deepagents学习打卡day02

import os
from typing import Literal
from langchain_openai import ChatOpenAI
from tavily import TavilyClient
from deepagents import create_deep_agent

# 1. 配置模型（通过硅基流动接入，当前免费模型即可跑通）
model = ChatOpenAI(
    model=os.environ.get("MODEL_NAME", "Qwen/Qwen2.5-7B-Instruct"),  # 复杂任务可改用 "zai-org/GLM-5.2"
    api_key=os.environ["SILICONFLOW_API_KEY"],
    base_url="https://api.siliconflow.cn/v1",
)

# 2. 初始化搜索客户端
tavily_client = TavilyClient(api_key=os.environ["TAVILY_API_KEY"])

# 3. 定义搜索工具
def internet_search(
    query: str,
    max_results: int = 5,
    topic: Literal["general", "news", "finance"] = "general",
    include_raw_content: bool = False,
):
    """Run a web search for the given query.

    Args:
        query: The search query string.
        max_results: Maximum number of results to return.
        topic: The topic category for the search.
        include_raw_content: Whether to include raw page content.
    """
    return tavily_client.search(
        query,
        max_results=max_results,
        include_raw_content=include_raw_content,
        topic=topic,
    )

# 4. 定义系统提示词
research_instructions = """你是一位专业的研究员。
你的工作是进行深入研究，然后撰写一份完整的研究报告。

你可以使用 internet_search 工具搜索互联网获取信息。
"""

# 5. 创建 Agent
agent = create_deep_agent(
    model=model,
    tools=[internet_search],
    system_prompt=research_instructions,
)

# 6. 运行
result = agent.invoke(
    {"messages": [{"role": "user", "content": "什么是 LangGraph？"}]}
)
print(result["messages"][-1].content)

cp ~/projects/research_deepagent/search_agent.py /mnt/c/Users/lenovo2026/Desktop/