{
  "workflow": {
    "id": 4611,
    "name": "Extract, summarize & analyze Amazon price drops with Bright Data & Google Gemini",
    "views": 984,
    "recentViews": 0,
    "totalViews": 984,
    "createdAt": "2025-06-02T23:56:04.415Z",
    "description": "![Extract, Summarize, Sentiment Analysis of Price Drops for Amazon Products via Bright Data.png](fileId:1435)\n\n### Notice\nCommunity nodes can only be installed on self-hosted instances of n8n.\n\n### Who this is for\nThis n8n-powered automation uses Bright Data's MCP Client to extract real-time data from a price drop site listing the amazon products, including price changes and related product details. \n\nThe extracted data is enriched with structured data transformation, content summarization, and sentiment analysis using Google Gemini LLM.\n\nThe Amazon Price Drop Intelligence Engine is designed for:\n\n- **Ecommerce Analysts** who need timely updates on competitor pricing trends\n\n- **Brand Managers** seeking to understand consumer sentiment around pricing\n\n- **Data Scientists** building pricing models or enrichment pipelines\n\n- **Affiliate Marketers** looking to optimize campaigns based on dynamic pricing\n\n- **AI Developers** automating product intelligence pipelines\n\n\n### What problem is this workflow solving?\n\nThis workflow solves several key pain points:\n\n**Reliable Scraping**: Uses Bright Data MCP, a managed crawling platform that handles proxies, captchas, and site structure changes automatically.\n\n**Insight Generation**: Transforms unstructured HTML into structured data and then into human-readable summaries using Google Gemini LLM.\n\n**Sentiment Context**: Goes beyond raw pricing data to reveal how customers feel about the price change, helping businesses and researchers measure consumer reaction.\n\n**Automated Reporting**: Aggregates and stores data for easy access and downstream automation (e.g., dashboards, notifications, pricing models).\n\n### What this workflow does\n\n**Scrape price drop site with Bright Data MCP**\n\nThe workflow begins by scraping targeted price drop site for Amazon listings using Bright Data's Model Context Protocol (MCP). \n\nYou can configure this to target:\n\n**Structured Data Extraction**\n\nOnce the HTML content is retrieved, Google Gemini is employed to:\n\n- Parse and structure the product information (title, price, discount, brand, ratings)\n\n**Summarization & Sentiment Analysis**\n\nThe extracted data is passed through an LLM chain to:\n\n- Generate a concise summary of the product and its recent price movement\n\n- Perform sentiment analysis on user reviews and public perception\n\n**Store the Results**\n\n- Save to disk for archiving or bulk processing\n\n- Updated in a Google Sheet, making it instantly shareable with your team or integrated into a BI dashboard\n\n\n### Pre-conditions\n\n1. Knowledge of Model Context Protocol (MCP) is highly essential. Please read this blog post - [model-context-protocol](https://www.anthropic.com/news/model-context-protocol)\n2. You need to have the [Bright Data](https://brightdata.com/) account and do the necessary setup as mentioned in the **Setup** section below.\n3. You need to have the Google Gemini API Key. Visit [Google AI Studio](https://aistudio.google.com/)\n3. You need to install the Bright Data MCP Server [@brightdata/mcp](https://www.npmjs.com/package/@brightdata/mcp)\n4. You need to install the [n8n-nodes-mcp](https://github.com/nerding-io/n8n-nodes-mcp)\n\n### Setup\n1. Please make sure to setup n8n locally with MCP Servers by navigating to [n8n-nodes-mcp](https://www.youtube.com/watch?v=NUb73ErUCsA)\n2. Please make sure to install the Bright Data MCP Server [@brightdata/mcp](https://www.npmjs.com/package/@brightdata/mcp)  on your local machine.\n3. Sign up at [Bright Data](https://brightdata.com/).\n4. Create a Web Unlocker proxy zone called mcp_unlocker on Bright Data control panel.\n5. Navigate to Proxies & Scraping and create a new Web Unlocker zone by selecting Web Unlocker API under Scraping Solutions.