AI & Vectors

The best vector database is the database you already have.

Supabase provides an open source toolkit for developing AI applications using Postgres and pgvector. Use the Supabase client libraries to store, index, and query your vector embeddings at scale.

The toolkit includes:

• A vector store and embeddings support using Postgres and pgvector.
• A Python client for managing unstructured embeddings.
• An embedding generation process using open source models directly in Edge Functions.
• Database migrations for managing structured embeddings.
• Integrations with all popular AI providers, such as OpenAI, Hugging Face, LangChain, and more.

Search#

You can use Supabase to build different types of search features for your app, including:

• Semantic search: search by meaning rather than exact keywords
• Keyword search: search by words or phrases
• Hybrid search: combine semantic search with keyword search

Examples#

Check out all of the AI templates and examples in our GitHub repository.

Headless Vector Search

A toolkit to perform vector similarity search on your knowledge base embeddings.

Image Search with OpenAI CLIP

Implement image search with the OpenAI CLIP Model and Supabase Vector.

Hugging Face inference

Generate image captions using Hugging Face.

OpenAI completions

Generate GPT text completions using OpenAI in Edge Functions.

Building ChatGPT Plugins

Use Supabase as a Retrieval Store for your ChatGPT plugin.

Vector search with Next.js and OpenAI

Learn how to build a ChatGPT-style doc search powered by Next.js, OpenAI, and Supabase.

Integrations#

Case studies#