Mein Arbeitsablauf 2

• •HTTP Webhook-Endpunkt (wird von n8n automatisch generiert)
• •Google Sheets API-Anmeldedaten

{
  "id": "VhEwspDqzu7ssFVE",
  "meta": {
    "instanceId": "f4b0efaa33080e7774e0d9285c40c7abcd2c6f7cf1a8b901fa7106170dd4cda3",
    "templateCredsSetupCompleted": true
  },
  "name": "My workflow 2",
  "tags": [],
  "nodes": [
    {
      "id": "48a84828-73de-4f4b-beb1-60e668342c11",
      "name": "Rechercheanfrage Webhook",
      "type": "n8n-nodes-base.webhook",
      "position": [
        -2048,
        624
      ],
      "webhookId": "5a9368a9-013f-41db-82cc-18be19ea6684",
      "parameters": {
        "path": "research-trigger",
        "options": {},
        "httpMethod": "POST",
        "responseMode": "responseNode"
      },
      "typeVersion": 1.1
    },
    {
      "id": "5d8a05fa-1528-4dc4-95cd-d99625a2221b",
      "name": "Recherchekonfigurationsprozessor",
      "type": "n8n-nodes-base.code",
      "position": [
        -1760,
        624
      ],
      "parameters": {
        "jsCode": "// Extract and validate research parameters\nconst body = $input.all()[0].json.body;\n\n// Default research configuration\nconst researchConfig = {\n  topic: body.topic || 'artificial intelligence trends',\n  depth: body.depth || 'comprehensive', // basic, detailed, comprehensive\n  sources: body.sources || ['web', 'academic', 'news'],\n  timeframe: body.timeframe || '6months',\n  language: body.language || 'en',\n  maxSources: body.maxSources || 10,\n  analysisType: body.analysisType || 'summary' // summary, detailed, comparative\n};\n\n// Generate search queries based on topic\nconst baseQueries = [\n  `${researchConfig.topic} latest developments`,\n  `${researchConfig.topic} research findings`,\n  `${researchConfig.topic} market analysis`,\n  `${researchConfig.topic} expert opinions`,\n  `${researchConfig.topic} case studies`\n];\n\n// Add specific queries based on depth\nif (researchConfig.depth === 'comprehensive') {\n  baseQueries.push(\n    `${researchConfig.topic} academic papers`,\n    `${researchConfig.topic} industry reports`,\n    `${researchConfig.topic} statistical data`,\n    `${researchConfig.topic} future predictions`\n  );\n}\n\nreturn [{\n  json: {\n    ...researchConfig,\n    searchQueries: baseQueries,\n    timestamp: new Date().toISOString(),\n    sessionId: `research_${Date.now()}`\n  }\n}];"
      },
      "typeVersion": 2
    },
    {
      "id": "19e3c76b-f0fb-4324-b212-585ab132bde5",
      "name": "Suchanfragen aufteilen",
      "type": "n8n-nodes-base.splitInBatches",
      "position": [
        -1456,
        624
      ],
      "parameters": {
        "options": {}
      },
      "typeVersion": 3
    },
    {
      "id": "6eb0ff10-aaf6-430f-aea0-7c0cbe950b95",
      "name": "Abfrageauswahl",
      "type": "n8n-nodes-base.code",
      "position": [
        -1152,
        624
      ],
      "parameters": {
        "jsCode": "// Get current batch data\nconst items = $input.all();\nconst currentItem = items[0].json;\nconst queries = currentItem.searchQueries;\nconst currentBatch = $('Split Search Queries').item.json;\n\n// Get current query\nconst currentQuery = queries[currentBatch.index];\n\nreturn [{\n  json: {\n    ...currentItem,\n    currentQuery: currentQuery,\n    batchIndex: currentBatch.index\n  }\n}];"
      },
      "typeVersion": 2
    },
    {
      "id": "99f73593-0ddd-4fc9-810f-8b1793cd8476",
      "name": "KI-Recherche-Scraper",
      "type": "n8n-nodes-scrapegraphai.scrapegraphAi",
      "position": [
        -848,
        624
      ],
      "parameters": {
        "userPrompt": "Research and extract comprehensive information about this topic. Provide: 1) Key findings and insights, 2) Important statistics or data points, 3) Expert quotes or opinions, 4) Recent developments, 5) Source credibility assessment. Format as structured JSON with fields: title, summary, keyPoints, statistics, quotes, sources, credibilityScore, datePublished, relevanceScore.",
        "websiteUrl": "={{ $json.currentQuery }}"
      },
      "typeVersion": 1
    },
    {
      "id": "da52e96d-0aa2-41ef-886e-bd396e0f42f2",
      "name": "Nachrichtenquellen-Scraper",
      "type": "n8n-nodes-scrapegraphai.scrapegraphAi",
      "position": [
        -848,
        832
      ],
      "parameters": {
        "userPrompt": "Extract recent news articles about this topic. For each article provide: headline, publication date, source, brief summary, and direct URL. Focus on credible news sources and recent publications within the last 6 months.",
        "websiteUrl": "https://www.google.com/search?q={{ encodeURIComponent($json.currentQuery) }}&tbm=nws"
      },
      "typeVersion": 1