\n6. In n8n, configure the Google Gemini(PaLM) Api account with the Google Gemini API key (or access through Vertex AI or proxy).\n7. In n8n, configure the credentials to connect with MCP Client (STDIO) account with the Bright Data MCP Server as shown below.\n\n![MCPClientAccount.png](fileId:1434)\n\nMake sure to copy the Bright Data API_TOKEN within the Environments textbox above as API_TOKEN=<your-token>\n\n### How to customize this workflow to your needs\n\n- **Target different platforms**: Switch Amazon for Walmart, eBay, or any ecommerce source using Bright Data’s flexible scraping infrastructure.\n\n- **Enrich with more LLM tasks**: Add brand tone analysis, category classification, or competitive benchmarking using Gemini prompts.\n\n- **Visualize output**: Pipe the Google Sheet to Looker Studio, Tableau, or Power BI.\n\n- **Notification integrations**: Add Slack, Discord, or email notifications for price drop alerts.",
    "workflow": {
      "id": "L6nGDqfxvxzlvDU2",
      "meta": {
        "instanceId": "885b4fb4a6a9c2cb5621429a7b972df0d05bb724c20ac7dac7171b62f1c7ef40",
        "templateCredsSetupCompleted": true
      },
      "name": "Extract, Summarize, Sentiment Analysis of Price Drops for Amazon Products via Bright Data",
      "tags": [
        {
          "id": "Kujft2FOjmOVQAmJ",
          "name": "Engineering",
          "createdAt": "2025-04-09T01:31:00.558Z",
          "updatedAt": "2025-04-09T01:31:00.558Z"
        },
        {
          "id": "ZOwtAMLepQaGW76t",
          "name": "Building Blocks",
          "createdAt": "2025-04-13T15:23:40.462Z",
          "updatedAt": "2025-04-13T15:23:40.462Z"
        },
        {
          "id": "ddPkw7Hg5dZhQu2w",
          "name": "AI",
          "createdAt": "2025-04-13T05:38:08.053Z",
          "updatedAt": "2025-04-13T05:38:08.053Z"
        }
      ],
      "nodes": [
        {
          "id": "c05c50b0-410e-428c-b9b9-c300b95b0ce8",
          "name": "When clicking ‘Test workflow’",
          "type": "n8n-nodes-base.manualTrigger",
          "position": [
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            -595
          ],
          "parameters": {},
          "typeVersion": 1
        },
        {
          "id": "e2bef95f-a42a-47f1-b49f-1e18a7a76fc5",
          "name": "Bright Data MCP Client List Tools",
          "type": "n8n-nodes-mcp.mcpClient",
          "position": [
            -540,
            -595
          ],
          "parameters": {},
          "credentials": {
            "mcpClientApi": {
              "id": "credential-id",
              "name": "mcpClientApi Credential"
            }
          },
          "typeVersion": 1
        },
        {
          "id": "03126e8e-b3ef-4bc1-966f-23050034e717",
          "name": "Set input fields",
          "type": "n8n-nodes-base.set",
          "position": [
            -320,
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          ],
          "parameters": {
            "options": {},
            "assignments": {
              "assignments": [
                {
                  "id": "0ac91db2-9848-40d4-b942-cd7288597ded",
                  "name": "price_drop_url",
                  "type": "string",
                  "value": "https://camelcamelcamel.com/top_drops?t=daily"
                },
                {
                  "id": "88826650-2a6f-4d19-8a2f-27b039296a00",
                  "name": "webhook_notification_url",
                  "type": "string",
                  "value": "https://webhook.site/24878284-919d-4d39-bff0-5f36bfae17b6"
                }
              ]
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        },
        {
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          "name": "Sticky Note2",
          "type": "n8n-nodes-base.stickyNote",
          "position": [
            160,
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          ],
          "parameters": {
            "color": 3,
            "width": 440,
            "height": 140,
            "content": "## Disclaimer\nThis template is only available on n8n self-hosted as it's making use of the community node for MCP Client."
