This workflow follows the Chainllm → Chainsummarization recipe pattern — see all workflows that pair these two integrations.
The workflow JSON
Copy or download the full n8n JSON below. Paste it into a new n8n workflow, add your credentials, activate. Full import guide →
{
"id": "sczRNO4u1HYc5YV7",
"meta": {
"templateCredsSetupCompleted": true
},
"name": "Extract & Summarize Wikipedia Data with Bright Data and Gemini AI",
"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": "0f4b4939-6356-4672-ae61-8d1daf66a168",
"name": "When clicking \u2018Test workflow\u2019",
"type": "n8n-nodes-base.manualTrigger",
"position": [
340,
-440
],
"parameters": {},
"typeVersion": 1
},
{
"id": "167e060a-c36c-462a-826c-81ef379c824b",
"name": "Google Gemini Chat Model For Summarization",
"type": "@n8n/n8n-nodes-langchain.lmChatGoogleGemini",
"position": [
1520,
-60
],
"parameters": {
"options": {},
"modelName": "models/gemini-2.0-flash-exp"
},
"credentials": {
"googlePalmApi": {
"name": "<your credential>"
}
},
"typeVersion": 1
},
{
"id": "a51f2634-8b59-4feb-be39-674e8f198714",
"name": "Google Gemini Chat Model2",
"type": "@n8n/n8n-nodes-langchain.lmChatGoogleGemini",
"position": [
1000,
-240
],
"parameters": {
"options": {},
"modelName": "models/gemini-2.0-pro-exp"
},
"credentials": {
"googlePalmApi": {
"name": "<your credential>"
}
},
"typeVersion": 1
},
{
"id": "a1ec001f-6e97-4efb-91d9-9a037fbf472c",
"name": "Summary Webhook Notifier",
"type": "n8n-nodes-base.httpRequest",
"position": [
1860,
-280
],
"parameters": {
"url": "https://webhook.site/ce41e056-c097-48c8-a096-9b876d3abbf7",
"options": {},
"sendBody": true,
"bodyParameters": {
"parameters": [
{
"name": "summary",
"value": "={{ $json.response.text }}"
}
]
}
},
"typeVersion": 4.2
},
{
"id": "f4dd93b5-2a33-4ac7-a0c9-9e0956bea363",
"name": "Sticky Note",
"type": "n8n-nodes-base.stickyNote",
"position": [
340,
-820
],
"parameters": {
"width": 400,
"height": 300,
"content": "## Note\n\nThis template deals with the Wikipedia data extraction and summarization of content with the Bright Data. \n\nThe LLM Data Extractor is responsible for producing a human readable content.\n\nThe Concise Summary Generator node is responsible for generating the concise summary of the Wikipedia extracted info.\n\n**Please make sure to update the Wikipedia URL with Bright Data Zone. Also make sure to set the Webhook Notification URL.**"
},
"typeVersion": 1
},
{
"id": "9bd6f913-c526-4e54-81f8-8885a0fe974f",
"name": "Sticky Note1",
"type": "n8n-nodes-base.stickyNote",
"position": [
780,
-820
],
"parameters": {
"width": 500,
"height": 300,
"content": "## LLM Usages\n\nGoogle Gemini Flash Exp model is being used to demonstrate the data extraction and summarization aspects.\n\nBasic LLM Chain is being used for extracting the html to text\n\nSummarization Chain is being used for summarization of the Wikipedia data.\n\n**Note - Replace Google Gemini with the Open AI or suitable LLM providers of your choice.**"
},
"typeVersion": 1
},
{
"id": "30008ce4-4de2-43c5-bb03-94db58262f86",
"name": "Wikipedia Web Request",
"type": "n8n-nodes-base.httpRequest",
"position": [
780,
-440
],
"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 }}"
},
{
"name": "format",
"value": "raw"
}
]
},
"genericAuthType": "httpHeaderAuth",
"headerParameters": {
"parameters": [
{}
]
}
},
"credentials": {
"httpHeaderAuth": {
"name": "<your credential>"
}
},
"typeVersion": 4.2
},
{
"id": "28656a7d-4bd8-41c8-8471-50d19d88e7f2",
"name": "LLM Data Extractor",
"type": "@n8n/n8n-nodes-langchain.chainLlm",
"position": [
1000,
-440
],
"parameters": {
"text": "={{ $json.data }}",
"messages": {
"messageValues": [
{
"message": "You are an expert Data Formatter. Make sure to format the data in a human readable manner. Please output the human readable content without your own thoughts"
}
]
},
"promptType": "define",
"hasOutputParser": true
},
"typeVersion": 1.6
},
{
"id": "7045af3b-9e74-42ef-92f0-f8d3266f2890",
