Flugcheck-in-Scraper für Airlines mit n8n, KI und Vektor-Datenbank-Speicherung

• •Möglicherweise sind Ziel-API-Anmeldedaten erforderlich
• •Google Sheets API-Anmeldedaten

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              "message": "=You are an intelligent parser trained to extract structured data from messy airline webpages.\n\nYour task is to extract and return well-structured airline check-in and policy details from raw text. Always return the result as a clean, valid JSON object using the exact schema described below.\n\n---\n\nExtraction Guidelines:\n\n* Ensure consistent JSON structure for every airline.\n* If a key has no value or content, **remove it** (do not return null, empty arrays, or empty objects).\n* Include any other useful data under `\"additional_info\"` if it doesn’t fit existing keys.\n* Always extract **direct URLs** wherever available.\n* Your output should be compact, valid, and readable JSON.\n\n---\n\nJSON Structure Format:\n\n1. Web Check-in Details\n\n* \"web\\_checkin\\_available\": true/false\n* \"checkin\\_url\": \"<URL>\"\n* \"checkin\\_methods\": \\[\"Online\", \"Mobile App\", \"Kiosk\", \"Airport Counter\"]\n* \"checkin\\_start\": \"<Start Time>\"\n* \"checkin\\_deadline\": \"<Deadline Time>\"\n* \"boarding\\_pass\\_options\":\n\n  * \"mobile\\_boarding\\_pass\\_available\": true/false\n  * \"printed\\_boarding\\_pass\\_required\": true/false\n  * \"additional\\_checkin\\_info\": \"<Extra instructions>\"\n\n2. Customer Support\n\n* \"customer\\_support\":\n\n  * \"phone\": \"<Phone Number>\"\n  * \"email\": \"<Email>\"\n  * \"support\\_url\": \"<Support URL>\"\n  * \"chat\\_url\": \"<Chat URL>\"\n  * \"operating\\_hours\": \"<Hours>\"\n  * \"additional\\_help\\_channels\": \\[\"WhatsApp\", \"Twitter Support\", \"Chatbot\"]\n\n3. Baggage Allowance\n\n* \"baggage\\_allowance\":\n\n  * \"hand\\_baggage\":\n\n    * \"weight\\_limit\": \"<Weight Limit>\"\n    * \"size\\_limit\": \"<Size Limit>\"\n    * \"additional\\_items\\_allowed\": \\[\"Handbag\", \"Laptop\", \"Baby Items\", \"Medical Equipment\"]\n    * \"special\\_conditions\": \"<Any special baggage conditions>\"\n  * \"checked\\_baggage\":\n\n    * \"general\\_rules\": \"<Baggage Rules>\"\n    * \"class\\_specific\\_limits\": \"<Limits for different travel classes>\"\n    * \"baggage\\_calculator\\_url\": \"<URL>\"\n    * \"oversized\\_special\\_baggage\": \"\\<Details on sports/music equipment>\"\n\n4. Refund & Cancellation Policy\n\n* \"refund\\_policy\":\n\n  * \"conditions\": \"<Refund conditions>\"\n  * \"processing\\_time\": \"<Processing Time>\"\n  * \"refund\\_policy\\_url\": \"<URL>\"\n* \"cancellation\\_policy\":\n\n  * \"conditions\": \"<Cancellation conditions>\"\n  * \"fees\\_or\\_penalties\": \"<Cancellation Fees>\"\n  * \"cancellation\\_policy\\_url\": \"<URL>\"\n\n5. Airport & Travel Guidelines\n\n* \"airport\\_travel\\_guidelines\":\n\n  * \"security\\_immigration\\_rules\": \"\\<Security & Immigration Rules>\"\n  * \"airport\\_checkin\\_requirements\": \"<Check-in document requirements>\"\n  * \"special\\_services\": \\[\"Priority Boarding\", \"Lounge Access\", \"Wheelchair Assistance\"]\n\n6. Frequently Asked Questions (FAQ)\n\n* \"faq\":\n\n  * \"faq\\_url\": \"<FAQ Page URL>\"\n  * \"questions\\_answers\": \\[\n    {\"question\": \"<Q1>\", \"answer\": \"<A1>\"},\n    {\"question\": \"<Q2>\", \"answer\": \"<A2>\"}\n    ]\n\n7. Additional Details\n\n* \"additional\\_info\": \"<Any extra info from the page not captured above>\"\n\n---\n\nOutput Rules Summary:\n\n* Always return valid JSON.\n* Do **not** include empty fields or structures (null, {}, or \\[]).\n* Place unrelated or extra info under \"additional\\_info\".\n\nUse this structure for every page, even if some values are missing. Just remove the missing fields completely in the output.\n\n\n"
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        "content": "\n\n### 📝 Web Check-in Details Extractor (LLM Prompt Guide)\n\n#### ✅ What is this?\n\nThis is a powerful AI prompt used inside the **\"Basic LLM Chain\"** node. It tells the AI how to **extract structured airline web check-in data** (like check-in time, baggage policy, cancellation rules) from messy airline webpages.\n\n#### 🎯 Why is it used?\n\nAirline websites often present data in unstructured formats. This LLM-based step:\n\n* Cleans the content scraped from airline URLs.\n* Extracts important travel-related info in a consistent JSON format.\n* Helps automate the enrichment of airline data stored in your Google Sheets and Vector DB.\n\n#### 🛠️ How to use it?\n\n1. **Input**: This node receives raw webpage content from the airline’s \"Web Check-in URL\".\n2. **Prompt**: It applies a fixed set of rules (in natural language) to guide the AI to convert the unstructured data into clean JSON.\n3. **Output**: The AI returns a **structured JSON** object with fields like:\n\n   * `checkin_url`\n   * `baggage_allowance`\n   * `refund_policy`\n   * `faq`\n   * `additional_info`\n4. The next nodes save this output to:\n\n   * Google Sheet (for visibility)\n   * PGVector (for semantic search)\n\n💡 **Pro Tip:** This works best when the HTML content is readable and includes useful labels like “Check-in”, “Cancellation”, “Support”, etc.\n\n\n"
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