Version updated for https://github.com/PieterDePauw/mdx-embeddings-actions to version v.0.0.9-alpha. This action is used across all versions by ? repositories. Action Type This is a Node action using Node version 20. Go to the GitHub Marketplace to find the latest changes. Action Summary This GitHub Action automates the conversion of markdown files into embeddings using OpenAI and stores them in a Supabase database. It allows for vector similarity search within documentation and websites, complementing the headless-vector-search repository by facilitating data storage and retrieval for vector-based queries. The action is available on the Marketplace and requires specific environment variables to be set as secrets for authentication and configuration.

Version updated for https://github.com/PieterDePauw/mdx-embeddings-actions to version v0.0.11. This action is used across all versions by ? repositories. Action Type This is a Node action using Node version 20. Go to the GitHub Marketplace to find the latest changes. Action Summary This GitHub Action automates the process of generating and storing document embeddings using OpenAI and Supabase. It reads markdown files from a specified directory, converts them into embeddings, and stores these embeddings in a PostgreSQL/Supabase database. This setup enables efficient vector similarity search within the documentation or website, facilitating quick access to relevant information.

Version updated for https://github.com/PieterDePauw/mdx-embeddings-actions to version v0.0.11. This action is used across all versions by ? repositories. Action Type This is a Node action using Node version 20. Go to the GitHub Marketplace to find the latest changes. Action Summary This GitHub Action converts markdown files into embeddings and stores them in a Postgres/Supabase database, allowing for vector similarity search within documentation. It integrates with OpenAI’s API to generate embeddings and uses Supabase as the storage backend for efficient retrieval. The action is designed to be used alongside the headless-vector-search repository for comprehensive documentation search capabilities.