vektorihakua

Vektorihaku, known in English as vector search or vector similarity search, is a method for searching through databases of high-dimensional data. Instead of comparing keywords or exact matches, vector search compares the semantic meaning of the data. This is achieved by representing data items, such as text, images, or audio, as numerical vectors in a multi-dimensional space. These vectors, often generated by machine learning models like neural networks, capture the underlying characteristics and relationships of the data.

The core principle of vector search is that items with similar meanings or content will have vectors that are close to each other in this high-dimensional space. When a query is made, it is also converted into a vector. The search then involves finding the vectors in the database that are closest to the query vector, typically using distance metrics like cosine similarity or Euclidean distance. This allows for sophisticated searches, such as finding images similar to a given image, retrieving documents related to a query's meaning rather than just its keywords, or recommending products based on user preferences.

Vector search is a fundamental component in many modern AI applications, including recommendation systems, image and video retrieval, natural language processing tasks like question answering and semantic search, and anomaly detection. The efficiency of vector search is crucial, leading to the development of specialized vector databases and indexing techniques designed to handle the computational challenges of searching through millions or billions of high-dimensional vectors.