pgvector

pgvector

PostgreSQL extension for storing vectors and running exact/approximate nearest-neighbor search in SQL.

Quick Navigation

• Installation: references/installation.md
• Core concepts and SQL recipes: references/core.md
• Indexing (HNSW / IVFFlat) and tuning: references/indexing.md
• Filtering, iterative scans, and performance: references/performance-and-filtering.md
• Types and functions reference (vector/halfvec/bit/sparsevec): references/types-and-functions.md
• Troubleshooting: references/troubleshooting.md
• Client libraries (priority):
  • Python: references/python.md
  • Go: references/go.md
  • Node (JS/TS): references/node.md
  • Java: references/java.md
  • Swift: references/swift.md

When to Use

• You need vector similarity search inside Postgres (keep vectors with relational data).
• You want SQL-native ANN indexes (HNSW or IVFFlat) with tunable recall/speed.
• You want consistent patterns to store/query embeddings across multiple application languages.

Quick Start (already installed)

Prerequisite: pgvector is installed on the Postgres server. See: references/installation.md.

Enable per database and run a first query:

CREATE EXTENSION vector;

CREATE TABLE items (id bigserial PRIMARY KEY, embedding vector(3));
INSERT INTO items (embedding) VALUES ('[1,2,3]'), ('[4,5,6]');

SELECT * FROM items ORDER BY embedding <-> '[3,1,2]' LIMIT 5;

Choosing distance operators

• L2 (Euclidean): use <->
• Inner product: use <#> (note: returns negative inner product)
• Cosine distance: use <=>
• L1: use <+>
• Binary vectors: Hamming <~> / Jaccard <%>

Indexing rules of thumb

• Exact search: no pgvector index; may use parallel scan on large tables.
• ANN search:
  • Prefer HNSW for better speed/recall, higher build time/memory.
  • Use IVFFlat when you need faster builds/lower memory.
• Create one index per distance function/operator class you plan to use.

Critical Prohibitions / Gotchas