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Description
Upscaledb is a high-speed key-value database that enhances storage efficiency and algorithms based on the unique characteristics of your data. It features optional compression that minimizes both file size and input/output operations, allowing for more data to reside in memory, which boosts performance and scalability during extensive table scans for querying and analyzing information. Upscaledb is capable of supporting all functionalities typical of a conventional SQL database, customized to align with the specific requirements of your application, and can be seamlessly integrated into your software. With its incredibly swift analytical capabilities and efficient database cursors, it serves as an ideal solution for processing data in scenarios where traditional SQL databases may falter in speed. This versatile database has found its applications across tens of millions of desktops, as well as on cloud servers, mobile devices, and various embedded systems. In a specific benchmark, a comprehensive table scan was conducted over 50 million records, yielding the highest retrieval speed, with the records set up as uint32 values, showcasing its remarkable efficiency. Furthermore, this performance highlights the potential of upscaledb to handle large datasets with ease, making it a preferred choice for developers seeking optimal data management solutions.
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
upscaledb
Website
upscaledb.com
Vendor Details
Company Name
MongoDB
Founded
2007
Country
United States
Website
blog.voyageai.com/2024/12/04/voyage-code-3/