A GitHub Action that converts your markdown files into embeddings and stores them in your Postgres/Supabase database, allowing you to perform vector similarity search inside your documentation and website.
This action is a companion to the headless-vector-search repo, which is used to store and retrieve the embeddings using OpenAI and Supabase.
You can find this action on the GitHub Marketplace.
In your knowledge base repository, create a new action called .github/workflows/generate_embeddings.yml with the following content:
name: 'generate_embeddings'
on: # run on main branch changes
push:
branches:
- main
jobs:
generate:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v3
- uses: supabase/[email protected] # Find the latest version in the Marketplace
with:
supabase-url: 'https://your-project-ref.supabase.co'
supabase-service-role-key: ${{ secrets.SUPABASE_SERVICE_ROLE_KEY }}
openai-key: ${{ secrets.OPENAI_KEY }}
docs-root-path: 'docs' # the path to the root of your md(x) filesMake sure to set SUPABASE_SERVICE_ROLE_KEY, and OPENAI_KEY as repository secrets in your repo settings (settings > secrets > actions).
See the instructions in the headless-vector-search for more information on how to query your database from your website.
See details in MAINTAINERS.md