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[
"Retriever-Ranker Architecture - Qdrant",
"Transcript:"
] | /tags/retriever-ranker-architecture/ | 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(21) | [
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] | p | For example, the question is hard rule at malt we have a local approach, and so we want to provide freelancers that are next to the project. | [
"tags",
"tags/retriever-ranker-architecture"
] | 12,200 |
[
"Retriever-Ranker Architecture - Qdrant",
"Transcript:"
] | /tags/retriever-ranker-architecture/ | 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(21) | [
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"tags",
"tags/retriever-ranker-architecture"
] | 12,201 |
[
"Retriever-Ranker Architecture - Qdrant",
"Transcript:"
] | /tags/retriever-ranker-architecture/ | 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(21) | [
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] | p | And on the other side, as you said, demetrius, we are talking about Rexis system here. | [
"tags",
"tags/retriever-ranker-architecture"
] | 12,202 |
[
"Retriever-Ranker Architecture - Qdrant",
"Transcript:"
] | /tags/retriever-ranker-architecture/ | 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(21) | [
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] | p | And so in a rexy system, you also have to take into account some other parameters, for example, the preferences of the freelancers and also the activity on the platform of the freelancer, for example. | [
"tags",
"tags/retriever-ranker-architecture"
] | 12,203 |
[
"Retriever-Ranker Architecture - Qdrant",
"Transcript:"
] | /tags/retriever-ranker-architecture/ | 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(21) | [
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] | p | And so in our system, we have to keep this in mind and to have this working. | [
"tags",
"tags/retriever-ranker-architecture"
] | 12,204 |
[
"Retriever-Ranker Architecture - Qdrant",
"Transcript:"
] | /tags/retriever-ranker-architecture/ | 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(21) | [
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] | p | And so if we do a big picture of how our system worked, we had an API with some alphilter at the beginning, then ML model that was mainly semantic and then some rescoring function with other parameters. | [
"tags",
"tags/retriever-ranker-architecture"
] | 12,205 |
[
"Retriever-Ranker Architecture - Qdrant",
"Transcript:"
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] | p | And so we decided to rework this architecture and to adopt a retriever ranker architecture. | [
"tags",
"tags/retriever-ranker-architecture"
] | 12,206 |
[
"Retriever-Ranker Architecture - Qdrant",
"Transcript:"
] | /tags/retriever-ranker-architecture/ | 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(21) | [
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"tags",
"tags/retriever-ranker-architecture"
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[
"Retriever-Ranker Architecture - Qdrant",
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] | /tags/retriever-ranker-architecture/ | 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(22) | [
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[
"Retriever-Ranker Architecture - Qdrant",
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] | p | So here is your wall databases, so your 600,000 freelancers. | [
"tags",
"tags/retriever-ranker-architecture"
] | 12,209 |
[
"Retriever-Ranker Architecture - Qdrant",
"Transcript:"
] | /tags/retriever-ranker-architecture/ | 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(22) | [
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] | p | And then you will have a first step that is called the retrieval, where we will constrict a subsets of your freelancers. | [
"tags",
"tags/retriever-ranker-architecture"
] | 12,210 |
[
"Retriever-Ranker Architecture - Qdrant",
"Transcript:"
] | /tags/retriever-ranker-architecture/ | 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(22) | [
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] | p | And then you can apply your wrong kill algorithm. | [
"tags",
"tags/retriever-ranker-architecture"
] | 12,211 |
[
"Retriever-Ranker Architecture - Qdrant",
"Transcript:"
] | /tags/retriever-ranker-architecture/ | 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(22) | [
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] | p | That is basically our current application. | [
"tags",
"tags/retriever-ranker-architecture"
] | 12,212 |
[
"Retriever-Ranker Architecture - Qdrant",
"Transcript:"
] | /tags/retriever-ranker-architecture/ | 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(22) | [
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"tags",
"tags/retriever-ranker-architecture"
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[
"Retriever-Ranker Architecture - Qdrant",
