Embeddings
Semantic search and vector representations for AI applications
5 models available
text-embedding-3-large
OpenAI's most capable embedding model. 3072 dimensions with excellent retrieval performance for RAG and semantic search.
BGE-M3
BAAI's versatile embedding model supporting dense, sparse, and multi-vector retrieval. Open-source and highly effective.
Cohere Embed v3
Cohere's multilingual embedding model. Supports 100+ languages with separate search and classification modes.
Jina Embeddings v3
Jina AI's latest embedding model with task-specific adapters. Supports flexible dimensions and multiple retrieval tasks.
text-embedding-3-small
OpenAI's efficient embedding model. 1536 dimensions with strong performance at lower cost, ideal for most use cases.
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