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[
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"tags",
"tags/llm-recommendation-system"
] | 16,676 |
[
"LLM Recommendation System - Qdrant",
"Top Takeaways:"
] | /tags/llm-recommendation-system/ | 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(6) | [
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"tags",
"tags/llm-recommendation-system"
] | 16,677 |
[
"LLM Recommendation System - Qdrant",
"Top Takeaways:"
] | /tags/llm-recommendation-system/ | 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(7) | [
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] | p | Filip discusses how AI can assist in finding the perfect songs for any mood. | [
"tags",
"tags/llm-recommendation-system"
] | 16,678 |
[
"LLM Recommendation System - Qdrant",
"Top Takeaways:"
] | /tags/llm-recommendation-system/ | 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(7) | [
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[
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[
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] | /tags/llm-recommendation-system/ | 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 > ol > li:nth-of-type(2) | [
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[
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[
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[
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[
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] | p | Fun Fact: Filip actually created a Spotify playlist in real-time during the video, based on the vibe and mood Demetrios described, showing just how powerful and interactive this AI music recommendation system can be! | [
"tags",
"tags/llm-recommendation-system"
] | 16,686 |
[
"LLM Recommendation System - Qdrant"
] | /tags/llm-recommendation-system/ | 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 > h2:nth-of-type(2) | [
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"More Quotes from Filip:"
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[
"LLM Recommendation System - Qdrant",
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[
"LLM Recommendation System - Qdrant",
"More Quotes from Filip:"
] | /tags/llm-recommendation-system/ | 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(11) | [
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] | p | “Once the user writes a query and the query mentions, like some kind of a mood, for example, I feel happy and it’s a sunny day and so on, you would get the similarity to the song that has this kind of language explanations and language intricacies in its description.”– | [
"tags",
"tags/llm-recommendation-system"
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[
"LLM Recommendation System - Qdrant",
"More Quotes from Filip:"
] | /tags/llm-recommendation-system/ | 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(11) | [
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] | p | “I’ve explored Qdrant and as I said with Spotify web API there are a lot of things to be done with these specific user-created recommendations.”– | [
"tags",
"tags/llm-recommendation-system"
] | 16,694 |
[
"LLM Recommendation System - Qdrant",
"More Quotes from Filip:"
] | /tags/llm-recommendation-system/ | 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(12) | [
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[
"LLM Recommendation System - Qdrant",
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[
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] | p | And can we get AI to help us on that? | [
"tags",
"tags/llm-recommendation-system"
] | 16,699 |
[
"LLM Recommendation System - Qdrant",
"Transcript:"
] | /tags/llm-recommendation-system/ | 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(13) | [
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"tags",
"tags/llm-recommendation-system"
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[
"LLM Recommendation System - Qdrant",
"Transcript:"
] | /tags/llm-recommendation-system/ | 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(13) | [
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[
"LLM Recommendation System - Qdrant",
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] | p | So I’ll let you share your screen. | [
"tags",
"tags/llm-recommendation-system"
] | 16,702 |
[
"LLM Recommendation System - Qdrant",
"Transcript:"
] | /tags/llm-recommendation-system/ | 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(13) | [
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] | 16,703 |
[
"LLM Recommendation System - Qdrant",
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[
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[
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[
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] | p | That’s great. | [
"tags",
"tags/llm-recommendation-system"
] | 16,707 |
[
"LLM Recommendation System - Qdrant",
"Transcript:"
] | /tags/llm-recommendation-system/ | 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(14) | [
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"tags",
"tags/llm-recommendation-system"
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[
"LLM Recommendation System - Qdrant",
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] | /tags/llm-recommendation-system/ | 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(14) | [
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[
"LLM Recommendation System - Qdrant",
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[
"LLM Recommendation System - Qdrant",
"Transcript:"
] | /tags/llm-recommendation-system/ | 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(15) | [
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] | p | There we go. | [
"tags",
"tags/llm-recommendation-system"
] | 16,715 |
[
"LLM Recommendation System - Qdrant",
"Transcript:"
] | /tags/llm-recommendation-system/ | 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(18) | [
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[
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] | p | So I had to use this slide because it was really well done as an introductory slide. | [
"tags",
"tags/llm-recommendation-system"
] | 16,718 |
[
"LLM Recommendation System - Qdrant",
"Transcript:"
] | /tags/llm-recommendation-system/ | 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(18) | [
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] | p | Thank you. Yeah. Thank you also for making it so. | [
"tags",
"tags/llm-recommendation-system"
] | 16,719 |
[
"LLM Recommendation System - Qdrant",
"Transcript:"
] | /tags/llm-recommendation-system/ | 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(18) | [
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] | p | Yeah, the idea was, and kind of the inspiration with music, we all listen to it. | [