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] | p | And on the other side, the ranking can contain more features, not only semantics, it less conference in time. | [
"tags",
"tags/retriever-ranker-architecture"
] | 12,214 |
[
"Retriever-Ranker Architecture - Qdrant",
"Transcript:"
] | /tags/retriever-ranker-architecture/ | 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(22) | [
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"tags",
"tags/retriever-ranker-architecture"
] | 12,215 |
[
"Retriever-Ranker Architecture - Qdrant",
"Transcript:"
] | /tags/retriever-ranker-architecture/ | 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(22) | [
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"tags",
"tags/retriever-ranker-architecture"
] | 12,216 |
[
"Retriever-Ranker Architecture - Qdrant",
"Transcript:"
] | /tags/retriever-ranker-architecture/ | 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(23) | [
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"tags",
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[
"Retriever-Ranker Architecture - Qdrant",
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] | /tags/retriever-ranker-architecture/ | 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(23) | [
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"tags",
"tags/retriever-ranker-architecture"
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[
"Retriever-Ranker Architecture - Qdrant",
"Transcript:"
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] | p | So let’s focus on the first part. | [
"tags",
"tags/retriever-ranker-architecture"
] | 12,219 |
[
"Retriever-Ranker Architecture - Qdrant",
"Transcript:"
] | /tags/retriever-ranker-architecture/ | 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(23) | [
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] | p | That’s what will interesting us today. | [
"tags",
"tags/retriever-ranker-architecture"
] | 12,220 |
[
"Retriever-Ranker Architecture - Qdrant",
"Transcript:"
] | /tags/retriever-ranker-architecture/ | 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(23) | [
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"tags",
"tags/retriever-ranker-architecture"
] | 12,221 |
[
"Retriever-Ranker Architecture - Qdrant",
"Transcript:"
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] | p | And so for that we will build this semantic space. | [
"tags",
"tags/retriever-ranker-architecture"
] | 12,222 |
[
"Retriever-Ranker Architecture - Qdrant",
"Transcript:"
] | /tags/retriever-ranker-architecture/ | 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(23) | [
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"tags",
"tags/retriever-ranker-architecture"
] | 12,223 |
[
"Retriever-Ranker Architecture - Qdrant",
"Transcript:"
] | /tags/retriever-ranker-architecture/ | 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(23) | [
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"tags",
"tags/retriever-ranker-architecture"
] | 12,224 |
[
"Retriever-Ranker Architecture - Qdrant",
"Transcript:"
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"tags",
"tags/retriever-ranker-architecture"
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[
"Retriever-Ranker Architecture - Qdrant",
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] | /tags/retriever-ranker-architecture/ | 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(24) | [
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"tags/retriever-ranker-architecture"
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[
"Retriever-Ranker Architecture - Qdrant",
"Transcript:"
] | /tags/retriever-ranker-architecture/ | 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(24) | [
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] | p | Keep this in mind, it will be interesting in our next slides. | [
"tags",
"tags/retriever-ranker-architecture"
] | 12,227 |
[
"Retriever-Ranker Architecture - Qdrant",
"Transcript:"
] | /tags/retriever-ranker-architecture/ | 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(24) | [
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"tags",
"tags/retriever-ranker-architecture"
] | 12,228 |
[
"Retriever-Ranker Architecture - Qdrant",
"Transcript:"
] | /tags/retriever-ranker-architecture/ | 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(24) | [
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] | p | The first one is a model, because we need a model to compute some vectors and to project our opportunity and our project and our freelancers in this space. | [
"tags",
"tags/retriever-ranker-architecture"
] | 12,229 |
[
"Retriever-Ranker Architecture - Qdrant",
"Transcript:"
] | /tags/retriever-ranker-architecture/ | 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(24) | [
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"tags",
"tags/retriever-ranker-architecture"
] | 12,230 |
[
"Retriever-Ranker Architecture - Qdrant",
"Transcript:"
] | /tags/retriever-ranker-architecture/ | 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(24) | [
