AI & RAG
Keep the changelog corpus current from Confluence release notes
On a schedule, pulls new and edited release-note pages from Confluence, chunks and embeds them.
How it runs
The automated pipeline, trigger to output.
- TriggerScheduled reindex run
- ActionFetch release-note pages edited since watermarkConfluence
- LogicSkip unchanged pages by content hash; parse the rest into entries
- ActionEmbed new and changed entriesOpenAI
- OutputUpsert entries and advance watermark in corpusPostgres
What it does
Maintains the retrieval corpus that every other changelog workflow queries. It scans the Confluence release-notes space for pages created or edited since the last run, parses each into version-tagged changelog entries, embeds them, and upserts into the Postgres vector store, removing stale chunks for re-edited pages.
When to use it
Run this as the ingestion backbone before deploying the answering workflows. Schedule it hourly or nightly so the corpus never drifts behind what engineering has published.
How it works
- 1A scheduled trigger fires (e.g., hourly).
- 2The flow queries Confluence for release-note pages updated since the stored watermark.
- 3Each page is parsed into discrete entries tagged with version, change type, and affected endpoints.
- 4A branch skips pages whose content hash is unchanged to avoid redundant embedding.
- 5New and changed entries are embedded and upserted into the Postgres corpus; obsolete chunks for edited pages are deleted and the watermark is advanced.
Set it up
What you configure once, before turning it on.
- 1Connect ConfluenceSpaces, pages, blueprints.
- 2Connect PostgresAny Postgres URL — query, write, migrate.
- 3Connect OpenAIModels, embeddings, files.
- 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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