# Calendar Generation Data Source Framework

A scalable, modular framework for managing evolving data sources in AI-powered content calendar generation. This framework provides a robust foundation for handling multiple data sources, quality gates, and AI prompt enhancement without requiring architectural changes as the system evolves.

## 🎯 **Overview**

The Calendar Generation Data Source Framework is designed to support the 12-step prompt chaining architecture for content calendar generation. It provides a scalable, maintainable approach to managing data sources that can evolve over time without breaking existing functionality.

### **Key Features**
- **Modular Architecture**: Individual modules for each data source and quality gate
- **Scalable Design**: Add new data sources without architectural changes
- **Quality Assurance**: Comprehensive quality gates with validation
- **AI Integration**: Strategy-aware prompt building with context
- **Evolution Management**: Version control and enhancement planning
- **Separation of Concerns**: Clean, maintainable code structure

## 🏗️ **Architecture**

### **Directory Structure**
```
calendar_generation_datasource_framework/
├── __init__.py                 # Package initialization and exports
├── interfaces.py               # Abstract base classes and interfaces
├── registry.py                 # Central data source registry
├── prompt_builder.py           # Strategy-aware prompt builder
├── evolution_manager.py        # Data source evolution management
├── data_sources/              # Individual data source modules
│   ├── __init__.py
│   ├── content_strategy_source.py
│   ├── gap_analysis_source.py
│   ├── keywords_source.py
│   ├── content_pillars_source.py
│   ├── performance_source.py
│   └── ai_analysis_source.py
└── quality_gates/             # Individual quality gate modules
    ├── __init__.py
    ├── quality_gate_manager.py
    ├── content_uniqueness_gate.py
    ├── content_mix_gate.py
    ├── chain_context_gate.py
    ├── calendar_structure_gate.py
    ├── enterprise_standards_gate.py
    └── kpi_integration_gate.py
```

### **Core Components**

#### **1. Data Source Interface (`interfaces.py`)**
Defines the contract for all data sources:
- `DataSourceInterface`: Abstract base class for data sources
- `DataSourceType`: Enumeration of data source types
- `DataSourcePriority`: Priority levels for processing
- `DataSourceValidationResult`: Standardized validation results

#### **2. Data Source Registry (`registry.py`)**
Central management system for data sources:
- Registration and unregistration of data sources
- Dependency management between sources
- Data retrieval with dependency resolution
- Source validation and status tracking

#### **3. Strategy-Aware Prompt Builder (`prompt_builder.py`)**
Builds AI prompts with full strategy context:
- Step-specific prompt generation
- Dependency-aware data integration
- Strategy context enhancement
- Quality gate integration

#### **4. Quality Gate Manager (`quality_gates/quality_gate_manager.py`)**
Comprehensive quality validation system:
- 6 quality gate categories
- Real-time validation during generation
- Quality scoring and threshold management
- Enterprise-level quality standards

#### **5. Evolution Manager (`evolution_manager.py`)**
Manages data source evolution:
- Version control and tracking
- Enhancement planning
- Evolution readiness assessment
- Backward compatibility management

## 📊 **Data Sources**

### **Current Data Sources**

#### **1. Content Strategy Source**
- **Type**: Strategy
- **Priority**: Critical
- **Purpose**: Provides comprehensive content strategy data
- **Fields**: 30+ strategic inputs including business objectives, target audience, content pillars, brand voice, editorial guidelines
- **Quality Indicators**: Data completeness, strategic alignment, content coherence

#### **2. Gap Analysis Source**
- **Type**: Analysis
- **Priority**: High
- **Purpose**: Identifies content gaps and opportunities
- **Fields**: Content gaps, keyword opportunities, competitor insights, recommendations
- **Quality Indicators**: Gap identification accuracy, opportunity relevance

#### **3. Keywords Source**
- **Type**: Research
- **Priority**: High
- **Purpose**: Provides keyword research and optimization data
- **Fields**: Primary keywords, long-tail keywords, search volume, competition level
- **Quality Indicators**: Keyword relevance, search volume accuracy

#### **4. Content Pillars Source**
- **Type**: Strategy
- **Priority**: Medium
- **Purpose**: Defines content pillar structure and distribution
- **Fields**: Pillar definitions, content mix ratios, theme distribution
- **Quality Indicators**: Pillar balance, content variety

#### **5. Performance Source**
- **Type**: Performance
- **Priority**: High
- **Purpose**: Provides historical performance data and metrics
- **Fields**: Content performance, audience metrics, conversion metrics
- **Quality Indicators**: Data accuracy, metric completeness

#### **6. AI Analysis Source**
- **Type**: AI
- **Priority**: High
- **Purpose**: Provides AI-generated strategic insights
- **Fields**: Strategic insights, content intelligence, audience intelligence, predictive analytics
- **Quality Indicators**: Intelligence accuracy, predictive reliability