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] | p | So to store the vector and also to perform the search. | [
"tags",
"tags/retriever-ranker-architecture"
] | 12,231 |
[
"Retriever-Ranker Architecture - Qdrant",
"Transcript:"
] | /tags/retriever-ranker-architecture/ | 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(24) | [
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] | p | So for the first part, for the model, I will give you some quick info about how we build it. | [
"tags",
"tags/retriever-ranker-architecture"
] | 12,232 |
[
"Retriever-Ranker Architecture - Qdrant",
"Transcript:"
] | /tags/retriever-ranker-architecture/ | 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(24) | [
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] | p | So for this part, it was more on the data scientist part. | [
"tags",
"tags/retriever-ranker-architecture"
] | 12,233 |
[
"Retriever-Ranker Architecture - Qdrant",
"Transcript:"
] | /tags/retriever-ranker-architecture/ | 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(24) | [
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] | p | So the data scientist started from an e five model. | [
"tags",
"tags/retriever-ranker-architecture"
] | 12,234 |
[
"Retriever-Ranker Architecture - Qdrant",
"Transcript:"
] | /tags/retriever-ranker-architecture/ | 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(25) | [
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] | p | Nicolas Mauti: | [
"tags",
"tags/retriever-ranker-architecture"
] | 12,235 |
[
"Retriever-Ranker Architecture - Qdrant",
"Transcript:"
] | /tags/retriever-ranker-architecture/ | 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(25) | [
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"tags",
"tags/retriever-ranker-architecture"
] | 12,236 |
[
"Retriever-Ranker Architecture - Qdrant",
"Transcript:"
] | /tags/retriever-ranker-architecture/ | 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(25) | [
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"tags",
"tags/retriever-ranker-architecture"
] | 12,237 |
[
"Retriever-Ranker Architecture - Qdrant",
"Transcript:"
] | /tags/retriever-ranker-architecture/ | 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(25) | [
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] | p | And as Malt is an european company, we have to have to model a multilingual model. | [
"tags",
"tags/retriever-ranker-architecture"
] | 12,238 |
[
"Retriever-Ranker Architecture - Qdrant",
"Transcript:"
] | /tags/retriever-ranker-architecture/ | 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(25) | [
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"tags",
"tags/retriever-ranker-architecture"
] | 12,239 |
[
"Retriever-Ranker Architecture - Qdrant",
"Transcript:"
] | /tags/retriever-ranker-architecture/ | 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(25) | [
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"tags",
"tags/retriever-ranker-architecture"
] | 12,240 |
[
"Retriever-Ranker Architecture - Qdrant",
"Transcript:"
] | /tags/retriever-ranker-architecture/ | 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(25) | [
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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. | [
"tags",
"tags/retriever-ranker-architecture"
] | 12,241 |
[
"Retriever-Ranker Architecture - Qdrant",
"Transcript:"
] | /tags/retriever-ranker-architecture/ | 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(25) | [
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] | p | And so this model is capable to take this into account. | [
"tags",
"tags/retriever-ranker-architecture"
] | 12,242 |
[
"Retriever-Ranker Architecture - Qdrant",
"Transcript:"
] | /tags/retriever-ranker-architecture/ | 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(25) | [
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] | p | And regarding the training, we use some past interaction on the platform to train it. | [
"tags",
"tags/retriever-ranker-architecture"
] | 12,243 |
[
"Retriever-Ranker Architecture - Qdrant",
"Transcript:"
] | /tags/retriever-ranker-architecture/ | 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(26) | [
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"tags",
"tags/retriever-ranker-architecture"
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[
"Retriever-Ranker Architecture - Qdrant",
"Transcript:"
] | /tags/retriever-ranker-architecture/ | 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(26) | [
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"tags",
"tags/retriever-ranker-architecture"
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[
"Retriever-Ranker Architecture - Qdrant",
"Transcript:"
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] | p | And so we use that to train this model. | [
"tags",
"tags/retriever-ranker-architecture"
] | 12,246 |
[
"Retriever-Ranker Architecture - Qdrant",
