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[
"Vector Image Search - Qdrant",
"Transcript:"
] | /tags/vector-image-search/ | 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(35) | [
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[
"Vector Image Search - Qdrant",
"Transcript:"
] | /tags/vector-image-search/ | 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(35) | [
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"tags",
"tags/vector-image-search"
] | 10,901 |
[
"Vector Image Search - Qdrant",
"Transcript:"
] | /tags/vector-image-search/ | 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(35) | [
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"tags",
"tags/vector-image-search"
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[
"Vector Image Search - Qdrant",
"Transcript:"
] | /tags/vector-image-search/ | 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(35) | [
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] | p | So here I was just showing the segmentation part because it’s a cool outcome of the model and it shows that the model works well here we are really here to build a representation of the image we cannot really play with segment anything for the matching, to my knowledge, at least. | [
"tags",
"tags/vector-image-search"
] | 10,903 |
[
"Vector Image Search - Qdrant",
"Transcript:"
] | /tags/vector-image-search/ | 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(36) | [
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[
"Vector Image Search - Qdrant",
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] | p | And I feel like, yeah, there’s one way of doing it, which is you hand picking the different embeddings that you think are going to be hard. | [
"tags",
"tags/vector-image-search"
] | 10,906 |
[
"Vector Image Search - Qdrant",
"Transcript:"
] | /tags/vector-image-search/ | 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(36) | [
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[
"Vector Image Search - Qdrant",
"Transcript:"
] | /tags/vector-image-search/ | 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(37) | [
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"Vector Image Search - Qdrant",
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[
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] | p | I don’t remember the names, but I definitely have seen demos online about how it’ll give you a 3d space and you can kind of explore the different embeddings and explore what’s going on I. | [
"tags",
"tags/vector-image-search"
] | 10,911 |
[
"Vector Image Search - Qdrant",
"Transcript:"
] | /tags/vector-image-search/ | 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(39) | [
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] | p | So tensorboard embeddings is a good tool for that. | [
"tags",
"tags/vector-image-search"
] | 10,914 |
[
"Vector Image Search - Qdrant",
"Transcript:"
] | /tags/vector-image-search/ | 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(39) | [
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] | p | I could actually demo it afterwards. | [
"tags",
"tags/vector-image-search"
] | 10,915 |
[
"Vector Image Search - Qdrant",
"Transcript:"
] | /tags/vector-image-search/ | 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(40) | [
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"tags",
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[
"Vector Image Search - Qdrant",
"Transcript:"
] | /tags/vector-image-search/ | 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(40) | [
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[
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] | /tags/vector-image-search/ | 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(40) | [
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[
"Vector Image Search - Qdrant",
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[
"Vector Image Search - Qdrant",
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] | p | So basically you have an embedding of like 1000 dimensions and it just reduces it to free dimensions. | [
"tags",
"tags/vector-image-search"
] | 10,922 |
[
"Vector Image Search - Qdrant",
"Transcript:"
] | /tags/vector-image-search/ | 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 so you can visualize it in a 3d space and you can see how close your embeddings are from each other. | [
"tags",
"tags/vector-image-search"
] | 10,923 |
[
"Vector Image Search - Qdrant",
"Transcript:"
] | /tags/vector-image-search/ | 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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[
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] | p | But it’s really for visualization purposes, not really for training purposes. | [
"tags",
"tags/vector-image-search"
] | 10,927 |
[
"Vector Image Search - Qdrant",
"Transcript:"
] | /tags/vector-image-search/ | 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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[
"Vector Image Search - Qdrant",
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[
"Vector Image Search - Qdrant",
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] | p | Yeah, I think that sounds like what I’m talking about. | [
"tags",
"tags/vector-image-search"
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[
"Vector Image Search - Qdrant",
"Transcript:"
] | /tags/vector-image-search/ | 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 | So good to know on both of these. | [
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[
"Vector Image Search - Qdrant",
"Transcript:"
] | /tags/vector-image-search/ | 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 | And you’re shooting me straight on it. | [
"tags",
"tags/vector-image-search"
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[
"Vector Image Search - Qdrant",
"Transcript:"
] | /tags/vector-image-search/ | 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 | Mike is asking a question in here, like text embedding, would that allow you to include an image with alternate text? | [
"tags",
"tags/vector-image-search"
] | 10,936 |
[
"Vector Image Search - Qdrant",
"Transcript:"
] | /tags/vector-image-search/ | 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(47) | [