## 🔍 **Quality Gates**

### **Quality Gate Categories**

#### **1. Content Uniqueness Gate**
- **Purpose**: Prevents duplicate content and keyword cannibalization
- **Validation**: Topic uniqueness, title diversity, keyword distribution
- **Threshold**: 0.9 (90% uniqueness required)

#### **2. Content Mix Gate**
- **Purpose**: Ensures balanced content distribution
- **Validation**: Content type balance, theme distribution, variety
- **Threshold**: 0.8 (80% balance required)

#### **3. Chain Context Gate**
- **Purpose**: Validates prompt chaining context preservation
- **Validation**: Step context continuity, data flow integrity
- **Threshold**: 0.85 (85% context preservation required)

#### **4. Calendar Structure Gate**
- **Purpose**: Ensures proper calendar structure and duration
- **Validation**: Structure completeness, duration appropriateness
- **Threshold**: 0.8 (80% structure compliance required)

#### **5. Enterprise Standards Gate**
- **Purpose**: Validates enterprise-level content standards
- **Validation**: Professional quality, brand compliance, industry standards
- **Threshold**: 0.9 (90% enterprise standards required)

#### **6. KPI Integration Gate**
- **Purpose**: Ensures KPI alignment and measurement framework
- **Validation**: KPI alignment, measurement framework, goal tracking
- **Threshold**: 0.85 (85% KPI integration required)

## 🚀 **Usage**

### **Basic Setup**

```python
from services.calendar_generation_datasource_framework import (
    DataSourceRegistry,
    StrategyAwarePromptBuilder,
    QualityGateManager,
    DataSourceEvolutionManager
)

# Initialize framework components
registry = DataSourceRegistry()
prompt_builder = StrategyAwarePromptBuilder(registry)
quality_manager = QualityGateManager()
evolution_manager = DataSourceEvolutionManager(registry)
```

### **Registering Data Sources**

```python
from services.calendar_generation_datasource_framework import ContentStrategyDataSource

# Create and register a data source
content_strategy = ContentStrategyDataSource()
registry.register_source(content_strategy)
```

### **Retrieving Data with Dependencies**

```python
# Get data from a source with its dependencies
data = await registry.get_data_with_dependencies("content_strategy", user_id=1, strategy_id=1)
```

### **Building Strategy-Aware Prompts**

```python
# Build a prompt for a specific step
prompt = await prompt_builder.build_prompt("step_1_content_strategy_analysis", user_id=1, strategy_id=1)
```

### **Quality Gate Validation**

```python
# Validate calendar data through all quality gates
validation_results = await quality_manager.validate_all_gates(calendar_data, "step_name")

# Validate specific quality gate
uniqueness_result = await quality_manager.validate_specific_gate("content_uniqueness", calendar_data, "step_name")
```

### **Evolution Management**

```python
# Check evolution status
status = evolution_manager.get_evolution_status()

# Get evolution plan for a source
plan = evolution_manager.get_evolution_plan("content_strategy")

# Evolve a data source
success = await evolution_manager.evolve_data_source("content_strategy", "2.5.0")
```

## 🔧 **Extending the Framework**

### **Adding a New Data Source**

1. **Create the data source module**:
```python
# data_sources/custom_source.py
from ..interfaces import DataSourceInterface, DataSourceType, DataSourcePriority, DataSourceValidationResult

class CustomDataSource(DataSourceInterface):
    def __init__(self):
        super().__init__("custom_source", DataSourceType.CUSTOM, DataSourcePriority.MEDIUM)
        self.version = "1.0.0"
    
    async def get_data(self, user_id: int, strategy_id: int) -> Dict[str, Any]:
        # Implement data retrieval logic
        return {"custom_data": "example"}
    
    async def validate_data(self, data: Dict[str, Any]) -> DataSourceValidationResult:
        # Implement validation logic
        validation_result = DataSourceValidationResult(is_valid=True, quality_score=0.8)
        return validation_result
    
    async def enhance_data(self, data: Dict[str, Any]) -> Dict[str, Any]:
        # Implement AI enhancement logic
        return {**data, "enhanced": True}
```

2. **Register the data source**:
```python
from .data_sources.custom_source import CustomDataSource

custom_source = CustomDataSource()
registry.register_source(custom_source)
```

3. **Update the package exports**:
```python
# data_sources/__init__.py
from .custom_source import CustomDataSource

__all__ = [
    # ... existing exports
    "CustomDataSource"
]
```

### **Adding a New Quality Gate**

1. **Create the quality gate module**:
```python
# quality_gates/custom_gate.py
class CustomGate:
    def __init__(self):
        self.name = "custom_gate"
        self.description = "Custom quality validation"
        self.pass_threshold = 0.8
        self.validation_criteria = ["Custom validation criteria"]
    
    async def validate(self, calendar_data: Dict[str, Any], step_name: str = None) -> Dict[str, Any]:
        # Implement validation logic
        return {
            "passed": True,
            "score": 0.9,
            "issues": [],
            "recommendations": []
        }
```

