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[
"Quantized Emdedding Models - Qdrant",
"Transcript:"
] | /tags/quantized-emdedding-models/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(41) | [
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] | p | And the next thing is, as I called out, we always want to take performance as a first class citizen. | [
"tags",
"tags/quantized-emdedding-models"
] | 16,472 |
[
"Quantized Emdedding Models - Qdrant",
"Transcript:"
] | /tags/quantized-emdedding-models/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(41) | [
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] | p | So we are looking at how we can allow you to change or adapt frozen Embeddings, let’s say open a Embedding or any other model to your specific domain. | [
"tags",
"tags/quantized-emdedding-models"
] | 16,473 |
[
"Quantized Emdedding Models - Qdrant",
"Transcript:"
] | /tags/quantized-emdedding-models/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(41) | [
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] | p | So maybe a separate toolkit within Fast Embed which is optional and not a part of the default path, because this is not something which you will use all the time. | [
"tags",
"tags/quantized-emdedding-models"
] | 16,474 |
[
"Quantized Emdedding Models - Qdrant",
"Transcript:"
] | /tags/quantized-emdedding-models/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(42) | [
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[
"Quantized Emdedding Models - Qdrant",
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"tags",
"tags/quantized-emdedding-models"
] | 16,476 |
[
"Quantized Emdedding Models - Qdrant",
"Transcript:"
] | /tags/quantized-emdedding-models/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(42) | [
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] | p | So we will do that. | [
"tags",
"tags/quantized-emdedding-models"
] | 16,477 |
[
"Quantized Emdedding Models - Qdrant",
"Transcript:"
] | /tags/quantized-emdedding-models/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(42) | [
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] | p | Yeah, that’s it. | [
"tags",
"tags/quantized-emdedding-models"
] | 16,478 |
[
"Quantized Emdedding Models - Qdrant",
"Transcript:"
] | /tags/quantized-emdedding-models/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(42) | [
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[
"Quantized Emdedding Models - Qdrant",
"Transcript:"
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] | p | There was somebody that talked about how you need to be good at your puns and that might be the best thing, best brag worthy stuff you’ve got. | [
"tags",
"tags/quantized-emdedding-models"
] | 16,486 |
[
"Quantized Emdedding Models - Qdrant",
"Transcript:"
] | /tags/quantized-emdedding-models/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(45) | [
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] | p | There’s also a question coming through that I want to ask you. | [
"tags",
"tags/quantized-emdedding-models"
] | 16,487 |
[
"Quantized Emdedding Models - Qdrant",
"Transcript:"
] | /tags/quantized-emdedding-models/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(45) | [
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] | p | Is it true that when we use Qdrant client add Fast Embedding is included? | [
"tags",
"tags/quantized-emdedding-models"
] | 16,488 |
[
"Quantized Emdedding Models - Qdrant",
"Transcript:"
] | /tags/quantized-emdedding-models/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(45) | [
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[
"Quantized Emdedding Models - Qdrant",
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] | p | What do you mean by do it? | [
"tags",
"tags/quantized-emdedding-models"
] | 16,491 |
[
"Quantized Emdedding Models - Qdrant",
"Transcript:"
] | /tags/quantized-emdedding-models/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(46) | [
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] | p | Yeah, I think it’s more just like you don’t have to add it on to Qdrant in any way or this is completely separated. | [
"tags",
"tags/quantized-emdedding-models"
] | 16,494 |
[
"Quantized Emdedding Models - Qdrant",
"Transcript:"
] | /tags/quantized-emdedding-models/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(48) | [
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] | p | Nirant Kasliwal: | [
"tags",
"tags/quantized-emdedding-models"
] | 16,495 |
[
"Quantized Emdedding Models - Qdrant",
"Transcript:"
] | /tags/quantized-emdedding-models/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(48) | [
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] | p | So this is client side. | [
"tags",
"tags/quantized-emdedding-models"
] | 16,496 |
[
"Quantized Emdedding Models - Qdrant",
"Transcript:"
] | /tags/quantized-emdedding-models/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(48) | [
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"tags",
"tags/quantized-emdedding-models"
] | 16,497 |
[
"Quantized Emdedding Models - Qdrant",
"Transcript:"
] | /tags/quantized-emdedding-models/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(48) | [
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"tags",
"tags/quantized-emdedding-models"
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[
"Quantized Emdedding Models - Qdrant",
"Transcript:"
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"tags",
"tags/quantized-emdedding-models"
] | 16,499 |
[
"Quantized Emdedding Models - Qdrant",
"Transcript:"
] | /tags/quantized-emdedding-models/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(48) | [
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"tags",
"tags/quantized-emdedding-models"
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[
"Quantized Emdedding Models - Qdrant",
"Transcript:"
] | /tags/quantized-emdedding-models/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(48) | [
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[
"Quantized Emdedding Models - Qdrant",
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] | p | Or let’s say two different ports for Qdrant and Discord within the sorry, Qdrant and Fast Embed in the same docker image or server. | [
"tags",
"tags/quantized-emdedding-models"
] | 16,502 |
[
"Quantized Emdedding Models - Qdrant",
"Transcript:"
] | /tags/quantized-emdedding-models/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(48) | [
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[
"Quantized Emdedding Models - Qdrant",
