ALwrity version 0.5.6
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ajaysi
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"""
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Core Interfaces for Calendar Generation Data Source Framework
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Defines the abstract interfaces and base classes for all data sources
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in the calendar generation system.
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"""
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import logging
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from abc import ABC, abstractmethod
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from datetime import datetime
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from typing import Dict, Any, Optional, List
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from enum import Enum
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logger = logging.getLogger(__name__)
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class DataSourceType(Enum):
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"""Enumeration of data source types."""
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STRATEGY = "strategy"
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ANALYSIS = "analysis"
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RESEARCH = "research"
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PERFORMANCE = "performance"
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AI = "ai"
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CUSTOM = "custom"
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class DataSourcePriority(Enum):
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"""Enumeration of data source priorities."""
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CRITICAL = 1
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HIGH = 2
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MEDIUM = 3
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LOW = 4
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OPTIONAL = 5
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class DataSourceInterface(ABC):
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"""
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Abstract interface for all data sources in the calendar generation system.
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This interface provides a standardized way to implement data sources
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that can be dynamically registered, validated, and enhanced with AI insights.
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"""
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def __init__(self, source_id: str, source_type: DataSourceType, priority: DataSourcePriority = DataSourcePriority.MEDIUM):
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"""
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Initialize a data source.
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Args:
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source_id: Unique identifier for the data source
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source_type: Type of data source (strategy, analysis, research, etc.)
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priority: Priority level for data source processing
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"""
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self.source_id = source_id
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self.source_type = source_type
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self.priority = priority
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self.is_active = True
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self.last_updated: Optional[datetime] = None
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self.data_quality_score: float = 0.0
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self.version: str = "1.0.0"
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self.metadata: Dict[str, Any] = {}
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logger.info(f"Initialized data source: {source_id} ({source_type.value})")
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@abstractmethod
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async def get_data(self, user_id: int, strategy_id: int) -> Dict[str, Any]:
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"""
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Retrieve data from this source.
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Args:
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user_id: User identifier
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strategy_id: Strategy identifier
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Returns:
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Dictionary containing the retrieved data
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"""
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raise NotImplementedError
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@abstractmethod
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async def validate_data(self, data: Dict[str, Any]) -> Dict[str, Any]:
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"""
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Validate and score data quality.
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Args:
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data: Data to validate
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Returns:
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Dictionary containing validation results and quality score
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"""
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raise NotImplementedError
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@abstractmethod
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async def enhance_data(self, data: Dict[str, Any]) -> Dict[str, Any]:
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"""
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Enhance data with AI insights.
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Args:
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data: Original data to enhance
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Returns:
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Enhanced data with AI insights
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"""
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raise NotImplementedError
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def get_metadata(self) -> Dict[str, Any]:
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"""
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Get source metadata for quality gates and monitoring.
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Returns:
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Dictionary containing source metadata
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"""
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return {
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"source_id": self.source_id,
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"source_type": self.source_type.value,
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"priority": self.priority.value,
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"is_active": self.is_active,
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"last_updated": self.last_updated.isoformat() if self.last_updated else None,
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"data_quality_score": self.data_quality_score,
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"version": self.version,
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"metadata": self.metadata
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}
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def update_metadata(self, key: str, value: Any) -> None:
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"""
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Update source metadata.
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Args:
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key: Metadata key
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value: Metadata value
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"""
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self.metadata[key] = value
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logger.debug(f"Updated metadata for {self.source_id}: {key} = {value}")
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def set_active(self, active: bool) -> None:
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"""
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Set the active status of the data source.
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Args:
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active: Whether the source should be active
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"""
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self.is_active = active
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logger.info(f"Set {self.source_id} active status to: {active}")
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def update_quality_score(self, score: float) -> None:
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"""
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Update the data quality score.
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Args:
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score: New quality score (0.0 to 1.0)
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"""
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if 0.0 <= score <= 1.0:
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self.data_quality_score = score
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logger.debug(f"Updated quality score for {self.source_id}: {score}")
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else:
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logger.warning(f"Invalid quality score for {self.source_id}: {score} (must be 0.0-1.0)")
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def mark_updated(self) -> None:
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"""Mark the data source as recently updated."""
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self.last_updated = datetime.utcnow()
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logger.debug(f"Marked {self.source_id} as updated at {self.last_updated}")
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def __str__(self) -> str:
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"""String representation of the data source."""
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return f"DataSource({self.source_id}, {self.source_type.value}, priority={self.priority.value})"
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def __repr__(self) -> str:
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"""Detailed string representation of the data source."""
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return f"DataSource(source_id='{self.source_id}', source_type={self.source_type}, priority={self.priority}, is_active={self.is_active}, quality_score={self.data_quality_score})"
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class DataSourceValidationResult:
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"""
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Standardized validation result for data sources.
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"""
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def __init__(self, is_valid: bool = True, quality_score: float = 0.0):
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self.is_valid = is_valid
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self.quality_score = quality_score
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self.missing_fields: List[str] = []
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self.recommendations: List[str] = []
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self.warnings: List[str] = []
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self.errors: List[str] = []
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self.metadata: Dict[str, Any] = {}
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def add_missing_field(self, field: str) -> None:
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"""Add a missing field to the validation result."""
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self.missing_fields.append(field)
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self.is_valid = False
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def add_recommendation(self, recommendation: str) -> None:
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"""Add a recommendation to the validation result."""
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self.recommendations.append(recommendation)
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def add_warning(self, warning: str) -> None:
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"""Add a warning to the validation result."""
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self.warnings.append(warning)
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def add_error(self, error: str) -> None:
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"""Add an error to the validation result."""
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self.errors.append(error)
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self.is_valid = False
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def to_dict(self) -> Dict[str, Any]:
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"""Convert validation result to dictionary."""
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return {
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"is_valid": self.is_valid,
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"quality_score": self.quality_score,
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"missing_fields": self.missing_fields,
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"recommendations": self.recommendations,
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"warnings": self.warnings,
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"errors": self.errors,
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"metadata": self.metadata
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}
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def __str__(self) -> str:
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"""String representation of validation result."""
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status = "VALID" if self.is_valid else "INVALID"
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return f"ValidationResult({status}, score={self.quality_score:.2f}, missing={len(self.missing_fields)}, errors={len(self.errors)})"
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