PostgreSQL Semantic Search Skill

PostgreSQL Semantic Search

Quick Start

1. Setup

CREATE EXTENSION IF NOT EXISTS vector;

CREATE TABLE documents (
    id SERIAL PRIMARY KEY,
    content TEXT NOT NULL,
    embedding vector(1536)  -- 1536-dim embedding
    -- Or: embedding halfvec(3072)  -- 3072-dim embedding (halfvec = 50% memory)
);

2. Basic Semantic Search

SELECT id, content, 1 - (embedding <=> query_vec) AS similarity
FROM documents
ORDER BY embedding <=> query_vec
LIMIT 10;

3. Add Index (> 10k documents)

CREATE INDEX ON documents USING hnsw (embedding vector_cosine_ops);

Docker Quick Start

# pgvector with PostgreSQL 17
docker run -d --name pgvector-db \
  -e POSTGRES_PASSWORD=postgres \
  -p 5432:5432 \
  pgvector/pgvector:pg17

# Or PostgreSQL 18
docker run -d --name pgvector-db \
  -e POSTGRES_PASSWORD=postgres \
  -p 5432:5432 \
  pgvector/pgvector:pg18

# ParadeDB (includes pgvector + pg_search + BM25)
docker run -d --name paradedb \
  -e POSTGRES_PASSWORD=postgres \
  -p 5432:5432 \
  paradedb/paradedb:latest  # `latest` is convenient for quick-start; pin to e.g. paradedb/paradedb:pg17 for reproducible builds

Connect: psql postgresql://postgres:postgres@localhost:5432/postgres

Cheat Sheet

Distance Operators

embedding <=> query  -- Cosine distance (1 - similarity)
embedding <-> query  -- L2/Euclidean distance
embedding <#> query  -- Negative inner product

Common Queries

-- Top 10 similar (cosine)
SELECT * FROM docs ORDER BY embedding <=> $1 LIMIT 10;

-- With similarity score
SELECT *, 1 - (embedding <=> $1) AS similarity FROM docs ORDER BY embedding <=> $1 LIMIT 10;

-- With a distance threshold — put the filter OUTSIDE a materialized CTE.
-- Filtering inline (WHERE (embedding <=> $1) < 0.3 ORDER BY ... LIMIT 10) makes
-- the executor apply the filter before the index returns LIMIT rows, so you get
-- fewer results than expected. pgvector documents this CTE form as the fix.
WITH nearest AS MATERIALIZED (
  SELECT id, content, embedding <=> $1 AS distance FROM docs
  ORDER BY distance LIMIT 10
) SELECT * FROM nearest WHERE distance < 0.3 ORDER BY distance;

-- Preload index (run on startup)
SELECT 1 FROM docs ORDER BY embedding <=> $1 LIMIT 1;

Index Quick Reference

-- HNSW (recommended)
CREATE INDEX ON docs USING hnsw (embedding vector_cosine_ops);

-- With tuning
CREATE INDEX ON docs USING hnsw (embedding vector_cosine_ops)
WITH (m = 24, ef_construction = 200);

-- Query-time recall
SET hnsw.ef_search = 100;

-- Iterative scan for filtered queries (pgvector 0.8+; OFF by default)
SET hnsw.iterative_scan = relaxed_order;    -- or strict_order
SET ivfflat.iterative_scan = relaxed_order; -- IVFFlat has no strict_order

Decision Trees

Choose Search Method

Query type?
├─ Conceptual/meaning-based → Pure vector search
├─ Exact terms/names → Pure keyword search (FTS)
├─ Fuzzy/typo-tolerant → pg_trgm trigram similarity
├─ Autocomplete/prefix → pg_trgm + prefix index
├─ Substring (LIKE/ILIKE) → pg_trgm GIN index
└─ Mixed/unknown → Hybrid search
    ├─ Simple setup → FTS + RRF (no extra extensions)
    ├─ Better ranking → BM25 + RRF (pg_search extension)
    └─ Full-featured → ParadeDB (Elasticsearch alternative)

Choose Index Type

Document count?
├─ < 10,000 → No index needed
├─ 10k - 1M → HNSW (best recall)
└─ > 1M → IVFFlat (less memory) or HNSW

Choose Vector Type

Choose by dimensions, not by provider — the column type only depends on embedding size and pgvector's HNSW index limits.

