Similarity search

Similarity Search (Nearest Neighbor Search) identifies data points most alike a query by comparing high-dimensional vector embeddings, not keywords.

This technology is mission-critical for modern AI: it translates complex, unstructured data (text, images, audio) into numerical vector embeddings, then uses algorithms like Approximate Nearest Neighbor (ANN) or metrics like Cosine Similarity to measure the 'distance' between them. The result is lightning-fast, semantically relevant retrieval from massive datasets, often billions of vectors deep. Key applications include powering recommendation engines (e.g., Netflix suggestions), semantic search in Natural Language Processing (NLP), and large-scale image retrieval systems.

https://milvus.io/