AI & RAG
Nightly Confluence Runbook Indexer for Datadog Knowledge Base
On a schedule, syncs Confluence runbook pages into a Postgres pgvector store and links them to Datadog monitor names so the spike-explainer flows always retrieve fresh.
How it runs
The automated pipeline, trigger to output.
- TriggerNightly schedule fires the runbook sync
- ActionList Confluence runbook pages changed since last runConfluence
- LogicSkip unchanged pages, chunk the rest
- ActionEmbed chunks and extract referenced metric/monitor namesOpenAI
- ActionUpsert chunks and prune stale rows in pgvectorPostgres
- OutputPost index summary to Slack ops channelSlack
What it does
Keeps the runbook knowledge base current. Every night it pulls changed runbook pages from Confluence, splits them into searchable chunks, generates embeddings, and upserts them into Postgres — tagging each chunk with the Datadog monitors and metrics it references so retrieval stays precise.
When to use it
Run this alongside any of the spike-answering flows. Use it when runbooks change often and you need the RAG layer to reflect the latest fix steps without anyone re-indexing by hand. It is the data-prep backbone for the rest of the catalog.
How it works
- 1A nightly schedule triggers the sync.
- 2The flow lists Confluence pages in the runbook space updated since the last run.
- 3Each page is chunked, and a logic step skips unchanged pages to save embedding cost.
- 4An LLM embeds each new chunk and extracts referenced metric and monitor names.
- 5Chunks and metadata are upserted into the Postgres pgvector table, with stale rows for deleted pages removed.
- 6A short summary of pages added, updated, and removed is posted to the team's Slack ops channel.
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.
- 4Connect SlackChannels, DMs, threads, mentions.
- 5Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 6Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 7Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
More AI & RAG workflows
RFP and security questionnaire drafter grounded in Coda
Drafts answers to inbound RFP and security questionnaire questions by retrieving approved language from your Coda hub, then files the cited draft for review before a rep sends it.
Detect Breaking API Changes from Spec Diffs and Alert Owners
Compares the new OpenAPI spec against the previous version on each GitLab merge, uses retrieval over the changelog to classify whether changes are breaking.
Grounded reply suggestions for inbound sales email
Reads inbound prospect emails, retrieves the matching answers from your Coda hub.
Coda-grounded sales answer bot with citations in Slack
Reps ask product, pricing, or competitive questions in Slack and get an answer drawn only from your Coda knowledge hub, with links to the exact docs and rows it pulled from.
On-Call Spec Answerer from Dropbox Engineering Corpus
Answers on-call questions posted in a Slack channel by retrieving the most relevant Dropbox engineering specs and replying with a grounded, source-cited answer in the thread.
Agentic Deep-Dive API Researcher for Hard Spec Questions
An agent fielded via webhook that answers multi-part API questions by iteratively searching OpenAPI specs, changelogs, and Confluence runbooks.
Run it inside a business
This workflow drops into a full company template. Import the org, and this is one of the playbooks its agents run.
Run this workflow in your colony.
14-day trial. No DevOps. No Sales call. Provisioned in under a minute.