Make sure that Docker daemon is installed and running:
sudo docker info
- If you do not see the server listed, start the Docker daemon.
- On Linux, Docker needs
sudoprivileges. To run Docker commands without
sudoprivileges, create a docker group and add your users (see Post-installation Steps for Linux for details).
Pull the image:
docker pull qdrant/qdrant
Run the container:
docker run -p 6333:6333 \ -v $(pwd)/path/to/data:/qdrant/storage \ qdrant/qdrant
With this command, you will start a Qdrant instance with the default configuration.
It will store all data in
By default, Qdrant uses port 6333, so at localhost:6333 you should see the welcome message.
Qdrant is written in Rust and can be compiled into a binary executable. This installation method can be helpful if you want to compile Qdrant for a specific processor architecture or if you do not want to use Docker for some reason.
Select the minimum set of processor instructions that will be available when using the service. The instruction set depends on the hardware at your disposal.
You can enable runtime selection of the architecture at the cost of a slightly bigger binary file size:
Or select specific architecture:
Build Qdrant with Cargo:
cargo build --release --bin qdrant
After a successful build, the binary is available at
You can use a ready-made Helm Chart to run Qdrant in your Kubeternetes cluster.
helm repo add qdrant https://qdrant.to/helm helm install qdrant-release qdrant/qdrant
Read further instructions in qdrant-helm repository.
Qdrant gets its operating parameters from the configuration file.
The configuration file is read when you start the service from the directory
The default values are stored in the file ./config/config.yaml.
You can overwrite values by adding new records to the file
./config/production.yaml. See an example here.
If you are using Docker, then running the service with a custom configuration will be as follows:
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
./path/to/custom_config.yaml is your custom configuration file with values to override.
Among other things, the configuration file allows you to specify the following settings:
- Optimizer parameters
- Network settings
- Default vector index parameters
- Storage settings
See the comments in the configuration file itself for details.
In addition to the service itself, Qdrant has a distinct python client, which has some additional features compared to clients generated from OpenAPI directly.
To install this client, just run the following command:
pip install qdrant-client
You can also use Qdrant natively in DocArray, where Qdrant serves as a high-performance document store to enable scalable vector search.
DocArray is a library from Jina AI for nested, unstructured data in transit, including text, image, audio, video, 3D mesh, etc. It allows deep-learning engineers to efficiently process, embed, search, recommend, store, and transfer the data with a Pythonic API.
To install DocArray with Qdrant support, please do
pip install "docarray[qdrant]"
More information can be found in DocArray’s documentations.