Hybrid RAG Production Pipeline: BM25 + HNSW Parallel Retrieval Merged via Reciprocal Rank Fusion + Cross-Encoder Reranker

Medium·high signal

Run sparse BM25 retrieval and dense HNSW vector retrieval in parallel against the same query, merge the result lists using Reciprocal Rank Fusion (which normalizes scores across both systems without tuning), then feed the top-K candidates to a cross-encoder reranker that jointly scores query and passage. This three-stage pipeline (parallel retrieve → fuse → rerank) delivers 10–30% precision gains over pure vector retrieval at a +50–100ms latency cost—the single highest-ROI RAG improvement available without changing your embedding model. Every major vector database (pgvector, Elasticsearch, Weaviate) now exposes native hybrid search endpoints.