Hugging Face to Notion
工作流概述
这是一个包含11个节点的复杂工作流，主要用于自动化处理各种任务。
工作流源代码
{
"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": "I5KdUzwhWnphQ862",
"name": "notion"
}
},
"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:
Core Introduction: Summarize the main contributions and objectives of the paper, highlighting its innovations and significance.
Keyword Extraction: List 2-5 keywords that best represent the research direction and techniques of the paper.
Key Data and Results: Extract important performance metrics, comparison results, and the paper's advantages over other studies.
Technical Details: Provide a brief overview of the methods, optimization techniques, and datasets mentioned in the paper.
Classification: Assign an appropriate academic classification based on the content of the paper.
Output as json：
{
\"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.\",
\"Keywords\": [
\"Paper Search Agent\",
\"Large Language Models\",
\"Reinforcement Learning\",
\"Academic Queries\",
\"Performance Benchmarking\"
],
\"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.\",
\"Technical_Details\": \"PaSa is optimized using reinforcement learning with the AutoScholarQuery synthetic dataset, demonstrating superior performance in multiple benchmarks.\",
\"Classification\": [
\"Artificial Intelligence (AI)\",
\"Academic Search and Information Retrieval\",
\"Natural Language Processing (NLP)\",
\"Reinforcement Learning\"
]
}
```"
},
{
"content": "={{ $json.abstract }}"
}
]
},
"jsonOutput": true
},
"credentials": {
"openAiApi": {
"id": "LmLcxHwbzZNWxqY6",
"name": "Unnamed credential"
}
},
"typeVersion": 1.8
},
{
"id": "f491cd7f-598e-46fd-b80c-04cfa9766dfd",
"name": "Store Abstract Notion",
"type": "n8n-nodes-base.notion",
"position": [
-300,
-300
],
"parameters": {
"options": {},
"resource": "databasePage",
"databaseId": {
"__rl": true,
"mode": "list",
"value": "17b67aba-1fcc-80ae-baa1-d88ffda7ae83",
"cachedResultUrl": "https://www.notion.so/17b67aba1fcc80aebaa1d88ffda7ae83",
"cachedResultName": "huggingface-abstract"
},
"propertiesUi": {
"propertyValues": [
{
"key": "URL|url",
"urlValue": "={{ 'https://huggingface.co'+$('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": "I5KdUzwhWnphQ862",
"name": "notion"
}
},
"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
}
]
]
}
}
}功能特点
- 自动检测新邮件
- AI智能内容分析
- 自定义分类规则
- 批量处理能力
- 详细的处理日志
技术分析
节点类型及作用
- Scheduletrigger
- If
- Splitinbatches
- Splitout
- Httprequest
复杂度评估
配置难度：
维护难度：
扩展性：
实施指南
前置条件
- 有效的Gmail账户
- n8n平台访问权限
- Google API凭证
- AI分类服务订阅
配置步骤
- 在n8n中导入工作流JSON文件
- 配置Gmail节点的认证信息
- 设置AI分类器的API密钥
- 自定义分类规则和标签映射
- 测试工作流执行
- 配置定时触发器（可选）
关键参数
| 参数名称 | 默认值 | 说明 |
|---|---|---|
| maxEmails | 50 | 单次处理的最大邮件数量 |
| confidenceThreshold | 0.8 | 分类置信度阈值 |
| autoLabel | true | 是否自动添加标签 |
最佳实践
优化建议
- 定期更新AI分类模型以提高准确性
- 根据邮件量调整处理批次大小
- 设置合理的分类置信度阈值
- 定期清理过期的分类规则
安全注意事项
- 妥善保管API密钥和认证信息
- 限制工作流的访问权限
- 定期审查处理日志
- 启用双因素认证保护Gmail账户
性能优化
- 使用增量处理减少重复工作
- 缓存频繁访问的数据
- 并行处理多个邮件分类任务
- 监控系统资源使用情况
故障排除
常见问题
邮件未被正确分类
检查AI分类器的置信度阈值设置，适当降低阈值或更新训练数据。
Gmail认证失败
确认Google API凭证有效且具有正确的权限范围，重新进行OAuth授权。
调试技巧
- 启用详细日志记录查看每个步骤的执行情况
- 使用测试邮件验证分类逻辑
- 检查网络连接和API服务状态
- 逐步执行工作流定位问题节点
错误处理
工作流包含以下错误处理机制：
- 网络超时自动重试（最多3次）
- API错误记录和告警
- 处理失败邮件的隔离机制
- 异常情况下的回滚操作