HR & IT Helpdesk Chatbot with Audio Transcription
工作流概述
这是一个包含27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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"meta": {
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"name": "HR & IT Helpdesk Chatbot with Audio Transcription",
"tags": [],
"nodes": [
{
"id": "c6cb921e-97ac-48f6-9d79-133993dd6ef7",
"name": "Sticky Note",
"type": "n8n-nodes-base.stickyNote",
"position": [
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],
"parameters": {
"color": 7,
"width": 780,
"height": 460,
"content": "## 1. Download & Extract Internal Policy Documents
[Read more about the HTTP Request Tool](https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-base.httprequest)
Begin by importing the PDF documents that contain your internal policies and FAQs—these will become the knowledge base for your Internal Helpdesk Assistant. For example, you can store a company handbook or IT/HR policy PDFs on a shared drive or cloud storage and reference a direct download link here.
In this demonstration, we'll use the **HTTP Request node** to fetch the PDF file from a given URL and then parse its text contents using the **Extract from File node**. Once extracted, these text chunks will be used to build the vector store that underpins your helpdesk chatbot’s responses.
[Example Employee Handbook with Policies](https://s3.amazonaws.com/scschoolfiles/656/employee_handbook_print_1.pdf)"
},
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"id": "450a254c-eec3-41ea-a11d-eb87b62ee4f4",
"name": "When clicking ‘Test workflow’",
"type": "n8n-nodes-base.manualTrigger",
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"id": "0972f31c-1f62-430c-8beb-bef8976cd0eb",
"name": "HTTP Request",
"type": "n8n-nodes-base.httpRequest",
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"parameters": {
"url": "https://s3.amazonaws.com/scschoolfiles/656/employee_handbook_print_1.pdf",
"options": {}
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"typeVersion": 4.2
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{
"id": "bf523255-39f5-410a-beb7-6331139c5f9b",
"name": "Extract from File",
"type": "n8n-nodes-base.extractFromFile",
"position": [
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20
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"parameters": {
"options": {},
"operation": "pdf"
},
"typeVersion": 1
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"parameters": {
"color": 7,
"width": 780,
"height": 1020,
"content": "## 2. Create Internal Policy Vector Store
[Read more about the In-Memory Vector Store](https://docs.n8n.io/integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.vectorstoreinmemory/)
Vector stores power the retrieval process by matching a user's natural language questions to relevant chunks of text. We'll transform your extracted internal policy text into vector embeddings and store them in a database-like structure.
We will be using PostgreSQL which has production ready vector support.
**How it works**
1. The text extracted in Step 1 is split into manageable segments (chunks).
2. An embedding model transforms these segments into numerical vectors.
3. These vectors, along with metadata, are stored in PostgreSQL.
4. When users ask a question, their query is embedded and matched to the most relevant vectors, improving the accuracy of the chatbot's response."
},
"typeVersion": 1
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{
"id": "8d6472ab-dcff-4d24-a320-109787bce52a",
"name": "Create HR Policies",
"type": "@n8n/n8n-nodes-langchain.vectorStorePGVector",
"position": [
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"parameters": {
"mode": "insert",
"options": {}
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"postgres": {
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"name": "Postgres account"
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"typeVersion": 1
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{
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"name": "Embeddings OpenAI",
"type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi",
"position": [
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"parameters": {
"options": {}
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"credentials": {
"openAiApi": {
"id": "J2D6m1evHLUJOMhO",
"name": "OpenAi account"
}
},