          },
          "typeVersion": 1
        },
        {
          "id": "377c7b92-4660-4130-a668-40752a52705a",
          "name": "Sticky Note4",
          "type": "n8n-nodes-base.stickyNote",
          "position": [
            -300,
            -880
          ],
          "parameters": {
            "color": 5,
            "width": 440,
            "height": 240,
            "content": "## LLM Usages\n\nGoogle Gemini LLM is being utilized for the structured data extraction handling."
          },
          "typeVersion": 1
        },
        {
          "id": "45f786b2-e785-4da5-af17-cd048bc93887",
          "name": "Sticky Note5",
          "type": "n8n-nodes-base.stickyNote",
          "position": [
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          ],
          "parameters": {
            "color": 7,
            "width": 440,
            "height": 360,
            "content": "## Logo\n\n\n![logo](https://images.seeklogo.com/logo-png/43/1/brightdata-logo-png_seeklogo-439974.png)\n"
          },
          "typeVersion": 1
        },
        {
          "id": "83d52f24-3c6e-4640-9e95-e41c6c9cfa37",
          "name": "Sticky Note3",
          "type": "n8n-nodes-base.stickyNote",
          "position": [
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          ],
          "parameters": {
            "width": 440,
            "height": 240,
            "content": "## Note\n\nDeals with the extraction of price drop information of ecommerce produce and scraping the site by leveraging the Bright Data MCP Client.\n\nThis workflow is also responsible for the structured data extraction, sentiment analysis and summarization of content.\n\n**Please make sure to set the input fields**"
          },
          "typeVersion": 1
        },
        {
          "id": "b0b39697-10e6-4066-8122-40775bb1472b",
          "name": "Split Out",
          "type": "n8n-nodes-base.splitOut",
          "position": [
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            -595
          ],
          "parameters": {
            "options": {},
            "fieldToSplitOut": "output"
          },
          "typeVersion": 1
        },
        {
          "id": "d73bf29c-b80c-47c4-b162-60a53bd4e0e0",
          "name": "Loop Over Items",
          "type": "n8n-nodes-base.splitInBatches",
          "position": [
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          ],
          "parameters": {
            "options": {}
          },
          "typeVersion": 3
        },
        {
          "id": "c6e5c79a-2a5b-40ae-a0e5-188d35f75c39",
          "name": "Wait",
          "type": "n8n-nodes-base.wait",
          "position": [
            936,
            -770
          ],
          "webhookId": "19a19f31-87fa-442c-85b2-472001ac344a",
          "parameters": {
            "amount": 10
          },
          "typeVersion": 1.1
        },
        {
          "id": "fa7525b9-86b0-4646-994f-38b85a00f0d9",
          "name": "Summarize Content",
          "type": "@n8n/n8n-nodes-langchain.chainSummarization",
          "position": [
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            -1120
          ],
          "parameters": {
            "options": {},
            "chunkingMode": "advanced"
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          "retryOnFail": true,
          "typeVersion": 2.1
        },
        {
          "id": "1f6dd841-144f-44a7-9220-fd7aaf2090bd",
          "name": "Sentiment Analysis",
          "type": "@n8n/n8n-nodes-langchain.informationExtractor",
          "position": [
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            -620
          ],
          "parameters": {
            "text": "=Perform sentiment analysis of  {{ $json.result.content[0].text }}",
            "options": {},
            "schemaType": "manual",
            "inputSchema": "{\n  \"$schema\": \"http://json-schema.org/schema#\",\n  \"title\": \"EcommerceSentimentSubset\",\n  \"type\": \"object\",\n  \"properties\": {\n    \"sentiment\": {\n      \"type\": \"string\",\n      \"enum\": [\"positive\", \"neutral\", \"negative\"],\n      \"description\": \"Categorized sentiment from NLP analysis\"\n    },\n    \"sentimentScore\": {\n      \"type\": \"number\",\n      \"minimum\": -1,\n      \"maximum\": 1,\n      \"description\": \"Numeric sentiment polarity score (-1 = very negative, 1 = very positive)\"\n    },\n    \"topics\": {\n      \"type\": \"array\",\n      \"description\": \"Key aspects mentioned in the review (e.g., battery, build quality, delivery)\",\n      \"items\": {\n        \"type\": \"string\"\n      }\n    }\n  }\n}\n"
          },
          "retryOnFail": true,
          "typeVersion": 1.1
        },
        {
          "id": "db2330d2-2490-4822-b5c5-519fecb95c1d",
          "name": "Google Gemini Chat Model for Summarize Content",
          "type": "@n8n/n8n-nodes-langchain.lmChatGoogleGemini",
          "position": [
            1380,
            -900
          ],
          "parameters": {
