Comprehensive n8n Creator Stats Automation Workflow
Automate the reporting of top n8n creators and workflows with this powerful workflow. By aggregating data from GitHub, g...
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📋 Optimisez vos ensembles de données vectorielles avec l'IA
💡 Description
Ce workflow n8n vous permet de créer des ensembles de données vectorielles optimisés pour les modèles de langage grâce à l'intégration de services avancés tels que Bright Data, Google Gemini et Pinecone. Idéal pour les entreprises cherchant à améliorer leurs capacités d'analyse et d'apprentissage automatique, ce processus automatisé extrait, formate et stocke efficacement les données web dans une base de données vectorielle. En réduisant le temps nécessaire à la préparation des données, ce workflow facilite la création d'applications IA plus intelligentes et réactives.
📈 Impact & ROI: Améliore l'efficacité opérationnelle en automatisant la préparation des données pour les projets IA, permettant ainsi un développement plus rapide et une meilleure exploitation des ressources analytiques.
🚀 Fonctionnalités Clés
- ✅ Extraction de données web précise
- ✅ Intégration fluide avec des modèles IA avancés
- ✅ Stockage efficace dans une base de données vectorielle
- ✅ Automatisation du processus de formatage et d'insertion
📊 Architecture Technique
21
Nodes
Nodes
14
Connexions
Connexions
3
Services
Services
🔌 Services Intégrés
Bright DataGoogle GeminiPinecone
🔧 Composition du Workflow
| Node | Type | Description |
|---|---|---|
| When clicking ‘Test workflow’ | manualTrigger | Traitement des données |
| AI Agent | @n8n/n8n-nodes-langchain.agent | Traitement des données |
| Pinecone Vector Store | @n8n/n8n-nodes-langchain.vectorStorePinecone | Traitement des données |
| Embeddings Google Gemini | @n8n/n8n-nodes-langchain.embeddingsGoogleGemini | Traitement des données |
| Default Data Loader | @n8n/n8n-nodes-langchain.documentDefaultDataLoader | Traitement des données |
| Recursive Character Text Splitter | @n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter | Division des données en plusieurs branches |
| Google Gemini Chat Model1 | @n8n/n8n-nodes-langchain.lmChatGoogleGemini | Traitement des données |
| Google Gemini Chat Model2 | @n8n/n8n-nodes-langchain.lmChatGoogleGemini | Traitement des données |
| Google Gemini Chat Model | @n8n/n8n-nodes-langchain.lmChatGoogleGemini | Traitement des données |
| Structured Output Parser | @n8n/n8n-nodes-langchain.outputParserStructured | Traitement des données |
| Sticky Note | stickyNote | Traitement des données |
| Set Fields - URL and Webhook URL | set | Traitement des données |
| Make a web request | httpRequest | Requête HTTP vers une API externe |
| Structured JSON Data Formatter | @n8n/n8n-nodes-langchain.chainLlm | Traitement des données |
| Webhook for structured data | httpRequest | Requête HTTP vers une API externe |
| Webhook for structured AI agent response | httpRequest | Requête HTTP vers une API externe |
| Sticky Note1 | stickyNote | Traitement des données |
| Sticky Note2 | stickyNote | Traitement des données |
| Sticky Note3 | stickyNote | Traitement des données |
| Information Extractor with Data Formatter | @n8n/n8n-nodes-langchain.informationExtractor | Traitement des données |
| Sticky Note4 | stickyNote | Traitement des données |
📖 Guide d'Implémentation
- Import du workflow: Téléchargez le fichier JSON et importez-le dans votre instance n8n
- Configuration des credentials: Configurez les accès pour chaque service utilisé
- Personnalisation: Adaptez les paramètres selon vos besoins spécifiques
