De Hugging Face vers Notion

Name: De Hugging Face vers Notion
Rating: 4.5 (10 reviews)
Author: AI Native

Intermédiaire

Ceci est unAIworkflow d'automatisation du domainecontenant 11 nœuds.Utilise principalement des nœuds comme If, Html, Notion, SplitOut, HttpRequest, combinant la technologie d'intelligence artificielle pour une automatisation intelligente. Analyser les articles Hugging Face avec IA et stocker dans Notion

Prérequis

•Clé API Notion
•Peut nécessiter les informations d'identification d'authentification de l'API cible
•Clé API OpenAI

Nœuds utilisés (11)

Catégorie

Intelligence Artificielle

Aperçu du workflow

Visualisation des connexions entre les nœuds, avec support du zoom et du déplacement

Déclencheur Planifié

Condition Si

Boucle sur les Éléments

Séparation

Requête d'Article Hugging Face

Extraction d'Article Hugging Face

Vérifier l'Existence de l'URL de l'Article

Requête de Détail d'Article Hugging Face

OpenAI Analyse de Résumé

Stockage de Résumé Notion

Extraction de Résumé d'Article Hugging Face

React Flow

Exporter le workflow

Copiez la configuration JSON suivante dans n8n pour importer et utiliser ce workflow

{
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  "meta": {
    "instanceId": "3294023dd650d95df294922b9d55d174ef26f4a2e6cce97c8a4ab5f98f5b8c7b",
    "templateCredsSetupCompleted": true
  },
  "name": "Hugging Face  to Notion",
  "tags": [],
  "nodes": [
    {
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      "name": "Déclencheur Planifié",
      "type": "n8n-nodes-base.scheduleTrigger",
      "position": [
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      ],
      "parameters": {
        "rule": {
          "interval": [
            {
              "field": "weeks",
              "triggerAtDay": [
                1,
                2,
                3,
                4,
                5
              ],
              "triggerAtHour": 8
            }
          ]
        }
      },
      "typeVersion": 1.2
    },
    {
      "id": "b1f4078e-ac77-47ec-995c-f52fd98fafef",
      "name": "Condition Si",
      "type": "n8n-nodes-base.if",
      "position": [
        -1360,
        -280
      ],
      "parameters": {
        "options": {},
        "conditions": {
          "options": {
            "version": 2,
            "leftValue": "",
            "caseSensitive": true,
            "typeValidation": "strict"
          },
          "combinator": "and",
          "conditions": [
            {
              "id": "7094d6db-1fa7-4b59-91cf-6bbd5b5f067e",
              "operator": {
                "type": "object",
                "operation": "empty",
                "singleValue": true
              },
              "leftValue": "={{ $json }}",
              "rightValue": ""
            }
          ]
        }
      },
      "typeVersion": 2.2
    },
    {
      "id": "afac08e1-b629-4467-86ef-907e4a5e8841",
      "name": "Boucle sur les Éléments",
      "type": "n8n-nodes-base.splitInBatches",
      "position": [
        -1760,
        -300
      ],
      "parameters": {
        "options": {
          "reset": false
        }
      },
      "typeVersion": 3
    },
    {
      "id": "807ba450-9c89-4f88-aa84-91f43e3adfc6",
      "name": "Séparation",
      "type": "n8n-nodes-base.splitOut",
      "position": [
        -1960,
        -300
      ],
      "parameters": {
        "options": {},
        "fieldToSplitOut": "url, url"
      },
      "typeVersion": 1
    },
    {
      "id": "08dd3f15-2030-48f2-ab0f-f85f797268e1",
      "name": "Requête d'Article Hugging Face",
      "type": "n8n-nodes-base.httpRequest",
      "position": [
        -2440,
        -300
      ],
      "parameters": {
        "url": "https://huggingface.co/papers",
        "options": {},
        "sendQuery": true,
        "queryParameters": {
          "parameters": [
            {
              "name": "date",
              "value": "={{ $now.minus(1,'days').format('yyyy-MM-dd') }}"
            }
          ]
        }
      },
      "typeVersion": 4.2
    },
    {
      "id": "f37ba769-d881-4aad-927d-ca1f4a68b9a1",
      "name": "Extraction d'Article Hugging Face",
      "type": "n8n-nodes-base.html",
      "position": [
        -2200,
        -300
      ],
      "parameters": {
        "options": {},
        "operation": "extractHtmlContent",
        "extractionValues": {
          "values": [
            {
              "key": "url",
              "attribute": "href",
              "cssSelector": ".line-clamp-3",
              "returnArray": true,
              "returnValue": "attribute"
            }
          ]
        }
      },
      "typeVersion": 1.2
    },
    {
      "id": "94ba99bf-a33b-4311-a4e6-86490e1bb9ad",
      "name": "Vérifier l'Existence de l'URL de l'Article",
      "type": "n8n-nodes-base.notion",
      "position": [
        -1540,
        -280
      ],
      "parameters": {
        "filters": {
          "conditions": [
            {
              "key": "URL|url",
              "urlValue": "={{ 'https://huggingface.co'+$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": "Requête de Détail d'Article Hugging Face",
      "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 Analyse de Résumé",
      "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": "={{ $json.abstract }}"
            }
          ]
        },
        "jsonOutput": true
      },
      "credentials": {
        "openAiApi": {
          "id": "LmLcxHwbzZNWxqY6",
          "name": "Unnamed credential"
        }
      },
      "typeVersion": 1.8
    },
    {
      "id": "f491cd7f-598e-46fd-b80c-04cfa9766dfd",
      "name": "Stockage de Résumé Notion",
      "type": "n8n-nodes-base.notion",
      "position": [
        -300,
        -300
      ],
      "parameters": {
        "options": {},
        "resource": "databasePage",
        "databaseId": {
          "__rl": true,
          "mode": "list",
          "value": "17b67aba-1fcc-80ae-baa1-d88ffda7ae83",
          "cachedResultUrl": "https://www.notion.so/17b67aba1fcc80aebaa1d88ffda7ae83",
          "cachedResultName": "huggingface-abstract"
        },
        "propertiesUi": {
          "propertyValues": [
            {
              "key": "URL|url",
              "urlValue": "={{ 'https://huggingface.co'+$('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": "Extraction de Résumé d'Article Hugging Face",
      "type": "n8n-nodes-base.html",
      "position": [
        -840,
        -300
      ],
      "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": {
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}

Foire aux questions

Comment utiliser ce workflow ?

Copiez le code de configuration JSON ci-dessus, créez un nouveau workflow dans votre instance n8n et sélectionnez "Importer depuis le JSON", collez la configuration et modifiez les paramètres d'authentification selon vos besoins.

Dans quelles scénarios ce workflow est-il adapté ?

Intermédiaire - Intelligence Artificielle

Est-ce payant ?

Ce workflow est entièrement gratuit et peut être utilisé directement. Veuillez noter que les services tiers utilisés dans le workflow (comme l'API OpenAI) peuvent nécessiter un paiement de votre part.