You’re saying vector DBs are the wrong abstraction, but companies keep throwing money at them. Why? Are they just slow to catch on, or are there legit cases where vectors actually make sense?
Founder of TopK here. There are legit use cases for vector-based retrieval (e.g. semantic search, recommendations, multi-modal search, etc.) but that only requires supporting vectors as a data type, not building the whole database around vectors as a first-class citizen (which is what vector DBs do). In practice, you also want to combine multiple vectors, text filters, and metadata alongside custom scoring functions to optimize relevance in your domain, which is not possible with a database built around a vector index.
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You’re saying vector DBs are the wrong abstraction, but companies keep throwing money at them. Why? Are they just slow to catch on, or are there legit cases where vectors actually make sense?
Founder of TopK here. There are legit use cases for vector-based retrieval (e.g. semantic search, recommendations, multi-modal search, etc.) but that only requires supporting vectors as a data type, not building the whole database around vectors as a first-class citizen (which is what vector DBs do). In practice, you also want to combine multiple vectors, text filters, and metadata alongside custom scoring functions to optimize relevance in your domain, which is not possible with a database built around a vector index.