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[
"Superpower your Semantic Search using Vector Database - Nicolas Mauti | Vector Space Talks - Qdrant",
"Transcript:"
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[
"Superpower your Semantic Search using Vector Database - Nicolas Mauti | Vector Space Talks - Qdrant",
"Transcript:"
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"blog",
"blog/semantic-search-vector-database"
] | 12,901 |
[
"Superpower your Semantic Search using Vector Database - Nicolas Mauti | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/semantic-search-vector-database/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(25) | [
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"blog",
"blog/semantic-search-vector-database"
] | 12,902 |
[
"Superpower your Semantic Search using Vector Database - Nicolas Mauti | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/semantic-search-vector-database/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(25) | [
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] | p | And as Malt is an european company, we have to have to model a multilingual model. | [
"blog",
"blog/semantic-search-vector-database"
] | 12,903 |
[
"Superpower your Semantic Search using Vector Database - Nicolas Mauti | Vector Space Talks - Qdrant",
"Transcript:"
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"blog",
"blog/semantic-search-vector-database"
] | 12,904 |
[
"Superpower your Semantic Search using Vector Database - Nicolas Mauti | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/semantic-search-vector-database/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(25) | [
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"blog",
"blog/semantic-search-vector-database"
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[
"Superpower your Semantic Search using Vector Database - Nicolas Mauti | Vector Space Talks - Qdrant",
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] | p | And this model is also able to take into account the structure of a profile of a freelancer profile because you have a description and job, some skills, some experiences. | [
"blog",
"blog/semantic-search-vector-database"
] | 12,906 |
[
"Superpower your Semantic Search using Vector Database - Nicolas Mauti | Vector Space Talks - Qdrant",
"Transcript:"
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"blog",
"blog/semantic-search-vector-database"
] | 12,907 |
[
"Superpower your Semantic Search using Vector Database - Nicolas Mauti | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/semantic-search-vector-database/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(25) | [
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"blog",
"blog/semantic-search-vector-database"
] | 12,908 |
[
"Superpower your Semantic Search using Vector Database - Nicolas Mauti | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/semantic-search-vector-database/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(26) | [
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[
"Superpower your Semantic Search using Vector Database - Nicolas Mauti | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/semantic-search-vector-database/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(26) | [
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[
"Superpower your Semantic Search using Vector Database - Nicolas Mauti | Vector Space Talks - Qdrant",
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] | p | And so we use that to train this model. | [
"blog",
"blog/semantic-search-vector-database"
] | 12,911 |
[
"Superpower your Semantic Search using Vector Database - Nicolas Mauti | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/semantic-search-vector-database/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(26) | [
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"blog",
"blog/semantic-search-vector-database"
] | 12,912 |
[
"Superpower your Semantic Search using Vector Database - Nicolas Mauti | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/semantic-search-vector-database/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(27) | [
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"blog",
"blog/semantic-search-vector-database"
] | 12,913 |
[
"Superpower your Semantic Search using Vector Database - Nicolas Mauti | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/semantic-search-vector-database/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(27) | [
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] | p | One question from my side, sorry to stop you right now. | [
"blog",
"blog/semantic-search-vector-database"
] | 12,914 |
[
"Superpower your Semantic Search using Vector Database - Nicolas Mauti | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/semantic-search-vector-database/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(27) | [
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[
"Superpower your Semantic Search using Vector Database - Nicolas Mauti | Vector Space Talks - Qdrant",
"Transcript:"
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[
"Superpower your Semantic Search using Vector Database - Nicolas Mauti | Vector Space Talks - Qdrant",
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"blog",
"blog/semantic-search-vector-database"
] | 12,917 |
[
"Superpower your Semantic Search using Vector Database - Nicolas Mauti | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/semantic-search-vector-database/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(28) | [
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"blog",
"blog/semantic-search-vector-database"
] | 12,918 |
[
"Superpower your Semantic Search using Vector Database - Nicolas Mauti | Vector Space Talks - Qdrant",
"Transcript:"
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] | p | And so we also review them, sometimes manually because sometimes the response are not so good or we don’t have exactly what we want or stuff like that, so we can review them. | [
"blog",
"blog/semantic-search-vector-database"
] | 12,919 |
[
"Superpower your Semantic Search using Vector Database - Nicolas Mauti | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/semantic-search-vector-database/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(28) | [
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"blog",
"blog/semantic-search-vector-database"
] | 12,920 |
[
"Superpower your Semantic Search using Vector Database - Nicolas Mauti | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/semantic-search-vector-database/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(28) | [