"Transcript:"
] | /tags/retriever-ranker-architecture/ | 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(26) | [
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] | p | And so at the end we get some embeddings with 384 dimensions. | [
"tags",
"tags/retriever-ranker-architecture"
] | 12,247 |
[
"Retriever-Ranker Architecture - Qdrant",
"Transcript:"
] | /tags/retriever-ranker-architecture/ | 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(27) | [
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] | p | Demetrios: | [
"tags",
"tags/retriever-ranker-architecture"
] | 12,248 |
[
"Retriever-Ranker Architecture - Qdrant",
"Transcript:"
] | /tags/retriever-ranker-architecture/ | 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(27) | [
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] | p | One question from my side, sorry to stop you right now. | [
"tags",
"tags/retriever-ranker-architecture"
] | 12,249 |
[
"Retriever-Ranker Architecture - Qdrant",
"Transcript:"
] | /tags/retriever-ranker-architecture/ | 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(27) | [
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] | p | Do you do any type of reviews or feedback and add that into the model? | [
"tags",
"tags/retriever-ranker-architecture"
] | 12,250 |
[
"Retriever-Ranker Architecture - Qdrant",
"Transcript:"
] | /tags/retriever-ranker-architecture/ | 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(28) | [
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] | p | Nicolas Mauti: | [
"tags",
"tags/retriever-ranker-architecture"
] | 12,251 |
[
"Retriever-Ranker Architecture - Qdrant",
"Transcript:"
] | /tags/retriever-ranker-architecture/ | 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(28) | [
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"tags",
"tags/retriever-ranker-architecture"
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[
"Retriever-Ranker Architecture - Qdrant",
"Transcript:"
] | /tags/retriever-ranker-architecture/ | 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(28) | [
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"tags",
"tags/retriever-ranker-architecture"
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[
"Retriever-Ranker Architecture - 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. | [
"tags",
"tags/retriever-ranker-architecture"
] | 12,254 |
[
"Retriever-Ranker Architecture - Qdrant",
"Transcript:"
] | /tags/retriever-ranker-architecture/ | 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(28) | [
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] | p | And also we are retraining the model regularly, so this way we can include new feedback from our freelancers. | [
"tags",
"tags/retriever-ranker-architecture"
] | 12,255 |
[
"Retriever-Ranker Architecture - Qdrant",
"Transcript:"
] | /tags/retriever-ranker-architecture/ | 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(28) | [
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] | p | So now we have our model and if we want to see how it looks. | [
"tags",
"tags/retriever-ranker-architecture"
] | 12,256 |
[
"Retriever-Ranker Architecture - Qdrant",
"Transcript:"
] | /tags/retriever-ranker-architecture/ | 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(28) | [
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] | p | So here I draw some ponds and color them by the category of our freelancer. | [
"tags",
"tags/retriever-ranker-architecture"
] | 12,257 |
[
"Retriever-Ranker Architecture - Qdrant",
"Transcript:"
] | /tags/retriever-ranker-architecture/ | 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(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. | [
"tags",
"tags/retriever-ranker-architecture"
] | 12,258 |
[
"Retriever-Ranker Architecture - Qdrant",
"Transcript:"
] | /tags/retriever-ranker-architecture/ | 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(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. | [
"tags",
"tags/retriever-ranker-architecture"
] | 12,259 |
[
"Retriever-Ranker Architecture - Qdrant",
"Transcript:"
] | /tags/retriever-ranker-architecture/ | 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(29) | [
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[
"Retriever-Ranker Architecture - Qdrant",
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"tags",
"tags/retriever-ranker-architecture"
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[
"Retriever-Ranker Architecture - Qdrant",
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] | p | And so now we have a good model. | [
"tags",
"tags/retriever-ranker-architecture"
] | 12,262 |
[
"Retriever-Ranker Architecture - Qdrant",
"Transcript:"
] | /tags/retriever-ranker-architecture/ | 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(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. | [
"tags",
"tags/retriever-ranker-architecture"
] | 12,263 |
[
"Retriever-Ranker Architecture - Qdrant",
"Transcript:"
