155 lines
6.3 KiB
Python
155 lines
6.3 KiB
Python
import os
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import asyncio
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from datetime import datetime
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from deepagents import create_deep_agent
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from openai import OpenAI
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from langchain.chat_models import init_chat_model
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from langchain.agents import create_agent
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from langgraph.checkpoint.memory import InMemorySaver, MemorySaver # 导入检查点工具
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from deepagents.backends import StoreBackend
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from loguru import logger
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def internet_search_tool(query: str):
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"""Run a web search"""
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logger.info(f"Running internet search for query: {query}")
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client = OpenAI(
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api_key=os.getenv('DASHSCOPE_API_KEY'),
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base_url=os.getenv('DASHSCOPE_BASE_URL'),
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)
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logger.info(f"create OpenAI")
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completion = client.chat.completions.create(
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model="qwen-plus",
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messages=[
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{'role': 'system', 'content': 'You are a helpful assistant.'},
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{'role': 'user', 'content': query}
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],
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extra_body={
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"enable_search": True
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}
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)
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logger.info(f"create completions")
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logger.info(f"OpenAI response: {completion.choices[0].message.content}")
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return completion.choices[0].message.content
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# System prompt to steer the agent to be an expert researcher
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today = datetime.now().strftime("%Y年%m月%d日")
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research_instructions = f"""你是一个智能助手。你的任务是帮助用户完成各种任务。
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你可以使用互联网搜索工具来获取信息。
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## `internet_search`
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使用此工具对给定查询进行互联网搜索。你可以指定返回结果的最大数量、主题以及是否包含原始内容。
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今天的日期是：{today}
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"""
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# Create the deep agent with memory
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model = init_chat_model(
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model="gpt-4.1-mini",
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model_provider='openai',
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api_key=os.getenv('OPENAI_API_KEY'),
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base_url=os.getenv('OPENAI_BASE_URL'),
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)
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checkpointer = InMemorySaver() # 创建内存检查点，自动保存历史
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agent = create_deep_agent( # state：thread会话级的状态
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tools=[internet_search_tool],
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system_prompt=research_instructions,
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model=model,
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checkpointer=checkpointer, # 添加检查点，启用自动记忆
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interrupt_on={'internet_search_tool':True}
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)
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# 多轮对话循环（使用 Checkpointer 自动记忆）
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printed_msg_ids = set() # 跟踪已打印的消息ID
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thread_id = "user_session_001" # 会话 ID，区分不同用户/会话
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config = {"configurable": {"thread_id": thread_id}, "metastore": {'assistant_id': 'owenliang'}} # 配置会话
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print("开始对话（输入 'exit' 退出）：")
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while True:
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user_input = input("\nHUMAN: ").strip()
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if user_input.lower() == 'exit':
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break
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# 使用 values 模式多次返回完整状态，这里按 message.id 去重，并按类型分类打印
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pending_resume = None
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while True:
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if pending_resume is None:
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request = {"messages": [{"role": "user", "content": user_input}]}
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else:
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from langgraph.types import Command as _Command
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request = _Command(resume=pending_resume)
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pending_resume = None
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for item in agent.stream(
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request,
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config=config,
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stream_mode="values",
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):
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state = item[0] if isinstance(item, tuple) and len(item) == 2 else item
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# 先检查是否触发了 Human-In-The-Loop 中断
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if isinstance(state, dict) and "__interrupt__" in state:
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interrupts = state["__interrupt__"] or []
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if interrupts:
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hitl_payload = interrupts[0].value
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action_requests = hitl_payload.get("action_requests", [])
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print("\n=== 需要人工审批的工具调用 ===")
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decisions: list[dict[str, str]] = []
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for idx, ar in enumerate(action_requests):
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name = ar.get("name")
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args = ar.get("args")
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print(f"[{idx}] 工具 {name} 参数: {args}")
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while True:
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choice = input(" 决策 (a=approve, r=reject): ").strip().lower()
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if choice in ("a", "r"):
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break
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decisions.append({"type": "approve" if choice == "a" else "reject"})
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# 下一轮调用改为 resume，同一轮用户回合继续往下跑
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pending_resume = {"decisions": decisions}
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break
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# 兼容 dict state 和 AgentState dataclass
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messages = state.get("messages", []) if isinstance(state, dict) else getattr(state, "messages", [])
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for msg in messages:
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msg_id = getattr(msg, "id", None)
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if msg_id is not None and msg_id in printed_msg_ids:
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continue
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if msg_id is not None:
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printed_msg_ids.add(msg_id)
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msg_type = getattr(msg, "type", None)
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if msg_type == "human":
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# 用户输入已经在命令行里，不再重复打印
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continue
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if msg_type == "ai":
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tool_calls = getattr(msg, "tool_calls", None) or []
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if tool_calls:
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# 这是发起工具调用的 AI 消息（TOOL CALL）
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for tc in tool_calls:
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tool_name = tc.get("name")
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args = tc.get("args")
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print(f"TOOL CALL [{tool_name}]: {args}")
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# 如果 AI 同时带有自然语言内容，也一起打印
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if getattr(msg, "content", None):
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print(f"AI: {msg.content}")
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continue
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if msg_type == "tool":
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# 工具执行结果（TOOL RESPONSE）
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tool_name = getattr(msg, "name", None) or "tool"
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print(f"TOOL RESPONSE [{tool_name}]: {msg.content}")
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continue
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# 兜底：其它类型直接打印出来便于调试
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print(f"[{msg_type}]: {getattr(msg, 'content', None)}")
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# 如果没有新的中断需要 resume，则整轮结束，等待下一轮用户输入
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if pending_resume is None:
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break
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