A vector database stores vectors—fixed-length numerical lists representing data features—and enables efficient search using Approximate Nearest Neighbor algorithms. These high-dimensional vectors encode characteristics of data such as text, images, or audio, derived via feature extraction, word embeddings, or deep learning. Vector databases support applications including similarity search, semantic search, and recommendations engines. They are crucial in retrieval-augmented generation (RAG), where embeddings of domain documents are stored to enhance large language model responses by retrieving relevant context based on a user query vector.
Techniques
The most important techniques for similarity search on high-dimensional vectors include:
- Hierarchical Navigable Small World (HNSW) graphs
- Locality-sensitive Hashing (LSH) and Sketching
- Product Quantization (PQ)
- Inverted Files
and combinations of these techniques.
In recent benchmarks, HNSW-based implementations have been among the best performers.89 Conferences such as the International Conference on Similarity Search and Applications, SISAP and the Conference on Neural Information Processing Systems (NeurIPS) host competitions on vector search in large databases.
Implementations
This is a dynamic list and may never be able to satisfy particular standards for completeness. You can help by adding missing items with reliable sources.
See also
- Curse of dimensionality – Difficulties arising when analyzing data with many aspects ("dimensions")
- Machine learning – Study of algorithms that improve automatically through experience
- Nearest neighbor search – Optimization problem in computer science
- Recommender system – System to predict users' preferences
External links
- Sawers, Paul (2024-04-20). "Why vector databases are having a moment as the AI hype cycle peaks". TechCrunch. Retrieved 2024-04-23.
References
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