I certainly see the value of large document retrieval and various forms of search.
However, what seems to be the business proposition is giving managers shallow access to documents but won't lead to rigorous information.
There's a few middle grounds where it can yield insights. like regulatory scenarios where you want to understand how public orgs satisfy permits with written plans.
however, what I don't believe will yield is the context size. when I want to explore my knowledge base, I need far more than 128k and there's sever orders of structure that language itself is not going to bridge.
Ya, a lot of knowledge is locked away in metadata, how documents are structured, in non-textual representations, or sometimes even just in the head of experts. We definitely want to be more than a shallow access to documents and we're building with that in mind. We're currently working to include more metadata and organizational understanding, with plans to tackle OCR, NL-to-SQL, code search and knowledge graphs in the future. While we don't have concrete dates for all of these, hopefully this gives some visibility into our vision at least!
Regarding the context size, the research community is doing some stunning work. If you're interested, you should check out the new Mamba (Transformers replacement) architecture: https://arxiv.org/abs/2312.00752
However, what seems to be the business proposition is giving managers shallow access to documents but won't lead to rigorous information.
There's a few middle grounds where it can yield insights. like regulatory scenarios where you want to understand how public orgs satisfy permits with written plans.
however, what I don't believe will yield is the context size. when I want to explore my knowledge base, I need far more than 128k and there's sever orders of structure that language itself is not going to bridge.