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How hybrid retrieval works
The two branches solve different retrieval problems. PostgreSQL full-text search matches a prepared tsvector document against a tsquery using @@, and can rank matches with functions such as ts_rank_cd. pgvector finds candidates by vector similarity. PostgreSQL describes tsvector as an optimized document representation and tsquery as a query representation; see the PostgreSQL 18 text-search introduction and text-search functions and operators.
The branch scores are not directly comparable: a text rank and a vector distance have different meanings and scales. RRF instead combines each document’s position in each branch’s ranked list. A document that ranks well in either list can therefore surface in the fused results, even if it does not appear in both.
The pgvector project describes combining PostgreSQL full-text search and vector search as hybrid search, and identifies RRF or a cross-encoder as fusion options in its hybrid-search guidance. RRF is a rank-fusion step, not a replacement for choosing suitable text and vector retrieval settings.
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Example: fuse two candidate lists in one statement
This illustrative query assigns branch-local ranks, unions the candidates, and sums an RRF contribution for every appearance. Bind $1 to the text query, $2 and $4 to the lexical and semantic candidate limits, $3 to the query vector, and $5 to the final result limit.
WITH
lexical AS (
SELECT id,
row_number() OVER (
ORDER BY ts_rank_cd(textsearch, query) DESC, id
) AS rank
FROM documents,
websearch_to_tsquery('english', $1) AS query
WHERE textsearch @@ query
ORDER BY ts_rank_cd(textsearch, query) DESC, id
LIMIT $2
),
semantic AS (
SELECT id,
row_number() OVER (
ORDER BY embedding <=> $3::vector, id
) AS rank
FROM documents
ORDER BY embedding <=> $3::vector, id
LIMIT $4
),
ranked AS (
SELECT id, rank, 'lexical' AS branch FROM lexical
UNION ALL
SELECT id, rank, 'semantic' AS branch FROM semantic
)
SELECT id,
sum(1.0 / (60 + rank)) AS rrf_score
FROM ranked
GROUP BY id
ORDER BY rrf_score DESC, id
LIMIT $5;This is a teaching outline, not a tested or universally optimal query. The 60 in the example is a parameter choice, not a proven best constant for your corpus. The example uses websearch_to_tsquery('english', ...) and the pgvector <=> distance operator; choose the text-search configuration and vector operator appropriate to your application. PostgreSQL documents text-search preparation and ranking in Controlling Text Search; pgvector’s README documents its operators and indexing options.
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What to adapt before using it
Text representation and query construction
Ensure textsearch is a tsvector built with the intended text-search configuration. The example’s English configuration is not suitable for every language or content model. PostgreSQL’s text-search types documentation explains tsvector and tsquery; its text-search controls documentation covers query construction and ranking.
Vector distance and index choice
Use a distance operator and index operator class that match the similarity objective and the index you configure. The example uses <=>; do not treat that operator as a universal choice. Consult the current pgvector README for supported operators, indexes, and version-specific guidance.
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Candidate depth, filters, and ties
The per-branch limits decide which documents are eligible for fusion. Limits that are too small can exclude relevant items before RRF sees them; larger limits may increase query work. There is no universally correct candidate depth in the cited documentation. Tune limits and filter placement against representative queries and your actual workload. The example uses id as a deterministic tie-breaker; use your stable document identifier.
Fusion and optional reranking
UNION ALL preserves candidates from either branch, and grouping sums contributions when a document appears in both. You can also evaluate weighted ranks or a later reranking stage, but weights and reranking introduce choices that should be judged on your application’s data. pgvector’s guidance names a cross-encoder as another fusion-related option; it does not establish that one approach is best for every workload.
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Validate quality and execution on your workload
A single SQL statement is an expression of the retrieval pipeline, not a performance guarantee. The official PostgreSQL and pgvector documentation establishes the relevant capabilities, not a general latency or relevance result for this particular query. Check both retrieval quality and database behavior on the schema, data, versions, and hardware you actually deploy.
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- Compare the fused list with lexical-only and vector-only results using representative queries and judged relevance.
- Check exact-term cases such as names, identifiers, and phrases, as well as semantically relevant queries that do not share the same wording.
- Vary candidate limits and assess whether the fused pool retains relevant documents without excessive database work.
- Inspect
EXPLAIN (ANALYZE, BUFFERS)to see the actual plan, index behavior, and resource use. - Measure latency and relevance with your real filters and data; do not infer either from the fact that the branches appear in one statement.
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