Retrieval Augmented Generation (RAG) Using Python

Harness the potential of Retrieval Augmented Generation (RAG) to build more robust and reliable AI applications. This book provides a comprehensive, hands-on approach to implementing RAG using Python, focusing on real-world NLP and AI use cases. You'll explore: The core concepts of RAG and its advantages over traditional language models. Practical Python implementations using popular libraries for NLP, vector databases, and large language model APIs. Techniques for efficient information retrieval, including semantic search and vector embeddings. Strategies for optimizing RAG pipelines for performance and accuracy. Applications in question answering, chatbots, document summarization, and more. Whether you're a seasoned developer or just starting with AI, this book equips you with the knowledge and skills to build powerful, context-aware applications with RAG.