AI & RAG

Nightly RFC + Code Index Builder for Confluence and GitHub

Runs nightly to pull updated Confluence RFC pages and GitHub source files, chunk and embed them.

CategoryAI & RAG
Enginesim
Difficultyintermediate
Triggerschedule
Steps5
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerNightly schedule fires
  • ActionFetch changed Confluence pages since watermarkConfluenceConfluence
  • ActionFetch files from new GitHub commitsGitHubGitHub
  • ActionChunk + embed documents with traceback IDsOpenAI
  • OutputUpsert vectors and prune deleted sources in SupabaseSupabaseSupabase

What it does

Keeps the retrieval index behind your RFC answer-bot current. Each night it fetches Confluence pages changed since the last run and GitHub files touched by recent commits, splits them into citation-friendly chunks, embeds them, and upserts the vectors into Supabase. Deleted pages and files are pruned so stale answers don't surface.

When to use it

Whenever you run a RAG answer-bot over Confluence and GitHub and need the index to track edits without manual reindexing. Run this before deploying any of the query-side templates.

How it works

  1. 1A nightly schedule fires the index build.
  2. 2The flow lists Confluence pages updated since the last watermark and GitHub files from new commits.
  3. 3Each document is chunked with stable IDs that carry the source URL and line range for later traceback.
  4. 4OpenAI generates embeddings for every new or changed chunk.
  5. 5Chunks are upserted into the Supabase vector table; rows for removed sources are deleted.
  6. 6The new watermark timestamp is written back for the next run.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect ConfluenceSpaces, pages, blueprints.
  2. 2
    Connect GitHubRepos, issues, pull requests, actions.
  3. 3
    Connect OpenAIModels, embeddings, files.
  4. 4
    Connect SupabaseTables, auth, storage, edge functions.
  5. 5
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  6. 6
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  7. 7
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

Run this workflow in your colony.

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