Embedding dimensions (N)?
├─ N ≤ 2000  → vector(N)   — HNSW indexable directly
├─ 2000 < N ≤ 4000 → halfvec(N) — vector(N)'s HNSW limit is 2000; halfvec extends to 4000
└─ N > 4000  → vector(N) without HNSW, or quantize via dimensionality reduction

Common embedding dimensions are 1536 and 3072, but sizes vary by provider and model — check the provider's docs for the embedding you're using.

For multilingual / non-English content, prefer multilingual-tuned embedding models (look for "multilingual" in the model name). Models tuned only on English may handle compound words and inflection poorly.

Storage vs. index trick for 2000 < N ≤ 4000: keep the column as vector(N) (full float4, useful for future re-embedding or re-ranking experiments) and only cast at index creation and query time. This preserves precision on disk while staying within HNSW's dimension limit.

CREATE INDEX ON docs USING hnsw ((embedding::halfvec(3072)) halfvec_cosine_ops);
-- Query must cast identically so the planner picks the index:
SELECT * FROM docs ORDER BY embedding::halfvec(3072) <=> $1 LIMIT 10;

If storage is tight or you never plan to re-embed, use halfvec(N) as the column type directly.

Measure before adopting

Every optimization in this skill (hybrid fusion, reranking, query expansion, embedding-model swaps) can regress on a specific corpus. Vendor and paper benchmarks are usually English, general-domain. Real counter-examples observed in production:

Query expansion (HyDE) regressing Hit@5 by tens of points on a domain corpus.
A widely recommended reranker regressing Hit@5 double-digits on multilingual text.

Rule: build a domain eval set (evaluation.md), then A/B each change. Adopt with ≥ +3 pp Hit@5 and p95 latency within budget; reject otherwise.

Operators

| Operator | Distance | Use Case | |----------|----------|----------| | <=> | Cosine | Text embeddings (default) | | <-> | L2/Euclidean | Image embeddings | | <#> | Inner product | Normalized vectors |

SQL Functions

Semantic Search

match_documents(query_vec, threshold, limit) - Basic search
match_documents_filtered(query_vec, metadata_filter, threshold, limit) - With JSONB filter
match_chunks(query_vec, threshold, limit) - Search document chunks

Fuzzy Search (pg_trgm)

fuzzy_search_trigram(query_text, threshold, limit) - Trigram similarity search
autocomplete_search(prefix, limit) - Prefix + fuzzy autocomplete
hybrid_search_fuzzy_semantic(query_text, query_vec, limit, rrf_k) - Fuzzy + vector RRF
weighted_fts_search(query_text, language, limit) - FTS with title/content weighting

Hybrid Search (FTS)

hybrid_search_fts(query_vec, query_text, limit, rrf_k, language) - FTS + RRF
hybrid_search_weighted(query_vec, query_text, limit, sem_weight, kw_weight) - Linear combination
hybrid_search_fallback(query_vec, query_text, limit) - Graceful degradation

Hybrid Search (BM25)

hybrid_search_bm25(query_vec, query_text, limit, rrf_k) - BM25 + RRF
hybrid_search_bm25_highlighted(...) - With snippet highlighting
hybrid_search_chunks_bm25(...) - For RAG with chunks

Re-ranking (Optional)

Two-stage retrieval improves precision: fast recall → precise rerank with a cross-encoder. Use when results need higher precision and you have <50 candidates after initial retrieval.

