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The Vector Database obsession came from RAG, which came from a marketing idea to calm down enterprise fears about hallucination with RAG. Will save this article
by noemit 7mo ago
The Vector Database obsession came from RAG, which came from a marketing idea to calm down enterprise fears about hallucination with RAG. Will save this article because I feel like I have this conversation weekly when people think they need a vector database for something they definitely do not.
- kencho 7mo agoExactly. We talk to teams every week who spent a month setting up pinecone or qdrant and then realize they just needed search that worked. The vector database became the default answer to every search problem because of the RAG hype cycle, even when the actual need is way simpler
- le-mark 7mo agoCan you describe when the actual need is much simpler? I mean throwing documents into elastic search is really easy and the search is really good.
- kencho 7mo agoone use case we're handling right now is for a large online auction marketplace. they needed to automatically categorize 40,000 newly uploaded images per week. no tags, no metadata from the sellers, just raw photos. elasticsearch can't look at an image and tell you it's a vintage rolex or a mid-century lamp. they needed search that understands visual content, not text that's the kind of problem where keyword search doesn't apply at all, no matter how good the engine is
- simonjgreen 7mo ago> Spent a month setting up Pinecone? Really?
- kencho 7mo agothere's a lot more in "setting up" than creating an account and a collection on pinecone or any other service
- alansaber 7mo agoThat RAG is marketing and doesn't significantly affect performance is incorrect. As to whether retrieval really benefits from vector DBs is another question.