Choosing the Right Retrieval Strategy for Production Systems: BM25 vs. Vector Search

This article explores the two dominant retrieval paradigms - BM25 and Vector Search - and how to combine them for effective search in production systems.

Details

The article delves into the strengths and weaknesses of BM25 and Vector Search. BM25 is a probabilistic ranking algorithm that uses term frequency, inverse document frequency, and document length normalization, making it effective for exact keyword matching but limited in understanding semantic intent. Vector Search, on the other hand, transforms text into dense numerical vectors where semantically similar content is geometrically close, enabling it to excel at semantic equivalence and natural language queries, but struggling with exact term matching. The article advocates for a hybrid retrieval approach, running both BM25 and Vector Search in parallel and combining the results using Reciprocal Rank Fusion (RRF). This approach leverages the strengths of both methods and addresses their individual limitations. The article also highlights common architecture mistakes, such as relying solely on Vector Search for RAG, ignoring chunk boundaries, and using a general-purpose embedding model on a specialized corpus.