Audience
AI researchers and developers in search of a solution providing an embedding model for code retrieval
About voyage-code-3
Voyage AI introduces voyage-code-3, a next-generation embedding model optimized for code retrieval. It outperforms OpenAI-v3-large and CodeSage-large by an average of 13.80% and 16.81% on a suite of 32 code retrieval datasets, respectively. It supports embeddings of 2048, 1024, 512, and 256 dimensions and offers multiple embedding quantization options, including float (32-bit), int8 (8-bit signed integer), uint8 (8-bit unsigned integer), binary (bit-packed int8), and ubinary (bit-packed uint8). With a 32 K-token context length, it surpasses OpenAI's 8K and CodeSage Large's 1K context lengths. Voyage-code-3 employs Matryoshka learning to create embeddings with a nested family of various lengths within a single vector. This allows users to vectorize documents into a 2048-dimensional vector and later use shorter versions (e.g., 256, 512, or 1024 dimensions) without re-invoking the embedding model.
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Integrations
Company Information
MongoDB
Founded: 2007
United States
blog.voyageai.com/2024/12/04/voyage-code-3/
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Product Details
Platforms Supported
Cloud
Training
Documentation
Live Online
Support
Online
voyage-code-3 Frequently Asked Questions
Q: What kinds of users and organization types does voyage-code-3 work with?
voyage-code-3 works with these users and organization types: Mid Size Business, Small Business, Enterprise, Freelance, Nonprofit, Government, and Startup.
Q: What languages does voyage-code-3 support in their product?
voyage-code-3 supports these languages: English.
Q: What kind of support options does voyage-code-3 offer?
voyage-code-3 offers support via online.
Q: What other applications or services does voyage-code-3 integrate with?
voyage-code-3 integrates with: Elasticsearch, Milvus, Qdrant, and Vespa.
Q: What type of training does voyage-code-3 provide?
voyage-code-3 provides training in the form of documentation and live online.
