production-agentic-rag-course README

The project's README from github.com/jamwithai/production-agentic-rag-course. As one markdown file: README.md.

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)

Python Version FastAPI OpenSearch Docker Status


RAG Architecture

📖 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

🏗️ 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:

🚀 Quick Start

📋 Prerequisites

⚡ Get Started

# 1. Clone and setup
git clone <repository-url>
cd arxiv-paper-curator

# 2. Configure environment (IMPORTANT!)
cp .env.example .env
# The .env file contains all necessary configuration for OpenSearch, 
# arXiv API, and service connections. Defaults work out of the box.
# You need to add Jina embeddings free api key and langfuse keys (check the blogs)

# 3. Install dependencies
uv sync

# 4. Start all services
docker compose up --build -d

# 5. Verify everything works
curl http://localhost:8000/api/v1/health

📚 Weekly Learning Path

Week Topic Blog Post Code Release
Week 0 The Mother of AI project - 6 phases The Mother of AI project -
Week 1 Infrastructure Foundation The Infrastructure That Powers RAG Systems week1.0
Week 2 Data Ingestion Pipeline Building Data Ingestion Pipelines for RAG week2.0
Week 3 OpenSearch ingestion & BM25 retrieval The Search Foundation Every RAG System Needs week3.0
Week 4 Chunking & Hybrid Search The Chunking Strategy That Makes Hybrid Search Work week4.0
Week 5 Complete RAG system The Complete RAG System week5.0
Week 6 Production monitoring & caching Production-ready RAG: Monitoring & Caching week6.0
Week 7 Agentic RAG & Telegram Bot Agentic RAG with LangGraph and Telegram week7.0

📥 Clone a specific week's release:

# Clone a specific week's code
git clone --branch <WEEK_TAG> https://github.com/jamwithai/arxiv-paper-curator
cd arxiv-paper-curator
uv sync
docker compose down -v
docker compose up --build -d

# Replace <WEEK_TAG> with: week1.0, week2.0, etc.

📊 Access Your Services

Service URL Purpose
API Documentation http://localhost:8000/docs Interactive API testing
Gradio RAG Interface http://localhost:7861 User-friendly chat interface
Langfuse Dashboard http://localhost:3000 RAG pipeline monitoring & tracing
Airflow Dashboard http://localhost:8080 Workflow management
OpenSearch Dashboards http://localhost:5601 Hybrid search engine UI

NOTE: Check airflow/simple_auth_manager_passwords.json.generated for Airflow username and password

📚 Week 1: Infrastructure Foundation ✅

Start here! Master the infrastructure that powers modern RAG systems.

🎯 Learning Objectives

🏗️ Architecture Overview

Week 1 Infrastructure Setup

Infrastructure Components:

📓 Setup Guide

# Launch the Week 1 notebook
uv run jupyter notebook notebooks/week1/week1_setup.ipynb

Completion Guide: Follow the Week 1 notebook for hands-on setup and verification steps.

📖 Deep Dive

Blog Post: The Infrastructure That Powers RAG Systems - Detailed walkthrough and production insights

📚 Week 2: Data Ingestion Pipeline ✅

Building on Week 1 infrastructure: Learn to fetch, process, and store academic papers automatically.

🎯 Learning Objectives

🏗️ Architecture Overview

Week 2 Data Ingestion Architecture

Data Pipeline Components:

📓 Implementation Guide

# Launch the Week 2 notebook  
uv run jupyter notebook notebooks/week2/week2_arxiv_integration.ipynb

Completion Guide: Follow the Week 2 notebook for hands-on implementation and verification steps.

📖 Deep Dive

Blog Post: Building Data Ingestion Pipelines for RAG - arXiv API integration and PDF processing

📚 Week 3: Keyword Search First - The Critical Foundation

Building on Weeks 1-2 foundation: Implement the keyword search foundation that professional RAG systems rely on.

🎯 Learning Objectives

🏗️ Architecture Overview

Week 3 OpenSearch Flow Architecture

Search Infrastructure Components:

📓 Setup Guide

# Launch the Week 3 notebook
uv run jupyter notebook notebooks/week3/week3_opensearch.ipynb

Completion Guide: Follow the Week 3 notebook for hands-on OpenSearch setup and BM25 search implementation.

