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