呼叫中心分析（DeepSeek模型双AI验证）
中级
这是一个CRM, AI Summarization领域的自动化工作流，包含 15 个节点。主要使用 Code, Webhook, HttpRequest, ManualTrigger, ChainLlm 等节点。 使用DeepSeek模型进行双AI验证的呼叫中心分析
前置要求
- •HTTP Webhook 端点（n8n 会自动生成）
- •可能需要目标 API 的认证凭证
工作流预览
可视化展示节点连接关系，支持缩放和平移
导出工作流
复制以下 JSON 配置到 n8n 导入，即可使用此工作流
{
"meta": {
"instanceId": "66ce8bb89c7868f862e0d2e755cd17c6a5aea7904e5504a5b2e292e317980443",
"templateCredsSetupCompleted": true
},
"nodes": [
{
"id": "613422d5-05db-4163-bcb0-3fdae9de260b",
"name": "当点击\"测试工作流\"时",
"type": "n8n-nodes-base.manualTrigger",
"position": [
-1120,
60
],
"parameters": {},
"typeVersion": 1
},
{
"id": "9ec28d5b-6ea0-4a57-911e-9f4546b739a2",
"name": "便签",
"type": "n8n-nodes-base.stickyNote",
"position": [
-520,
-240
],
"parameters": {
"color": 7,
"width": 340,
"height": 440,
"content": "## 生成报告"
},
"typeVersion": 1
},
{
"id": "cf30eb86-2aad-4d33-a5c2-737239e63636",
"name": "便签1",
"type": "n8n-nodes-base.stickyNote",
"position": [
-140,
-240
],
"parameters": {
"color": 7,
"width": 340,
"height": 440,
"content": "## 双重检查"
},
"typeVersion": 1
},
{
"id": "d799760f-83c5-4603-8eb8-3857807b364a",
"name": "HTTP 请求",
"type": "n8n-nodes-base.httpRequest",
"position": [
300,
-140
],
"parameters": {
"url": "YOUR_CALL_BACK_API",
"method": "POST",
"options": {},
"jsonBody": "={\n data: \"{{$node['Report'].json.text}}\"\n}",
"sendBody": true,
"specifyBody": "json"
},
"typeVersion": 4.2
},
{
"id": "bf0a8ab6-06e0-4564-96af-ddcf6795a845",
"name": "报告",
"type": "@n8n/n8n-nodes-langchain.chainLlm",
"position": [
-480,
-140
],
"parameters": {
"text": "=You are a CRM data analyst assistant. Your task is to analyze the provided CRM data and generate valuable insights in Markdown format.\n\nYou will receive JSON data extracted from a CRM system that may include information about:\n- Call canter agents metrics.\n\n# ANALYSIS REQUIREMENTS\nAnalyze the data considering:\n1. Lead conversion rates and quality metrics\n2. Upsall\n3. Rank the agents with small description about every one.\n\n# OUTPUT FORMAT\nStructure your analysis in Markdown.\n\n# GUIDELINES\n- Focus on actionable insights rather than just describing the data\n- Use bullet points and tables when appropriate to improve readability\n- Include both positive findings and areas for improvement\n- Reference specific data points to support your analysis\n- Prioritize quality over quantity in your recommendations\n- Be concise yet thorough\n- If there are data quality issues or missing information, note these limitations\n- If you detect any unusual patterns or anomalies, highlight them\n\n# DATA\n```\n{{ JSON.stringify($input.first().json.body) }}\n```",
"promptType": "define"
},
"typeVersion": 1.6
},
{
"id": "f51d601e-898c-4dd6-894b-2e4911a334db",
"name": "DeepSeek 推理",
"type": "@n8n/n8n-nodes-langchain.lmChatDeepSeek",
"position": [
-400,
60
],
"parameters": {
"model": "deepseek-reasoner",
"options": {}
},
"credentials": {
"deepSeekApi": {
"id": "ltpFxb7M3kHaEBFD",
"name": "DeepSeek account"
}
},
"typeVersion": 1
},
{
"id": "9393be48-aebc-4f6c-b445-014633e0e289",
"name": "DeepSeek 聊天",
"type": "@n8n/n8n-nodes-langchain.lmChatDeepSeek",
"position": [
-20,
80
],
"parameters": {
"options": {}
},
"credentials": {
"deepSeekApi": {
"id": "ltpFxb7M3kHaEBFD",
