CHATBOTS

Discord /ask slash command that answers from a Postgres knowledge base

Gives members a /ask slash command that retrieves the most relevant FAQ entries from your Postgres knowledge base and returns a concise, cited reply right in the channel.

CategoryChatbots
Enginesim
Difficultyintermediate
Triggerevent
Steps5
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerMember runs /ask slash commandDiscordDiscord
  • ActionQuery Postgres knowledge base for matchesPostgreSQLPostgres
  • LogicDid the search return any rows?
  • ActionSummarize matched entries into a cited answerOpenAI
  • OutputReply to the slash command with the answerDiscordDiscord

What it does

Adds an on-demand /ask command to your server. Instead of scanning every message, the bot only responds when a member explicitly invokes it. It searches a Postgres-backed FAQ table for the closest matching entries and composes a short answer that quotes the source rows, so members get an authoritative reply without pinging a human.

When to use it

When your FAQ content is larger than a single pinned post and lives in a structured store. The explicit slash command keeps the bot quiet in busy channels and avoids reacting to every casual message, which is better for high-traffic communities.

How it works

  1. 1A member runs the /ask slash command with their question as the argument.
  2. 2The bot queries the Postgres knowledge_base table for entries matching the question keywords.
  3. 3A branch checks whether any matching rows were returned.
  4. 4If matches exist, an LLM call summarizes them into a concise answer that cites the entry titles.
  5. 5The bot replies to the slash command with the answer.
  6. 6If no matches exist, it returns a friendly fallback inviting the member to open a support thread.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect DiscordCommunity channels + voice + bots.
  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.

Run this workflow in your colony.

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