jamwithai/production-agentic-rag-course

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) 📖 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 Complete Week 7 architecture showing Telegram bot integration with the agentic RAG system LangGraph Agentic RAG Workflow 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 # 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 Complete infrastructure setup with Docker Compose FastAPI development with automatic documentation and health checks PostgreSQL database configuration and management OpenSearch hybrid search engine setup Ollama local LLM service configuration Service orchestration and health monitoring Professional development environment with code quality tools 🏗️ Architecture Overview Infrastructure Components: FastAPI : REST endpoints with async support (Port 8000) PostgreSQL 16 : Paper metadata storage (Port 5432) OpenSearch 2.19 : Search engine with dashboards (Ports 9200, 5601) Apache Airflow 3.0 : Workflow orchestration (Port 8080) Ollama : Local LLM server (Port 11434) 📓 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 arXiv API integration with rate limiting and retry logic Scientific PDF parsing using Docling Automated data ingestion pipelines with Apache Airflow Metadata extraction and storage workflows Complete paper processing from API to database 🏗️ Architecture Overview Data Pipeline Components: MetadataFetcher : 🎯 Main orchestrator coordinating the entire pipeline ArxivClient : Rate-limited paper fetching with retry logic PDFParserService : Docling-powered scientific document processing Airflow DAGs : Automated daily paper ingestion workflows PostgreSQL Storage : Structured paper metadata and content 📓 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 Why keyword search is essential for RAG systems (foundation first approach) OpenSearch index management, mappings, and search optimization BM25 algorithm and the math behind effective keyword search Query DSL for building complex search queries with filters and boosting Search analytics for measuring relevance and performance Production patterns used by real companies 🏗️ Architecture Overview Search Infrastructure Components: OpenSearch Service : src/services/opensearch/ - Professional search service implementation Search API : src/routers/search.py - Search API endpoints with BM25 scoring Learning Materials : notebooks/week3/ - Complete OpenSearch integration guide Quality Metrics : Precision, recall, and relevance scoring 📓 Setup Guide # Launch the Week 3 notebook uv run jupyter notebook notebooks/week3/week3_opensearch.