Readme
Source
- Canonical Cookbook page: https://developers.openai.com/cookbook/examples/vector_databases/supabase/readme
- OpenAI Cookbook source: https://github.com/openai/openai-cookbook/blob/main/examples/vector_databases/supabase/README.md
- Raw source: https://raw.githubusercontent.com/openai/openai-cookbook/main/examples/vector_databases/supabase/README.md
- Source path:
examples/vector_databases/supabase/README.md - Source kind:
examples - Source format:
.md - License basis: OpenAI Cookbook repository MIT license.
- Content hash:
5cffac60cb5a7a849fcd735d5dbc794ec9885c4dd583532c08e7fb22e5aed7e8
Classification
- Primary category: RAG / retrieval / vector databases
- Wiki collection: 2026-05-15-openai-cookbook
- Taxonomy page: openai-cookbook-taxonomy
- Topic hub: openai-cookbook
Summary
Supabase Vector Database Supabase is an open-source Firebase alternative built on top of Postgres, a production-grade SQL database. Supabase Vector is a vector toolkit built on pgvector, a Postgres extension that allows you to store your embeddings inside the same database that holds the rest of your application data. When combined with pgvector’s indexing a…
What This Teaches
- How to connect OpenAI models with retrieval, embeddings, or external knowledge stores.
Implementation Use Cases
- Use as a concrete implementation reference when building OpenAI API systems in this category.
- Compare against current official API docs before copying model names, SDK calls, or parameters into production code.
- Preserve this page as a mirrored source; prefer synthesis pages for personal recommendations or project-specific decisions.
Mirrored Content
Supabase Vector Database
Supabase is an open-source Firebase alternative built on top of Postgres, a production-grade SQL database.
Supabase Vector is a vector toolkit built on pgvector, a Postgres extension that allows you to store your embeddings inside the same database that holds the rest of your application data. When combined with pgvector’s indexing algorithms, vector search remains fast at large scales.
Supabase adds an ecosystem of services and tools on top of Postgres that makes app development as quick as possible, including:
- Auto-generated REST APIs
- Auto-generated GraphQL APIs
- Realtime APIs
- Authentication
- File storage
- Edge functions
We can use these services alongside pgvector to store and query embeddings within Postgres.
OpenAI Cookbook Examples
Below are guides and resources that walk you through how to use OpenAI embedding models with Supabase Vector.
| Guide | Description |
|---|---|
| Semantic search | Store, index, and query embeddings at scale using pgvector |