Ratings and Reviews 0 Ratings

Total
ease
features
design
support

This software has no reviews. Be the first to write a review.

Write a Review

Ratings and Reviews 0 Ratings

Total
ease
features
design
support

This software has no reviews. Be the first to write a review.

Write a Review

Alternatives to Consider

  • Gemini Enterprise Agent Platform Reviews & Ratings
    961 Ratings
    Company Website
  • FISPAN Reviews & Ratings
    5 Ratings
    Company Website
  • Pipeliner CRM Reviews & Ratings
    750 Ratings
    Company Website
  • Parasoft Reviews & Ratings
    144 Ratings
    Company Website
  • NMI Payments Reviews & Ratings
    109 Ratings
    Company Website
  • Reflectiz Reviews & Ratings
    29 Ratings
    Company Website
  • Creatio Reviews & Ratings
    523 Ratings
    Company Website
  • Sage Intacct Reviews & Ratings
    8,335 Ratings
    Company Website
  • Wiz Reviews & Ratings
    1,452 Ratings
    Company Website
  • RaimaDB Reviews & Ratings
    12 Ratings
    Company Website

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 NVIDIA NeMo Retriever?

NVIDIA NeMo Retriever comprises a collection of microservices tailored for the development of high-precision multimodal extraction, reranking, and embedding workflows, all while prioritizing data privacy. It facilitates quick and context-aware responses for various AI applications, including advanced retrieval-augmented generation (RAG) and agentic AI functions. Within the NVIDIA NeMo ecosystem and leveraging NVIDIA NIM, NeMo Retriever equips developers with the ability to effortlessly integrate these microservices, linking AI applications to vast enterprise datasets, no matter their storage location, and providing options for specific customizations to suit distinct requirements. This comprehensive toolkit offers vital elements for building data extraction and information retrieval pipelines, proficiently gathering both structured and unstructured data—ranging from text to charts and tables—transforming them into text formats, and efficiently eliminating duplicates. Additionally, the embedding NIM within NeMo Retriever processes these data segments into embeddings, storing them in a highly efficient vector database, which is optimized by NVIDIA cuVS, thus ensuring superior performance and indexing capabilities. As a result, the overall user experience and operational efficiency are significantly enhanced, enabling organizations to fully leverage their data assets while upholding a strong commitment to privacy and accuracy in their processes. By employing this innovative solution, businesses can navigate the complexities of data management with greater ease and effectiveness.

Media

Media

Integrations Supported

Elasticsearch
Milvus
NVIDIA NIM
NVIDIA NeMo
Qdrant
Vespa
Weaviate

Integrations Supported

Elasticsearch
Milvus
NVIDIA NIM
NVIDIA NeMo
Qdrant
Vespa
Weaviate

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
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

NVIDIA

Date Founded

1993

Company Location

United States

Company Website

developer.nvidia.com/nemo-retriever

Categories and Features

Categories and Features

Popular Alternatives

Popular Alternatives

Voyage AI Reviews & Ratings

Voyage AI

MongoDB
voyage-4-large Reviews & Ratings

voyage-4-large

Voyage AI
NVIDIA NeMo Reviews & Ratings

NVIDIA NeMo

NVIDIA