Multi-vector retrieval assigns multiple embedding vectors to each document — one per token (as in ColBERT), per sentence, or per semantic aspect. This captures richer information than a single pooled vector, improving recall for documents that cover multiple topics. The trade-off is increased storage requirements and more complex indexing, though techniques like quantization and efficient late interaction help manage costs.