Stop Choosing Between BM25 and Vector Search: Implement Hybrid Search with RRF

Combine dense embeddings and sparse keyword search using Reciprocal Rank Fusion to eliminate retrieval failure modes in production RAG systems.

The Bottleneck in Production

Most production RAG pipelines start with pure vector search. It works reliably during initial demos, but fails quietly once users begin searching for exact product IDs, error codes, or domain-specific identifiers.

Vector search translates text into semantic coordinate spaces. Because dense models optimize for high-level concepts, they tend to blur fine-grained details. A search for ERR-502-BAD-GATEWAY might retrieve general network troubleshooting docs instead of the exact runbook for error 502.

Conversely, relying purely on keyword search (BM25) breaks when users paraphrase. A query for "reduce database memory footprint" completely misses an article titled "Mitigating PostgreSQL RAM Saturation" if there is no direct keyword overlap.