📖 Deep Dive

Blog Post: The Search Foundation Every RAG System Needs - Complete BM25 implementation with OpenSearch

📚 Week 4: Chunking & Hybrid Search - The Semantic Layer

Building on Week 3 foundation: Add the semantic layer that makes search truly intelligent.

🎯 Learning Objectives

🏗️ Architecture Overview

Week 4 Hybrid Search Architecture

Hybrid Search Infrastructure Components:

📓 Setup Guide

# Launch the Week 4 notebook
uv run jupyter notebook notebooks/week4/week4_hybrid_search.ipynb

Completion Guide: Follow the Week 4 notebook for hands-on implementation and verification steps.

📖 Deep Dive

Blog Post: The Chunking Strategy That Makes Hybrid Search Work - Production chunking and RRF fusion implementation

📚 Week 5: Complete RAG Pipeline with LLM Integration

Building on Week 4 hybrid search: Add the LLM layer that turns search into intelligent conversation.

🎯 Learning Objectives

🏗️ Architecture Overview

Week 5 Complete RAG System Architecture

Complete RAG Infrastructure Components:

📓 Setup Guide

# Launch the Week 5 notebook
uv run jupyter notebook notebooks/week5/week5_complete_rag_system.ipynb

# Launch Gradio interface
uv run python gradio_launcher.py
# Open http://localhost:7861

Completion Guide: Follow the Week 5 notebook for hands-on LLM integration and RAG pipeline implementation.

📖 Deep Dive

Blog Post: The Complete RAG System - Complete RAG system with local LLM integration and optimization techniques

📚 Week 6: Production Monitoring and Caching

Building on Week 5 complete RAG system: Add observability, performance optimization, and production-grade monitoring.

🎯 Learning Objectives

🏗️ Architecture Overview

Week 6 Monitoring & Caching Architecture

Production Infrastructure Components:

📓 Setup Guide

# Launch the Week 6 notebook
uv run jupyter notebook notebooks/week6/week6_cache_testing.ipynb

Completion Guide: Follow the Week 6 notebook for hands-on Langfuse tracing and Redis caching implementation.

📖 Deep Dive

Blog Post: Production-ready RAG: Monitoring & Caching - Production-ready RAG with monitoring and caching

📚 Week 7: Agentic RAG with LangGraph and Telegram Bot

Building on Week 6 production system: Add intelligent reasoning, multi-step decision-making, and Telegram bot integration for mobile-first AI interactions.

🎯 Learning Objectives

🏗️ Architecture Overview

Week 7 Agentic RAG & Telegram Architecture

Agentic RAG Infrastructure Components:

📓 Setup Guide

# Launch the Week 7 notebook
uv run jupyter notebook notebooks/week7/week7_agentic_rag.ipynb

Completion Guide: Follow the Week 7 notebook for hands-on LangGraph agentic RAG and Telegram bot implementation.

📖 Deep Dive

Blog Post: Agentic RAG with LangGraph and Telegram - Building intelligent agents with decision-making, adaptive retrieval, and mobile access

⚙️ Configuration

Setup:

cp .env.example .env
# Edit .env for your environment

Key Variables:

Complete Configuration: See .env.example for all available options and detailed documentation.

🔧 Reference & Development Guide

🛠️ Technology Stack

Service Purpose Status
FastAPI REST API with automatic docs ✅ Ready
PostgreSQL 16 Paper metadata and content storage ✅ Ready
OpenSearch 2.19 Hybrid search engine (BM25 + Vector) ✅ Ready
Apache Airflow 3.0 Workflow automation ✅ Ready
Jina AI Embedding generation (Week 4) ✅ Ready
Ollama Local LLM serving (Week 5) ✅ Ready
Redis High-performance caching (Week 6) ✅ Ready
Langfuse RAG pipeline observability (Week 6) ✅ Ready

Development Tools: UV, Ruff, MyPy, Pytest, Docker Compose

🏗️ Project Structure

arxiv-paper-curator/
├── src/                    # Main application code
│   ├── routers/            # API endpoints (search, ask, papers)
│   ├── services/           # Business logic (opensearch, ollama, agents, cache)
│   ├── models/             # Database models (SQLAlchemy)
│   ├── schemas/            # Pydantic validation schemas
│   └── config.py           # Environment configuration
├── notebooks/              # Weekly learning materials (week1-7)
├── airflow/                # Workflow orchestration (DAGs)
├── tests/                  # Test suite
└── compose.yml             # Docker service orchestration

