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Introduction to Qdrant (Vector Database) Using Python

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Vijay Anand Ramakrishnan

1:45:29

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  • 1. Introduction.mp4
    02:26
  • 2. Vector Databases.mp4
    04:03
  • 3. Components of a Vector Databases.mp4
    03:10
  • 4. Vector Embeddings.mp4
    02:22
  • 5. Vector Embeddings.html
  • 6. Vector Similarity Metrics.mp4
    02:24
  • 7. Vector Similarity.html
  • 1. Introduction and Installation.mp4
    09:16
  • 2. Qdrant Storage Model.mp4
    02:24
  • 3. Qdrant Storage Model.html
  • 4. Collections.mp4
    04:59
  • 5. Collections.html
  • 6. Points.mp4
    07:10
  • 7. Points.html
  • 8. Loading a Dataset into Qdrant.mp4
    03:35
  • 9. Vector Similarity Search in Qdrant - Part 1.mp4
    05:06
  • 10. Similarity Search - Part 1.html
  • 11. Vector similarity search in Qdrant - Part 2.mp4
    06:10
  • 12. Similarity Search - Part 2.html
  • Files.zip
  • 1. Payload Indexes.mp4
    04:05
  • 2. Payload Indexes.html
  • 3. Vector Index.mp4
    05:33
  • 4. Indexing the Vectors.html
  • 5. Vector Quantization - Part 1.mp4
    04:40
  • 6. Quantization - Part 1.html
  • 7. Vector Quantization - Part 2.mp4
    06:58
  • 8. Vector Quantization - Part 2.html
  • 9. Snapshots.mp4
    01:46
  • 10. Configuring Qdrant.mp4
    06:24
  • 11. Optimizers.mp4
    03:32
  • 12. Qdrant - Async Python Client.mp4
    03:26
  • Files.zip
  • 1. Qdrant + Tensorflow.mp4
    05:07
  • 2. Qdrant + OpenAI.mp4
    05:10
  • 3. Qdrant + LangChain.mp4
    04:29
  • Files.zip
  • 1. Conclusion.mp4
    01:14

    • Description

    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

    Who is this for?

  • Data Scientists
  • AI Engineers
  • Machine Learning Engineers
  • MLOps Engineers
  • Data Scientists
  • Anyone who is motivated to learn and work with a Vector database

    • What You Need to Know?

  • Python
  • Fundamentals of Docker and Docker Compose
  • Basic Linux commands

    • More details

    Description

    Qdrant is an Open Source vector database with in-built vector similarity search engine. Qdrant is written in Rust and is proven to be fast and reliable even under high load in production environment. Qdrant provides convenient API to store, search and manage vectors along with the associated payload for the vectors.


    This course will provide you with solid practical Skills in Qdrant using its Python interface.  Before you begin, you are required to have basic knowledge on


    • Python Programming

    • Linux Commands

    • Docker and Docker Compose


    Some of the highlights of this course are


    • All lectures have been designed from the ground up to make the complex topics easy to understand

    • Ample working examples demonstrated in the video lectures

    • Downloadable Python notebooks for the examples that were used in the course

    • Precise and informative video lectures

    • Quiz at the end of every important video lectures

    • Covers a wide range of fundamental topics in Qdrant


    After completing this course, you will be able to


    • Install and work with Qdrant using Python

    • Manage Collections in Qdrant

    • Perform vector search on vectors stored in Qdrant collection

    • Filter the search results

    • Create and manage snapshots

    • Use Qdrant to build scalable real-world AI apps


    This course will be updated periodically and enroll now to get lifelong access to this course!

    Who this course is for:

    • Data Scientists
    • AI Engineers
    • Machine Learning Engineers
    • MLOps Engineers
    • Data Scientists
    • Anyone who is motivated to learn and work with a Vector database

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    Category

    Python

    Vijay Anand Ramakrishnan
    Vijay Anand Ramakrishnan
    Instructor's Courses
    My name is Vijay Anand and I am working as a database consultant. I have been into multiple roles in the Software industry for the past 11+ years. I have authored a book on ClickHouse titled "Up and Running With ClickHouse".I am into creating online courses on new and promising Database systems in my spare time. I enjoy solving interesting problems with software and I am an Open-Source enthusiast.
    Udemy
    Udemy
    View courses Udemy
    Students take courses primarily to improve job-related skills.Some courses generate credit toward technical certification. Udemy has made a special effort to attract corporate trainers seeking to create coursework for employees of their company.
    • language english
    • Training sessions 24
    • duration 1:45:29
    • Release Date 2024/05/18

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