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Music Recommendation System with Qdrant Vector Search and Audio Embeddings 

Qdrant - Vector Database & Search Engine
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In this beginner tutorial, you will build a song recommendation engine based on music genres and other metadata. As part of this instruction set, you will generate embeddings from audio and upload them to Qdrant for storage, search and recommendations. Follow along with our documentation: t.ly/OmcTJ
All of our tutorials are available on Qdrant's main Documentation page:
qdrant.tech/do...

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7 окт 2024

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Комментарии : 7   
@kevon217
@kevon217 Год назад
Great walkthrough!
@karndeepsingh
@karndeepsingh 11 месяцев назад
Thanks for the tutorial. I want to understand, How we can do hybrid search? How we can fuse rank from BM25 + cosine similarity from embedding to prepare a hybrid rank to get approximate ranking based on BM25 model and semantic based search? Is it possible with qdranr?
@edwardmckenzie7295
@edwardmckenzie7295 9 месяцев назад
I keep getting when i want to install the modules "ModuleNotFoundError Traceback (most recent call last) Cell In[13], line 1 ----> 1 from transformers import AutoFeatureExtractor, AutoModel 2 from IPython.display import Audio as player 3 from datasets import load_dataset, Audio ModuleNotFoundError: No module named 'transformers'
@jolual27
@jolual27 7 месяцев назад
You have to pip install transformers in your environment
@jolual27
@jolual27 5 месяцев назад
You have to pip install transformers library
@elimelethlopez6859
@elimelethlopez6859 4 месяца назад
❤❤❤❤
@nisargjaiswal4264
@nisargjaiswal4264 Год назад
Content seems to be good but please improve audio quality ita very irritating
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