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[
"Vector Space Talks - Qdrant",
"Transcript:"
] | /tags/vector-space-talks/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div:nth-of-type(1) > 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. | [
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"tags/vector-space-talks"
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[
"Vector Space Talks - Qdrant",
"Transcript:"
] | /tags/vector-space-talks/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div:nth-of-type(1) > div > div:nth-of-type(2) > article > p:nth-of-type(55) | [
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"tags",
"tags/vector-space-talks"
] | 11,903 |
[
"Vector Space Talks - Qdrant",
"Transcript:"
] | /tags/vector-space-talks/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div:nth-of-type(1) > div > div:nth-of-type(2) > article > p:nth-of-type(55) | [
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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-space-talks"
] | 11,906 |
[
"Vector Space Talks - Qdrant",
"Transcript:"
] | /tags/vector-space-talks/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div:nth-of-type(1) > div > div:nth-of-type(2) > article > p:nth-of-type(55) | [
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[
"Vector Space Talks - Qdrant",
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] | /tags/vector-space-talks/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div:nth-of-type(1) > div > div:nth-of-type(2) > article > p:nth-of-type(55) | [
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[
"Vector Space Talks - Qdrant",
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[
"Vector Space Talks - Qdrant",
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] | /tags/vector-space-talks/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div:nth-of-type(1) > 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-space-talks"
] | 11,911 |
[
"Vector Space Talks - Qdrant",
"Transcript:"
] | /tags/vector-space-talks/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div:nth-of-type(1) > div > div:nth-of-type(2) > article > p:nth-of-type(56) | [
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"tags",
"tags/vector-space-talks"
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[
"Vector Space Talks - Qdrant",
"Transcript:"
] | /tags/vector-space-talks/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div:nth-of-type(1) > div > div:nth-of-type(2) > article > p:nth-of-type(56) | [
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"tags",
"tags/vector-space-talks"
] | 11,913 |
[
"Vector Space Talks - Qdrant",
"Transcript:"
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] | p | The challenges we faced was really about bathrooms and empty rooms because all bathrooms kind of look similar. | [
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[
"Vector Space Talks - Qdrant",
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"tags/vector-space-talks"
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[
"Vector Space Talks - Qdrant",
"Transcript:"
] | /tags/vector-space-talks/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div:nth-of-type(1) > div > div:nth-of-type(2) > article > p:nth-of-type(56) | [
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"tags",
"tags/vector-space-talks"
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[
"Vector Space Talks - Qdrant",
"Transcript:"
] | /tags/vector-space-talks/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div:nth-of-type(1) > div > div:nth-of-type(2) > article > p:nth-of-type(56) | [
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"tags",
"tags/vector-space-talks"
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[
"Vector Space Talks - Qdrant",
"Transcript:"
] | /tags/vector-space-talks/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div:nth-of-type(1) > div > div:nth-of-type(2) > article > p:nth-of-type(56) | [
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"tags/vector-space-talks"
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[
"Vector Space Talks - Qdrant",
"Transcript:"
] | /tags/vector-space-talks/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div:nth-of-type(1) > div > div:nth-of-type(2) > article > p:nth-of-type(57) | [
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[
"Vector Space Talks - Qdrant",
"Transcript:"
] | /tags/vector-space-talks/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div:nth-of-type(1) > div > div:nth-of-type(2) > article > p:nth-of-type(57) | [
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"tags",
"tags/vector-space-talks"
] | 11,920 |
[
"Vector Space Talks - Qdrant",
"Transcript:"
] | /tags/vector-space-talks/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div:nth-of-type(1) > div > div:nth-of-type(2) > article > p:nth-of-type(57) | [
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"tags",
"tags/vector-space-talks"
] | 11,921 |
[
"Vector Space Talks - Qdrant",
"Transcript:"
] | /tags/vector-space-talks/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div:nth-of-type(1) > 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-space-talks"
] | 11,922 |
[
"Vector Space Talks - Qdrant",
"Transcript:"
] | /tags/vector-space-talks/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div:nth-of-type(1) > div > div:nth-of-type(2) > article > p:nth-of-type(57) | [
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[
"Vector Space Talks - Qdrant",
"Transcript:"
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[
"Vector Space Talks - Qdrant",
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[
"Vector Space Talks - Qdrant",
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"Vector Space Talks - Qdrant",
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"tags",
"tags/vector-space-talks"
] | 11,927 |
[
"Vector Space Talks - Qdrant",
"Transcript:"
] | /tags/vector-space-talks/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div:nth-of-type(1) > div > div:nth-of-type(2) > article > p:nth-of-type(58) | [
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"tags",
"tags/vector-space-talks"
] | 11,930 |
[
"Vector Space Talks - Qdrant",
"Transcript:"
] | /tags/vector-space-talks/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div:nth-of-type(1) > div > div:nth-of-type(2) > article > p:nth-of-type(58) | [
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[
"Vector Space Talks - Qdrant",
