A Python library for generating high-quality sentence, text, and image embeddings. It simplifies the process of converting text into dense vector representations, which are fundamental for similarity search and storage in vector databases.

Advanced multi-vector retrieval model creating token-level embeddings with late interaction mechanism, featuring denoised supervision and improved memory efficiency over original ColBERT.

Learned sparse embedding model built on DeepImpact architecture, outperforming BM25 by up to 44% on TREC benchmarks for high-precision keyword search and hybrid retrieval.

State-of-the-art general-purpose and multilingual embedding model from Voyage AI that ranks first across eight domains spanning 100 datasets, outperforming OpenAI and Cohere models by significant margins.