RAG with Python Cookbook

Chapter 1. Getting Started with RAG

Large language models (LLMs) have transformed how we approach complex cognitive tasks—from writing production code to analyzing financial reports to translating dozens of languages. Complementary foundation models now handle vision, speech synthesis and recognition, audio processing, image generation, and multimodal reasoning, all built on similar transformer architectures that can process and generate human-like content across multiple domains.

Despite these capabilities, these models have fundamental structural limitations. They don’t have access to your private or confidential data unless you provide it. Their context windows limit how much information they can consider at a time, which makes long documents expensive or impossible to analyze in a single pass. And when these models lack the right information, they tend to hallucinate rather than admit uncertainty.

Retrieval augmented generation (RAG) addresses all three problems at once. It gives the model controlled access to external knowledge, lets it work with far more information than fits into a single prompt, and grounds its answers in retrieved evidence instead of guesswork. When a user asks a question, the system first retrieves relevant information from a knowledge source and then passes that context to the model to generate a response.

Figure 1-1 shows the simplest form of a RAG system. A retriever searches a knowledge source such as a vector store or database and returns relevant ...