What is vector search?
Vector search is a retrieval method that finds information based on similarity in meaning rather than only matching exact keywords. It represents queries and searchable content as numerical vectors, allowing the system to identify conceptually related results even when different words are used.
Can vector search find results without matching exact keywords?
Yes. Vector search can retrieve semantically similar information even when the query and result use different terminology. Because it compares numerical representations of meaning, it can recognize conceptual relationships that traditional exact keyword matching may overlook.
What is a vector in AI search?
A vector is a numerical representation of data, such as text or an image, within a multidimensional space. In AI search, vectors capture characteristics of the underlying information so systems can compare them mathematically and identify items with similar meaning or features.
What are embeddings, and why does vector search need them?
Embeddings are numerical representations that encode meaningful characteristics of text, images, or other data. Vector search uses these embeddings to calculate similarity, enabling it to find related information based on semantic meaning instead of relying entirely on matching words.
How does vector search measure similarity between two pieces of content?
Vector search compares the mathematical distance or similarity between their vector representations using measures such as cosine similarity, dot product, or Euclidean distance. Vectors positioned closer according to the selected measure are generally treated as more similar.
What is a vector database?
A vector database is designed to store, index, and retrieve high-dimensional vector embeddings efficiently. It allows applications to search large collections of information according to vector similarity, making it useful for semantic search, recommendation systems, RAG, and other AI applications.
When should vector search be used instead of keyword search?
Vector search is useful when meaning and context matter more than exact terminology, such as natural-language questions, recommendation systems, and semantic retrieval. Keyword search remains valuable for exact names, codes, or phrases, and many modern systems combine both approaches through hybrid search.
Can vector search work with images and other non-text data?
Yes. Vector search can work with images, audio, video, and other data when suitable models convert them into embeddings. This enables applications to retrieve conceptually or visually similar items and makes vector search an important technology for multimodal AI systems.