pgvector
pgvector Postgres extension. Covers vector types, distance operators, indexing (HNSW/IVFFlat), and client library usage. Use when storing vectors in PostgreSQL, running nearest-neighbor searches, or configuring HNSW/IVFFlat indexes. Keywords: pgvector, PostgreSQL, vector search, HNSW, IVFFlat.
Works with
---
name: pgvector
description: pgvector Postgres extension. Covers vector types, distance operators, indexing (HNSW/IVFFlat), and client library usage. Use when storing vectors in PostgreSQL, running nearest-neighbor searches, or configuring HNSW/IVFFlat indexes. Keywords: pgvector, PostgreSQL, vector search, HNSW, IVFFlat.
license: MIT
---
# 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:
```sql
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
- Approximate indexes can change results (recall vs speed).
- Index usage typically requires `ORDER BY <distance-op> ... LIMIT ...`.
- `<#>` returns negative inner product; multiply by `-1` to get the actual value.
- `NULL` vectors are not indexed; for cosine distance, zero vectors are not indexed.
## Links
- Docs / repo: https://github.com/pgvector/pgvector
- Client libs:
- Python: https://github.com/pgvector/pgvector-python
- Go: https://github.com/pgvector/pgvector-go
- Node: https://github.com/pgvector/pgvector-node
- Java: https://github.com/pgvector/pgvector-java
- Swift: https://github.com/pgvector/pgvector-swift
- Releases/tags: https://github.com/pgvector/pgvector/tagsMore Database skills
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