Vector database is not a separate database category

Re:Vector database is not a separate database category by deepGem on Tuesday October 03, 2023 @08:30AM (#37750969)

At the expense of latency ? when in fact latency is the most important aspect for any search. Any idea on how fast the searches from the client are ?

retrieve the embedding index and to run an indexed search to identify the data to be retrieved. Please bear with the layman like questioning -

So if the data is {obj: "obj1, "data": {"name": "atlas", "embedding": "1123124234" } What is an embedding index ? Is it something like {"1123124234": "obj1"} ?

From what I understand the query will be "geography" whose embedding will be "12311111" and now you have to run a KNN for a match which will return {"name": "atlas", "embedding": "1123124234"}

Not sure where the embedding index comes into play here.

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Re:Vector database is not a separate database category by avereveard on Tuesday October 03, 2023 @11:07AM (#37752920)

Eh, sure latency is suboptimal. But if you have a LLM in the mix, that latency will dominate the overall response time. At that point you might not care about how performant your index is, and since performance/cost is non linear, it can translate to very significant savings

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