Retrieval-Augmented Generation (RAG) systems depend heavily on efficient vector search to deliver relevant context to language models. Understanding the underlying algorithms—KNN, IVF, and HNSW—is crucial for engineers looking to optimize latency and accuracy. KNN provides exact but slow results, while IVF and HNSW offer approximate search with significant speed gains. Faiss, a library from Meta, implements these algorithms efficiently and is widely adopted in production systems. Choosing the right algorithm involves trade-offs between recall, speed, and memory usage, which vary by dataset size and query patterns. This topic page explores these algorithms, their practical implementations, and how to make informed decisions for your RAG pipeline. Whether you're building a small prototype or scaling to millions of vectors, mastering these techniques is essential for effective retrieval.
This tutorial covers core vector database retrieval algorithms—KNN, IVF, HNSW—and their implementation with Faiss, a key skill for RAG systems. While the content is standard, the topic remains highly relevant for engineers optimizing retrieval performance. The practical focus on algorithm selection and trade-offs offers lasting value.