KI-gesteuerter Buchempfehlungs-Engine

• •Möglicherweise sind Ziel-API-Anmeldedaten erforderlich

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  "name": "AI-Driven Book Recommendation Engine",
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          "systemMessage": "=You are a helper that identifies and lists only the types of books mentioned in the message.\n\nWhich types of books are being asked for in this message?\n\nReply with the types in lowercase, one on each line. Don’t include anything else — no headers, labels, or symbols.\n\nInput:\n{{ $json.textPlain }}"
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        "html": "=<p>We're sorry, but no books were found for the subject <em>{{$node[\"Check API Response\"].json[\"name\"]}}</em>. </p>\n\n<p>Please try a different subject or update your n8n workflow accordingly.</p>\n\n<p>You can explore a list of available subjects at <a href=\"https://openlibrary.org/subjects\" target=\"_blank\">Open Library Subjects</a>.</p>\n",
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        "subject": "=Book not found in {{$node[\"Check API Response\"].json[\"name\"]}}",
        "toEmail": "john.doe@example.com",
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        "content": "## Overview: \n\nThis workflow triggers on book request emails, uses Ollama LLM to extract intent, queries a Book Search API for data (title, summary, details), handles no-book cases, formats and enhances the data, generates a personalized email, and sends it to the user.\n\n✅ Ideal for automated book suggestions using LLMs and structured APIs.\n✅ Great for newsletter, reading clubs, and educational bots.\n"
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        "content": "## How to Run:\n\n1. Ensure the email trigger is connected to a valid inbox (IMAP/Gmail).\n2. Verify the Ollama node is running with the correct model.\n3. Set valid credentials for the book search API.\n4. Click **“Execute Workflow”** or activate the workflow to listen in real-time.\n5. Send a test email with a book request (e.g., “Recommend me a sci-fi novel.”).\n6. Check the sent mailbox for the recommendation email.\n\n## Tip: \nCustomize the book genre parser or API call to match your use case.\n"
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        "content": "## Email Input Processing\n\nTriggers on new book request emails and uses Ollama LLM to extract user intent."
      },
      "typeVersion": 1
    },
    {
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        "content": "## Output Generation\n\nFormats data, refines it with code, creates a personalized email, and sends it to the user."
      },
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    {
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        "content": "## Book Data Retrieval\n\nGenerates a search query, fetches book data (title, summary, details) via API, and validates the response."
      },
      "typeVersion": 1
    },
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        "content": "## Data Preparation\n\nExtracts summary, ensures data readiness, and gathers additional book details."
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