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] | p | So now we have our model and if we want to see how it looks. | [
"blog",
"blog/semantic-search-vector-database"
] | 12,921 |
[
"Superpower your Semantic Search using Vector Database - Nicolas Mauti | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/semantic-search-vector-database/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(28) | [
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] | p | So here I draw some ponds and color them by the category of our freelancer. | [
"blog",
"blog/semantic-search-vector-database"
] | 12,922 |
[
"Superpower your Semantic Search using Vector Database - Nicolas Mauti | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/semantic-search-vector-database/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(28) | [
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] | p | So on the platform the freelancer can have category, for example tech or graphic or soon designer or this kind of category. | [
"blog",
"blog/semantic-search-vector-database"
] | 12,923 |
[
"Superpower your Semantic Search using Vector Database - Nicolas Mauti | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/semantic-search-vector-database/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(28) | [
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] | p | And so we can see that our space seems to be well organized, where the tech freelancer are close to each other and the graphic designer for example, are far from the tech family. | [
"blog",
"blog/semantic-search-vector-database"
] | 12,924 |
[
"Superpower your Semantic Search using Vector Database - Nicolas Mauti | Vector Space Talks - Qdrant",
"Transcript:"
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"blog",
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] | 12,925 |
[
"Superpower your Semantic Search using Vector Database - Nicolas Mauti | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/semantic-search-vector-database/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(29) | [
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"blog",
"blog/semantic-search-vector-database"
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[
"Superpower your Semantic Search using Vector Database - Nicolas Mauti | Vector Space Talks - Qdrant",
"Transcript:"
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] | p | And so now we have a good model. | [
"blog",
"blog/semantic-search-vector-database"
] | 12,927 |
[
"Superpower your Semantic Search using Vector Database - Nicolas Mauti | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/semantic-search-vector-database/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(29) | [
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] | p | So okay, now we have our model, we have to find a way to operate it, so to store this vector and to perform our search. | [
"blog",
"blog/semantic-search-vector-database"
] | 12,928 |
[
"Superpower your Semantic Search using Vector Database - Nicolas Mauti | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/semantic-search-vector-database/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(29) | [
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] | p | And so for that, Vectordb seems to be the good candidate. | [
"blog",
"blog/semantic-search-vector-database"
] | 12,929 |
[
"Superpower your Semantic Search using Vector Database - Nicolas Mauti | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/semantic-search-vector-database/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(29) | [
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] | p | But if you follow the news, you can see that vectordb is very trendy and there is plenty of actor on the market. | [
"blog",
"blog/semantic-search-vector-database"
] | 12,930 |
[
"Superpower your Semantic Search using Vector Database - Nicolas Mauti | Vector Space Talks - Qdrant",
"Transcript:"
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] | p | And so it could be hard to find your loved one. | [
"blog",
"blog/semantic-search-vector-database"
] | 12,931 |
[
"Superpower your Semantic Search using Vector Database - Nicolas Mauti | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/semantic-search-vector-database/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(29) | [
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] | p | And so I will try to give you the criteria we had and why we choose Qdrant at the end. | [
"blog",
"blog/semantic-search-vector-database"
] | 12,932 |
[
"Superpower your Semantic Search using Vector Database - Nicolas Mauti | Vector Space Talks - Qdrant",
"Transcript:"
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[
"Superpower your Semantic Search using Vector Database - Nicolas Mauti | Vector Space Talks - Qdrant",
"Transcript:"
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[
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] | p | So I think I already talked about this ponds, but yeah, we needed performances. | [
"blog",
"blog/semantic-search-vector-database"
] | 12,935 |
[
"Superpower your Semantic Search using Vector Database - Nicolas Mauti | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/semantic-search-vector-database/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(30) | [
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] | p | The second ones was about inn quality. | [
"blog",
"blog/semantic-search-vector-database"
] | 12,936 |
[
"Superpower your Semantic Search using Vector Database - Nicolas Mauti | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/semantic-search-vector-database/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(30) | [
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] | p | As I said before, we cannot do a KnN search, brute force search each time. | [
"blog",
"blog/semantic-search-vector-database"
] | 12,937 |
[
"Superpower your Semantic Search using Vector Database - Nicolas Mauti | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/semantic-search-vector-database/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(30) | [
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] | p | And so we have to find a way to approximate but to be close enough and to be good enough on these points. | [
"blog",
"blog/semantic-search-vector-database"
] | 12,938 |
[
"Superpower your Semantic Search using Vector Database - Nicolas Mauti | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/semantic-search-vector-database/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(30) | [
