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Description
Voyager® stands out as the preferred integrated library solution for numerous top-tier libraries around the globe, forming the essential framework for their operational systems. With its user-friendly graphical interface, Voyager is designed on open systems technology and adheres to industry standards, enabling seamless integration with pre-existing library infrastructures and the flexibility to grow alongside future demands. This system not only works in harmony with established library technologies but also embraces innovative advancements. The selection of core technologies, standards, and programming language support has been meticulously curated to align with the dynamic requirements faced by libraries today. The Voyager client/server architecture facilitates Web-based public access cataloging and authority management, alongside modules for acquisitions, serials, circulation, and course reserves. Additionally, it offers advanced reporting capabilities and system administration features, which are included as part of the standard offering, making it a comprehensive solution for modern library operations. Ultimately, Voyager equips libraries with robust tools to enhance their services and better serve their communities.
Description
Voyage AI has unveiled voyage-code-3, an advanced embedding model specifically designed to enhance code retrieval capabilities. This innovative model achieves superior performance, surpassing OpenAI-v3-large and CodeSage-large by averages of 13.80% and 16.81% across a diverse selection of 32 code retrieval datasets. It accommodates embeddings of various dimensions, including 2048, 1024, 512, and 256, and provides an array of embedding 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 a context length of 32 K tokens, voyage-code-3 exceeds the limitations of OpenAI's 8K and CodeSage Large's 1K context lengths, offering users greater flexibility. Utilizing an innovative approach known as Matryoshka learning, it generates embeddings that feature a layered structure of varying lengths within a single vector. This unique capability enables users to transform documents into a 2048-dimensional vector and subsequently access shorter dimensional representations (such as 256, 512, or 1024 dimensions) without the need to re-run the embedding model, thus enhancing efficiency in code retrieval tasks. Additionally, voyage-code-3 positions itself as a robust solution for developers seeking to improve their coding workflow.
API Access
Has API
API Access
Has API
Integrations
Elasticsearch
Milvus
Qdrant
Vespa
Weaviate
Pricing Details
No price information available.
Free Trial
Free Version
Pricing Details
No price information available.
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
Ex Libris
Founded
2009
Website
exlibrisgroup.com/products/voyager-integrated-library-system/
Vendor Details
Company Name
MongoDB
Founded
2007
Country
United States
Website
blog.voyageai.com/2024/12/04/voyage-code-3/
Product Features
Library Management
Acquisition Management
Barcode Scanning
Barcoding / RFID
Catalog Management
Church Libraries
Circulation Management
Fee Collection
Inventory Management
Law Libraries
OPAC
Patron Management
Periodicals Management
Private Libraries
Public Libraries
Reserve Shelf Management
School Libraries
Search
Self Check-in / Check-Out
Serials Management