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What is voyage-3-large?

Voyage AI has launched voyage-3-large, a groundbreaking multilingual embedding model that demonstrates superior performance across eight diverse domains, including law, finance, and programming, boasting an average enhancement of 9.74% compared to OpenAI-v3-large and 20.71% over Cohere-v3-English. The model utilizes cutting-edge Matryoshka learning alongside quantization-aware training, enabling it to deliver embeddings in dimensions of 2048, 1024, 512, and 256, while supporting various quantization formats such as 32-bit floating point, signed and unsigned 8-bit integer, and binary precision, which greatly reduces costs for vector databases without compromising retrieval quality. Its ability to manage a 32K-token context length is particularly noteworthy, as it significantly surpasses OpenAI's 8K limit and Cohere's mere 512 tokens. Extensive tests across 100 datasets from multiple fields underscore its remarkable capabilities, with the model's flexible precision and dimensionality options leading to substantial storage savings while maintaining high-quality output. This significant development establishes voyage-3-large as a strong contender in the embedding model arena, setting new standards for both adaptability and efficiency in data processing. Overall, its innovative features not only enhance performance in various applications but also promise to transform the landscape of multilingual embedding technologies.

What is Aquarium?

Aquarium's cutting-edge embedding technology adeptly identifies critical performance issues in your model while linking you to the necessary data for resolution. By leveraging neural network embeddings, you can reap the rewards of advanced analytics without the headaches of infrastructure management or troubleshooting embedding models. This platform allows you to seamlessly uncover the most urgent patterns of failure within your datasets. Furthermore, it offers insights into the nuanced long tail of edge cases, helping you determine which challenges to prioritize first. You can sift through large volumes of unlabeled data to identify atypical scenarios with ease. The incorporation of few-shot learning technology enables the swift initiation of new classes with minimal examples. The larger your dataset grows, the more substantial the value we can deliver. Aquarium is crafted to effectively scale with datasets comprising hundreds of millions of data points. Moreover, we provide dedicated solutions engineering resources, routine customer success meetings, and comprehensive user training to help our clients fully leverage our offerings. For organizations with privacy concerns, we also feature an anonymous mode, ensuring that you can utilize Aquarium without compromising sensitive information, thereby placing a strong emphasis on security. In conclusion, with Aquarium, you can significantly boost your model's performance while safeguarding the integrity of your data, ultimately fostering a more efficient and secure analytical environment.

Media

Media

Integrations Supported

Cohere
LangChain
OneSignal
PyTorch
Snowflake
Voyage AI

Integrations Supported

API Availability

API Availability

Pricing Information

Pricing not provided
Free Trial Offered?

Pricing Information

$1,250 per month
Free Version
Free Trial Offered?

Supported Platforms

SaaS

Supported Platforms

SaaS

Customer Service / Support

Standard Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
On-Site Training

Training Options

Documentation Hub
Online Training

Company Facts

Organization Name

MongoDB

Date Founded

2007

Company Location

United States

Company Website

blog.voyageai.com/2025/01/07/voyage-3-large/

Company Facts

Organization Name

Aquarium

Company Website

www.aquariumlearning.com

Categories and Features

Embedding Models

Not specified

Categories and Features

AI Observability

Not specified

Embedding Models

Not specified

Machine Learning

Not specified

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