"name": "Concise Summary Generator",
"type": "@n8n/n8n-nodes-langchain.chainSummarization",
"position": [
1440,
-280
],
"parameters": {
"options": {
"summarizationMethodAndPrompts": {
"values": {
"prompt": "Write a concise summary of the following:\n\n\n\"{text}\"\n"
}
}
},
"chunkingMode": "advanced"
},
"typeVersion": 2
},
{
"id": "0cc843c1-252a-4c18-9856-5c7dfc732072",
"name": "Set Wikipedia URL with Bright Data Zone",
"type": "n8n-nodes-base.set",
"notes": "Set the URL which you are interested to scrap the data",
"position": [
560,
-440
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "1c132dd6-31e4-453b-a8cf-cad9845fe55b",
"name": "url",
"type": "string",
"value": "https://en.wikipedia.org/wiki/Cloud_computing?product=unlocker&method=api"
},
{
"id": "0fa387df-2511-4228-b6aa-237cceb3e9c7",
"name": "zone",
"type": "string",
"value": "web_unlocker1"
}
]
}
},
"notesInFlow": true,
"typeVersion": 3.4
},
{
"id": "6cb9930f-1924-4762-8150-f5cd0e063348",
"name": "Sticky Note2",
"type": "n8n-nodes-base.stickyNote",
"position": [
940,
-500
],
"parameters": {
"color": 4,
"width": 380,
"height": 420,
"content": "## Basic LLM Chain Data Extractor\n"
},
"typeVersion": 1
},
{
"id": "47811535-bce5-4946-aaa6-baef87db1100",
"name": "Sticky Note3",
"type": "n8n-nodes-base.stickyNote",
"position": [
1400,
-340
],
"parameters": {
"color": 5,
"width": 340,
"height": 420,
"content": "## Summarization Chain\n"
},
"typeVersion": 1
}
],
"active": false,
"settings": {
"executionOrder": "v1"
},
"versionId": "5b5e78fb-6e5a-4b92-838c-6c4060618e9c",
"connections": {
"LLM Data Extractor": {
"main": [
[
{
"node": "Concise Summary Generator",
"type": "main",
"index": 0
}
]
]
},
"Wikipedia Web Request": {
"main": [
[
{
"node": "LLM Data Extractor",
"type": "main",
"index": 0
}
]
]
},
"Concise Summary Generator": {
"main": [
[
{
"node": "Summary Webhook Notifier",
"type": "main",
"index": 0
}
]
]
},
"Google Gemini Chat Model2": {
"ai_languageModel": [
[
{
"node": "LLM Data Extractor",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"When clicking \u2018Test workflow\u2019": {
"main": [
[
{
"node": "Set Wikipedia URL with Bright Data Zone",
"type": "main",
"index": 0
}
]
]
},
"Set Wikipedia URL with Bright Data Zone": {
"main": [
[
{
"node": "Wikipedia Web Request",
"type": "main",
"index": 0
}
]
]
},
"Google Gemini Chat Model For Summarization": {
"ai_languageModel": [
[
{
"node": "Concise Summary Generator",
"type": "ai_languageModel",
"index": 0
}
]
]
}
}
}
Credentials you'll need
Each integration node will prompt for credentials when you import. We strip credential IDs before publishing — you'll add your own.
googlePalmApihttpHeaderAuth
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How this works
Quickly extract and summarise key facts from any Wikipedia page to fuel your research or content creation, saving hours of manual reading and note-taking. This workflow suits writers, researchers, and marketers who need concise insights without sifting through dense articles. It leverages Bright Data for robust web requests to fetch Wikipedia content, followed by Gemini AI to intelligently extract and condense the information into a digestible summary.
Use this workflow when you require on-demand, event-triggered processing of Wikipedia data for reports or blogs, especially for topics needing rapid overviews. Avoid it for real-time applications or non-Wikipedia sources, as it's tailored specifically to that platform's structure. Common variations include swapping Gemini for another AI model like OpenAI or adding email notifications to share summaries directly.
About this workflow
Extract & Summarize Wikipedia Data with Bright Data and Gemini AI. Uses manualTrigger, lmChatGoogleGemini, httpRequest, stickyNote. Event-driven trigger; 12 nodes.
Source: https://github.com/Zie619/n8n-workflows — original creator credit. Request a take-down →
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