2. **Register the quality gate**:
```python
# quality_gates/quality_gate_manager.py
from .custom_gate import CustomGate

self.gates["custom_gate"] = CustomGate()
```

## 🧪 **Testing**

### **Running Framework Tests**

```bash
cd backend
python test_calendar_generation_datasource_framework.py
```

### **Test Coverage**

The framework includes comprehensive tests for:
- **Framework Initialization**: Component setup and registration
- **Data Source Registry**: Source management and retrieval
- **Data Source Validation**: Quality assessment and validation
- **Prompt Builder**: Strategy-aware prompt generation
- **Quality Gates**: Validation and scoring
- **Evolution Manager**: Version control and enhancement
- **Framework Integration**: End-to-end functionality
- **Scalability Features**: Custom source addition and evolution

## 📈 **Performance & Scalability**

### **Performance Characteristics**
- **Data Source Registration**: O(1) constant time
- **Data Retrieval**: O(n) where n is dependency depth
- **Quality Gate Validation**: O(m) where m is number of gates
- **Prompt Building**: O(d) where d is data source dependencies

### **Scalability Features**
- **Modular Design**: Add new components without architectural changes
- **Dependency Management**: Automatic dependency resolution
- **Evolution Support**: Version control and backward compatibility
- **Quality Assurance**: Comprehensive validation at each step
- **Extensibility**: Easy addition of new data sources and quality gates

## 🔒 **Quality Assurance**

### **Quality Metrics**
- **Data Completeness**: Percentage of required fields present
- **Data Quality**: Accuracy and reliability of data
- **Strategic Alignment**: Alignment with content strategy
- **Content Uniqueness**: Prevention of duplicate content
- **Enterprise Standards**: Professional quality compliance

### **Quality Thresholds**
- **Critical Sources**: 0.9+ quality score required
- **High Priority Sources**: 0.8+ quality score required
- **Medium Priority Sources**: 0.7+ quality score required
- **Quality Gates**: 0.8-0.9+ threshold depending on gate type

## 🛠️ **Maintenance & Evolution**

### **Version Management**
- **Semantic Versioning**: Major.Minor.Patch versioning
- **Backward Compatibility**: Maintains compatibility with existing implementations
- **Migration Support**: Automated migration between versions
- **Deprecation Warnings**: Clear deprecation notices for removed features

### **Evolution Planning**
- **Enhancement Tracking**: Track planned enhancements and improvements
- **Priority Management**: Prioritize enhancements based on impact
- **Resource Allocation**: Allocate development resources efficiently
- **Risk Assessment**: Assess risks before implementing changes

## 📚 **Integration with 12-Step Prompt Chaining**

This framework is designed to support the 12-step prompt chaining architecture for content calendar generation:

### **Phase 1: Foundation (Steps 1-3)**
- **Step 1**: Content Strategy Analysis (Content Strategy Source)
- **Step 2**: Gap Analysis Integration (Gap Analysis Source)
- **Step 3**: Keyword Research (Keywords Source)

### **Phase 2: Structure (Steps 4-6)**
- **Step 4**: Content Pillar Definition (Content Pillars Source)
- **Step 5**: Calendar Framework (All Sources)
- **Step 6**: Content Mix Planning (Content Mix Gate)

### **Phase 3: Generation (Steps 7-9)**
- **Step 7**: Daily Content Generation (All Sources)
- **Step 8**: Content Optimization (Performance Source)
- **Step 9**: AI Enhancement (AI Analysis Source)

### **Phase 4: Validation (Steps 10-12)**
- **Step 10**: Quality Validation (All Quality Gates)
- **Step 11**: Strategy Alignment (Strategy Alignment Gate)
- **Step 12**: Final Integration (All Components)

## 🤝 **Contributing**

### **Development Guidelines**
1. **Follow Modular Design**: Keep components independent and focused
2. **Maintain Quality Standards**: Ensure all quality gates pass
3. **Add Comprehensive Tests**: Include tests for new functionality
4. **Update Documentation**: Keep README and docstrings current
5. **Follow Naming Conventions**: Use consistent naming patterns

### **Code Standards**
- **Type Hints**: Use comprehensive type hints
- **Docstrings**: Include detailed docstrings for all methods
- **Error Handling**: Implement proper exception handling
- **Logging**: Use structured logging for debugging
- **Validation**: Validate inputs and outputs

## 📄 **License**

This framework is part of the ALwrity AI Writer project and follows the project's licensing terms.

## 🆘 **Support**

For issues, questions, or contributions:
1. Check the existing documentation
2. Review the test files for usage examples
3. Consult the implementation plan document
4. Create an issue with detailed information

---

**Framework Version**: 2.0.0
**Last Updated**: January 2025
**Status**: Production Ready
**Compatibility**: Python 3.8+, AsyncIO