    },
    {
      "id": "0ee6cf16-02e5-4a3b-b068-dd76a1351718",
      "name": "Akademische Quellen-Scraper",
      "type": "n8n-nodes-scrapegraphai.scrapegraphAi",
      "position": [
        -848,
        1024
      ],
      "parameters": {
        "userPrompt": "Extract academic papers and research studies. For each paper provide: title, authors, publication year, journal/conference, citation count, abstract summary, and DOI/URL if available. Focus on peer-reviewed sources and recent publications.",
        "websiteUrl": "https://scholar.google.com/scholar?q={{ encodeURIComponent($json.currentQuery) }}"
      },
      "typeVersion": 1
    },
    {
      "id": "3228908f-f816-4a0c-889b-abf756281eb8",
      "name": "Recherchequellen zusammenführen",
      "type": "n8n-nodes-base.merge",
      "position": [
        -560,
        832
      ],
      "parameters": {
        "mode": "combine",
        "options": {},
        "mergeByFields": {
          "values": [
            {}
          ]
        }
      },
      "typeVersion": 2.1
    },
    {
      "id": "90b55ee1-3404-4db2-aec1-6d6219043c09",
      "name": "Recherchedatenprozessor",
      "type": "n8n-nodes-base.code",
      "position": [
        -256,
        832
      ],
      "parameters": {
        "jsCode": "// Combine and process all research data\nconst allItems = $input.all();\nconst researchData = allItems[0].json;\nconst newsData = allItems[1]?.json || {};\nconst academicData = allItems[2]?.json || {};\n\n// Extract and structure the research findings\nconst processedData = {\n  sessionId: researchData.sessionId,\n  query: researchData.currentQuery,\n  batchIndex: researchData.batchIndex,\n  timestamp: new Date().toISOString(),\n  \n  // General research findings\n  generalFindings: {\n    title: researchData.result?.title || 'Research Findings',\n    summary: researchData.result?.summary || '',\n    keyPoints: researchData.result?.keyPoints || [],\n    statistics: researchData.result?.statistics || [],\n    credibilityScore: researchData.result?.credibilityScore || 0\n  },\n  \n  // News findings\n  newsFindings: {\n    articles: newsData.result?.articles || [],\n    totalArticles: newsData.result?.articles?.length || 0\n  },\n  \n  // Academic findings\n  academicFindings: {\n    papers: academicData.result?.papers || [],\n    totalPapers: academicData.result?.papers?.length || 0\n  },\n  \n  // Meta information\n  sourceTypes: ['general', 'news', 'academic'],\n  totalSources: (researchData.result?.sources?.length || 0) + \n                (newsData.result?.articles?.length || 0) + \n                (academicData.result?.papers?.length || 0)\n};\n\nreturn [{\n  json: processedData\n}];"
      },
      "typeVersion": 2
    },
    {
      "id": "7eb34b80-f6d2-4e80-83f5-529d4748cbec",
      "name": "Recherchedatenspeicherung",
      "type": "n8n-nodes-base.googleSheets",
      "position": [
        352,
        832
      ],
      "parameters": {
        "columns": {
          "value": {},
          "schema": [
            {
              "id": "sessionId",
              "type": "string",
              "display": true,
              "required": false,
              "displayName": "Session ID",
              "defaultMatch": false,
              "canBeUsedToMatch": true
            },
            {
              "id": "query",
              "type": "string",
              "display": true,
              "required": false,
              "displayName": "Research Query",
              "defaultMatch": false,
              "canBeUsedToMatch": true
            },
            {
              "id": "timestamp",
              "type": "string",
              "display": true,
              "required": false,
              "displayName": "Timestamp",
              "defaultMatch": false,
              "canBeUsedToMatch": true
            },
            {
              "id": "analysis",
              "type": "string",
              "display": true,
              "required": false,
              "displayName": "AI Analysis",
              "defaultMatch": false,
              "canBeUsedToMatch": true
            },
            {
              "id": "totalSources",
              "type": "number",
              "display": true,
              "required": false,
              "displayName": "Total Sources",
              "defaultMatch": false,
              "canBeUsedToMatch": true
            }
          ],
          "mappingMode": "autoMapInputData",
          "matchingColumns": []
        },
        "options": {},
        "operation": "append",
        "sheetName": {
          "__rl": true,
          "mode": "name",
          "value": "Research_Data"
