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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.
A toolkit to perform vector similarity search on your knowledge base embeddings.
Implement image search with the OpenAI CLIP Model and Supabase Vector.
Generate image captions using Hugging Face.
Generate GPT text completions using OpenAI in Edge Functions.
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
OpenAI is an AI research and deployment company. Supabase provides a way to use OpenAI in your applications.
A fully managed service that offers a choice of high-performing foundation models from leading AI companies.
Call Fireworks AI models from Edge Functions and search Fireworks embeddings with pgvector.
Hugging Face is an open-source provider of NLP technologies. Supabase provides a way to use Hugging Face's models in your applications.
LangChain is a language-agnostic, open-source, and self-hosted API for text translation, summarization, and sentiment analysis.
LlamaIndex is a data framework for your LLM applications.
Case studies
Berri AI Boosts Productivity by Migrating from AWS RDS to Supabase with pgvector
Learn how Berri AI overcame challenges with self-hosting their vector database on AWS RDS and successfully migrated to Supabase.
Firecrawl switches from Pinecone to Supabase for Postgres vector embeddings
How Firecrawl boosts efficiency and accuracy of chat powered search for documentation using Supabase with pgvector
Markprompt: GDPR-Compliant AI Chatbots for Docs and Websites
AI-powered chatbot platform, Markprompt, empowers developers to deliver efficient and GDPR-compliant prompt experiences on top of their content, by leveraging Supabase's secure and privacy-focused database and authentication solutions