What is Vector Store?
A vector store is a specialized database that stores high-dimensional vectors (embeddings) and supports fast similarity searches to find the most relevant items.
It works by indexing vectors generated from data like text, images, or audio using machine learning models. Queries are converted into vectors and compared against stored ones using metrics such as cosine similarity or Euclidean distance.
Key techniques include approximate nearest neighbor (ANN) algorithms like HNSW or IVF to scale efficiently to millions or billions of vectors while balancing speed and accuracy.
Vector stores often attach metadata to vectors, enabling filtered searches that combine semantic similarity with structured conditions.
Example
A company stores embeddings of product descriptions in a vector store; when a customer searches 'wireless headphones with noise cancellation,' the system quickly retrieves the most similar product vectors and returns matching items.
Why it matters
Vector stores power retrieval-augmented generation (RAG) systems that give large language models access to up-to-date or domain-specific knowledge without retraining.
Frequently asked questions
Regular databases store structured data and use exact keyword matches, while vector stores focus on semantic similarity between high-dimensional vectors.
Related terms
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.
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.
Semantic search retrieves information by understanding the meaning and intent of a query rather than relying on exact keyword matches.
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.
Cosine similarity measures how similar two vectors are by computing the cosine of the angle between them, ignoring their magnitudes.