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Hugging Face to Notion

Automate the discovery and summarization of new research papers from Hugging Face and store them directly in your Notion database. This powerful workflow leverages a scheduled trigger to periodically request the latest papers from Hugging Face, intelligently extracts key details like the paper URL and abstract using HTML parsing, and then checks your Notion database to prevent duplicate entries. For new papers, it fetches the full paper details and uses OpenAI to analyze and summarize the abstract, before finally storing all relevant information, including the OpenAI-generated summary, into your Notion workspace. This workflow is ideal for researchers, academics, or anyone needing to stay current with the latest AI and machine learning advancements without manually sifting through new publications. It saves significant time and effort by automating the entire research discovery, summarization, and organization process, ensuring you have a curated, up-to-date repository of relevant papers readily available in Notion.
11 nodesschedule trigger114 views0 copiesProductivity
OpenAINotion

Workflow JSON

{"id": "FU3MrLkaTHmfdG4n", "meta": {"instanceId": "3294023dd650d95df294922b9d55d174ef26f4a2e6cce97c8a4ab5f98f5b8c7b", "templateCredsSetupCompleted": true}, "name": "Hugging Face to Notion", "tags": [], "nodes": [{"id": "32d5bfee-97f1-4e92-b62e-d09bdd9c3821", "name": "Schedule 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": "If", "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": "Loop Over Items", "type": "n8n-nodes-base.splitInBatches", "position": [-1760, -300], "parameters": {"options": {"reset": false}}, "typeVersion": 3}, {"id": "807ba450-9c89-4f88-aa84-91f43e3adfc6", "name": "Split Out", "type": "n8n-nodes-base.splitOut", "position": [-1960, -300], "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": "={{ $now.minus(1,'days').format('yyyy-MM-dd') }}"}]}}, "typeVersion": 4.2}, {"id": "f37ba769-d881-4aad-927d-ca1f4a68b9a1", "name": "Extract Hugging Face Paper", "type": "n8n-nodes-base.html", "position": [-2200, -300], "parameters": {"options": {}, "operation": "extractHtmlContent", "extractionValues": {"values": [{"key": "url", "attribute": "href", "cssSelector": ".line-clamp-3", "returnArray": true, "returnValue": "attribute"}]}}, "typeVersion": 1.2}, {"id": "94ba99bf-a33b-4311-a4e6-86490e1bb9ad", "name": "Check Paper URL Existed", "type": "n8n-nodes-base.notion", "position": [-1540, -280], "parameters": {"filters": {"conditions": [{"key": "URL|url", "urlValue": "={{ 'https://huggingface.co'+$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": "", "name": "[Your notionApi]"}}, "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\uff1a\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": "={{ $json.abstract }}"}]}, "jsonOutput": true}, "credentials": {"openAiApi": {"id": "", "name": "[Your openAiApi]"}}, "typeVersion": 1.8}, {"id": "f491cd7f-598e-46fd-b80c-04cfa9766dfd", "name": "Store Abstract Notion", "type": "n8n-nodes-base.notion", "position": [-300, -300], "parameters": {"options": {}, "resource": "databasePage", "databaseId": {"__rl": true, "mode": "list", "value": "17b67aba-1fcc-80ae-baa1-d88ffda7ae83", "cachedResultUrl": "https://www.notion.so/17b67aba1fcc80aebaa1d88ffda7ae83", "cachedResultName": "huggingface-abstract"}, "propertiesUi": {"propertyValues": [{"key": "URL|url", "urlValue": "={{ 'https://huggingface.co'+$('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": "", "name": "[Your notionApi]"}}, "typeVersion": 2.2}, {"id": "d5816a1c-d1fa-4be2-8088-57fbf68e6b43", "name": "Extract Hugging Face Paper Abstract", "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": {"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}]]}}}

How to Import This Workflow

  1. 1Copy the workflow JSON above using the Copy Workflow JSON button.
  2. 2Open your n8n instance and go to Workflows.
  3. 3Click Import from JSON and paste the copied workflow.

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