{
  "workflow": {
    "id": 2418,
    "name": "Easy image captioning with Gemini 1.5 Pro",
    "views": 13836,
    "recentViews": 1,
    "totalViews": 13836,
    "createdAt": "2024-09-18T10:17:10.042Z",
    "description": "This n8n workflow demonstrates how to automate image captioning tasks using Gemini 1.5 Pro - a multimodal LLM which can accept and analyse images. This is a really simple example of how easy it is to build and leverage powerful AI models in your repetitive tasks.\n\n## How it works\n* For this demo, we'll import a public image from a popular stock photography website, Pexel.com, into our workflow using the HTTP request node.\n* With multimodal LLMs, there is little do preprocess other than ensuring the image dimensions fit within the LLMs accepted limits. Though not essential, we'll resize the image using the Edit image node to achieve fast processing.\n* The image is used as an input to the basic LLM node by defining a \"user message\" entry with the binary (data) type.\n* The LLM node has the Gemini 1.5 Pro language model attached and we'll prompt it to generate a caption title and text appropriate for the image it sees.\n* Once generated, the generated caption text is positioning over the original image to complete the task. We can calculate the positioning relative to the amount of characters produced using the code node.\n\nAn example of the combined image and caption can be found here: [https://res.cloudinary.com/daglih2g8/image/upload/f_auto,q_auto/v1/n8n-workflows/l5xbb4ze4wyxwwefqmnc](https://res.cloudinary.com/daglih2g8/image/upload/f_auto,q_auto/v1/n8n-workflows/l5xbb4ze4wyxwwefqmnc)\n\n## Requirements\n* Google Gemini API Key.\n* Access to Google Drive.\n\n## Customising the workflow\n\n* Not using Google Gemini? n8n's basic LLM node supports the standard syntax for image content for models that support it - try using GPT4o, Claude or LLava (via Ollama).\n\n* Google Drive is only used for demonstration purposes. Feel free to swap this out for other triggers such as webhooks to fit your use case.\n\n",
    "workflow": {
      "meta": {
        "instanceId": "408f9fb9940c3cb18ffdef0e0150fe342d6e655c3a9fac21f0f644e8bedabcd9"
      },
      "nodes": [
        {
          "id": "0b64edf1-57e0-4704-b78c-c8ab2b91f74d",
          "name": "When clicking ‘Test workflow’",
          "type": "n8n-nodes-base.manualTrigger",
          "position": [
            480,
            300
          ],
          "parameters": {},
          "typeVersion": 1
        },
        {
          "id": "a875d1c5-ccfe-4bbf-b429-56a42b0ca778",
          "name": "Google Gemini Chat Model",
          "type": "@n8n/n8n-nodes-langchain.lmChatGoogleGemini",
          "position": [
            1280,
            720
          ],
          "parameters": {
            "options": {},
            "modelName": "models/gemini-1.5-flash"
          },
          "credentials": {
            "googlePalmApi": {
              "id": "credential-id",
              "name": "googlePalmApi Credential"
            }
          },
          "typeVersion": 1
        },
        {
          "id": "a5e00543-dbaa-4e62-afb0-825ebefae3f3",
          "name": "Structured Output Parser",
          "type": "@n8n/n8n-nodes-langchain.outputParserStructured",
          "position": [
            1480,
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          ],
          "parameters": {
            "jsonSchemaExample": "{\n\t\"caption_title\": \"\",\n\t\"caption_text\": \"\"\n}"
          },
          "typeVersion": 1.2
        },
        {
          "id": "bb9af9c6-6c81-4e92-a29f-18ab3afbe327",
          "name": "Get Info",
          "type": "n8n-nodes-base.editImage",
          "position": [
            1100,
            400
          ],
          "parameters": {
            "operation": "information"
          },
          "typeVersion": 1
        },
        {
          "id": "8a0dbd5d-5886-484a-80a0-486f349a9856",
          "name": "Resize For AI",
          "type": "n8n-nodes-base.editImage",
          "position": [
            1100,
            560
          ],
          "parameters": {
            "width": 512,
            "height": 512,
            "options": {},
            "operation": "resize"
          },
          "typeVersion": 1
        },
        {
          "id": "d29f254a-5fa3-46fa-b153-19dfd8e8c6a7",
          "name": "Calculate Positioning",
          "type": "n8n-nodes-base.code",
          "position": [
            2020,
            720
          ],
          "parameters": {
            "mode": "runOnceForEachItem",