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"tags",
"tags/vector-image-search"
] | 10,937 |
[
"Vector Image Search - Qdrant",
"Transcript:"
] | /tags/vector-image-search/ | 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(47) | [
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[
"Vector Image Search - Qdrant",
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[
"Vector Image Search - Qdrant",
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[
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] | p | So it sounds like a way to meet regulatory accessibility requirements if you have. | [
"tags",
"tags/vector-image-search"
] | 10,941 |
[
"Vector Image Search - Qdrant",
"Transcript:"
] | /tags/vector-image-search/ | 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 | I think it was probably around where you were talking about the multimodal and text to guide the embeddings and potentially would having that allow you to include an image with alternate text? | [
"tags",
"tags/vector-image-search"
] | 10,942 |
[
"Vector Image Search - Qdrant",
"Transcript:"
] | /tags/vector-image-search/ | 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(49) | [
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[
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"tags",
"tags/vector-image-search"
] | 10,944 |
[
"Vector Image Search - Qdrant",
"Transcript:"
] | /tags/vector-image-search/ | 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(49) | [
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"tags",
"tags/vector-image-search"
] | 10,945 |
[
"Vector Image Search - Qdrant",
"Transcript:"
] | /tags/vector-image-search/ | 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(49) | [
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] | p | It’s what I understand. | [
"tags",
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[
"Vector Image Search - Qdrant",
"Transcript:"
] | /tags/vector-image-search/ | 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(49) | [
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] | p | My idea was just if you could create an embedding that could combine a text inputs and the image inputs, and basically it would be trained in such a way that the text would basically be used as a guidance of the image to only encode the parts of the image which are required for your task to not be disturbed by the noisy. | [
"tags",
"tags/vector-image-search"
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[
"Vector Image Search - Qdrant",
"Transcript:"
] | /tags/vector-image-search/ | 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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[
"Vector Image Search - Qdrant",
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"tags",
"tags/vector-image-search"
] | 10,949 |
[
"Vector Image Search - Qdrant",
"Transcript:"
] | /tags/vector-image-search/ | 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 | All right, Mike, let us know if that answers the question or if you have more. Yes. | [
"tags",
"tags/vector-image-search"
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[
"Vector Image Search - Qdrant",
"Transcript:"
] | /tags/vector-image-search/ | 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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[
"Vector Image Search - Qdrant",
"Transcript:"
] | /tags/vector-image-search/ | 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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] | p | So I’ll let you keep cruising and I’ll try not to derail it again. | [
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"tags/vector-image-search"
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[
"Vector Image Search - Qdrant",
"Transcript:"
] | /tags/vector-image-search/ | 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(52) | [
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[
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] | p | I wanted to stop you and ask some questions. | [
"tags",
"tags/vector-image-search"
] | 10,959 |
[
"Vector Image Search - Qdrant",
"Transcript:"
] | /tags/vector-image-search/ | 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(53) | [
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[
"Vector Image Search - Qdrant",
"Transcript:"
] | /tags/vector-image-search/ | 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(53) | [
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] | p | So as I was saying, you have two agencies coming to take different pictures of the same property. | [
"tags",
"tags/vector-image-search"
] | 10,963 |
[
"Vector Image Search - Qdrant",
"Transcript:"
] | /tags/vector-image-search/ | 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(53) | [
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] | p | And the thing is that they may not put exactly the same price or the same surface or the same location. | [
"tags",
"tags/vector-image-search"
] | 10,964 |
[
"Vector Image Search - Qdrant",
"Transcript:"
] | /tags/vector-image-search/ | 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(53) | [
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"tags",
"tags/vector-image-search"
] | 10,965 |
[
"Vector Image Search - Qdrant",
"Transcript:"
] | /tags/vector-image-search/ | 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(53) | [
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"tags",
"tags/vector-image-search"
] | 10,966 |
[
"Vector Image Search - Qdrant",
"Transcript:"
] | /tags/vector-image-search/ | 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(53) | [
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[
"Vector Image Search - Qdrant",
"Transcript:"
] | /tags/vector-image-search/ | 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(53) | [
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[
"Vector Image Search - Qdrant",
"Transcript:"
] | /tags/vector-image-search/ | 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/vector-image-search"
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[
"Vector Image Search - Qdrant",
"Transcript:"
] | /tags/vector-image-search/ | 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 it was basically the task to match images of the same properties. | [
