From Content Quality to Compression: The Evolution of Embedding Models at Cohere with Nils Reimers - Qdrant

This post details Cohere's advancements in embedding models, focusing on content quality estimation, compression-aware training to reduce memory footprint, and the application of reinforcement learning from human feedback. It also highlights the importance of evaluating embedding quality in relative terms and previews new features like input type support for Langchain and improved compression techniques. The discussion frames these advancements within the context of using vector databases like Qdrant.