class AnalysisType(str, Enum):
    DOCUMENT = "document"    # Document analysis and summarization
    CHAT = "chat"           # Conversational AI interactions
    SEO = "seo"             # Content optimization
    CRYPTO = "crypto"       # Financial market analysis
    CUSTOM = "custom"       # Generic processing

def deconstruct_input(self, user_input: str, analysis_type: AnalysisType) -> dict:
    """Extract structured context from raw user input."""
    context = {
        'intent': self._classify_intent(user_input),
        'domain': self._detect_domain(user_input),
        'complexity': self._assess_complexity(user_input),
        'constraints': self._extract_constraints(user_input)
    }
 
    # Apply type-specific extraction rules
    if analysis_type == AnalysisType.DOCUMENT:
        context.update(self._extract_document_context(user_input))
    elif analysis_type == AnalysisType.SEO:
        context.update(self._extract_seo_context(user_input))
 
    return self._apply_intelligent_defaults(context, analysis_type)

class RequestValidator:
    def validate_and_enhance(self, context: dict, analysis_type: AnalysisType) -> dict:
        """Validate request context and enhance with missing elements."""
 
        # Layer 1: Basic completeness check
        completeness_score = self._assess_completeness(context)
 
        # Layer 2: Type-specific validation
        validation_result = self._validate_by_type(context, analysis_type)
 
        # Layer 3: Quality enhancement
        enhanced_context = self._enhance_context(context, validation_result)
 
        # Layer 4: Feasibility check
        if not self._is_feasible(enhanced_context):
            enhanced_context = self._apply_fallback_strategy(enhanced_context)
 
        return enhanced_context

def _detect_missing_context(self, context: dict) -> List[str]:
    missing = []
 
    if not context.get('target_audience'):
        missing.append('audience')
 
    if not context.get('desired_outcome'):
        missing.append('outcome')
 
    if not context.get('constraints'):
        missing.append('constraints')
 
    return missing

def _fill_missing_context(self, context: dict, missing: List[str]) -> dict:
    """Fill missing context based on analysis patterns."""
 
    if 'audience' in missing:
        context['target_audience'] = self._infer_audience(
            context.get('domain', 'general')
        )
 
    if 'outcome' in missing:
        context['desired_outcome'] = self._infer_outcome(
            context.get('intent', 'analysis')
        )
 
    return context

class PromptTemplateEngine:
    def get_document_template(self) -> str:
        return """
        You are Ash, a world-class AI document analysis specialist with expertise
        in extracting actionable insights from complex information.
 
        MISSION: Transform user input into precise, structured document analysis
        using the 4-D methodology.
 
        ## 1. DECONSTRUCT
        Extract:
        - Document type and purpose
        - Key stakeholders and audience
        - Critical information needs
        - Success criteria
 
        ## 2. DIAGNOSE
        Validate:
        - Information completeness
        - Analysis feasibility
        - Resource requirements
        - Potential limitations
 
        ## 3. DEVELOP
        Create analysis covering:
        1. **Executive Summary**: Core findings in 2-3 sentences
        2. **Key Insights**: 3-5 most important discoveries
        3. **Supporting Evidence**: Data and examples
        4. **Actionable Recommendations**: Specific next steps
        5. **Quality Assessment**: Reliability and gaps
 
        ## 4. DELIVER
        Format: Professional, scannable, actionable
        Tone: Authoritative but accessible
        Length: Comprehensive but concise
        """
 
    def get_seo_template(self) -> str:
        return """
        You are Ash, an elite SEO content strategist with deep expertise
        in creating content that ranks and converts.
 
        MISSION: Transform user input into SEO-optimized content strategy
        using the 4-D methodology.
 
        ## 1. DECONSTRUCT
        Identify:
        - Target keywords and intent
        - Content type and format
        - Audience and user journey stage
        - Competitive landscape
 
        ## 2. DIAGNOSE
        Assess:
        - Keyword difficulty and opportunity
        - Content gaps and advantages
        - Technical SEO considerations
        - Conversion potential
 
        ## 3. DEVELOP
        Create:
        1. **Title & Meta**: Optimized for CTR and relevance
        2. **Content Structure**: H2/H3 hierarchy with keyword distribution
        3. **Content Body**: Valuable, engaging, search-optimized
        4. **Internal Linking**: Strategic link opportunities
        5. **Call-to-Action**: Conversion-focused next steps
 
