What is Vector Index?
A vector index is a specialized data structure that organizes high-dimensional vectors (embeddings) to support fast similarity searches, such as finding the nearest neighbors to a query vector.
It works by using algorithms like HNSW, IVF, or product quantization to partition or graph the vectors, allowing approximate nearest-neighbor (ANN) queries instead of brute-force comparison of every vector.
The index trades a small amount of accuracy for large gains in speed and scalability, making it practical to search millions or billions of vectors in milliseconds.
Vector indexes are typically stored inside vector databases and are updated incrementally as new embeddings are added.
Example
An e-commerce site converts product descriptions into 768-dimensional vectors and builds a vector index; when a shopper searches for 'comfortable running shoes,' the system quickly retrieves the top-10 most similar product vectors.
Why it matters
Vector indexes power semantic search, retrieval-augmented generation (RAG), and recommendation systems, enabling modern AI applications to find relevant information at scale.
Frequently asked questions
Traditional indexes speed up exact matches on scalar values; vector indexes enable fast approximate similarity searches on high-dimensional numeric vectors.
Related terms
A vector database is a specialized database designed to store and query high-dimensional vector embeddings, enabling fast similarity searches instead of traditional exact-match queries.
An embedding (or vector embedding) is a way to represent words, sentences, or other data as dense numerical vectors in a high-dimensional space so that similar items end up close together.
Cosine similarity measures how similar two vectors are by computing the cosine of the angle between them, ignoring their magnitudes.
Batch size is the number of training examples processed together in a single forward and backward pass during model training.
Chunking is the process of breaking large datasets, documents, or files into smaller, fixed-size or semantically meaningful segments. It is a common data preprocessing step in AI/ML pipelines to manage memory and enable efficient processing.
Data augmentation is a technique that artificially increases the size and diversity of a training dataset by creating modified versions of existing data samples.