Use External Knowledge - Build a Cache-Augmented Generation (CAG) System

Large Language Models (LLMs) have revolutionized how we interact with information, but they have a fundamental limitation: their knowledge is generally frozen at the time of training. They don’t inherently know about your specific project documents, the latest company reports, or real-time web content. To build truly useful AI applications, we often need to augment these models with external, up-to-date, or private knowledge.

While complex techniques like Retrieval-Augmented Generation (RAG) exist to dynamically fetch relevant information snippets, they introduce their own challenges, including retrieval latency and the complexity of building and maintaining the retrieval pipeline itself. But what if there’s a simpler way, especially when the knowledge base isn’t constantly changing or astronomically large?

This tutorial introduces Cache-Augmented Generation (CAG)¹, a straightforward yet powerful approach that leverages the dramatically increased context windows of modern LLMs. Instead of intricate retrieval mechanisms, CAG takes a more direct route: it loads the entire relevant external knowledge base directly into the LLM’s context window alongside the user’s query. Think of it like giving the LLM an open-book exam where the entire textbook is available on its desk for every question.

In this hands-on guide, you will build CogVault CAG, a practical application demonstrating this technique. CogVault allows users to upload their documents (PDFs, text files) or provide web page URLs, and then engage in a conversation with an AI assistant that uses the full content of these sources as its knowledge base. We will focus on building this system using local tools, including Ollama for running the LLM, LangChain for structuring the interaction, Docling for document processing, and Streamlit for the user interface.

What is a Cache-Augmented Generation (CAG)?

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