Embedding Models

Embedding models convert text into dense vector representations, enabling semantic search, clustering, and similarity comparison. They are essential for building search engines, recommendation systems, and knowledge retrieval applications.

• BGE Large 1.5: Maps English text to a 1024-dimensional dense vector space, supporting high-quality semantic search and retrieval tasks.

• BGE m3: Handles long multilingual text and maps it to a 1024-dimensional vector space for cross-lingual search and analysis.

• Paraphrase Multilingual MPNet v2: A sentence transformer model that maps text to a 768-dimensional vector space, enabling efficient paraphrase detection and semantic similarity tasks.

• Qwen3 VL Embedding 8B: A multimodal embedding model that maps text and images to a 4096-dimensional vector space, enabling cross-modal semantic search and visual document retrieval across 30+ languages.