"name": "DeepSeek account"
}
},
"typeVersion": 1
},
{
"id": "4a8dc360-fd3f-46a5-89e1-87ece59b0bb6",
"name": "示例数据",
"type": "n8n-nodes-base.code",
"position": [
-860,
60
],
"parameters": {
"jsCode": "return {\n \"body\": \n // You can use any data as JSON\n // this is just example\n // data start here\n [\n {\n \"user\": {\n \"id\": 15,\n \"full_name\": \"lisa confirmation\",\n },\n \"productivity\": 44.67,\n \"total_leads\": 465,\n \"total_confirmed\": 291,\n \"total_delivred\": 130,\n \"total_in_proccess\": 119,\n \"total_cancled\": 0,\n \"total_returned\": 13,\n \"total_assign\": 495,\n \"total_need_confirmation\": 0,\n \"total_recheck\": 22,\n \"upsell\": 59,\n \"upsell_delivered\": 27,\n \"confirmation_rate\": 62.58\n },\n {\n \"user\": {\n \"id\": 1346,\n \"full_name\": \"Sallam Confirmation\",\n },\n \"productivity\": 42.29,\n \"total_leads\": 374,\n \"total_confirmed\": 253,\n \"total_delivred\": 107,\n \"total_in_proccess\": 96,\n \"total_cancled\": 0,\n \"total_returned\": 21,\n \"total_assign\": 459,\n \"total_need_confirmation\": 1,\n \"total_recheck\": 1,\n \"upsell\": 62,\n \"upsell_delivered\": 31,\n \"confirmation_rate\": 67.65\n }\n ]\n // data end here\n}"
},
"typeVersion": 2
},
{
"id": "41e36370-fa7a-4f6e-a439-15127dfc432d",
"name": "便签2",
"type": "n8n-nodes-base.stickyNote",
"position": [
-1160,
-20
],
"parameters": {
"width": 480,
"height": 240,
"content": "## 测试工作流"
},
"typeVersion": 1
},
{
"id": "e0d473e8-7a2e-4473-9af7-5f2f835990db",
"name": "便签8",
"type": "n8n-nodes-base.stickyNote",
"position": [
-1160,
240
],
"parameters": {
"width": 480,
"height": 80,
"content": "## 仅用于测试"
},
"typeVersion": 1
},
{
"id": "b0c31c3b-1fd0-485e-a54b-9a3045bcf09e",
"name": "便签7",
"type": "n8n-nodes-base.stickyNote",
"position": [
-1160,
400
],
"parameters": {
"color": 4,
"width": 1540,
"height": 100,
"content": "## 需要更多帮助或有任何建议？"
},
"typeVersion": 1
},
{
"id": "9a1f03ee-167f-44e3-ae12-61ab8d3789f2",
"name": "便签6",
"type": "n8n-nodes-base.stickyNote",
"position": [
-520,
-360
],
"parameters": {
"color": 7,
"width": 720,
"height": 80,
"content": "## 在此处修改"
},
"typeVersion": 1
},
{
"id": "d91de9c8-835b-4232-a76b-e27554ad595d",
"name": "Webhook",
"type": "n8n-nodes-base.webhook",
"position": [
-980,
-300
],
"webhookId": "b408defb-315d-4676-b4c4-1dcebe81ffc0",
"parameters": {
"path": "b408defb-315d-4676-b4c4-1dcebe81ffc0",
"options": {},
"httpMethod": [
"POST",
"GET"
],
"multipleMethods": true
},
"typeVersion": 2
},
{
"id": "04c2da18-10c9-44df-8084-52b5901ecc18",
"name": "便签9",
"type": "n8n-nodes-base.stickyNote",
"position": [
-1020,
-380
],
"parameters": {
"color": 4,
"width": 200,
"height": 240,
"content": "## 生产环境"
},
"typeVersion": 1
},
{
"id": "a1fb1cbc-391c-4918-b48f-8b44116921b8",
"name": "重新检查",
"type": "@n8n/n8n-nodes-langchain.chainLlm",
"position": [
-100,
-140
],
"parameters": {
