Vector database
A vector database is a type of database designed to store and search “vectors”—arrays of numbers that represent data as points in a high-dimensional space. These vectors are often produced by machine learning models (for example, embedding text, images, audio, or user behavior). Similar items end up close to each other
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What “vector database” means
A vector database is a type of database designed to store and search “vectors”—arrays of numbers that represent data as points in a high-dimensional space. These vectors are often produced by machine learning models (for example, embedding text, images, audio, or user behavior). Similar items end up close to each other in that space, enabling efficient similarity search.
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How it’s used
Common tasks include semantic search (finding meaning, not just matching keywords), recommendation systems, clustering, deduplication, and retrieval for AI applications (often called Retrieval-Augmented Generation, or RAG). Instead of exact matches, queries are converted into vectors and the database returns the nearest vectors based on a similarity metric such as cosine similarity or dot product.
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Key components and considerations
Vector databases typically include: (1) an embedding pipeline (how data becomes vectors), (2) an index for fast nearest-neighbor search (e.g., approximate methods for speed), and (3) metadata storage to filter results (like language, time, or category). Important considerations include latency/throughput, index accuracy vs. speed trade-offs, scalability, and how updates/deletions are handled.
Client endpoint
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