Time-Weighted Retriever Implementation Using Langchain

Dear Pinecone community,

I am currently utilizing Pinecone in conjunction with Langchain for a document retrieval project. Each record in my index is associated with a ‘created_at’ timestamp.

I am seeking to implement a time-weighted retriever strategy that biases the retrieval process towards more recent documents. Are there any guidances on any recommended approaches or best practices for configuring Pinecone to prioritize newer documents based on their timestamp? Additionally, if there are methods that are particularly easy to implement using Langchain, I would appreciate any insights on those as well.

Hi @congxing, and welcome to the Pinecone forums!

Thank you for your question.

This is an interesting approach and it makes sense.

It sounds like you may want to try a post-retrieval re-ranking step, where your application code first receives the most relevant items back from your LangChain vectorstore, and then performs some custom logic to re-rank the items according to recency.

That would probably most easily be achieved by using LangChain’s similarity_search method, like so:

query="What is Pinecone?"

When you receive the documents, you could then filter and re-sort them based on their created_at values.

You might also look into our metadata filtering/querying capabilities, but depending on your exact query, you may need to re-implement some of the code that LangChain is abstracting away for you if you want more control.

Does that make sense and help at all?