I am getting "error code: 502" errors for the last few minutes on two different vectorize indices
I am getting "error code: 502" errors for the last few minutes on two different vectorize indices
14 Replies
The body literally only says "error code: 502" if I see this right. I am doing a normal request (that used to work a few days ago) on /query. It uses topK: 2 with a namespace filter and returnMetadata: true
This happens on both of my indices, if a cloudflare staff can DM me, I can provide the specific indices maybe there's more info on the cause on your isde
Greetings! Are there any hints on when we can expect vector DELETE and GET to be available through the REST api?
we're working on vector DELETE. For GET, is that get vector by id?
Hi @Vy, yes, that'd be what I'm interested in
thanks, we should be able to tackle GET same time as DELETE
Hey all!
I have a question regarding billing, if using metadata filters, would total queried vector dimensions be calculated towards the entire index or only the vectors matching the metadata filter?
I am currently studying the use case of partitioning user documents using filters, but don't want to be billed on a search through all vectors for a question regarding a single document
Sorry everyone, dumb question, it seems I misunderstood the pricing, a query is billed as 1 queried vector regardless of how many vectors are in the index, right? Now it makes sense why my estimations were coming out so absurd
no, number of vectors in your index does impact billing since that's the total search space for a query. See https://developers.cloudflare.com/vectorize/platform/pricing/#calculating-vector-dimensions for some examples.
Pricing · Vectorize
Vectorize bills based on:
But that would affect the "stored vectors" cost, correct?
My understanding is that the "per query" cost is not affected by the stored vectors count (as in, if I have 100000 vectors stored, the cost per query is the same as if I have 200000, but the stored vectors bill would increase)
Is my understanding correct in that sense?
If this doesn't make sense, help us understand where you're stuck. There's also some examples further down:
Put another way:
- If you store 10k vectors of 1536 dimension each and make ZERO queries, it's
(10000)*1536*(0.040/1000000) = $0.6144 for the month
- If you query that index 50k times that month, it's (10000+50000)*1536*(0.040/1000000) = $3.68. The query component is $3.072. You just add them up.In the end it does make sense!
The docs are quite clear, I just got it wrong at first, since the provided formula includes both the query and storage costs, I at first assumed that index size would affect per query costs
I think it would be clearer to newcomers if the pricing examples had the cost components (stored and queried vectors) shown (and calculated) separately.
It was just a misunderstanding in my part, as clearly there are two unrelated cost components defined on the top of the article, but the calculation including both factors kinda threw me off!
Thanks a lot for your help, the costs are clear to me now
Was this question answered? Will the queried vector formula be affected when using metadata filters?
In other words, can we save money by smartly partitioning and querying with metadata. It seems namespaces are the only way to do this at the moment, but are limited to 1000.
Unknown User•11mo ago
Message Not Public
Sign In & Join Server To View