Database Deployments

A Vector Database deployment gives you a managed, single-tenant vector database with its own hostname and generated credentials, running on the platform's Kubernetes infrastructure. Use it as the vector store behind Knowledge Bases and retrieval nodes without operating the database yourself.

What you can deploy

The supported engine is Weaviate, currently at engine version 1.25.0. Each deployment is installed from a Helm chart with its own persistent storage, API-key authentication enabled, and anonymous access disabled.

Create a database deployment

Pick the Vector Database type

On the Deployments page, click Add new deployment, select Vector Database ("Deploy open-source vector databases like Weaviate in 1 click."), and click Next.

Fill in the deployment form

The Add new deployment panel asks for:

• Name and Description.
• Engine — Weaviate.
• Engine version — 1.25.0.
• Resource profile — the compute the database runs on.
• Advanced configuration — Replicas (defaults to 1) and Storage size (defaults to 10Gi), the size of the persistent volume backing the database.

Create

Click Create. You see a "Vector database was created" confirmation and land on the database page. The deployment starts in pending status and is ready when it reaches running.

The database page

The header shows the deployment's name, status, Hostname (with a copy button), and Deployed by, plus a delete action. Two tabs sit below it:

• ENDPOINT — a ready-to-copy Python snippet that connects to the deployment with the official Weaviate client, prefilled with your hostname.
• ACCESS — the deployment's credentials: each user with a reveal/copy control for its API key.

Connection details and credentials

Every database deployment exposes:

Two users are generated at deploy time, each with its own random API key:

• admin — full read-write access.
• reader — read-only access.

Read them from the ACCESS tab, or via the API: GET /v1/databases/{database_id}/credentials returns {"data": {"credentials": {"users": [{"username": "...", "password": "..."}]}}}, where password is the user's API key. Credentials are excluded from all other database responses.

Connect from code

The ENDPOINT tab shows this pattern with your hostname filled in:

# pip install weaviate-client
import os
import weaviate
from weaviate.auth import AuthApiKey

hostname = "<your-database-hostname>"
weaviate_api_key = os.getenv("WEAVIATE_API_KEY")  # from the Access tab

client = weaviate.connect_to_custom(
    http_host=hostname,
    http_port=443,
    http_secure=True,
    grpc_host=hostname,
    grpc_port=50051,
    grpc_secure=True,
    auth_credentials=AuthApiKey(weaviate_api_key),
)

client.connect()
print(client.is_ready())
client.close()

Use it from workflow Connections

To use the deployment in workflows and Knowledge Bases, create a Weaviate Connection that points at it:

1. Create a new Connection of type Weaviate.
2. Set the deployment type to Custom (not Weaviate Cloud).
3. Fill in HTTP Host = your database hostname, HTTP Port = 443, gRPC Host = the same hostname, gRPC Port = 50051.
4. Set API Key to the admin key from the ACCESS tab (use reader for read-only consumers).

The Connection then appears anywhere a Weaviate vector store can be selected — including as the storage backend of a Knowledge Base.

Manage via the management API

Database deployments are managed on https://api.getdynamiq.ai with a Personal Access Token:

POST /v1/databases takes:

curl -X POST "https://api.getdynamiq.ai/v1/databases" \
  -H "Authorization: Bearer $DYNAMIQ_PERSONAL_ACCESS_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "name": "kb-vectors",
    "project_id": "11111111-1111-1111-1111-111111111111",
    "resource_profile_id": "22222222-2222-2222-2222-222222222222",
    "engine": "weaviate",
    "engine_version": "1.25.0",
    "parameters": {"replicas": 1, "storage": {"size": "10Gi"}}
  }'

Next steps