Vector Search
The process of finding the most similar vectors in a vector database to a given query vector. It enables retrieving semantically similar content at scale.
Why It Matters
Vector search is the core technology behind modern semantic search, RAG, and recommendation systems. Its speed and accuracy determine the quality of AI-powered retrieval.
Example
Searching for product images similar to a query image by comparing their vector representations — finding visually similar items in a catalog of millions.
Think of it like...
Like a bloodhound following a scent — instead of matching exact keywords, it follows the 'scent' of meaning to find the most relevant results.
Related Terms
Vector Database
A specialized database designed to store, index, and search high-dimensional vector embeddings efficiently. It enables fast similarity searches across millions or billions of vectors.
Semantic Search
Search that understands the meaning and intent behind a query rather than just matching keywords. It uses embeddings to find results that are conceptually related even if they use different words.
Embedding
A numerical representation of data (text, images, etc.) as a vector of numbers in a high-dimensional space. Similar items are placed closer together in this space, enabling machines to understand semantic relationships.
Approximate Nearest Neighbor
An algorithm that finds vectors approximately closest to a query vector, trading perfect accuracy for dramatic speed improvements. ANN makes vector search practical at scale.
Cosine Similarity
A metric that measures the similarity between two vectors by calculating the cosine of the angle between them. Values range from -1 (opposite) to 1 (identical), with 0 meaning unrelated.