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Documentation Complète
📋 Automatisation de la Veille Technologique avec Hugging Face et Notion
💡 Description
Ce workflow automatise la collecte et l'analyse des articles publiés sur Hugging Face, en les intégrant directement dans une base de données Notion pour une gestion efficace des informations. Il s'exécute quotidiennement pour extraire les derniers articles, analyse leur contenu à l'aide d'OpenAI pour en tirer des insights pertinents, et les stocke dans Notion. Cela permet aux équipes de recherche et développement de rester à jour avec les dernières innovations en intelligence artificielle sans effort manuel, optimisant ainsi le temps passé sur la veille technologique.
📈 Impact & ROI: Ce workflow améliore considérablement la productivité des équipes en automatisant la veille technologique, réduisant ainsi le temps consacré à la recherche manuelle et augmentant la capacité à réagir rapidement aux nouvelles tendances.
🚀 Fonctionnalités Clés
- ✅ Collecte automatique des articles récents - Économie de temps
- ✅ Analyse approfondie des contenus - Insights enrichis
- ✅ Intégration directe avec Notion - Organisation centralisée
- ✅ Utilisation d'OpenAI pour l'analyse - Précision accrue
📊 Architecture Technique
11
Nodes
Nodes
11
Connexions
Connexions
3
Services
Services
🔌 Services Intégrés
Hugging FaceNotionOpenAI
🔧 Composition du Workflow
| Node | Type | Description |
|---|---|---|
| Schedule Trigger | scheduleTrigger | Traitement des données |
| If | if | Condition logique pour router le flux |
| Loop Over Items | splitInBatches | Division des données en plusieurs branches |
| Split Out | splitOut | Division des données en plusieurs branches |
| Request Hugging Face Paper | httpRequest | Requête HTTP vers une API externe |
| Extract Hugging Face Paper | html | Traitement des données |
| Check Paper URL Existed | notion | Traitement des données |
| Request Hugging Face Paper Detail | httpRequest | Requête HTTP vers une API externe |
| OpenAI Analysis Abstract | @n8n/n8n-nodes-langchain.openAi | Traitement des données |
| Store Abstract Notion | notion | Traitement des données |
| Extract Hugging Face Paper Abstract | html | 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
{
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"meta": {
"instanceId": "3294023dd650d95df294922b9d55d174ef26f4a2e6cce97c8a4ab5f98f5b8c7b",
"templateCredsSetupCompleted": true
},
"name": "Hugging Face to Notion",
"tags": [],
"nodes": [
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"name": "Schedule Trigger",
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"parameters": {
"rule": {
"interval": [
{
"field": "weeks",
"triggerAtDay": [
1,
2,
3,
4,
5
],
"triggerAtHour": 8
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},
"typeVersion": 1.2
},
{
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"name": "If",
"type": "n8n-nodes-base.if",
"position": [
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],
"parameters": {
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"conditions": {
"options": {
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"leftValue": "",
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"typeValidation": "strict"
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"combinator": "and",
"conditions": [
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"operator": {
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"operation": "empty",
"singleValue": true
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"leftValue": "={{ $json }}",
"rightValue": ""
}
]
}
},
"typeVersion": 2.2
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{
"id": "afac08e1-b629-4467-86ef-907e4a5e8841",
"name": "Loop Over Items",
"type": "n8n-nodes-base.splitInBatches",
"position": [
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"parameters": {
"options": {
"reset": false
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"typeVersion": 3
