AI & RAG
Confluence Space Embedding Index Builder
On a schedule, chunks every page in the team's Confluence space, generates embeddings with a Hugging Face model.
How it runs
The automated pipeline, trigger to output.
- TriggerNightly schedule fires
- ActionFetch all pages in the team spaceConfluence
- LogicChunk pages and diff against existing index
- ActionGenerate embeddings via Hugging FaceHugging Face
- OutputUpsert vectors and prune deleted pages in PostgresPostgres
What it does
Builds and maintains the vector index that powers grounded retrieval over one Confluence space. On a schedule it fetches all pages, splits them into overlapping chunks, generates embeddings using a Hugging Face model, and upserts the vectors plus page metadata into a Postgres table. Stale or deleted pages are pruned so the index mirrors the live space.
When to use it
Use it as the indexing backbone for any of your team's RAG answer bots. Run it nightly so retrieval reflects the latest documentation without manual reindexing.
How it works
- 1A nightly schedule triggers the flow.
- 2The flow lists and fetches every page in the configured Confluence space.
- 3A logic step chunks page text and diffs against the existing index to find new, changed, and removed pages.
- 4Hugging Face generates embeddings for changed chunks.
- 5Vectors and metadata are upserted into Postgres, and rows for deleted pages are removed.
Set it up
What you configure once, before turning it on.
- 1Connect ConfluenceSpaces, pages, blueprints.
- 2Connect Hugging FaceModels, datasets, spaces — the open-source hub.
- 3Connect PostgresAny Postgres URL — query, write, migrate.
- 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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