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Description

Nomic Embed is a comprehensive collection of open-source, high-performance embedding models tailored for a range of uses, such as multilingual text processing, multimodal content integration, and code analysis. Among its offerings, Nomic Embed Text v2 employs a Mixture-of-Experts (MoE) architecture that efficiently supports more than 100 languages with a remarkable 305 million active parameters, ensuring fast inference. Meanwhile, Nomic Embed Text v1.5 introduces flexible embedding dimensions ranging from 64 to 768 via Matryoshka Representation Learning, allowing developers to optimize for both performance and storage requirements. In the realm of multimodal applications, Nomic Embed Vision v1.5 works in conjunction with its text counterparts to create a cohesive latent space for both text and image data, enhancing the capability for seamless multimodal searches. Furthermore, Nomic Embed Code excels in embedding performance across various programming languages, making it an invaluable tool for developers. This versatile suite of models not only streamlines workflows but also empowers developers to tackle a diverse array of challenges in innovative ways.

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

Screenshots View All

Screenshots View All

Integrations

Baseten
Elasticsearch
Go
Java
JavaScript
Milvus
PHP
Python
Qdrant
Ruby
Vespa
Weaviate

Integrations

Baseten
Elasticsearch
Go
Java
JavaScript
Milvus
PHP
Python
Qdrant
Ruby
Vespa
Weaviate

Pricing Details

Free
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

Nomic

Country

United States

Website

www.nomic.ai/embed

Vendor Details

Company Name

Voyage AI

Founded

2023

Country

United States

Website

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

Product Features

Alternatives

Alternatives

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