Key rule: rerankers must be wrapped so a failure (missing key, HTTP error, timeout) returns null and the caller falls back to original retrieval order — never let a reranker outage break search.

For provider comparison, generic Promise<T | null> wrapper, and self-hosted options, see reranking.md.

Multilingual / non-English content tips

When the corpus is non-English (Finnish, German, French, Spanish, etc.):

FTS language config: pass the matching language to to_tsvector(language, text) to apply the built-in snowball stemmer (e.g., 'finnish' handles opiskelija → opiskelij). For mixed-language corpora, use 'simple' and rely on prefix/trigram fallbacks instead.
Combine stemmer + unaccent for accent-insensitive matching ("café" matches "cafe"). See hybrid-search.md → Custom FTS configuration for the 3-step DDL pattern.
Prefix tsquery for languages with rich inflection (no full morphology engine required): build the tsquery manually with :* on each token, so kartta:* matches kartta, karttaa, karttoja. websearch_to_tsquery cannot emit :*. Use the hardened prefix_tsquery(regconfig, text) in fuzzy-search.md → Prefix Matching for Agglutinative Languages — it sanitizes tsquery metacharacters from user input and falls back to websearch_to_tsquery for quoted phrases. Do not hand-roll a version without that sanitizing; raw input containing &, |, !, ( or : raises a syntax error.
Compound-word fallback: pair semantic search with pg_trgm similarity to catch compound-word misses (e.g., a query for "ammattikorkea" should still find "ammattikorkeakoulu").
BM25 stemmer in ParadeDB: tokenize with { "type": "default", "stemmer": "<language>" } — a raw tokenizer only matches full fields.
Multilingual embeddings: prefer models explicitly trained on your target language(s). English-only embeddings often miss inflected forms and compound words. The gap can be large — several percentage points of Hit@5 on non-English retrieval is realistic. Benchmark your specific language + domain before committing.
Cross-language RRF fusion for monolingual corpora: when the corpus is one language and queries arrive in many, run two hybrid passes per off-language query (original-language embedding + translated-language embedding, same FTS text) and RRF-merge. Recovers domain terms that cross-lingual embeddings collapse. See hybrid-search.md → Cross-language RRF fusion pattern.
Per-language indexing for multilingual content: when translated content exists, add language_code to the chunk table (default to the original language so existing rows backfill), include it in the uniqueness constraint, and scope ingest writes/deletes to one language. Search stays language-agnostic; native-language queries hit native embeddings directly.
```
ALTER TABLE chunks ADD COLUMN language_code TEXT NOT NULL DEFAULT 'en';
ALTER TABLE chunks DROP CONSTRAINT chunks_doc_chunk_unique;
ALTER TABLE chunks ADD CONSTRAINT chunks_doc_chunk_lang_unique
  UNIQUE (doc_id, chunk_index, language_code);
CREATE INDEX chunks_doc_lang_idx ON chunks (doc_id, language_code);
```

References

fuzzy-search.md - pg_trgm, fuzzy matching, LIKE/ILIKE, autocomplete, advanced FTS
paradedb.md - ParadeDB full-text search (Elasticsearch alternative)
vector-types.md - vector vs halfvec, dimensions, storage
indexing.md - HNSW, IVFFlat, GIN parameters
hybrid-search.md - FTS, BM25, RRF algorithms
performance.md - Cold-start, memory, HNSW vs IVFFlat
evaluation.md - Eval-set construction, Hit@K / MRR, adoption thresholds, reranker/expansion benchmarking
reranking.md - Two-stage retrieval, graceful fallback, when rerankers regress

Scripts

setup.sql - Extension and table setup
semantic_search.sql - Semantic search functions
hybrid_search_fts.sql - FTS hybrid functions
hybrid_search_bm25.sql - BM25 hybrid functions
fuzzy_search.sql - pg_trgm fuzzy search, autocomplete, weighted FTS
indexes.sql - Index creation scripts
embeddings.ts - Embedding generation helpers (TypeScript)

