Vector Databases

Chapter 4. Semantic Search with SQLite3

In this chapter, we will build a personal knowledge management system using semantic search over Reddit content. You will learn how to search by meaning rather than by keywords by combining SQLite’s familiar SQL interface with vector similarity search.

In Chapter 3, we explored FAISS as a dedicated vector similarity search engine. Here we use sqlite-vss, a SQLite extension that wraps FAISS, integrating vector search into the relational database world. This combination lets us perform semantic retrieval together with relational metadata filtering in a single SQL workflow. In practice, most sqlite-vss queries retrieve nearest-neighbor candidates first and then apply metadata filters as a second step (filters are not pushed down into the FAISS search), so we’ll use an “overfetch-then-filter” pattern to reliably return enough results.

Our goal is practical: imagine that you have saved hundreds of Reddit posts across various topics. With this system, you can query for posts similar in meaning to “deep learning for edge devices,” regardless of exact keywords, while filtering by time period or community. By the end of this chapter, you will have a working application demonstrating these capabilities.

Understanding the SQLite Vector Similarity Search Extension

The SQLite Vector Similarity Search (VSS) extension (sqlite-vss) adds vector storage and similarity search to SQLite. It transforms SQLite into a capable vector database suitable for personal-scale ...