community-automations/supabase-vector

Research & Intel

PublicClaude subagent

Supabase Vector Pipeline

Vector pipelines drift fast: re-ingesting a file creates duplicate chunks, and nobody can tell which version is live. This flow keeps insert, upsert, and retrieval in one place.

sonnet3 daysSupabasepgvectorOpenAI EmbeddingsEdge Functions
ClaudeClaude
ROI for
README.md

Why this subagent

Vector pipelines drift fast: re-ingesting a file creates duplicate chunks, and nobody can tell which version is live. This flow keeps insert, upsert, and retrieval in one place.

Everything sits in Supabase on pgvector, with upserts keyed to a stable document ID so re-ingesting replaces old chunks instead of piling them up. Every operation is logged with its source ID, so you can always trace what got embedded and when.

How it runs

    • Read

      Used at step 01 to kick off the pipeline.

    • Write

      Used at step 01 to kick off the pipeline.

    • WebFetch

      Used at step 01 to kick off the pipeline.

    • WebSearch

      Used at step 01 to kick off the pipeline.

Sample output

json
// Sample output
// (generated when the pipeline finishes)

Run insert, upsert, and retrieval over a Supabase pgvector store with stable IDs and a logged audit trail.

Unlock the rest

The full agent definition, install snippet, and starter task are gated for community members.

Members get the full `.md` agent file, the npm / pnpm install one-liners, a starter prompt that we've tuned against real runs, and the open-source repo when this automation ships there. One email, magic link, done.