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.

Install uv:

On macOS and Linux:

curl -LsSf https://astral.sh/uv/install.sh | sh

On Windows:

powershell -c "irm https://astral.sh/uv/install.ps1 | iex"

Create a new virtual environment:

uv venv
source .venv/bin/activate  # On macOS/Linux
# or
.venv\Scripts\activate  # On Windows

Alternative: Using Poetry

If you prefer Poetry for dependency management, it offers robust project management with automatic virtual environment handling and dependency lock files.

Install Poetry:

On macOS and Linux:

curl -sSL https://install.python-poetry.org | python3 -

On Windows (PowerShell):

(Invoke-WebRequest -Uri https://install.python-poetry.org -UseBasicParsing).Content | py -

Create a new project or add dependencies:

# Initialize a new Poetry project
poetry init

# Activate the virtual environment
poetry shell

Python Version Requirements: You’ll need Python 3.10 or higher, as required by the qdrant-client library.

Installing Dependencies

With your virtual environment activated, install the required libraries:

Using uv (recommended):

uv pip install "qdrant-client>=1.16.2" "fastembed>=0.8.0"

Using Poetry:

poetry add "qdrant-client>=1.16.2" "fastembed>=0.8.0"

Using pip:

pip install "qdrant-client>=1.16.2" "fastembed>=0.8.0"

What These Libraries Do

• qdrant-client: The official Python client for Qdrant, providing both synchronous and asynchronous APIs for vector search operations. This library contains full type definitions and supports all Qdrant features.

• fastembed: A fast, lightweight library for generating embeddings, maintained by the Qdrant team. It includes support for multi-vector embeddings which we’ll use extensively in this course. (Note: fastembed=0.7.5 or above required for this course)

Verification Steps

Let’s verify that everything is installed correctly.

Test your imports:

from qdrant_client import QdrantClient
from fastembed import TextEmbedding

print("All dependencies installed successfully!")

Quick connection test:

If you set up Qdrant in the previous lesson, verify you can connect:

# For Qdrant Cloud
client = QdrantClient(
    url="https://your-cluster-url.cloud.qdrant.io",
    api_key="your-api-key"
)

# For local Qdrant
# client = QdrantClient(url="http://localhost:6333")

print(f"Connected to Qdrant: {client.get_collections()}")

Next Steps

With your Python environment configured and dependencies installed, you’re ready to dive into Module 1, where we’ll explore the fundamentals of multi-vector search and understand how it differs from traditional single-vector approaches.