Qdrant Setup

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

Module 0

Qdrant Setup

Before diving into multi-vector search, you need a running Qdrant instance. Whether you choose Qdrant Cloud for a managed solution or a local deployment, this lesson will get you up and running.

Multi-vector search requires specific collection configurations that differ from traditional single-vector setups. We’ll cover the essentials to prepare your environment.

Qdrant Cloud Setup (Recommended)

Qdrant Cloud is the fastest way to get started with multi-vector search. It provides a fully managed, production-ready vector database with automatic backups, high availability, and secure TLS connections. Both Qdrant Cloud and the open-source version provide the same feature set - Cloud simply handles the infrastructure for you.

Installing Dependencies

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

Module 0

Installing Dependencies

To work with multi-vector search in Qdrant, you’ll need several Python libraries: Qdrant client for search and FastEmbed for multi-vector embeddings.

We’ll set up a clean Python environment and install everything you need to start experimenting with multi-vector representations.

Python Environment Setup

Using uv (Recommended)

For this course, we recommend using uv, a modern Python package manager that’s significantly faster and more reliable than traditional pip. It handles virtual environments and dependencies with better performance and dependency resolution.

Module 0: Setting Up Dependencies on Qdrant - Vector Search Engine

Qdrant Setup

Qdrant Setup

Qdrant Cloud Setup (Recommended)

Installing Dependencies

Installing Dependencies

Python Environment Setup

Using uv (Recommended)