AI & RAG

Discord engineering answer bot grounded in Confluence + GitLab wiki

Answers engineers' questions in a Discord support channel by retrieving from Confluence spaces and GitLab project wikis, then posting a cited answer in-thread.

CategoryAI & RAG
Enginesim
Difficultyintermediate
Triggerevent
Steps6
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerNew message in Discord engineering channelDiscordDiscord
  • ActionRetrieve passages from Confluence spacesConfluenceConfluence
  • ActionRetrieve passages from GitLab project wikisGitLabGitLab
  • LogicRelevance gate: any chunk above score threshold?
  • ActionCompose cited answer with OpenAIOpenAI
  • OutputReply in Discord thread with sourcesDiscordDiscord

What it does

Watches a Discord channel for engineering questions, searches your internal Confluence spaces and GitLab project wikis for relevant passages, and replies in a thread with a grounded answer plus source links. When retrieval turns up nothing trustworthy, it says so and tags the on-call docs owner instead of fabricating an answer.

When to use it

When your team keeps asking the same "how do I deploy X / where's the runbook for Y" questions in chat and the answers already live in Confluence or GitLab wikis. Cuts interrupt-driven pings and stops tribal knowledge from getting lost.

How it works

  1. 1A new message in the watched Discord channel fires the trigger.
  2. 2The message is embedded and used to retrieve top passages from Confluence spaces and GitLab wikis in parallel.
  3. 3A relevance gate checks whether any retrieved chunk clears the score threshold.
  4. 4If yes, OpenAI composes an answer constrained to the retrieved text with inline citations.
  5. 5If no, the flow routes to an escalation path that pings the docs owner.
  6. 6The answer (or escalation notice) is posted back as a Discord thread reply with source URLs.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect DiscordCommunity channels + voice + bots.
  2. 2
    Connect ConfluenceSpaces, pages, blueprints.
  3. 3
    Connect GitLabRepos, MRs, pipelines, registry.
  4. 4
    Connect OpenAIModels, embeddings, files.
  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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