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Generate BigQuery SQL from Natural Language Queries using GPT-4o Chat

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Created by: Robert Breen || rbreen

Robert Breen

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Last update 4 days ago

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Give business users a chat box; get back valid BigQuery SQL and live query results.
The workflow:

  1. Captures a plain-language question from a chat widget or internal portal.
  2. Fetches the current table + column schema from your BigQuery dataset (via INFORMATION_SCHEMA).
  3. Feeds both the schema and the question to GPT-4o so it can craft a syntactically correct SQL query using only fields that truly exist.
  4. Executes the AI-generated SQL in BigQuery and returns the results.
  5. Stores a short-term memory by session, enabling natural follow-up questions.

Perfect for analysts, customer-success teams, or any stakeholder who needs data without writing SQL.

⚙️ Setup Instructions

  1. Import the workflow

    • n8n → Workflows → Import from File (or Paste JSON) → Save

  2. Add credentials

    Service Where to create credentials Node(s) to update
    OpenAI <https://platform.openai.com> → Create API key OpenAI Chat Model
    Google BigQuery Google Cloud Console → IAM & Admin → Service Account JSON key Google BigQuery (schema + query)

  3. Point the schema fetcher to your dataset

    • In Google BigQuery1 you’ll see:
      SELECT table_name, column_name, data_type
FROM `n8nautomation-453001.email_leads_schema.INFORMATION_SCHEMA.COLUMNS`
    • Replace n8nautomation-453001.email_leads_schema with YOUR_PROJECT.YOUR_DATASET.
    • Keep the rest of the query the same—BigQuery’s INFORMATION_SCHEMA always surfaces table_name, column_name, and data_type.

  4. Update the execution node

    • Open Google BigQuery (the second BigQuery node).
    • In Project ID select your project.
    • The SQL Query field is already {{ $json.output.query }} so it will run whatever the AI returns.

  5. (Optional)Embed the chat interface

  6. Test end-to-end

    • Open the embedded chat widget.
    • Ask: “How many distinct email leads were created last week?”
    • After a few seconds the workflow will return a table of results—or an error if the schema lacks the requested fields.
    • As specific questions about your data

  7. Activate

    • Toggle Active so the chat assistant is available 24/7.

🧩 Customization Ideas

  • Row-limit safeguard: automatically append LIMIT 1000 to every query.
  • Chart rendering: send query results to Google Sheets + Looker Studio for instant visuals.
  • Slack bot: forward both the question and the SQL result to a Slack channel for team visibility.
  • Schema caching: store the INFORMATION_SCHEMA result for 24 hours to cut BigQuery costs.

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