"typeVersion": 1.2
},
{
"id": "e25418af-65bb-4628-9b26-ec59cae7b2b4",
"name": "Default Data Loader",
"type": "@n8n/n8n-nodes-langchain.documentDefaultDataLoader",
"position": [
760,
340
],
"parameters": {
"options": {},
"jsonData": "={{ $('Extract from File').item.json.text }}",
"jsonMode": "expressionData"
},
"typeVersion": 1
},
{
"id": "a4538deb-8406-4a5b-9b1e-4e2f859943c8",
"name": "Recursive Character Text Splitter",
"type": "@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter",
"position": [
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"parameters": {
"options": {},
"chunkSize": 2000
},
"typeVersion": 1
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{
"id": "7ee0e861-1576-4b0c-b2ef-3fc023371907",
"name": "Telegram Trigger",
"type": "n8n-nodes-base.telegramTrigger",
"position": [
1420,
240
],
"webhookId": "65f501de-3c14-4089-9b9d-8956676bebf3",
"parameters": {
"updates": [
"message"
],
"additionalFields": {}
},
"credentials": {
"telegramApi": {
"id": "jSdrxiRKb8yfG6Ty",
"name": "Telegram account"
}
},
"typeVersion": 1.1
},
{
"id": "bcf1e82e-0e83-4783-a59f-857a6d1528b6",
"name": "Verify Message Type",
"type": "n8n-nodes-base.switch",
"position": [
1620,
240
],
"parameters": {
"rules": {
"values": [
{
"outputKey": "Text",
"conditions": {
"options": {
"version": 2,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"operator": {
"type": "array",
"operation": "contains",
"rightType": "any"
},
"leftValue": "={{ $json.message.keys()}}",
"rightValue": "text"
}
]
},
"renameOutput": true
},
{
"outputKey": "Audio",
"conditions": {
"options": {
"version": 2,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
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"operator": {
"type": "array",
"operation": "contains",
"rightType": "any"
},
"leftValue": "={{ $json.message.keys()}}",
"rightValue": "voice"
}
]
},
"renameOutput": true
}
]
},
"options": {
"fallbackOutput": "extra"
}
},
"typeVersion": 3.2,
"alwaysOutputData": false
},
{
"id": "d403f864-c781-48fc-a62b-de0c8bfedf06",
"name": "OpenAI",
"type": "@n8n/n8n-nodes-langchain.openAi",
"position": [
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],
"parameters": {
"options": {},
"resource": "audio",
"operation": "transcribe",
"binaryPropertyName": "=data"
},
"credentials": {
"openAiApi": {
"id": "J2D6m1evHLUJOMhO",
"name": "OpenAi account"
}
},
"typeVersion": 1.8
},
{
"id": "5b17c8f1-4bee-4f2a-abcb-74fe72d4cdfd",
"name": "Telegram1",
"type": "n8n-nodes-base.telegram",
"position": [
2120,
380
],
"parameters": {
"fileId": "={{ $json.message.voice.file_id }}",
"resource": "file"
},
"credentials": {
"telegramApi": {
"id": "jSdrxiRKb8yfG6Ty",
"name": "Telegram account"
}
},
"typeVersion": 1.2
},
{
"id": "cc6862cb-acfc-465b-b142-dd5fdc12fb13",
"name": "Unsupported Message Type",
"type": "n8n-nodes-base.telegram",
"position": [
2200,
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],
"parameters": {
"text": "I'm not able to process this message type.",
"chatId": "={{ $json.message.chat.id }}",
"additionalFields": {}
},
"credentials": {
"telegramApi": {
"id": "jSdrxiRKb8yfG6Ty",
"name": "Telegram account"
}
},
"typeVersion": 1.2
},
{
"id": "8b97aaa1-ea0d-4b11-89c9-9ac6376c0760",
"name": "AI Agent",
"type": "@n8n/n8n-nodes-langchain.agent",
"position": [
2860,
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],
"parameters": {
"text": "={{ $json.text }}",
"options": {
"systemMessage": "You are a helpful assistant for HR and employee policies"
},
"promptType": "define"
},
"typeVersion": 1.7
},
{
"id": "e0d5416e-a799-46a2-83e3-fa6919ec0e36",
"name": "OpenAI Chat Model",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"position": [
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],
"parameters": {
"options": {}
},
"credentials": {
"openAiApi": {
"id": "J2D6m1evHLUJOMhO",
"name": "OpenAi account"
}
},
"typeVersion": 1.1
},
{
"id": "9149f41d-692e-49bc-ad70-848492d2c345",
"name": "Postgres Chat Memory",
"type": "@n8n/n8n-nodes-langchain.memoryPostgresChat",
"position": [
3060,
840
],
"parameters": {
"sessionKey": "={{ $('Telegram Trigger').item.json.message.chat.id }}",
"sessionIdType": "customKey"
},
"credentials": {
"postgres": {
"id": "wQK6JXyS5y1icHw3",
"name": "Postgres account"
}
},
"typeVersion": 1.3
},
{
"id": "a1f68887-da44-4bff-86fc-f607a5bd0ab6",
"name": "Answer questions with a vector store",
