Hugging Face nach Notion

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

Fortgeschritten

Dies ist ein AI-Bereich Automatisierungsworkflow mit 11 Nodes. Hauptsächlich werden If, Html, Notion, SplitOut, HttpRequest und andere Nodes verwendet, kombiniert mit KI-Technologie für intelligente Automatisierung. KI-Analyse der Hugging Face-Papiere und Speicherung in Notion

Voraussetzungen

•Notion API Key
•Möglicherweise sind Ziel-API-Anmeldedaten erforderlich
•OpenAI API Key

Verwendete Nodes (11)

Kategorie

Künstliche Intelligenz

Workflow-Vorschau

Visualisierung der Node-Verbindungen, mit Zoom und Pan

Zeitplan-Trigger

Wenn

Über Elemente iterieren

Aufteilen

Hugging Face-Papier anfordern

Hugging Face-Papier extrahieren

Prüfen, ob Papier-URL existiert

Hugging Face-Papierdetails anfordern

OpenAI Abstract analysieren

Abstract speichern Notion

Hugging Face-Papier-Abstract extrahieren

React Flow

Workflow exportieren

Kopieren Sie die folgende JSON-Konfiguration und importieren Sie sie in n8n

{
  "id": "FU3MrLkaTHmfdG4n",
  "meta": {
    "instanceId": "3294023dd650d95df294922b9d55d174ef26f4a2e6cce97c8a4ab5f98f5b8c7b",
    "templateCredsSetupCompleted": true
  },
  "name": "Hugging Face  to Notion",
  "tags": [],
  "nodes": [
    {
      "id": "32d5bfee-97f1-4e92-b62e-d09bdd9c3821",
      "name": "Zeitplan-Trigger",
      "type": "n8n-nodes-base.scheduleTrigger",
      "position": [
        -2640,
        -300
      ],
      "parameters": {
        "rule": {
          "interval": [
            {
              "field": "weeks",
              "triggerAtDay": [
                1,
                2,
                3,
                4,
                5
              ],
              "triggerAtHour": 8
            }
          ]
        }
      },
      "typeVersion": 1.2
    },
    {
      "id": "b1f4078e-ac77-47ec-995c-f52fd98fafef",
      "name": "Wenn",
      "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": "Über Elemente iterieren",
      "type": "n8n-nodes-base.splitInBatches",
      "position": [
        -1760,
        -300
      ],
      "parameters": {
        "options": {
          "reset": false
        }
      },
      "typeVersion": 3
    },
    {
      "id": "807ba450-9c89-4f88-aa84-91f43e3adfc6",
      "name": "Aufteilen",
      "type": "n8n-nodes-base.splitOut",
      "position": [
        -1960,
        -300
      ],
      "parameters": {
        "options": {},
        "fieldToSplitOut": "url, url"
      },
      "typeVersion": 1
    },
    {
      "id": "08dd3f15-2030-48f2-ab0f-f85f797268e1",
      "name": "Hugging Face-Papier anfordern",
      "type": "n8n-nodes-base.httpRequest",
      "position": [
        -2440,
        -300
      ],
      "parameters": {
        "url": "https://huggingface.co/papers",
        "options": {},
        "sendQuery": true,
        "queryParameters": {
          "parameters": [
            {
              "name": "date",
              "value": "={{ $now.minus(1,'days').format('yyyy-MM-dd') }}"
            }
          ]
        }
      },
      "typeVersion": 4.2
    },
    {
      "id": "f37ba769-d881-4aad-927d-ca1f4a68b9a1",
      "name": "Hugging Face-Papier extrahieren",
      "type": "n8n-nodes-base.html",
      "position": [
        -2200,
        -300
      ],
      "parameters": {
        "options": {},
        "operation": "extractHtmlContent",
        "extractionValues": {
          "values": [
            {
              "key": "url",
              "attribute": "href",
              "cssSelector": ".line-clamp-3",
              "returnArray": true,
              "returnValue": "attribute"
            }
          ]
        }
      },
      "typeVersion": 1.2
    },
    {
      "id": "94ba99bf-a33b-4311-a4e6-86490e1bb9ad",
      "name": "Prüfen, ob Papier-URL existiert",
      "type": "n8n-nodes-base.notion",
      "position": [
        -1540,
        -280
      ],
      "parameters": {
        "filters": {
          "conditions": [
            {
              "key": "URL|url",
              "urlValue": "={{ 'https://huggingface.co'+$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": "Hugging Face-Papierdetails anfordern",
      "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 Abstract analysieren",
      "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": "={{ $json.abstract }}"
            }
          ]
        },
        "jsonOutput": true
      },
      "credentials": {
        "openAiApi": {
          "id": "LmLcxHwbzZNWxqY6",
          "name": "Unnamed credential"
        }
      },
      "typeVersion": 1.8
    },
    {
      "id": "f491cd7f-598e-46fd-b80c-04cfa9766dfd",
      "name": "Abstract speichern Notion",
      "type": "n8n-nodes-base.notion",
      "position": [
        -300,
        -300
      ],
      "parameters": {
        "options": {},
        "resource": "databasePage",
        "databaseId": {
          "__rl": true,
          "mode": "list",
          "value": "17b67aba-1fcc-80ae-baa1-d88ffda7ae83",
          "cachedResultUrl": "https://www.notion.so/17b67aba1fcc80aebaa1d88ffda7ae83",
          "cachedResultName": "huggingface-abstract"
        },
        "propertiesUi": {
          "propertyValues": [
            {
              "key": "URL|url",
              "urlValue": "={{ 'https://huggingface.co'+$('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": "Hugging Face-Papier-Abstract extrahieren",
      "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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  }
}

Häufig gestellte Fragen

Wie verwende ich diesen Workflow?

Kopieren Sie den obigen JSON-Code, erstellen Sie einen neuen Workflow in Ihrer n8n-Instanz und wählen Sie "Aus JSON importieren". Fügen Sie die Konfiguration ein und passen Sie die Anmeldedaten nach Bedarf an.

Für welche Szenarien ist dieser Workflow geeignet?

Fortgeschritten - Künstliche Intelligenz

Ist es kostenpflichtig?

Dieser Workflow ist völlig kostenlos. Beachten Sie jedoch, dass Drittanbieterdienste (wie OpenAI API), die im Workflow verwendet werden, möglicherweise kostenpflichtig sind.