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] | p | And so otherwise we won’t be leveraged the performance of our model. | [
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"blog/semantic-search-vector-database"
] | 12,939 |
[
"Superpower your Semantic Search using Vector Database - Nicolas Mauti | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/semantic-search-vector-database/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(30) | [
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"blog/semantic-search-vector-database"
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[
"Superpower your Semantic Search using Vector Database - Nicolas Mauti | Vector Space Talks - Qdrant",
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"blog",
"blog/semantic-search-vector-database"
] | 12,941 |
[
"Superpower your Semantic Search using Vector Database - Nicolas Mauti | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/semantic-search-vector-database/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(30) | [
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"blog",
"blog/semantic-search-vector-database"
] | 12,942 |
[
"Superpower your Semantic Search using Vector Database - Nicolas Mauti | Vector Space Talks - Qdrant",
"Transcript:"
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[
"Superpower your Semantic Search using Vector Database - Nicolas Mauti | Vector Space Talks - Qdrant",
"Transcript:"
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] | p | You can just have a three step process and do filtering, semantic search and then ranking, or do semantic search, filtering and then ranking. | [
"blog",
"blog/semantic-search-vector-database"
] | 12,944 |
[
"Superpower your Semantic Search using Vector Database - Nicolas Mauti | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/semantic-search-vector-database/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(31) | [
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] | p | But in both cases, you will have some troubles if you do that. | [
"blog",
"blog/semantic-search-vector-database"
] | 12,945 |
[
"Superpower your Semantic Search using Vector Database - Nicolas Mauti | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/semantic-search-vector-database/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(31) | [
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[
"Superpower your Semantic Search using Vector Database - Nicolas Mauti | Vector Space Talks - Qdrant",
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"blog/semantic-search-vector-database"
] | 12,947 |
[
"Superpower your Semantic Search using Vector Database - Nicolas Mauti | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/semantic-search-vector-database/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(31) | [
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"blog",
"blog/semantic-search-vector-database"
] | 12,948 |
[
"Superpower your Semantic Search using Vector Database - Nicolas Mauti | Vector Space Talks - Qdrant",
"Transcript:"
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"blog",
"blog/semantic-search-vector-database"
] | 12,949 |
[
"Superpower your Semantic Search using Vector Database - Nicolas Mauti | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/semantic-search-vector-database/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(31) | [
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"blog",
"blog/semantic-search-vector-database"
] | 12,950 |
[
"Superpower your Semantic Search using Vector Database - Nicolas Mauti | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/semantic-search-vector-database/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(31) | [
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] | p | And as the number of freelancers you have will grow, your binary namask will also grow. | [
"blog",
"blog/semantic-search-vector-database"
] | 12,951 |
[
"Superpower your Semantic Search using Vector Database - Nicolas Mauti | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/semantic-search-vector-database/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(32) | [
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"blog",
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] | 12,952 |
[
"Superpower your Semantic Search using Vector Database - Nicolas Mauti | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/semantic-search-vector-database/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(32) | [
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"blog",
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[
"Superpower your Semantic Search using Vector Database - Nicolas Mauti | Vector Space Talks - Qdrant",
"Transcript:"
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] | p | And regarding the performance, it will be degraded as your freelancer base grow. | [
"blog",
"blog/semantic-search-vector-database"
] | 12,954 |
[
"Superpower your Semantic Search using Vector Database - Nicolas Mauti | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/semantic-search-vector-database/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(32) | [
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[
"Superpower your Semantic Search using Vector Database - Nicolas Mauti | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/semantic-search-vector-database/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(32) | [
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] | p | A lot of vector database and Qdrants is one of them using hash NSW algorithm to do your inn search. | [
"blog",
"blog/semantic-search-vector-database"
] | 12,956 |
[
"Superpower your Semantic Search using Vector Database - Nicolas Mauti | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/semantic-search-vector-database/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(32) | [
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[
"Superpower your Semantic Search using Vector Database - Nicolas Mauti | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/semantic-search-vector-database/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(32) | [
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"blog",
"blog/semantic-search-vector-database"
] | 12,958 |
[
"Superpower your Semantic Search using Vector Database - Nicolas Mauti | Vector Space Talks - Qdrant",
"Transcript:"
