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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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] | p | And afterwards we usually pass it to an LLM, which will generate an answer. | [
"tags",
"tags/vector-image-search"
] | 11,000 |
[
"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 | But here in this talk, we won’t focus on the overall product, but really on the vector search part. | [
"tags",
"tags/vector-image-search"
] | 11,001 |
[
"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 two projects was one, as I told you, a rack for my nutrition company, so endosion with around a few hundred thousand of pages, and the second one was for medical companies, so for the doctors. | [
"tags",
"tags/vector-image-search"
] | 11,002 |
[
"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 it was really about the documentation search rather than the LLM, because you cannot output any mistake. | [
"tags",
"tags/vector-image-search"
] | 11,003 |
[
"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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[
"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(58) | [
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[
"Vector Image Search - Qdrant",
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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(58) | [
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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(58) | [
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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(58) | [
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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(58) | [
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[
"Vector Image Search - Qdrant",
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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(58) | [
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[
"Vector Image Search - Qdrant",
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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(58) | [
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] | p | So since we use GPT four afterwards, because it’s currently the best LLM, it was also easier to use adatu to start with, but we may use a better one afterwards because as I’m saying, it’s not the best one if you refer to the MTAB. | [
"tags",
"tags/vector-image-search"
] | 11,011 |
[
"Vector Image Search - Qdrant",
"Transcript:"
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"tags/vector-image-search"
] | 11,012 |
[
"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(58) | [
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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(58) | [
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] | p | All the benchmarks are just for one language and it’s not as well taking into account most of the languages, like it’s only considering English, Polish and Chinese. | [
"tags",
"tags/vector-image-search"
] | 11,014 |
[
"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(59) | [
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[
"Vector Image Search - Qdrant",
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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(59) | [
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[
"Vector Image Search - Qdrant",
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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(59) | [
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] | p | So like most of the best performing models are currently closed source APIs and hence closed source data sets, and so we don’t know how they’ve been trained. | [
"tags",
"tags/vector-image-search"
] | 11,017 |
[
"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(59) | [
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] | p | So they probably trained themselves on these data sets. | [
"tags",
"tags/vector-image-search"
] | 11,018 |
[
"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(59) | [
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[
"Vector Image Search - Qdrant",
"Transcript:"
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] | p | So I assume they did it to gain some points in these data sets. | [
"tags",
"tags/vector-image-search"
] | 11,020 |
[
"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(60) | [
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] | p | Demetrios: | [
"tags",
"tags/vector-image-search"
] | 11,021 |
[
"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(60) | [
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[
"Vector Image Search - Qdrant",
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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(61) | [
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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(61) | [
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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(61) | [
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[
"Vector Image Search - Qdrant",
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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(62) | [
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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(62) | [
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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(63) | [
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[
"Vector Image Search - Qdrant",
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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(63) | [
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[
"Vector Image Search - Qdrant",
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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(63) | [
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] | p | Since both projects are very similar, I’ll jump into the conclusion for both of them together. | [
"tags",
"tags/vector-image-search"
] | 11,032 |
[
"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(63) | [
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"tags",
"tags/vector-image-search"
] | 11,033 |
[
"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(63) | [
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"tags",
"tags/vector-image-search"
] | 11,034 |
[
"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(63) | [
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] | p | And also, quick note, because I did not do it from the start, is that when using an API model, it’s much faster to use it in asynchronous mode like the embedding equation went something like ten times or 100 times faster. | [
"tags",
"tags/vector-image-search"
] | 11,035 |
[
"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(63) | [
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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(63) | [
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[
"Vector Image Search - Qdrant",
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[
"Vector Image Search - Qdrant",
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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(64) | [
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] | p | So you can pop it in a second, get the free version, and using the feature which allows to put the vectors on disk instead of storing them on ram, which makes it a bit slower, you can easily support few hundred thousand of vectors and with a very decent response time. | [
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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(64) | [
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] | p | The challenge we faced is that mostly for the notion, so like mostly in notion, we have a lot of pages which are just a title because they are empty, et cetera. | [
"tags",
"tags/vector-image-search"
] | 11,040 |
[
"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(64) | [
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"tags",
"tags/vector-image-search"
] | 11,041 |
[