        },
        "documentId": {
          "__rl": true,
          "mode": "url",
          "value": ""
        }
      },
      "typeVersion": 4.5
    },
    {
      "id": "d093ce1d-9716-4254-89b7-4b8bffd23b48",
      "name": "Rechercheabschlussantwort",
      "type": "n8n-nodes-base.respondToWebhook",
      "position": [
        656,
        832
      ],
      "parameters": {
        "options": {},
        "respondWith": "json",
        "responseBody": "={{ JSON.stringify({\n  status: 'completed',\n  sessionId: $json.sessionId,\n  message: 'Research analysis completed successfully',\n  totalSources: $json.totalSources,\n  timestamp: $json.timestamp\n}) }}"
      },
      "typeVersion": 1.1
    },
    {
      "id": "8398d709-67b8-4ad4-90f0-d2c041d4678e",
      "name": "Webhook Auslöseanleitung",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -2160,
        -448
      ],
      "parameters": {
        "color": 2,
        "width": 520,
        "height": 1732,
        "content": "# Step 1: Research Request Webhook 🎯\n\nThis webhook endpoint receives research requests and initiates the deep research process.\n\n## Request Format\n```json\n{\n  \"topic\": \"artificial intelligence in healthcare\",\n  \"depth\": \"comprehensive\",\n  \"sources\": [\"web\", \"academic\", \"news\"],\n  \"timeframe\": \"6months\",\n  \"maxSources\": 15,\n  \"analysisType\": \"detailed\"\n}\n```\n\n## Configuration\n- **Method**: POST\n- **Path**: /research-trigger\n- **Authentication**: Optional API key\n- **Rate Limiting**: Configurable\n\n## Depth Levels\n- **Basic**: Quick overview with 3-5 sources\n- **Detailed**: Comprehensive analysis with 8-12 sources\n- **Comprehensive**: Deep dive with 15+ sources and academic papers\n\n## Source Types\n- **Web**: General web content and industry sites\n- **News**: Recent news articles and press releases\n- **Academic**: Peer-reviewed papers and research studies"
      },
      "typeVersion": 1
    },
    {
      "id": "965963f7-6f98-4954-a0f0-916ab00477be",
      "name": "Konfigurationsanleitung",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -1600,
        -448
      ],
      "parameters": {
        "color": 2,
        "width": 520,
        "height": 1748,
        "content": "# Step 2: Research Configuration Processor 🔧\n\nThis node processes and validates the incoming research request, setting up the research parameters.\n\n## What it does\n- Validates and sanitizes input parameters\n- Sets default values for missing parameters\n- Generates multiple search queries based on topic\n- Creates unique session ID for tracking\n- Configures research depth and scope\n\n## Query Generation Strategy\n- **Base Queries**: Core topic searches\n- **Depth-Specific**: Additional queries for comprehensive research\n- **Time-Sensitive**: Recent developments and trends\n- **Multi-Angle**: Different perspectives and viewpoints\n\n## Customization Options\n- Modify query generation logic\n- Add industry-specific search patterns\n- Implement custom validation rules\n- Configure default research parameters"
      },
      "typeVersion": 1
    },
    {
      "id": "47a160d4-d829-4133-93fa-aa4dbd41f785",
      "name": "KI-Scraping-Anleitung",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -1040,
        -448
      ],
      "parameters": {
        "color": 3,
        "width": 520,
        "height": 1748,
        "content": "# Step 3: Multi-Source AI Scraping 🤖\n\nThree parallel AI-powered scrapers collect data from different source types for comprehensive research coverage.\n\n## AI Research Scraper\n- **Purpose**: General web research and industry insights\n- **Focus**: Key findings, statistics, expert opinions\n- **Output**: Structured insights with credibility scores\n\n## News Sources Scraper\n- **Purpose**: Recent news and current developments\n- **Focus**: Headlines, publication dates, credible sources\n- **Output**: Timestamped news articles with summaries\n\n## Academic Sources Scraper\n- **Purpose**: Peer-reviewed research and scholarly articles\n- **Focus**: Academic papers, citations, research studies\n- **Output**: Scientific literature with metadata\n\n## ScrapeGraphAI Benefits\n- **AI-Powered**: Intelligent content extraction\n- **Structured Output**: Consistent data format\n- **Source Validation**: Credibility assessment\n- **Multi-Language**: Global research capability"
      },
      "typeVersion": 1
    },
    {
      "id": "503cdf42-cee7-4b44-a2fd-4f4a4a134f60",
      "name": "Verarbeitungs- & Analyseanleitung",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -464,
        -448
      ],
      "parameters": {
        "color": 3,
        "width": 520,
