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RAG in Action: Building Retrieval-Augmented Generation Systems with Python
Paperback
Publisher: Independently Published
Published: Apr 13 2026
Pages: 262
Weight: 1.00
Height: 0.55 Width: 7.50 Depth: 9.25
Language: English
You've seen what LLMs can do. You've also seen what they can't - answer questions about your company's internal documents, your proprietary data, or anything that happened after their training cutoff. The result: confident, fluent, completely wrong. If you've tried to fix this with a basic RAG prototype and found that retrieval quality was poor, answers were unfaithful, or latency was unacceptable, you already know the problem. Shallow tutorials got you started. They won't get you to production.
RAG in Action is the hands-on engineering guide that bridges that gap. Written for working Python developers, it moves beyond blog-post basics to show you exactly how to build reliable, production-grade Retrieval-Augmented Generation systems - with complete, runnable code at every step and honest guidance on tradeoffs.
Inside this book, you'll learn how to:
- Design robust ingestion pipelines that handle messy real-world documents - PDFs, Word files, HTML, CSVs, and more
- Apply the right chunking strategy for your corpus, from fixed-size splits to semantic and hierarchical chunking
- Select, evaluate, and optimize embedding models - including open-source alternatives and quantized options for cost efficiency
- Choose and configure the right vector database (Chroma, Qdrant, Pinecone, pgvector, and more) for your use case
- Implement advanced retrieval techniques including hybrid search, HyDE, RAG Fusion, and cross-encoder re-ranking
- Evaluate your system rigorously using RAGAS and LLM-as-judge frameworks - and know what to fix when it fails
Along the way, you'll build:
- A production-grade enterprise document Q&A system with hybrid retrieval, citation grounding, semantic caching, and a FastAPI backend
- An agentic research assistant that decomposes complex questions, retrieves iteratively from multiple sources, and streams synthesized answers
- A GraphRAG pipeline combining vector and knowledge-graph retrieval for relationship-aware reasoning
- A multimodal ingestion and retrieval system capable of handling figures, charts, tables, and scanned pages
This book is for Python developers who have worked (or dabbled) with LLMs before and are ready to build something real. Whether you're designing a document Q&A system from scratch, rescuing a struggling prototype, or evaluating tooling decisions as a technical lead, RAG in Action gives you the structured, current, complete resource the RAG space has been missing.
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