：23℃，晴，风力2级'
|
||
|
||
|
||
tools = [get_weather]
|
||
llm_with_tools = llm.bind_tools(tools) # 让llm学会调用工具节点
|
||
|
||
|
||
# --- 核心组件:拆解AgentExecutor ---
|
||
# ReAct Step1:Thought(LLM决策)
|
||
def call_model(state:MessagesState):
|
||
# 构造带system prompt 的完整消息列表(仅用于本次LLM调用)
|
||
message_for_llm = [SystemMessage(content=sys_prompt)]+state["messages"]
|
||
response = llm_with_tools.invoke(message_for_llm)
|
||
# 此处只会返回新生成的消息，不包含prompt，防止污染历史
|
||
return {"messages":[response]} # 新消息追加到状态
|
||
|
||
|
||
# ReAct Step2-3:Action + Observation
|
||
tool_node = ToolNode(tools)
|
||
|
||
# ReAct Step4:Loop Controller(是否循环)
|
||
def should_continue(state:MessagesState):
|
||
last_msg = state["messages"][-1]
|
||
if hasattr(last_msg,"tool_calls") and last_msg.tool_calls:
|
||
return "tools" # 有工具调用 -> 执行工具
|
||
return END # 无工具调用 -> 返回答案
|
||
|
||
|
||
# --- 构建 ReAct 循环图---
|
||
workflow = StateGraph(MessagesState)
|
||
|
||
workflow.add_node("agent",call_model) # Thought
|
||
workflow.add_node("tools",tool_node) # Action + Observation
|
||
|
||
workflow.add_edge(START,"agent")
|
||
|
||
# 条件边:Thought -> 决定下一步
|
||
workflow.add_conditional_edges(
|
||
"agent", # 从哪个节点出发
|
||
should_continue, # 决定下一步去哪
|
||
{
|
||
"tools":"tools", # 如果返回tools，去tools节点
|
||
END:END # 如果返回END，直接结束工作流
|
||
}
|
||
)
|
||
|
||
workflow.add_edge("tools","agent")
|
||
|
||
|
||
|
||
# 编译时启用记忆
|
||
app = workflow.compile(checkpointer=MemorySaver())
|
||
|
||
|
||
|
||
if __name__ == '__main__':
|
||
session_id = "user123"
|
||
config = {
|
||
"configurable":{"thread_id":session_id}
|
||
}
|
||
while 1:
|
||
user_input = input('\n你：')
|
||
if user_input.strip().lower() == 'quit':
|
||
break
|
||
|
||
result = app.invoke(
|
||
{'messages':[HumanMessage(content=user_input)]},
|
||
config=config
|
||
)
|
||
|
||
ai_msg = result["messages"][-1]
|
||
print(f'AI：{ai_msg.content}')
|
||
|
||
|