Extract personal data with a self-hosted LLM Mistral NeMo
工作流概述
这是一个包含13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": "HMoUOg8J7RzEcslH",
"meta": {
"instanceId": "3f91626b10fcfa8a3d3ab8655534ff3e94151838fd2709ecd2dcb14afb3d061a",
"templateCredsSetupCompleted": true
},
"name": "Extract personal data with a self-hosted LLM Mistral NeMo",
"tags": [],
"nodes": [
{
"id": "7e67ae65-88aa-4e48-aa63-2d3a4208cf4b",
"name": "When chat message received",
"type": "@n8n/n8n-nodes-langchain.chatTrigger",
"position": [
-500,
20
],
"webhookId": "3a7b0ea1-47f3-4a94-8ff2-f5e1f3d9dc32",
"parameters": {
"options": {}
},
"typeVersion": 1.1
},
{
"id": "e064921c-69e6-4cfe-a86e-4e3aa3a5314a",
"name": "Ollama Chat Model",
"type": "@n8n/n8n-nodes-langchain.lmChatOllama",
"position": [
-280,
420
],
"parameters": {
"model": "mistral-nemo:latest",
"options": {
"useMLock": true,
"keepAlive": "2h",
"temperature": 0.1
}
},
"credentials": {
"ollamaApi": {
"id": "vgKP7LGys9TXZ0KK",
"name": "Ollama account"
}
},
"typeVersion": 1
},
{
"id": "fe1379da-a12e-4051-af91-9d67a7c9a76b",
"name": "Auto-fixing Output Parser",
"type": "@n8n/n8n-nodes-langchain.outputParserAutofixing",
"position": [
-200,
220
],
"parameters": {
"options": {
"prompt": "Instructions:
--------------
{instructions}
--------------
Completion:
--------------
{completion}
--------------
Above, the Completion did not satisfy the constraints given in the Instructions.
Error:
--------------
{error}
--------------
Please try again. Please only respond with an answer that satisfies the constraints laid out in the Instructions:"
}
},
"typeVersion": 1
},
{
"id": "b6633b00-6ebb-43ca-8e5c-664a53548c17",
"name": "Structured Output Parser",
"type": "@n8n/n8n-nodes-langchain.outputParserStructured",
"position": [
60,
400
],
"parameters": {
"schemaType": "manual",
"inputSchema": "{
\"type\": \"object\",
\"properties\": {
\"name\": {
\"type\": \"string\",
\"description\": \"Name of the user\"
},
\"surname\": {
\"type\": \"string\",
\"description\": \"Surname of the user\"
},
\"commtype\": {
\"type\": \"string\",
\"enum\": [\"email\", \"phone\", \"other\"],
\"description\": \"Method of communication\"
},
\"contacts\": {
\"type\": \"string\",
\"description\": \"Contact details. ONLY IF PROVIDED\"
},
\"timestamp\": {
\"type\": \"string\",
\"format\": \"date-time\",
\"description\": \"When the communication occurred\"
},
\"subject\": {
\"type\": \"string\",
\"description\": \"Brief description of the communication topic\"
}
},
\"required\": [\"name\", \"commtype\"]
}"
},
"typeVersion": 1.2
},
{
"id": "23681a6c-cf62-48cb-86ee-08d5ce39bc0a",
"name": "Basic LLM Chain",
"type": "@n8n/n8n-nodes-langchain.chainLlm",
"onError": "continueErrorOutput",
"position": [
-240,
20
],
"parameters": {
"messages": {
"messageValues": [
{
"message": "=Please analyse the incoming user request. Extract information according to the JSON schema. Today is: \"{{ $now.toISO() }}\""
}
]
},
"hasOutputParser": true
},
"typeVersion": 1.5
},
{
"id": "8f4d1b4b-58c0-41ec-9636-ac555e440821",
"name": "On Error",
"type": "n8n-nodes-base.noOp",
"position": [
200,
140
],
"parameters": {},
"typeVersion": 1
},
{
"id": "f4d77736-4470-48b4-8f61-149e09b70e3e",
"name": "Sticky Note",
"type": "n8n-nodes-base.stickyNote",
"position": [
-560,
-160
],
"parameters": {
"color": 2,
"width": 960,
"height": 500,
"content": "## Update data source
When you change the data source, remember to update the `Prompt Source (User Message)` setting in the **Basic LLM Chain node**."