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] | p | And so your quality of your matching will degrade. | [
"blog",
"blog/semantic-search-vector-database"
] | 12,959 |
[
"Superpower your Semantic Search using Vector Database - Nicolas Mauti | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/semantic-search-vector-database/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(32) | [
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"blog",
"blog/semantic-search-vector-database"
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[
"Superpower your Semantic Search using Vector Database - Nicolas Mauti | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/semantic-search-vector-database/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(33) | [
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[
"Superpower your Semantic Search using Vector Database - Nicolas Mauti | Vector Space Talks - Qdrant",
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] | p | So, no, if we go to post filtering here, I think the issue is more clear. | [
"blog",
"blog/semantic-search-vector-database"
] | 12,962 |
[
"Superpower your Semantic Search using Vector Database - Nicolas Mauti | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/semantic-search-vector-database/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(33) | [
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[
"Superpower your Semantic Search using Vector Database - Nicolas Mauti | Vector Space Talks - Qdrant",
"Transcript:"
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"blog",
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[
"Superpower your Semantic Search using Vector Database - Nicolas Mauti | Vector Space Talks - Qdrant",
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] | p | If you apply some very aggressive filtering and you exclude a lot of freelancer with your filtering, you will have to ask for a lot of freelancer in your vector database and so your performances will be impacted. | [
"blog",
"blog/semantic-search-vector-database"
] | 12,965 |
[
"Superpower your Semantic Search using Vector Database - Nicolas Mauti | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/semantic-search-vector-database/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(33) | [
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"blog",
"blog/semantic-search-vector-database"
] | 12,966 |
[
"Superpower your Semantic Search using Vector Database - Nicolas Mauti | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/semantic-search-vector-database/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(33) | [
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] | p | So we cannot do pre filtering or post filtering. | [
"blog",
"blog/semantic-search-vector-database"
] | 12,967 |
[
"Superpower your Semantic Search using Vector Database - Nicolas Mauti | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/semantic-search-vector-database/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(33) | [
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"blog",
"blog/semantic-search-vector-database"
] | 12,968 |
[
"Superpower your Semantic Search using Vector Database - Nicolas Mauti | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/semantic-search-vector-database/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(33) | [
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] | p | And so Qdrant is one of them, you have other one in the market. | [
"blog",
"blog/semantic-search-vector-database"
] | 12,969 |
[
"Superpower your Semantic Search using Vector Database - Nicolas Mauti | Vector Space Talks - Qdrant",
"Transcript:"
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[
"Superpower your Semantic Search using Vector Database - Nicolas Mauti | Vector Space Talks - Qdrant",
"Transcript:"
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"blog",
"blog/semantic-search-vector-database"
] | 12,971 |
[
"Superpower your Semantic Search using Vector Database - Nicolas Mauti | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/semantic-search-vector-database/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(34) | [
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] | p | And this filter is the geospatial filtering and a few of databases under this filtering, and I think Qdrant is one of them that supports it. | [
"blog",
"blog/semantic-search-vector-database"
] | 12,972 |
[
"Superpower your Semantic Search using Vector Database - Nicolas Mauti | Vector Space Talks - Qdrant",
"Transcript:"
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"blog",
"blog/semantic-search-vector-database"
] | 12,973 |
[
"Superpower your Semantic Search using Vector Database - Nicolas Mauti | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/semantic-search-vector-database/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(34) | [
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"blog",
"blog/semantic-search-vector-database"
] | 12,974 |
[
"Superpower your Semantic Search using Vector Database - Nicolas Mauti | Vector Space Talks - Qdrant",
"Transcript:"
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] | p | And so now that I said all of that, we had three candidates that we tested and we benchmarked them. | [
"blog",
"blog/semantic-search-vector-database"
] | 12,975 |
[
"Superpower your Semantic Search using Vector Database - Nicolas Mauti | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/semantic-search-vector-database/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(34) | [
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"blog",
"blog/semantic-search-vector-database"
] | 12,976 |
[
"Superpower your Semantic Search using Vector Database - Nicolas Mauti | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/semantic-search-vector-database/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(34) | [
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] | p | And on this slide you can see Pycon for example, and Pycon was excluded because of the lack of geospatial filtering. | [
"blog",
"blog/semantic-search-vector-database"
] | 12,977 |
[
"Superpower your Semantic Search using Vector Database - Nicolas Mauti | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/semantic-search-vector-database/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(34) | [
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] | p | And so we benchmark them regarding the qps. | [
"blog",
"blog/semantic-search-vector-database"
] | 12,978 |
[
