postgres-semantic-search

What this skill does

# PostgreSQL Semantic Search

## Quick Start

### 1. Setup

```sql
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

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

### 3. Add Index (> 10k documents)

```sql
CREATE INDEX ON documents USING hnsw (embedding vector_cosine_ops);
```

### Docker Quick Start

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

# Or PostgreSQL 18 (latest)
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

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

### Common Queries

```sql
-- 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 2 DESC LIMIT 10;

-- With threshold (parenthesize the distance — keeps it clear and precedence-safe)
SELECT * FROM docs WHERE (embedding <=> $1) < 0.3 ORDER BY 1 LIMIT 10;

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

### Index Quick Reference

```sql
-- 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+)
SET hnsw.iterative_scan = relaxed_order;
SET ivfflat.iterative_scan = on;
```

## 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.

```sql
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](references/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](references/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](references/hybrid-search.md#custom-fts-configuration-eg-language--unaccent) for the 3-step DDL pattern.
- **Prefix tsquery** for languages with rich inflection (no full morphology engine required):

  ```sql
  CREATE OR REPLACE FUNCTION prefix_tsquery(p text)
  RETURNS tsquery LANGUAGE sql IMMUTABLE AS $$
    SELECT to_tsquery('simple',