{
  "workflow": {
    "id": 3854,
    "name": "Google trend data extract & summarization with Bright Data & Google Gemini",
    "views": 3299,
    "recentViews": 1,
    "totalViews": 3299,
    "createdAt": "2025-05-04T00:11:41.788Z",
    "description": "### Who this is for\nThe Google Trend Data Extract & Summarization workflow is ideal for trend researchers, digital marketers, content strategists, and AI developers who want to automate the extraction, summarization, and distribution of Google Trends data. This end-to-end solution helps transform trend signals into human-readable insights and delivers them across multiple channels.\n\nIt is built for:\n\n- **Market Researchers** - Tracking trends by topic or region\n\n- **Content Strategists** - Identifying content opportunities from trending data\n\n- **SEO Analysts** - Monitoring search volume and shifts in keyword popularity\n\n- **Growth Hackers** - Reacting quickly to real-time search behavior\n\n- **AI & Automation Engineers** - Creating automated trend monitoring systems\n\n### What problem is this workflow solving?\nGoogle Trends data can provide rich insights into user interests, but the raw data is not always structured or easily interpretable at scale. Manually extracting, cleaning, and summarizing trends from multiple regions or categories is time-consuming.\n\nThis workflow solves the following problems:\n\n- Automates the conversion of markdown or scraped HTML into clean textual input\n\n- Transforms unstructured data into structured format ready for processing\n\n- Uses AI summarization to generate easy-to-read insights from Google Trends\n\n- Distributes summaries via email and webhook notifications\n\n- Persists responses to disk for archiving, auditing, or future analytics\n\n### What this workflow does\n\n1. Receives input: Sets an URL for the data extraction and analysis.\n\n2. Uses Bright Data’s Web Unlocker to extract content from relevant site.\n\n3. Markdown to Textual Data Extractor: Converts markdown content into plaintext using n8n’s Function or Markdown nodes\n\n4. Structured Data Extract: Parses the plaintext into structured JSON suitable for AI processing\n\n5. Summarize Google Trends: Sends structured data to Google Gemini with a summarization prompt to extract key takeaways\n\n6. Send Summary via Gmail: Composes an email with the AI-generated summary and sends it to a designated recipient\n\n7. Persist to Disk: Writes the AI structured data to disk\n\n8. Webhook Notification: Sends the summarized response to an external system (e.g., Slack, Notion, Zapier) using a webhook\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:1253)\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\n1. **Update Source** : Update the workflow input to read from Google Sheet or Airbase etc.\n2. **Gemini Prompt Tuning** : \n- Customize prompts to extract summaries like:\n\t- Summarize the most significant trend shifts\n\t- Generate content ideas from the trending search topics\n3. **Email Personalization** : Configure Gmail node to:\n - Use dynamic subject lines like: Weekly Google Trends Summary – {{date}}\n- Send to multiple stakeholders or mailing lists\n\n4. **File Storage Customization** : \n- Save with timestamps, e.g., trends_summary_2025-04-29.json\n-  Extend to S3 or cloud drive integrations\n5. **Webhook Use Cases** : \n- Send summary to:\n\t- Internal dashboards\n\t- Slack channels\n\t- Automation tools like Make, Zapier etc.\n\n\n",
    "workflow": {
      "id": "9Or3kzIEI2tskRyR",
      "meta": {
        "instanceId": "885b4fb4a6a9c2cb5621429a7b972df0d05bb724c20ac7dac7171b62f1c7ef40",
        "templateCredsSetupCompleted": true
      },
      "name": "Google Trend Data Extract, Summarization with Bright Data & Google Gemini",
      "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": "29e6ce01-c42f-4155-add1-8a5cfff56967",
          "name": "When clicking ‘Test workflow’",
          "type": "n8n-nodes-base.manualTrigger",
          "position": [
            200,
            -420
          ],
          "parameters": {},
          "typeVersion": 1
        },
        {
          "id": "6abf0439-8286-4198-9b5e-226a7bf805dc",
          "name": "Sticky Note",
          "type": "n8n-nodes-base.stickyNote",
          "position": [
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          ],
          "parameters": {
            "width": 400,
            "height": 300,
            "content": "## Note\n\nThis workflow deals with the structured data extraction by utilizing 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
        },
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          "type": "n8n-nodes-base.stickyNote",
          "position": [
            620,
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          "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 structured data extraction.\n\nSummarization Chain is being used for building the summary."
