Install and run Qdrant on Axion

Prepare the vector database environment

In this section, you prepare a SUSE Linux Enterprise Server (SLES) arm64 virtual machine and deploy Qdrant, an open-source vector database designed for efficient similarity search and vector indexing.

Qdrant enables applications to store and retrieve embeddings — numerical vector representations of data such as text, images, and audio. These embeddings allow applications to perform semantic search and AI-powered retrieval.

Running Qdrant on Google Axion Arm-based infrastructure enables efficient execution of modern AI workloads including semantic search, recommendation systems, and chatbot retrieval pipelines.

Architecture overview

The deployment creates a simple vector search system where embeddings are generated and stored in Qdrant, enabling fast semantic similarity queries.

    

        
        
SUSE Linux Enterprise Server (arm64)
        |
        v
Docker Container Runtime
        |
        v
Qdrant Vector Database
        |
        v
Vector Embeddings Storage
        |
        v
Semantic Similarity Search

    

Update the system

Update package repositories and installed packages.

    

        
        
sudo zypper refresh
sudo zypper update -y

    

Install required packages

Install Docker and Python dependencies.

    

        
        
sudo zypper install -y docker python3 python3-pip git
sudo zypper install -y python311 python311-pip

    

Create a virtual environment

Create and activate a virtual environment to isolate Python dependencies and avoid system-level package conflicts.

    

        
        
python3.11 -m venv qdrant-env
source qdrant-env/bin/activate
pip install --upgrade pip

    

Your prompt changes to show (qdrant-env) when the environment is active. Use this environment for all subsequent Python commands in this Learning Path.

Verify Python installation:

    

        
        
python3.11 --version

    

The output is similar to:

    

        
        Python 3.11.10

        
    

Why this matters:

• Python 3.11 provides improved performance and memory efficiency.
• It ensures compatibility with modern AI libraries used in vector search pipelines.

Enable Docker

Start and enable the Docker service.

    

        
        
sudo systemctl enable docker
sudo systemctl start docker
sudo usermod -aG docker $USER ; newgrp docker

    

The newgrp command avoids the need to logout and back in for the docker group permissions to take effect.

Verify Docker installation

    

        
        
docker --version

    

The output is similar to:

    

        
        Docker version 28.5.1-ce, build f8215cc26

        
    

Docker runs Qdrant in an isolated container environment.

Run the Qdrant vector database

Start the Qdrant container.

    

        
        
docker run -d \
-p 6333:6333 \
-p 6334:6334 \
-v $(pwd)/qdrant_storage:/qdrant/storage \
qdrant/qdrant

    

This command:

• Runs Qdrant in detached mode
• Exposes ports 6333 and 6334
• Creates persistent storage for vector data

The output is similar to:

    

        
        latest: Pulling from qdrant/qdrant
3ea009573b47: Pull complete
4f4fb700ef54: Pull complete
ea8055cf6833: Pull complete
9d7bb093ff98: Pull complete
13053c6d0c21: Pull complete
c017fa517b2b: Pull complete
3e2c95baf78f: Pull complete
b940a5cd37f5: Pull complete
Digest: sha256:f1c7272cdac52b38c1a0e89313922d940ba50afd90d593a1605dbbc214e66ffb
Status: Downloaded newer image for qdrant/qdrant:latest
1af9f6ac9cef017016837667f68aeed22a74f0f6352effd568dfa188337820c0

        
    

Verify Qdrant

Check running containers.

    

        
        
docker ps

    

The output is similar to:

    

        
        1af9f6ac9cef   qdrant/qdrant   "./entrypoint.sh"   13 seconds ago   Up 11 seconds   0.0.0.0:6333-6334->6333-6334/tcp, [::]:6333-6334>6333-6334/tcp   inspiring_dijkstra

        
    

This confirms the Qdrant container is running successfully.

Test the Qdrant API

Verify the Qdrant service by calling the REST API.

    

        
        
curl http://localhost:6333

    

You should see an output similar to:

    

        
        {"title":"qdrant - vector search engine","version":"1.17.0","commit":"4ab6d2ee0f6c718667e553b1055f3e944fef025f"}gcpuser@qdrant-arm64~>

        
    

This confirms the vector database service is reachable and ready for use.

What you’ve learned and what’s next

In this section, you learned how to:

• Prepare a SUSE Linux arm64 environment on Axion
• Install Docker and Python dependencies
• Deploy the Qdrant vector database container
• Verify that the vector database is running correctly
• Access the Qdrant API endpoint

In the next section, you will generate vector embeddings using a transformer model and store them in Qdrant, enabling semantic search and AI-powered retrieval.