118 lines
3.6 KiB
Python
118 lines
3.6 KiB
Python
import os
|
|
from config import OPENAI_API_KEY,LANGCHAIN_API_KEY
|
|
from langchain_openai import ChatOpenAI
|
|
from langchain.tools import tool
|
|
from langchain_core.messages import HumanMessage
|
|
from langgraph.graph import StateGraph, MessagesState, START, END
|
|
from langgraph.prebuilt import ToolNode
|
|
from langgraph.checkpoint.memory import MemorySaver
|
|
|
|
# LangSmith调试
|
|
os.environ["LANGCHAIN_TRACING_V2"] = "true" # 总开关，决定启用追踪功能
|
|
os.environ["LANGCHAIN_PROJECT"] = "human_approval" # 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"
|
|
)
|
|
|
|
|
|
# 定义一个敏感工具：发送邮件(模拟)
|
|
@tool
|
|
def send_email(to, content):
|
|
"""模拟发送邮件"""
|
|
return f'邮件已发送至{to},内容为：{content}'
|
|
|
|
|
|
# 工具绑定到llm
|
|
tools = [send_email]
|
|
llm_with_tools = llm.bind_tools(tools)
|
|
|
|
# Node函数与Edge节点
|
|
tool_node = ToolNode(tools)
|
|
|
|
|
|
def call_model(state: MessagesState):
|
|
response = llm_with_tools.invoke(state['messages'])
|
|
return {"messages":[response]}
|
|
|
|
|
|
def should_continue(state: MessagesState):
|
|
last_msg = state['messages'][-1]
|
|
if hasattr(last_msg, "tool_calls") and last_msg.tool_calls:
|
|
return "tools"
|
|
return END
|
|
|
|
|
|
|
|
# 构建基础ReAct图
|
|
workflow = StateGraph(MessagesState)
|
|
|
|
workflow.add_node("agent", call_model)
|
|
workflow.add_node("tools", tool_node)
|
|
workflow.add_edge(START, "agent")
|
|
|
|
workflow.add_conditional_edges(
|
|
"agent",
|
|
should_continue,
|
|
{
|
|
"tools": "tools",
|
|
END: END
|
|
}
|
|
)
|
|
|
|
workflow.add_edge("tools", "agent")
|
|
|
|
app = workflow.compile(
|
|
# 在内存里做状态持久化
|
|
checkpointer=MemorySaver(),
|
|
interrupt_before=["tools"] # 选择要人工审批的节点 -- 负责在哪里停，之后的代码负责停了之后怎么办
|
|
)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
config = {
|
|
"configurable":{"thread_id":"user123"}
|
|
}
|
|
user_input = "请帮我给 boss@example.com 发一封邮件，内容是：会议推迟到明天下午3点。不要询问其他细节。"
|
|
|
|
print("用户输入:",user_input)
|
|
print("\nAgent正在思考...\n")
|
|
|
|
# 初识输入
|
|
inputs = {"messages":[HumanMessage(content=user_input)]}
|
|
|
|
while 1:
|
|
# 触发工作流执行，推进到下一个中断点或自然结束
|
|
# inputs注入事件;config确定回话id;"values":完整记录每步结果
|
|
for _ in app.stream(inputs,config,stream_mode="values"): # 流式执行
|
|
pass # 必须迭代生成器，才能实际执行工作流
|
|
|
|
# 获取当前状态
|
|
snapshot = app.get_state(config)
|
|
next_tasks = snapshot.next # 返回下一步要执行的节点名列表
|
|
|
|
# 如果没有下一步，说明工作流已结束
|
|
if not next_tasks:
|
|
final_msg = snapshot.values['messages'][-1]
|
|
print(f'\n最终回复:{final_msg.content}')
|
|
break
|
|
|
|
# 如果下一步是需要审批的节点
|
|
if "tools" in next_tasks:
|
|
last_msg = snapshot.values['messages'][-1]
|
|
tool_call = last_msg.tool_calls[0]
|
|
print(f'\n⚠️ Agent准备执行操作：')
|
|
print(f' 工具名称：{tool_call["name"]}')
|
|
print(f' 参数：{tool_call["args"]}')
|
|
|
|
approval = input("\n✅ 是否批准执行？(输入 'yes' 继续，其他取消): ").strip().lower()
|
|
if approval == "yes":
|
|
print('\n 继续执行...')
|
|
inputs = None # 表示从断点继续，无新输入
|
|
else:
|
|
print("\n❌ 操作已取消，流程终止")
|
|
break |