Build a Retrieval-Augmented Generation System

Learn to build an advanced Retrieval-Augmented Generation (RAG) system using LangChain, Ollama, and hybrid search. Process documents, create embeddings, and query your knowledge base with a local LLM.

Updated Jun 20, 202513 min read

Large Language Models (LLMs) are powerful, but their knowledge is fundamentally limited to the data they were trained on. This creates a gap when we need them to reason about private documents, real-time information, or specialized domains. While the Cache-Augmented Generation (CAG) approach—loading entire documents into the context—is simple and effective for smaller knowledge bases, it doesn't scale. What happens when your knowledge base is thousands of pages long or consists of countless documents?

This is where Retrieval-Augmented Generation (RAG) becomes essential. Instead of relying on the LLM's static internal knowledge or trying to fit everything into its context window, RAG systems dynamically retrieve the most relevant pieces of information from an external knowledge base and provide them to the LLM as targeted context to answer a specific query. This approach allows an LLM to generate responses that are grounded in external, up-to-date, and verifiable facts.

In this tutorial, you will build CogVault RAG, a RAG application that demonstrates how to transform raw documents into a queryable knowledge base. You will learn to implement a multi-stage pipeline that includes document ingestion, chunking, hybrid search (combining keyword and semantic search), and re-ranking to ensure the LLM receives the highest quality context. We will build this entire system using local tools, including Ollama for the LLM, LangChain for the RAG framework, and Streamlit for the user interface.