Supabase Vector leverages PostgreSQL with the pgvector extension to enable efficient storage, indexing, and similarity search of high-dimensional vector embeddings within the Supabase platform. It combines vector search with Supabase's authentication, row-level security, Edge Functions, and real-time capabilities.¹⁾

Supabase integrates pgvector as a PostgreSQL extension, allowing vectors to be stored alongside relational data in the same schema:²⁾

• Enable with CREATE EXTENSION IF NOT EXISTS vector WITH SCHEMA extensions;
• Store embeddings in dedicated VECTOR columns
• Support for HNSW and IVFFlat indexing
• Hybrid semantic and keyword queries by combining embeddings with text columns
• Standard SQL operations (joins, filters, aggregations) alongside vector search

Supabase Edge Functions (serverless TypeScript/Deno functions) handle embedding generation and processing:³⁾

• Process incoming data and generate embeddings in real-time
• Integrate with OpenAI, Hugging Face, Cohere, and other embedding APIs
• Upsert embeddings into pgvector tables
• Deployable as database webhooks triggered on row changes
• No external infrastructure required

Since Supabase Edge Runtime v1.36.0, the gte-small model (384 dimensions) runs natively within Edge Functions:⁴⁾

• No external API calls required for embedding generation
• const model = new Supabase.ai.Session('gte-small')
• Suitable for lightweight semantic search applications
• Reduces latency and external dependencies

Supabase exposes vector search through RPC functions callable via client SDKs:⁵⁾

• Create PostgreSQL functions using pgvector operators (⇔, ↔, <#>)
• Call via Supabase JS/Python SDKs using supabase.rpc()
• Support for top-k retrieval with distance metrics
• Combinable with metadata filters and row-level security policies

Vector Buckets (launched 2026) provide S3-backed vector storage for larger-scale workloads:⁶⁾

• Up to 50 million vectors per index
• Queryable via JavaScript SDK and Postgres Foreign Data Wrappers
• Multi-index buckets for multi-tenant applications

Supabase facilitates retrieval-augmented generation workflows:⁷⁾

• Store document chunks with their embeddings in pgvector tables
• Retrieve top-k similar chunks via RPC similarity search
• Feed retrieved context to LLMs (OpenAI, Anthropic, etc.) for generation
• Hybrid RAG combining semantic search with keyword filtering
• Edge Functions orchestrate the full embed-search-generate pipeline

Row-level security (RLS) applies to vector tables like any PostgreSQL relation:⁸⁾

• Enforce policies on SELECT, INSERT, UPDATE operations involving embeddings
• Filter similarity searches by user ID or organization
• GDPR-compliant data isolation for multi-tenant applications
• No custom application-level access control needed

Supabase provides an automated embedding generation pipeline:⁹⁾

• Triggers – detect content changes and enqueue embedding requests
• pgmq – queue embedding generation for processing and retries
• pg_net – asynchronous HTTP requests to Edge Functions from Postgres
• pg_cron – scheduled processing and retry of failed embeddings
• Generic and reusable across multiple tables and content types

Supabase Vector excels in applications requiring unified relational and vector data with built-in authentication and security.¹⁰⁾

• pgvector
• Pinecone
• Hugging Face