postgres-hybrid-text-search

# Hybrid Text Search Hybrid search combines keyword search (BM25) with semantic search (vector embeddings) to get the best of both: exact keyword matching and meaning-based retrieval. Use Reciprocal Rank Fusion (RRF) to merge results from both methods into a single ranked list. This guide covers combining [pg_textsearch](https://github.com/timescale/pg_textsearch) (BM25) with [pgvector](https://github.com/pgvector/pgvector). Requires both extensions. For high-volume setups, filtering, or advanced pgvector tuning (binary quantization, HNSW parameters), see the **pgvector-semantic-search** skill. pg_textsearch is a new BM25 text search extension for PostgreSQL, fully open-source and available hosted on Tiger Cloud as well as for self-managed deployments. It provides true BM25 ranking, which often improves relevance compared to PostgreSQL's built-in ts_rank and can offer better performance at scale. Note: pg_textsearch is currently in prerelease and not yet recommended for production use. pg_textsearch currently supports PostgreSQL 17 and 18. ## When to Use Hybrid Search - **Use hybrid** when queries mix specific terms (product names, codes, proper nouns) with conceptual intent - **Use semantic only** when meaning matters more than exact wording (e.g., "how to fix slow queries" should match "query optimization") - **Use keyword only** when exact matches are critical (e.g., error codes, SKUs, legal citations) Hybrid search typically improves recall over either method alone,...