{
  "workflow": {
    "id": 3305,
    "name": "🗼 AI powered supply chain control tower with BigQuery and GPT-4o",
    "views": 7595,
    "recentViews": 1,
    "totalViews": 7595,
    "createdAt": "2025-03-24T10:55:56.536Z",
    "description": "*Tags: Supply Chain, Logistics, Control Tower*\n\n### Context\n\nHey! I’m [Samir](https://samirsaci.com), a Supply Chain Engineer and Data Scientist from Paris, and the founder of [LogiGreen Consulting](https://logi-green.com).\n\nWe design tools to help companies improve their **logistics processes** using data analytics, AI, and automation—to **reduce costs and minimize environmental impact**.\n\n> Let’s use N8N to build smarter and more sustainable supply chains!\n\n📬 For business inquiries, you can add me on [LinkedIn](https://www.linkedin.com/in/samir-saci)\n\n### Who is this template for?\n\nThis workflow template is designed for **logistics operations** that need a monitoring solution for their **distribution chains**.\n\n![Supply Chain Control Tower](https://miro.medium.com/v2/resize:fit:1050/1*0qKAg2B2ZpC-HH0TXmRewA.png) \n\nConnected to your Transportation Management Systems, this AI agent can answer any question about the shipments handled by your distribution teams.\n\n### How does it work?\n\nThe workflow is connected to a **Google BigQuery** table that stores **outbound order data** (customer deliveries).\n\n![BigQuery Table](https://www.samirsaci.com/content/images/size/w1000/2025/03/image-14.png)\n\nHere’s what the AI agent does:\n1. 🤔 Receives a user question via chat.  \n2. 🧠 Understands the request and **generates the correct SQL query**.  \n3. ✅ Executes the SQL query using a **BigQuery** node.  \n4. 💬 Responds to the user in **plain English**.\n\n[![Example of Interactions](https://www.samirsaci.com/content/images/size/w1000/2025/03/Chat-Screen.png)](https://www.loom.com/share/50271f9d50214d7184830985497a75ec?sid=d0c410dc-29f1-488f-b89a-4011de0ded07) \n\nThanks to the chat memory, users can ask follow-up questions to dive deeper into the data.\n\n### What do I need to get started?\n\nThis workflow requires **no advanced programming skills**.\n\nYou’ll need:\n- A **Google BigQuery account** with an SQL table storing transactional records.\n- An **OpenAI API key** (GPT-4o) for the chat model.\n\n### Next Steps\n\nFollow the sticky notes in the workflow to configure each node and start using **AI to support your supply chain operations**.\n\n[![Tutorial Screenshot](https://www.samirsaci.com/content/images/2025/04/image.png)](https://www.loom.com/share/50271f9d50214d7184830985497a75ec?sid=d0c410dc-29f1-488f-b89a-4011de0ded07)  \n\n[🎥 Watch My Tutorial](https://www.loom.com/share/50271f9d50214d7184830985497a75ec?sid=d0c410dc-29f1-488f-b89a-4011de0ded07)\n\n🚀 Curious how N8N can transform your logistics operations?  \n\n### Notes\n\n- The chat trigger can easily be replaced with **Teams**, **Telegram**, or **Slack** for a better user experience.\n- You can also connect this to a customer chat window using a **webhook**.\n\n*This workflow was built using N8N version 1.82.1*  \n*Submitted: March 24, 2025*\n",
    "workflow": {
      "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": "AI Control Tower Agent",
          "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": "Sticky Note1",
          "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": "Sticky Note2",
          "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": "Call Query Tool",
          "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": "Sticky Note",
          "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": "Query Database",
          "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": "Trigger Executed by the AI Tool",
          "type": "n8n-nodes-base.executeWorkflowTrigger",
          "position": [
            960,
            180
          ],
          "parameters": {
            "workflowInputs": {
              "values": [
                {
                  "name": "query"
                }
              ]
            }
          },
          "typeVersion": 1.1
        },
        {
          "id": "42a2801e-582e-4340-83af-ef0041eab4f9",
          "name": "Sanitising the Query",
          "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": "Chat Memory",
          "type": "@n8n/n8n-nodes-langchain.memoryBufferWindow",
          "position": [
            480,
            480
          ],
          "parameters": {},
          "typeVersion": 1.3
        },
        {
          "id": "e1408ac1-24da-4d38-8fdf-c110a54d3f55",
          "name": "Chat with the User",
          "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": "Sticky Note3",
          "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": {
        "Chat Memory": {
          "ai_memory": [
            [
              {
                "node": "AI Control Tower Agent",
                "type": "ai_memory",
                "index": 0
              }
            ]
          ]
        },
        "Call Query Tool": {
          "ai_tool": [
            [
              {
                "node": "AI Control Tower Agent",
                "type": "ai_tool",
                "index": 0
              }
            ]
          ]
        },
        "OpenAI Chat Model": {
          "ai_languageModel": [
            [
              {
                "node": "AI Control Tower Agent",
                "type": "ai_languageModel",
                "index": 0
              }
            ]
          ]
        },
