Vector Search Glossary¶

This glossary defines key terms related to vector search in ScyllaDB. It covers core concepts essential to understanding how vectors are stored, indexed, and queried.

• Vector - An ordered list of numbers (floats) representing data, such as text, images, or audio, in a way that captures its meaning or features.

• Vector Type - A native ScyllaDB column type used to store fixed-length numeric vectors directly in a table for similarity search. See Data Types - Vectors in the ScyllaDB documentation.

• Vector Search Index - In ScyllaDB, a USearch index built on a vector column that accelerates similarity queries. Unlike traditional indexes (for exact matches or ranges), a vector search index is optimized for approximate nearest neighbor (ANN) lookups over high-dimensional data.

• USearch Index - A high-performance, in-memory vector index library developed by Unum, designed for fast approximate nearest-neighbor (ANN) search. ScyllaDB uses USearch as the underlying engine for its Vector Search Index to deliver low-latency similarity queries and efficient memory utilization.

• ANN (Approximate Nearest Neighbor) Search - A search technique that efficiently finds data points in a large dataset that are most similar to a given query vector. Instead of looking for an exact match, ANN speeds up the search by accepting results that are close enough - making it ideal for working with large datasets and high-dimensional vector spaces in applications like semantic search, recommendations, and generative AI.

• Similarity Search - A technique for finding items in a dataset that are most similar to a query vector, using a distance or similarity measure. It is commonly used in high-dimensional vector spaces to retrieve approximate matches efficiently.

• Semantic Search - A type of similarity search that compares the meaning of a query and data items using vector embeddings. It enables context-aware retrieval by focusing on semantic relevance rather than exact terms.

• Embedding - A vector generated by a machine learning model to represent raw data in a numerical form. ScyllaDB can store and query embeddings generated by an external tool and inserted into the database.

• Similarity Function (Distance Metric) - A mathematical function that measures how close two vectors are. In ScyllaDB, three similarity functions are supported: dot product, cosine similarity, and Euclidean distance.