          },
          "typeVersion": 1
        },
        {
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          "name": "Markdown to Textual Data Extractor",
          "type": "@n8n/n8n-nodes-langchain.chainLlm",
          "position": [
            860,
            -420
          ],
          "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",
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          "position": [
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          ],
          "parameters": {
            "options": {},
            "assignments": {
              "assignments": [
                {
                  "id": "3aedba66-f447-4d7a-93c0-8158c5e795f9",
                  "name": "url",
                  "type": "string",
                  "value": "https://trends.google.com/trends/explore?gprop=youtube&hl=en-US"
                },
                {
                  "id": "4e7ee31d-da89-422f-8079-2ff2d357a0ba",
                  "name": "zone",
                  "type": "string",
                  "value": "web_unlocker1"
                }
              ]
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          "typeVersion": 3.4
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        {
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          "name": "Initiate a Webhook Notification for Markdown to Textual Data Extraction",
          "type": "n8n-nodes-base.httpRequest",
          "position": [
            1320,
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          ],
          "parameters": {
            "url": "https://webhook.site/3c36d7d1-de1b-4171-9fd3-643ea2e4dd76",
            "options": {},
            "sendBody": true,
            "bodyParameters": {
              "parameters": [
                {
                  "name": "content",
                  "value": "={{ $json.text }}"
                }
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          "typeVersion": 4.2
        },
        {
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          "name": "Google Gemini Chat Model for Data Extract",
          "type": "@n8n/n8n-nodes-langchain.lmChatGoogleGemini",
          "position": [
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          ],
          "parameters": {
            "options": {},
            "modelName": "models/gemini-2.0-flash-exp"
          },
          "credentials": {
            "googlePalmApi": {
              "id": "credential-id",
              "name": "googlePalmApi Credential"
            }
          },
          "typeVersion": 1
        },
        {
          "id": "30d3b080-d35a-422d-990d-0df0d73b96a8",
          "name": "Perform Bright Data Web Request",
          "type": "n8n-nodes-base.httpRequest",
          "position": [
            640,
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          ],
          "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": "18acbc0a-f0e2-4f5b-b98c-dec69c656a7e",
          "name": "Create a binary data",
          "type": "n8n-nodes-base.function",
          "position": [
            1980,
            -640
          ],
          "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": "1c386966-85ae-4b30-a485-259f1eb0727b",
          "name": "Structured Data Extractor",
          "type": "@n8n/n8n-nodes-langchain.informationExtractor",
          "position": [
            1280,
            -420
          ],
          "parameters": {
            "text": "=Extract the Google Trend Data in JSON.\n\nHere's the content:\n\n {{ $json.text }}",
            "options": {},
            "schemaType": "manual",
            "inputSchema": "{\n\t\"type\": \"array\",\n\t\"properties\": {\n\t\t\"topics\": {\n\t\t\t\"type\": \"string\"\n\t\t},\"desc\": {\n\t\t\t\"type\": \"string\"\n\t\t}\n\t}\n}"
          },
          "typeVersion": 1
        },
        {
          "id": "aa7b5dd7-53c7-4197-b2e8-886832cad82e",
          "name": "Summarize Google Trends",
          "type": "@n8n/n8n-nodes-langchain.chainSummarization",
          "position": [
            1760,
            -420
          ],
          "parameters": {
            "options": {},
            "chunkingMode": "advanced"
          },
          "typeVersion": 2
        },
        {
          "id": "25f0a115-ba3a-4ec6-8fe6-8e33e6302a2b",
          "name": "Initiate a Webhook Notification for Summarization",
          "type": "n8n-nodes-base.httpRequest",
          "position": [
            2200,
            -420
          ],
          "parameters": {
            "url": "https://webhook.site/3c36d7d1-de1b-4171-9fd3-643ea2e4dd76",
            "options": {},
            "sendBody": true,
            "bodyParameters": {
              "parameters": [
                {
                  "name": "content",
                  "value": "={{ $json.response.text }}"
                }
              ]
            }
          },
          "typeVersion": 4.2
        },
        {
          "id": "50b55d73-5506-439c-8e82-e198f3b4f431",
          "name": "Write the file to disk",
          "type": "n8n-nodes-base.readWriteFile",
          "position": [
            2200,
            -640
          ],
          "parameters": {
            "options": {},
            "fileName": "d:\\google-trends.json",
            "operation": "write"
          },
          "typeVersion": 1
        },
        {
          "id": "a163f8d3-2b5c-48a5-8a1d-26c0caba6383",
          "name": "Google Gemini Chat Model for Summarization",
          "type": "@n8n/n8n-nodes-langchain.lmChatGoogleGemini",
          "position": [
            1860,
            -200
          ],
          "parameters": {
            "options": {},
            "modelName": "models/gemini-2.0-flash-exp"
          },
          "credentials": {
            "googlePalmApi": {
              "id": "credential-id",
              "name": "googlePalmApi Credential"
            }
          },
          "typeVersion": 1
        },
        {
          "id": "9e3db8e9-ad4c-4247-841e-1f5f4937b93c",
          "name": "Google Gemini Chat Model for Structured Data Extract",
          "type": "@n8n/n8n-nodes-langchain.lmChatGoogleGemini",
          "position": [
            1380,
            -200
          ],
          "parameters": {
            "options": {},
            "modelName": "models/gemini-2.0-flash-exp"
          },
          "credentials": {
            "googlePalmApi": {
              "id": "credential-id",
              "name": "googlePalmApi Credential"
