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About

Combine semantic relevance and user feedback to reliably retrieve the optimal document chunks in your retrieval augmented generation system. Combine semantic relevance and document freshness in your search system, because more recent results tend to be more accurate. Build a real-time personalized ecommerce product feed with user vectors constructed from SKU embeddings the user interacted with. Discover behavioral clusters of your customers using a vector index in your data warehouse. Describe and load your data, use spaces to construct your indices and run queries - all in-memory within a Python notebook.

About

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.

Platforms Supported

Windows Not Supported
Mac Not Supported
Linux Not Supported
Cloud Supported
On-Premises Not Supported
iPhone Not Supported
iPad Not Supported
Android Not Supported
Chromebook Not Supported

Platforms Supported

Windows Not Supported
Mac Not Supported
Linux Not Supported
Cloud Supported
On-Premises Not Supported
iPhone Not Supported
iPad Not Supported
Android Not Supported
Chromebook Not Supported

Audience

Organizations wanting a data engineer solution to turn data into vector embeddings

Audience

AI researchers and developers in search of a solution providing an embedding model for code retrieval

Support

Phone Support Not Supported
24/7 Live Support Not Supported
Online Supported

Support

Phone Support Not Supported
24/7 Live Support Not Supported
Online Supported

API

Offers API Not Supported

API

Offers API Not Supported

Screenshots and Videos

Screenshots and Videos

Pricing

No information available.
Free Version Not Supported
Free Trial Not Supported

Pricing

No information available.
Free Version Not Supported
Free Trial Not Supported

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

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Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software

Training

Documentation Supported
Webinars Not Supported
Live Online Supported
In Person Not Supported

Training

Documentation Supported
Webinars Not Supported
Live Online Supported
In Person Not Supported

Company Information

Superlinked
Founded: 2021
United States
superlinked.com

Company Information

MongoDB
Founded: 2007
United States
blog.voyageai.com/2024/12/04/voyage-code-3/

Alternatives

Alternatives

Voyage AI

Voyage AI

MongoDB
txtai

txtai

NeuML
voyage-4-large

voyage-4-large

Voyage AI
Codestral Embed

Codestral Embed

Mistral AI

Categories

Categories

Embedding Models Supported

Integrations

Elasticsearch Not Supported
Milvus Not Supported
Python Supported
Qdrant Not Supported
Vespa Not Supported
Weaviate Supported

Integrations

Elasticsearch Supported
Milvus Supported
Python Not Supported
Qdrant Supported
Vespa Supported
Weaviate Not Supported
Claim Superlinked and update features and information
Claim Superlinked and update features and information
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Claim voyage-code-3 and update features and information