Introduction To Qdrant (Vector Database) Using Python

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

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 onPython ProgrammingLinux CommandsDocker and Docker ComposeSome of the highlights of this course areAll lectures have been designed from the ground up to make the complex topics easy to understandAmple working examples demonstrated in the video lecturesDownloadable Python notebooks for the examples that were used in the coursePrecise and informative video lecturesQuiz at the end of every important video lecturesCovers a wide range of fundamental topics in Qdrant After completing this course, you will be able toInstall and work with Qdrant using PythonManage Collections in QdrantPerform vector search on vectors stored in Qdrant collection Filter the search resultsCreate and manage snapshotsUse Qdrant to build scalable real-world AI appsThis course will be updated periodically and enroll now to get lifelong access to this course!

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 Vector Databases

Lecture 3 Components of a Vector Databases

Lecture 4 Vector Embeddings

Lecture 5 Vector Similarity Metrics

Section 2: Qdrant - Basics

Lecture 6 Introduction and Installation

Lecture 7 Qdrant Storage Model

Lecture 8 Collections

Lecture 9 Points

Lecture 10 Loading a Dataset into Qdrant

Lecture 11 Vector Similarity Search in Qdrant - Part 1

Lecture 12 Vector similarity search in Qdrant - Part 2

Section 3: Qdrant - Advanced

Lecture 13 Payload Indexes

Lecture 14 Vector Index

Lecture 15 Vector Quantization - Part 1

Lecture 16 Vector Quantization - Part 2

Lecture 17 Snapshots

Lecture 18 Configuring Qdrant

Lecture 19 Optimizers

Lecture 20 Qdrant - Async Python Client

Section 4: Qdrant - Examples (Optional)

Lecture 21 Qdrant + Tensorflow

Lecture 22 Qdrant + OpenAI

Lecture 23 Qdrant + LangChain

Section 5: Conclusion

Lecture 24 Conclusion

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