Qdrant Provider

Manage Qdrant vector database deployments and collections through the Pragmatiks platform. Deploy self-hosted Qdrant instances to any Kubernetes cluster, or manage collections on existing Qdrant Cloud or local instances.

Resources

Resource	Type Slug	Description
Database	`qdrant/database`	Self-hosted Qdrant deployment with persistent storage and LoadBalancer access
Collection	`qdrant/collection`	Vector collection for similarity search on any Qdrant instance

Prerequisites

For Database resources: A kubernetes/config resource pointing at the target cluster. The cluster must support LoadBalancer services for external access.
For Collection resources: A running Qdrant instance -- either a Database resource from this provider, a Qdrant Cloud cluster, or any self-hosted Qdrant server accessible via HTTP.
API key (optional): Required for Qdrant Cloud. For self-hosted, can be generated automatically or provided explicitly.

Installation

pragma providers install qdrant

Database (`qdrant/database`)

Deploys a Qdrant vector database to a Kubernetes cluster as a StatefulSet with persistent storage. Creates a headless Service for pod DNS, a StatefulSet with configurable replicas and storage, and a LoadBalancer Service for external HTTP and gRPC access.

Config:

Field	Type	Required	Mutable	Default	Description
`config`	dependency	yes	no	--	`kubernetes/config` resource for cluster access
`replicas`	int	no	yes	`1`	Number of Qdrant StatefulSet pods
`image`	string	no	yes	`"qdrant/qdrant:latest"`	Qdrant Docker image
`api_key`	string	no	yes	--	Explicit API key for authentication (mutually exclusive with `generate_api_key`)
`generate_api_key`	bool	no	yes	`false`	Generate a secure 32-character hex API key
`storage`	object	no	yes	--	Persistent volume configuration (see below)
`resources`	object	no	yes	--	CPU and memory limits (see below)

StorageConfig:

Field	Type	Default	Description
`size`	string	`"10Gi"`	Persistent volume size
`class`	string	`"standard-rwo"`	Kubernetes storage class name

ResourceConfig:

Field	Type	Default	Description
`memory`	string	`"2Gi"`	Memory limit for each Qdrant pod
`cpu`	string	`"1"`	CPU limit for each Qdrant pod

Outputs: url, grpc_url, api_key

Example:

resources:
  - name: my-qdrant
    provider: qdrant
    type: database
    config:
      config:
        $ref: my-kubernetes-config
      replicas: 1
      generate_api_key: true
      storage:
        size: 20Gi
        class: premium-rwo
      resources:
        memory: 4Gi
        cpu: "2"

Behavior:

Create: Deploys headless Service, StatefulSet, and LoadBalancer Service sequentially. Waits for each to be ready and for the LoadBalancer to receive an external IP, up to 19 minutes in total.
Update: Reapplies all child Kubernetes resources with the full configuration, waiting for the StatefulSet only when its settings changed, then reapplies the LoadBalancer Service. The config dependency is immutable; changing it requires delete and recreate.
Delete: Explicitly deletes child Kubernetes resources (LoadBalancer Service, StatefulSet, headless Service). The stored vectors live on the StatefulSet's PersistentVolumeClaims; whether they are deleted with the StatefulSet follows the kubernetes provider's StatefulSet.
Health: Delegates to the underlying StatefulSet health check.
Logs: Streams pod logs from the underlying StatefulSet.

Collection (`qdrant/collection`)

Manages a vector collection on any Qdrant instance for similarity search. Works with Qdrant Cloud (with API key), self-hosted instances, or Database resources from this provider.

Config:

Field	Type	Required	Mutable	Default	Description
`url`	string	no	no	`"http://localhost:6333"`	Qdrant server URL (immutable)
`api_key`	string	no	yes	--	API key for Qdrant Cloud or secured instances
`name`	string	yes	no	--	Collection name within Qdrant (immutable)
`vectors`	object	yes	no	--	Vector configuration (immutable, see below)
`on_disk`	bool	no	yes	`false`	Store vectors on disk instead of in memory

VectorConfig:

Field	Type	Default	Description
`size`	int	--	Vector dimension (must match your embedding model output)
`distance`	string	`"Cosine"`	Distance metric: `Cosine`, `Euclid`, or `Dot`

