n8n-workflows/workflows/FU3MrLkaTHmfdG4n_Hugging_Face__to_Notion.json

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  "name": "Hugging Face  to Notion",
  "tags": [],
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      "name": "Request Hugging Face Paper",
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      "parameters": {
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        "sendQuery": true,
        "queryParameters": {
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              "value": "={{ $now.minus(1,'days').format('yyyy-MM-dd') }}"
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              "urlValue": "={{ 'https://huggingface.co'+$json.url }}",
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      "name": "Request Hugging Face Paper Detail",
      "type": "n8n-nodes-base.httpRequest",
      "position": [
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      "parameters": {
        "url": "={{ 'https://huggingface.co'+$('Split Out').item.json.url }}",
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    },
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      "name": "OpenAI Analysis Abstract",
      "type": "@n8n/n8n-nodes-langchain.openAi",
      "position": [
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      ],
      "parameters": {
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          "mode": "list",
          "value": "gpt-4o-2024-11-20",
          "cachedResultName": "GPT-4O-2024-11-20"
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        "options": {},
        "messages": {
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            {
              "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": "={{ $json.abstract }}"
            }
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        "jsonOutput": true
      },
      "credentials": {
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            {
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              "urlValue": "={{ 'https://huggingface.co'+$('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
            },
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              "key": "Classification|rich_text",
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            },
            {
              "key": "Technical_Details|rich_text",
              "textContent": "={{ $json.message.content.Technical_Details }}"
            },
            {
              "key": "Data_and_Results|rich_text",
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            },
            {
              "key": "keywords|rich_text",
              "textContent": "={{ $json.message.content.Keywords.join(',') }}"
            },
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