        "Chat with the User": {
          "main": [
            [
              {
                "node": "AI Control Tower Agent",
                "type": "main",
                "index": 0
              }
            ]
          ]
        },
        "Sanitising the Query": {
          "main": [
            [
              {
                "node": "Query Database",
                "type": "main",
                "index": 0
              }
            ]
          ]
        },
        "Trigger Executed by the AI Tool": {
          "main": [
            [
              {
                "node": "Sanitising the Query",
                "type": "main",
                "index": 0
              }
            ]
          ]
        }
      }
    },
    "lastUpdatedBy": 29,
    "workflowInfo": {
      "nodeCount": 12,
      "nodeTypes": {
        "n8n-nodes-base.code": {
          "count": 1
        },
        "n8n-nodes-base.stickyNote": {
          "count": 4
        },
        "n8n-nodes-base.googleBigQuery": {
          "count": 1
        },
        "@n8n/n8n-nodes-langchain.agent": {
          "count": 1
        },
        "@n8n/n8n-nodes-langchain.chatTrigger": {
          "count": 1
        },
        "@n8n/n8n-nodes-langchain.lmChatOpenAi": {
          "count": 1
        },
        "@n8n/n8n-nodes-langchain.toolWorkflow": {
          "count": 1
        },
        "n8n-nodes-base.executeWorkflowTrigger": {
          "count": 1
        },
        "@n8n/n8n-nodes-langchain.memoryBufferWindow": {
          "count": 1
        }
      }
    },
    "status": "published",
    "user": {
      "name": "Samir Saci",
      "username": "samirsaci",
      "bio": "Automation, AI and Analytics for Supply Chain Operations, Business Optimisation and Content Creation for Marketing.\nLinkedin: www.linkedin.com/in/samir-saci",
      "verified": true,
      "links": [
        "https://linktr.ee/samirsaci"
      ],
      "avatar": "https://gravatar.com/avatar/d3644476a59e5813c5f00a7933977e2be8805cbdc574958b9cc6a53b45bedd6a?r=pg&d=retro&size=200"
    },
    "nodes": [
      {
        "id": 479,
        "icon": "file:googleBigQuery.svg",
        "name": "n8n-nodes-base.googleBigQuery",
        "codex": {
          "data": {
            "resources": {
              "primaryDocumentation": [
                {
                  "url": "https://docs.n8n.io/integrations/builtin/app-nodes/n8n-nodes-base.googlebigquery/"
                }
              ],
              "credentialDocumentation": [
                {
                  "url": "https://docs.n8n.io/integrations/builtin/credentials/google/oauth-single-service/"
                }
              ]
            },
            "categories": [
              "Data & Storage",
              "Development"
            ],
            "nodeVersion": "1.0",
            "codexVersion": "1.0"
          }
        },
        "group": "[\"input\"]",
        "defaults": {
          "name": "Google BigQuery"
        },
        "iconData": {
          "type": "file",
          "fileBuffer": "data:image/svg+xml;base64,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"
        },
        "displayName": "Google BigQuery",
        "typeVersion": 2,
        "nodeCategories": [
          {
            "id": 3,
            "name": "Data & Storage"
          },
          {
            "id": 5,
            "name": "Development"
          }
        ]
      },
      {
        "id": 565,
        "icon": "fa:sticky-note",
        "name": "n8n-nodes-base.stickyNote",
        "codex": {
          "data": {
            "alias": [
              "Comments",
              "Notes",
              "Sticky"
            ],
            "categories": [
              "Core Nodes"
            ],
            "nodeVersion": "1.0",
            "codexVersion": "1.0",
            "subcategories": {
              "Core Nodes": [
                "Helpers"
              ]
            }
          }
        },
        "group": "[\"input\"]",
        "defaults": {
          "name": "Sticky Note",
          "color": "#FFD233"
        },
        "iconData": {
          "icon": "sticky-note",
          "type": "icon"
        },
        "displayName": "Sticky Note",
        "typeVersion": 1,
        "nodeCategories": [
          {
            "id": 9,
            "name": "Core Nodes"
          }
        ]
      },
      {
        "id": 834,
        "icon": "file:code.svg",
        "name": "n8n-nodes-base.code",
        "codex": {
          "data": {
            "alias": [
              "cpde",
              "Javascript",
              "JS",
              "Python",
              "Script",
              "Custom Code",
              "Function"
            ],
            "details": "The Code node allows you to execute JavaScript in your workflow.",
            "resources": {
              "primaryDocumentation": [
                {
                  "url": "https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-base.code/"
                }
              ]
            },
            "categories": [
              "Development",
              "Core Nodes"
            ],
            "nodeVersion": "1.0",
            "codexVersion": "1.0",
            "subcategories": {
              "Core Nodes": [
                "Helpers",
                "Data Transformation"
              ]
            }
          }
        },
        "group": "[\"transform\"]",
        "defaults": {
          "name": "Code"
        },
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