pragmatiks-qdrant-provider 0.23.0

Qdrant provider for Pragmatiks

Qdrant Provider

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

Resources

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

Prerequisites

• For Database resources: A GKE cluster managed by the gcp provider (gcp/gke resource). 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 GKE cluster as a Kubernetes 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
cluster	dependency	yes	no	--	GKE cluster to deploy to (gcp/gke resource)
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:
      cluster:
        $ref: my-gke-cluster
      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. Cluster changes require 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. Self-hosted Qdrant database
  - name: vector-db
    provider: qdrant
    type: database
    config:
      cluster:
        $ref: my-cluster
      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:

1. No authentication -- omit both api_key and generate_api_key (suitable for development)
2. Generated key -- set generate_api_key: true to auto-generate a secure 32-character hex key
3. 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