What is the data given to the llm by langchain on the embedding query

[gao-stu]

I know that when I use vector store, I give text vectors and query vectors to llm.
I would like to know what kind of query questions langchain gives llm. While executing this line of code " answer = qa.run(query )"
What does text and query become to llm.
Here's my code

    docs_path = r'E:\TSTOR\LLM\text\9_6keti'
    loader = DirectoryLoader(docs_path, glob="**/*.txt", show_progress=True, use_multithreading=True)
    docs = loader.load()
    savelocal = r"E:\TSTOR\LLM\text\vector_store_local_dataset_suanfa"
    db = FAISS.load_local(savelocal, OpenAIEmbeddings())
    text_splitter = CharacterTextSplitter.from_tiktoken_encoder(chunk_size=100, chunk_overlap=0)
    texts = text_splitter.split_documents(docs)
    vector_store = FAISS.from_documents(texts, embeddings)
    savelocal = r"E:\TSTOR\LLM\text\vector_store_local_dataset_suanfa"

    vector_store.save_local(savelocal)
    embeddings = OpenAIEmbeddings()
    retriever = db.as_retriever()
    qa = RetrievalQA.from_chain_type(llm=OpenAI(), chain_type="stuff", retriever=retriever, verbose=True)

    query = "xxxxxxx"
    answer = qa.run(query)
    print("answer：%s" % answer)

[dosubot[bot]]

Answer generated by a 🤖

Answer

From your question, I understand that you're curious about the data that LangChain provides to the Language Model (LLM) when executing the qa.run(query) command. Specifically, you're interested in understanding what the query and text become when passed to the LLM.

In the context of your code, when you execute qa.run(query), LangChain provides the LLM with two key pieces of information:

1. The query itself, which is a string that represents the question you're asking the model. In your code, this is represented by the string "xxxxxxx".

2. The retriever object, which is essentially a representation of your document corpus that the LLM can understand and search through. This is created by transforming your documents into vectors using OpenAIEmbeddings() and storing them in a FAISS vector store.

So, when you run qa.run(query), LangChain uses the retriever to find the most relevant documents in your corpus related to the query. These documents, along with the query, are then fed to the LLM, which generates an answer based on this information.

Here's the relevant part of your code:

    retriever = db.as_retriever()
    qa = RetrievalQA.from_chain_type(llm=OpenAI(), chain_type="stuff", retriever=retriever, verbose=True)

    query = "xxxxxxx"
    answer = qa.run(query)
    print("answer：%s" % answer)

In this snippet, db.as_retriever() creates the retriever from your document corpus, and qa.run(query) feeds the query and the retriever to the LLM.

This is one potential explanation based on the provided context. If you find this explanation accurate and feel it's not covered in the existing documentation, I encourage you to make a pull request to update the documentation. This would be greatly beneficial for other users who might have the same question in the future.

Sources

• docs/snippets/modules/chains/popular/vector_db_qa.mdx
• langchain/indexes/vectorstore.py

---

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

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[gao-stu]

Thank you for your answer. It is very useful. I also want to know whether the text and query given by langchain to llm are string or vectors. Can I debug and see them? I wonder if the text and query given by langchain to llm are not what I want, can I adjust them.

[dosubot[bot]]

Answer generated by a 🤖

Answer

I understand that you're working with the LangChain Python framework and you're curious about the data types of the text and query parameters that LangChain provides to the Language Model (LLM). You're also interested in debugging these values and potentially adjusting them if they don't meet your expectations.

In LangChain, the text and query parameters provided to the LLM are indeed strings. You can confirm this by looking at the question: str parameter in the _get_docs and _aget_docs methods in the RetrievalQA class, as well as in the query and query_with_sources methods in the VectorStoreIndexWrapper class.

To debug and view these values, you can simply print them before passing them to the qa.run(query) command. Here's an example:

print("Query: ", query)
answer = qa.run(query)
print("Answer: ", answer)

If the text and query values aren't what you expect, you can adjust them by modifying the query or text strings before passing them to the qa.run(query) command. For instance, you can add additional context to the query or modify the text string like so:

query = "Additional context. " + query
text = "Modified text. " + text
answer = qa.run(query)
print("Answer: ", answer)

Remember, the text string is the context that the LLM uses to generate the answer, so modifying it can change the answer that the LLM generates.

You can also set logging for the queries to get more information about what's happening during the execution of your code. This can be useful for debugging purposes. You can do this by importing the logging module and setting the logging level for the langchain.retrievers.multi_query logger to logging.INFO.

import logging

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

This will print information about the queries that are being executed to the console, which can help you understand what's happening during the execution of your code.

Please note that this is one potential solution based on the information provided. If you find this solution helpful and believe it could benefit other users, I encourage you to make a pull request to update the documentation. This way, you can contribute to the LangChain community and help other developers who might encounter the same issue.

Sources

• docs/extras/use_cases/question_answering/index.mdx
• langchain/chains/retrieval_qa/base.py
• langchain/indexes/vectorstore.py
• docs/extras/modules/chains/additional/openai_functions_retrieval_qa.ipynb
• docs/extras/modules/data_connection/retrievers/how_to/MultiQueryRetriever.ipynb

---

This response is meant to be useful, save you time, and share context. 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.

[shashankdeshpande]

You can trace it by enabling debug mode initially

import langchain
langchain.debug = True

In your example, since you have defined chain_type as stuff, Langchain will call LLM with user query along with all relevant documents that has been fetched through retriever to identify an answer.

For document QA use case, I will suggest you to quickly check this short course - LangChain: Chat with Your Data

Here are more relevant references that might be helpful -

• https://python.langchain.com/docs/use_cases/question_answering/
• https://learn.deeplearning.ai/langchain/lesson/5/question-and-answer