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If I understand correctly, the vecstore product is an api that wraps a vector database (among other features). So the pitch here is something like “you don’t ne
by kenforthewin 8mo ago
If I understand correctly, the vecstore product is an api that wraps a vector database (among other features). So the pitch here is something like “you don’t need a vector database, you need an api wrapper to a vector database that we manage for you”.
- _puk 8mo agoSo the classic build vs buy question
- SirHumphrey 8mo agoI was asking myself the whole article “what does this mysterious semantic search api actually do?” and was a bit underwhelmed when the result came out to be - managed vector database.
- EspadaV9 8mo agoIf you need to store vector, surely the easiest solution is to run `CREATE EXTENSION vector`. When, or if, you need more, then look at alternatives, but I'm sure most people will have a much easier time just adding it to their existing database.
- canpan 8mo agoPgvector is definitely the easiest way to get started. I did not really get the problem the article was trying to sell. Setting up an ingestion pipeline to your existing db, vs ingesting into yet another db seems to not solve a problem I have. If there was one thing I wish pgvector was better at, it would be to allow composite indexes (ie find vector where category). But it's a minor point.
- kencho 8mo agothe article was aimed more at teams that don't have an existing postgres setup and are evaluating standalone vector databases from scratch. if you're already running postgres with pgvector, you're in a good spot
- EspadaV9 8mo agoI was genuinely surprised just how easy it was to get a fully working RAG set up with Postgres. It was a few hours over a weekend to get something "working" and then probably a bit less time a following weekend to have a nicer database structure and rebuild it learning from the mistakes during the first attempt. The harder part comes next, because that involves multiple tables of user provided data, multi tenancy with a shared core vector schema, and all the actual business logic, so I've put it all on hold for a real breakdown now, but I wouldn't expect it to be much of a problem with what I've found so far with pgvector, and Postgres in general.
- deleted 8mo ago[deleted]
- Raro 8mo agoIf this were on Mad Men: "You don't need a hamburger... you need McDonald's".
- kencho 8mo agofair point on the framing. we do use vector search internally, so calling it "not a vector database" is a stretch. the argument i was trying to make is more narrow: most teams evaluating pinecone or qdrant don't need to operate the vector layer themselves. they need search results from an api call. whether that api uses vectors, BM25, or hamsters under the hood shouldn't matter to them. i could have been clearer about that