怎么实现到指定网址检索来回答问题

[WangXusheng1225]

怎么实现到指定网址检索来回答问题

[dosubot[bot]]

🤖

嘿，@WangXusheng1225！很高兴再次见到你。希望一切都好！

在Langchain-Chatchat的代码库中，已经实现了在指定网址上检索以回答问题的功能。具体实现方式如下：

首先，定义了一个异步函数 search_engine_iter(query: str)，这个函数接收一个字符串参数 query，这个参数就是要在网上搜索的问题。然后，这个函数调用了 search_engine_chat 函数，将 query 作为参数传入，并指定搜索引擎为 "bing"。这个函数会返回一个响应，响应中包含了搜索结果。

然后，这个函数通过一个异步循环从响应的 body_iterator 中读取数据。每次循环，都会从 body_iterator 中读取一个 json 字符串，然后将这个字符串解析为 Python 对象。这个对象中包含了搜索结果的答案和相关文档。

最后，这个函数返回搜索结果的答案。

另外，还定义了一个函数 search_internet(query: str)，这个函数接收一个字符串参数 query，然后调用 search_engine_iter 函数，并等待其返回结果。这个函数的返回值就是搜索结果的答案。

所以，如果你想在指定网址上检索以回答问题，你可以调用 search_internet 函数，将你的问题作为参数传入。这个函数会返回搜索结果的答案。

例如，如果你想知道今天是星期几，你可以这样调用这个函数：

result = search_internet("今天星期几")
print("答案:",result)

这段代码会在网上搜索 "今天星期几" 这个问题，然后打印出搜索结果的答案。

你可以在这个链接中查看相关的代码：server/agent/tools/search_internet.py

此外，Langchain-Chatchat的代码库还支持使用Bing、DuckDuckGo和Metaphor等搜索引擎在特定网站上进行搜索以回答问题。这是通过 lookup_search_engine 函数实现的，该函数使用指定的搜索引擎执行搜索。它接收一个查询，搜索引擎的名称，以及其他可选参数。它使用 SEARCH_ENGINES 字典根据提供的搜索引擎名称选择适当的搜索函数。然后，使用 search_result2docs 函数将搜索结果转换为文档。

这是支持此功能的代码片段：

SEARCH_ENGINES = {"bing": bing_search,
                  "duckduckgo": duckduckgo_search,
                  "metaphor": metaphor_search,
                  }

async def lookup_search_engine(
        query: str,
        search_engine_name: str,
        top_k: int = SEARCH_ENGINE_TOP_K,
        split_result: bool = False,
):
    search_engine = SEARCH_ENGINES[search_engine_name]
    results = await run_in_threadpool(search_engine, query, result_len=top_k, split_result=split_result)
    docs = search_result2docs(results)
    return docs

search_engine_chat 函数是使用 lookup_search_engine 函数根据用户的查询和指定的搜索引擎执行搜索的主要函数。然后，它使用搜索结果生成响应。

你可以在这个链接中查看相关的代码：server/chat/search_engine_chat.py

Sources

• server/agent/tools/search_internet.py
• server/chat/search_engine_chat.py

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[WangXusheng1225]

[dosubot]

🤖

Based on the context you provided, it seems like you're on the right track. The lookup_search_engine function is indeed used to perform a search based on the user's query and the specified search engine. The results of this search are then passed to a large language model for processing to generate an answer.

Here's a step-by-step guide on how you can do this:

1. First, you need to call the lookup_search_engine function with the appropriate parameters to get the search results. This function returns a list of Document objects, where each Document represents a search result.

docs = await lookup_search_engine(query, search_engine_name, top_k, split_result=split_result)

2. Next, you need to convert the Document objects into a string format that can be processed by the large language model. In this case, the page_content of each Document is concatenated into a single string, separated by newline characters.

context = "\n".join([doc.page_content for doc in docs])

3. Now, you can pass the context string to the large language model for processing. In this case, the context string is passed to the LLMChain object, which represents the large language model. The acall method of the LLMChain object is then called with the context string and the original query to generate an answer.

chain = LLMChain(prompt=chat_prompt, llm=model)
task = asyncio.create_task(wrap_done(
    chain.acall({"context": context, "question": query}),
    callback.done),
)

4. The acall method returns an AsyncIterable object that yields the tokens of the generated answer. These tokens can be collected and concatenated into a single string to form the final answer.

answer = ""
async for token in callback.aiter():
    answer += token

5. Finally, the answer is returned in a JSON format.

yield json.dumps({"answer": answer,
                  "docs": source_documents},
                 ensure_ascii=False)

You can find more details about this process in the server/chat/search_engine_chat.py file.

As for the structure of the Document object, it has two main attributes: page_content and metadata. The page_content attribute contains the snippet of the search result, and the metadata attribute is a dictionary that contains additional information about the search result, such as the source link and the title.

I hope this helps! If you have any more questions, feel free to ask.

Sources

• server/chat/search_engine_chat.py

About Dosu

This response is meant to be useful and save you time. It is not meant to be a precise solution, but rather a starting point for your own research.

Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

If you want to continue the conversation, start your reply with @dosu-bot.

[zRzRzRzRzRzRzR]

确实没有这个功能，目前需要把网址自己弄成固定的知识库