            "jsCode": "const { size, output } = $input.item.json;\n\nconst lineHeight = 35;\nconst fontSize = Math.round(size.height / lineHeight);\nconst maxLineLength = Math.round(size.width/fontSize) * 2;\nconst text = `\"${output.caption_title}\". ${output.caption_text}`;\nconst numLinesOccupied = Math.round(text.length / maxLineLength);\n\nconst verticalPadding = size.height * 0.02;\nconst horizontalPadding = size.width * 0.02;\nconst rectPosX = 0;\nconst rectPosY = size.height - (verticalPadding * 2.5) - (numLinesOccupied * fontSize);\nconst textPosX = horizontalPadding;\nconst textPosY = size.height - (numLinesOccupied * fontSize) - (verticalPadding/2);\n\nreturn {\n  caption: {\n    fontSize,\n    maxLineLength,\n    numLinesOccupied,\n    rectPosX,\n    rectPosY,\n    textPosX,\n    textPosY,\n    verticalPadding,\n    horizontalPadding,\n  }\n}\n"
          },
          "typeVersion": 2
        },
        {
          "id": "12a7f2d6-8684-48a5-aa41-40a8a4f98c79",
          "name": "Apply Caption to Image",
          "type": "n8n-nodes-base.editImage",
          "position": [
            2380,
            560
          ],
          "parameters": {
            "options": {},
            "operation": "multiStep",
            "operations": {
              "operations": [
                {
                  "color": "=#0000008c",
                  "operation": "draw",
                  "endPositionX": "={{ $json.size.width }}",
                  "endPositionY": "={{ $json.size.height }}",
                  "startPositionX": "={{ $json.caption.rectPosX }}",
                  "startPositionY": "={{ $json.caption.rectPosY }}"
                },
                {
                  "font": "/usr/YOUR_AWS_SECRET_KEY_HERE.ttf",
                  "text": "=\"{{ $json.output.caption_title }}\". {{ $json.output.caption_text }}",
                  "fontSize": "={{ $json.caption.fontSize }}",
                  "fontColor": "#FFFFFF",
                  "operation": "text",
                  "positionX": "={{ $json.caption.textPosX }}",
                  "positionY": "={{ $json.caption.textPosY }}",
                  "lineLength": "={{ $json.caption.maxLineLength }}"
                }
              ]
            }
          },
          "typeVersion": 1
        },
        {
          "id": "4d569ec8-04c2-4d21-96e1-86543b26892d",
          "name": "Sticky Note",
          "type": "n8n-nodes-base.stickyNote",
          "position": [
            -120,
            80
          ],
          "parameters": {
            "width": 423.75,
            "height": 431.76353488372104,
            "content": "## Try it out!\n\n### This workflow takes an image and generates a caption for it using AI. The OpenAI node has been able to do this for a while but this workflow demonstrates how to achieve the same with other multimodal vision models such as Google's Gemini.\n\nAdditional, we'll use the Edit Image node to overlay the generated caption onto the image. This can be useful for publications or can be repurposed for copyrights and/or watermarks.\n\n### Need Help?\nJoin the [Discord](https://discord.com/invite/XPKeKXeB7d) or ask in the [Forum](https://community.n8n.io/)!\n"
          },
          "typeVersion": 1
        },
        {
          "id": "45d37945-5a7a-42eb-8c8c-5940ea276072",
          "name": "Merge Image & Caption",
          "type": "n8n-nodes-base.merge",
          "position": [
            1620,
            400
          ],
          "parameters": {
            "mode": "combine",
            "options": {},
            "combineBy": "combineByPosition"
          },
          "typeVersion": 3
        },
        {
          "id": "53a26842-ad56-4c8d-a59d-4f6d3f9e2407",
          "name": "Merge Caption & Positions",
          "type": "n8n-nodes-base.merge",
          "position": [
            2200,
            560
          ],
          "parameters": {
            "mode": "combine",
            "options": {},
            "combineBy": "combineByPosition"
          },
          "typeVersion": 3
        },
        {
          "id": "b6c28913-b16a-4c59-aa49-47e9bb97f86d",
          "name": "Get Image",
          "type": "n8n-nodes-base.httpRequest",
          "position": [
            680,
            300
          ],
          "parameters": {
            "url": "https://images.pexels.com/photos/1267338/pexels-photo-1267338.jpeg?auto=compress&cs=tinysrgb&w=600",
            "options": {}
          },
          "typeVersion": 4.2
        },
        {
          "id": "6c25054d-8103-4be9-bea7-6c3dd47f49a3",
          "name": "Sticky Note1",
          "type": "n8n-nodes-base.stickyNote",
          "position": [
            340,
            80
          ],
          "parameters": {
            "color": 7,
            "width": 586.25,
            "height": 486.25,
            "content": "## 1. Import an Image \n[Read more about the HTTP request node](https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-base.httprequest)\n\nFor this demonstration, we'll grab an image off Pexels.com - a popular free stock photography site - by using the HTTP request node to download.\n\nIn your own workflows, this can be replaces by other triggers such as webhooks."
