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60 lines
1.7 KiB
Markdown
60 lines
1.7 KiB
Markdown
---
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id: postgres-full-text-search
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title: PostgreSQL Full-Text Search
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type: documentation
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tags:
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- postgres
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- fts
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- tsvector
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- search
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status: published
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author: asep
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created_at: 2026-08-20
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updated_at: 2026-08-20
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---
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# PostgreSQL Full-Text Search
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MCPedia's keyword search is backed by PostgreSQL's native full-text search (FTS), not
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an external engine. The `documents` table carries a generated `tsvector` column that
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combines the title (weight `A`) and body (weight `B`).
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## Generated search vector
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The column is `generatedAlwaysAs`, so it is always consistent with the row and needs
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no trigger:
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```ts
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searchVector: tsvector("search_vector")
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.notNull()
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.generatedAlwaysAs(
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sql`setweight(to_tsvector('simple', coalesce(${documents.title}, '')), 'A') ||
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setweight(to_tsvector('simple', coalesce(${documents.body}, '')), 'B')`,
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),
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```
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The `'simple'` config disables stemming, so mixed identifier/English queries (e.g.
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`websocket`, `tsvector`) match literally. A GIN index on `searchVector` keeps lookups
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fast.
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## Query + ranking
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Search parses the user query with `websearch_to_tsquery` (or `plainto_tsquery`), then
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ranks with `ts_rank`:
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```sql
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SELECT *, ts_rank(search_vector, q) AS rank
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FROM documents, websearch_to_tsquery('simple', $1) q
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WHERE search_vector @@ q
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ORDER BY rank DESC;
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```
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A headline snippet for the UI comes from `ts_headline`, which bolds the matched lexemes.
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## Hybrid fusion
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Semantic search (embedding cosine) and FTS are fused with **Reciprocal Rank Fusion**
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(RRF) in `packages/search`. Each result set is ranked, scored `1/(k + rank)`, and the
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summed scores re-rank the union — no cross-score normalization needed, which is robust
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when the two signals live on different scales.
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