"tags",
"tags/llm-recommendation-system"
] | 16,720 |
[
"LLM Recommendation System - Qdrant",
"Transcript:"
] | /tags/llm-recommendation-system/ | 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(18) | [
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[
"LLM Recommendation System - Qdrant",
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] | /tags/llm-recommendation-system/ | 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(18) | [
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[
"LLM Recommendation System - Qdrant",
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[
"LLM Recommendation System - Qdrant",
"Transcript:"
] | /tags/llm-recommendation-system/ | 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(19) | [
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"tags",
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[
"LLM Recommendation System - Qdrant",
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] | /tags/llm-recommendation-system/ | 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(19) | [
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[
"LLM Recommendation System - Qdrant",
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] | p | Or if we try to work for a few hours and try to get concentrated and try to code for hours, we can do the same and then we click play, but it’s not what we’re looking for again. | [
"tags",
"tags/llm-recommendation-system"
] | 16,726 |
[
"LLM Recommendation System - Qdrant",
"Transcript:"
] | /tags/llm-recommendation-system/ | 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(19) | [
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[
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] | p | So the obvious first question is how do we even capture a vibe and feel of a song? | [
"tags",
"tags/llm-recommendation-system"
] | 16,729 |
[
"LLM Recommendation System - Qdrant",
"Transcript:"
] | /tags/llm-recommendation-system/ | 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(19) | [
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] | p | So initially, one approach that’s popular and that works quite well is basically using a data set that has a lot of features. | [
"tags",
"tags/llm-recommendation-system"
] | 16,730 |
[
"LLM Recommendation System - Qdrant",
"Transcript:"
] | /tags/llm-recommendation-system/ | 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(19) | [
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] | p | So Spotify has one data set like this and there are many others open source ones which include different features like loudness, key tempo, different kind of details related to the acoustics, the melody and so on. | [
"tags",
"tags/llm-recommendation-system"
] | 16,731 |
[
"LLM Recommendation System - Qdrant",
"Transcript:"
] | /tags/llm-recommendation-system/ | 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(19) | [
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"tags",
"tags/llm-recommendation-system"
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[
"LLM Recommendation System - Qdrant",
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] | /tags/llm-recommendation-system/ | 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(20) | [
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[
"LLM Recommendation System - Qdrant",
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] | p | And this is kind of a way that a lot of song recommendation systems are built. | [
"tags",
"tags/llm-recommendation-system"
] | 16,734 |
[
"LLM Recommendation System - Qdrant",
"Transcript:"
] | /tags/llm-recommendation-system/ | 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(20) | [
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[
"LLM Recommendation System - Qdrant",
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] | /tags/llm-recommendation-system/ | 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(20) | [
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[
"LLM Recommendation System - Qdrant",
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"tags",
"tags/llm-recommendation-system"
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[
"LLM Recommendation System - Qdrant",
"Transcript:"
] | /tags/llm-recommendation-system/ | 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(20) | [
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] | p | So my idea was, okay, is it possible to use human language to transfer this experience, this feeling that we have, and just use that and try to maybe encapsulate these features of songs. | [
"tags",
"tags/llm-recommendation-system"
] | 16,738 |
[
"LLM Recommendation System - Qdrant",
"Transcript:"
] | /tags/llm-recommendation-system/ | 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(20) | [
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] | p | And instead of having a data set, just have descriptions of songs or sentences that explain different aspects of a song. | [
"tags",
"tags/llm-recommendation-system"
] | 16,739 |
[
"LLM Recommendation System - Qdrant",
"Transcript:"
] | /tags/llm-recommendation-system/ | 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(20) | [
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] | p | So, as I said, this is a bit of a less traditional approach, and it’s more of kind of testing the waters, but it worked to a decent extent. | [
"tags",
"tags/llm-recommendation-system"
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[
"LLM Recommendation System - Qdrant",
"Transcript:"
] | /tags/llm-recommendation-system/ | 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(20) | [
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] | p | So what I did was, first I created a data set where I queried a large language model. | [
"tags",
"tags/llm-recommendation-system"
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[
"LLM Recommendation System - Qdrant",
"Transcript:"
] | /tags/llm-recommendation-system/ | 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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[
"LLM Recommendation System - Qdrant",
"Transcript:"
] | /tags/llm-recommendation-system/ | 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/llm-recommendation-system"
] | 16,743 |
[
"LLM Recommendation System - Qdrant",
"Transcript:"
] | /tags/llm-recommendation-system/ | 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/llm-recommendation-system"
] | 16,744 |
[
"LLM Recommendation System - Qdrant",
"Transcript:"
] | /tags/llm-recommendation-system/ | 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/llm-recommendation-system"
] | 16,745 |
[
"LLM Recommendation System - Qdrant",
"Transcript:"
] | /tags/llm-recommendation-system/ | 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/llm-recommendation-system"
] | 16,746 |
[
