RAG with Python Cookbook. Practical Recipes from Data Preprocessing to LLM Agents

As businesses race to unlock the full potential of large language models (LLMs), a critical challenge has emerged: How do you connect these tools to real-time, external data to solve real-world problems? Retrieval-augmented generation (RAG) is the answer. By combining LLMs with information retrieval, RAG empowers you to build everything from intelligent chatbots to autonomous, task-solving agents. Packed with over 70 practical recipes, this go-to guide tackles a wide range of GenAI applications through structured hands-on learning. Author Dominik Polzer provides the tools you need to design, implement, and optimize RAG systems for your unique use cases. Whether you're working with simple data retrieval or designing cutting-edge autonomous agents, this cookbook will help you stay ahead of the curve. Learn core RAG components including embedding, retrieval, and generation techniques Understand advanced workflows like semantic-aware chunking and multi-query prompting Build custom solutions such as chatbots and autonomous agents for specific data challenges Continuously evaluate and optimize systems for accuracy, relevance, and performance