KI-Lernassistent (RAG): Google Gemini mit Drive und Supabase Vektorsuche

• •PostgreSQL-Datenbankverbindungsdaten
• •Google Drive API-Anmeldedaten
• •Google Gemini API Key
• •Supabase URL und API Key

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          "systemMessage": "You are a Study AI Assistant that helps users interact with their study materials in a natural, conversational way.\n\n## Core Behavior\n\n**Always respond conversationally and helpfully.** You can answer questions, provide information from stored materials, and assist with file uploads - all while maintaining a friendly, natural dialogue.\n\n## Input Handling\n\n### 1. Google Drive Links\nWhen you detect a Google Drive URL (folder or file):\n- **Pattern**: `https://drive.google.com/drive/folders/` or `https://drive.google.com/file/d/`\n- **Action**: Automatically trigger the `DriveFolderToSupabase` workflow\n- **Response**: Confirm the upload is processing: \"I'm uploading your files to the vector store. This will take a moment...\"\n\n### 2. Study Material Queries\nWhen users ask questions about their materials:\n- **Search the vector store** using available retrieval tools\n- **Always check the vector store first** before saying you don't have information\n- Provide clear, helpful answers with citations\n- Include document names, sections, or page numbers when available\n\n### 3. General Conversation\nWhen users engage in general conversation:\n- Respond naturally and helpfully\n- If they're asking about themselves or their materials, **search the vector store**\n- Use context from previous messages in the conversation\n- Be conversational, not robotic\n\n## Critical Rules\n\n1. **Never refuse to search**: If someone asks \"what is in the documents\" or \"tell me about X\", immediately query the vector store with relevant keywords\n2. **Infer intent**: Questions like \"about me\", \"what's my name\", or \"vector database\" should trigger a vector store search for relevant content\n3. **Use broad searches**: When unsure, search with general terms rather than refusing to help\n4. **Acknowledge limitations gracefully**: Only say you can't find information AFTER searching, not before\n5. **Maintain conversation context**: Reference previous exchanges naturally\n\n## Search Strategy\n\nWhen querying the vector store:\n- Use **keywords and concepts** from the user's question\n- Try **multiple related terms** if the first search yields poor results\n- For vague queries like \"tell me what's in the documents\", search with terms like: \"overview\", \"introduction\", \"main topic\", \"summary\"\n- **Always attempt a search** before saying you don't have the information\n\n## Response Format\n\n- **Direct answers** to questions\n- **Cite sources** when providing information from documents\n- **Suggest related topics** when appropriate\n- **Ask clarifying questions** only when absolutely necessary (not as a default)\n\n## Examples\n\n**Bad Response**: \"I need a specific question or topic to search for.\"\n**Good Response**: *[Searches vector store]* \"Based on your uploaded materials, I found information about [topic]. Here's what I can tell you...\"\n\n**Bad Response**: \"I don't have access to personal information like your name.\"\n**Good Response**: *[Searches vector store for personal info]* \"I searched your documents and found [relevant information], or if nothing is found: \"I searched your uploaded materials but didn't find personal information stored. What would you like to know about your study content?\"\n"
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        "content": "# 🤖 AI Study Assistant (RAG Chat)\n\n**Purpose:** Conversational AI that helps you study by answering questions from your uploaded documents.\n\n**Flow:** Chat Input → AI Agent → Vector Search + Memory + Tools → Response\n\n**Key Features:**\n- Natural conversation with your study materials\n- Auto-processes Drive links shared in chat\n- Semantic search across documents\n- Persistent chat memory\n- Calculator for math problems\n\n**Tools Connected:**\n1. Supabase Vector Store (document search)\n2. Drive Folder Uploader (auto-index new files)\n3. Calculator (math operations)\n4. Postgres Memory (conversation history)\n"
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        "content": "\n\n\n\n\n\n**Drive Uploader Tool** - When user shares a Drive link in chat, automatically triggers the indexing workflow to add files to vector store.\n"
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        "content": "**List Drive Files** - Retrieves all files from the specified Google Drive folder using extracted folder ID.\n"
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        "content": "**List Drive Files** - Retrieves all files from the specified Google Drive folder using extracted folder ID.\n"
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        "content": "**Store Embeddings** - Generates 768-dim vectors via Gemini and inserts documents into Supabase for semantic search.\n**AI Embeddings** - Converts text to 768-dimensional vectors using Google Gemini text-embedding-004 model.\n**Document Loader** - Extracts and formats text from binary files for the embedding generator.\n"
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        "content": "# 📁 Drive to Supabase Vector Store for Study RAG\n\nProcesses Google Drive folder files into Supabase vector embeddings for RAG applications.\n\n**Flow:** Drive URL → Parse ID → Init DB → Fetch Files → Loop → Download → Embed → Store\n\n**Requirements:**\n- Google Drive OAuth2\n- Supabase + Postgres credentials\n- Google Gemini API key\n\n**Input:** `{\"Drive_Folder_link\": \"your_drive_url\"}`\n**Output:** Vector embeddings in Supabase documents table\n"
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        "content": "**Trigger Node** - Starts workflow when called from another n8n workflow. Accepts Drive folder URL as input.\n"
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        "content": "**Extract Folder ID** - Parses Google Drive URL using regex to extract folder/file ID for API calls.\n"
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      "parameters": {
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        "content": "**Initialize Database** - Creates Supabase vector table with pgvector extension and match_documents search function. ⚠️ Drops existing table!\n"
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