          },
          "typeVersion": 1
        },
        {
          "id": "d1b708e2-31c3-4cd1-a353-678bc33d4022",
          "name": "Sticky Note2",
          "type": "n8n-nodes-base.stickyNote",
          "position": [
            960,
            140
          ],
          "parameters": {
            "color": 7,
            "width": 888.75,
            "height": 783.75,
            "content": "## 2. Using Vision Model to Generate Caption\n[Learn more about the Basic LLM Chain](https://docs.n8n.io/integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.chainllm)\n\nn8n's basic LLM node supports multimodal input by allowing you to specify either a binary or an image url to send to a compatible LLM. This makes it easy to start utilising this powerful feature for visual classification or OCR tasks which have previously depended on more dedicated OCR models.\n\nHere, we've simply passed our image binary as a \"user message\" option, asking the LLM to help us generate a caption title and text which is appropriate for the given subject. Once generated, we'll pass this text along with the image to combine them both."
          },
          "typeVersion": 1
        },
        {
          "id": "36a39871-340f-4c44-90e6-74393b9be324",
          "name": "Sticky Note3",
          "type": "n8n-nodes-base.stickyNote",
          "position": [
            1880,
            280
          ],
          "parameters": {
            "color": 7,
            "width": 753.75,
            "height": 635,
            "content": "## 3. Overlay Caption on Image \n[Read more about the Edit Image node](https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-base.editimage)\n\nFinally, we’ll perform some basic calculations to place the generated caption onto the image. With n8n's user-friendly image editing features, this can be done entirely within the workflow!\n\nThe Code node tool is ideal for these types of calculations and is used here to position the caption at the bottom of the image. To create the overlay, the Edit Image node enables us to insert text onto the image, which we’ll use to add the generated caption."
          },
          "typeVersion": 1
        },
        {
          "id": "d175fe97-064e-41da-95fd-b15668c330c4",
          "name": "Sticky Note4",
          "type": "n8n-nodes-base.stickyNote",
          "position": [
            2660,
            280
          ],
          "parameters": {
            "width": 563.75,
            "height": 411.25,
            "content": "**FIG 1.** Example input image with AI generated caption\n![Example Output](https://res.cloudinary.com/daglih2g8/image/upload/f_auto,q_auto/v1/n8n-workflows/l5xbb4ze4wyxwwefqmnc#full-width)"
          },
          "typeVersion": 1
        },
        {
          "id": "23db0c90-45b6-4b85-b017-a52ad5a9ad5b",
          "name": "Image Captioning Agent",
          "type": "@n8n/n8n-nodes-langchain.chainLlm",
          "position": [
            1280,
            560
          ],
          "parameters": {
            "text": "Generate a caption for this image.",
            "messages": {
              "messageValues": [
                {
                  "message": "=You role is to provide an appropriate image caption for user provided images.\n\nThe individual components of a caption are as follows: who, when, where, context and miscellaneous. For a really good caption, follow this template: who + when + where + context + miscellaneous\n\nGive the caption a punny title."
                },
                {
                  "type": "HumanMessagePromptTemplate",
                  "messageType": "imageBinary"
                }
              ]
            },
            "promptType": "define",
            "hasOutputParser": true
          },
          "typeVersion": 1.4
        }
      ],
      "pinData": {},
      "connections": {
        "Get Info": {
          "main": [
            [
              {
                "node": "Merge Image & Caption",
                "type": "main",
                "index": 0
              }
            ]
          ]
        },
        "Get Image": {
          "main": [
            [
              {
                "node": "Resize For AI",
                "type": "main",
                "index": 0
              },
              {
                "node": "Get Info",
                "type": "main",
                "index": 0
              }
            ]
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        },
        "Resize For AI": {
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                "node": "Image Captioning Agent",
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              }
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        "Calculate Positioning": {
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        "Image Captioning Agent": {
          "main": [
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                "node": "Merge Image & Caption",
                "type": "main",
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          ]
        },
        "Google Gemini Chat Model": {
          "ai_languageModel": [
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              {
                "node": "Image Captioning Agent",
                "type": "ai_languageModel",
                "index": 0
              }
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        "Structured Output Parser": {
          "ai_outputParser": [
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                "node": "Image Captioning Agent",
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    "status": "published",
    "user": {
      "name": "Jimleuk",
      "username": "jimleuk",
      "bio": "Founder @ Subworkflow.ai - the fastest way to build durable RAG applications.\n\nFreelance AI Automation Engineer based in London, UK. Since 2024, my n8n templates have documented my journey into applied AI and have helped hundreds of businesses and organisations get up to speed with AI automation. Today, I continue to explore use-cases as AI evolves and occasionally upload templates which I find novel and interesting.\n\nSubscribe to the RSS Feed: https://cdn.subworkflow.ai/n8n-templates/rss.xml",
      "verified": true,
      "links": [
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            "details": "The Edit Image node allows you to manipulate and edit images. Use this node when you want to:\n\n- Blur an image\n- Add a border to an image\n- Create a new image\n- Crop an image\n- Composite an image on top of another\n- Draw on an image\n- Get information about the image\n- Rotate an image\n- Change the size of an image\n- Shear an image along the X or Y axis\n- Add text to the image",
            "resources": {
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            "codexVersion": "1.0",
            "subcategories": {
              "Core Nodes": [
                "Files",
                "Data Transformation"
              ]
            }
          }
        },
        "group": "[\"transform\"]",
        "defaults": {
          "name": "Edit Image",
          "color": "#553399"
        },
        "iconData": {
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          "type": "icon"
        },
        "displayName": "Edit Image",
        "typeVersion": 1,
        "nodeCategories": [
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            "name": "Core Nodes"
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          {
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            "name": "Marketing"
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        ]
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      {
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        "icon": "file:httprequest.svg",
        "name": "n8n-nodes-base.httpRequest",
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              "API",
              "Request",
              "URL",
              "Build",
              "cURL"
            ],
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                  "label": "2021: The Year to Automate the New You with n8n"
                },
                {
                  "url": "https://n8n.io/blog/why-business-process-automation-with-n8n-can-change-your-daily-life/",
                  "icon": "🧬",
                  "label": "Why business process automation with n8n can change your daily life"
                },
                {
                  "url": "https://n8n.io/blog/automatically-pulling-and-visualizing-data-with-n8n/",
                  "icon": "📈",
                  "label": "Automatically pulling and visualizing data with n8n"
                },
                {
                  "url": "https://n8n.io/blog/learn-how-to-automatically-cross-post-your-content-with-n8n/",
                  "icon": "✍️",
                  "label": "Learn how to automatically cross-post your content with n8n"
                },
                {
                  "url": "https://n8n.io/blog/automatically-adding-expense-receipts-to-google-sheets-with-telegram-mindee-twilio-and-n8n/",
                  "icon": "🧾",
                  "label": "Automatically Adding Expense Receipts to Google Sheets with Telegram, Mindee, Twilio, and n8n"
                },
                {
                  "url": "https://n8n.io/blog/running-n8n-on-ships-an-interview-with-maranics/",
                  "icon": "🛳",
                  "label": "Running n8n on ships: An interview with Maranics"
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                {
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                  "label": "5 tasks you can automate with the new Notion API "
                },
                {
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                  "icon": "📜",
                  "label": "Celebrating World Poetry Day with a daily poem in Telegram"
                },
                {
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                  "icon": "💡",
                  "label": "15 Google apps you can combine and automate to increase productivity"
                },
                {
                  "url": "https://n8n.io/blog/automate-designs-with-bannerbear-and-n8n/",
                  "icon": "🎨",
                  "label": "Automate Designs with Bannerbear and n8n"
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                {
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                  "icon": " 🕸️",
                  "label": "How uProc scraped a multi-page website with a low-code workflow"
                },
                {
                  "url": "https://n8n.io/blog/building-an-expense-tracking-app-in-10-minutes/",
                  "icon": "📱",
                  "label": "Building an expense tracking app in 10 minutes"
                },
                {
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                {
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                },
                {
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                  "icon": "✨",
                  "label": "How Common Knowledge use workflow automation for activism"
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                {
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                  "icon": "🤟",
                  "label": "Creating scheduled text affirmations with n8n"
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                {
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                  "icon": "🛵",
                  "label": "How Goomer automated their operations with over 200 n8n workflows"
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                {
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                  "label": "7 no-code workflow automations for Amazon Web Services"
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              "Core Nodes"