- Test: Exécutez le workflow en mode test pour vérifier le bon fonctionnement
- Activation: Activez le workflow pour une exécution automatique
🏷️ Tags
Structure JSON
Voir le code JSON complet
{
"id": "3Lih0LVosR8dZbla",
"meta": {
"instanceId": "885b4fb4a6a9c2cb5621429a7b972df0d05bb724c20ac7dac7171b62f1c7ef40",
"templateCredsSetupCompleted": true
},
"name": "Create AI-Ready Vector Datasets for LLMs with Bright Data, Gemini & Pinecone",
"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": "0a468953-e348-420e-a6b3-c55fb20d3cbf",
"name": "When clicking ‘Test workflow’",
"type": "n8n-nodes-base.manualTrigger",
"position": [
200,
-710
],
"parameters": [],
"typeVersion": 1
},
{
"id": "3725e480-246f-4f32-b0a7-b946cacbe830",
"name": "AI Agent",
"type": "@n8n\/n8n-nodes-langchain.agent",
"position": [
1236,
-60
],
"parameters": {
"text": "=Format the below search result\n\n{{ $json.output.search_result }}",
"options": [],
"promptType": "define",
"hasOutputParser": true
},
"typeVersion": 1.8
},
{
"id": "30a12b8e-02f5-4b2e-bf9f-20fd9658405e",
"name": "Pinecone Vector Store",
"type": "@n8n\/n8n-nodes-langchain.vectorStorePinecone",
"position": [
1628,
-10
],
"parameters": {
"mode": "insert",
"options": [],
"pineconeIndex": {
"__rl": true,
"mode": "list",
"value": "hacker-news",
"cachedResultName": "hacker-news"
}
},
"credentials": {
"pineconeApi": {
"id": "wdfRQ6NE8yjCDFhY",
"name": "PineconeApi account"
}
},
"typeVersion": 1.1
},
{
"id": "1738dea6-fa4f-4a8d-a6fb-2f01feb1a6d5",
"name": "Embeddings Google Gemini",
"type": "@n8n\/n8n-nodes-langchain.embeddingsGoogleGemini",
"position": [
1612,
210
],
"parameters": {
"modelName": "models\/text-embedding-004"
},
"credentials": {
"googlePalmApi": {
"id": "YeO7dHZnuGBVQKVZ",
"name": "Google Gemini(PaLM) Api account"
}
},
"typeVersion": 1
},
{
"id": "e6443541-de71-4d26-ad58-d7c72868a190",
"name": "Default Data Loader",
"type": "@n8n\/n8n-nodes-langchain.documentDefaultDataLoader",
"position": [
1760,
220
],
"parameters": {
"options": [],
"jsonData": "={{ $('Information Extractor with Data Formatter').item.json.output.search_result }}",
"jsonMode": "expressionData"
},
"typeVersion": 1
},
{
"id": "09ffc8cd-096f-47fe-937d-f8ab4fb41266",
"name": "Recursive Character Text Splitter",
"type": "@n8n\/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter",
"position": [
1820,
410
],
"parameters": {
"options": []
},
"typeVersion": 1
},
{
"id": "90cc9aa4-0931-4c52-8734-e4e0de820205",
"name": "Google Gemini Chat Model1",
"type": "@n8n\/n8n-nodes-langchain.lmChatGoogleGemini",
"position": [
1240,
160
],
"parameters": {
"options": [],
"modelName": "models\/gemini-2.0-flash-exp"
},
"credentials": {
"googlePalmApi": {
"id": "YeO7dHZnuGBVQKVZ",
"name": "Google Gemini(PaLM) Api account"
}
},
"typeVersion": 1
},
{
"id": "1090a4af-7e5d-446b-a537-3afe48cd4909",
"name": "Google Gemini Chat Model2",
"type": "@n8n\/n8n-nodes-langchain.lmChatGoogleGemini",
"position": [
948,
-340
],
"parameters": {
"options": [],
"modelName": "models\/gemini-2.0-flash-exp"
},
"credentials": {
"googlePalmApi": {
"id": "YeO7dHZnuGBVQKVZ",
"name": "Google Gemini(PaLM) Api account"
}
},
"typeVersion": 1
},
{
"id": "324c530c-0a03-411e-acb0-d82e9dc635cf",
"name": "Google Gemini Chat Model",
"type": "@n8n\/n8n-nodes-langchain.lmChatGoogleGemini",
"position": [
948,
160
],
"parameters": {
"options": [],
"modelName": "models\/gemini-2.0-flash-exp"
},
"credentials": {
"googlePalmApi": {
"id": "YeO7dHZnuGBVQKVZ",
"name": "Google Gemini(PaLM) Api account"
}
},
"typeVersion": 1
},
{
"id": "3226a2d6-ade1-4d6a-95c5-0be4d787a947",
"name": "Structured Output Parser",
