Comment by dennis16384
5 hours ago
"With experience and support from" is a nice landing trick!
How do you extract and relate to each other the facts from the documents that require comprehension and not simple similarity matching using common embeddings models?
Haha thanks, the reader can try and guess which is which;)
We actually don't use embeddings or vector similarity, since those tend not to work well in specialist domains (e.g. for the OfficeQA benchmark where we have 90k pages talking about US treasury numbers, they would be mostly mapped to a very small embedding space because it's all the same topic, with small variations across years, expense categories etc.).
We use LLMs for the extraction and comparison as well, and we route between different models depending on the complexity of the comprehension of the given step required (and by this I mean routing between our pipeline steps; we currently do not dynamically try to judge individual cases for complexity like OpenRouter Fusion).