RAG Agents with n8n & Vector Databases
Xây dựng RAG systems với n8n - Knowledge base chatbots, document QA, semantic search.
📋 Mô tả khóa học
RAG (Retrieval-Augmented Generation) cho phép AI trả lời dựa trên documents của bạn. Khóa học này giúp bạn build RAG systems với n8n, connect vector databases, và create knowledge base chatbots.
🎯 Bạn sẽ học được gì?
- ✅ Vector database basics (Pinecone, Supabase)
- ✅ Document ingestion workflows
- ✅ RAG pipelines trong n8n
- ✅ Semantic search implementation
- ✅ Context-aware AI responses
- ✅ Knowledge base automation
👥 Khóa học dành cho ai?
- n8n users building chatbots
- Teams needing internal knowledge bases
- Customer support automation
- Document-heavy businesses
📚 Chương trình học (14 bài)
Module 1: RAG Fundamentals
- What is RAG - Concept, architecture, use cases
- Embeddings Explained - What vectors are
- Vector Database Options - Pinecone, Supabase, Qdrant
- n8n Vector Store Nodes - Setup and configuration
Module 2: Document Processing
- Document Loaders - PDF, Word, web pages
- Text Splitting - Chunking strategies
- Embedding Documents - Creating vector representations
- Indexing Workflows - Automated document ingestion
Module 3: RAG Workflows
- Query Pipeline - Search → retrieve → generate
- Context Management - Relevant chunks selection
- Response Generation - Grounded answers
- Citation & Sources - Reference original docs
Module 4: Production
- Chatbot Interface - Telegram, Slack, web
- Testing & Optimization - Improve accuracy
🛠️ Tools Used
- n8n
- Pinecone / Supabase Vector
- OpenAI Embeddings
- Document processing libraries
🚀 Dự án chính
- Company Knowledge Base Chatbot - Internal wiki assistant
- AI Tutor with Course Materials - Educational Q&A
- Document QA System - PDF analysis bot
- Smart Search for Notion - Semantic search over notes
⚙️ Prerequisites
- ✅ n8n + GenAI Integration course
- ✅ Understanding of AI basics
- ✅ Documents/content to index
Thời lượng: 6-8 tuần (5-6 giờ/tuần)
Level: Advanced
Pathway: n8n Automation