"type": "@n8n\/n8n-nodes-langchain.outputParserStructured",
"position": [
1400,
160
],
"parameters": {
"jsonSchemaExample": "[{\n\t\"id\": \"<string>\",\n\t\"title\": \"<string>\",\n \"summary\": \"<string>\",\n \"keywords\": [\"\"],\n \"topics\": [\"\"]\n}]"
},
"typeVersion": 1.2
},
{
"id": "a739a314-900a-4ef7-9cc2-1b65374e2e05",
"name": "Sticky Note",
"type": "n8n-nodes-base.stickyNote",
"position": [
40,
-360
],
"parameters": {
"width": 480,
"height": 220,
"content": "## Note\nPlease make sure to set the URL for web crawling. \n\nWeb-Unlocker Product is being utilized for performing the web scrapping. \n\nThis workflow is utilizing the Basic LLM Chain, Information Extraction with the AI Agents for formatting, extracting and persisting the response in PineCone Vector Database"
},
"typeVersion": 1
},
{
"id": "3dca6d46-c423-4fb5-a6e4-c2aa2852d51c",
"name": "Set Fields - URL and Webhook URL",
"type": "n8n-nodes-base.set",
"notes": "Set the URL which you are interested to scrap the data",
"position": [
420,
-710
],
"parameters": {
"options": [],
"assignments": {
"assignments": [
{
"id": "1c132dd6-31e4-453b-a8cf-cad9845fe55b",
"name": "url",
"type": "string",
"value": "https:\/\/news.ycombinator.com?product=unlocker&method=api"
},
{
"id": "90f3272b-d13d-44e2-8b4c-0943648cfce9",
"name": "webhook_url",
"type": "string",
"value": "https:\/\/webhook.site\/bc804ce5-4a45-4177-a68a-99c80e5c86e6"
}
]
}
},
"notesInFlow": true,
"typeVersion": 3.4
},
{
"id": "216a3261-a398-484c-9bf4-ca5966b829b6",
"name": "Make a web request",
"type": "n8n-nodes-base.httpRequest",
"position": [
640,
-260
],
"parameters": {
"url": "https:\/\/api.brightdata.com\/request",
"method": "POST",
"options": [],
"sendBody": true,
"sendHeaders": true,
"authentication": "genericCredentialType",
"bodyParameters": {
"parameters": [
{
"name": "zone",
"value": "web_unlocker1"
},
{
"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": "0c74e21c-3007-4297-b6ab-8ee17f4c6436",
"name": "Structured JSON Data Formatter",
"type": "@n8n\/n8n-nodes-langchain.chainLlm",
"position": [
860,
-560
],
"parameters": {
"text": "=Format the below response and produce a textual data. Output the response as per the below JSON schema.\n\nHere's the input: {{ $json.data }}\nHere's the JSON schema: \n\n[{\n \"rank\": { \"type\": \"integer\" },\n \"title\": { \"type\": \"string\" },\n \"site\": { \"type\": \"string\" },\n \"points\": { \"type\": \"integer\" },\n \"user\": { \"type\": \"string\" },\n \"age\": { \"type\": \"string\" },\n \"comments\": { \"type\": \"string\" }\n}]",
"messages": {
"messageValues": [
{
"message": "You are an expert data formatter"
}
]
},
"promptType": "define"
},
"typeVersion": 1.6
},
{
"id": "012d4bb0-2b58-47cd-9cea-b4e0dced9082",
"name": "Webhook for structured data",
"type": "n8n-nodes-base.httpRequest",
"position": [
1314,
-860
],
"parameters": {
"url": "={{ $json.webhook_url }}",
"options": [],
"sendBody": true,
"bodyParameters": {
"parameters": [
{
"name": "response",
"value": "={{ $json.text }}"
}
]
}
},
"typeVersion": 4.2
},
{
"id": "93b35e5e-6f52-4aeb-8f1b-39cc495beefe",
"name": "Webhook for structured AI agent response",
"type": "n8n-nodes-base.httpRequest",
"position": [
1750,
-660
],
"parameters": {
"url": "={{ $json.webhook_url }}",
"options": [],
"sendBody": true,
"bodyParameters": {
"parameters": [
{
"name": "response",
"value": "={{ $json.output }}"
}
]
}
},
"typeVersion": 4.2
},
{
"id": "251b4251-255c-48c6-999b-02227fa2de9b",
"name": "Sticky Note1",
"type": "n8n-nodes-base.stickyNote",
"position": [
800,
-620
],
"parameters": {
"width": 360,
"height": 420,
