HNSW (Hierarchical Navigable Small World) is an efficient algorithm for approximate nearest neighbor (ANN) search, designed to handle large-scale, high-dimensional data. It builds a graph-based index where data points are nodes, and edges represent their proximity. The algorithm organizes the graph into hierarchical layers. The top layers have fewer nodes and represent coarse-grained views of the dataset, while the lower layers have denser connections and finer granularity. During a search, HNSW starts at the top layer and navigates down, finding the nearest neighbors quickly by skipping irrelevant nodes. HNSW is valued for its balance of speed and accuracy, making it suitable for real-time applications like recommendation systems, image retrieval, and natural language queries. It’s commonly integrated into vector databases for managing embeddings efficiently.
What is HNSW?
- Evaluating Your RAG Applications: Methods and Metrics
- Getting Started with Milvus
- Vector Database 101: Everything You Need to Know
- AI & Machine Learning
- Natural Language Processing (NLP) Advanced Guide
- All learn series →
Recommended AI Learn Series
VectorDB for GenAI Apps
Zilliz Cloud is a managed vector database perfect for building GenAI applications.
Try Zilliz Cloud for FreeKeep Reading
How do recommender systems predict user preferences?
Recommender systems predict user preferences by analyzing both historical data and user behavior to suggest content that
What is the role of automation in big data workflows?
Automation plays a crucial role in big data workflows by streamlining various processes and improving efficiency. In big
What is a hyperparameter in neural networks?
A hyperparameter is a parameter that controls the training process of a neural network, but is set before training begin