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Voyage AI provides cutting-edge embedding and rerankers. Using voyageai API requires voyageai package, which can be installed using pip install voyageai. Voyage AI embeddings are used to generate embeddings for text data. The embeddings can be used for various tasks like semantic search, clustering, and classification. You also need to set the VOYAGE_API_KEY environment variable to use the VoyageAI API. Supported models are:
  • voyage-4-large (best retrieval quality, 1024 default dimensions, supports 256/512/1024/2048)
  • voyage-4 (balanced general-purpose, 1024 default dimensions, supports 256/512/1024/2048)
  • voyage-4-lite (optimized for latency/cost, 1024 default dimensions, supports 256/512/1024/2048)
  • voyage-context-3
  • voyage-3.5
  • voyage-3.5-lite
  • voyage-3
  • voyage-3-lite
  • voyage-finance-2
  • voyage-multilingual-2
  • voyage-law-2
  • voyage-code-2
  • voyage-multimodal-3.5 (multimodal - supports text, images, and video)
Multimodal Model: voyage-multimodal-3.5 supports text, images, and video inputs. It outputs 1024-dimensional embeddings by default, configurable via the output_dimension parameter (256, 512, 1024, 2048). See the VoyageAI multimodal embeddings documentation for more details.
Supported parameters (to be passed in create method) are: Usage Example:

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Multimodal Example

The voyage-multimodal-3.5 model can embed text alongside images. You can use image URLs, file paths, or PIL Image objects: