openai agent第二弹：deepresearch原理介绍

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已于 2025-02-06 00:08:27 修改 · 3k 阅读

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于 2025-02-05 22:45:19 首次发布

文章目录

技术原理
类似开源项目
参考资料

2月2日openai上线了第二个agent: deep research，具体功能类似24年11月google gemini发布的deep research。

技术原理

deep research 使用端到端强化学习，训练模型在不同领域推理和复杂浏览任务的能力；这种方法的核心原则是，模型学会自主规划和执行多步骤过程以找到相关数据，包括基于实时信息进行回溯和适应的能力。此过程允许模型处理诸如浏览用户上传的文件、生成和细化图形以及引用网络来源等任务。

类似开源项目

OpenDeepResearcher

开源地址：https://github.com/mshumer/OpenDeepResearcher

该项目侧重于用asyncio和aiohttp进行异步编程和请求响应，以此项目为例，具体进行深度研究的流程如下：

根据用户输入的研究主题，生成多个相关的query:

async def generate_search_queries_async(session, user_query):
    """
    Ask the LLM to produce up to four precise search queries (in Python list format)
    based on the user’s query.
    """
    prompt = (
        "You are an expert research assistant. Given the user's query, generate up to four distinct, "
        "precise search queries that would help gather comprehensive information on the topic. "
        "Return only a Python list of strings, for example: ['query1', 'query2', 'query3']."
    )
    messages = [
        {
   
   "role": "system", "content": "You are a helpful and precise research assistant."},
        {
   
   "role": "user", "content": f"User Query: {
     
     user_query}\n\n{
     
     prompt}"}
    ]
    response = await call_openrouter_async(session, messages)
    if response:
        try:
            # Expect exactly a Python list (e.g., "['query1', 'query2']")
            search_queries = eval(response)
            if isinstance(search_queries, list):
                return search_queries
            else:
                print("LLM did not return a list. Response:", response)
                return []
        except Exception as e:
            print("Error parsing search queries:", e, "\nResponse:", response