AI & RAG

Slack 'have we seen this before?' incident lookup command

Lets any engineer paste an error or symptom into Slack and get back, in-thread, the most similar past incidents from the postmortem corpus with their causes, fixes, and links.

CategoryAI & RAG
Enginesim
Difficultybeginner
Triggerchat
Steps5
Setup~5 min

How it runs

The automated pipeline, trigger to output.

  • TriggerEngineer asks the bot in SlackSlack
  • ActionEmbed question and vector-search postmortem corpusPostgreSQLPostgres
  • LogicHandle no-strong-match case honestly
  • ActionSummarize matches grounded on retrieved postmortemsOpenAI
  • OutputReply in Slack thread with ranked similar incidentsSlack

What it does

This gives on-call engineers a self-serve way to ask the corpus directly. An engineer mentions the bot or runs a slash command in Slack with a pasted error, log line, or plain-English symptom. The workflow embeds the query, retrieves the closest postmortems from Postgres, and replies in-thread with a ranked list of similar incidents — each summarized with its root cause, the resolution, and a link — all grounded strictly on retrieved writeups.

When to use it

When engineers want an interactive 'have we seen this before?' during an active incident or while triaging, without leaving Slack to dig through a wiki. Complements the automatic Sentry and PagerDuty finders.

How it works

  1. 1An engineer triggers the bot in Slack with an error or symptom.
  2. 2The query is embedded and vector-searched against the postmortem corpus in Postgres.
  3. 3A gate handles the no-strong-match case with an honest 'nothing similar found' reply.
  4. 4An LLM summarizes the matches grounded only on retrieved postmortems, with citations.
  5. 5The ranked answer is posted back as a Slack thread reply.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect SlackChannels, DMs, threads, mentions.
  2. 2
    Connect PostgresAny Postgres URL — query, write, migrate.
  3. 3
    Connect OpenAIModels, embeddings, files.
  4. 4
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  5. 5
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  6. 6
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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