            "options": {},
            "modelName": "models/gemini-2.0-flash-exp"
          },
          "credentials": {
            "googlePalmApi": {
              "id": "credential-id",
              "name": "googlePalmApi Credential"
            }
          },
          "typeVersion": 1
        },
        {
          "id": "b887a1a5-4de1-43bb-b51c-fd218d5ca2e6",
          "name": "Google Gemini Chat Model for Sentiment Analysis",
          "type": "@n8n/n8n-nodes-langchain.lmChatGoogleGemini",
          "position": [
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            -400
          ],
          "parameters": {
            "options": {},
            "modelName": "models/gemini-2.0-flash-exp"
          },
          "credentials": {
            "googlePalmApi": {
              "id": "credential-id",
              "name": "googlePalmApi Credential"
            }
          },
          "typeVersion": 1
        },
        {
          "id": "e14757c2-3457-445b-9acf-5391de2d61fc",
          "name": "Merge",
          "type": "n8n-nodes-base.merge",
          "position": [
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            -770
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          "parameters": {},
          "typeVersion": 3.1
        },
        {
          "id": "fc1dc2fd-e407-4e7c-acf8-5d29ebb18956",
          "name": "Update Google Sheets",
          "type": "n8n-nodes-base.googleSheets",
          "position": [
            2192,
            -520
          ],
          "parameters": {
            "columns": {
              "value": {
                "output": "={{ $json.data.toJsonString() }}"
              },
              "schema": [
                {
                  "id": "output",
                  "type": "string",
                  "display": true,
                  "removed": false,
                  "required": false,
                  "displayName": "output",
                  "defaultMatch": false,
                  "canBeUsedToMatch": true
                }
              ],
              "mappingMode": "defineBelow",
              "matchingColumns": [
                "output"
              ],
              "attemptToConvertTypes": false,
              "convertFieldsToString": false
            },
            "options": {},
            "operation": "appendOrUpdate",
            "sheetName": {
              "__rl": true,
              "mode": "list",
              "value": "gid=0",
              "cachedResultUrl": "https://docs.google.com/spreadsheets/d/10gAihQMT8-h8Mpehe9j-xxN4oTTpg8qwToI-I-Eauew/edit#gid=0",
              "cachedResultName": "Sheet1"
            },
            "documentId": {
              "__rl": true,
              "mode": "list",
              "value": "1a1yb4XSMQ0Vs0Rg2RCwrcIVXwDN3ImXW_4OUebURKZI",
              "cachedResultUrl": "https://docs.google.com/spreadsheets/d/1a1yb4XSMQ0Vs0Rg2RCwrcIVXwDN3ImXW_4OUebURKZI/edit?usp=drivesdk",
              "cachedResultName": "Pricedrop Info"
            }
          },
          "credentials": {
            "googleSheetsOAuth2Api": {
              "id": "credential-id",
              "name": "googleSheetsOAuth2Api Credential"
            }
          },
          "typeVersion": 4.5
        },
        {
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          "name": "Webhook Notification for Price Drop Info",
          "type": "n8n-nodes-base.httpRequest",
          "position": [
            2192,
            -770
          ],
          "parameters": {
            "url": "={{ $('Set input fields').item.json.webhook_notification_url }}",
            "options": {},
            "sendBody": true,
            "bodyParameters": {
              "parameters": [
                {
                  "name": "response",
                  "value": "={{ $json.data.toJsonString() }}"
                }
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          "typeVersion": 4.2
        },
        {
          "id": "707977ff-cdfb-4020-86f4-5d0be44ee52f",
          "name": "Google Gemini Chat Model",
          "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": "bbc02162-176f-4131-9f63-d5e39fb52b9b",
          "name": "Structured Output Parser",
          "type": "@n8n/n8n-nodes-langchain.outputParserStructured",
          "position": [
            320,
            -360
          ],
          "parameters": {
            "jsonSchemaExample": "[{\n\"id\": \"unique_id\",\n\"title\": \n\"Atosa MBF8005GR 52″ Reach-In Refrigerator for Restaurant Deli Cafeteria Steak House | Top Mount Compressor, 43.2 Cu. Ft. | 2-Solid Swing Door, 6 Adjustable Epoxy Coated Shelves | Stainless Steel, 115v\",\n\"price\": \n\"$2,919.48\",\n\"savings\": \n\"Save 18% ($649.36)\",\n\"link\": \n\"https://camelcamelcamel.com/product/07BH0Y75B4KUYI1YP78IB/go?context=top%5Fdrops&ctx%5Fpid=94751977&ctx%5Fcid=-1&ctx%5Faid=-1&ctx%5Fact=buy&ctx%5Fsrc=buy-button&&sjc=1\"\n}]"
          },
          "typeVersion": 1.2
        },
        {