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] | p | Yeah, and I think if I understood it, I understood that question a little bit differently, where it’s just like this comes with Qdrant out of the box. | [
"tags",
"tags/quantized-emdedding-models"
] | 16,505 |
[
"Quantized Emdedding Models - Qdrant",
"Transcript:"
] | /tags/quantized-emdedding-models/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(50) | [
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[
"Quantized Emdedding Models - Qdrant",
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] | p | Yes, I think that’s a good way to look at it. | [
"tags",
"tags/quantized-emdedding-models"
] | 16,507 |
[
"Quantized Emdedding Models - Qdrant",
"Transcript:"
] | /tags/quantized-emdedding-models/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(50) | [
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"tags",
"tags/quantized-emdedding-models"
] | 16,508 |
[
"Quantized Emdedding Models - Qdrant",
"Transcript:"
] | /tags/quantized-emdedding-models/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(50) | [
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] | p | Let’s say our default model is picked for English and it’s mostly tested on open domain open web data. | [
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[
"Quantized Emdedding Models - Qdrant",
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"tags",
"tags/quantized-emdedding-models"
] | 16,510 |
[
"Quantized Emdedding Models - Qdrant",
"Transcript:"
] | /tags/quantized-emdedding-models/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(50) | [
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"tags",
"tags/quantized-emdedding-models"
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[
"Quantized Emdedding Models - Qdrant",
"Transcript:"
] | /tags/quantized-emdedding-models/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(50) | [
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"tags",
"tags/quantized-emdedding-models"
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[
"Quantized Emdedding Models - Qdrant",
"Transcript:"
] | /tags/quantized-emdedding-models/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(51) | [
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[
"Quantized Emdedding Models - Qdrant",
"Transcript:"
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[
"Quantized Emdedding Models - Qdrant",
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[
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"tags",
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[
"Quantized Emdedding Models - Qdrant",
"Transcript:"
] | /tags/quantized-emdedding-models/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(54) | [
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] | p | We support a reasonable range of models, including a few multilingual ones. | [
"tags",
"tags/quantized-emdedding-models"
] | 16,521 |
[
"Quantized Emdedding Models - Qdrant",
"Transcript:"
] | /tags/quantized-emdedding-models/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(54) | [
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"tags",
"tags/quantized-emdedding-models"
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[
"Quantized Emdedding Models - Qdrant",
"Transcript:"
] | /tags/quantized-emdedding-models/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(54) | [
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] | p | So, for instance, let’s say this is how you would have to manually specify, let’s say, passage or query. | [
"tags",
"tags/quantized-emdedding-models"
] | 16,523 |
[
"Quantized Emdedding Models - Qdrant",
"Transcript:"
] | /tags/quantized-emdedding-models/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(54) | [
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[
"Quantized Emdedding Models - Qdrant",
"Transcript:"
] | /tags/quantized-emdedding-models/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(54) | [
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[
"Quantized Emdedding Models - Qdrant",
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] | p | So this is taken care of. | [
"tags",
"tags/quantized-emdedding-models"
] | 16,526 |
[
"Quantized Emdedding Models - Qdrant",
"Transcript:"
] | /tags/quantized-emdedding-models/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(54) | [
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"Quantized Emdedding Models - Qdrant",
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[
"Quantized Emdedding Models - Qdrant",
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] | p | And as a very important shout out in Embedding retrieval, when we use Embedding for retrieval or vector similarity search, it’s the relative ordering which matters. | [
"tags",
"tags/quantized-emdedding-models"
] | 16,531 |
[
"Quantized Emdedding Models - Qdrant",
"Transcript:"
] | /tags/quantized-emdedding-models/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(55) | [
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] | p | So, for instance, we cannot say that zero nine is always good. | [
"tags",
"tags/quantized-emdedding-models"
] | 16,532 |
[
"Quantized Emdedding Models - Qdrant",
"Transcript:"
] | /tags/quantized-emdedding-models/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(55) | [
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] | p | It could also mean that the best match is, let’s say, 0.6 in your domain. | [
"tags",
"tags/quantized-emdedding-models"
] | 16,533 |
[
"Quantized Emdedding Models - Qdrant",
"Transcript:"
] | /tags/quantized-emdedding-models/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(55) | [
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[
"Quantized Emdedding Models - Qdrant",
"Transcript:"
] | /tags/quantized-emdedding-models/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(55) | [
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"tags",
"tags/quantized-emdedding-models"
] | 16,535 |
[
"Quantized Emdedding Models - Qdrant",
"Transcript:"
] | /tags/quantized-emdedding-models/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(55) | [
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] | p | So I would suggest that you calibrate that for your queries and domain. | [
"tags",
"tags/quantized-emdedding-models"
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[
"Quantized Emdedding Models - Qdrant",
"Transcript:"
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[
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] | p | Even if you just, let’s say, start with five to ten questions, which you handwrite based on your understanding of the domain, you will do a lot better than just picking a threshold at random. | [
