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Description
The software is developed and maintained by a team of highly skilled and seasoned programmers who possess extensive experience in the shipping industry. This module is specifically crafted to efficiently calculate and estimate voyages in a quick and user-friendly manner, accommodating all forms of voyage estimation. It utilizes either the FIFO or Average method to compute the costs associated with fuel supply. Moreover, it generates reports that analyze voyages and juxtapose estimated figures against actual calculations. Another vital component, the Crew module, focuses on the adaptable management of onboard human resources. It actively tracks certificates and their validity for vessels, sending timely reminders before expiration dates. Additionally, it updates the Crew List for every ship, monitoring the status of crew members—indicating who is proposed or rejected and when individuals are ready for their next embarkation. We consistently employ best practices and, when necessary, re-engineer existing workflows to guarantee that our solutions provide a competitive edge while facilitating effective cost management. This approach not only enhances operational efficiency but also contributes to a more streamlined decision-making process.
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
Action Pc
Founded
2006
Country
Greece
Website
www.actionseas.gr
Vendor Details
Company Name
MongoDB
Founded
2007
Country
United States
Website
blog.voyageai.com/2024/12/04/voyage-code-3/
Product Features
Shipping
Air Shipping
Bills of Lading
Container Shipping
Freight Shipping
Ground Shipping
Import / Export
Ocean Shipping
Parcel Shipping
Quotes / Estimates
Shipment Tracking
Warehouse Management