Callcenter-Analyse (DeepSeek-Modell mit doppelter KI-Validierung)

• •HTTP Webhook-Endpunkt (wird von n8n automatisch generiert)
• •Möglicherweise sind Ziel-API-Anmeldedaten erforderlich

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        "content": "## Generate report\nUsing deepseek R1"
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        "content": "## Double-check\nUsing deepseek V3"
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        "url": "YOUR_CALL_BACK_API",
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        "jsonBody": "={\n  data: \"{{$node['Report'].json.text}}\"\n}",
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      "id": "bf0a8ab6-06e0-4564-96af-ddcf6795a845",
      "name": "Bericht",
      "type": "@n8n/n8n-nodes-langchain.chainLlm",
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      "parameters": {
        "text": "=You are a CRM data analyst assistant. Your task is to analyze the provided CRM data and generate valuable insights in Markdown format.\n\nYou will receive JSON data extracted from a CRM system that may include information about:\n- Call canter agents metrics.\n\n# ANALYSIS REQUIREMENTS\nAnalyze the data considering:\n1. Lead conversion rates and quality metrics\n2. Upsall\n3. Rank the agents with small description about every one.\n\n# OUTPUT FORMAT\nStructure your analysis in Markdown.\n\n# GUIDELINES\n- Focus on actionable insights rather than just describing the data\n- Use bullet points and tables when appropriate to improve readability\n- Include both positive findings and areas for improvement\n- Reference specific data points to support your analysis\n- Prioritize quality over quantity in your recommendations\n- Be concise yet thorough\n- If there are data quality issues or missing information, note these limitations\n- If you detect any unusual patterns or anomalies, highlight them\n\n# DATA\n```\n{{ JSON.stringify($input.first().json.body) }}\n```",
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    {
      "id": "f51d601e-898c-4dd6-894b-2e4911a334db",
      "name": "DeepSeek Reasoning",
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        "model": "deepseek-reasoner",
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      "credentials": {
        "deepSeekApi": {
          "id": "ltpFxb7M3kHaEBFD",
          "name": "DeepSeek account"
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    {
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          "name": "DeepSeek account"
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        "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}"
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        "content": "## Test Workflow\nClick this button to test the workflow with example data"
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        "content": "## Just to test"
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        "content": "## Do you need more help or have any suggestions?\nContact me at mediaplus.ma@gmail.com"
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        "content": "## Change here\nYou can edit/add details about your goal by changing the AI promps.\n"
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        "path": "b408defb-315d-4676-b4c4-1dcebe81ffc0",
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        "content": "## Production"
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      "name": "Erneut prüfen",
      "type": "@n8n/n8n-nodes-langchain.chainLlm",
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        "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 }}",
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