pgvector is an open-source PostgreSQL extension that adds native support for storing, indexing, and querying high-dimensional vectors. It enables efficient similarity search within existing PostgreSQL databases, allowing vector operations alongside traditional relational data.¹⁾

Install pgvector from source or via package managers:²⁾

• Linux – apt install postgresql-16-pgvector
• macOS – brew install pgvector
• Managed services – pre-installed on AWS RDS, Google Cloud SQL, Supabase, Neon

Enable in a database with:

CREATE EXTENSION vector;

Create a vector column with a fixed dimension count (e.g., 1536 for OpenAI embeddings):

CREATE TABLE items (
  id BIGSERIAL PRIMARY KEY,
  content TEXT,
  embedding VECTOR(1536)
);

pgvector introduces the vector data type for fixed-length arrays of floating-point numbers:³⁾

• Dimensions specified at column creation
• Integrity validation on insert
• Aggregate functions: avg(vector), sum(vector)
• Combinable with SQL joins, filters, and aggregations

pgvector provides operators for different similarity metrics:⁴⁾

Example query:

SELECT * FROM items
ORDER BY embedding <=> query_embedding
LIMIT 10;

IVFFlat (Inverted File with Flat compression) partitions the vector space into clusters using k-means:⁵⁾

• Searches only the closest cluster(s) instead of all vectors
• lists parameter controls the number of clusters (recommended: sqrt(n) for n rows)
• ivfflat.probes at query time controls how many clusters to search
• Requires data before index creation (needs data for centroid calculation)
• Lower memory usage than HNSW
• Best for smaller datasets or when memory is constrained

CREATE INDEX ON items USING ivfflat (embedding vector_cosine_ops)
WITH (lists = 100);

HNSW (Hierarchical Navigable Small World) uses a graph structure for high-recall approximate nearest neighbor search:⁶⁾

• Builds a multi-layer graph of connected nodes
• Higher layers for coarse navigation, lower layers for precise search
• Key tunable parameters:
  • m – max connections per node (default 16; higher improves recall, increases index size)
  • ef_construction – search depth during build (default 64; higher improves quality)
• hnsw.ef_search at query time controls search effort (default 40)
• Supports parallel index builds
• Best for large datasets requiring high recall and low latency

CREATE INDEX ON items USING hnsw (embedding vector_cosine_ops)
WITH (m = 16, ef_construction = 128);

Key tuning strategies for production pgvector deployments:⁷⁾

• Increase maintenance_work_mem for faster index builds (2-4GB recommended)
• Increase max_parallel_maintenance_workers for parallel HNSW builds
• Normalize vectors for cosine distance operations
• Use table partitioning for very large datasets
• Monitor with standard PostgreSQL tools
• Set hnsw.ef_search based on recall requirements

pgvector excels when vectors need to live alongside relational data, avoiding data silos and synchronization overhead.⁸⁾

• Supabase Vector
• Pinecone
• Hugging Face