"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(64) | [
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] | p | So often the documents were retrieved were document with very little content, which was a bit frustrating. | [
"tags",
"tags/vector-image-search"
] | 11,042 |
[
"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(64) | [
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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(64) | [
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"tags",
"tags/vector-image-search"
] | 11,044 |
[
"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(64) | [
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] | p | So you can use matrix rules, but basically you need to divide your documents in content which semantically makes sense and it’s not always trivial. | [
"tags",
"tags/vector-image-search"
] | 11,045 |
[
"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(64) | [
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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(65) | [
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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(65) | [
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] | p | So to handle these changes, a good option would be to use models handing differently question and documents like Bg or cohere. | [
"tags",
"tags/vector-image-search"
] | 11,048 |
[
"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(65) | [
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] | p | Basically they use the same model but trained differently on long documents and questions which allow them to map them differently in the space. | [
"tags",
"tags/vector-image-search"
] | 11,049 |
[
"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(65) | [
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] | p | And my guess is that using such model documents, which are only a title, et cetera, will not be as close as the question as they are right now because they will be considered differently. | [
"tags",
"tags/vector-image-search"
] | 11,050 |
[
"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(65) | [
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] | p | So I hope it will help this problem. | [
"tags",
"tags/vector-image-search"
] | 11,051 |
[
"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(65) | [
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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(65) | [
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] | p | Heap research so for the keyword problem I was mentioning here, so in the recent release, Cudran just enabled sparse vectors which make actually TFEdev vectors possible. | [
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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(65) | [
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"tags",
"tags/vector-image-search"
] | 11,054 |
[
"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(65) | [
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"tags",
"tags/vector-image-search"
] | 11,055 |
[
"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(66) | [
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] | p | And so you can build a debrief search combining the TFDF search for keyword search and the other search for semantic search to get the best of both worlds and overcome this issue. | [
"tags",
"tags/vector-image-search"
] | 11,057 |
[
"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(66) | [
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] | p | And finally, I’m actually quite surprised that with all the work that is going on, generative AI and rag, nobody has started working on a model to help with chunking. | [
"tags",
"tags/vector-image-search"
] | 11,058 |
[
"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(66) | [
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] | p | It’s like one of the biggest challenge, and I feel like it’s quite doable to have a model which will our model, or some kind of algorithm which will understand the structure of your documentation and understand why it semantically makes sense to chunk your documents. | [
"tags",
"tags/vector-image-search"
] | 11,059 |
[
"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(66) | [
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[
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[
"Vector Image Search - Qdrant",
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[
"Vector Image Search - Qdrant",
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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(67) | [
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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(67) | [
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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(67) | [
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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(67) | [
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[
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[
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[
"Vector Image Search - Qdrant",
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] | p | This is why I mentioned this project is mostly about the document search. | [
"tags",
"tags/vector-image-search"
] | 11,072 |
[
"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(68) | [
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"tags",
"tags/vector-image-search"
] | 11,073 |
[
"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(68) | [
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"tags",
"tags/vector-image-search"
] | 11,074 |
[
"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(68) | [
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"tags",
"tags/vector-image-search"
] | 11,075 |
[
"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(68) | [
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"tags",
"tags/vector-image-search"
] | 11,076 |
[
"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(68) | [
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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(68) | [
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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(69) | [
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"tags/vector-image-search"
] | 11,079 |
[
"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(69) | [
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] | p | So it’s really the way, it’s really a product problem rather than an algorithm problem, an algorithmic problem, yeah. | [
"tags",
"tags/vector-image-search"
] | 11,080 |
[
"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(69) | [
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] | p | Yeah, makes sense. | [
"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(70) | [
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[
"Vector Image Search - Qdrant",
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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(70) | [
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[
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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(71) | [
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[
"Vector Image Search - Qdrant",
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[
"Vector Image Search - Qdrant",
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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(72) | [
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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(73) | [
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[
"Vector Image Search - Qdrant",
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[
"Vector Image Search - Qdrant",
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] | p | Yeah, because I think that’s another really incredibly hard problem is like, oh yeah, we’re just sending all this sensitive information over to the IDA model to create embeddings with it. | [
"tags",
"tags/vector-image-search"
] | 11,099 |
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