| 1. Conclusion.mp4 | 12.2 MB | ||
| 1. Introduction and Installation.mp4 | 66.6 MB | ||
| 1. Introduction.mp4 | 21.7 MB | ||
| 1. Payload Indexes.mp4 | 24.3 MB | ||
| 1. Qdrant + Tensorflow.mp4 | 41.3 MB | ||
| 1.1 4.Indexes.ipynb | 4.4 KB | ||
| 1.1 7.tf_example.ipynb | 8.7 KB | ||
| 1.1 docker-compose.yaml | 307.2 B | ||
| 10. Configuring Qdrant.mp4 | 44.4 MB | ||
| 10. Similarity Search - Part 1.html | 102.4 B | ||
| 11. Optimizers.mp4 | 25.7 MB | ||
| 11. Vector similarity search in Qdrant - Part 2.mp4 | 46.3 MB | ||
| 12. Qdrant - Async Python Client.mp4 | 21.6 MB | ||
| 12. Similarity Search - Part 2.html | 102.4 B | ||
| 12.1 async_example.py | 512 B | ||
| 2. Payload Indexes.html | 102.4 B | ||
| 2. Qdrant + OpenAI.mp4 | 42.3 MB | ||
| 2. Qdrant Storage Model.mp4 | 14 MB | ||
| 2. Vector Databases.mp4 | 28.8 MB | ||
| 2.1 8.openai_example.ipynb | 9.8 KB | ||
| 3. Components of a Vector Databases.mp4 | 55 MB | ||
| 3. Qdrant + LangChain.mp4 | 35 MB | ||
| 3. Qdrant Storage Model.html | 102.4 B | ||
| 3. Vector Index.mp4 | 24.6 MB | ||
| 3.1 9.langchain_example.ipynb | 4.6 KB | ||
| 3.2 nobel_physics_2023.txt | 2.2 KB | ||
| 4. Collections.mp4 | 29.6 MB | ||
| 4. Indexing the Vectors.html | 102.4 B | ||
| 4. Vector Embeddings.mp4 | 45.4 MB | ||
| 4.1 1.collections.ipynb | 5.3 KB | ||
| 5. Collections.html | 102.4 B | ||
| 5. Vector Embeddings.html | 102.4 B | ||
| 5. Vector Quantization - Part 1.mp4 | 25.7 MB | ||
| 5.1 5.quantization.ipynb | 4.7 KB | ||
| 6. Points.mp4 | 40.8 MB | ||
| 6. Quantization - Part 1.html | 102.4 B | ||
| 6. Vector Similarity Metrics.mp4 | 45.7 MB | ||
| 6.1 2.Points.ipynb | 11.4 KB | ||
| 7. Points.html | 102.4 B | ||
| 7. Vector Quantization - Part 2.mp4 | 28.3 MB | ||
| 7. Vector Similarity.html | 102.4 B | ||
| 8. Loading a Dataset into Qdrant.mp4 | 17.9 MB | ||
| 8. Vector Quantization - Part 2.html | 102.4 B | ||
| 9. Snapshots.mp4 | 11.8 MB | ||
| 9. Vector Similarity Search in Qdrant - Part 1.mp4 | 38.5 MB | ||
| 9.1 3.search.ipynb | 137.1 KB | ||
| 9.1 6.snapshots.ipynb | 3.8 KB | ||
| Bonus Resources.txt | 409.6 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| ▲ 49 total files | |||
Introduction to Qdrant (Vector Database) Using Python
https://DevCourseWeb.com
Published 3/2024
Created by Vijay Anand Ramakrishnan
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 24 Lectures ( 1h 45m ) | Size: 787 MB
Learn the basics of Qdrant (Vector Database), Indexing the data, snapshots, Python Client with examples and more !
What you'll learn:
Basics of Vector databases
Introduction to Qdrant and Installing Qdrant
Collections, Segments and Points in Qdrant
Vector and payload fields in a Collection
Vector and Payload indexing
Vector similarity search on a Collection and filtering the results based on payload
Quantizing the vectors
Configuring Qdrant Server
Requirements:
Python
Fundamentals of Docker and Docker Compose
Basic Linux commands
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| 3.8 GB | freecoursewb | 1 day | 0 | 0 | |
| 1.6 GB | freecoursewb | 1 day | 0 | 0 | |
| 1.6 GB | freecoursewb | 1 day | 0 | 0 | |
| 649 MB | freecoursewb | 1 day | 0 | 0 | |
| 533.1 MB | freecoursewb | 2 days | 9 | 5 |
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