Vector representation is a method used to encode data, such as words, documents, images, or other entities, into numerical forms that can be easily processed by machine learning algorithms. In this representation, entities are expressed as vectors—mathematical objects that have both magnitude and d...
Vector similarity search uses machine learning to translate the similarity of text, images, or audio into a vector space, making search faster, more accurate, and more scalable.
Two major types of vector search algorithms are k-nearest neighbors (kNN) and approximate nearest neighbor (ANN). BetweenkNN and ANN, the latter offers a balance between accuracy and efficiency, making it better suited for large-scale applications. Some well-known ANN algorithms include Inverted ...
There are several vector search algorithms used to find vectors in the database that are most similar, or closest, to the query vector. Among the commonly used algorithms are approximate nearest neighbor and K-nearest neighbor; both use mathematical techniques to compute the similarity among and p...
Similarity search, or finding approximate nearest neighbors, is becoming an increasingly important tool to find the closest matches for a given query object in large scale database. Recently, learning hashing-based methods have attracted considerable attention due to their computational and memory ...
In this article Algorithms used in vector search How nearest neighbor search works Similarity metrics used to measure nearness Scores in a vector search results Show 2 more During vector query execution, the search engine looks for similar vectors to find the best candidates to return in ...
There are also various algorithms which can be used to search a vector database to find similarity. These include: ANN (approximate nearest neighbor): an algorithm that uses distance algorithms to locate nearby vectors. kNN (k-nearest neighbors): an algorithm that uses proximity to make predictio...
For increased oversight, the ranking algorithms should provide some interpretability into why certain passages are deemed relevant. Modeling Diverse Relationships A core limitation of standard vector search is its singular focus on semantic similarity. ...
While it is possible for you to create your own chunking algorithms, utilizing this functionality could save you time and aid in faster development with a pre-packaged SQL function. View Documentation Chainable Utility Functions for Vectors
AnalyticDB for MySQL supports integrated query by usingk-nearest neighbor (KNN) and radius nearest neighbor (RNN) algorithms. For example, you can compare the similarity between two sets of vectors. Real-time updates AnalyticDB for MySQL supports high-concurrency real-time writes and updates. You...