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pgvector for Postgres Vector Search Cheat Sheet

pgvector for Postgres Vector Search Cheat Sheet

Back to Generative AI
Updated 2026-05-21
Next Topic: Pinecone (Vector Database) Cheat Sheet

pgvector is an open-source PostgreSQL extension that adds vector storage and similarity search directly inside Postgres, turning an ordinary relational database into a capable vector store without any external infrastructure. It matters because it lets teams build RAG pipelines, semantic search, and recommendation engines while keeping all their data in a single ACID-compliant system they already operate. The key mental model to carry into the tables: every vector distance operator you use in a query must match the operator class chosen when the index was built β€” a mismatch silently falls back to a sequential scan that can be thousands of times slower.

What This Cheat Sheet Covers

This topic spans 17 focused tables and 109 indexed concepts. Below is a complete table-by-table outline of this topic, spanning foundational concepts through advanced details.

Table 1: Installation and Extension SetupTable 2: Vector Data TypesTable 3: Distance OperatorsTable 4: Index Types β€” HNSW vs IVFFlatTable 5: HNSW Index ParametersTable 6: IVFFlat Index ParametersTable 7: Index Operator ClassesTable 8: Expression Indexes and Quantization PatternsTable 9: Filtered Vector SearchTable 10: Hybrid Search (Vector + Full-Text)Table 11: Index Build PerformanceTable 12: REINDEX and VACUUM MaintenanceTable 13: ANN Recall TuningTable 14: Key Vector FunctionsTable 15: Cloud Platform IntegrationTable 16: Python Client IntegrationTable 17: Production and Operational Patterns

Table 1: Installation and Extension Setup

Enabling pgvector is a single SQL statement, but the exact steps vary by platform. Every major managed Postgres provider β€” Supabase, Neon, AWS RDS, Aurora, Azure, and Google Cloud SQL β€” ships with pgvector pre-installed; you only need to activate it per-database.

TypeExampleDescription
Enable extension
CREATE EXTENSION IF NOT EXISTS vector;
Activates pgvector in the current database; note the registered name is vector, not pgvector.
Verify installation
SELECT extversion FROM pg_extension WHERE extname = 'vector';
Confirms the extension is active and shows the installed version.
Enable on Supabase
CREATE EXTENSION vector WITH SCHEMA extensions;
Supabase best practice places pgvector in the extensions schema to keep the public schema clean.
Enable on Azure
SHOW azure.extensions; then CREATE EXTENSION vector;
Azure requires allowlisting vector (not pgvector) in the server parameter before the CREATE EXTENSION command can succeed.

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