There's more technical detail here: https://www.pinecone.io/blog/integrated-inference/ https://www.pinecone.io/blog/integrated-inference/ > Is this announcemen

There's more technical detail here: https://www.pinecone.io/blog/integrated-inference/ https://www.pinecone.io/blog/integrated-inference/ > Is this announcement that pinecone is adding their own? TLDR: they trained their own embeddings model and rely on Cohere for ranking. Pinecone (the database) uses this model automatically to generate and store embeddings. > I assumed that a specific flavour of LLM was needed, an “embedding model” to generate the vectors. You're mostly right, with one caveat: embeddings models aren't really LLMs in that they're not very large: they just map semantic meaning to numerical space. > Is it better or worse than the models here: https://ollama.com/search?c=embedding https://ollama.com/search?c=embedding For example? This is the golden question. As far as I know, there is no appropriate benchmarking/eval data about this. I think the real value is the first-class integration between their model and their service.