Stock Q&A Workflow
工作流概述
这是一个包含17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
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"templateId": "1960"
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"name": "Stock Q&A Workflow",
"tags": [],
"nodes": [
{
"id": "ec3b86be-4113-4fd5-8365-02adb67693e9",
"name": "Embeddings OpenAI1",
"type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi",
"position": [
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"options": {}
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{
"id": "42fd8020-3861-4d0f-a7a2-70e2c35f0bed",
"name": "On new manual Chat Message",
"type": "@n8n/n8n-nodes-langchain.manualChatTrigger",
"disabled": true,
"position": [
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"position": [
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"parameters": {
"color": 6,
"width": 903.0896125323785,
"height": 733.5099670584011,
"content": "## Step 2: Setup the Q&A
### The incoming message from the webhook is queried from the Supabase Vector Store. The response is provided in the response webhook. "
},
"typeVersion": 1
},
{
"id": "472b4800-745a-4337-9545-163247f7e9ae",
"name": "Retrieval QA Chain",
"type": "@n8n/n8n-nodes-langchain.chainRetrievalQa",
"position": [
1880,
240
],
"parameters": {
"query": "={{ $json.body.input }}"
},
"typeVersion": 1
},
{
"id": "e58bd82d-abc6-44ed-8e93-ec5436126d66",
"name": "Respond to Webhook",
"type": "n8n-nodes-base.respondToWebhook",
"position": [
2280,
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],
"parameters": {
"options": {},
"respondWith": "text",
"responseBody": "={{ $json.response.text }}"
},
"typeVersion": 1
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{
"id": "04bbf01e-8269-47c7-897d-4ea94a1bd1c0",
"name": "Vector Store Retriever",
"type": "@n8n/n8n-nodes-langchain.retrieverVectorStore",
"position": [
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"parameters": {
"topK": 5
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{
"id": "feee6d68-2e0d-4d40-897e-c1d833a13bf2",
"name": "Webhook1",
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"webhookId": "679f4afb-189e-4f04-9dc0-439eec2ec5f1",
"parameters": {
"path": "19f5499a-3083-4783-93a0-e8ed76a9f742",
"options": {},
"httpMethod": "POST",
"responseMode": "responseNode"
},
"typeVersion": 1.1
},
{
"id": "1b8d251f-7069-4d7d-b6d6-4bfa683d4ad1",
"name": "When clicking \"Execute Workflow\"",
"type": "n8n-nodes-base.manualTrigger",
"position": [
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"parameters": {},
"typeVersion": 1
},
{
"id": "b746a7a4-ed94-4332-bf7b-65aadcf54130",
"name": "Google Drive",
"type": "n8n-nodes-base.googleDrive",
"position": [
580,
260
],
"parameters": {
"fileId": {
"__rl": true,
"mode": "list",
"value": "1LZezppYrWpMStr4qJXtoIX-Dwzvgehll",
"cachedResultUrl": "https://drive.google.com/file/d/1LZezppYrWpMStr4qJXtoIX-Dwzvgehll/view?usp=drivesdk",
"cachedResultName": "crowdstrike.pdf"
},
"options": {},
"operation": "download"
},
"credentials": {
"googleDriveOAuth2Api": {
"id": "1tsDIpjUaKbXy0be",
"name": "Google Drive account"
}
},
"typeVersion": 3
},
{
"id": "83a7d470-f934-436d-ba3f-1ae7c776f5a5",
"name": "Binary to Document",
"type": "@n8n/n8n-nodes-langchain.documentBinaryInputLoader",
"position": [
860,
480
],
"parameters": {
"loader": "pdfLoader",
"options": {}
},
"typeVersion": 1
},
{
"id": "b52b4a90-99a1-49cc-a6f0-7551d6754496",
"name": "Recursive Character Text Splitter",
"type": "@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter",
"position": [
860,
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"parameters": {
"options": {},
"chunkSize": 3000,
"chunkOverlap": 200
},
"typeVersion": 1
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{
"id": "b525e130-2029-4f55-a603-1fdc05a19c17",
"name": "Embeddings OpenAI",
"type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi",
"position": [
1160,
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],
"parameters": {
"options": {}
