🗼 Tour de contrôle de la chaîne d'approvisionnement piloté par l'IA (utilise BigQuery et GPT-4o)

• •Clé API OpenAI

{
  "meta": {
    "instanceId": "6a5e68bcca67c4cdb3e0b698d01739aea084e1ec06e551db64aeff43d174cb23"
  },
  "nodes": [
    {
      "id": "53b36910-966f-45ba-a425-a3260a55059f",
      "name": "OpenAI Chat Model",
      "type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
      "position": [
        340,
        480
      ],
      "parameters": {
        "model": {
          "__rl": true,
          "mode": "list",
          "value": "gpt-4o-mini"
        },
        "options": {}
      },
      "typeVersion": 1.2
    },
    {
      "id": "177235e8-c925-43d0-9695-10f072e26350",
      "name": "Agent de la tour de contrôle IA",
      "type": "@n8n/n8n-nodes-langchain.agent",
      "position": [
        380,
        240
      ],
      "parameters": {
        "options": {
          "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"
        }
      },
      "typeVersion": 1.8
    },
    {
      "id": "5366cc5f-85d3-44d2-9b1b-62febfcb44e3",
      "name": "Note adhésive 1",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -100,
        -120
      ],
      "parameters": {
        "color": 7,
        "width": 200,
        "height": 520,
        "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"
      },
      "typeVersion": 1
    },
    {
      "id": "4218a062-12f8-437d-ab22-5a653a3089b2",
      "name": "Note adhésive 2",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        140,
        -120
      ],
      "parameters": {
        "color": 7,
        "width": 700,
        "height": 740,
        "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)"
      },
      "typeVersion": 1
    },
    {
      "id": "c5967f58-00e8-4f03-9110-913547f7ab9c",
      "name": "Outil d'appel de requête",
      "type": "@n8n/n8n-nodes-langchain.toolWorkflow",
      "position": [
        640,
        440
      ],
      "parameters": {
        "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",
        "workflowInputs": {
          "value": {
            "query": "={{ $fromAI(\"query\", \"SQL query to run\") }}"
          },
          "schema": [
            {
              "id": "query",
              "type": "string",
              "display": true,
              "removed": false,
              "required": false,
              "displayName": "query",
              "defaultMatch": false,
              "canBeUsedToMatch": true
            }
          ],
          "mappingMode": "defineBelow",
          "matchingColumns": [
            "query"
          ],
          "attemptToConvertTypes": false,
          "convertFieldsToString": false
        }
      },
      "typeVersion": 2
    },
    {
      "id": "429813c8-b07f-4551-aeea-1744a1225449",
      "name": "Note adhésive",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        900,
        -120
      ],
      "parameters": {
        "width": 760,
        "height": 460,
        "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"
      },
      "typeVersion": 1
    },
    {
      "id": "bede0624-8923-4af0-8adc-8be22d556066",
      "name": "Interroger la base de données",
      "type": "n8n-nodes-base.googleBigQuery",
      "position": [
        1520,
        180
      ],
      "parameters": {
        "options": {},
        "sqlQuery": "={{ $json.query }}",
        "projectId": {
          "__rl": true,
          "mode": "list",
          "value": "=",
          "cachedResultUrl": "=",
          "cachedResultName": "="
        }
      },
      "notesInFlow": true,
      "typeVersion": 2.1
    },
    {
      "id": "137e4dbc-db8d-4ec7-a3e0-478dde6ef27c",
      "name": "Déclencheur exécuté par l'outil IA",
      "type": "n8n-nodes-base.executeWorkflowTrigger",
      "position": [
        960,
        180
      ],
      "parameters": {
        "workflowInputs": {
          "values": [
            {
              "name": "query"
            }
          ]
        }
      },
      "typeVersion": 1.1
    },
    {
      "id": "42a2801e-582e-4340-83af-ef0041eab4f9",
      "name": "Assainissement de la requête",
      "type": "n8n-nodes-base.code",
      "position": [
        1240,
        180
      ],
      "parameters": {
        "jsCode": "return [\n  {\n    json: {\n      query: $input.first().json.query.replace(/```sql|```/g, \"\").trim()\n    }\n  }\n];\n"
      },
      "typeVersion": 2
    },
    {
      "id": "7c86fda0-116c-47ad-aaf5-8b83d2c083c6",
      "name": "Mémoire de chat",
      "type": "@n8n/n8n-nodes-langchain.memoryBufferWindow",
      "position": [
        480,
        480
      ],
      "parameters": {},
      "typeVersion": 1.3
    },
    {
      "id": "e1408ac1-24da-4d38-8fdf-c110a54d3f55",
      "name": "Chat avec l'utilisateur",
      "type": "@n8n/n8n-nodes-langchain.chatTrigger",
      "position": [
        -60,
        240
      ],
      "webhookId": "ee7c418b-d7d6-41f9-8e87-0f71b8ae1cf9",
      "parameters": {
        "options": {}
      },
      "typeVersion": 1.1
    },
    {
      "id": "bc49829b-45f2-4910-9c37-907271982f14",
      "name": "Note adhésive 3",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        900,
        380
      ],
      "parameters": {
        "width": 780,
        "height": 540,
        "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)"
      },
      "typeVersion": 1
    }
  ],
  "pinData": {},
  "connections": {
    "7c86fda0-116c-47ad-aaf5-8b83d2c083c6": {
      "ai_memory": [
        [
          {
            "node": "177235e8-c925-43d0-9695-10f072e26350",
            "type": "ai_memory",
            "index": 0
          }
        ]
      ]
    },
    "c5967f58-00e8-4f03-9110-913547f7ab9c": {
      "ai_tool": [
        [
          {
            "node": "177235e8-c925-43d0-9695-10f072e26350",
            "type": "ai_tool",
            "index": 0
          }
        ]
      ]
    },
    "53b36910-966f-45ba-a425-a3260a55059f": {
      "ai_languageModel": [
        [
          {
            "node": "177235e8-c925-43d0-9695-10f072e26350",
            "type": "ai_languageModel",
            "index": 0
          }
        ]
      ]
    },
    "e1408ac1-24da-4d38-8fdf-c110a54d3f55": {
      "main": [
        [
          {
            "node": "177235e8-c925-43d0-9695-10f072e26350",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "42a2801e-582e-4340-83af-ef0041eab4f9": {
      "main": [
        [
          {
            "node": "bede0624-8923-4af0-8adc-8be22d556066",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "137e4dbc-db8d-4ec7-a3e0-478dde6ef27c": {
      "main": [
        [
          {
            "node": "42a2801e-582e-4340-83af-ef0041eab4f9",
            "type": "main",
            "index": 0
          }
        ]
      ]
    }
  }
}