Cohere released Embed 4, an artificial intelligence (AI) embedding tool, last week. The Toronto-based AI firm pitched the new search and retrieval tool to businesses building and deploying AI applications and agents. The company, which builds enterprise-focused AI models and tools, claims that Embed 4 can understand complex, multimodal documents and efficiently surface information that an AI system requires to complete a task. The AI model is also said to help businesses save on data storage costs by sharing compressed embeddings instead of full documents.
In a blog post, Cohere announced the launch of Embed 4 and detailed the new product. It is a multimodal embedding tool that adds search and retrieval capability to existing AI systems. The tool is currently available directly from the company’s website, Microsoft Azure AI Foundry, and Amazon SageMaker. It is also available for private deployment into any virtual private cloud (VPC) or on-premise environment.
All AI models use a system dubbed Retrieval-Augmented Generation (RAG) to find information from their knowledge base. Essentially, it is a command that prompts search and retrieval of particular information based on keywords, ranking, and other rule-based algorithms. Embed 4 is essentially an AI model that replaces this function for data from outside sources.
Cohere says the embedding tool can be added to any existing AI system, be it an AI application or an agent. Enterprises that usually use such tools internally, either use the third-party AI model’s search engine or custom build search engines. The AI firm claims that Embed 4 is a better option than either of those two solutions.
Embed 4’s biggest unique proposition is its multimodality support. It can contextually understand documents that not only contain text, but also those that contain images, graphs, tables, diagrams, and code. Additionally, the AI tool supports more than 100 languages, including Arabic, Japanese, Korean, and French, to let businesses globally seamlessly look up their data.
Cohere also highlighted that Embed 4 was trained against noisy real-world data, which means imperfect documents, including those with spelling mistakes, formatting issues, or different page orientation and can also be retrieved by the AI tool without harming the accuracy of the search results.
Additionally, the AI model is equipped with domain-specific understanding of data from regulated industries such as finance, healthcare, and manufacturing. This means Embed 4 can be deployed in VPC and on-premise environments to keep data secure.