        ## 4. DELIVER
        Output: SEO-optimized, user-focused, actionable content
        """

def optimize_prompt_for_provider(self, base_prompt: str, provider: str) -> str:
    """Optimize prompt for specific AI provider characteristics."""
 
    optimizations = {
        'openai': {
            'max_context': 128000,
            'strengths': ['reasoning', 'structured_output'],
            'format_preferences': ['json', 'markdown']
        },
        'grok': {
            'max_context': 32000,
            'strengths': ['creativity', 'conversational'],
            'format_preferences': ['natural_language', 'bullet_points']
        }
    }
 
    config = optimizations.get(provider, optimizations['openai'])
    return self._apply_provider_optimizations(base_prompt, config)

class AIOrchestrator:
    def __init__(self):
        self.registry = ai_service_registry
        self.fallback_manager = FallbackManager()
        self.performance_monitor = PerformanceMonitor()
 
    async def execute_analysis(self,
                             optimized_prompt: str,
                             context: dict) -> AnalysisResult:
        """
        Execute analysis using optimal service selection and fallback.
        """
 
        # Select optimal services based on requirements
        services = self._select_optimal_services(context)
 
        # Execute with monitoring and fallback
        try:
            result = await self._execute_with_monitoring(
                optimized_prompt,
                services
            )
            return result
 
        except ServiceUnavailableError:
            return await self._execute_with_fallback(
                optimized_prompt,
                context
            )

def _select_optimal_services(self, context: dict) -> ServicePlan:
    """Select optimal AI services for the given context."""
 
    factors = {
        'complexity': context.get('complexity', 'medium'),
        'domain': context.get('domain', 'general'),
        'response_time_priority': context.get('urgency', 'normal'),
        'cost_priority': context.get('budget', 'balanced'),
        'quality_priority': context.get('quality_level', 'high')
    }
 
    # Score available services
    service_scores = {}
    available_services = self.registry.get_healthy_services()
 
    for service_id, service in available_services.items():
        score = self._calculate_service_score(service, factors)
        service_scores[service_id] = score
 
    # Select optimal combination
    return self._create_service_plan(service_scores, factors)

async def _execute_multi_provider_analysis(self,
                                         prompt: str,
                                         services: ServicePlan) -> dict:
    """
    Execute analysis using multiple providers in parallel or sequence.
    """
 
    results = {}
 
    # Phase 1: Prompt optimization (usually OpenAI)
    if services.prompt_optimizer:
        results['optimized_prompt'] = await services.prompt_optimizer.process(
            prompt
        )
 
    # Phase 2: Primary analysis (best fit for task)
    if services.primary_analyzer:
        results['analysis'] = await services.primary_analyzer.process(
            results.get('optimized_prompt', prompt)
        )
 
    # Phase 3: Quality validation (if required)
    if services.validator and results.get('analysis'):
        results['validation'] = await services.validator.validate(
            results['analysis']
        )
 
    return self._structure_final_result(results, services)

class PerformanceMonitor:
    async def track_analysis_performance(self,
                                       analysis_id: str,
                                       start_time: float) -> dict:
        """Track and analyze performance metrics."""
 
        end_time = time.time()
        duration_ms = int((end_time - start_time) * 1000)
 
        metrics = {
            'analysis_id': analysis_id,
            'duration_ms': duration_ms,
            'services_used': await self._get_services_used(analysis_id),
            'token_usage': await self._calculate_token_usage(analysis_id),
            'cost_estimate': await self._estimate_cost(analysis_id),
            'quality_score': await self._assess_quality(analysis_id)
        }
 
        # Store for analysis and optimization
        await self._store_performance_data(metrics)
 
        return metrics

class FallbackManager:
    async def execute_with_fallback(self,
                                   prompt: str,
                                   context: dict) -> AnalysisResult:
        """Execute analysis with comprehensive fallback strategies."""
 
        fallback_strategies = [
            self._primary_service_strategy,
            self._alternative_provider_strategy,
            self._simplified_analysis_strategy,
            self._cached_response_strategy,
            self._graceful_degradation_strategy
        ]
 
        for strategy in fallback_strategies:
            try:
                result = await strategy(prompt, context)
                if self._validate_result(result):
                    return result
            except Exception as e:
                logger.warning(f"Fallback strategy failed: {e}")
                continue
 
