Techniques & Methods
Vector Representation
Vector representation converts discrete objects—words, documents, images, users—into points in a continuous high-dimensional space where mathematical operations (distance, similarity) become meaningful. It is the lingua franca of modern AI systems.
Cosine similarity between vectors measures semantic closeness, enabling efficient nearest-neighbor search across millions of documents. This is how semantic search and RAG retrieval systems find relevant content.
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Related Terms
Techniques & Methods
Word Embedding
Technique representing words as dense vectors that capture semantic similarity.
Model Components
Embeddings
Dense numerical vectors that represent text, images, or other content in a high-dimensional space where semantically similar items are geometrically close — the foundational data structure for semantic search and RAG retrieval.
Techniques & Methods
Semantic Search
Search technology that retrieves results based on the meaning of a query rather than exact keyword matches — using embeddings to represent queries and documents as vectors and finding nearest neighbors in semantic space.
Miscellaneous
Vector Store
Specialized database for storing, indexing, and efficiently retrieving high-dimensional vector embeddings.