bge-m3

BGE-M3 is a versatile embedding model designed for high-performance retrieval across diverse linguistic landscapes. Unlike traditional embedding models limited to a single modality or language, M3 focuses on 'multi-linguality, multi-functionality, and multi-granularity.' For developers, this means a single model can handle dense retrieval, sparse retrieval (BM25-style), and multi-vector reranking simultaneously. It significantly expands the context window compared to earlier BGE iterations, allowing for the processing of longer documents without aggressive truncation. This makes it an ideal backbone for RAG pipelines where hybrid search is required to balance semantic meaning with keyword precision across global datasets.