Data science
fromMedium
11 hours agoIVF vs HNSW Indexing in Milvus
Approximate Nearest Neighbor indexes in Milvus enable fast vector searches, achieving over 95% recall with significantly higher throughput compared to brute-force methods.
In the previous lesson, you learned how to turn text into embeddings - compact, high-dimensional vectors that capture semantic meaning. By computing cosine similarity between these vectors, you could find which sentences or paragraphs were most alike. That worked beautifully for a small handcrafted corpus of 30-40 paragraphs. But what if your dataset grows to millions of documents or billions of image embeddings? Suddenly, your brute-force search breaks down - and that's where Approximate Nearest Neighbor (ANN) methods come to the rescue.