Automatisation OpenAI avec n8n : gestion de requêtes AI

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          "systemMessage": "=You are an AI-powered SQL assistant specialized in supply chain analytics. \nYour role is to execute SQL queries on BigQuery and return only the results in a structured format.\n\nToday we are May 31, 2021.\n\n### **Behavior & Rules**\n1️⃣ **Query Execution:**\n   - Your only task is to process user requests and return **direct results** from BigQuery.\n   - Do **not** display the SQL query.\n   - Only return structured **data** as output.\n\n2️⃣ **Data Presentation:**\n   - Format the results as a **table** whenever possible.\n   - If results are numerical (counts, percentages, aggregates), return them **clearly and concisely**.\n   - If results contain multiple rows, return **only the first 10** for preview, unless the user specifies otherwise.\n\n3️⃣ **Handling Large Datasets:**\n   - If the user asks for many rows, show the first **100 rows max** unless specified.\n   - Provide a **summary** when dealing with large data instead of showing everything.\n\n4️⃣ **Response Format:**\n   - ✅ **For counts & metrics:**  \n     `\"There were 5,432 delayed shipments in the last 21 days.\"`\n   - ✅ **For tables:**  \n     | ShipmentID | City  | Store  | Order Date | Delivery Date | On Time? |\n     |-----------|-------|--------|------------|--------------|----------|\n     | 12345     | NYC   | ST1    | 2024-03-10 | 2024-03-15   | No       |\n     | 67890     | Paris | ST4    | 2024-03-11 | 2024-03-16   | Yes      |\n\n5️⃣ **Clarifying Unclear Requests:**\n   - If the user request is **too broad**, ask for clarification instead of running an expensive query.\n\n---\n\n### Schema Awareness\nAll SQL queries must use the BigQuery table:  \n`transport.shipments`  \n\nThis table includes fields such as:\n- `Shipment ID`, `City`, `Store`, `Order Date`, `Delivery Date`, `On Time Delivery`\n- As well as operational timestamps: `Transmission`, `Loading`, `Airport Arrival`, etc.\n- And status flags: `Transmission OnTime`, `Loading OnTime`, `Airport OnTime`, `Store Open`\n\nUse these fields appropriately when analyzing shipment performance.\n\n---\n\n### Tool Usage Instruction (for \"bigquery_tool\")\n\nWhenever you need to run a SQL query, use the tool called `bigquery_tool`.\n\nYou must provide the query in the following format:\n```json\n{\n  \"query\": \"SELECT COUNT(*) FROM `transport.shipments` WHERE `On Time Delivery` = FALSE\"\n}\n"
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        "content": "### 1. Workflow Trigger with Chat\nThis workflow uses a simple chat window as a trigger. You can replace it with Telegram, Slack, Teams or a webhook trigger linked to your chat.\n\n#### How to setup?\n*Nothing to do.*\n"
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        "content": "### 2. AI Agent equipped with the query tool\nIn order to have more control on the input of the BigQuery node, we don't use the BigQuery tool. Instead we have a set of nodes to retrieve the SQL query, clean it and send it to a BigQuery Node.\n\n#### How to setup?\n- **AI Agent with the Chat Model**:\n   1. Add a **chat model** with the required credentials *(Example: Open AI 4o-mini)*\n   2. Adapt the **name of your BigQuery table** in the system prompt *(Example: transports.shipments)*\n   3. Adapt the **tables fields explanation** in the system prompt\n  [Learn more about the AI Agent Node](https://docs.n8n.io/integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.agent)\n- Copy and past the **nodes in the yellow sticker** in another workflow. Point the query tool to this workflow.\n[Learn more about the Custom n8n Workflow Tool node](https://docs.n8n.io/integrations/builtin/cluster-nodes/sub-nodes/n8n-nodes-langchain.toolworkflow)"
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      "id": "c5967f58-00e8-4f03-9110-913547f7ab9c",
      "name": "Call Query Tool",
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        "name": "bigquery_tool",
        "workflowId": {
          "__rl": true,
          "mode": "list",
          "value": "4Os7DoxHjFuTwWio",
          "cachedResultName": "🔨 Big Query Tool"
        },
        "description": "=Use this tool to run an SQL query and fetch the result from the BigQuery database.\n\nThe tool expects input in the following format:\n{\n  \"query\": \"SELECT COUNT(*) FROM `transport.shipments` WHERE `On Time Delivery` = FALSE\"\n}\n\nOnly provide the SQL query as a string inside the 'query' key. Do not include code formatting (like ```sql), comments, or explanations. The tool will return only the raw result from the database.\n",
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          "value": {
            "query": "={{ $fromAI(\"query\", \"SQL query to run\") }}"
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            {
              "id": "query",
              "type": "string",
              "display": true,
              "removed": false,
              "required": false,
              "displayName": "query",
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        "content": "### 3. Big Query Workflow\nExecute the SQL query generated by the AI agent in Big Query. Retrieve the results and send them back to the AI Agent.\n\n### How to set up?\n- Paste these nodes in a separate workflow so you can use it with multiple agents.\n- **Google BigQuery API**:\n   1. Add your Google Translate API credentials\n   2. The project in which your table is located\n  [Learn more about the Google BigQuery Node](https://docs.n8n.io/integrations/builtin/app-nodes/n8n-nodes-base.googlebigquery)\n"
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      "name": "Query Database",
      "type": "n8n-nodes-base.googleBigQuery",
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        "options": {},
        "sqlQuery": "={{ $json.query }}",
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      "name": "Trigger Executed by the AI Tool",
      "type": "n8n-nodes-base.executeWorkflowTrigger",
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        "workflowInputs": {
          "values": [
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              "name": "query"
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      "parameters": {
        "jsCode": "return [\n  {\n    json: {\n      query: $input.first().json.query.replace(/```sql|```/g, \"\").trim()\n    }\n  }\n];\n"
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      "webhookId": "ee7c418b-d7d6-41f9-8e87-0f71b8ae1cf9",
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      "parameters": {
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        "content": "### 4. Do you need more details?\nFind a step-by-step guide in this tutorial\n![Guide](https://www.samirsaci.com/content/images/2025/04/image.png)\n[🎥 Watch My Tutorial](https://www.loom.com/share/50271f9d50214d7184830985497a75ec?sid=d0c410dc-29f1-488f-b89a-4011de0ded07)"
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