Robust to noisy data
Noisy data often contains errors, outliers, and irrelevant information that hinder an embedding model’s ability to discern meaningful patterns or relationships within the data. Our Embed model understands your data’s nuances, making it highly accurate even when dealing with noisy real-world datasets.
Better retrievals for RAG
The effectiveness of RAG is dependent on multiple components, including embedding models that power search systems to retrieve relevant information. Embed’s elevated accuracy facilitates highly relevant and fewer search results, saving time and computational resources for retrievals.
Make sense of large text archives by grouping similar texts based on their meaning (as captured by embeddings). Uncover patterns like often commonly asked questions or grouping similar types of issues.
Use Embed with a wide variety of vector databases that directly integrate with the Embed model.
“From our experience, Cohere’s support is #1, and the embeddings endpoint has given us excellent results, outperforming other models”