"tags",
"tags/quantized-emdedding-models"
] | 16,539 |
[
"Quantized Emdedding Models - Qdrant",
"Transcript:"
] | /tags/quantized-emdedding-models/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(57) | [
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] | p | Okay, thanks for that. | [
"tags",
"tags/quantized-emdedding-models"
] | 16,542 |
[
"Quantized Emdedding Models - Qdrant",
"Transcript:"
] | /tags/quantized-emdedding-models/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(57) | [
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] | p | So there’s a question coming through in the chat from Shreya asking how is the latency in comparison to elasticsearch? | [
"tags",
"tags/quantized-emdedding-models"
] | 16,543 |
[
"Quantized Emdedding Models - Qdrant",
"Transcript:"
] | /tags/quantized-emdedding-models/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(58) | [
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[
"Quantized Emdedding Models - Qdrant",
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[
"Quantized Emdedding Models - Qdrant",
"Transcript:"
] | /tags/quantized-emdedding-models/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(58) | [
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] | p | I believe that’s a Qdrant benchmark question and I’m not sure how is elastics HNSW index, because I think that will be the fair comparison. | [
"tags",
"tags/quantized-emdedding-models"
] | 16,546 |
[
"Quantized Emdedding Models - Qdrant",
"Transcript:"
] | /tags/quantized-emdedding-models/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(58) | [
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] | p | I also believe elastics HNSW index puts some limitations on how many vectors they can store with the payload. | [
"tags",
"tags/quantized-emdedding-models"
] | 16,547 |
[
"Quantized Emdedding Models - Qdrant",
"Transcript:"
] | /tags/quantized-emdedding-models/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(58) | [
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] | p | So it’s not an apples to apples comparison. | [
"tags",
"tags/quantized-emdedding-models"
] | 16,548 |
[
"Quantized Emdedding Models - Qdrant",
"Transcript:"
] | /tags/quantized-emdedding-models/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(58) | [
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] | p | It’s almost like comparing, let’s say, a single page with the entire book, because that’s typically the ratio from what I remember I also might be a few months outdated on this, but I think the intent behind that question is, is Qdrant fast enough for what Qdrant does? | [
"tags",
"tags/quantized-emdedding-models"
] | 16,549 |
[
"Quantized Emdedding Models - Qdrant",
"Transcript:"
] | /tags/quantized-emdedding-models/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(58) | [
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"tags",
"tags/quantized-emdedding-models"
] | 16,550 |
[
"Quantized Emdedding Models - Qdrant",
"Transcript:"
] | /tags/quantized-emdedding-models/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(58) | [
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"tags",
"tags/quantized-emdedding-models"
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[
"Quantized Emdedding Models - Qdrant",
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] | /tags/quantized-emdedding-models/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(58) | [
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"tags/quantized-emdedding-models"
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[
"Quantized Emdedding Models - Qdrant",
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[
"Quantized Emdedding Models - Qdrant",
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[
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] | p | So I think if a Twitter scale company, which probably does about anywhere between two and 5 million tweets a day, if they can embed and use Qdrant to serve that similarity search, I think most people should be okay with that latency and throughput requirements. | [
"tags",
"tags/quantized-emdedding-models"
] | 16,555 |
[
"Quantized Emdedding Models - Qdrant",
"Transcript:"
] | /tags/quantized-emdedding-models/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(60) | [
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] | p | I mean, you called it Fast Embed for a reason, right? | [
"tags",
"tags/quantized-emdedding-models"
] | 16,558 |
[
"Quantized Emdedding Models - Qdrant",
"Transcript:"
] | /tags/quantized-emdedding-models/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(44) | [
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[
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] | p | And given the variety of models and the embedding sizes available, how do you determine the most suitable models and embedding sizes? | [
"tags",
"tags/quantized-emdedding-models"
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[
"Quantized Emdedding Models - Qdrant",
"Transcript:"
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"tags",
"tags/quantized-emdedding-models"
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[
"Quantized Emdedding Models - Qdrant",
"Transcript:"
] | /tags/quantized-emdedding-models/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(62) | [
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[
"Quantized Emdedding Models - Qdrant",
"Transcript:"
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[
"Quantized Emdedding Models - Qdrant",
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] | p | And I will give you all these three answers in that order. | [
"tags",
"tags/quantized-emdedding-models"
] | 16,569 |
[
"Quantized Emdedding Models - Qdrant",
"Transcript:"
] | /tags/quantized-emdedding-models/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(63) | [
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] | p | So the academic and the gold standard way of doing this would probably look something like this. | [
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"tags/quantized-emdedding-models"
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[
"Quantized Emdedding Models - Qdrant",
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] | p | You will go at a known benchmark, which might be, let’s say, something like Kilt K-I-L-T or multilingual text embedding benchmark, also known as MTEB or Beer, which is beir one of these three benchmarks. | [
"tags",
"tags/quantized-emdedding-models"
] | 16,571 |
Subsets and Splits