Unlock the Power of Text Embeddings with Azure AI's Cohere Embed v3 - English

Azure AI has integrated the cutting-edge Cohere Embed v3 - English model, a state-of-the-art text embedding model that transforms text into 1024-dimensional numerical vectors. This model is designed to excel in various tasks including semantic search, retrieval-augmented generation (RAG), classification, and clustering.

Model Overview

The Cohere Embed v3 - English model is renowned for its high performance on prominent benchmarks like the Massive Text Embedding Benchmark (MTEB) and the Benchmark for Evaluating Information Retrieval (BEIR). It particularly shines in zero-shot dense retrieval scenarios.

Performance

This model evaluates the content quality and relevance of documents within the vector space, allowing for superior ranking based on both topic match and information quality. This capability is invaluable for processing noisy, real-world data.

Features

• Includes a mandatory parameter input_type which can be set to search_document, search_query, classification, or clustering to optimize embeddings for specific tasks.
• Available in float32 and int8 embeddings, the latter offering a 4x memory saving and approximately 30% speed-up in search while maintaining 99.99% of the search quality.

Integration with Azure AI

The model is available as a serverless API in Azure Machine Learning Studio with a pay-as-you-go, token-based billing system. Users can deploy and consume the model via Azure AI Studio and Azure Machine Learning. Additionally, it integrates seamlessly with Azure AI Search, enabling efficient storage and search over embeddings, leading to significant memory cost reductions and speed improvements while maintaining high search quality.

Applications

Cohere Embed v3 - English is highly effective for semantic search, RAG systems, and other applications requiring precise and relevant text embeddings. It empowers generative models to access and retrieve pertinent information from a company's data, providing comprehensive and detailed responses.

Deployment and Usage

Users can deploy the model as a serverless API in Azure Machine Learning Studio. The API can be utilized through the Azure AI Model Inference API schema or the native Cohere Embed v3 API schema. To get started, users need to set up their credentials for both Cohere and Azure AI Search, generate embeddings using the Cohere API, and then integrate them into Azure AI Search.