Embeddings & Vector Search

Dense vector representations, similarity algorithms, and indexing structures that power semantic search and retrieval systems.

Where to Start

This cluster covers 1 topic. Here's a suggested reading order from fundamentals to advanced.

Embedding

Start here

Embeddings are dense vector representations that map words, sentences, or other data into continuous numerical spaces where semantic relationships are preserved as geometric distances. Neural networks learn these representations during training, positioning similar meanings close together and dissimilar meanings far apart. Embeddings power semantic search, recommendation systems, and retrieval-augmented generation by enabling machines to measure meaning through vector similarity rather than exact keyword matching. Also known as: Embeddings, Vector Embedding.

6 articles