Vector embeddings & language: how models turn words into geometry

This post explains the linguistic challenges and evolution of creating vector embeddings from text. It details how models learn to represent meaning as geometry, moving from static word embeddings (word2vec, GloVe) to contextual embeddings (BERT) that account for ambiguity and context. It describes pooling strategies for sentence/document embeddings and the encoding of relationships (e.g., 'capital of') and biases within vector spaces. The post also introduces Approximate Nearest Neighbor (ANN) algorithms like HNSW for efficient retrieval and highlights Redis's integration of vector search with operational data and AI context engines like Redis Iris.