AI & RAG
Index Notion postmortems into a searchable incident corpus
On a schedule, pulls new and edited postmortem pages from a Notion database, chunks and embeds them.
How it runs
The automated pipeline, trigger to output.
- TriggerScheduled index refresh
- ActionQuery Notion for postmortems edited since last runNotion
- LogicSkip drafts and empty pages
- ActionChunk and embed postmortem sectionsOpenAI
- OutputUpsert vectors and metadata into Postgres corpusPostgres
What it does
This is the ingestion side of the 'have we seen this before' system. On a recurring schedule it scans your Notion postmortem database for pages created or edited since the last run, splits each writeup into sections (summary, timeline, root cause, remediation), embeds them, and upserts the vectors plus metadata into a Postgres store. Old chunks for re-edited pages are replaced so the corpus never goes stale.
When to use it
Run this once and forget it if your postmortems live in Notion. It keeps the retrieval index in lockstep with what engineers actually write, so similar-incident lookups stay grounded in current knowledge.
How it works
- 1A scheduled trigger fires (for example, hourly).
- 2The Notion postmortem database is queried for pages edited since the last watermark.
- 3A branch skips pages with no real content or still in draft.
- 4Each page is chunked, embedded, and the vectors are upserted into Postgres, replacing prior chunks for that page.
- 5The run watermark is advanced so the next pass only sees new edits.
Set it up
What you configure once, before turning it on.
- 1Connect NotionPages, databases, comments.
- 2Connect OpenAIModels, embeddings, files.
- 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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