2 ms·
I'll add txtai (https://github.com/neuml/txtai https://github.com/neuml/txtai) to the list. There is still plenty of room for innovation in this space. Just ne
by dmezzetti 3y ago
I'll add txtai (https://github.com/neuml/txtai https://github.com/neuml/txtai) to the list.
There is still plenty of room for innovation in this space. Just need to focus on the right projects that are innovating and not the ones (re)working on problems solved in 2020/2021.
- zeroCalories 3y agoI agree. Honestly even the fundamentals of vector databases aren't really "solved" in the way they are for other databases. Vector indexing, embedding generation, horizontal scaling, etc. can probably still improve a lot. And don't forget, even if Postgres and MySQL are the only traditional databases in town, every tech company had their own SQL database once. Many of them are still around too. No need to get pissy about these companies.
- dmezzetti 3y agoAgreed. For example, here is a post about integrating vector search results with semantic graphs for RAG - https://news.ycombinator.com/item?id=39141420 https://news.ycombinator.com/item?id=39141420 And here's a post on an alternative way to integrate vectors with traditional databases (Postgres, MySQL) - https://neuml.hashnode.dev/external-database-integration https://neuml.hashnode.dev/external-database-integration As others have said in this thread, cosine similarity on arrays of vectors isn't novel. But there are many possibilities past that, many we haven't thought of yet too.