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{
"id": "807ba450-9c89-4f88-aa84-91f43e3adfc6",
"name": "Split Out",
"type": "n8n-nodes-base.splitOut",
"position": [
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],
"parameters": {
"options": [],
"fieldToSplitOut": "url, url"
},
"typeVersion": 1
},
{
"id": "08dd3f15-2030-48f2-ab0f-f85f797268e1",
"name": "Request Hugging Face Paper",
"type": "n8n-nodes-base.httpRequest",
"position": [
-2440,
-300
],
"parameters": {
"url": "https:\/\/huggingface.co\/papers",
"options": [],
"sendQuery": true,
"queryParameters": {
"parameters": [
{
"name": "date",
"value": "={{ $now.minus(1,'days').format('yyyy-MM-dd') }}"
}
]
}
},
"typeVersion": 4.2
},
{
"id": "f37ba769-d881-4aad-927d-ca1f4a68b9a1",
"name": "Extract Hugging Face Paper",
"type": "n8n-nodes-base.html",
"position": [
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-300
],
"parameters": {
"options": [],
"operation": "extractHtmlContent",
"extractionValues": {
"values": [
{
"key": "url",
"attribute": "href",
"cssSelector": ".line-clamp-3",
"returnArray": true,
"returnValue": "attribute"
}
]
}
},
"typeVersion": 1.2
},
{
"id": "94ba99bf-a33b-4311-a4e6-86490e1bb9ad",
"name": "Check Paper URL Existed",
"type": "n8n-nodes-base.notion",
"position": [
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-280
],
"parameters": {
"filters": {
"conditions": [
{
"key": "URL|url",
"urlValue": "={{ 'https:\/\/huggingface.co'+$json.url }}",
"condition": "equals"
}
]
},
"options": [],
"resource": "databasePage",
"operation": "getAll",
"databaseId": {
"__rl": true,
"mode": "list",
"value": "17b67aba-1fcc-80ae-baa1-d88ffda7ae83",
"cachedResultUrl": "https:\/\/www.notion.so\/17b67aba1fcc80aebaa1d88ffda7ae83",
"cachedResultName": "huggingface-abstract"
},
"filterType": "manual"
},
"credentials": {
"notionApi": {
"id": "I5KdUzwhWnphQ862",
"name": "notion"
}
},
"typeVersion": 2.2,
"alwaysOutputData": true
},
{
"id": "ece8dee2-e444-4557-aad9-5bdcb5ecd756",
"name": "Request Hugging Face Paper Detail",
"type": "n8n-nodes-base.httpRequest",
"position": [
-1080,
-300
],
"parameters": {
"url": "={{ 'https:\/\/huggingface.co'+$('Split Out').item.json.url }}",
"options": []
},
"typeVersion": 4.2
},
{
"id": "53b266fe-e7c4-4820-92eb-78a6ba7a6430",
"name": "OpenAI Analysis Abstract",
"type": "@n8n\/n8n-nodes-langchain.openAi",
"position": [
-640,
-300
],
"parameters": {
"modelId": {
"__rl": true,
"mode": "list",
"value": "gpt-4o-2024-11-20",
"cachedResultName": "GPT-4O-2024-11-20"
},
"options": [],
"messages": {
"values": [
{
"role": "system",
"content": "Extract the following key details from the paper abstract:\n\nCore Introduction: Summarize the main contributions and objectives of the paper, highlighting its innovations and significance.\nKeyword Extraction: List 2-5 keywords that best represent the research direction and techniques of the paper.\nKey Data and Results: Extract important performance metrics, comparison results, and the paper's advantages over other studies.\nTechnical Details: Provide a brief overview of the methods, optimization techniques, and datasets mentioned in the paper.\nClassification: Assign an appropriate academic classification based on the content of the paper.\n\n\nOutput as json：\n{\n \"Core_Introduction\": \"PaSa is an advanced Paper Search agent powered by large language models that can autonomously perform a series of decisions (including invoking search tools, reading papers, and selecting relevant references) to provide comprehensive and accurate results for complex academic queries.\",\n \"Keywords\": [\n \"Paper Search Agent\",\n \"Large Language Models\",\n \"Reinforcement Learning\",\n \"Academic Queries\",\n \"Performance Benchmarking\"\n ],\n \"Data_and_Results\": \"PaSa outperforms existing baselines (such as Google, GPT-4, chatGPT) in tests using AutoScholarQuery (35k academic queries) and RealScholarQuery (real-world academic queries). For example, PaSa-7B exceeds Google with GPT-4o by 37.78% in recall@20 and 39.90% in recall@50.\",\n \"Technical_Details\": \"PaSa is optimized using reinforcement learning with the AutoScholarQuery synthetic dataset, demonstrating superior performance in multiple benchmarks.\",\n \"Classification\": [\n \"Artificial Intelligence (AI)\",\n \"Academic Search and Information Retrieval\",\n \"Natural Language Processing (NLP)\",\n \"Reinforcement Learning\"\n ]\n}\n```"