            }
          },
          "typeVersion": 1
        },
        {
          "id": "122d3269-e932-48e0-af01-e2c421650e16",
          "name": "Send Summary to Gmail",
          "type": "n8n-nodes-base.gmail",
          "position": [
            2200,
            -160
          ],
          "webhookId": "a57ca2f7-42dc-4ee9-808d-85455bb7c12f",
          "parameters": {
            "sendTo": "user@example.com",
            "message": "={{ $json.response.text }}",
            "options": {},
            "subject": "Google Trends Summary"
          },
          "credentials": {
            "gmailOAuth2": {
              "id": "credential-id",
              "name": "gmailOAuth2 Credential"
            }
          },
          "typeVersion": 2.1
        }
      ],
      "active": false,
      "pinData": {},
      "settings": {
        "executionOrder": "v1"
      },
      "versionId": "bc73fbca-1218-47bd-93cf-b308b424894d",
      "connections": {
        "Create a binary data": {
          "main": [
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              {
                "node": "Write the file to disk",
                "type": "main",
                "index": 0
              }
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        "Write the file to disk": {
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        "Summarize Google Trends": {
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                "node": "Initiate a Webhook Notification for Summarization",
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                "node": "Send Summary to Gmail",
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        "Structured Data Extractor": {
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        "Set URL and Bright Data Zone": {
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              {
                "node": "Perform Bright Data Web Request",
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            ]
          ]
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        "Perform Bright Data Web Request": {
          "main": [
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                "node": "Markdown to Textual Data Extractor",
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        "Markdown to Textual Data Extractor": {
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                "node": "Initiate a Webhook Notification for Markdown to Textual Data Extraction",
                "type": "main",
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        "Google Gemini Chat Model for Data Extract": {
          "ai_languageModel": [
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                "node": "Markdown to Textual Data Extractor",
                "type": "ai_languageModel",
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        "Google Gemini Chat Model for Summarization": {
          "ai_languageModel": [
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                "node": "Summarize Google Trends",
                "type": "ai_languageModel",
                "index": 0
              }
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        "Google Gemini Chat Model for Structured Data Extract": {
          "ai_languageModel": [
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                "type": "ai_languageModel",
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    "lastUpdatedBy": 29,
    "workflowInfo": {
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      "nodeTypes": {
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        "n8n-nodes-base.readWriteFile": {
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        "@n8n/n8n-nodes-langchain.chainLlm": {
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        "@n8n/n8n-nodes-langchain.chainSummarization": {
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        },
        "@n8n/n8n-nodes-langchain.lmChatGoogleGemini": {
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        "@n8n/n8n-nodes-langchain.informationExtractor": {
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      }
    },
    "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/"
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      "avatar": "https://gravatar.com/avatar/7b74fe44a7ad32db7c783b972deb5848a4b1f043377bce4039737ed66da8305f?r=pg&d=retro&size=200"
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              "Javascript",
              "Custom Code",
              "Script",
              "cpde"
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            "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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                  "label": "How to host virtual coffee breaks with n8n"
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                  "icon": "store",
                  "label": "6 e-commerce workflows to power up your Shopify s"
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                {
                  "url": "https://n8n.io/blog/5-workflow-automations-for-mattermost-that-we-love-at-n8n/",
                  "icon": "🤖",
                  "label": "5 workflow automations for Mattermost that we love at n8n"
                },
                {
                  "url": "https://n8n.io/blog/tracking-time-spent-in-meetings-with-google-calendar-twilio-and-n8n/",
                  "icon": "🗓",
                  "label": "Tracking Time Spent in Meetings With Google Calendar, Twilio, and n8n"
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              "Notes",
              "Sticky"
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            "subcategories": {
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                "Root Nodes"
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            "name": "Langchain"
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        "icon": "file:readWriteFile.svg",
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        "icon": "file:google.svg",
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                "Root Nodes"
              ],
              "Language Models": [
                "Chat Models (Recommended)"
              ]
            }
          }
        },
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        "name": "Market Research"
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}