10 chapters42m total
Complete RAG In Python
A comprehensive, developer-first guide to mastering Retrieval-Augmented Generation (RAG) in Python. Build production-grade semantic search pipelines covering document ingestion, text splitting, embeddings, vector stores, re-ranking, and pipeline evaluation.
ragpythonvector-dbembeddingssemantic-searchgenai
Table of Contents
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Chapter 1: Introduction to Advanced RAG
Chapter 2: Getting the Data (Document Loaders)
Chapter 3: Advanced Text Splitters & Semantic Chunking
Chapter 4: Embeddings, Vectors, and Dimensionality
Chapter 5: Vector Databases in Production
Chapter 6: Building the LCEL Retrieval Pipeline
Chapter 7: Advanced Retrieval Algorithms
Chapter 8: Re-ranking and Context Compression
Chapter 9: Conversational RAG & State Management
Chapter 10: RAG Evaluation & Observability (RAGAs)