AI & RAG

Nightly Postmortem Knowledge Index Builder

On a schedule, pulls new and updated postmortems from Confluence, chunks and embeds them, and upserts the vectors into Postgres so the retrieval corpus stays current.

CategoryAI & RAG
Enginesim
Difficultyintermediate
Triggerschedule
Steps6
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerNightly schedule fires
  • ActionFetch changed postmortems from ConfluenceConfluenceConfluence
  • LogicSkip run if nothing changed
  • ActionGenerate embeddings for new chunksOpenAI
  • ActionUpsert vectors into Postgres pgvector storePostgreSQLPostgres
  • OutputPost index summary to Slack ops channelSlack

What it does

This is the ingestion backbone for the rest of your runbook assistants. On a nightly schedule it finds postmortems and runbooks changed since the last run, splits them into retrievable chunks, generates embeddings, and upserts them into a pgvector table in Postgres — keeping search fresh without manual re-indexing.

When to use it

Run it as the foundation under any RAG assistant in this collection. Essential when postmortems are written frequently and stale retrieval would surface outdated guidance during incidents.

How it works

  1. 1A scheduled trigger fires nightly.
  2. 2Confluence is queried for pages created or modified since the last successful run.
  3. 3A logic step skips the run cleanly when nothing changed.
  4. 4OpenAI generates embeddings for each new or revised chunk.
  5. 5Vectors and metadata are upserted into the Postgres vector store, replacing prior chunks for edited pages.
  6. 6A summary of indexed, updated, and skipped pages is posted to a Slack ops channel.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect ConfluenceSpaces, pages, blueprints.
  2. 2
    Connect OpenAIModels, embeddings, files.
  3. 3
    Connect PostgresAny Postgres URL — query, write, migrate.
  4. 4
    Connect SlackChannels, DMs, threads, mentions.
  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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