What distance metrics does Weaviate support?

Weaviate supports cosine similarity, dot product, squared Euclidean, Manhattan, and Hamming distance. Cosine is the default for text embeddings because it compares angle regardless of magnitude. The metric is set per collection in the vector index configuration and cannot be changed without reindexing. Dot product is faster when vectors are pre-normalized, Euclidean suits some image and geometry embeddings, and Hamming works with binary vectors. Most text RAG workloads use cosine or dot product depending on the embedding model's training regime.