What is a Vector Index?

A query vector is a high-dimensional representation of the item you are searching for. In a vector search system, the query vector is compared against other vectors in a vector database to find the most similar results, improving search speed and semantic search accuracy.

Vector embeddings are numeric representations of items such as text or images. They are stored within specialized data structures that support similarity search. Choosing the right data structure and vector space ensures that vector embeddings can be retrieved quickly even in large datasets.

Vector databases often rely on complex data structures such as Hierarchical Navigable Small World (HNSW) graphs to balance search speed and accuracy. These structures reduce the search space and help manage high dimensional vectors effectively, enabling fast vector search over big datasets.

Natural language processing (NLP) techniques generate vector representations of words or sentences. These vectors are stored in a vector index so that semantic search can return results based on meaning rather than exact keywords. Hash functions and other indexing methods are used to organize these vectors efficiently.

The search space refers to the set of vectors that the system must examine to find similar items. By using efficient indexing methods and reducing the search space, vector databases can maintain high search speed even as datasets grow. Choosing appropriate indexing algorithms and data structures ensures scalable performance.