Introduction to Qdrant (Vector Database) Using Python

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Introduction to Qdrant (Vector Database) Using Python (Size: 787.7 MB)
  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

Description


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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