] | /tags/retriever-ranker-architecture/ | 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(29) | [
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] | p | And so for that, Vectordb seems to be the good candidate. | [
"tags",
"tags/retriever-ranker-architecture"
] | 12,264 |
[
"Retriever-Ranker Architecture - Qdrant",
"Transcript:"
] | /tags/retriever-ranker-architecture/ | 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(29) | [
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"tags",
"tags/retriever-ranker-architecture"
] | 12,265 |
[
"Retriever-Ranker Architecture - Qdrant",
"Transcript:"
] | /tags/retriever-ranker-architecture/ | 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(29) | [
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"tags",
"tags/retriever-ranker-architecture"
] | 12,266 |
[
"Retriever-Ranker Architecture - Qdrant",
"Transcript:"
] | /tags/retriever-ranker-architecture/ | 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(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. | [
"tags",
"tags/retriever-ranker-architecture"
] | 12,267 |
[
"Retriever-Ranker Architecture - Qdrant",
"Transcript:"
] | /tags/retriever-ranker-architecture/ | 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(29) | [
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[
"Retriever-Ranker Architecture - Qdrant",
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] | p | So I think I already talked about this ponds, but yeah, we needed performances. | [
"tags",
"tags/retriever-ranker-architecture"
] | 12,270 |
[
"Retriever-Ranker Architecture - Qdrant",
"Transcript:"
] | /tags/retriever-ranker-architecture/ | 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(30) | [
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] | p | The second ones was about inn quality. | [
"tags",
"tags/retriever-ranker-architecture"
] | 12,271 |
[
"Retriever-Ranker Architecture - Qdrant",
"Transcript:"
] | /tags/retriever-ranker-architecture/ | 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(30) | [
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] | p | As I said before, we cannot do a KnN search, brute force search each time. | [
"tags",
"tags/retriever-ranker-architecture"
] | 12,272 |
[
"Retriever-Ranker Architecture - Qdrant",
"Transcript:"
] | /tags/retriever-ranker-architecture/ | 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(30) | [
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"tags",
"tags/retriever-ranker-architecture"
] | 12,273 |
[
"Retriever-Ranker Architecture - Qdrant",
"Transcript:"
] | /tags/retriever-ranker-architecture/ | 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(30) | [
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] | p | And so otherwise we won’t be leveraged the performance of our model. | [
"tags",
"tags/retriever-ranker-architecture"
] | 12,274 |
[
"Retriever-Ranker Architecture - Qdrant",
"Transcript:"
] | /tags/retriever-ranker-architecture/ | 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(30) | [
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] | p | And the last one, and I didn’t talk a lot about this before, is filtering. | [
"tags",
"tags/retriever-ranker-architecture"
] | 12,275 |
[
"Retriever-Ranker Architecture - Qdrant",
"Transcript:"
] | /tags/retriever-ranker-architecture/ | 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(30) | [
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"tags",
"tags/retriever-ranker-architecture"
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[
"Retriever-Ranker Architecture - Qdrant",
"Transcript:"
] | /tags/retriever-ranker-architecture/ | 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(30) | [
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"tags",
"tags/retriever-ranker-architecture"
] | 12,277 |
[
"Retriever-Ranker Architecture - Qdrant",
"Transcript:"
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] | p | Nicolas Mauti: | [
"tags",
"tags/retriever-ranker-architecture"
] | 12,278 |
[
"Retriever-Ranker Architecture - Qdrant",
"Transcript:"
] | /tags/retriever-ranker-architecture/ | 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(31) | [
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"tags",
"tags/retriever-ranker-architecture"
] | 12,279 |
[
"Retriever-Ranker Architecture - Qdrant",
"Transcript:"
] | /tags/retriever-ranker-architecture/ | 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(31) | [
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] | p | But in both cases, you will have some troubles if you do that. | [
"tags",
"tags/retriever-ranker-architecture"
] | 12,280 |
[
"Retriever-Ranker Architecture - Qdrant",
"Transcript:"
] | /tags/retriever-ranker-architecture/ | 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(31) | [
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[
"Retriever-Ranker Architecture - Qdrant",
"Transcript:"
] | /tags/retriever-ranker-architecture/ | 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(31) | [
