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生产级 Agentic RAG 课程系统课程·英文原文 ↗仓库 ↗

The Mother of AI Project ​

Phase 1 RAG Systems: arXiv Paper Curator ​

A Learner-Focused Journey into Production RAG Systems

Learn to build modern AI systems from the ground up through hands-on implementation

Master the most in-demand AI engineering skills: RAG (Retrieval-Augmented Generation)

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<img src="/mirror/30/3085cd9bd207560afac2d73781f2b86057e97db3.webp" alt="RAG Architecture" width="700">

📖 About This Course ​

This 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.

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.

🎯 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.

By 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.

🎓 What You'll Build ​

  • Week 1: Complete infrastructure with Docker, FastAPI, PostgreSQL, OpenSearch, and Airflow
  • Week 2: Automated data pipeline fetching and parsing academic papers from arXiv
  • Week 3: Production BM25 keyword search with filtering and relevance scoring
  • Week 4: Intelligent chunking + hybrid search combining keywords with semantic understanding
  • Week 5: Complete RAG pipeline with local LLM, streaming responses, and Gradio interface
  • Week 6: Production monitoring with Langfuse tracing and Redis caching for optimized performance
  • Week 7: Agentic RAG with LangGraph and Telegram Bot for mobile access

🏗️ System Architecture Evolution ​

Week 7: Agentic RAG & Telegram Bot Integration ​

Week 7 Telegram and Agentic AI Architecture

Complete Week 7 architecture showing Telegram bot integration with the agentic RAG system

LangGraph Agentic RAG Workflow ​

LangGraph Agentic RAG Flow

Detailed LangGraph workflow showing decision nodes, document grading, and adaptive retrieval

Week 7 Code walkthrough + blog: Agentic RAG with LangGraph and Telegram

Key Innovations in Week 7:

  • Intelligent Decision-Making: Agents evaluate and adapt retrieval strategies
  • Document Grading: Automatic relevance assessment with semantic evaluation
  • Query Rewriting: Adaptive query refinement when results are insufficient
  • Guardrails: Out-of-domain detection prevents hallucination
  • Mobile Access: Telegram bot for conversational AI on any device
  • Transparency: Full reasoning step tracking for debugging and trust

🚀 Quick Start ​

📋 Prerequisites ​

  • Docker Desktop (with Docker Compose)
  • Python 3.12+
  • UV Package Manager (Install Guide)
  • 8GB+ RAM and 20GB+ free disk space

⚡ Get Started ​

bash
# 1. Clone and setup