vector space models

An introduction to embedding models: how they turn text, images, or code into dense vectors that capture meaning for tasks like semantic search, clustering, recommendations, and RAG. Explains training and dimensionality, similarity metrics (cosine), vector databases, chunking, and practical tips on normalization, evaluation, and limits such as bias and context loss.