In order to find text similarity by comparing string embeddings, is it necessary to use a vector database? Alternatively, could the same results be achieved by averaging the embeddings of a set of strings and then calculating the distance between the average and the embedding of another string? In this context, would a vector database provide any additional benefits, or is it possible to achieve the same results without one? Additionally, I am wondering if the pricing of vector database solutions such as Pinecone and Milvus is justified for my use case, or if there are other more cost-effective options available.

Great stuff, looking forward to using it.

Would love to use as an API.

I have some questions, do you retrain the model for each theme, or are you using embeddings, or just train on a big model that has all the themes in it