},
"credentials": {
"openAiApi": {
"id": "fOF5kro9BJ6KMQ7n",
"name": "OpenAi account"
}
},
"typeVersion": 1
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{
"id": "5358c53f-55f9-431d-8956-c6bae7ad25bc",
"name": "Sticky Note",
"type": "n8n-nodes-base.stickyNote",
"position": [
540,
120
],
"parameters": {
"color": 6,
"width": 772.0680602743597,
"height": 732.3675002130781,
"content": "## Step 1: Upserting the PDF
### Fetch file from Google Drive, split it into chunks and insert into Supabase index
"
},
"typeVersion": 1
},
{
"id": "fb91e2da-0eeb-47a5-aa49-65bf56986857",
"name": "Qdrant Vector Store",
"type": "@n8n/n8n-nodes-langchain.vectorStoreQdrant",
"position": [
940,
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],
"parameters": {
"mode": "insert",
"options": {},
"qdrantCollection": {
"__rl": true,
"mode": "id",
"value": "=crowd"
}
},
"credentials": {
"qdrantApi": {
"id": "U5CpjAgFeXziP3I1",
"name": "QdrantApi account"
}
},
"typeVersion": 1
},
{
"id": "89e14837-d1fc-4b1e-9ebc-7cf3e7fd9a70",
"name": "Qdrant Vector Store1",
"type": "@n8n/n8n-nodes-langchain.vectorStoreQdrant",
"position": [
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],
"parameters": {
"qdrantCollection": {
"__rl": true,
"mode": "id",
"value": "={{ $json.body.company }}"
}
},
"credentials": {
"qdrantApi": {
"id": "U5CpjAgFeXziP3I1",
"name": "QdrantApi account"
}
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{
"id": "c619245b-5ea0-4354-974d-21ec6b8efa93",
"name": "OpenAI Chat Model",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"position": [
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460
],
"parameters": {
"options": {}
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"credentials": {
"openAiApi": {
"id": "fOF5kro9BJ6KMQ7n",
"name": "OpenAi account"
}
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"name": "Sticky Note2",
"type": "n8n-nodes-base.stickyNote",
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"parameters": {
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"width": 710.9124489067698,
"height": 726.4452519516944,
"content": "## Start here: Step-by Step Youtube Tutorial :star:
[](https://www.youtube.com/watch?v=pMvizUx5n1g)
"
},
"typeVersion": 1
}
],
"active": true,
"pinData": {},
"settings": {},
"versionId": "463aec94-26a6-436d-8732-fc01d637c6ae",
"connections": {
"Webhook1": {
"main": [
[
{
"node": "Retrieval QA Chain",
"type": "main",
"index": 0
}
]
]
},
"Google Drive": {
"main": [
[
{
"node": "Qdrant Vector Store",
"type": "main",
"index": 0
}
]
]
},
"Embeddings OpenAI": {
"ai_embedding": [
[
{
"node": "Qdrant Vector Store",
"type": "ai_embedding",
"index": 0
}
]
]
},
"OpenAI Chat Model": {
"ai_languageModel": [
[
{
"node": "Retrieval QA Chain",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Binary to Document": {
"ai_document": [
[
{
"node": "Qdrant Vector Store",
"type": "ai_document",
"index": 0
}
]
]
},
"Embeddings OpenAI1": {
"ai_embedding": [
[
{
"node": "Qdrant Vector Store1",
"type": "ai_embedding",
"index": 0
}
]
]
},
"Retrieval QA Chain": {
"main": [
[
{
"node": "Respond to Webhook",
"type": "main",
"index": 0
}
]
]
},
"Qdrant Vector Store1": {
"ai_vectorStore": [
[
{
"node": "Vector Store Retriever",
"type": "ai_vectorStore",
"index": 0
}
]
]
},
"Vector Store Retriever": {
"ai_retriever": [
[
{
"node": "Retrieval QA Chain",
"type": "ai_retriever",
"index": 0
}
]
]
},
"On new manual Chat Message": {
"main": [
[
{
"node": "Retrieval QA Chain",
"type": "main",
"index": 0
}
]
]
},
"When clicking \"Execute Workflow\"": {
"main": [
[
{
"node": "Google Drive",
"type": "main",
"index": 0
}
]
]
},
"Recursive Character Text Splitter": {
"ai_textSplitter": [
[
{
"node": "Binary to Document",
"type": "ai_textSplitter",
"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.Embeddingsopenai
- @N8N/N8N Nodes Langchain.Manualchattrigger
- Stickynote
- @N8N/N8N Nodes Langchain.Chainretrievalqa
- Respondtowebhook
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