{ "name": "NotionFlow", "nodes": [ { "parameters": { "rule": { "interval": [ { "field": "weeks", "triggerAtDay": [ 1, 2, 3, 4, 5 ], "triggerAtHour": 8 } ] } }, "id": "fc8d2cfa-eb97-4815-be83-38e198f54e8a", "name": "Schedule Trigger", "type": "n8n-nodes-base.scheduleTrigger", "position": [ 0, 0 ], "typeVersion": 1.2 }, { "parameters": { "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": "" } ] }, "options": {} }, "id": "f819ce79-6f0c-468a-bb47-423e679410a7", "name": "If", "type": "n8n-nodes-base.if", "position": [ 1280, 20 ], "typeVersion": 2.2 }, { "parameters": { "options": { "reset": false } }, "id": "4901bfad-8bfa-48d7-aa3b-74f21cc04ac4", "name": "Loop Over Items", "type": "n8n-nodes-base.splitInBatches", "position": [ 880, 0 ], "typeVersion": 3 }, { "parameters": { "fieldToSplitOut": "url, url", "options": {} }, "id": "81948b55-eefb-4565-b7d1-2e3ae7ce0712", "name": "Split Out", "type": "n8n-nodes-base.splitOut", "position": [ 680, 0 ], "typeVersion": 1 }, { "parameters": { "url": "https://huggingface.co/papers", "sendQuery": true, "queryParameters": { "parameters": [ { "name": "date", "value": "={{ $now.minus(1,'days').format('yyyy-MM-dd') }}" } ] }, "options": {} }, "id": "966a60be-ecf1-441f-a4e7-0806ad70d5cc", "name": "Request Hugging Face Paper", "type": "n8n-nodes-base.httpRequest", "position": [ 200, 0 ], "typeVersion": 4.2 }, { "parameters": { "operation": "extractHtmlContent", "extractionValues": { "values": [ { "key": "url", "cssSelector": ".line-clamp-3", "returnValue": "attribute", "attribute": "href", "returnArray": true } ] }, "options": {} }, "id": "4ee70f27-9a23-4ef1-9f87-4acb2d6a3730", "name": "Extract Hugging Face Paper", "type": "n8n-nodes-base.html", "position": [ 440, 0 ], "typeVersion": 1.2 }, { "parameters": { "pageId": { "__rl": true, "value": "https://www.notion.so/Sites-1ea17e4de8c380f48856d5410122a9b2?pvs=12", "mode": "url" }, "title": "n8n", "options": {} }, "id": "cdeb1ff1-3299-4e02-a9ed-6f90bd3c30f7", "name": "Check Paper URL Existed", "type": "n8n-nodes-base.notion", "position": [ 1100, 20 ], "typeVersion": 2.2, "alwaysOutputData": true, "credentials": { "notionApi": { "name": "" } } }, { "parameters": { "url": "={{ 'https://huggingface.co'+$('Split Out').item.json.url }}", "options": {} }, "id": "d6c2e4c0-e53e-4faf-a198-1f92bd6f0aaa", "name": "Request Hugging Face Paper Detail", "type": "n8n-nodes-base.httpRequest", "position": [ 1560, 0 ], "typeVersion": 4.2 }, { "parameters": { "modelId": { "__rl": true, "mode": "list", "value": "gpt-4o-2024-11-20", "cachedResultName": "GPT-4O-2024-11-20" }, "messages": { "values": [ { "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```", "role": "system" }, { "content": "={{ $json.abstract }}" } ] }, "jsonOutput": true, "options": {} }, "id": "856bdf6c-f9ad-4554-a429-b80d00e9f5b9", "name": "OpenAI Analysis Abstract", "type": "@n8n/n8n-nodes-langchain.openAi", "position": [ 2000, 0 ], "typeVersion": 1.8, "credentials": { "openAiApi": { "name": "" } } }, { "parameters": { "pageId": { "__rl": true, "value": "https://www.notion.so/Sites-1ea17e4de8c380f48856d5410122a9b2?pvs=12", "mode": "url" }, "title": "n8n", "options": {} }, "id": "4a77cfea-a77d-4b0f-9a6d-9fdaf7543c02", "name": "Store Abstract Notion", "type": "n8n-nodes-base.notion", "position": [ 2340, 0 ], "typeVersion": 2.2, "credentials": { "notionApi": { "name": "" } } }, { "parameters": { "operation": "extractHtmlContent", "extractionValues": { "values": [ { "key": "abstract", "cssSelector": ".text-gray-700" }, { "key": "title", "cssSelector": ".text-2xl" } ] }, "options": {} }, "id": "5d2c6183-b737-4f64-b6ed-399474c6fdb5", "name": "Extract Hugging Face Paper Abstract", "type": "n8n-nodes-base.html", "position": [ 1800, 0 ], "typeVersion": 1.2 } ], "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 } ] ] } }, "active": false, "settings": { "executionOrder": "v1" }, "versionId": "98b66eab-0084-4c70-aa42-a9f879886baf", "meta": { "templateCredsSetupCompleted": true }, "id": "duthpuofablT4FxK", "tags": [] }