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Generate BigQuery SQL from natural language queries using GPT-4o chat

Robert BreenRobert Breen
2018 views
2/3/2026
Task ManagementMicrosoft TeamsTrelloNotificationsProductivity
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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.

Contact

  • Email: rbreen@ynteractive.com
  • Website: https://ynteractive.com
  • YouTube: https://www.youtube.com/@ynteractivetraining
  • LinkedIn: https://www.linkedin.com/in/robertbreen

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

This n8n workflow leverages the power of AI to convert natural language questions into executable Google BigQuery SQL queries. It simplifies data querying for users who may not be proficient in SQL, allowing them to interact with their BigQuery data using plain English.

What it does

This workflow automates the following steps:

  1. Listens for Natural Language Queries: It starts by receiving a natural language question (e.g., "Show me the total sales for the last quarter").
  2. Maintains Conversation Context: It uses a simple memory buffer to keep track of the conversation history, allowing for more coherent and context-aware SQL generation.
  3. Generates SQL with AI Agent: An AI Agent, powered by an OpenAI Chat Model (like GPT-4o), processes the natural language query and the conversation history to generate a BigQuery SQL query.
  4. Structures Output: A structured output parser ensures the generated SQL is correctly formatted and ready for execution.
  5. Executes BigQuery SQL: It then executes the generated SQL query against your specified Google BigQuery dataset.
  6. Aggregates Results: The results from BigQuery are aggregated into a single output.
  7. Prepares Response: A Code node processes the BigQuery results, preparing them for a user-friendly response.
  8. Merges and Responds: Finally, it merges the original query with the BigQuery results to provide a comprehensive answer back to the user.

Prerequisites/Requirements

To use this workflow, you will need:

  • n8n Instance: A running n8n instance.
  • OpenAI API Key: An API key for OpenAI (for the Chat Model, e.g., GPT-4o).
  • Google BigQuery Account: Access to a Google BigQuery project and dataset.
  • Google BigQuery Credentials: Configured Google BigQuery credentials in n8n with appropriate permissions to execute queries.

Setup/Usage

  1. Import the workflow: Download the provided JSON and import it into your n8n instance.
  2. Configure Credentials:
    • OpenAI Chat Model: Configure your OpenAI API Key credential in the "OpenAI Chat Model" node.
    • Google BigQuery: Configure your Google BigQuery credentials in the "Google BigQuery" node. Ensure these credentials have the necessary permissions to read from your BigQuery tables.
  3. Customize BigQuery Query (Optional): The "Google BigQuery" node is set up to execute a query. You might need to adjust the project, dataset, and table names within the generated SQL or provide schema information to the AI agent for better results.
  4. Activate the workflow: Once configured, activate the workflow.
  5. Send a chat message: Use the "When chat message received" trigger to send a natural language query to the workflow. The output will contain the generated SQL and the results from BigQuery.

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