"LLM Recommendation System - Qdrant",
"Transcript:"
] | /tags/llm-recommendation-system/ | 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 on. | [
"tags",
"tags/llm-recommendation-system"
] | 16,747 |
[
"LLM Recommendation System - Qdrant",
"Transcript:"
] | /tags/llm-recommendation-system/ | 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 the idea was, as I said, again, to only use song names as queries for this large language model. | [
"tags",
"tags/llm-recommendation-system"
] | 16,748 |
[
"LLM Recommendation System - Qdrant",
"Transcript:"
] | /tags/llm-recommendation-system/ | 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 | So not have the full data sets with multiple features, but just song name, and kind of use this pretrained ability of all these LLMs to get this info that I was looking for. | [
"tags",
"tags/llm-recommendation-system"
] | 16,749 |
[
"LLM Recommendation System - Qdrant",
"Transcript:"
] | /tags/llm-recommendation-system/ | 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 | So an example of the generated data was this. | [
"tags",
"tags/llm-recommendation-system"
] | 16,750 |
[
"LLM Recommendation System - Qdrant",
"Transcript:"
] | /tags/llm-recommendation-system/ | 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 | So this song called Deep Sea Creature. | [
"tags",
"tags/llm-recommendation-system"
] | 16,751 |
[
"LLM Recommendation System - Qdrant",
"Transcript:"
] | /tags/llm-recommendation-system/ | 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/llm-recommendation-system"
] | 16,752 |
[
"LLM Recommendation System - Qdrant",
"Transcript:"
] | /tags/llm-recommendation-system/ | 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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[
"LLM Recommendation System - Qdrant",
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] | /tags/llm-recommendation-system/ | 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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[
"LLM Recommendation System - Qdrant",
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] | p | It will make you feel like you’re descending into the unknown and so on. | [
"tags",
"tags/llm-recommendation-system"
] | 16,755 |
[
"LLM Recommendation System - Qdrant",
"Transcript:"
] | /tags/llm-recommendation-system/ | 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/llm-recommendation-system"
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[
"LLM Recommendation System - Qdrant",
"Transcript:"
] | /tags/llm-recommendation-system/ | 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/llm-recommendation-system"
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[
"LLM Recommendation System - Qdrant",
"Transcript:"
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] | p | So that was the first step. | [
"tags",
"tags/llm-recommendation-system"
] | 16,758 |
[
"LLM Recommendation System - Qdrant",
"Transcript:"
] | /tags/llm-recommendation-system/ | 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 | So the next step was how to encode this text. | [
"tags",
"tags/llm-recommendation-system"
] | 16,759 |
[
"LLM Recommendation System - Qdrant",
"Transcript:"
] | /tags/llm-recommendation-system/ | 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/llm-recommendation-system"
] | 16,760 |
[
"LLM Recommendation System - Qdrant",
"Transcript:"
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] | p | And this led me to sentence transformers and sentence Bird. | [
"tags",
"tags/llm-recommendation-system"
] | 16,761 |
[
"LLM Recommendation System - Qdrant",
"Transcript:"
] | /tags/llm-recommendation-system/ | 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 the usual issue with kind of doing this sentence similarity in the past was this, what I have highlighted here. | [
"tags",
"tags/llm-recommendation-system"
] | 16,762 |
[
"LLM Recommendation System - Qdrant",
"Transcript:"
] | /tags/llm-recommendation-system/ | 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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[
"LLM Recommendation System - Qdrant",
"Transcript:"
] | /tags/llm-recommendation-system/ | 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 | So this is actually a quote from a paper that Nils published a few years ago. | [
"tags",
"tags/llm-recommendation-system"
] | 16,764 |
[
"LLM Recommendation System - Qdrant",
"Transcript:"
] | /tags/llm-recommendation-system/ | 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 | So, basically, the way that this similarity was done was using cross encoders in the past, and that worked well, but it was really slow and unscalable. | [
"tags",
"tags/llm-recommendation-system"
] | 16,765 |
[
"LLM Recommendation System - Qdrant",
"Transcript:"
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] | p | So Nils and his colleague created this kind of model, which helped scale this and make this a lot quicker, but also keep a lot of the accuracy. | [
"tags",
"tags/llm-recommendation-system"
] | 16,766 |
[
"LLM Recommendation System - Qdrant",
"Transcript:"
] | /tags/llm-recommendation-system/ | 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 | So Bert and Roberta were used, but they were not, as I said, quite scalable or useful for larger applications. | [
"tags",
"tags/llm-recommendation-system"
] | 16,767 |
[
"LLM Recommendation System - Qdrant",
"Transcript:"
] | /tags/llm-recommendation-system/ | 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 | So that’s how sentence Bert was created. | [
"tags",
"tags/llm-recommendation-system"
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[
"LLM Recommendation System - Qdrant",
"Transcript:"
] | /tags/llm-recommendation-system/ | 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 | So the idea here was that there would be, like, a Siamese network that would train the model so that there could be, like, two bird models, and then the training would be done using this like zero, one and two tags, where kind of the sentences would be compared, whether there is entailment, neutrality or contradiction. | [
"tags",
"tags/llm-recommendation-system"
] | 16,769 |
[
"LLM Recommendation System - Qdrant",
"Transcript:"
] | /tags/llm-recommendation-system/ | 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 | So how similar these sentences are to each other. | [
"tags",
"tags/llm-recommendation-system"
] | 16,770 |
[
"LLM Recommendation System - Qdrant",
"Transcript:"
] | /tags/llm-recommendation-system/ | 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 | And by training a model like this and doing mean pooling, in the end, the model performed quite well and was able to kind of encapsulate this language intricacies of sentences. | [
"tags",
"tags/llm-recommendation-system"
] | 16,771 |
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