},
"typeVersion": 1
},
{
"id": "5fd273c8-e61d-452b-8eac-8ac4b7fff6c2",
"name": "Sticky Note1",
"type": "n8n-nodes-base.stickyNote",
"position": [
-560,
340
],
"parameters": {
"color": 2,
"width": 440,
"height": 220,
"content": "## Configure local LLM
Ollama offers additional settings
to optimize model performance
or memory usage."
},
"typeVersion": 1
},
{
"id": "63cbf762-0134-48da-a6cd-0363e870decd",
"name": "Sticky Note2",
"type": "n8n-nodes-base.stickyNote",
"position": [
0,
340
],
"parameters": {
"color": 2,
"width": 400,
"height": 220,
"content": "## Define JSON Schema"
},
"typeVersion": 1
},
{
"id": "9625294f-3cb4-4465-9dae-9976e0cf5053",
"name": "Extract JSON Output",
"type": "n8n-nodes-base.set",
"position": [
200,
-80
],
"parameters": {
"mode": "raw",
"options": {},
"jsonOutput": "={{ $json.output }}
"
},
"typeVersion": 3.4
},
{
"id": "2c6fba3b-0ffe-4112-b904-823f52cc220b",
"name": "Sticky Note3",
"type": "n8n-nodes-base.stickyNote",
"position": [
-560,
200
],
"parameters": {
"width": 960,
"height": 120,
"content": "If the LLM response does not pass
the **Structured Output Parser** checks,
**Auto-Fixer** will call the model again with a different
prompt to correct the original response."
},
"typeVersion": 1
},
{
"id": "c73ba1ca-d727-4904-a5fd-01dd921a4738",
"name": "Sticky Note6",
"type": "n8n-nodes-base.stickyNote",
"position": [
-560,
460
],
"parameters": {
"height": 80,
"content": "The same LLM connects to both **Basic LLM Chain** and to the **Auto-fixing Output Parser**.
"
},
"typeVersion": 1
},
{
"id": "193dd153-8511-4326-aaae-47b89d0cd049",
"name": "Sticky Note7",
"type": "n8n-nodes-base.stickyNote",
"position": [
200,
440
],
"parameters": {
"width": 200,
"height": 100,
"content": "When the LLM model responds, the output is checked in the **Structured Output Parser**"
},
"typeVersion": 1
}
],
"active": false,
"pinData": {},
"settings": {
"executionOrder": "v1"
},
"versionId": "9f3721a8-f340-43d5-89e7-3175c29c2f3a",
"connections": {
"Basic LLM Chain": {
"main": [
[
{
"node": "Extract JSON Output",
"type": "main",
"index": 0
}
],
[
{
"node": "On Error",
"type": "main",
"index": 0
}
]
]
},
"Ollama Chat Model": {
"ai_languageModel": [
[
{
"node": "Auto-fixing Output Parser",
"type": "ai_languageModel",
"index": 0
},
{
"node": "Basic LLM Chain",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Structured Output Parser": {
"ai_outputParser": [
[
{
"node": "Auto-fixing Output Parser",
"type": "ai_outputParser",
"index": 0
}
]
]
},
"Auto-fixing Output Parser": {
"ai_outputParser": [
[
{
"node": "Basic LLM Chain",
"type": "ai_outputParser",
"index": 0
}
]
]
},
"When chat message received": {
"main": [
[
{
"node": "Basic LLM Chain",
"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
- @N8N/N8N Nodes Langchain.Chattrigger
- @N8N/N8N Nodes Langchain.Lmchatollama
- @N8N/N8N Nodes Langchain.Outputparserautofixing
- @N8N/N8N Nodes Langchain.Outputparserstructured
- @N8N/N8N Nodes Langchain.Chainllm
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