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[ DevCourseWeb com ] Introduction to Qdrant (Vector Database) Using Python
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Get Bonus Downloads Here.url (Size: 787.72 MB) (Files: 49)
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1. Introduction
1. Introduction.mp4
2. Vector Databases.mp4
3. Components of a Vector Databases.mp4
4. Vector Embeddings.mp4
5. Vector Embeddings.html
6. Vector Similarity Metrics.mp4
7. Vector Similarity.html
2. Qdrant - Basics
1. Introduction and Installation.mp4
1.1 docker-compose.yaml
10. Similarity Search - Part 1.html
11. Vector similarity search in Qdrant - Part 2.mp4
12. Similarity Search - Part 2.html
2. Qdrant Storage Model.mp4
3. Qdrant Storage Model.html
4. Collections.mp4
4.1 1.collections.ipynb
5. Collections.html
6. Points.mp4
6.1 2.Points.ipynb
7. Points.html
8. Loading a Dataset into Qdrant.mp4
9. Vector Similarity Search in Qdrant - Part 1.mp4
9.1 3.search.ipynb
3. Qdrant - Advanced
1. Payload Indexes.mp4
1.1 4.Indexes.ipynb
10. Configuring Qdrant.mp4
11. Optimizers.mp4
12. Qdrant - Async Python Client.mp4
12.1 async_example.py
2. Payload Indexes.html
3. Vector Index.mp4
4. Indexing the Vectors.html
5. Vector Quantization - Part 1.mp4
5.1 5.quantization.ipynb
6. Quantization - Part 1.html
7. Vector Quantization - Part 2.mp4
8. Vector Quantization - Part 2.html
9. Snapshots.mp4
9.1 6.snapshots.ipynb
4. Qdrant - Examples (Optional)
1. Qdrant + Tensorflow.mp4
1.1 7.tf_example.ipynb
2. Qdrant + OpenAI.mp4
2.1 8.openai_example.ipynb
3. Qdrant + LangChain.mp4
3.1 9.langchain_example.ipynb
3.2 nobel_physics_2023.txt
5. Conclusion
1. Conclusion.mp4
Bonus Resources.txt
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