Mit Bright Data und Gemini AI Daten von Wikipedia extrahieren und zusammenfassen
Dies ist ein Other, AI-Bereich Automatisierungsworkflow mit 12 Nodes. Hauptsächlich werden Set, HttpRequest, ManualTrigger, ChainLlm, ChainSummarization und andere Nodes verwendet, kombiniert mit KI-Technologie für intelligente Automatisierung. Extrahieren und Zusammenfassen von Wikipedia-Daten mit Bright Data und Gemini AI
- •Möglicherweise sind Ziel-API-Anmeldedaten erforderlich
- •Google Gemini API Key
Verwendete Nodes (12)
Kategorie
{
"id": "sczRNO4u1HYc5YV7",
"meta": {
"instanceId": "885b4fb4a6a9c2cb5621429a7b972df0d05bb724c20ac7dac7171b62f1c7ef40",
"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": "Bei Klick auf 'Workflow testen'",
"type": "n8n-nodes-base.manualTrigger",
"position": [
340,
-440
],
"parameters": {},
"typeVersion": 1
},
{
"id": "167e060a-c36c-462a-826c-81ef379c824b",
"name": "Google Gemini Chat-Modell für Zusammenfassung",
"type": "@n8n/n8n-nodes-langchain.lmChatGoogleGemini",
"position": [
1520,
-60
],
"parameters": {
"options": {},
"modelName": "models/gemini-2.0-flash-exp"
},
"credentials": {
"googlePalmApi": {
"id": "YeO7dHZnuGBVQKVZ",
"name": "Google Gemini(PaLM) Api account"
}
},
"typeVersion": 1
},
{
"id": "a51f2634-8b59-4feb-be39-674e8f198714",
"name": "Google Gemini Chat-Modell2",
"type": "@n8n/n8n-nodes-langchain.lmChatGoogleGemini",
"position": [
1000,
-240
],
"parameters": {
"options": {},
"modelName": "models/gemini-2.0-pro-exp"
},
"credentials": {
"googlePalmApi": {
"id": "YeO7dHZnuGBVQKVZ",
"name": "Google Gemini(PaLM) Api account"
}
},
"typeVersion": 1
},
{
"id": "a1ec001f-6e97-4efb-91d9-9a037fbf472c",
"name": "Zusammenfassungs-Webhook-Benachrichtiger",
"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": "Haftnotiz",
"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": "Haftnotiz1",
"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-Webanfrage",
"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": {
"id": "kdbqXuxIR8qIxF7y",
"name": "Header Auth account"
}
},
"typeVersion": 4.2
},
{
"id": "28656a7d-4bd8-41c8-8471-50d19d88e7f2",
"name": "LLM-Datenextraktor",
"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": "Präziser Zusammenfassungsgenerator",
"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": "Wikipedia-URL mit Bright Data Zone setzen",
"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": "Haftnotiz2",
"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": "Haftnotiz3",
"type": "n8n-nodes-base.stickyNote",
"position": [
1400,
-340
],
"parameters": {
"color": 5,
"width": 340,
"height": 420,
"content": "## Summarization Chain\n"
},
"typeVersion": 1
}
],
"active": false,
"pinData": {},
"settings": {
"executionOrder": "v1"
},
"versionId": "5b5e78fb-6e5a-4b92-838c-6c4060618e9c",
"connections": {
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},
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[
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"node": "28656a7d-4bd8-41c8-8471-50d19d88e7f2",
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},
"7045af3b-9e74-42ef-92f0-f8d3266f2890": {
"main": [
[
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"node": "a1ec001f-6e97-4efb-91d9-9a037fbf472c",
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},
"a51f2634-8b59-4feb-be39-674e8f198714": {
"ai_languageModel": [
[
{
"node": "28656a7d-4bd8-41c8-8471-50d19d88e7f2",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"0f4b4939-6356-4672-ae61-8d1daf66a168": {
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},
"167e060a-c36c-462a-826c-81ef379c824b": {
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"type": "ai_languageModel",
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]
]
}
}
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Für welche Szenarien ist dieser Workflow geeignet?
Fortgeschritten - Sonstiges, Künstliche Intelligenz
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Ranjan Dailata
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