"tags",
"tags/vector-image-search"
] | 10,972 |
[
"Vector Image Search - Qdrant",
"Transcript:"
] | /tags/vector-image-search/ | 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/vector-image-search"
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[
"Vector Image Search - Qdrant",
"Transcript:"
] | /tags/vector-image-search/ | 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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[
"Vector Image Search - Qdrant",
"Transcript:"
] | /tags/vector-image-search/ | 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 like on the pictures I shown, I just showed, the model was quite good at identifying. | [
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[
"Vector Image Search - Qdrant",
"Transcript:"
] | /tags/vector-image-search/ | 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 | It was from the same property. | [
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"tags/vector-image-search"
] | 10,976 |
[
"Vector Image Search - Qdrant",
"Transcript:"
] | /tags/vector-image-search/ | 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 | Here we used cudrant for this project was a bit more recent. | [
"tags",
"tags/vector-image-search"
] | 10,977 |
[
"Vector Image Search - Qdrant",
"Transcript:"
] | /tags/vector-image-search/ | 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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[
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] | /tags/vector-image-search/ | 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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[
"Vector Image Search - Qdrant",
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[
"Vector Image Search - Qdrant",
"Transcript:"
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] | p | So we had 15 million vectors and without this indexing, the search could take up to 20, 30 seconds. | [
"tags",
"tags/vector-image-search"
] | 10,983 |
[
"Vector Image Search - Qdrant",
"Transcript:"
] | /tags/vector-image-search/ | 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 | And with indexing it was like in a split second. | [
"tags",
"tags/vector-image-search"
] | 10,984 |
[
"Vector Image Search - Qdrant",
"Transcript:"
] | /tags/vector-image-search/ | 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 it was a killer feature for us. | [
"tags",
"tags/vector-image-search"
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[
"Vector Image Search - Qdrant",
"Transcript:"
] | /tags/vector-image-search/ | 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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[
"Vector Image Search - Qdrant",
"Transcript:"
] | /tags/vector-image-search/ | 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(56) | [
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[
"Vector Image Search - Qdrant",
"Transcript:"
] | /tags/vector-image-search/ | 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(56) | [
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] | p | Since using the metadata we managed to every time reduce the task down to a search of 1000 vector. | [
"tags",
"tags/vector-image-search"
] | 10,988 |
[
"Vector Image Search - Qdrant",
"Transcript:"
] | /tags/vector-image-search/ | 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(56) | [
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"tags",
"tags/vector-image-search"
] | 10,989 |
[
"Vector Image Search - Qdrant",
"Transcript:"
] | /tags/vector-image-search/ | 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(56) | [
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"tags",
"tags/vector-image-search"
] | 10,990 |
[
"Vector Image Search - Qdrant",
"Transcript:"
] | /tags/vector-image-search/ | 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(56) | [
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[
"Vector Image Search - Qdrant",
"Transcript:"
] | /tags/vector-image-search/ | 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(56) | [
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[
"Vector Image Search - Qdrant",
"Transcript:"
] | /tags/vector-image-search/ | 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(56) | [
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] | p | The model would often put high similarity scores between two bathroom of different properties and same for the empty rooms. | [
"tags",
"tags/vector-image-search"
] | 10,993 |
[
"Vector Image Search - Qdrant",
"Transcript:"
] | /tags/vector-image-search/ | 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(56) | [
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"tags/vector-image-search"
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[
"Vector Image Search - Qdrant",
"Transcript:"
] | /tags/vector-image-search/ | 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(56) | [
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"tags/vector-image-search"
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[
"Vector Image Search - Qdrant",
"Transcript:"
] | /tags/vector-image-search/ | 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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[
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] | p | And again, same thing on the future of image embedding. | [
"tags",
"tags/vector-image-search"
] | 10,997 |
[
"Vector Image Search - Qdrant",
"Transcript:"
] | /tags/vector-image-search/ | 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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"tags",
"tags/vector-image-search"
] | 10,998 |
[
"Vector Image Search - Qdrant",
"Transcript:"
] | /tags/vector-image-search/ | 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 the principle of retribution generation for those of you who are not familiar with it is just you take some documents, you have an embedding model here, an embedding model trained on text and not on images, which will output representations from these documents, put it in a vector database, and then when a user will ask a question over the documentation, it will create an embedding of the request and retrieve the most similar documents. | [
"tags",
"tags/vector-image-search"
] | 10,999 |
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