"type": "@n8n/n8n-nodes-langchain.toolVectorStore",
"position": [
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],
"parameters": {
"name": "hr_employee_policies",
"description": "data for HR and employee policies"
},
"typeVersion": 1
},
{
"id": "76220fe4-2448-4b32-92d8-68c564cc702d",
"name": "Postgres PGVector Store",
"type": "@n8n/n8n-nodes-langchain.vectorStorePGVector",
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"parameters": {
"options": {}
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"credentials": {
"postgres": {
"id": "wQK6JXyS5y1icHw3",
"name": "Postgres account"
}
},
"typeVersion": 1
},
{
"id": "055fd294-7483-45ce-b58a-c90075199f5f",
"name": "OpenAI Chat Model1",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"position": [
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],
"parameters": {
"options": {}
},
"credentials": {
"openAiApi": {
"id": "J2D6m1evHLUJOMhO",
"name": "OpenAi account"
}
},
"typeVersion": 1.1
},
{
"id": "cc13eac7-8163-45bf-8d8a-9cf72659e357",
"name": "Embeddings OpenAI1",
"type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi",
"position": [
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],
"parameters": {
"options": {}
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"credentials": {
"openAiApi": {
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"name": "OpenAi account"
}
},
"typeVersion": 1.2
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{
"id": "d46e415e-75ff-46b8-b382-cdcda216b1ed",
"name": "Telegram",
"type": "n8n-nodes-base.telegram",
"position": [
4200,
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],
"parameters": {
"text": "={{ $json.output }}",
"chatId": "={{ $('Telegram Trigger').first().json.message.chat.id }}",
"additionalFields": {}
},
"credentials": {
"telegramApi": {
"id": "jSdrxiRKb8yfG6Ty",
"name": "Telegram account"
}
},
"typeVersion": 1.2
},
{
"id": "ddf623a1-0a5e-48c9-b897-6a339895a891",
"name": "Edit Fields",
"type": "n8n-nodes-base.set",
"position": [
2120,
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],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "403b336f-87ce-4bef-a5f2-1640425f8198",
"name": "text",
"type": "string",
"value": "={{ $json.message.text }}"
}
]
},
"includeOtherFields": true
},
"typeVersion": 3.4
},
{
"id": "4ae84e17-cfc1-425c-930d-949da7308b78",
"name": "Sticky Note2",
"type": "n8n-nodes-base.stickyNote",
"position": [
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],
"parameters": {
"color": 4,
"width": 1300,
"height": 1020,
"content": "## 3. Handling Messages with Fallback Support
This workflow processes Telegram messages to handle **text** and **voice** inputs, with a fallback for unsupported message types. Here’s how it works:
1. **Trigger Node**:
- The workflow starts with a Telegram trigger that listens for incoming messages.
2. **Message Type Check**:
- The workflow verifies the type of message received:
- **Text Message**: If the message contains `$json.message.text`, it is sent directly to the agent.
- **Voice Message**: If the message contains `$json.message.voice`, the audio is transcribed into text using a transcription service, and the result is sent to the agent.
3. **Fallback Path**:
- If the message is neither text nor voice, a fallback response is returned:
`\"Sorry, I couldn’t process your message. Please try again.\"`
4. **Unified Output**:
- Both text messages and transcribed voice messages are converted into the same format before sending to the agent, ensuring consistency in handling.
"
},
"typeVersion": 1
},
{
"id": "86ad4e08-ef2d-405e-8861-bff38e1db651",
"name": "Sticky Note3",
"type": "n8n-nodes-base.stickyNote",
"position": [
220,
220
],
"parameters": {
"width": 260,
"height": 80,
"content": "The setup needs to be run at the start or when data is changed"
},
"typeVersion": 1
},
{
"id": "b05c4437-00fb-40f6-87fa-8dc564b16005",
"name": "Sticky Note4",
"type": "n8n-nodes-base.stickyNote",
"position": [
2680,
-280
],
"parameters": {
"color": 4,
"width": 1180,
"height": 1420,
"content": "## 4. HR & IT AI Agent Provides Helpdesk Support
n8n's AI agents allow you to create intelligent and interactive workflows that can access and retrieve data from internal knowledgebases. In this workflow, the AI agent is configured to provide answers for HR and IT queries by performing Retrieval-Augmented Generation (RAG) on internal documents.
