Exact and approximate nearest-neighbor search
Exact search is the default and provides perfect recall. HNSW and IVFFlat indexes enable approximate search with a configurable speed-versus-recall tradeoff.
pgvector
Freemium
pgvector is an open-source PostgreSQL extension for storing vectors and running exact or approximate nearest-neighbor searches alongside relational data. It supports multiple vector formats, distance functions, and standard PostgreSQL operations for applications that need similarity search.
What is pgvector?
pgvector is an open-source PostgreSQL extension for storing vector data and performing vector similarity search. It keeps embeddings with application data in PostgreSQL rather than requiring a separate vector database, while allowing similarity queries to be combined with normal SQL operations.
The extension performs exact nearest-neighbor search by default, which provides perfect recall for the search operation. HNSW and IVFFlat indexes can be added for approximate search when lower query latency or larger collections justify trading some recall for speed. pgvector also supports quantization for workloads with many vectors.
Users interact with pgvector through PostgreSQL SQL. The extension supports vector storage, nearest-neighbor queries, distance calculations, aggregate averages, updates, deletes, upserts, bulk loading with COPY, and queries against vectors from other rows.
What can pgvector do?
Multiple vector representations
The extension supports single-precision, half-precision, binary, and sparse vectors, allowing storage choices to match the data and workload.
Six distance functions
SQL queries can use L2, inner product, cosine, L1, Hamming, and Jaccard distance. Hamming and Jaccard are available for binary vectors.
SQL-based data operations
Vectors can be added to new or existing tables and then inserted, updated, deleted, upserted, averaged, or loaded in bulk with PostgreSQL’s `COPY` command.
PostgreSQL-native indexing and operations
Similarity queries can be used with PostgreSQL filtering, joins, aggregates, ACID compliance, and point-in-time recovery rather than being handled outside the relational database.
Quantization and ongoing optimization
Quantization is provided for scaling collections with many vectors. The project also documents iterative index scans and performance improvements to HNSW and IVFFlat across releases.
Use Cases
“Add similarity search to an existing PostgreSQL application”
Teams can add an `embedding` column to an existing table and query similar rows without moving the related records into a separate search system.
“Build semantic retrieval workflows”
Applications can store embeddings and retrieve the nearest rows using cosine, inner-product, or L2 distance, then use the returned records in an application workflow.
“Combine vector search with relational filters”
SQL queries can exclude rows, apply conditions, join related tables, or limit results while ordering by vector distance.
“Scale a larger vector collection with indexes”
When exact search becomes too slow for a workload, teams can evaluate HNSW or IVFFlat indexes and use quantization where the collection contains many vectors.
“Support varied embedding storage formats”
Projects that need reduced precision, binary representations, or sparse vectors can choose among pgvector’s supported vector types and corresponding distance operations.
Frequently Asked Questions
What is pgvector?
pgvector is an open-source extension that adds vector similarity search and vector data types to PostgreSQL.
How do I enable pgvector in a database?
After installing the extension, run `CREATE EXTENSION vector;` once in each database where it is needed. You can then create a column such as `embedding vector(3)` and use SQL to insert and query vectors.
Does pgvector perform exact or approximate search?
It performs exact nearest-neighbor search by default. Adding an HNSW or IVFFlat index enables approximate search, which trades some recall for speed.
Which distance functions are supported?
The documented operators support L2 distance, negative inner product, cosine distance, L1 distance, Hamming distance for binary vectors, and Jaccard distance for binary vectors.
Which PostgreSQL versions are supported?
The installation instructions state that the current release supports PostgreSQL 13 and later. The changelog also records support for PostgreSQL 18.
Quick Facts
- Category
- Developer Tool
- Product type
- Open-source PostgreSQL extension
- Primary function
- Vector storage and similarity search
- Query interface
- SQL through any language with a PostgreSQL client
- Vector formats
- Single-precision, half-precision, binary, and sparse
- Installation
- Source build, Docker, package managers, PostgreSQL.app, and hosted providers
pgvector Alternatives
Spice AI
spice.ai
Open-source SQL query and hybrid search engine for data-intensive apps and AI agents, with zero ETL.
Redis
redis.io
Redis is a real-time data platform and database for applications, with Redis Cloud providing a fully managed deployment on major public cloud services. It supports application data, caching, search, and AI-oriented workflows through Redis and Redis Stack.
SereneDB
serenedb.com
SereneDB is a search-OLAP database for real-time full-text, vector, hybrid search, and analytical queries over the same data. It uses SQL and the PostgreSQL wire protocol, helping developers connect with familiar clients while keeping search and analytics in one engine.
Upstash Vector
upstash.com
Upstash Vector is a serverless vector database for storing and querying embeddings and metadata in AI and machine-learning applications. It supports dense, sparse, and hybrid indexes through a REST API and SDKs for Python, TypeScript, Go, and PHP.
MongoDB
mongodb.com
MongoDB is a modern data platform built around MongoDB Atlas, combining operational data, vector search, and stream processing for application workloads.
ParadeDB
paradedb.com
ParadeDB adds search, vector retrieval, and aggregations to PostgreSQL.