AI & RAG
Keep the support knowledge index fresh from Confluence changes
On a schedule, pulls newly created and edited Confluence pages, re-embeds them with OpenAI, and upserts the vectors into Supabase so retrieval always reflects the latest docs.
How it runs
The automated pipeline, trigger to output.
- TriggerScheduled reindex run
- ActionFetch pages changed since last sync from ConfluenceConfluence
- LogicSkip run if no pages changed
- ActionChunk and embed updated pages with OpenAIOpenAI
- OutputUpsert vectors and prune stale chunks in SupabaseSupabase
What it does
Maintains the vector index that every RAG support workflow reads from. It periodically detects which Confluence pages changed since the last run, regenerates their embeddings, and writes them back to Supabase — removing stale chunks for deleted or rewritten pages so answers never quote outdated docs.
When to use it
Run this alongside any docs-grounded answering workflow. Essential when your knowledge base changes weekly and you can't afford support bots citing superseded instructions.
How it works
- 1A schedule (for example, hourly) fires the trigger.
- 2Confluence is queried for pages created or modified since the last successful sync.
- 3A branch skips the run cleanly when nothing has changed.
- 4Changed pages are chunked and embedded with OpenAI.
- 5The new vectors are upserted into Supabase and obsolete chunks for those pages are deleted, keeping the index in lockstep with the source.
Set it up
What you configure once, before turning it on.
- 1Connect ConfluenceSpaces, pages, blueprints.
- 2Connect OpenAIModels, embeddings, files.
- 3Connect SupabaseTables, auth, storage, edge functions.
- 4Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 5Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 6Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
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Run this workflow in your colony.
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