"Superpower your Semantic Search using Vector Database - Nicolas Mauti | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/semantic-search-vector-database/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(35) | [
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"blog",
"blog/semantic-search-vector-database"
] | 12,981 |
[
"Superpower your Semantic Search using Vector Database - Nicolas Mauti | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/semantic-search-vector-database/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(35) | [
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[
"Superpower your Semantic Search using Vector Database - Nicolas Mauti | Vector Space Talks - Qdrant",
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] | p | They used brute force knn for that. | [
"blog",
"blog/semantic-search-vector-database"
] | 12,983 |
[
"Superpower your Semantic Search using Vector Database - Nicolas Mauti | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/semantic-search-vector-database/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(35) | [
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"blog",
"blog/semantic-search-vector-database"
] | 12,984 |
[
"Superpower your Semantic Search using Vector Database - Nicolas Mauti | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/semantic-search-vector-database/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(35) | [
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] | p | And so they give you a measure of your precision of your inn algorithm. | [
"blog",
"blog/semantic-search-vector-database"
] | 12,985 |
[
"Superpower your Semantic Search using Vector Database - Nicolas Mauti | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/semantic-search-vector-database/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(35) | [
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] | p | For this metric, you could see that elasticsearch was a little bit better than Qdrants, but in fact we were able to tune a little bit the parameter of the AsHNSW algorithm and indexes. | [
"blog",
"blog/semantic-search-vector-database"
] | 12,986 |
[
"Superpower your Semantic Search using Vector Database - Nicolas Mauti | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/semantic-search-vector-database/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(35) | [
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"blog",
"blog/semantic-search-vector-database"
] | 12,987 |
[
"Superpower your Semantic Search using Vector Database - Nicolas Mauti | Vector Space Talks - Qdrant",
"Transcript:"
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[
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] | p | So at the end we decided to go with Qdrant. | [
"blog",
"blog/semantic-search-vector-database"
] | 12,989 |
[
"Superpower your Semantic Search using Vector Database - Nicolas Mauti | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/semantic-search-vector-database/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(36) | [
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] | p | So we have, I think all know we have our model and we have our tool to operate them, to operate our model. | [
"blog",
"blog/semantic-search-vector-database"
] | 12,990 |
[
"Superpower your Semantic Search using Vector Database - Nicolas Mauti | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/semantic-search-vector-database/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(36) | [
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] | p | So a final part of this presentation will be about the deployment. | [
"blog",
"blog/semantic-search-vector-database"
] | 12,991 |
[
"Superpower your Semantic Search using Vector Database - Nicolas Mauti | Vector Space Talks - Qdrant",
"Transcript:"
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] | p | I will talk about it a little bit because I think it’s interesting and it’s also part of my job as a development engineer. | [
"blog",
"blog/semantic-search-vector-database"
] | 12,992 |
[
"Superpower your Semantic Search using Vector Database - Nicolas Mauti | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/semantic-search-vector-database/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(36) | [
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] | p | So regarding the deployment, first we decided to deploy a Qdrant in a cluster configuration. | [
"blog",
"blog/semantic-search-vector-database"
] | 12,993 |
[
"Superpower your Semantic Search using Vector Database - Nicolas Mauti | Vector Space Talks - Qdrant",
"Transcript:"
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] | p | We decided to start with three nodes and so we decided to get our collection. | [
"blog",
"blog/semantic-search-vector-database"
] | 12,994 |
[
"Superpower your Semantic Search using Vector Database - Nicolas Mauti | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/semantic-search-vector-database/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(36) | [
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"blog",
"blog/semantic-search-vector-database"
] | 12,995 |
[
"Superpower your Semantic Search using Vector Database - Nicolas Mauti | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/semantic-search-vector-database/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(36) | [
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] | p | And so we decided to split our collection between three nodes. | [
"blog",
"blog/semantic-search-vector-database"
] | 12,996 |
[
"Superpower your Semantic Search using Vector Database - Nicolas Mauti | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/semantic-search-vector-database/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(37) | [
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"blog",
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] | 12,997 |
[
"Superpower your Semantic Search using Vector Database - Nicolas Mauti | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/semantic-search-vector-database/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(37) | [
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"blog",
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[
"Superpower your Semantic Search using Vector Database - Nicolas Mauti | Vector Space Talks - Qdrant",
"Transcript:"
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] | p | So you have a shard of a collection on each node, and then for each shard you have one replica. | [
"blog",
"blog/semantic-search-vector-database"
] | 12,999 |
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