As others have correctly pointed out, to make a vector search or recommendation application requires a lot more than similarity alone. We have seen the HNSW become commoditised and the real value lies elsewhere. Just because a database has vector functionality doesn’t mean it will actually service anything beyond “hello world” type semantic search applications. IMHO these have questionable value, much like the simple Q and A RAG applications that have proliferated. The elephant in the room with these systems is that if you are relying on machine learning models to produce the vectors you are going to need to invest heavily in the ML components of the system. Domain specific models are a must if you want to be a serious contender to an existing search system and all the usual considerations still apply regarding frequent retraining and monitoring of the models. Currently this is left as an exercise to the reader - and a very large one at that. We (https://github.com/marqo-ai/marqo, I am a co-founder) are investing heavily into making the ML production worthy and continuous learning from feedback of the models as part of the system. Lots of other things to think about in how you represent documents with multiple vectors, multimodality, late interactions, the interplay between embedding quality and HNSW graph quality (i.e. recall) and much more.
PheonixPharts|2 years ago
In general I find they're incredible good for being able to rapidly build out search engines for things that would it would normally be difficult to do with plain text.
The most obvious example is code search where you can describe the function's behavior and get a match. But you could also make a searchable list of recipes that would allow a user to search something like "a hearty beef dish for a cold fall night". Or searching support tickets where full text might not match, "all the cases where users had trouble signing on".
Interestingly Q & A is ultimately a (imho fairly boring) implementation of this pattern.
The really nice part is that you can implement working demos of this projects in just a few lines of code once you have the vector db set up. Once you start thinking in terms of semantic search over text matching, you realize you can build old-Google style search engines for basically any text available to you.
One thing that is a bit odd about the space is, from what I've experienced and heard, is that setup and performance on most of this products is not all that great. Given that you can implement the demo version of a vector db in a few lines of numpy, you would hope that investing in a full vector db product we get you an easily scalable solution.