Common Patterns

TypeScript Integration (Supabase)

// Semantic search
const { data } = await supabase.rpc('match_documents', {
  query_embedding: embedding,
  match_threshold: 0.7,
  match_count: 10
});

// Hybrid search
const { data } = await supabase.rpc('hybrid_search_fts', {
  query_embedding: embedding,
  query_text: userQuery,
  match_count: 10,
  rrf_k: 60,
  fts_language: 'simple'
});

Drizzle ORM

import { sql } from 'drizzle-orm';

const results = await db.execute(sql`
  SELECT * FROM match_documents(
    ${embedding}::vector(1536),
    0.7,
    10
  )
`);

Troubleshooting

| Symptom | Cause | Solution | |---------|-------|----------| | Index not used | < 10k rows or planner choice | Normal for small tables, check with EXPLAIN | | Slow first query (30-60s) | HNSW cold-start | SELECT pg_prewarm('idx_name') or preload query | | Poor recall | Low ef_search | SET hnsw.ef_search = 100 or higher | | FTS returns nothing | Wrong language config | Use 'simple' for mixed/unknown languages | | Memory error on index build | maintenance_work_mem too low | Increase to 2GB+ | | Cosine similarity > 1 | Vectors not normalized | Normalize before insert or use L2 | | Slow inserts | Index overhead | Batch inserts, consider IVFFlat | | Fuzzy search slow | Missing trigram index | CREATE INDEX USING gin (col gin_trgm_ops) | | ILIKE '%x%' slow | No pg_trgm GIN index | Enable pg_trgm + create GIN trigram index | | % operator error | pg_trgm not installed | CREATE EXTENSION IF NOT EXISTS pg_trgm |

Compatibility

pgvector: 0.8.6+ recommended as the safe floor. Feature history: 0.7.0 added halfvec/bit/sparsevec, 0.8.0 added iterative scans. Correctness history: 0.6.0–0.8.1 carry a parallel-HNSW-build buffer overflow (CVE-2026-3172 — leaks data from other relations or crashes the server), 0.8.2 fixed it, 0.8.3 fixed possible HNSW index corruption during vacuum, 0.8.4 fixed further HNSW vacuum errors, 0.8.6 fixed an IVFFlat build overflow on 32-bit. Verify current state in the CHANGELOG — the GitHub Releases tab is empty, releases ship as tags.
pg_search: Since 0.25.0 pg_search depends on pgvector's vector type — install pgvector first. Check ParadeDB releases for latest.
PostgreSQL: pgvector supports 13+; pg_search ships prebuilt binaries for 15+. Prefer the newest major your host offers.

Related Skills

| Need | Skill | |------|-------| | General Postgres performance, indexes, RLS, connection pooling | /supabase-postgres-best-practices | | Chatbot orchestration, session DB, tool calls, HITL, feedback | /nextjs-chatbot | | AI SDK usage for embeddings and retrieval | /ai-sdk |

For ParadeDB-specific questions, always apply the Documentation Fetch Policy in references/paradedb.md — live docs at https://docs.paradedb.com/llms-full.txt are the authoritative source.

External Documentation

Core

pgvector GitHub - Official extension, latest features
PostgreSQL FTS - Built-in full-text search

Embedding providers

OpenAI Embeddings - model list + dimensions
Voyage Embeddings - includes multilingual model
Cohere Embed - model list
HuggingFace Hub - open-weight embeddings

Reranker providers

Cohere Rerank
Voyage Rerank
Zerank
Sentence Transformers - self-hosted cross-encoders

Hosting / extensions

Supabase Vector Guide - Supabase-specific integration
ParadeDB pg_search - BM25 extension documentation
ParadeDB AI Docs - Fetch for latest ParadeDB API (always current)

Agent Skills: PostgreSQL Semantic Search

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