There are so many options for vector databases that it's so confusing. But those are just a piece of the puzzle when you create applications using large languag

There are so many options for vector databases that it's so confusing. But those are just a piece of the puzzle when you create applications using large language models. As mentioned in the comments, you have to choose an embeddings model, the LLM, and manage all the interaction in between. With Vectara (full disclosure: I work there; https://vectara.com https://vectara.com) we provide a simple API to implement applications with Grounded Generation (aka retrieval augmented generation). The embeddings model, the vector store, the retrieval engine and all the other functionality - implemented by the Vectara platform, so you don't have to choose which vector DB to use, which embeddings model to use, and so on. Makes life easy and simple, and you can focus on developing your application.