Individual skill
postgres-hybrid-text-search
Use this skill to implement hybrid search combining BM25 keyword search with semantic vector search using Reciprocal Rank Fusion (RRF). **Trigger when user asks to:** - Combine keyword and semantic search - Implement hybrid search or multi-modal retrieval - Use BM25/pg_textsearch with pgvector together - Implement RRF (Reciprocal Rank Fusion) for search - Build search that handles both exact terms and meaning **Keywords:** hybrid search, BM25, pg_textsearch, RRF, reciprocal rank fusion, keyword search, full-text search, reranking, cross-encoder Covers: pg_textsearch BM25 index setup, parallel query patterns, client-side RRF fusion (Python/TypeScript), weighting strategies, and optional ML reranking.
skills/postgres-hybrid-text-search/SKILL.md
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# 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)