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[
"Vector Space Talks - Qdrant",
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"Vector Space Talks - Qdrant",
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[
"Vector Space Talks - Qdrant",
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] | /tags/vector-space-talks/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div:nth-of-type(1) > div > div:nth-of-type(2) > article > p:nth-of-type(58) | [
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[
"Vector Space Talks - Qdrant",
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[
"Vector Space Talks - Qdrant",
"Transcript:"
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[
"Vector Space Talks - Qdrant",
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] | /tags/vector-space-talks/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div:nth-of-type(1) > div > div:nth-of-type(2) > article > p:nth-of-type(59) | [
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[
"Vector Space Talks - Qdrant",
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] | p | So they probably trained themselves on these data sets. | [
"tags",
"tags/vector-space-talks"
] | 11,941 |
[
"Vector Space Talks - Qdrant",
"Transcript:"
] | /tags/vector-space-talks/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div:nth-of-type(1) > div > div:nth-of-type(2) > article > p:nth-of-type(59) | [
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"tags",
"tags/vector-space-talks"
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[
"Vector Space Talks - Qdrant",
"Transcript:"
] | /tags/vector-space-talks/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div:nth-of-type(1) > div > div:nth-of-type(2) > article > p:nth-of-type(59) | [
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] | p | So I assume they did it to gain some points in these data sets. | [
"tags",
"tags/vector-space-talks"
] | 11,943 |
[
"Vector Space Talks - Qdrant",
"Transcript:"
] | /tags/vector-space-talks/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div:nth-of-type(1) > div > div:nth-of-type(2) > article > p:nth-of-type(60) | [
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[
"Vector Space Talks - Qdrant",
"Transcript:"
] | /tags/vector-space-talks/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div:nth-of-type(1) > div > div:nth-of-type(2) > article > p:nth-of-type(61) | [
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] | p | Yeah, that’s why the multilingual is super important for these use cases. | [
"tags",
"tags/vector-space-talks"
] | 11,951 |
[
"Vector Space Talks - Qdrant",
"Transcript:"
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] | p | Again, for this one there are models for French working much better than other two, so we may change it afterwards, but right now the performance we have is decent. | [
"tags",
"tags/vector-space-talks"
] | 11,954 |
[
"Vector Space Talks - Qdrant",
"Transcript:"
] | /tags/vector-space-talks/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div:nth-of-type(1) > 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-space-talks"
] | 11,955 |
[
"Vector Space Talks - Qdrant",
"Transcript:"
] | /tags/vector-space-talks/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div:nth-of-type(1) > div > div:nth-of-type(2) > article > p:nth-of-type(63) | [
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] | p | So Ada two is good for understanding diverse context, wide range of documentation, medical contents, technical content, et cetera, without any fine tuning. | [
"tags",
"tags/vector-space-talks"
] | 11,956 |
[
"Vector Space Talks - Qdrant",
"Transcript:"
] | /tags/vector-space-talks/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div:nth-of-type(1) > div > div:nth-of-type(2) > article > p:nth-of-type(63) | [
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"tags",
"tags/vector-space-talks"
] | 11,957 |
[
"Vector Space Talks - Qdrant",
"Transcript:"
] | /tags/vector-space-talks/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div:nth-of-type(1) > 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-space-talks"
] | 11,958 |
[
"Vector Space Talks - Qdrant",
"Transcript:"
] | /tags/vector-space-talks/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div:nth-of-type(1) > div > div:nth-of-type(2) > article > p:nth-of-type(63) | [
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] | p | So it was definitely, it changed a lot of things. | [
"tags",
"tags/vector-space-talks"
] | 11,959 |
[
"Vector Space Talks - Qdrant",
"Transcript:"
] | /tags/vector-space-talks/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div:nth-of-type(1) > div > div:nth-of-type(2) > article > p:nth-of-type(63) | [
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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. | [
"tags",
"tags/vector-space-talks"
] | 11,962 |
[
"Vector Space Talks - Qdrant",
"Transcript:"
] | /tags/vector-space-talks/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div:nth-of-type(1) > div > div:nth-of-type(2) > article > p:nth-of-type(64) | [
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"tags",
"tags/vector-space-talks"
] | 11,963 |
[
"Vector Space Talks - Qdrant",
"Transcript:"
] | /tags/vector-space-talks/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div:nth-of-type(1) > div > div:nth-of-type(2) > article > p:nth-of-type(64) | [
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"tags",
"tags/vector-space-talks"
] | 11,964 |
[
"Vector Space Talks - Qdrant",
"Transcript:"
] | /tags/vector-space-talks/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div:nth-of-type(1) > div > div:nth-of-type(2) > article > p:nth-of-type(64) | [
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"tags/vector-space-talks"
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[
"Vector Space Talks - Qdrant",
"Transcript:"
] | /tags/vector-space-talks/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div:nth-of-type(1) > div > div:nth-of-type(2) > article > p:nth-of-type(64) | [
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"tags",
"tags/vector-space-talks"
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[
"Vector Space Talks - Qdrant",
"Transcript:"
] | /tags/vector-space-talks/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div:nth-of-type(1) > div > div:nth-of-type(2) > article > p:nth-of-type(64) | [
