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features
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Description

Cohere's Embed stands out as a premier multimodal embedding platform that effectively converts text, images, or a blend of both into high-quality vector representations. These vector embeddings are specifically tailored for various applications such as semantic search, retrieval-augmented generation, classification, clustering, and agentic AI. The newest version, embed-v4.0, introduces the capability to handle mixed-modality inputs, permitting users to create a unified embedding from both text and images. It features Matryoshka embeddings that can be adjusted in dimensions of 256, 512, 1024, or 1536, providing users with the flexibility to optimize performance against resource usage. With a context length that accommodates up to 128,000 tokens, embed-v4.0 excels in managing extensive documents and intricate data formats. Moreover, it supports various compressed embedding types such as float, int8, uint8, binary, and ubinary, which contributes to efficient storage solutions and expedites retrieval in vector databases. Its multilingual capabilities encompass over 100 languages, positioning it as a highly adaptable tool for applications across the globe. Consequently, users can leverage this platform to handle diverse datasets effectively while maintaining performance efficiency.

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 No 

API Access

Has API No 

Screenshots View All

Screenshots View All

Integrations

Cohere Yes 
Elasticsearch No 
Milvus No 
Qdrant No 
Vespa No 
voyage-4-large Yes 

Integrations

Cohere No 
Elasticsearch Yes 
Milvus Yes 
Qdrant Yes 
Vespa Yes 
voyage-4-large No 

Pricing Details

$0.47 per image
Free Trial No 
Free Version No 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Types of Training

Training Docs Yes 
Webinars Yes 
Live Training (Online) Yes 
In Person No 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) Yes 
In Person No 

Vendor Details

Company Name

Cohere

Founded

2019

Country

Canada

Website

cohere.com/embed

Vendor Details

Company Name

MongoDB

Founded

2007

Country

United States

Website

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

Product Features

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Alternatives

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