RAG with Python Cookbook, Deepak Dhyani, 2026

RAG with Python Cookbook, Deepak Dhyani, 2026.

   This book is written for engineers, architects, data scientists, and technical practitioners who want to build robust and production-grade RAG applications. By the end of this book, you will have the skills to design complete RAG pipelines, optimize retrieval workflows, integrate LLMs effectively, and implement advanced agentic retrieval strategies using modern Python libraries




Topic-based document splitting.
Topic-based splitting is an advanced document segmentation technique where document chunks are divided not by size or structure, but by semantic coherence, where each chunk represents a distinct concept, theme, or topic.

Rather than arbitrarily slicing a document at fixed lengths or structural breaks, this approach splits the document based on the topic of the content, which ensures that each chunk's context to a topic is not lost.

Topic-based splitting is a semantically aware strategy that improves how documents are prepared for retrieval. It aligns closely with the strengths of modern LLMs and ensures that the knowledge fed into the system is relevant, coherent, and well-structured.

Contents.
1. Foundation of Retrieval-augmented Generation.
2. Document Loaders for RAG Pipelines.
3. Document Splitting Techniques.
4. Embedding Strategies for Vector Retrieval.
5. Vector Stores for Semantic Retrieval.
6. Efficient Retrieval from Vector Store.
7. Response Generation with LLM in RAG Systems.
8. Prompt Engineering for RAG Systems.
9. Effective Search for RAG Systems.
10. Implementing RAG with Chains.
11. Agentic RAG with Dynamic Retrieval.
Index.



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