Udemy - Introduction to Qdrant (Vector Database) Using Python
Udemy - Introduction to Qdrant (Vector Database) Using Python metadata, size 787.71 MB, 47 files, category video, indexed at 2026-07-28.
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Infohashfdf485c8318768bcc412c270625f36c9358100ff
总大小787.7 MB
格式mp4
分类视频
文件数47
发掘时间（北京时间）2026-07-28 17:46:49
最后活跃（北京时间）2026-07-28 17:46:49
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magnet:?xt=urn:btih:fdf485c8318768bcc412c270625f36c9358100ff&dn=Udemy%20-%20Introduction%20to%20Qdrant%20%28Vector%20Database%29%20Using%20Python
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文件列表
| 文件名 | 格式 | 大小 |
|---|---|---|
| 1. Introduction and Installation.mp4 | mp4 | 66.6 MB |
| 2. Vector Databases.mp4 | mp4 | 28.8 MB |
| 3. Components of a Vector Databases.mp4 | mp4 | 55.0 MB |
| 4. Vector Embeddings.mp4 | mp4 | 45.4 MB |
| 5. Vector Embeddings.html | html | 133 B |
| 6. Vector Similarity Metrics.mp4 | mp4 | 45.7 MB |
| 7. Vector Similarity.html | html | 133 B |
| 1. Introduction.mp4 | mp4 | 21.7 MB |
| 1.1 docker-compose.yaml | yaml | 262 B |
| 2. Qdrant Storage Model.mp4 | mp4 | 14.0 MB |
| 3. Qdrant Storage Model.html | html | 133 B |
| 4. Collections.mp4 | mp4 | 29.6 MB |
| 4.1 1.collections.ipynb | ipynb | 5.3 KB |
| 5. Collections.html | html | 133 B |
| 6. Points.mp4 | mp4 | 40.8 MB |
| 6.1 2.Points.ipynb | ipynb | 11.4 KB |
| 7. Points.html | html | 133 B |
| 8. Loading a Dataset into Qdrant.mp4 | mp4 | 17.9 MB |
| 9. Vector Similarity Search in Qdrant - Part 1.mp4 | mp4 | 38.5 MB |
| 9.1 3.search.ipynb | ipynb | 137.1 KB |
| 10. Similarity Search - Part 1.html | html | 133 B |
| 11. Vector similarity search in Qdrant - Part 2.mp4 | mp4 | 46.3 MB |
| 12. Similarity Search - Part 2.html | html | 133 B |
| 1. Payload Indexes.mp4 | mp4 | 24.3 MB |
| 1.1 4.Indexes.ipynb | ipynb | 4.4 KB |
| 2. Payload Indexes.html | html | 133 B |
| 3. Vector Index.mp4 | mp4 | 24.6 MB |
| 4. Indexing the Vectors.html | html | 133 B |
| 5. Vector Quantization - Part 1.mp4 | mp4 | 25.7 MB |
| 5.1 5.quantization.ipynb | ipynb | 4.7 KB |
| 6. Quantization - Part 1.html | html | 133 B |
| 7. Vector Quantization - Part 2.mp4 | mp4 | 28.3 MB |
| 8. Vector Quantization - Part 2.html | html | 133 B |
| 9. Snapshots.mp4 | mp4 | 11.8 MB |
| 9.1 6.snapshots.ipynb | ipynb | 3.8 KB |
| 10. Configuring Qdrant.mp4 | mp4 | 44.4 MB |
| 11. Optimizers.mp4 | mp4 | 25.7 MB |
| 12. Qdrant - Async Python Client.mp4 | mp4 | 21.6 MB |
| 12.1 async_example.py | py | 546 B |
| 1. Qdrant + Tensorflow.mp4 | mp4 | 41.3 MB |
| 1.1 7.tf_example.ipynb | ipynb | 8.7 KB |
| 2. Qdrant + OpenAI.mp4 | mp4 | 42.3 MB |
| 2.1 8.openai_example.ipynb | ipynb | 9.8 KB |
| 3. Qdrant + LangChain.mp4 | mp4 | 35.0 MB |
| 3.1 9.langchain_example.ipynb | ipynb | 4.6 KB |
| 3.2 nobel_physics_2023.txt | txt | 2.2 KB |
| 1. Conclusion.mp4 | mp4 | 12.2 MB |
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