### How It Works:
- **Internal Knowledgebase Access**: A **Vector store tool** is used to connect the agent to the HR & IT knowledgebase built earlier in the workflow. This enables the agent to fetch accurate and specific answers for employee queries.
- **Chat Memory**: A **Chat memory subnode** tracks the conversation, allowing the agent to maintain context across multiple queries from the same user, creating a personalized and cohesive experience.
- **Dynamic Query Responses**: Whether employees ask about policies, leave balances, or technical troubleshooting, the agent retrieves relevant data from the vector store and crafts a natural language response.
By integrating the AI agent with a vector store and chat memory, this workflow empowers your HR & IT helpdesk chatbot to provide quick, accurate, and conversational support to employees.
PostgrSQL is used for all steps to simplify development in production."
},
"typeVersion": 1
},
{
"id": "b266ca42-de62-4341-9aff-33ee0ac68045",
"name": "Sticky Note5",
"type": "n8n-nodes-base.stickyNote",
"position": [
3900,
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],
"parameters": {
"color": 4,
"width": 540,
"height": 280,
"content": "## 5. Send Message
The simplest and most important part :)"
},
"typeVersion": 1
}
],
"active": false,
"pinData": {},
"settings": {
"executionOrder": "v1"
},
"versionId": "7b1d11ca-9b56-4c5f-9189-26d536c24b76",
"connections": {
"OpenAI": {
"main": [
[
{
"node": "AI Agent",
"type": "main",
"index": 0
}
]
]
},
"AI Agent": {
"main": [
[
{
"node": "Telegram",
"type": "main",
"index": 0
}
]
]
},
"Telegram1": {
"main": [
[
{
"node": "OpenAI",
"type": "main",
"index": 0
}
]
]
},
"Edit Fields": {
"main": [
[
{
"node": "AI Agent",
"type": "main",
"index": 0
}
]
]
},
"HTTP Request": {
"main": [
[
{
"node": "Extract from File",
"type": "main",
"index": 0
}
]
]
},
"Telegram Trigger": {
"main": [
[
{
"node": "Verify Message Type",
"type": "main",
"index": 0
}
]
]
},
"Embeddings OpenAI": {
"ai_embedding": [
[
{
"node": "Create HR Policies",
"type": "ai_embedding",
"index": 0
}
]
]
},
"Extract from File": {
"main": [
[
{
"node": "Create HR Policies",
"type": "main",
"index": 0
}
]
]
},
"OpenAI Chat Model": {
"ai_languageModel": [
[
{
"node": "AI Agent",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Embeddings OpenAI1": {
"ai_embedding": [
[
{
"node": "Postgres PGVector Store",
"type": "ai_embedding",
"index": 0
}
]
]
},
"OpenAI Chat Model1": {
"ai_languageModel": [
[
{
"node": "Answer questions with a vector store",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Default Data Loader": {
"ai_document": [
[
{
"node": "Create HR Policies",
"type": "ai_document",
"index": 0
}
]
]
},
"Verify Message Type": {
"main": [
[
{
"node": "Edit Fields",
"type": "main",
"index": 0
}
],
[
{
"node": "Telegram1",
"type": "main",
"index": 0
}
],
[
{
"node": "Unsupported Message Type",
"type": "main",
"index": 0
}
]
]
},
"Postgres Chat Memory": {
"ai_memory": [
[
{
"node": "AI Agent",
"type": "ai_memory",
"index": 0
}
]
]
},
"Postgres PGVector Store": {
"ai_vectorStore": [
[
{
"node": "Answer questions with a vector store",
"type": "ai_vectorStore",
"index": 0
}
]
]
},
"Recursive Character Text Splitter": {
"ai_textSplitter": [
[
{
"node": "Default Data Loader",
"type": "ai_textSplitter",
"index": 0
}
]
]
},
"When clicking ‘Test workflow’": {
"main": [
[
{
"node": "HTTP Request",
"type": "main",
"index": 0
}
]
]
},
"Answer questions with a vector store": {
"ai_tool": [
[
{
"node": "AI Agent",
"type": "ai_tool",
"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
- Stickynote
- Manualtrigger
- Httprequest
- Extractfromfile
- @N8N/N8N Nodes Langchain.Vectorstorepgvector
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