"content": "## AI Data Formatter\n"
},
"typeVersion": 1
},
{
"id": "f62463cd-6be3-4942-a636-de980a3154b4",
"name": "Sticky Note2",
"type": "n8n-nodes-base.stickyNote",
"position": [
1560,
-160
],
"parameters": {
"color": 4,
"width": 520,
"height": 720,
"content": "## Vector Database Persistence\n"
},
"typeVersion": 1
},
{
"id": "ad20cc91-766a-4a57-be54-6f0d09a784eb",
"name": "Sticky Note3",
"type": "n8n-nodes-base.stickyNote",
"position": [
1260,
-920
],
"parameters": {
"color": 3,
"width": 680,
"height": 440,
"content": "## Webhook Notification Handler\n"
},
"typeVersion": 1
},
{
"id": "37ab5c0f-d36e-4131-844d-20a22d3f2861",
"name": "Information Extractor with Data Formatter",
"type": "@n8n\/n8n-nodes-langchain.informationExtractor",
"position": [
860,
-60
],
"parameters": {
"text": "={{ $json.data }}",
"options": {
"systemPromptTemplate": "You are an expert HTML extractor. Your job is to analyze the search result and extract the content as a collection on items"
},
"attributes": {
"attributes": [
{
"name": "search_result",
"description": "Search Response"
}
]
}
},
"typeVersion": 1
},
{
"id": "e04e189a-8ba9-4ef4-9a49-fc13daf00828",
"name": "Sticky Note4",
"type": "n8n-nodes-base.stickyNote",
"position": [
800,
-160
],
"parameters": {
"color": 5,
"width": 720,
"height": 720,
"content": "## Data Extraction\/Formatting with the AI Agent\n"
},
"typeVersion": 1
}
],
"active": false,
"pinData": [],
"settings": {
"executionOrder": "v1"
},
"versionId": "799fb406-600d-45a5-b926-24b8844f33a5",
"connections": {
"AI Agent": {
"main": [
[
{
"node": "Pinecone Vector Store",
"type": "main",
"index": 0
},
{
"node": "Webhook for structured AI agent response",
"type": "main",
"index": 0
}
]
]
},
"Make a web request": {
"main": [
[
{
"node": "Structured JSON Data Formatter",
"type": "main",
"index": 0
},
{
"node": "Information Extractor with Data Formatter",
"type": "main",
"index": 0
}
]
]
},
"Default Data Loader": {
"ai_document": [
[
{
"node": "Pinecone Vector Store",
"type": "ai_document",
"index": 0
}
]
]
},
"Pinecone Vector Store": {
"ai_tool": [
[]
]
},
"Embeddings Google Gemini": {
"ai_embedding": [
[
{
"node": "Pinecone Vector Store",
"type": "ai_embedding",
"index": 0
}
]
]
},
"Google Gemini Chat Model": {
"ai_languageModel": [
[
{
"node": "Information Extractor with Data Formatter",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Structured Output Parser": {
"ai_outputParser": [
[
{
"node": "AI Agent",
"type": "ai_outputParser",
"index": 0
}
]
]
},
"Google Gemini Chat Model1": {
"ai_languageModel": [
[
{
"node": "AI Agent",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Google Gemini Chat Model2": {
"ai_languageModel": [
[
{
"node": "Structured JSON Data Formatter",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Structured JSON Data Formatter": {
"main": [
[
{
"node": "Webhook for structured data",
"type": "main",
"index": 0
}
]
]
},
"Set Fields - URL and Webhook URL": {
"main": [
[
{
"node": "Make a web request",
"type": "main",
"index": 0
},
{
"node": "Webhook for structured data",
"type": "main",
"index": 0
},
{
"node": "Webhook for structured AI agent response",
"type": "main",
"index": 0
}
]
]
},
"Recursive Character Text Splitter": {
"ai_textSplitter": [
[
{
"node": "Default Data Loader",
"type": "ai_textSplitter",
"index": 0
}
]
]
},
"When clicking ‘Test workflow’": {
"main": [
[
{
"node": "Set Fields - URL and Webhook URL",
"type": "main",
"index": 0
}
]
]
},
"Information Extractor with Data Formatter": {
"main": [
[
{
"node": "AI Agent",
"type": "main",
"index": 0
}
]
]
}
}
}
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