        "height": 1748,
        "content": "# Step 4: Data Processing & AI Analysis 🧠\n\nAdvanced data processing and AI-powered analysis to generate actionable insights from collected research data.\n\n## Research Data Processor\n- **Combines**: All source types into unified structure\n- **Validates**: Data quality and completeness\n- **Enriches**: Metadata and source attribution\n- **Structures**: For optimal analysis and storage\n\n## AI Research Analyst\n- **Model**: GPT-4 for sophisticated analysis\n- **Analysis Types**: Summary, trends, conflicts, reliability\n- **Output**: Executive summary with actionable insights\n- **Temperature**: Low (0.3) for consistent, factual analysis\n\n## Analysis Components\n1. **Executive Summary**: High-level overview\n2. **Key Insights**: Major findings and trends\n3. **Reliability Assessment**: Source credibility evaluation\n4. **Recommendations**: Actionable next steps\n5. **Further Research**: Suggested investigation areas"
      },
      "typeVersion": 1
    },
    {
      "id": "0105d893-94ce-465d-9ef8-8f144280f0c9",
      "name": "Speicherungs- & Antwortanleitung",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        144,
        -432
      ],
      "parameters": {
        "color": 4,
        "width": 840,
        "height": 1716,
        "content": "# Step 5: Data Storage & Response 📊\n\nSecure storage of research findings and structured response delivery for seamless integration with other systems.\n\n## Google Sheets Storage\n- **Sheet Structure**: Research_Data with comprehensive columns\n- **Data Retention**: Historical research for trend analysis\n- **Access Control**: Secure OAuth2 authentication\n- **Format**: Structured data ready for analysis and reporting\n\n## Response Delivery\n- **Format**: JSON with status and metadata\n- **Content**: Session ID, completion status, source count\n- **Integration**: Ready for webhook consumers and APIs\n- **Tracking**: Unique session IDs for research correlation\n\n## Data Management Features\n- **Versioning**: Track research iterations\n- **Export**: Multiple format support\n- **Sharing**: Team collaboration capabilities\n- **Analytics**: Built-in Google Sheets analysis tools\n\n## Use Cases\n- **Market Research**: Competitive analysis and trends\n- **Academic Research**: Literature reviews and citations\n- **Business Intelligence**: Industry insights and reports"
      },
      "typeVersion": 1
    }
  ],
  "active": false,
  "pinData": {},
  "settings": {
    "executionOrder": "v1"
  },
  "versionId": "076dd376-d6cb-4851-b335-e074cd47911c",
  "connections": {
    "6eb0ff10-aaf6-430f-aea0-7c0cbe950b95": {
      "main": [
        [
          {
            "node": "99f73593-0ddd-4fc9-810f-8b1793cd8476",
            "type": "main",
            "index": 0
          },
          {
            "node": "da52e96d-0aa2-41ef-886e-bd396e0f42f2",
            "type": "main",
            "index": 0
          },
          {
            "node": "0ee6cf16-02e5-4a3b-b068-dd76a1351718",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "99f73593-0ddd-4fc9-810f-8b1793cd8476": {
      "main": [
        [
          {
            "node": "3228908f-f816-4a0c-889b-abf756281eb8",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "da52e96d-0aa2-41ef-886e-bd396e0f42f2": {
      "main": [
        [
          {
            "node": "3228908f-f816-4a0c-889b-abf756281eb8",
            "type": "main",
            "index": 1
          }
        ]
      ]
    },
    "19e3c76b-f0fb-4324-b212-585ab132bde5": {
      "main": [
        [
          {
            "node": "6eb0ff10-aaf6-430f-aea0-7c0cbe950b95",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "7eb34b80-f6d2-4e80-83f5-529d4748cbec": {
      "main": [
        [
          {
            "node": "d093ce1d-9716-4254-89b7-4b8bffd23b48",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "3228908f-f816-4a0c-889b-abf756281eb8": {
      "main": [
        [
          {
            "node": "90b55ee1-3404-4db2-aec1-6d6219043c09",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "90b55ee1-3404-4db2-aec1-6d6219043c09": {
      "main": [
        [
          {
            "node": "7eb34b80-f6d2-4e80-83f5-529d4748cbec",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "48a84828-73de-4f4b-beb1-60e668342c11": {
      "main": [
        [
          {
            "node": "5d8a05fa-1528-4dc4-95cd-d99625a2221b",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "5d8a05fa-1528-4dc4-95cd-d99625a2221b": {
      "main": [
        [
          {
            "node": "19e3c76b-f0fb-4324-b212-585ab132bde5",
            "type": "main",
            "index": 0
          }
        ]
      ]
    }
  }
}