supabase/embeddings-generator
GitHub Action to generate embeddings from the markdown files in your repository.
This tool helps automate the process of making your documentation searchable using AI. It takes your existing Markdown documentation files, converts their content into machine-readable numerical representations (embeddings), and stores them in your Supabase database. This enables you to add powerful search capabilities to your documentation or website, making it easier for users to find relevant information. It's designed for developers managing project documentation or website content.
118 stars.
Use this if you want to automatically update and synchronize your Markdown documentation with a vector database for AI-powered search.
Not ideal if you need a solution for non-Markdown content, or if you prefer to manually manage embedding generation and database storage.
Stars
118
Forks
27
Language
TypeScript
License
MIT
Category
Last pushed
Mar 11, 2026
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/embeddings/supabase/embeddings-generator"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Related tools
Azure/azure-search-vector-samples
A repository of code samples for Vector search capabilities in Azure AI Search.
curiosity-ai/catalyst
🚀 Catalyst is a C# Natural Language Processing library built for speed. Inspired by spaCy's...
vector-ai/vectorai
Vector AI — A platform for building vector based applications. Encode, query and analyse data...
wagtail/wagtail-vector-index
Store Wagtail pages & Django models as embeddings in vector databases
curiosity-ai/hnsw-sharp
C# library for approximate nearest neighbors search using Hierarchical Navigable Small World graphs