0.2版本知识库问答如何实现MultiQueryRetriever?

[yangpeng-space]

计划通过MultiQueryRetriever来提升效果，找到了MultiQueryRetriever示例：
from langchain.chat_models import ChatOpenAI
from langchain.retrievers.multi_query import MultiQueryRetriever

question = "What are the approaches to Task Decomposition?"
llm = ChatOpenAI(temperature=0)
retriever_from_llm = MultiQueryRetriever.from_llm(
retriever=vectordb.as_retriever(), llm=llm
)

Set logging for the queries

import logging

logging.basicConfig()
logging.getLogger("langchain.retrievers.multi_query").setLevel(logging.INFO)

unique_docs = retriever_from_llm.get_relevant_documents(query=question)
len(unique_docs)

具体需要修改项目中knowledge_base_chat.py的哪些内容来实现MultiQueryRetriever?

[dosubot]

To implement MultiQueryRetriever in the knowledge_base_chat.py file, you will need to make several modifications. Here's a general guide on what to change:

1. Import the Necessary Modules: Add the imports for MultiQueryRetriever and any other necessary modules at the beginning of the file.

   from langchain.retrievers.multi_query import MultiQueryRetriever
   from langchain.chat_models import ChatOpenAI

2. Initialize the LLM: Set up the ChatOpenAI model, which will be used by the MultiQueryRetriever.

   llm = ChatOpenAI(temperature=0)

3. Configure the Retriever: Replace the existing document retrieval logic with MultiQueryRetriever. You will need to convert your vectordb object to a retriever and then use it to initialize MultiQueryRetriever.

   retriever_from_llm = MultiQueryRetriever.from_llm(
       retriever=vectordb.as_retriever(), llm=llm
   )

4. Retrieve Documents: Use the MultiQueryRetriever to get relevant documents. Replace the existing document search logic with the following:

   unique_docs = await run_in_threadpool(
       retriever_from_llm.get_relevant_documents, query=query
   )

5. Logging (Optional): If you want to enable logging for the queries, configure the logging as shown in your example.

   import logging
   
   logging.basicConfig()
   logging.getLogger("langchain.retrievers.multi_query").setLevel(logging.INFO)

6. Adjust the Flow: Ensure that the rest of the code that processes the retrieved documents and generates responses is compatible with the output from MultiQueryRetriever.

These changes will integrate MultiQueryRetriever into your knowledge_base_chat.py file, allowing you to enhance the retrieval process with multiple queries. Make sure to test the implementation thoroughly to ensure it works as expected.

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