langchain vectorstore question and answer from a single embedding in vectorstore – Langchain

The Problem:

I have created a vectorstore from a series of paragraphs from a text document. The text of the document has been split into non-overlapping paragraphs for a good reason, as these represent different information. These paragraphs have metadata that has been included. I would like to retrieve the different embeddings in my FAISS vectorstore and then query those individually, using as a query something like "What’s this paragraph about?". Is there any option to query a specific embedding or to use as a retriever a single specific embedding? In any case, I would like to gain access of the original paragraph I’m querying together with its metadata.

The Solutions:

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Solution 1: Use filter option in as_retriever()

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In order to query a specific embedding or to use a single specific embedding as a retriever, you can use the filter option in the as_retriever() function. This will allow you to filter the results by the metadata of the paragraphs, such as the paragraph ID or the page number.

Here’s how modified code would look like:

filter_dict = {"paragraph_id": 19, "page": 5}

qa_chain = RetrievalQA.from_chain_type(
    llm=ChatOpenAI(temperature = 0.0, model='gpt-4'), 
    chain_type="stuff", 
    retriever=db.as_retriever(filter=filter_dict), 
    verbose=False
)

label_output = qa_chain.run(query="What is this paragraph about?")
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Solution 2: Use index IDs to filter specific embeddings

1. Look into the index IDs assigned within the vector database and store them into an iterable object.
2. Iterate through the list of index IDs and use the ID as part of the filter_dict.

import faiss

# Get the index IDs from the vector database
index_ids = [doc.index_id for doc in paragraphs_document_list]

# Iterate through the index IDs and use the ID as part of the filter_dict
for index_id in index_ids:
    filter_dict = {"index_id": index_id}
    results = db.similarity_search(query, filter=filter_dict, k=1, fetch_k=1)

Solution 3: Using Pinecone for Metadata Filtering

Pinecone provides the ability to index your data with metadata and filter your search results based on that metadata. To use Pinecone, you can follow these steps:

1. Import the Pinecone library and create a Pinecone instance:

from pinecone.client import Pinecone

vectorstore = Pinecone(index, embed.embed_query, "text")

2. Index your data with metadata:

vectorstore.add_documents(documents, metadata={"paragraph_id": 19, "page": 5})

3. Search for similar vectors with metadata filtering:

results = vectorstore.search(query, filters={"paragraph_id": 19, "page": 5})

This approach allows you to query specific embeddings based on their metadata and retrieve the original paragraph and its metadata along with the search results.

Video Explanation: