{
  "workflow": {
    "id": 3846,
    "name": "Extract & analyze brand content with Bright Data and Google Gemini",
    "views": 629,
    "recentViews": 0,
    "totalViews": 629,
    "createdAt": "2025-05-03T13:17:38.073Z",
    "description": "### Who this is for?\n\nThe Brand Content Extract, Summarization & Sentiment Analysis workflow is designed for professionals and teams who need to monitor, understand, and act on public brand perception at scale. \n\nIt is ideal for:\n\n1. **Brand Managers** - Looking to track how their brand is portrayed online.\n\n2. **Marketing Analysts** - Seeking insights from competitor and industry content.\n\n3. **PR & Communications Teams** - Evaluating media tone and potential reputation risks.\n\n4. **Data Scientists & AI Developers** - Automating content intelligence pipelines.\n\n5. **Growth Hackers** - Performing large-scale web listening for campaign optimization.\n\n### What problem is this workflow solving?\n\nManually tracking and interpreting how your brand is mentioned across blogs, news sites, or product reviews is labor-intensive and unscalable. Traditional scraping tools return raw data but lack insights like summarization, sentiment analysis etc.\n\nThis workflow addresses:\n\n- Scalable extraction of brand-related content using Bright Data's infrastructure.\n\n- Textual data extract for easy decision-making or alerting.\n\n- Automated summarization of verbose or multi-paragraph articles using Gemini.\n\n- Sentiment analysis of how a brand is being portrayed.\n\n### What this workflow does\n\n- Receives input: A brand URL for the data extraction and analysis.\n\n- Uses Bright Data's Web Unlocker to extract content from relevant sites.\n\n- Cleans and preprocesses the scraped content for readability.\n\n- Sends the content to Google Gemini for:\n\n\t- Enriched results including:\n\n\t\t- Cleaned content\n\n\t\t- Summary\n\n\t\t- Sentiment Analysis\n\n\t\t- Sends the response to a target system via Webhook notification\n\t\t- Perists the response to disk\n\n### Setup\n\n- Sign up at [Bright Data](https://brightdata.com/).\n- Navigate to Proxies & Scraping and create a new Web Unlocker zone by selecting Web Unlocker API under Scraping Solutions.\n- In n8n, configure the Header Auth account under Credentials (Generic Auth Type: Header Authentication).\n![Header Authentication.png](fileId:1229)\nThe Value field should be set with the \n**Bearer XXXXXXXXXXXXXX**. The XXXXXXXXXXXXXX should be replaced by the Web Unlocker Token.\n- A Google Gemini API key (or access through Vertex AI or proxy).\n- Update the **Set URL and Bright Data Zone** for setting the brand content URL and the Bright Data Zone name.\n- Update the Webhook HTTP Request node with the Webhook endpoint of your choice.\n\n### How to customize this workflow to your needs\n\n- **Update Source** : Update the workflow input to read from Google Sheet or Airbase for dynamically tracking multiple brands or topics.\n- **AI Prompt Customization** : Tailor Gemini prompts for: \n\t- Summary length (brief vs. detailed)\n\n\t- Detailed Sentiment with the custom structured data format.\n\n\t- Brand-specific tone detection (e.g., trust, excitement, dissatisfaction)\n\n- **Output Destinations**: Configure the output node to send the responses to various platforms, such as Slack, CRM systems, or databases.",
    "workflow": {
      "id": "wTI77cpLkbxsRQat",
      "meta": {
        "instanceId": "885b4fb4a6a9c2cb5621429a7b972df0d05bb724c20ac7dac7171b62f1c7ef40",
        "templateCredsSetupCompleted": true
      },
      "name": "Brand Content Extract, Summarize & Sentiment Analysis with Bright Data",
      "tags": [
        {
          "id": "Kujft2FOjmOVQAmJ",
          "name": "Engineering",
          "createdAt": "2025-04-09T01:31:00.558Z",
          "updatedAt": "2025-04-09T01:31:00.558Z"
        },
        {
          "id": "ddPkw7Hg5dZhQu2w",
          "name": "AI",
          "createdAt": "2025-04-13T05:38:08.053Z",
          "updatedAt": "2025-04-13T05:38:08.053Z"
        }
      ],
      "nodes": [
        {
          "id": "646ef542-c601-4103-87e6-6fa9616d8c52",
          "name": "When clicking ‘Test workflow’",
          "type": "n8n-nodes-base.manualTrigger",
          "position": [
            120,
            -560
          ],
          "parameters": {},
          "typeVersion": 1
        },
        {
          "id": "00b4ce90-c4f2-41c4-8943-7db3d0c3f81a",
          "name": "Sticky Note",
          "type": "n8n-nodes-base.stickyNote",
          "position": [
            100,
            -320
          ],
          "parameters": {
            "width": 400,
            "height": 300,
            "content": "## Note\n\nThis workflow deals with the brand content extraction by utilizing the Bright Data Web Unlocker Product.\n\nThe Basic LLM Chain, Information Extraction, Summarization Chain are being used to demonstrate the usage of the N8N AI capabilities.\n\n**Please make sure to set the web URL of your interest within the \"Set URL and Bright Data Zone\" node and update the Webhook Notification URL**"
          },
          "typeVersion": 1
        },
        {
          "id": "5cc35b9b-7483-404e-96a3-1688f7b9078b",
          "name": "Sticky Note1",
          "type": "n8n-nodes-base.stickyNote",
          "position": [
            540,
            -320
          ],
          "parameters": {
            "width": 480,
            "height": 300,
            "content": "## LLM Usages\n\nGoogle Gemini Flash Exp model is being used.\n\nBasic LLM Chain Data Extractor.\n\nInformation Extraction is being used for the handling the custom sentiment analysis with the structured response.\n\nSummarization Chain is being used for the creation of a concise summary of the extracted brand content."
          },
          "typeVersion": 1
        },
        {
          "id": "e15f32de-58d9-4ea6-9d5c-f63975d1090d",
          "name": "Markdown to Textual Data Extractor",
          "type": "@n8n/n8n-nodes-langchain.chainLlm",
          "position": [
            1240,
            -440
          ],
          "parameters": {
            "text": "=You need to analyze the below markdown and convert to textual data. Please do not output with your own thoughts. Make sure to output with textual data only with no links, scripts, css etc.\n\n{{ $json.data }}",
            "messages": {
              "messageValues": [
                {
                  "message": "You are a markdown expert"
                }
              ]
            },
            "promptType": "define"
          },
          "typeVersion": 1.6
        },
        {
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          "name": "Set URL and Bright Data Zone",
          "type": "n8n-nodes-base.set",
          "position": [
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          ],
          "parameters": {
            "options": {},
            "assignments": {
              "assignments": [
                {
                  "id": "3aedba66-f447-4d7a-93c0-8158c5e795f9",
                  "name": "url",
                  "type": "string",
                  "value": "https://www.amazon.com/TP-Link-Dual-Band-Archer-BE230-HomeShield/dp/B0DC99N2T8"
                },
                {
                  "id": "4e7ee31d-da89-422f-8079-2ff2d357a0ba",
                  "name": "zone",
                  "type": "string",
                  "value": "web_unlocker1"
                }
              ]
            }
          },
          "typeVersion": 3.4
        },
        {
          "id": "9783e878-e864-4632-9b89-d78567204053",
          "name": "AI Sentiment Analyzer with the structured response",
          "type": "@n8n/n8n-nodes-langchain.informationExtractor",
          "position": [
            1740,
            100
          ],
          "parameters": {
            "text": "=Perform the sentiment analysis on the below content and output with the structured information.\n\nHere's the content:\n\n{{ $('Perform Bright Data Web Request').item.json.data }}",
            "options": {
              "systemPromptTemplate": "You are an expert sentiment analyzer."
            },
            "schemaType": "manual",
            "inputSchema": "{\n  \"$schema\": \"http://json-schema.org/schema#\",\n  \"title\": \"SentimentAnalysisResponseArray\",\n  \"type\": \"array\",\n  \"items\": {\n    \"type\": \"object\",\n    \"properties\": {\n      \"sentiment\": {\n        \"type\": \"string\",\n        \"enum\": [\"Positive\", \"Neutral\", \"Negative\"],\n        \"description\": \"The overall sentiment of the content.\"\n      },\n      \"confidence_score\": {\n        \"type\": \"number\",\n        \"minimum\": 0,\n        \"maximum\": 1,\n        \"description\": \"Confidence score of the sentiment classification.\"\n      },\n      \"sentence\": {\n        \"type\": \"string\",\n        \"description\": \"A natural language statement explaining the sentiment.\"\n      }\n    },\n    \"required\": [\"sentiment\", \"confidence_score\", \"sentence\"],\n    \"additionalProperties\": false\n  }\n}\n"
          },
          "typeVersion": 1
        },
        {
          "id": "41352a53-7821-4247-905e-7995e1e6e382",
          "name": "Initiate a Webhook Notification for Markdown to Textual Data Extraction",
          "type": "n8n-nodes-base.httpRequest",
          "position": [
            1720,
            -460
          ],
          "parameters": {
            "url": "https://webhook.site/3c36d7d1-de1b-4171-9fd3-643ea2e4dd76",
            "options": {},
            "sendBody": true,
            "bodyParameters": {
              "parameters": [
                {
                  "name": "summary",
                  "value": "={{ $json.text }}"
                }
              ]
            }
          },
          "typeVersion": 4.2
        },
        {
          "id": "9717b5df-f148-4c8c-95d4-cb7c54837228",
          "name": "Initiate a Webhook Notification for AI Sentiment Analyzer",
          "type": "n8n-nodes-base.httpRequest",
          "position": [
            2120,
            100
          ],
          "parameters": {
            "url": "https://webhook.site/3c36d7d1-de1b-4171-9fd3-643ea2e4dd76",
            "options": {},
            "sendBody": true,
            "bodyParameters": {
              "parameters": [
                {
                  "name": "summary",
                  "value": "={{ $json.output }}"
                }
              ]
            }
          },
          "typeVersion": 4.2
        },
        {
          "id": "88733b5f-cbb0-42a6-898c-7a1ccc94bef7",
          "name": "Google Gemini Chat Model for Summary",
          "type": "@n8n/n8n-nodes-langchain.lmChatGoogleGemini",
          "position": [
            1260,
            -780
          ],
          "parameters": {
            "options": {},
            "modelName": "models/gemini-2.0-flash-exp"
          },
          "credentials": {
            "googlePalmApi": {
              "id": "credential-id",
              "name": "googlePalmApi Credential"
            }
          },
          "typeVersion": 1
        },
        {
          "id": "560e3d33-61d8-4db6-b1df-89f4e915f3f1",
          "name": "Google Gemini Chat Model for Data Extract",
          "type": "@n8n/n8n-nodes-langchain.lmChatGoogleGemini",
          "position": [
            1320,
            -220
          ],
          "parameters": {
            "options": {},
            "modelName": "models/gemini-2.0-flash-exp"
          },
          "credentials": {
            "googlePalmApi": {
              "id": "credential-id",
              "name": "googlePalmApi Credential"
            }
          },
          "typeVersion": 1
        },
        {
          "id": "1b07608f-7174-46e8-af27-3abf100d9e3a",
          "name": "Google Gemini Chat Model for Sentiment Analyzer",
          "type": "@n8n/n8n-nodes-langchain.lmChatGoogleGemini",
          "position": [
            1820,
            320
          ],
          "parameters": {
            "options": {},
            "modelName": "models/gemini-2.0-flash-exp"
          },
          "credentials": {
            "googlePalmApi": {
              "id": "credential-id",
              "name": "googlePalmApi Credential"
            }
          },
          "typeVersion": 1
        },
        {
          "id": "b6b6df94-d3fc-45ee-a339-5a368ea000eb",
          "name": "Initiate a Webhook Notification for Summarization",
          "type": "n8n-nodes-base.httpRequest",
          "position": [
            1660,
            -820
          ],
          "parameters": {
            "url": "https://webhook.site/3c36d7d1-de1b-4171-9fd3-643ea2e4dd76",
            "options": {},
            "sendBody": true,
            "bodyParameters": {
              "parameters": [
                {
                  "name": "summary",
                  "value": "={{ $json.response.text }}"
                }
              ]
            }
          },
          "typeVersion": 4.2
        },
        {
          "id": "f3e60ecd-5d07-4df0-a413-327b24db23ab",
          "name": "Perform Bright Data Web Request",
          "type": "n8n-nodes-base.httpRequest",
          "position": [
            560,
            -560
          ],
          "parameters": {
            "url": "https://api.brightdata.com/request",
            "method": "POST",
            "options": {},
            "sendBody": true,
            "sendHeaders": true,
            "authentication": "genericCredentialType",
            "bodyParameters": {
              "parameters": [
                {
                  "name": "zone",
                  "value": "={{ $json.zone }}"
                },
                {
                  "name": "url",
                  "value": "={{ $json.url }}?product=unlocker&method=api"
                },
                {
                  "name": "format",
                  "value": "raw"
                },
                {
                  "name": "data_format",
                  "value": "markdown"
                }
              ]
            },
            "genericAuthType": "httpHeaderAuth",
            "headerParameters": {
              "parameters": [
                {}
              ]
            }
          },
          "credentials": {
            "httpHeaderAuth": {
              "id": "credential-id",
              "name": "httpHeaderAuth Credential"
            }
          },
          "typeVersion": 4.2
        },
        {
          "id": "9030085f-5b05-41d9-94ee-668ee29df815",
          "name": "Summarize Content",
          "type": "@n8n/n8n-nodes-langchain.chainSummarization",
          "position": [
            1240,
            -980
          ],
          "parameters": {
            "options": {
              "summarizationMethodAndPrompts": {
                "values": {
                  "prompt": "Write a concise summary of the following:\n\n\n\"{text}\"\n\n"
                }
              }
            },
            "chunkingMode": "advanced"
          },
          "typeVersion": 2
        },
        {
          "id": "fe93c4a6-de3b-481d-ba6c-5f315f5279c4",
          "name": "Create a binary data for textual data",
          "type": "n8n-nodes-base.function",
          "position": [
            1720,
            -220
          ],
          "parameters": {
            "functionCode": "items[0].binary = {\n  data: {\n    data: new Buffer(JSON.stringify(items[0].json, null, 2)).toString('base64')\n  }\n};\nreturn items;"
          },
          "typeVersion": 1
        },
        {
          "id": "0811c300-1302-49b5-a334-ac8f960a5b8c",
          "name": "Create a binary data for sentiment analysis",
          "type": "n8n-nodes-base.function",
          "position": [
            2120,
            320
          ],
          "parameters": {
            "functionCode": "items[0].binary = {\n  data: {\n    data: new Buffer(JSON.stringify(items[0].json, null, 2)).toString('base64')\n  }\n};\nreturn items;"
          },
          "typeVersion": 1
        },
        {
          "id": "01d798b7-7c62-4240-9d5e-f2e67ca047ae",
          "name": "Write the AI Sentiment analysis file to disk",
          "type": "n8n-nodes-base.readWriteFile",
          "position": [
            2520,
            320
          ],
          "parameters": {
            "options": {},
            "fileName": "d:\\Brand-Content-Sentiment-Analysis.json",
            "operation": "write"
          },
          "typeVersion": 1
        },
        {
          "id": "f9faf283-ba8d-48e1-860e-2bb660cb9c1e",
          "name": "Write the textual file to disk",
          "type": "n8n-nodes-base.readWriteFile",
          "position": [
            2100,
            -220
          ],
          "parameters": {
            "options": {},
            "fileName": "d:\\Brand-Content-Textual.json",
            "operation": "write"
          },
          "typeVersion": 1
        },
        {
          "id": "2c47c271-4456-4fc4-9a54-20784365a4af",
          "name": "Create a binary data for summary",
          "type": "n8n-nodes-base.function",
          "position": [
            1660,
            -1060
          ],
          "parameters": {
            "functionCode": "items[0].binary = {\n  data: {\n    data: new Buffer(JSON.stringify(items[0].json, null, 2)).toString('base64')\n  }\n};\nreturn items;"
          },
          "typeVersion": 1
        },
        {
          "id": "c5f33f8d-93eb-47ac-a42f-717b39f4d7c2",
          "name": "Write the summary file to disk",
          "type": "n8n-nodes-base.readWriteFile",
          "position": [
            1880,
            -1060
          ],
          "parameters": {
            "options": {},
            "fileName": "d:\\Brand-Content-Summary.json",
            "operation": "write"
          },
          "typeVersion": 1
        },
        {
          "id": "72938f7b-20c1-45d3-9348-878d6e0b8d60",
          "name": "Sticky Note2",
          "type": "n8n-nodes-base.stickyNote",
          "position": [
            1200,
            -1080
          ],
          "parameters": {
            "color": 4,
            "width": 1100,
            "height": 460,
            "content": "## Summarization"
          },
          "typeVersion": 1
        },
        {
          "id": "fcf1d1ad-d516-41bc-bf76-73ebb920ecba",
          "name": "Sticky Note3",
          "type": "n8n-nodes-base.stickyNote",
          "position": [
            1720,
            40
          ],
          "parameters": {
            "color": 6,
            "width": 1000,
            "height": 480,
            "content": "## Sentiment Analysis"
          },
          "typeVersion": 1
        },
        {
          "id": "9c44d01f-e30b-4597-ad74-09fa54b4ec84",
          "name": "Sticky Note4",
          "type": "n8n-nodes-base.stickyNote",
          "position": [
            1200,
            -520
          ],
          "parameters": {
            "color": 3,
            "width": 1100,
            "height": 480,
            "content": "## Textual Data Extract"
          },
          "typeVersion": 1
        }
      ],
      "active": false,
      "pinData": {},
      "settings": {
        "executionOrder": "v1"
      },
      "versionId": "317a5d48-95c6-4425-a14a-6b2fec9e0802",
      "connections": {
        "Summarize Content": {
          "main": [
            [
              {
                "node": "Initiate a Webhook Notification for Summarization",
                "type": "main",
                "index": 0
              },
              {
                "node": "Create a binary data for summary",
                "type": "main",
                "index": 0
              }
            ]
          ]
        },
        "Set URL and Bright Data Zone": {
          "main": [
            [
              {
                "node": "Perform Bright Data Web Request",
                "type": "main",
                "index": 0
              }
            ]
          ]
        },
        "Perform Bright Data Web Request": {
          "main": [
            [
              {
                "node": "Markdown to Textual Data Extractor",
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              },
              {
                "node": "Summarize Content",
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              }
            ]
          ]
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        "Create a binary data for summary": {
          "main": [
            [
              {
                "node": "Write the summary file to disk",
                "type": "main",
                "index": 0
              }
            ]
          ]
        },
        "When clicking ‘Test workflow’": {
          "main": [
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              {
                "node": "Set URL and Bright Data Zone",
                "type": "main",
                "index": 0
              }
            ]
          ]
        },
        "Markdown to Textual Data Extractor": {
          "main": [
            [
              {
                "node": "AI Sentiment Analyzer with the structured response",
                "type": "main",
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              },
              {
                "node": "Initiate a Webhook Notification for Markdown to Textual Data Extraction",
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        },
        "Google Gemini Chat Model for Summary": {
          "ai_languageModel": [
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              {
                "node": "Summarize Content",
                "type": "ai_languageModel",
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              }
            ]
          ]
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        "Create a binary data for textual data": {
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                "node": "Write the textual file to disk",
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        "Create a binary data for sentiment analysis": {
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                "node": "Write the AI Sentiment analysis file to disk",
                "type": "main",
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              }
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        "Google Gemini Chat Model for Sentiment Analyzer": {
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              {
                "node": "AI Sentiment Analyzer with the structured response",
                "type": "ai_languageModel",
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        "AI Sentiment Analyzer with the structured response": {
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                "node": "Initiate a Webhook Notification for AI Sentiment Analyzer",
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                "node": "Create a binary data for sentiment analysis",
                "type": "main",
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              }
            ]
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        },
        "Initiate a Webhook Notification for AI Sentiment Analyzer": {
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            []
          ]
        },
        "Initiate a Webhook Notification for Markdown to Textual Data Extraction": {
          "main": [
            []
          ]
        }
      }
    },
    "lastUpdatedBy": 29,
    "workflowInfo": {
      "nodeCount": 23,
      "nodeTypes": {
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        },
        "n8n-nodes-base.function": {
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        },
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        },
        "n8n-nodes-base.httpRequest": {
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        },
        "n8n-nodes-base.manualTrigger": {
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        },
        "n8n-nodes-base.readWriteFile": {
          "count": 3
        },
        "@n8n/n8n-nodes-langchain.chainLlm": {
          "count": 1
        },
        "@n8n/n8n-nodes-langchain.chainSummarization": {
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        },
        "@n8n/n8n-nodes-langchain.lmChatGoogleGemini": {
          "count": 3
        },
        "@n8n/n8n-nodes-langchain.informationExtractor": {
          "count": 1
        }
      }
    },
    "status": "published",
    "user": {
      "name": "Ranjan Dailata",
      "username": "ranjancse",
      "bio": "A Professional based out of India specialized in handling AI-powered automations. Contact me at ranjancse@gmail.com ",
      "verified": true,
      "links": [
        "https://www.linkedin.com/in/ranjan-dailata/"
      ],
      "avatar": "https://gravatar.com/avatar/7b74fe44a7ad32db7c783b972deb5848a4b1f043377bce4039737ed66da8305f?r=pg&d=retro&size=200"
    },
    "nodes": [
      {
        "id": 14,
        "icon": "fa:code",
        "name": "n8n-nodes-base.function",
        "codex": {
          "data": {
            "alias": [
              "Code",
              "Javascript",
              "Custom Code",
              "Script",
              "cpde"
            ],
            "details": "The Function node allows you to execute JavaScript in your workflow. Unlike the Function Item node, this node does not operate on incoming node data per-item. Instead, you must iterate over multiple items of incoming data yourself. This can be useful if you're performing data transformation where you want to manipulate the number of items being outputted by the node (i.e. 1 item is inputted in with nested object, 10 items are outputted without any nested objects)",
            "resources": {
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        "icon": "fa:sticky-note",
        "name": "n8n-nodes-base.stickyNote",
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              "Notes",
              "Sticky"
            ],
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            "name": "Core Nodes"
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            "nodeVersion": "1.0",
            "codexVersion": "1.0"
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        "group": "[\"trigger\"]",
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          "color": "#909298"
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          "type": "icon"
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            "name": "Core Nodes"
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      },
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        "id": 1121,
        "icon": "fa:link",
        "name": "@n8n/n8n-nodes-langchain.chainSummarization",
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            "alias": [
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            ],
            "resources": {
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              ]
            },
            "categories": [
              "AI",
              "Langchain"
            ],
            "subcategories": {
              "AI": [
                "Chains",
                "Root Nodes"
              ]
            }
          }
        },
        "group": "[\"transform\"]",
        "defaults": {
          "name": "Summarization Chain",
          "color": "#909298"
        },
        "iconData": {
          "icon": "link",
          "type": "icon"
        },
        "displayName": "Summarization Chain",
        "typeVersion": 2,
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            "name": "AI"
          },
          {
            "id": 26,
            "name": "Langchain"
          }
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        "id": 1123,
        "icon": "fa:link",
        "name": "@n8n/n8n-nodes-langchain.chainLlm",
        "codex": {
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            "alias": [
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            ],
            "resources": {
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              "Langchain"
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            "subcategories": {
              "AI": [
                "Chains",
                "Root Nodes"
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            }
          }
        },
        "group": "[\"transform\"]",
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          "name": "Basic LLM Chain",
          "color": "#909298"
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          "icon": "link",
          "type": "icon"
        },
        "displayName": "Basic LLM Chain",
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            "name": "AI"
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            "id": 26,
            "name": "Langchain"
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        "icon": "file:readWriteFile.svg",
        "name": "n8n-nodes-base.readWriteFile",
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              "Binary",
              "File",
              "Text",
              "Open",
              "Import",
              "Save",
              "Export",
              "Disk",
              "Transfer"
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            "codexVersion": "1.0",
            "subcategories": {
              "Core Nodes": [
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        "group": "[\"input\"]",
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        },
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          "fileBuffer": "data:image/svg+xml;base64,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"
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        "nodeCategories": [
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            "name": "Core Nodes"
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        ]
      },
      {
        "id": 1262,
        "icon": "file:google.svg",
        "name": "@n8n/n8n-nodes-langchain.lmChatGoogleGemini",
        "codex": {
          "data": {
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                }
              ]
            },
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              "Langchain"
            ],
            "subcategories": {
              "AI": [
                "Language Models",
                "Root Nodes"
              ],
              "Language Models": [
                "Chat Models (Recommended)"
              ]
            }
          }
        },
        "group": "[\"transform\"]",
        "defaults": {
          "name": "Google Gemini Chat Model"
        },
        "iconData": {
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          "fileBuffer": "data:image/svg+xml;base64,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"
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            "name": "Langchain"
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        "icon": "fa:project-diagram",
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            "subcategories": {
              "AI": [
                "Chains",
                "Root Nodes"
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            }
          }
        },
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        },
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    "categories": [
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        "id": 32,
        "name": "Market Research"
      },
      {
        "id": 49,
        "name": "AI Summarization"
      }
    ],
    "image": [
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        "url": "https://n8niostorageaccount.blob.core.windows.net/n8nio-strapi-blobs-prod/assets/Header_Authentication_376f746fd0.png"
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    ]
  }
}