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[
"Retriever-Ranker Architecture - Qdrant",
"Transcript:"
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] | p | If you do that, in fact, you will have, so we’ll have this kind of architecture. | [
"tags",
"tags/retriever-ranker-architecture"
] | 12,283 |
[
"Retriever-Ranker Architecture - Qdrant",
"Transcript:"
] | /tags/retriever-ranker-architecture/ | 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(31) | [
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"tags",
"tags/retriever-ranker-architecture"
] | 12,284 |
[
"Retriever-Ranker Architecture - Qdrant",
"Transcript:"
] | /tags/retriever-ranker-architecture/ | 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(31) | [
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] | p | And so with that, you will basically create a binary mask on your freelancers pool. | [
"tags",
"tags/retriever-ranker-architecture"
] | 12,285 |
[
"Retriever-Ranker Architecture - Qdrant",
"Transcript:"
] | /tags/retriever-ranker-architecture/ | 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(31) | [
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] | p | And as the number of freelancers you have will grow, your binary namask will also grow. | [
"tags",
"tags/retriever-ranker-architecture"
] | 12,286 |
[
"Retriever-Ranker Architecture - Qdrant",
"Transcript:"
] | /tags/retriever-ranker-architecture/ | 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(32) | [
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[
"Retriever-Ranker Architecture - Qdrant",
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"tags",
"tags/retriever-ranker-architecture"
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[
"Retriever-Ranker Architecture - Qdrant",
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"tags",
"tags/retriever-ranker-architecture"
] | 12,289 |
[
"Retriever-Ranker Architecture - Qdrant",
"Transcript:"
] | /tags/retriever-ranker-architecture/ | 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(32) | [
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] | p | And also you will have another problem. | [
"tags",
"tags/retriever-ranker-architecture"
] | 12,290 |
[
"Retriever-Ranker Architecture - Qdrant",
"Transcript:"
] | /tags/retriever-ranker-architecture/ | 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(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. | [
"tags",
"tags/retriever-ranker-architecture"
] | 12,291 |
[
"Retriever-Ranker Architecture - Qdrant",
"Transcript:"
] | /tags/retriever-ranker-architecture/ | 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(32) | [
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"tags",
"tags/retriever-ranker-architecture"
] | 12,292 |
[
"Retriever-Ranker Architecture - Qdrant",
"Transcript:"
] | /tags/retriever-ranker-architecture/ | 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(32) | [
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"tags",
"tags/retriever-ranker-architecture"
] | 12,293 |
[
"Retriever-Ranker Architecture - Qdrant",
"Transcript:"
] | /tags/retriever-ranker-architecture/ | 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(32) | [
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] | p | And so your quality of your matching will degrade. | [
"tags",
"tags/retriever-ranker-architecture"
] | 12,294 |
[
"Retriever-Ranker Architecture - Qdrant",
"Transcript:"
] | /tags/retriever-ranker-architecture/ | 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(32) | [
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"tags",
"tags/retriever-ranker-architecture"
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[
"Retriever-Ranker Architecture - Qdrant",
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] | /tags/retriever-ranker-architecture/ | 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(33) | [
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[
"Retriever-Ranker Architecture - Qdrant",
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] | p | So, no, if we go to post filtering here, I think the issue is more clear. | [
"tags",
"tags/retriever-ranker-architecture"
] | 12,297 |
[
"Retriever-Ranker Architecture - Qdrant",
"Transcript:"
] | /tags/retriever-ranker-architecture/ | 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(33) | [
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] | p | You will have this kind of architecture. | [
"tags",
"tags/retriever-ranker-architecture"
] | 12,298 |
[
"Retriever-Ranker Architecture - Qdrant",
"Transcript:"
] | /tags/retriever-ranker-architecture/ | 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(33) | [
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] | p | And so, in fact, if you do that, you will have to retrieve a lot of freelancer for your vector database. | [
"tags",
"tags/retriever-ranker-architecture"
] | 12,299 |
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