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[
"Vector Space Talk - Qdrant",
"Transcript:"
] | /tags/vector-space-talk/ | 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(42) | [
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[
"Vector Space Talk - Qdrant",
"Transcript:"
] | /tags/vector-space-talk/ | 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(43) | [
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[
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[
"Vector Space Talk - Qdrant",
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[
"Vector Space Talk - Qdrant",
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] | /tags/vector-space-talk/ | 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(44) | [
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[
"Vector Space Talk - Qdrant",
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] | p | And it is already consolidated data sets, which we again used for benchmarking purposes. | [
"tags",
"tags/vector-space-talk"
] | 15,178 |
[
"Vector Space Talk - Qdrant",
"Transcript:"
] | /tags/vector-space-talk/ | 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(44) | [
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"tags",
"tags/vector-space-talk"
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[
"Vector Space Talk - Qdrant",
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[
"Vector Space Talk - Qdrant",
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[
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[
"Vector Space Talk - Qdrant",
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"tags",
"tags/vector-space-talk"
] | 15,183 |
[
"Vector Space Talk - Qdrant",
"Transcript:"
] | /tags/vector-space-talk/ | 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(45) | [
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[
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[
"Vector Space Talk - Qdrant",
"Transcript:"
] | /tags/vector-space-talk/ | 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(46) | [
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"tags/vector-space-talk"
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[
"Vector Space Talk - Qdrant",
"Transcript:"
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] | p | Okay, cool. Good. | [
"tags",
"tags/vector-space-talk"
] | 15,191 |
[
"Vector Space Talk - Qdrant",
"Transcript:"
] | /tags/vector-space-talk/ | 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(47) | [
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[
"Vector Space Talk - Qdrant",
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"tags/vector-space-talk"
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[
"Vector Space Talk - Qdrant",
"Transcript:"
] | /tags/vector-space-talk/ | 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(47) | [
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[
"Vector Space Talk - Qdrant",
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] | /tags/vector-space-talk/ | 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(47) | [
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"tags/vector-space-talk"
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[
"Vector Space Talk - Qdrant",
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] | p | If so, not right now, but this is actually a great idea and we will try to build it as soon as possible. | [
"tags",
"tags/vector-space-talk"
] | 15,199 |
[
"Vector Space Talk - Qdrant",
"Transcript:"
] | /tags/vector-space-talk/ | 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(49) | [
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] | p | Where you have the drag and you can see like within this area, you have X amount of hotels, and these are what they look like, et cetera, et cetera. | [
"tags",
"tags/vector-space-talk"
] | 15,202 |
[
"Vector Space Talk - Qdrant",
"Transcript:"
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] | p | All right, so, yeah, feel free to share any slides you have, otherwise I can hit you with another question in the meantime, which is I’m wondering about the configurations you used for the HNSW index in Qdrant and what were the number of edges per node and the number of neighbors to consider during the index building. | [
"tags",
"tags/vector-space-talk"
] | 15,207 |
[
"Vector Space Talk - Qdrant",
"Transcript:"
] | /tags/vector-space-talk/ | 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(51) | [
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[
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] | p | So should I go with the slide first or should I answer your question first? | [
"tags",
"tags/vector-space-talk"
] | 15,210 |
[
"Vector Space Talk - Qdrant",
"Transcript:"
] | /tags/vector-space-talk/ | 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(53) | [
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[
"Vector Space Talk - Qdrant",
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] | p | So, for HNSW configuration, we have specified the value of M, which is, I think, basically the layers as 64, and the value for EF construct is 256. | [
"tags",
"tags/vector-space-talk"
] | 15,215 |
[
"Vector Space Talk - Qdrant",
"Transcript:"
] | /tags/vector-space-talk/ | 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 Talk - Qdrant",
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[
"Vector Space Talk - Qdrant",
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[
"Vector Space Talk - Qdrant",
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] | /tags/vector-space-talk/ | 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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[
"Vector Space Talk - Qdrant",
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] | p | In that case, the Python client for Qdrant also provides a parameter called exact, and if we specify it as true, then it basically does not use indexing and it makes a full search on the whole vector collection, basically. | [
"tags",
"tags/vector-space-talk"
] | 15,223 |
[
"Vector Space Talk - Qdrant",
"Transcript:"
] | /tags/vector-space-talk/ | 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/vector-space-talk"
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[
"Vector Space Talk - Qdrant",
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[
"Vector Space Talk - Qdrant",
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"tags/vector-space-talk"
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[
"Vector Space Talk - Qdrant",
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[
"Vector Space Talk - Qdrant",
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[
"Vector Space Talk - Qdrant",
"Transcript:"
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[
"Vector Space Talk - Qdrant",
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[
"Vector Space Talk - Qdrant",
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[
"Vector Space Talk - Qdrant",
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[
"Vector Space Talk - Qdrant",
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[
"Vector Space Talk - Qdrant",
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[
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[
"Vector Space Talk - Qdrant",
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] | p | Okay, if anything comes up yeah, I’ll remind you, but hit us with the slides, man. | [
"tags",
"tags/vector-space-talk"
] | 15,243 |
[
"Vector Space Talk - Qdrant",
"Transcript:"
] | /tags/vector-space-talk/ | 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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[
"Vector Space Talk - Qdrant",
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] | p | So I have an architecture diagram of what the solution looks like right now. | [
"tags",
"tags/vector-space-talk"
] | 15,247 |
[
"Vector Space Talk - Qdrant",
"Transcript:"
] | /tags/vector-space-talk/ | 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(62) | [
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[
"Vector Space Talk - Qdrant",
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[
"Vector Space Talk - Qdrant",
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"tags/vector-space-talk"
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[
"Vector Space Talk - Qdrant",
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[
"Vector Space Talk - Qdrant",
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[
"Vector Space Talk - Qdrant",
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[
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"tags",
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[
"Vector Space Talk - Qdrant",
"Transcript:"
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] | p | Did you go through different models or was it just this one worked well enough and you went with it? | [
"tags",
"tags/vector-space-talk"
] | 15,258 |
[
"Vector Space Talk - Qdrant",
"Transcript:"
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"tags",
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[
"Vector Space Talk - Qdrant",
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] | /tags/vector-space-talk/ | 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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[
"Vector Space Talk - Qdrant",
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] | p | So we did a lot of experiments. | [
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[
"Vector Space Talk - Qdrant",
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"tags",
"tags/vector-space-talk"
] | 15,264 |
[
"Vector Space Talk - Qdrant",
"Transcript:"
] | /tags/vector-space-talk/ | 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-talk"
] | 15,265 |
[
"Vector Space Talk - Qdrant",
"Transcript:"
] | /tags/vector-space-talk/ | 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 we have deployed it in a serverless inference endpoint in SageMaker. | [
"tags",
"tags/vector-space-talk"
] | 15,266 |
[
"Vector Space Talk - Qdrant",
"Transcript:"
] | /tags/vector-space-talk/ | 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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[
"Vector Space Talk - Qdrant",
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] | p | We have exposed this API gateway in such a way that multiple clients can connect to it and they can use this entity resolution service according to their use cases. | [
"tags",
"tags/vector-space-talk"
] | 15,271 |
Subsets and Splits