laedwards-dtt/qdrant-azure

Qdrant Vector Database on Azure Cloud

Qdrant Vector Database on Azure Cloud

This project combines the power of the Qdrant Vector Database with the Microsoft Azure Cloud allowing you to bring Vector Search and Embeddings storage to your AI products.

Getting started

You have several options for how to get Qdrant running on Azure:

• Azure Kubernetes Service
• Qdrant Container in Docker

Prerequisites

To get started, users will need access to an Azure subscription.

To deploy using the Deploy to Azure button which leverages an ARM template, you need write access on the resources you're deploying and access to all operations on the Microsoft.Resources/deployments resource type.

Installation

Azure Kubernetes Service

To deploy Qdrant to a cluster running in Azure Kubernetes Services, go to the Azure-Kubernetes-Svc folder and follow instructions in the README.md to deploy to a Kubernetes cluster with Load Balancer on Azure Kubernetes Services (AKS).

You can quickly create an Azure Kubernetes Service cluster by clicking the Deploy to Azure button below. After creating your AKS cluster, go to the Azure-Kubernetes-Svc folder to deploy Qdrant into the AKS cluster using Helm.

AKS Prerequisites

PLEASE NOTE! ensure that you have a resource group and ssh key in that resource created before selecting the Deploy to Azure Button below. More details can be found in README.md in Azure Kubernetes Service README.md

Docker (Local)

To develop against and run Qdrant locally.

VS Code Dev Container This project contains a dev container configuration which can be used for local development. To learn more about using a dev container, please review Developing inside a Container.

Docker Locally To run the Qdrant vector database running in Docker locally, please follow the instructions from Qdrant's website: Install Qdrant with Docker

To run Qdrant with Docker locally, you can use the following command using default values stored in the file .config/config.yaml located in the Local-Docker-Deployment folder.

docker run -p 6333:6333 \
    -v $(pwd)/path/to/data:/qdrant/storage \
    -v $(pwd)/path/to/custom_config.yaml:/qdrant/config/production.yaml \
    qdrant/qdrant

You can overwrite values by creating and adding new records to a file ./config/production.yaml. An example of the production.yaml file located in the Local-Docker-Deployment directory. Please review the Qdrant documentation to learn more information on configuration options for Qdrant.

Resources for Learning More

• Semantic Kernel Blog: The Power of Persistent Memory with Semantic Kernel and Qdrant Vector Database
• Qdrant Vector Search [Vector Database]
• Qdrant Integration with OpenAI
• Azure Container Instances (ACI)
• Azure Kubernetes Service (AKS)
• Docker Desktop