Quick Overview
Quick Overview This workflow reindexes Markdown documentation into a Supabase Postgres pgvector table by fetching source docs from an HTTP API, chunking and embedding them via a Supabase Edge Function, upserting the vectors, and deleting stale chunks on a daily schedule or on-demand webhook. How it works Runs daily on a schedule or triggers when a POST request hits the webhook endpoint. Fetches Markdown sources (for example FAQ and blog posts) from a configured HTTP API endpoint. Strips frontmatter, splits content on H2 sections, chunks long sections with overlap, and batches chunks for embedding. For each batch, calls a Supabase Edge Function to generate embeddings for the chunk texts. Upserts each chunk’s source, index, content, and pgvector embedding into a Supabase Postgres rag_chunks table using conflict updates. After processing batches, deletes rows in rag_chunks whose updated_at timestamp is older than the current run to remove stale chunks. Setup Add an HTTP Header Auth credential for the sources API request and for calling the Supabase Edge Function. Add Supabase Postgres credentials with access to the database where the vector table lives. Create a public.rag_chunks table with a pgvector embedding column (matching your model’s dimensions) and a primary key on (source, chunk_idx). Update the sources API URL, the Supabase Edge Function /embed URL, and (optionally) the daily schedule time and batch/chunk sizing constants to match your environment. An n8n automation workflow template by Filip Mijic.
Filip Mijic
web
free
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