Thanks for your contribution to the Universe! :)
I'm hacking on a in-process DB on top of LMDB+Lance(for now, hilbert space kung-fu with a custom matryoshka embedding setup with separate spatial + temporal + internal and content derived anchors will replace that ~last-century~ last-year tech) + roaring bitmaps as the primary indexing engine with the same or similar goal[0] and will definitely deep-dive into yours.
I'm also trying to index users unstructured documents and workflows(tabs, emails, files, notes, identities etc)
- Organize them into semantically meaningful user or agent created context or directory-like virtual trees (the same photo of a nice kitchen may be surfaced under `/travel/barcelona` and `/arch/interieour/kitches`)
- ..where tree nodes are mapped to bitmaps - `/travel/barcelona` does a fast and cheap `travel` AND `barcelona`, want to "zoom-out" you just go one directory up to `/travel` and see all documents tagged with travel)
- You can use multiple timelines - extract that fancy md-converted en-wiki hf dataset into a wikipedia db dataset + timeline, tag your personal timeline as "personal" - wanna know the zeitgeist of your grandmothers birth date - search for it with timelines personal + wikipedia in layered mode and you'll get everything that happened or was happening during that time.
- You can have long-running stateful query sessions and refine your searches dynamically - search for "winter" and get all documents with a winter scenery or mentioning winter - refine with "nice view" then "laptop" - citing a recent example[1]
- Documents have relations that would be cumbersome to map in a virtual tree structure(worth an experiment due to the zoom-in/out you get with context bitmap trees though) - hence on top of the initial structure you can use graph edges(also powered by bitmaps - as most indexes are)
- All vector queries always run on top of a candidate set you get by the bitmap/bitmap-based filter algebra hence searching through 100k+ docs is usually pretty fast
Anyhow, let me stop here, thank you once again!
[0] https://github.com/canvas-ui/canvas-synapsd (sorry for the sloppy ai readme, no time to resurrect my old one with the updated APIs)
[1] https://demo.cnvs.ai/pub/c/aks6zaf8