Yes. pgvector supports hybrid keyword and semantic search when you pair its vector-similarity retrieval with PostgreSQL full-text search. The pgvector project recommends this combination and demonstrates combining the separate result lists with Reciprocal Rank Fusion (RRF); it also names a cross-encoder as an option. Hybrid search is a pattern built from these components, not a single special pgvector operator.
What keyword search and semantic search each do
PostgreSQL full-text search finds lexical matches
PostgreSQL converts document text into a tsvector and a user’s search into a tsquery. The @@ operator checks whether the document matches the query. You can rank matches with ts_rank_cd, which uses cover density. PostgreSQL’s query-conversion functions include plainto_tsquery and websearch_to_tsquery; the latter is designed for a more familiar, forgiving web-search style of input. PostgreSQL full-text search introduction and text-search functions and operators.
pgvector finds semantically similar candidates
pgvector stores embeddings in PostgreSQL and lets you order documents by vector distance to a query embedding. The project’s hybrid-search example uses cosine distance, with the <=> operator, to create a semantic ranking. pgvector hybrid-search documentation.
How to combine pgvector and full-text search
- Store both representations. Keep each document’s text and embedding in PostgreSQL, and give both retrieval paths a shared document identifier. The project’s example uses a documents table with content and an embedding. pgvector hybrid-search documentation.
- Retrieve keyword matches. Convert the user’s text query into a
tsquery, match documents with@@, and optionally rank them withts_rank_cd. For web-style query input, considerwebsearch_to_tsquery. PostgreSQL text-search functions and operators. - Retrieve semantic matches. Create an embedding for the query, order documents by distance to it, and take a candidate set. The documented example uses cosine distance with
<=>. pgvector hybrid-search documentation. - Combine or refine the candidates. Join the two result lists by document ID and combine their ranks with RRF, or use a cross-encoder as a further ranking option. pgvector hybrid-search documentation.
Choosing a way to combine results
| Approach | What it does | What the documentation establishes |
|---|---|---|
| Reciprocal Rank Fusion (RRF) | Combines the keyword and semantic rankings by adding reciprocal-rank contributions for each document. | The pgvector README includes a Python example that joins result sets by document ID and sums those contributions. It does not provide a benchmark showing when RRF is best. pgvector hybrid-search documentation. |
| Cross-encoder | Offers another way to combine or refine retrieved results. | The README names this option but does not provide a quantitative comparison with RRF. pgvector hybrid-search documentation. |
Use the approach that performs well for your application’s queries and corpus. The documented examples establish the implementation pattern, not a universally optimal ranking method or a guaranteed improvement in relevance.
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What to consider when implementing hybrid search
- Query behavior: Full-text search matches lexical content and supports query operators and ranking informed by lexical, proximity, and structural information. Vector retrieval ranks by embedding similarity, which can surface semantically related content without relying only on matching words. PostgreSQL full-text search introduction.
- Text indexing: PostgreSQL says text-search indexes are optional, but usually desirable when a column is searched regularly. PostgreSQL text-search indexes.
- Workload and relevance: Vector-index choices and tuning depend on your workload; the hybrid-search example does not benchmark them. Assess retrieval quality against representative searches from your own application rather than assuming a fusion method will suit every corpus.
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