Outputs: name

Example (Qdrant Cloud):

resources:
  - name: company-docs
    provider: qdrant
    type: collection
    config:
      url: https://xyz-abc.eu-central.aws.cloud.qdrant.io:6333
      api_key:
        $ref: qdrant-api-key
      name: company-docs
      vectors:
        size: 1536
        distance: Cosine
      on_disk: true

Example (self-hosted via Database resource):

resources:
  - name: embeddings
    provider: qdrant
    type: collection
    config:
      url: ${{ my-qdrant.outputs.url }}
      api_key: ${{ my-qdrant.outputs.api_key }}
      name: embeddings
      vectors:
        size: 768
        distance: Cosine

Behavior:

Create: Creates the collection if it does not already exist. Idempotent -- if the collection exists, leaves it as is.
Observe: Reports the collection present when the server has a collection with the configured name.
Update: Applies on_disk to the live collection in place, keeping its vectors. Fails if the live vector size or distance differs from the config. Vector configuration, collection name and server URL changes are not allowed; changing them requires delete and recreate.
Delete: Deletes the collection and all its vectors. Idempotent -- succeeds if the collection does not exist.
Health: Reads the live collection status. Green is healthy, yellow and grey are degraded, red or a missing collection is unhealthy. Details carry the point, indexed vector and segment counts.

Common Patterns

Knowledge Base with Embeddings

Deploy a Qdrant database and create collections for a RAG pipeline:

resources:
  # 1. GKE cluster (from GCP provider)
  - name: my-cluster
    provider: gcp
    type: gke
    config:
      project_id: my-project
      location: europe-west4
      name: my-cluster
      credentials:
        $ref: gcp-credentials

  # 2. Kubernetes config authenticating against the GKE cluster
  - name: my-kubernetes-config
    provider: kubernetes
    type: config
    config:
      mode: gke_cluster
      cluster:
        $ref: my-cluster

  # 3. Self-hosted Qdrant database
  - name: vector-db
    provider: qdrant
    type: database
    config:
      config:
        $ref: my-kubernetes-config
      generate_api_key: true
      storage:
        size: 50Gi

  # 3. Collection for document embeddings
  - name: documents
    provider: qdrant
    type: collection
    config:
      url: ${{ vector-db.outputs.url }}
      api_key: ${{ vector-db.outputs.api_key }}
      name: documents
      vectors:
        size: 1536
        distance: Cosine
      on_disk: true

  # 4. Collection for FAQ embeddings (smaller model)
  - name: faq
    provider: qdrant
    type: collection
    config:
      url: ${{ vector-db.outputs.url }}
      api_key: ${{ vector-db.outputs.api_key }}
      name: faq
      vectors:
        size: 384
        distance: Cosine

Qdrant Cloud with Multiple Collections

Use Qdrant Cloud instead of self-hosting:

resources:
  - name: product-search
    provider: qdrant
    type: collection
    config:
      url: https://my-cluster.eu-central.aws.cloud.qdrant.io:6333
      api_key:
        $ref: qdrant-cloud-key
      name: products
      vectors:
        size: 768
        distance: Dot

  - name: image-search
    provider: qdrant
    type: collection
    config:
      url: https://my-cluster.eu-central.aws.cloud.qdrant.io:6333
      api_key:
        $ref: qdrant-cloud-key
      name: images
      vectors:
        size: 512
        distance: Cosine

Configuration

API Keys

Qdrant supports optional API key authentication. For the Database resource, you have three options:

No authentication -- omit both api_key and generate_api_key (suitable for development)
Generated key -- set generate_api_key: true to auto-generate a secure 32-character hex key
Explicit key -- set api_key to a specific value (useful when rotating keys or matching existing config)

For Collection resources connecting to Qdrant Cloud, the api_key field is required and should reference a secret.

Vector Dimensions

Choose the vectors.size to match your embedding model:

Embedding Model	Dimensions
OpenAI `text-embedding-3-large`	3072
OpenAI `text-embedding-3-small`	1536
OpenAI `text-embedding-ada-002`	1536
Cohere `embed-english-v3.0`	1024
Sentence Transformers (all-MiniLM-L6-v2)	384
Google `text-embedding-004`	768

Distance Metrics

Metric	Best For
`Cosine`	Text embeddings, normalized vectors (most common)
`Euclid`	Spatial data, when absolute distance matters
`Dot`	Pre-normalized embeddings, maximum inner product search

Development

# Lint and type check
task qdrant:check

# Format
task qdrant:format

Metadata

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