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What is Cohere Embed?

Cohere's Embed emerges as a leading multimodal embedding solution that adeptly transforms text, images, or a combination of the two into superior vector representations. These vector embeddings are designed for a multitude of uses, including semantic search, retrieval-augmented generation, classification, clustering, and autonomous AI applications. The latest iteration, embed-v4.0, enhances functionality by enabling the processing of mixed-modality inputs, allowing users to generate a cohesive embedding that incorporates both text and images. It includes Matryoshka embeddings that can be customized in dimensions of 256, 512, 1024, or 1536, giving users the ability to fine-tune performance in relation to resource consumption. With a context length that supports up to 128,000 tokens, embed-v4.0 is particularly effective at managing large documents and complex data formats. Additionally, it accommodates various compressed embedding types such as float, int8, uint8, binary, and ubinary, which aid in efficient storage solutions and quick retrieval in vector databases. Its multilingual support spans over 100 languages, making it an incredibly versatile tool for global applications. As a result, users can utilize this platform to efficiently manage a wide array of datasets, all while upholding high performance standards. This versatility ensures that it remains relevant in a rapidly evolving technological landscape.

What is Contextwaise?

Contextwaise is a cutting-edge API and agent SDK tailored for teams focused on building retrieval-augmented generation (RAG) applications, AI agents, and various large language model (LLM) products. It assesses retrieved materials, including passages, web pages, chat logs, and tool outputs, in line with the specific requirements of the task at hand, delivering pertinent context while respecting a designated token limit prior to the model's engagement. Equipped with a flexible REST API, it supports requests in any programming language, while its TypeScript and Python SDKs enhance agent workflows and integrate effortlessly with Vercel AI SDK, Anthropic SDK, and OpenAI SDK. The output systematically preserves the sequence of submissions, generating reports that outline token modifications, potential cost savings, and rationales for excluded content. Contextwaise also includes features such as request logging, adjustable budgets, and quality assurance; its agent SDKs can disclose segments of excluded tool outputs for additional clarification. By employing this solution, engineering teams can significantly reduce unnecessary model inputs while maintaining their retrieval resources, prompts, and interactions with model providers, thus simplifying the development workflow. This optimization not only improves efficiency but also empowers teams to advance their AI-driven applications more effectively and creatively. Ultimately, Contextwaise serves as an essential tool for fostering innovation in the AI landscape.

Media

Media

No images available

Integrations Supported

Cohere
voyage-4-large

Integrations Supported

Anthropic
OpenAI
Vercel AI SDK

API Availability

API Availability

Has API

Pricing Information

$0.47 per image

Pricing Information

$15/month
Free includes 1M context tokens once. Pro is $15/month for 100M tokens/month, then $0.18 per started 1M tokens. Scale is $49/month for 350M tokens/month, then $0.15 per started 1M tokens. Enterprise has custom pricing under an annual contract and offers self-hosted deployment.
Free Version

Supported Platforms

SaaS

Supported Platforms

SaaS
On-Prem

Customer Service / Support

Web-Based Support

Customer Service / Support

Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training

Training Options

Documentation Hub

Company Facts

Organization Name

Cohere

Date Founded

2019

Company Location

Canada

Company Website

cohere.com/embed

Company Facts

Organization Name

GITMORE

Date Founded

2026

Company Location

United Kingdom

Company Website

contextwaise.com

Categories and Features

Categories and Features

LLM API

Not specified

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