},
{
"content": "={{ $json.abstract }}"
}
]
},
"jsonOutput": true
},
"credentials": {
"openAiApi": {
"id": "LmLcxHwbzZNWxqY6",
"name": "Unnamed credential"
}
},
"typeVersion": 1.8
},
{
"id": "f491cd7f-598e-46fd-b80c-04cfa9766dfd",
"name": "Store Abstract Notion",
"type": "n8n-nodes-base.notion",
"position": [
-300,
-300
],
"parameters": {
"options": [],
"resource": "databasePage",
"databaseId": {
"__rl": true,
"mode": "list",
"value": "17b67aba-1fcc-80ae-baa1-d88ffda7ae83",
"cachedResultUrl": "https:\/\/www.notion.so\/17b67aba1fcc80aebaa1d88ffda7ae83",
"cachedResultName": "huggingface-abstract"
},
"propertiesUi": {
"propertyValues": [
{
"key": "URL|url",
"urlValue": "={{ 'https:\/\/huggingface.co'+$('Split Out').item.json.url }}"
},
{
"key": "title|title",
"title": "={{ $('Extract Hugging Face Paper Abstract').item.json.title }}"
},
{
"key": "abstract|rich_text",
"textContent": "={{ $('Extract Hugging Face Paper Abstract').item.json.abstract.substring(0,2000) }}"
},
{
"key": "scrap-date|date",
"date": "={{ $today.format('yyyy-MM-dd') }}",
"includeTime": false
},
{
"key": "Classification|rich_text",
"textContent": "={{ $json.message.content.Classification.join(',') }}"
},
{
"key": "Technical_Details|rich_text",
"textContent": "={{ $json.message.content.Technical_Details }}"
},
{
"key": "Data_and_Results|rich_text",
"textContent": "={{ $json.message.content.Data_and_Results }}"
},
{
"key": "keywords|rich_text",
"textContent": "={{ $json.message.content.Keywords.join(',') }}"
},
{
"key": "Core Introduction|rich_text",
"textContent": "={{ $json.message.content.Core_Introduction }}"
}
]
}
},
"credentials": {
"notionApi": {
"id": "I5KdUzwhWnphQ862",
"name": "notion"
}
},
"typeVersion": 2.2
},
{
"id": "d5816a1c-d1fa-4be2-8088-57fbf68e6b43",
"name": "Extract Hugging Face Paper Abstract",
"type": "n8n-nodes-base.html",
"position": [
-840,
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],
"parameters": {
"options": [],
"operation": "extractHtmlContent",
"extractionValues": {
"values": [
{
"key": "abstract",
"cssSelector": ".text-gray-700"
},
{
"key": "title",
"cssSelector": ".text-2xl"
}
]
}
},
"typeVersion": 1.2
}
],
"active": true,
"pinData": [],
"settings": {
"executionOrder": "v1"
},
"versionId": "4b0ec2a3-253d-46d5-a4d4-1d9ff21ba4a3",
"connections": {
"If": {
"main": [
[
{
"node": "Request Hugging Face Paper Detail",
"type": "main",
"index": 0
}
],
[
{
"node": "Loop Over Items",
"type": "main",
"index": 0
}
]
]
},
"Split Out": {
"main": [
[
{
"node": "Loop Over Items",
"type": "main",
"index": 0
}
]
]
},
"Loop Over Items": {
"main": [
[],
[
{
"node": "Check Paper URL Existed",
"type": "main",
"index": 0
}
]
]
},
"Schedule Trigger": {
"main": [
[
{
"node": "Request Hugging Face Paper",
"type": "main",
"index": 0
}
]
]
},
"Store Abstract Notion": {
"main": [
[
{
"node": "Loop Over Items",
"type": "main",
"index": 0
}
]
]
},
"Check Paper URL Existed": {
"main": [
[
{
"node": "If",
"type": "main",
"index": 0
}
]
]
},
"OpenAI Analysis Abstract": {
"main": [
[
{
"node": "Store Abstract Notion",
"type": "main",
"index": 0
}
]
]
},
"Extract Hugging Face Paper": {
"main": [
[
{
"node": "Split Out",
"type": "main",
"index": 0
}
]
]
},
"Request Hugging Face Paper": {
"main": [
[
{
"node": "Extract Hugging Face Paper",
"type": "main",
"index": 0
}
]
]
},
"Request Hugging Face Paper Detail": {
"main": [
[
{
"node": "Extract Hugging Face Paper Abstract",
"type": "main",
"index": 0
}
]
]
},
"Extract Hugging Face Paper Abstract": {
"main": [
[
{
"node": "OpenAI Analysis Abstract",
"type": "main",
"index": 0
}
]
]
}
}
}
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