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] | p | Basically, if you want your retrieval process to work well, you have to divide your documents the right way to create the embeddings. | [
"tags",
"tags/vector-space-talks"
] | 11,967 |
[
"Vector Space Talks - Qdrant",
"Transcript:"
] | /tags/vector-space-talks/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div:nth-of-type(1) > div > div:nth-of-type(2) > article > p:nth-of-type(64) | [
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"tags/vector-space-talks"
] | 11,968 |
[
"Vector Space Talks - Qdrant",
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[
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[
"Vector Space Talks - Qdrant",
"Transcript:"
] | /tags/vector-space-talks/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div:nth-of-type(1) > div > div:nth-of-type(2) > article > p:nth-of-type(65) | [
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"tags",
"tags/vector-space-talks"
] | 11,971 |
[
"Vector Space Talks - Qdrant",
"Transcript:"
] | /tags/vector-space-talks/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div:nth-of-type(1) > div > div:nth-of-type(2) > article > p:nth-of-type(65) | [
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"tags",
"tags/vector-space-talks"
] | 11,972 |
[
"Vector Space Talks - Qdrant",
"Transcript:"
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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-space-talks"
] | 11,973 |
[
"Vector Space Talks - Qdrant",
"Transcript:"
] | /tags/vector-space-talks/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div:nth-of-type(1) > 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-space-talks"
] | 11,974 |
[
"Vector Space Talks - Qdrant",
"Transcript:"
] | /tags/vector-space-talks/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div:nth-of-type(1) > div > div:nth-of-type(2) > article > p:nth-of-type(65) | [
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] | 11,975 |
[
"Vector Space Talks - Qdrant",
"Transcript:"
] | /tags/vector-space-talks/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div:nth-of-type(1) > div > div:nth-of-type(2) > article > p:nth-of-type(65) | [
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"tags",
"tags/vector-space-talks"
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[
"Vector Space Talks - Qdrant",
"Transcript:"
] | /tags/vector-space-talks/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div:nth-of-type(1) > div > div:nth-of-type(2) > article > p:nth-of-type(65) | [
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"tags",
"tags/vector-space-talks"
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[
"Vector Space Talks - Qdrant",
"Transcript:"
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] | p | But as a sparse vector it’s much better. | [
"tags",
"tags/vector-space-talks"
] | 11,978 |
[
"Vector Space Talks - Qdrant",
"Transcript:"
] | /tags/vector-space-talks/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div:nth-of-type(1) > div > div:nth-of-type(2) > article > p:nth-of-type(66) | [
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"tags/vector-space-talks"
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[
"Vector Space Talks - Qdrant",
"Transcript:"
] | /tags/vector-space-talks/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div:nth-of-type(1) > div > div:nth-of-type(2) > article > p:nth-of-type(66) | [
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"tags",
"tags/vector-space-talks"
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[
"Vector Space Talks - Qdrant",
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[
"Vector Space Talks - Qdrant",
"Transcript:"
] | /tags/vector-space-talks/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div:nth-of-type(1) > div > div:nth-of-type(2) > article > p:nth-of-type(67) | [
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[
"Vector Space Talks - Qdrant",
"Transcript:"
] | /tags/vector-space-talks/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div:nth-of-type(1) > div > div:nth-of-type(2) > article > p:nth-of-type(67) | [
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[
"Vector Space Talks - Qdrant",
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] | /tags/vector-space-talks/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div:nth-of-type(1) > div > div:nth-of-type(2) > article > p:nth-of-type(67) | [
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[
"Vector Space Talks - Qdrant",
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[
"Vector Space Talks - Qdrant",
"Transcript:"
] | /tags/vector-space-talks/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div:nth-of-type(1) > div > div:nth-of-type(2) > article > p:nth-of-type(67) | [
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] | p | This is a very good question. | [
"tags",
"tags/vector-space-talks"
] | 11,994 |
[
"Vector Space Talks - Qdrant",
"Transcript:"
] | /tags/vector-space-talks/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div:nth-of-type(1) > div > div:nth-of-type(2) > article > p:nth-of-type(68) | [
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"tags",
"tags/vector-space-talks"
] | 11,995 |
[
"Vector Space Talks - Qdrant",
"Transcript:"
] | /tags/vector-space-talks/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div:nth-of-type(1) > div > div:nth-of-type(2) > article > p:nth-of-type(68) | [
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] | p | Basically what we do is that we use chainlit, which is a very good tool for chatting, and then you can put a react front in front of it to make it very custom. | [
"tags",
"tags/vector-space-talks"
] | 11,996 |
[
"Vector Space Talks - Qdrant",
"Transcript:"
] | /tags/vector-space-talks/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div:nth-of-type(1) > div > div:nth-of-type(2) > article > p:nth-of-type(68) | [
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"tags",
"tags/vector-space-talks"
] | 11,997 |
[
"Vector Space Talks - Qdrant",
"Transcript:"
] | /tags/vector-space-talks/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div:nth-of-type(1) > div > div:nth-of-type(2) > article > p:nth-of-type(68) | [
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"tags",
"tags/vector-space-talks"
] | 11,998 |
[
"Vector Space Talks - Qdrant",
"Transcript:"
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] | p | And so we know the answer may have some problems. | [
"tags",
"tags/vector-space-talks"
] | 11,999 |
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