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          "name": "Structure Data Extract Using LLM",
          "type": "@n8n/n8n-nodes-langchain.chainLlm",
          "position": [
            120,
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          ],
          "parameters": {
            "text": "=Extract structured data from  {{ $json.result.content[0].text }}",
            "batching": {},
            "promptType": "define",
            "hasOutputParser": true
          },
          "retryOnFail": true,
          "typeVersion": 1.7
        },
        {
          "id": "6a0bc566-2eb8-4eea-bf11-d040f352fbf7",
          "name": "MCP Client for Price Drop Data Extract",
          "type": "n8n-nodes-mcp.mcpClient",
          "position": [
            -100,
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          ],
          "parameters": {
            "toolName": "scrape_as_markdown",
            "operation": "executeTool",
            "toolParameters": "={\n  \"url\": \"{{ $json.price_drop_url }}\"\n} "
          },
          "credentials": {
            "mcpClientApi": {
              "id": "credential-id",
              "name": "mcpClientApi Credential"
            }
          },
          "typeVersion": 1
        },
        {
          "id": "22d7cba2-5d8f-4e2a-8d99-e1dd2b232fd3",
          "name": "MCP Client for Price Drop Data Extract Within a Loop",
          "type": "n8n-nodes-mcp.mcpClient",
          "position": [
            1156,
            -770
          ],
          "parameters": {
            "toolName": "scrape_as_markdown",
            "operation": "executeTool",
            "toolParameters": "={\n  \"url\": \"{{ $json.link }}\"\n} "
          },
          "credentials": {
            "mcpClientApi": {
              "id": "credential-id",
              "name": "mcpClientApi Credential"
            }
          },
          "typeVersion": 1
        },
        {
          "id": "1ccf68b1-ab0c-4288-8892-f6736639f952",
          "name": "Aggregate",
          "type": "n8n-nodes-base.aggregate",
          "position": [
            1972,
            -770
          ],
          "parameters": {
            "options": {},
            "aggregate": "aggregateAllItemData"
          },
          "typeVersion": 1
        },
        {
          "id": "cf8bdb20-18e5-4d29-b582-65b2b9c7aa32",
          "name": "Recursive Character Text Splitter",
          "type": "@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter",
          "position": [
            1580,
            -900
          ],
          "parameters": {
            "options": {},
            "chunkSize": 5000
          },
          "typeVersion": 1
        },
        {
          "id": "3207d699-e620-4324-b71e-29aec2ffaf9a",
          "name": "Sticky Note",
          "type": "n8n-nodes-base.stickyNote",
          "position": [
            1880,
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          ],
          "parameters": {
            "color": 5,
            "width": 560,
            "height": 640,
            "content": "## Output Data Handling "
          },
          "typeVersion": 1
        },
        {
          "id": "26874b27-2d9e-4564-ade2-87f236b250e0",
          "name": "Sticky Note1",
          "type": "n8n-nodes-base.stickyNote",
          "position": [
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          ],
          "parameters": {
            "color": 4,
            "width": 1080,
            "height": 980,
            "content": "## Loop and Extract Data\n\nPerform Summarization & Sentiment Analysis"
          },
          "typeVersion": 1
        }
      ],
      "active": false,
      "pinData": {},
      "settings": {
        "executionOrder": "v1"
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      "versionId": "c9e4ce77-7ccb-47dd-90b8-e9b45036ca5e",
      "connections": {
        "Wait": {
          "main": [
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              {
                "node": "MCP Client for Price Drop Data Extract Within a Loop",
                "type": "main",
                "index": 0
              }
            ]
          ]
        },
        "Merge": {
          "main": [
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              {
                "node": "Aggregate",
                "type": "main",
                "index": 0
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            ]
          ]
        },
        "Aggregate": {
          "main": [
            [
              {
                "node": "Update Google Sheets",
                "type": "main",
                "index": 0
              },
              {
                "node": "Webhook Notification for Price Drop Info",
                "type": "main",
                "index": 0
              }
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        "Split Out": {
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                "node": "Loop Over Items",
                "type": "main",