📡 API Endpoints Reference

Endpoint Method Description Week
/health GET Service health check Week 1
/api/v1/papers GET List stored papers Week 2
/api/v1/papers/{id} GET Get specific paper Week 2
/api/v1/search POST BM25 keyword search Week 3
/api/v1/hybrid-search/ POST Hybrid search (BM25 + Vector) Week 4

API Documentation: Visit http://localhost:8000/docs for interactive API explorer

🔧 Essential Commands

# View all available commands
make help

# Quick workflow
make start         # Start all services
make health        # Check all services health
make test          # Run tests
make stop          # Stop services

All Available Commands

Command Description
make start Start all services
make stop Stop all services
make restart Restart all services
make status Show service status
make logs Show service logs
make health Check all services health
make setup Install Python dependencies
make format Format code
make lint Lint and type check
make test Run tests
make test-cov Run tests with coverage
make clean Clean up everything

Direct Commands (Alternative)

# If you prefer using commands directly
docker compose up --build -d    # Start services
docker compose ps               # Check status
docker compose logs            # View logs
uv run pytest                 # Run tests

🎓 Target Audience

Who Why
AI/ML Engineers Learn production RAG architecture beyond tutorials
Software Engineers Build end-to-end AI applications with best practices
Data Scientists Implement production AI systems using modern tools

🛠️ Troubleshooting

Common Issues:

Get Help:

💰 Cost Structure

This course is completely free! You'll only need minimal costs for optional services:

🎉 Ready to Start Your AI Engineering Journey?

Begin with the Week 1 setup notebook and build your first production RAG system!

For learners who want to master modern AI engineering

Built with love by Shirin Khosravi Jam & Shantanu Ladhwe

Star History

Star History Chart

📄 License

MIT License - see LICENSE file for details.

production-agentic-rag-course (jamwithai/production-agentic-rag-course)

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9.8K ·GitHub
Production-agentic-rag-course
What is this?This is a hands-on, week-by-week course that teaches you to build 'arXiv Paper Curator', a production-grade Retrieval-Augmented Generation (RAG) system that fetches academic papers, indexes them, and answers research questions about them.
Is this a ready-made app or a course I follow step by step?It is a learner-focused course delivered through a GitHub repo: each week has its own notebook, blog post, and code release that progressively builds the same arXiv research-assistant application.
How do I get started with the course?Clone a specific week's code release (starting with Week 1) and follow that week's notebook to stand up the Docker Compose stack covering FastAPI, PostgreSQL, OpenSearch, and Airflow.
What do I need installed before starting?You need Docker Desktop with Compose, Python 3.12+, the UV package manager, and at least 8GB of RAM plus 20GB of free disk space.
Why does the course start with keyword search instead of vectors?The course deliberately teaches BM25 keyword search as the foundation (Week 3) before adding embeddings for hybrid search (Week 4), because that is the order successful production systems follow, unlike AI-first tutorials.
Does the RAG pipeline keep my data local?Yes, from Week 5 onward the system answers questions using a locally-run LLM served through Ollama instead of a cloud API.
What makes this course different from typical RAG tutorials?It follows the professional path used by real companies: build solid BM25 keyword search first, then add vector embeddings for hybrid retrieval, instead of jumping straight to vector search.
What do I end up with by Week 7?An agentic RAG system built with LangGraph that grades retrieved documents, rewrites queries, applies guardrails against out-of-domain questions, and is reachable through a Telegram bot for mobile access.
How much does caching speed up responses?Week 6 adds Redis caching alongside Langfuse tracing, which the course reports delivers a 150-400x speedup on repeated queries.
Can I jump straight to a later week?Each week has its own clonable code release, but the system is built incrementally, so later weeks assume the infrastructure and services set up in earlier weeks.
What are similar projects?agentic-rag-for-dummies, goclaw, DocsGPT, m_flow, serviette, XRAG, ai-agents-the-definitive-guide, ruflo
Which topics is it in?bm25, telegram-bot, langgraph, arxiv, retrieval-augmented-generation, jupyter-notebook, gradio, multi-agent-systems, vector-search, caching, semantic-search, fastapi
Creator
jamwithai
Size
10.1 MB
Stars
9.8K
Updated
CommitApr 05Releaseweek7.0Nov 26
Age
1y
Commits · 90 Days