I have a collection of long documents, which I am trying to chunk and index with a SentenceSplitter. How can I help to ensure that RAG retrieval returns results at a document level?
To be clear, what I mean is, I want to index chunks, but when I perform a search like, "find me conversations with rude speakers", that I get back a list of the best matching documents and not just the best matching nodes as the latter may result in just returning 10 segments/nodes from the same 'very rude conversation'.
The chunking helps with search, but does llama-index provide any sort of built in mechanism to facilitate the retrieval filtering?
Ideally my goal is to support things like "find me the rudest conversations from last week" or something along those lines. I'm close but this document-level post-filter has got me stuck.
@Logan M thanks, but I guess this would still require me to filter the responses, as opposed to incorporating this concept into the retrieval and scoring process itself?
I'm using OpenSearch as the backend and I was hoping to do something similar to what is supportedin Elastic, where the vector search response can return the top K documents directly instead of just the top K segments
The issue I"m trying to overcome is if I retrieve the 'top 5' hits but all five hits come from the same document, then I end up only looking at 1 conversation.
