Build a Simple RAG Pipeline in 30 Minutes

Learn to construct a modular Retrieval-Augmented Generation (RAG) pipeline in Python that enables document ingestion, embedding generation, and end-to-end quality evaluation through a single command-line interface. Master the fundamental architecture of RAG systems by setting up a database for document storage, implementing core components for text processing and retrieval, and establishing evaluation mechanisms to test pipeline performance. Discover how to connect all pipeline components seamlessly, execute the complete workflow, and iteratively upgrade the system for improved results. Gain hands-on experience with practical RAG implementation techniques including document chunking, vector embeddings, similarity search, and response generation while building a production-ready pipeline that can be easily extended and customized for various use cases.