De Hugging Face a Notion

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

Intermedio

Este es unAIflujo de automatización del dominio deautomatización que contiene 11 nodos.Utiliza principalmente nodos como If, Html, Notion, SplitOut, HttpRequest, combinando tecnología de inteligencia artificial para lograr automatización inteligente. Usar AI para analizar papers de Hugging Face y almacenarlos en Notion

Requisitos previos

•Clave de API de Notion
•Pueden requerirse credenciales de autenticación para la API de destino
•Clave de API de OpenAI

Nodos utilizados (11)

Categoría

Inteligencia Artificial

Vista previa del flujo de trabajo

Visualización de las conexiones entre nodos, con soporte para zoom y panorámica

Disparador Programado

Iterar sobre Elementos

Separar

Solicitar Artículo de Hugging Face

Extraer Artículo de Hugging Face

Verificar si la URL del Artículo Existe

Solicitar Detalles del Artículo de Hugging Face

OpenAI Analizar Resumen

Almacenar Resumen Notion

Extraer Resumen del Artículo de Hugging Face

React Flow

Exportar flujo de trabajo

Copie la siguiente configuración JSON en n8n para importar y usar este flujo de trabajo

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

Preguntas frecuentes

¿Cómo usar este flujo de trabajo?

Copie el código de configuración JSON de arriba, cree un nuevo flujo de trabajo en su instancia de n8n y seleccione "Importar desde JSON", pegue la configuración y luego modifique la configuración de credenciales según sea necesario.

¿En qué escenarios es adecuado este flujo de trabajo?

Intermedio - Inteligencia Artificial

¿Es de pago?

Este flujo de trabajo es completamente gratuito, puede importarlo y usarlo directamente. Sin embargo, tenga en cuenta que los servicios de terceros utilizados en el flujo de trabajo (como la API de OpenAI) pueden requerir un pago por su cuenta.