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What is voyage-code-3?

Voyage AI has introduced voyage-code-3, a cutting-edge embedding model meticulously crafted to improve code retrieval performance. This groundbreaking model consistently outperforms OpenAI-v3-large and CodeSage-large by impressive margins of 13.80% and 16.81%, respectively, across a wide array of 32 distinct code retrieval datasets. It supports embeddings in several dimensions, including 2048, 1024, 512, and 256, while offering multiple quantization options such as float (32-bit), int8 (8-bit signed integer), uint8 (8-bit unsigned integer), binary (bit-packed int8), and ubinary (bit-packed uint8). With an extended context length of 32 K tokens, voyage-code-3 surpasses the limitations imposed by OpenAI's 8K and CodeSage Large's 1K context lengths, granting users enhanced flexibility. This model employs an innovative Matryoshka learning technique, allowing it to create embeddings with a layered structure of varying lengths within a single vector. As a result, users can convert documents into a 2048-dimensional vector and later retrieve shorter dimensional representations (such as 256, 512, or 1024 dimensions) without having to re-execute the embedding model, significantly boosting efficiency in code retrieval tasks. Furthermore, voyage-code-3 stands out as a powerful tool for developers aiming to optimize their coding processes and streamline workflows effectively. This advancement promises to reshape the landscape of code retrieval, making it a vital resource for software development.

What is Ex Libris Voyager?

Voyager® is recognized as the leading integrated library solution by many prestigious libraries worldwide, serving as the backbone of their operational frameworks. Designed with a user-friendly graphical interface, Voyager utilizes open systems technology and complies with industry standards, ensuring smooth integration with existing library setups and adaptability to future needs. This system not only complements traditional library technologies but also welcomes cutting-edge innovations. The careful selection of essential technologies, standards, and programming languages has been tailored to meet the evolving challenges faced by contemporary libraries. With a client/server architecture that supports web-based public access cataloging and authority management, Voyager also includes modules for acquisitions, serials, circulation, and course reserves. Furthermore, it provides enhanced reporting tools and system administration features as part of its standard package, positioning itself as a holistic solution for today’s library operations. In conclusion, Voyager empowers libraries with powerful resources that significantly improve their services and help them effectively meet the needs of their communities. This adaptability and comprehensive nature make it an invaluable asset in the ever-evolving landscape of library management.

Media

Media

Integrations Supported

Elasticsearch
Milvus
Qdrant
Vespa

Integrations Supported

API Availability

API Availability

Pricing Information

Pricing not provided

Pricing Information

Pricing not provided

Supported Platforms

SaaS

Supported Platforms

SaaS

Customer Service / Support

Web-Based Support

Customer Service / Support

Web-Based Support

Training Options

Documentation Hub
Online Training

Training Options

Documentation Hub
Webinars
On-Site Training

Company Facts

Organization Name

MongoDB

Date Founded

2007

Company Location

United States

Company Website

blog.voyageai.com/2024/12/04/voyage-code-3/

Company Facts

Organization Name

Ex Libris

Date Founded

2009

Company Website

exlibrisgroup.com/products/voyager-integrated-library-system/

Categories and Features

Embedding Models

Not specified

Categories and Features

Library Management

Not specified

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