dataflow.platform.models.base

Base Platform Models - Foundation Layer

This module provides the foundation platform model for all Haive systems with intelligent inheritance patterns. All platform models inherit from BasePlatform to ensure consistent behavior and capabilities.

Key Features: - Pure Pydantic models (no __init__ methods) - Platform-based inheritance architecture - Intelligent design patterns with composition - Comprehensive validation and field management

Classes

BasePlatform	Foundation platform model for all Haive systems.
PlatformStatus	Platform operational status.

Module Contents

class dataflow.platform.models.base.BasePlatform(/, **data: Any)

Bases: pydantic.BaseModel

Foundation platform model for all Haive systems.

This is the base class that all platform models inherit from, providing: - Core platform identification and metadata - Standard configuration and state management - Lifecycle tracking (created_at, updated_at, status) - Capability flags for different platform features - Comprehensive validation for platform fields

Design Philosophy: - Pure Pydantic model (no __init__ method) - All configuration via Field definitions - Validation through field validators - Extensible through inheritance

Examples

Basic platform creation:

platform = BasePlatform(
    platform_id="my-platform",
    platform_name="My Platform",
    description="A sample platform"
)

With capabilities:

platform = BasePlatform(
    platform_id="advanced-platform",
    platform_name="Advanced Platform",
    description="Platform with enhanced capabilities",
    supports_discovery=True,
    supports_health_monitoring=True,
    supports_authentication=True
)

add_metadata(key: str, value: Any) → None

Add metadata entry to platform.

Parameters:

• key – Metadata key

• value – Metadata value

get_capability_summary() → Dict[str, bool]

Get summary of all platform capabilities.

Returns:

Dictionary mapping capability names to their status

update_status(new_status: PlatformStatus, update_timestamp: bool = True) → None

Update platform status and optionally timestamp.

Parameters:

• new_status – New platform status

• update_timestamp – Whether to update the updated_at timestamp

classmethod validate_platform_id(v: str) → str

Validate platform ID format.

Platform IDs must be: - Lowercase only - Alphanumeric characters - Hyphens (-) and underscores (_) allowed - No spaces or special characters

Parameters:

v – Platform ID to validate

Returns:

Validated platform ID

Raises:

ValueError – If platform ID format is invalid

classmethod validate_version(v: str) → str

Validate semantic version format.

Versions must follow semantic versioning (semver) format: - MAJOR.MINOR.PATCH - Optional pre-release: 1.0.0-beta.1 - Optional build metadata: 1.0.0+20210101

Parameters:

v – Version string to validate

Returns:

Validated version string

Raises:

ValueError – If version format is invalid

config: Dict[str, Any] = None

created_at: datetime.datetime = None

description: str = None

metadata: Dict[str, Any] = None

model_config

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

platform_id: str = None

platform_name: str = None

status: PlatformStatus = None

supports_authentication: bool = None

supports_caching: bool = None

supports_discovery: bool = None

supports_health_monitoring: bool = None

updated_at: datetime.datetime | None = None

version: str = None

class dataflow.platform.models.base.PlatformStatus

Bases: str, enum.Enum

Platform operational status.

ACTIVE = 'active'

ERROR = 'error'

INACTIVE = 'inactive'

INITIALIZING = 'initializing'

MAINTENANCE = 'maintenance'

STARTING = 'starting'

STOPPING = 'stopping'