MongoDB AI Applications Program Partner Spotlight: Cohere Brings Leading AI Foundation Models to the Enterprise

Companies can now search PDFs at scale using MongoDB and Nomic Embed. This solution enables efficient and precise search, categorization, and recommendation systems by generating a vector representation or embedding data objects. Nomic Embed is particularly useful for processing large PDFs due to its long context length of 8192 tokens, high throughput capabilities, and adjustable embedding size. By storing embeddings in MongoDB Atlas Vector Search, users can create advanced retrieval-augmented generation (RAG) applications that combine AI with natural language processing for improved search accuracy. This technology has various industry use cases, including financial services, retail, and manufacturing, where it helps automate data extraction and analysis from large volumes of PDFs.