Generar SQL de BigQuery a partir de consultas en lenguaje natural con chat GPT-4o

• •Clave de API de OpenAI

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        "content": "Talk-to-Data: Instant BigQuery SQL Generator\n### Need more help?\n- **LinkedIn:** https://www.linkedin.com/in/robertbreen\n- **Email:** rbreen@ynteractive.com  "
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        "content": "\n#### ⚙️ Setup Instructions\n\n1. **Import the workflow**  \n   - n8n → **Workflows → Import from File** (or **Paste JSON**) → **Save**\n\n2. **Add credentials**  \n   | Service | Where to create credentials | Node(s) to update |\n   |---------|----------------------------|-------------------|\n   | **OpenAI** | <https://platform.openai.com> → Create API key | **OpenAI Chat Model** |\n   | **Google BigQuery** | Google Cloud Console → IAM & Admin → Service Account JSON key | **Google BigQuery** (schema + query) |\n\n3. **Point the schema fetcher to your dataset**  \n   - In **Google BigQuery1** you’ll see:  \n     ```sql\n     SELECT table_name, column_name, data_type\n     FROM `n8nautomation-453001.email_leads_schema.INFORMATION_SCHEMA.COLUMNS`\n     ```  \n   - Replace **`n8nautomation-453001.email_leads_schema`** with **`YOUR_PROJECT.YOUR_DATASET`**.  \n   - Keep the rest of the query the same—BigQuery’s `INFORMATION_SCHEMA` always surfaces `table_name`, `column_name`, and `data_type`.\n\n4. **Update the execution node**  \n   - Open **Google BigQuery** (the second BigQuery node).  \n   - In **Project ID** select your project.  \n   - The **SQL Query** field is already `{{ $json.output.query }}` so it will run whatever the AI returns.\n\n5. **(Optional)Embed the chat interface**  \n\n6. **Test end-to-end**  \n   - Open the embedded chat widget.  \n   - Ask: *“How many distinct email leads were created last week?”*  \n   - After a few seconds the workflow will return a table of results—or an error if the schema lacks the requested fields.\n   - As specific questions about your data\n\n8. **Activate**  \n   - Toggle **Active** so the chat assistant is available 24/7.\n\n"
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