Generative AI models often face limitations in accessing company-specific data, which can be mitigated by using Retrieval-Augmented Generation (RAG) systems. RAG combines embedding models and vector databases to enhance generative models with relevant information from company data, allowing for comprehensive summaries and detailed follow-up questions. Cohere has released new embedding models, Embed v3, which support English and multilingual data with improved performance on benchmarks like MTEB and BEIR. These models incorporate a new input parameter, input_type, to optimize embeddings for tasks like search, classification, and clustering, ensuring high-quality results. By measuring both topic similarity and content quality, these models provide better search results, especially in noisy datasets, as demonstrated with the TREC-COVID dataset. They also excel in multi-hop queries, enhancing RAG system performance. Embed v3 undergoes training stages focusing on topic similarity, content quality, and compression-aware capabilities, making it suitable for large-scale applications. The models are evaluated on benchmarks such as MTEB, BEIR, and MIRACL, showcasing their broad capabilities and high performance across multiple languages, making them a valuable tool for applications involving diverse datasets.