"text": "=You are a Data Analysis Verification Expert. Your task is to evaluate whether an AI-generated report accurately and completely analyzes the provided CRM data. You will assess the report quality and determine if it's compatible with the original input.\n\n# INPUT\nYou will receive:\n1. The original call center agent metrics data (JSON)\n2. The AI-generated analysis report in Markdown\n\n# VERIFICATION REQUIREMENTS\nEvaluate the report for:\n1. Factual accuracy - Do all numbers, rankings, and statements accurately reflect the data?\n2. Comprehensiveness - Does the report cover all required areas? (Lead conversion, Upsell, Agent ranking)\n3. Insight quality - Does the report provide meaningful insights beyond basic data description?\n4. Completeness - Are all agents included in the analysis?\n5. Format compliance - Is the report properly formatted in Markdown with appropriate sections?\n\n# OUTPUT FORMAT\nReturn a JSON object with the following structure:\n```json\n{\n \"verified\": true/false,\n \"score\": 1-10,\n \"quality_assessment\": \"Brief 2-4 sentence evaluation of report quality\",\n \"missing_elements\": [\"List any required elements missing from the report\"],\n \"inaccuracies\": [\"List any factual errors or misinterpretations\"],\n \"improvement_suggestions\": [\"Specific suggestions for report improvement\"]\n}\n```\n\n# EVALUATION CRITERIA\n- \"verified\": Set to true ONLY if the report is factually accurate, includes all agents, covers all required areas, and provides meaningful insights.\n- \"score\": Rate from 1-10 where:\n * 1-3: Poor report with major inaccuracies or missing elements\n * 4-6: Adequate report with some issues\n * 7-8: Good report with minor issues\n * 9-10: Excellent report with comprehensive analysis\n\n# GUIDELINES\n- Be thorough and precise in your verification\n- Check all numerical claims against the original data\n- Verify that all agents are properly ranked and described\n- Check that lead conversion rates and upsell metrics are accurately analyzed\n- Assess whether the insights are actionable and valuable\n- Maintain a balanced perspective, noting both strengths and weaknesses\n\n# ORIGINAL DATA\n{{ JSON.stringify($node[\"Example data\"].json.chatInput) }}\n\n# AI-GENERATED REPORT\n{{ $json.text }}",
"promptType": "define"
},
"typeVersion": 1.6
}
],
"pinData": {},
"connections": {
"Report": {
"main": [
[
{
"node": "Recheck",
"type": "main",
"index": 0
}
]
]
},
"Recheck": {
"main": [
[
{
"node": "HTTP Request",
"type": "main",
"index": 0
}
]
]
},
"Webhook": {
"main": [
[
{
"node": "Report",
"type": "main",
"index": 0
}
]
]
},
"Example data": {
"main": [
[
{
"node": "Report",
"type": "main",
"index": 0
}
]
]
},
"DeepSeek Chat": {
"ai_languageModel": [
[
{
"node": "Recheck",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"DeepSeek Reasonning": {
"ai_languageModel": [
[
{
"node": "Report",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"When clicking ‘Test workflow’": {
"main": [
[
{
"node": "Example data",
"type": "main",
"index": 0
}
]
]
}
}
}常见问题
如何使用这个工作流？
复制上方的 JSON 配置代码，在您的 n8n 实例中创建新工作流并选择「从 JSON 导入」，粘贴配置后根据需要修改凭证设置即可。
这个工作流适合什么场景？
中级 - 客户关系管理, AI 摘要总结
需要付费吗？
本工作流完全免费，您可以直接导入使用。但请注意，工作流中使用的第三方服务（如 OpenAI API）可能需要您自行付费。
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工作流信息
难度等级
中级
节点数量15
分类2
节点类型7
作者
Omar Akoudad
@mediaplusmaAutomation, Code, and Analytics for E-commerce businesses, We help businesses streamline operations using n8n, AI agents, and data science to enhance efficiency and scalability.
外部链接
在 n8n.io 查看 →
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