Configuration

RAIT Connector can be configured in two ways:

1. Environment Variables (recommended for production)
2. Direct Parameters (useful for testing or multiple configurations)

Using Environment Variables

Set environment variables in your system or deployment:

# RAIT Configuration
export RAIT_API_URL="https://api.raitracker.com"
export RAIT_CLIENT_ID="your-client-id"
export RAIT_CLIENT_SECRET="your-secret"

# Azure Configuration
export AZURE_OPENAI_ENDPOINT="https://your.openai.azure.com"
export AZURE_OPENAI_API_KEY="your-key"
# ... more variables

Then initialize without parameters:

from rait_connector import RAITClient

client = RAITClient()  # Reads from environment

Using Direct Parameters

Pass configuration directly (overrides environment variables):

from rait_connector import RAITClient

client = RAITClient(
    rait_api_url="https://api.raitracker.com",
    rait_client_id="your-client-id",
    rait_client_secret="your-secret",
    azure_openai_endpoint="https://your.openai.azure.com",
    azure_openai_api_key="your-key",
    azure_openai_deployment="gpt-4",
    # ... more parameters
)

Configuration Precedence

1. Direct parameters to RAITClient() (highest priority)
2. Environment variables
3. Default values (only for optional settings)

RAIT Settings

Parameter	Environment Variable	Required	Description
rait_api_url	RAIT_API_URL	Yes	RAIT API endpoint
rait_client_id	RAIT_CLIENT_ID	Yes	Client ID for authentication
rait_client_secret	RAIT_CLIENT_SECRET	Yes	Client secret
rait_ingest_url	RAIT_INGEST_URL	Yes	API Gateway ingest URL — all log types (evaluation, telemetry, calibration) are routed here

Azure Settings

Authentication

Parameter	Environment Variable	Required	Description
azure_client_id	AZURE_CLIENT_ID	Yes	Azure AD client ID
azure_tenant_id	AZURE_TENANT_ID	Yes	Azure AD tenant ID
azure_client_secret	AZURE_CLIENT_SECRET	Yes	Azure AD client secret

OpenAI

Parameter	Environment Variable	Required	Description
azure_openai_endpoint	AZURE_OPENAI_ENDPOINT	Yes	OpenAI endpoint URL
azure_openai_api_key	AZURE_OPENAI_API_KEY	Yes	OpenAI API key
azure_openai_deployment	AZURE_OPENAI_DEPLOYMENT	Yes	Deployment name
azure_openai_api_version	AZURE_OPENAI_API_VERSION	No	API version (default: 2024-12-01-preview)

Resources

Parameter	Environment Variable	Required	Description
azure_subscription_id	AZURE_SUBSCRIPTION_ID	Yes	Subscription ID
azure_resource_group	AZURE_RESOURCE_GROUP	Yes	Resource group
azure_project_name	AZURE_PROJECT_NAME	Yes	AI project name
azure_account_name	AZURE_ACCOUNT_NAME	Yes	Account name
azure_ai_project_url	AZURE_AI_PROJECT_URL	No	AI project URL

Telemetry

Parameter	Environment Variable	Required	Description
azure_log_analytics_workspace_id	AZURE_LOG_ANALYTICS_WORKSPACE_ID	No*	Log Analytics workspace ID for telemetry

*Required only when using telemetry features (fetch_telemetry, post_telemetry).

Multiple Configurations

Create multiple clients with different configurations:

# Production client
prod_client = RAITClient(
    rait_api_url="https://api.raitracker.com",
    rait_client_id="prod-client-id",
    rait_client_secret="prod-secret"
)

# Development client
dev_client = RAITClient(
    rait_api_url="https://api.raitracker.com",
    rait_client_id="dev-client-id",
    rait_client_secret="dev-secret"
)

Best Practices

1. Use environment variables in production for security
2. Never commit credentials to version control
3. Use separate credentials for dev/staging/prod
4. Validate configuration on startup
5. Use direct parameters for testing and CI/CD

Validation

Check if configuration is valid:

from rait_connector import RAITClient
from rait_connector.exceptions import AuthenticationError

try:
    client = RAITClient()
    metrics = client.get_enabled_metrics()
    print(f"Configuration valid! Found {len(metrics)} metric dimensions")
except AuthenticationError as e:
    print(f"Authentication failed: {e}")
except Exception as e:
    print(f"Configuration error: {e}")