What is Qdrant?

info@qdrant.tech (Andrey Vasnetsov) — Mon, 01 Jan 0001 00:00:00 +0000

Introduction

Vector databases are a relatively new way for interacting with abstract data representations derived from opaque machine learning models such as deep learning architectures. These representations are often called vectors or embeddings and they are a compressed version of the data used to train a machine learning model to accomplish a task like sentiment analysis, speech recognition, object detection, and many others.

These new databases shine in many applications like semantic search and recommendation systems, and here, we’ll learn about one of the most popular and fastest growing vector databases in the market, Qdrant.

Understanding Vector Search in Qdrant

info@qdrant.tech (Andrey Vasnetsov) — Mon, 01 Jan 0001 00:00:00 +0000

How Does Vector Search Work in Qdrant?

If you are still trying to figure out how vector search works, please read ahead. This document describes how vector search is used, covers Qdrant’s place in the larger ecosystem, and outlines how you can use Qdrant to augment your existing projects.

For those who want to start writing code right away, visit our Complete Beginners tutorial to build a search engine in 5-15 minutes.

Overview on Qdrant - Vector Database

What is Qdrant?

Introduction

Understanding Vector Search in Qdrant

How Does Vector Search Work in Qdrant?