Vector Indexing

Vector Indexing is the core data structure (e.g., HNSW) that organizes high-dimensional vector embeddings, enabling millisecond-latency Approximate Nearest Neighbor (ANN) similarity search at massive scale.

This technology is mission-critical for modern AI applications: it transforms brute-force, O(n) vector search into an efficient, sub-linear process, typically O(log n) or better. Vector indexing algorithms, such as Hierarchical Navigable Small World (HNSW) or Inverted File (IVF), structure the embeddings to minimize the search space. For example, HNSW builds a multi-layer graph, allowing a query vector to quickly traverse to its nearest neighbors, bypassing millions of irrelevant data points. This speed is non-negotiable for use cases like Retrieval-Augmented Generation (RAG) and semantic search, where systems must retrieve the most contextually relevant data from billions of vectors in real-time.

https://weaviate.io/developers/weaviate/concepts/vector-index