production-agentic-rag-course

概要

この実戦コースでは、RAGテクノロジーを使用して完全な研究アシスタントシステムを構築することを教えています。インフラストラクチャ、データ処理、ハイブリッド検索、インテリジェントなRAGおよびTelegram統合をカバーします。業界のベストプラクティスと専門的な検索の基盤に焦点を当てます。

README プレビュー

# The Mother of AI Project\n## Phase 1 RAG Systems: arXiv Paper Curator\n\n\n  A Learner-Focused Journey into Production RAG Systems\n  Learn to build modern AI systems from the ground up through hands-on implementation\n  Master the most in-demand AI engineering skills: RAG (Retrieval-Augmented Generation)\n\n\n\n  \n  \n  \n  \n  \n\n\n\n\n\n  \n    \n  \n\n\n## 📖 About This Course\n\nThis is a **learner-focused project** where you'll build a complete research assistant system that automatically fetches academic papers, understands their content, and answers your research questions using advanced RAG techniques.\n\n**The arXiv Paper Curator** will teach you to build a **production-grade RAG system using industry best practices**. Unlike tutorials that jump straight to vector search, we follow the **professional path**: master keyword search foundations first, then enhance with vectors for hybrid retrieval.\n\n> **🎯 The Professional Difference:** We build RAG systems the way successful companies do - solid search foundations enhanced with AI, not AI-first approaches that ignore search fundamentals.\n\nBy the end of this course, you'll have your own AI research assistant and the deep technical skills to build production RAG systems for any domain.\n\n### **🎓 What You'll Build**\n\n- **Week 1:** Complete infrastructure with Docker, FastAPI, PostgreSQL, OpenSearch, and Airflow\n- **Week 2:** Automated data pipeline fetching and parsing academic papers from arXiv  \n- **Week 3:** Production BM25 keyword search with filtering and relevance scoring\n- **Week 4:** Intelligent chunking + hybrid search combining keywords with semantic understanding\n- **Week 5:** Complete RAG pipeline with local LLM, streaming responses, and Gradio interface\n- **Week 6:** Production monitoring with Langfuse tracing and Redis caching for optimized performance\n- **Week 7:** **Agentic RAG with LangGraph and Telegram Bot for mobile access**\n\n---\n\n## 🏗️ System Architecture Evolution\n\