[ddematheu]

Co-founder here :)

Today, it is mostly about convenience. We provide abstractions in the form of a pipeline that encompasses a data source, embed and sink definition. This means that you don't have to think about embedding your query or what class you used to add the data into the vector DB.

In the future, we have some additional abstractions that we are adding that will add more convenience. For example, we are working on a concept of pipeline collections so that you can search across multiple indexes but get unified results. We are also adding more automation around metadata given that as part of the pipeline configuration we know what metadata was added and examples of it, so we can help translate queries into hybrid search. I think about it as a self-query retriever from Langchain or Llama Index but that automatically has context of the data at hand. (no need to provide attributes)

Are there any specific retrieval capabilities you are looking for?