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)  -- text-embedding-3-small
    -- Or: embedding halfvec(3072)  -- text-embedding-3-large (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 (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

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 2 DESC LIMIT 10;

-- With threshold
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

-- 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
└─ 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

Embedding model?
├─ text-embedding-3-small (1536) → vector(1536)
├─ text-embedding-3-large (3072) → halfvec(3072) (50% memory savings)
└─ Other models → vector(dimensions)

Operators

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

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.

When to Use

• Results need higher precision
• Using < 50 candidates after initial search
• Have budget for API calls (Cohere) or compute (local models)

Options

TypeScript Example (Cohere)

import { CohereClient } from 'cohere-ai';

const cohere = new CohereClient({ token: process.env.COHERE_API_KEY });

async function rerankResults(query: string, documents: string[]) {
  const response = await cohere.rerank({
    model: 'rerank-v4.0-fast',

...