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

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

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