Pure vector search is not always enough. If you have built a semantic search system with pgvector and noticed that exact keyword matches sometimes get buried under loosely related results, you have run into the fundamental limitation of embedding-only retrieval. Hybrid search fixes this by combining vector similarity with traditional keyword matching. In PostgreSQL, you can do both in a single query, without any additional infrastructure.
This guide covers:
- Why pure vector search falls short for exact-term queries
- How Reciprocal Rank Fusion (RRF) works
- Full SQL implementation combining pgvector and tsvector
- Python wrapper and LangChain integration
- Metadata filtering, tuning, and performance considerations
Read the full post on Rivestack: https://rivestack.io/blog/hybrid-search-pgvector-postgres
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