Production Agentic RAG Course

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GitHub Python MIT

Description

A production-focused Agentic RAG course teaching how to build scalable, reliable RAG agent systems with indexing strategies, retrieval optimization, and monitoring.

Key Features

  • 7-week progressive course covering infrastructure to Agentic RAG
  • Production-grade hybrid search: BM25 keyword + vector semantic retrieval
  • LangGraph integration for intelligent decision-making and query rewriting
  • Production monitoring with Langfuse tracing and Redis caching
  • Telegram Bot integration for mobile conversational access
  • One-click deployment with Docker Compose

Use Cases

πŸ’‘ Learn end-to-end production-grade RAG system development
πŸ’‘ Build automated academic paper retrieval and Q&A assistants
πŸ’‘ Master Agentic RAG with intelligent routing and adaptive retrieval
πŸ’‘ Deploy reliable AI systems with monitoring and caching

Tags

rag production course agent scaling

Categories

πŸ“š RAG Tools πŸ“Š Observability

Quick Start

1. Clone the repository and navigate to the project directory
2. Copy `.env.example` to `.env` and configure API keys
3. Install dependencies: `uv sync`
4. Start services: `docker compose up --build -d`
5. Access `http://localhost:7861` for the Gradio interface
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