        # Final fallback: return helpful error with suggestions
        return self._create_fallback_response(context)

# DECONSTRUCT
context = {
    'analysis_type': 'document',
    'document_type': 'business_report',
    'timeframe': 'quarterly',
    'stakeholders': ['executives', 'investors'],
    'focus_areas': ['performance', 'trends', 'risks']
}
 
# DIAGNOSE
validation = {
    'completeness': 0.8,
    'feasibility': 1.0,
    'enhancement_needed': ['specific_metrics', 'comparison_period']
}
 
# DEVELOP
optimized_prompt = """
Analyze this quarterly business report with focus on:
1. Key Performance Indicators and trends
2. Financial health and cash flow implications
3. Market position and competitive advantages
4. Risk factors and mitigation strategies
5. Strategic recommendations for next quarter
 
Format as executive summary with actionable insights.
"""
 
# DELIVER
services_selected = {
    'prompt_optimizer': 'openai_gpt4',
    'primary_analyzer': 'grok_beta',
    'quality_validator': 'internal_validator'
}

# DECONSTRUCT
context = {
    'analysis_type': 'seo',
    'industry': 'fashion',
    'niche': 'sustainable_fashion',
    'content_type': 'blog_post',
    'target_keywords': ['sustainable fashion', 'eco-friendly clothing'],
    'audience': 'environmentally_conscious_consumers'
}
 
# DIAGNOSE
validation = {
    'keyword_difficulty': 'medium',
    'content_opportunity': 'high',
    'competitive_gap': 'authenticity_stories'
}
 
# DEVELOP
optimized_prompt = """
Create comprehensive SEO content for sustainable fashion brand:
 
1. Title: Engaging, keyword-rich (60 chars max)
2. Meta description: Compelling, CTA-focused (160 chars)
3. Content structure:
   - Hook: Sustainability pain point
   - Problem: Fast fashion impact
   - Solution: Sustainable alternatives
   - Proof: Brand story and materials
   - Action: Specific next steps
4. Keywords: Natural integration of primary and LSI terms
5. Internal links: Connect to product and educational pages
"""
 
# DELIVER
services_selected = {
    'prompt_optimizer': 'openai_gpt4',
    'content_generator': 'grok_beta',
    'seo_validator': 'internal_seo_checker'
}

@pytest.mark.asyncio
async def test_complete_4d_workflow():
    """Test end-to-end 4-D Engine processing."""
 
    # Test input
    user_input = "Analyze customer feedback data for product improvements"
    analysis_type = AnalysisType.CUSTOM
 
    # Execute complete workflow
    result = await four_d_engine.process(
        user_input=user_input,
        analysis_type=analysis_type,
        user_context={'industry': 'saas', 'urgency': 'high'}
    )
 
    # Validate 4-D phases
    assert 'deconstruct_result' in result
    assert 'diagnose_result' in result
    assert 'develop_result' in result
    assert 'deliver_result' in result
 
    # Validate final output
    assert result['analysis_type'] == analysis_type
    assert 'optimized_prompt' in result
    assert 'analysis_result' in result
    assert 'services_used' in result
    assert 'performance_metrics' in result
 
    # Validate quality
    assert len(result['analysis_result']) > 200
    assert result['performance_metrics']['quality_score'] > 7.0

from app.services.four_d_engine import FourDEngine
from app.services.analysis_service import AnalysisType
 
# Initialize engine
engine = FourDEngine()
 
# Simple document analysis
result = await engine.process(
    user_input="Analyze this market research report",
    analysis_type=AnalysisType.DOCUMENT,
    user_context={
        'industry': 'technology',
        'audience': 'investors',
        'urgency': 'high'
    }
)
 
# Access results
print(f"Analysis: {result['analysis_result']}")
print(f"Services used: {result['services_used']}")
print(f"Performance: {result['performance_metrics']}")

# Custom engine configuration
engine_config = {
    'enable_fallback': True,
    'max_response_time': 5000,  # milliseconds
    'quality_threshold': 8.0,
    'cost_optimization': True,
    'cache_results': True
}
 
engine = FourDEngine(config=engine_config)
 
# Custom service preferences
service_preferences = {
    'prompt_optimizer': 'prefer_openai',
    'analyzer': 'prefer_grok',
    'fallback_strategy': 'aggressive'
}
 
result = await engine.process(
    user_input=user_input,
    analysis_type=analysis_type,
    user_context=context,
    service_preferences=service_preferences
)