CHATBOTS

Schema-Grounded SQL Helper for the Warehouse

Converts a plain-English data question in Slack into a validated Snowflake query, grounding column choices in the live schema and dictionary.

CategoryChatbots
Enginepaperclip
Difficultyadvanced
Triggerchat
Steps6
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerSlack natural-language data questionSlack
  • ActionLoad schema grounding context from SnowflakeSnowflakeSnowflake
  • ActionAdd definitions + join hints from CodaCodaCoda
  • ActionDraft SQL and dry-run validate against SnowflakeSnowflakeSnowflake
  • LogicRetry self-correction if validation fails
  • OutputPost validated SQL + result preview to SlackSlack

What it does

Lets non-SQL users ask questions like "weekly active accounts by plan for the last quarter" and get back a correct, runnable Snowflake query. The bot grounds every table and column it picks in the actual warehouse schema so it never invents fields.

When to use it

When stakeholders flood analysts with ad-hoc pulls and you want a first-draft query they can run or hand off, without hallucinated column names.

How it works

  1. 1A Slack message poses a natural-language data question.
  2. 2The flow loads the relevant table and column metadata from Snowflake INFORMATION_SCHEMA to build a grounding context.
  3. 3It enriches with business definitions and join hints from the Coda dictionary.
  4. 4An agent drafts SQL, then a validation step runs a dry-run/LIMIT query against Snowflake to confirm it parses and the columns exist.
  5. 5If validation fails, it self-corrects once; on success the bot posts the SQL plus a small result preview back to Slack.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect SlackChannels, DMs, threads, mentions.
  2. 2
    Connect SnowflakeWarehouses, queries, shares.
  3. 3
    Connect CodaDocs, packs, automations.
  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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