Vektorihakukone

Vektorihakukone, also known as a vector search engine, is a type of search system that stores and searches data based on vector embeddings. Unlike traditional keyword-based search engines that look for exact or similar word matches, vector search engines operate on numerical representations of data, typically text, images, or audio. These numerical representations, called embeddings, capture the semantic meaning or characteristics of the data.

The process begins with an embedding model, which transforms raw data into high-dimensional vectors. For example, a sentence like "The cat sat on the mat" would be converted into a vector of numbers. Similarly, an image of a cat would also be represented as a vector. The core idea is that semantically similar items will have vectors that are close to each other in the vector space.

When a query is made, it is also converted into a vector using the same embedding model. The vector search engine then finds the vectors in its database that are closest to the query vector, using distance metrics like cosine similarity or Euclidean distance. This allows for retrieval of information based on conceptual similarity rather than just keyword matching. For instance, a search for "feline companion" might return results related to "cat" even if the word "cat" is not explicitly present in the query.

Vektorihakukoneet are increasingly used in applications requiring deep understanding of content, such as recommendation systems, semantic search, plagiarism detection, and image similarity search. They enable more nuanced and context-aware search experiences.