Contextual embedding in RavenDB (or how to save 220,000,000 USD)

A while ago, MongoDB purchased VoyageAI for 220 million dollars. Since then, they have released a couple of dedicated embedding models. For example, you can read their blog post on voyage-context-4.The key premise in those sorts of models is that you can feed the model text of any size, and it will automatically handle generating embedding vectors, smart chunking, providing context, etc.I ran into this recently and was curious to see how this can work. In particular, since RavenDB handles both embedding generation and vector search, I decided to do a full evaluation of MongoDB’s way of chunking. MongoDB built their own model to achieve this, but RavenDB’s approach to embedding generation is to rely on any embedding model you prefer to use.Before we get into the full details, let’s talk for a second about what the point of contextual embedding is, so we are all on the same page.Embedding models take your data and translate it into a multidimensional mathematical space based on its…