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              "Core Nodes": [
                "Helpers"
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            }
          }
        },
        "group": "[\"output\"]",
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          "name": "HTTP Request",
          "color": "#0004F5"
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        "iconData": {
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            "name": "Development"
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            "id": 9,
            "name": "Core Nodes"
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      },
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        "id": 24,
        "icon": "file:merge.svg",
        "name": "n8n-nodes-base.merge",
        "codex": {
          "data": {
            "alias": [
              "Join",
              "Concatenate",
              "Wait"
            ],
            "resources": {
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                {
                  "url": "https://n8n.io/blog/how-to-sync-data-between-two-systems/",
                  "icon": "🏬",
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                },
                {
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                  "icon": "🎫",
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                {
                  "url": "https://n8n.io/blog/migrating-community-metrics-to-orbit-using-n8n/",
                  "icon": "📈",
                  "label": "Migrating Community Metrics to Orbit using n8n"
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                {
                  "url": "https://n8n.io/blog/build-your-own-virtual-assistant-with-n8n-a-step-by-step-guide/",
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                },
                {
                  "url": "https://n8n.io/blog/sending-automated-congratulations-with-google-sheets-twilio-and-n8n/",
                  "icon": "🙌",
                  "label": "Sending Automated Congratulations with Google Sheets, Twilio, and n8n "
                },
                {
                  "url": "https://n8n.io/blog/aws-workflow-automation/",
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                  "url": "https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-base.merge/"
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            "categories": [
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            "nodeVersion": "1.0",
            "codexVersion": "1.0",
            "subcategories": {
              "Core Nodes": [
                "Flow",
                "Data Transformation"
              ]
            }
          }
        },
        "group": "[\"transform\"]",
        "defaults": {
          "name": "Merge"
        },
        "iconData": {
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            "name": "Core Nodes"
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        ]
      },
      {
        "id": 565,
        "icon": "fa:sticky-note",
        "name": "n8n-nodes-base.stickyNote",
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              "Notes",
              "Sticky"
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        "group": "[\"input\"]",
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          "name": "Sticky Note",
          "color": "#FFD233"
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              "Javascript",
              "JS",
              "Python",
              "Script",
              "Custom Code",
              "Function"
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            "resources": {
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                  "url": "https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-base.code/"
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                "Data Transformation"
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        "group": "[\"transform\"]",
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          "name": "Code"
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            "name": "Development"
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            "name": "Core Nodes"
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        "icon": "fa:mouse-pointer",
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                }
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            "nodeVersion": "1.0",
            "codexVersion": "1.0"
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        },
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          "color": "#909298"
        },
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          "type": "icon"
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        "displayName": "Manual Trigger",
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            "name": "Core Nodes"
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        ]
      },
      {
        "id": 1123,
        "icon": "fa:link",
        "name": "@n8n/n8n-nodes-langchain.chainLlm",
        "codex": {
          "data": {
            "alias": [
              "LangChain"
            ],
            "resources": {
              "primaryDocumentation": [
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                }
              ]
            },
            "categories": [
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              "Langchain"
            ],
            "subcategories": {
              "AI": [
                "Chains",
                "Root Nodes"
              ]
            }
          }
        },
        "group": "[\"transform\"]",
        "defaults": {
          "name": "Basic LLM Chain",
          "color": "#909298"
        },
        "iconData": {
          "icon": "link",
          "type": "icon"
        },
        "displayName": "Basic LLM Chain",
        "typeVersion": 2,
        "nodeCategories": [
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            "id": 25,
            "name": "AI"
          },
          {
            "id": 26,
            "name": "Langchain"
          }
        ]
      },
      {
        "id": 1179,
        "icon": "fa:code",
        "name": "@n8n/n8n-nodes-langchain.outputParserStructured",
        "codex": {
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            "alias": [
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              "zod"
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            "resources": {
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                  "url": "https://docs.n8n.io/integrations/builtin/cluster-nodes/sub-nodes/n8n-nodes-langchain.outputparserstructured/"
                }
              ]
            },
            "categories": [
              "AI",
              "Langchain"
            ],
            "subcategories": {
              "AI": [
                "Output Parsers"
              ]
            }
          }
        },
        "group": "[\"transform\"]",
        "defaults": {
          "name": "Structured Output Parser"
        },
        "iconData": {
          "icon": "code",
          "type": "icon"
        },
        "displayName": "Structured Output Parser",
        "typeVersion": 1,
        "nodeCategories": [
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            "id": 25,
            "name": "AI"
          },
          {
            "id": 26,
            "name": "Langchain"
          }
        ]
      },
      {
        "id": 1262,
        "icon": "file:google.svg",
        "name": "@n8n/n8n-nodes-langchain.lmChatGoogleGemini",
        "codex": {
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                }
              ]
            },
            "categories": [
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              "Langchain"
            ],
            "subcategories": {
              "AI": [
                "Language Models",
                "Root Nodes"
              ],
              "Language Models": [
                "Chat Models (Recommended)"
              ]
            }
          }
        },
        "group": "[\"transform\"]",
        "defaults": {
          "name": "Google Gemini Chat Model"
        },
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    "image": []
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}