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, ready

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 5 minutes).
Update: Reapplies all child Kubernetes resources with updated configuration. Skips reapply if config is unchanged. Changing the config dependency requires delete and recreate.
Delete: Explicitly removes child Kubernetes resources (LoadBalancer Service, StatefulSet, headless Service).
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	yes	`"http://localhost:6333"`	Qdrant server URL
`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	yes	--	Vector configuration (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, indexed_vectors_count, points_count, status

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, returns its current info.
Update: Recreates the collection if vector configuration changes (size, distance, on_disk). This is destructive and deletes all existing vectors. Collection name changes are not allowed.
Delete: Deletes the collection and all its vectors. Idempotent -- succeeds if the collection does not exist.

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

# Run tests
task qdrant:test

# Lint and type check
task qdrant:check

# Format
task qdrant:format

Metadata

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