Text Embeddings
Convert text to dense vector representations for search, clustering, and RAG
Embedding models map text to fixed-size dense vectors. Texts with similar meaning produce vectors that are close together. Use embeddings for semantic search, clustering, anomaly detection, and RAG retrieval.
Available Models
- OpenAI Text Embeddings 3 Large – High-precision embeddings for semantic search, RAG, and classification (3072 dimensions)
- BGE-M3 – Multilingual dense and sparse embeddings for hybrid retrieval (1024 dimensions)