Thank you :). I updated the README to have some more explanation of the steps.
The chunking algorithm chunks by logical section (intro, abstract, authors, etc.) and also utilizes recursive subdivision chunking (chunk at 512 characters, then 256, then 128...). It is quite naive still but it works OK for now. An improvement would perhaps involve more advanced techniques like knowledge graph precomputation.
Reranking works by instead of embedding each text chunk as a vector and performing cosine similarity nearest neighbor search, you use a Cross-Encoder model that compares two texts and outputs a similarity score. Specifically, I chose Cohere's Reranker that specializes in comparing Query and Answer chunk pairs.
The chunking algorithm chunks by logical section (intro, abstract, authors, etc.) and also utilizes recursive subdivision chunking (chunk at 512 characters, then 256, then 128...). It is quite naive still but it works OK for now. An improvement would perhaps involve more advanced techniques like knowledge graph precomputation.
Reranking works by instead of embedding each text chunk as a vector and performing cosine similarity nearest neighbor search, you use a Cross-Encoder model that compares two texts and outputs a similarity score. Specifically, I chose Cohere's Reranker that specializes in comparing Query and Answer chunk pairs.