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Embedding Models in Model Library

Embedding Models in Model Library

12 embedding models from OpenAI, Google, and Cohere now available for retrieval, search, and RAG workflows.

What's New

Embedding models are now integrated into the Model Library — browse, filter, and use them directly when configuring assistants for retrieval, search, and RAG workflows.

Available Models

  • OpenAI: text-embedding-3-large (3072d), text-embedding-3-small (1536d), ada-002 (1536d).

  • Google: gemini-embedding-001 in 768, 1536, and 3072 dimensions.

  • Cohere: embed-v4.0 (256–1536d), embed-english-v3.0, embed-multilingual-v3.0, embed-multilingual-light-v3.0.

New API Endpoints

  • GET /v1/embeddings — List all available embedding models.

  • GET /v1/embeddings?provider=openai — Filter by provider.

  • GET /v1/embeddings?dimensions=3072 — Filter by vector dimensions.

  • GET /v1/embeddings/:id — Get model details including pricing and capabilities.

How to Use It

Specify embedding_provider and embedding_model_name directly when creating or updating assistants. The system automatically handles chunking, dimension matching, and vector store indexing.

Migration Notes

  • Fully backward compatible — existing integrations unchanged.

  • Default embedding model remains text-embedding-3-small unless explicitly changed.

  • Switching embedding models on an existing assistant triggers automatic re-indexing of stored documents.

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