AI 기반 RAG 문서 처리 및 챗봇 - Google Drive, Supabase, OpenAI

• •Google Drive API 인증 정보
• •OpenAI API Key
• •Google Gemini API Key
• •Supabase URL과 API Key

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        "content": "## 📁 Data Processing from Google Drive\nAutomatically monitors your Google Drive folder and processes new files. When you upload a PDF, CSV, or Google Doc, this section downloads the file, extracts all the text content, and prepares it for AI analysis. Think of it as your automatic document reader that never sleeps."
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        "width": 1940,
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        "content": "## 🧠 RAG Data Upload Pipeline\nTakes your extracted document text and makes it AI-ready. It creates smart summaries, breaks documents into digestible chunks, adds context to each piece, and stores everything in a searchable database. This is where your documents become \"AI-searchable\" knowledge that can answer questions."
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        "text": "=Create metadata title and metadata description based on the document specified below (below ##Document). Metadata title and metadata description will be used to seperate data in vector DB that will be used for RAG.\n\n##Document\n{{ $('Document Data').item.json.data }}",
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          "systemMessage": "=You're an internal company knowledge assistants. Your job is to answer questions using the files in the Vector Database. Here's how to do it:\n\nCheck Vector Database First\n- Search for similar test chunks using RAG.\n- If you find matches, use them to answer the question. \n- If you find relevant matches and need additional context to support the answer, prioritize retrieving it from documents that share the same metadata \"file_id\" & \"title\" before exploring other sources.\"\n\nNo Answer Found?\n- Clearly say: \"I couldn't find this in the databases\"\n- Never guess or invent answers.\n\nExample Response:\n\"Netflix grew revenue in Streaming services by 20% in 2024.\""
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      "position": [
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      "parameters": {
        "color": 4,
        "width": 1180,
        "height": 760,
        "content": "## AI Chat Agent - Chat with your documents stored in Supabase Vector Store\nour personal AI assistant that can answer questions about all your uploaded documents. Ask anything in plain English - it searches through your document database, finds relevant information, and gives you accurate answers with source references. It remembers your conversation and never makes up information."
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        "content": "## 📄 AI-Powered RAG Document Processing & Chatbot with Google Drive, Supabase, OpenAI\n\n**What This Workflow Does:**\n- Watches a Google Drive folder for new documents\n- Extracts text from PDF/CSV files\n- Enhances the text using AI (Google Gemini)\n- Chunks and embeds the text using OpenAI\n- Stores vector data in Supabase for semantic search\n- Enables users to chat with documents using OpenAI"
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        "content": "## 📋 Data Processing & RAG Workflow\n\n### 📁 Step 1: Google Drive Trigger  \nWatches a specific Google Drive folder for new file uploads. Triggers every minute.\n\n### 🔁 Step 2: Loop Over Uploaded Files  \nEnsures each uploaded file is processed one-by-one to avoid bulk processing issues.\n\n### 🆔 Step 3: Set File ID  \nExtracts and stores the Google Drive file ID for reference in later steps.\n\n### 📥 Step 4: Download File  \nDownloads the file (PDF, CSV, or Google Docs). Google Docs are auto-converted to PDF.\n\n### 🧭 Step 5: File Type Switch  \nRoutes file based on type:\n- PDF → Go to PDF Text Extractor\n- CSV → Go to CSV Extractor\n\n### 📄 Step 6A: Extract Text from PDF  \nExtracts raw text content from the PDF file.\n\n### 📊 Step 6B: Extract Text from CSV  \nExtracts and formats CSV data into a readable string.\n\n### 📦 Step 7: Format Document Data  \nWraps extracted text into JSON: `{\"data\": \"text content\"}`.\n\n### 🧠 Step 8: Generate Metadata (Gemini)  \nUses Gemini AI to create a document title and description.\n\n### ✂️ Step 9: Split into Chunks  \nSplits the document text into 1000-character chunks with 200-character overlap, intelligently at paragraph/sentence/word level.\n\n### 🔄 Step 10: Split Out Chunks  \nConverts array of chunks into individual workflow items.\n\n### 🚫 Step 11: Limit to 20 Chunks  \nLimits processing to first 20 chunks to control API and processing load.\n\n### 🧠 Step 12: Enhance Chunk Context (Gemini)  \nEach chunk is enhanced for better clarity, continuity, and context using Gemini.\n\n### 🧩 Step 13: Summarize  \nCombines enhanced chunks using `###SPLIT###` separator for further processing.\n\n### 🧷 Step 14: Re-Split Enhanced Chunks  \nSplits combined text back into chunks using `###SPLIT###` separator.\n\n### 🧬 Step 15: Generate Embeddings (OpenAI)  \nConverts text chunks into vector embeddings for semantic search.\n\n### 🗂️ Step 16: Add Metadata  \nEach chunk is wrapped with metadata (file ID, title, description) for vector DB.\n\n### 🧠 Step 17: Store in Supabase Vector DB  \nStores embeddings in Supabase vector store for later semantic retrieval.\n\n"
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        "content": "\n## SETUP REQUIRED  \n**Author:** [Billy Christi](https://n8n.io/creators/billy/)\n\n---\n\n### 📚 Database: Supabase Table `documents`\n\nTo manage and store embeddings and metadata, we use a Supabase table with the following schema:\n\n```sql\nCREATE TABLE public.documents (\n  id bigserial PRIMARY KEY,         -- Unique identifier\n  content text,                     -- Textual content of the document\n  metadata jsonb,                   -- Additional metadata (e.g., title, source)\n  embedding vector                  -- Vector embeddings (for similarity search, etc.)\n);\n````\n### 🔐 Required Credentials & Keys\n✅ **Google Drive Credential** \n✅ **Supabase API Key & Project URL** \n✅ **OpenAI API Key**\n✅ **Google Gemini API Key**\n\n\n"
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        "content": "\n## 🤖 CHAT INTERFACE WORKFLOW\n\n### 💬 Step 1: Chat Trigger  \nListens for user chat input (e.g., question about a document).\n\n### 🧠 Step 2: Simple Memory  \nStores last 10 messages to maintain conversation context.\n\n### 🔍 Step 3: Vector Search (Supabase)  \nSearches Supabase vector DB for relevant chunks related to the user's question.\n\n### 🧬 Step 4: Convert Question to Embedding  \nUses OpenAI to embed the question text for similarity search.\n\n### 🗨️ Step 5: Generate Response (OpenAI GPT-4o-mini)  \nUses context + question to generate a natural language answer.\n\n### 🧠 Step 6: AI Agent  \nFinal agent logic:\n- Searches vector DB first\n- Answers based on found chunks\n- Attributes response to correct document\n- Admits if answer not found (no hallucination)"
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        "content": "📌 **Understanding the Limit Node**\nThe **Limit Node** restricts the number of records or items processed in your workflow. This is especially useful for:\n✅ **Testing** your setup with a smaller dataset\n✅ **Controlling resource usage** during processing\n\n💡 **You can**:\n* **Adjust the limit value** to suit your workflow's needs\n* **Remove the Limit Node** altogether to process all available data"
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