Vector Databases

Chapter 6. Building a Retrieval-Augmented Generation System with SQLite VSS and Ollama

In previous chapters, we focused on individual components of vector search: creating embeddings and performing similarity queries. Now it’s time to assemble these pieces into a functional retrieval-augmented generation (RAG) system.

Unlike production-scale web applications that rely on distributed cloud clusters, our goal here is to build a high-performance, private, entirely local RAG system that runs on a single desktop. We will use SQLite VSS for our search engine and Ollama as our local LLM “brain.”

RAG systems solve a fundamental limitation of LLMs: their knowledge is frozen at training time, preventing access to private or recent information. By augmenting LLMs with a retrieval mechanism, we create a system that answers questions using up-to-date, domain-specific knowledge.

Our goal is to build a question-answering system that responds intelligently to queries about Reddit content. When a user asks a question, the system will (1) search stored Reddit posts to find the most relevant information, (2) retrieve the best-matching content chunks, (3) provide this context to an LLM, and (4) generate a natural language answer based solely on the retrieved information.

This approach ensures factual accuracy by grounding the LLM’s responses in actual data, significantly reducing (though not eliminating) hallucinations and enabling the system to work with private or specialized content absent from ...