| 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. |
| metadata | {"version":"0.8.6","release_date":"2026-07-29"} |
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
- 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
