Introduction To Qdrant (Vector Database) Using Python
Date: 20 February 2026 | Posted by: AD-TEAM
Introduction To Qdrant (Vector Database) Using Python
Published 3/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 708.49 MB | Duration: 1h 45m
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
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RapidGator
NitroFlare
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