                "index": 0
              }
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          ]
        },
        "Loop Over Items": {
          "main": [
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            [
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                "type": "main",
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        "Set input fields": {
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              {
                "node": "MCP Client for Price Drop Data Extract",
                "type": "main",
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        "Summarize Content": {
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                "node": "Merge",
                "type": "main",
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        },
        "Sentiment Analysis": {
          "main": [
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              {
                "node": "Merge",
                "type": "main",
                "index": 1
              }
            ]
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        "Update Google Sheets": {
          "main": [
            [
              {
                "node": "Loop Over Items",
                "type": "main",
                "index": 0
              }
            ]
          ]
        },
        "Google Gemini Chat Model": {
          "ai_languageModel": [
            [
              {
                "node": "Structure Data Extract Using LLM",
                "type": "ai_languageModel",
                "index": 0
              }
            ]
          ]
        },
        "Structured Output Parser": {
          "ai_outputParser": [
            [
              {
                "node": "Structure Data Extract Using LLM",
                "type": "ai_outputParser",
                "index": 0
              }
            ]
          ]
        },
        "Structure Data Extract Using LLM": {
          "main": [
            [
              {
                "node": "Split Out",
                "type": "main",
                "index": 0
              }
            ]
          ]
        },
        "Bright Data MCP Client List Tools": {
          "main": [
            [
              {
                "node": "Set input fields",
                "type": "main",
                "index": 0
              }
            ]
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                "Root Nodes"
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                "Root Nodes"
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        },
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        },
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            "name": "Langchain"
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            "name": "AI"
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            "name": "Langchain"
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        "icon": "fa:grip-lines-vertical",
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              "AI": [
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            "name": "Langchain"
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              "Array",
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            "subcategories": {
              "Core Nodes": [
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              ]
            }
          }
        },
        "group": "[\"transform\"]",
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        },
        "iconData": {
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        "id": 1239,
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        "name": "n8n-nodes-base.splitOut",
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            "codexVersion": "1.0",
            "subcategories": {
              "Core Nodes": [
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              ]
            }
          }
        },
        "group": "[\"transform\"]",
        "defaults": {
          "name": "Split Out"
        },
        "iconData": {
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        "icon": "file:google.svg",
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                "Root Nodes"
              ],
              "Language Models": [
                "Chat Models (Recommended)"
              ]
            }
          }
        },
        "group": "[\"transform\"]",
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        },
        "iconData": {
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        "icon": "fa:project-diagram",
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                "Root Nodes"
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            }
          }
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        "name": "Market Research"
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      }
    ]
  }
}