meta_platform_implementation.py

#!/usr/bin/env python3
"""
AIRDOCS Meta-Platform Implementation
Intelligent AI Orchestration Layer for Specialized AI Services
"""

import asyncio
import aiohttp
import time
from typing import Dict, Any, List, Optional
from dataclasses import dataclass
from enum import Enum
import json
import logging

# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class ServiceStatus(Enum):
    HEALTHY = "healthy"
    DEGRADED = "degraded"
    UNAVAILABLE = "unavailable"

class ContentCategory(Enum):
    ACADEMIC_PAPERS = "academic_papers"
    PRESENTATIONS = "presentations"
    BUSINESS_REPORTS = "business_reports"
    RESEARCH_REPORTS = "research_reports"
    MARKETING_CAMPAIGNS = "marketing_campaigns"

@dataclass
class AIServiceConfig:
    """Configuration for external AI service."""
    name: str
    api_endpoint: str
    auth_type: str  # oauth2, api_key, bearer
    credentials: Dict[str, str]
    rate_limit: int  # requests per minute
    timeout: int  # seconds
    retry_attempts: int
    quality_score: int  # 1-100
    specialization: str
    cost_per_request: float
    free_credits: int

@dataclass
class RequestContext:
    """Context for AI service request."""
    user_id: str
    content_category: ContentCategory
    prompt: str
    quality_level: int  # 1-3
    output_formats: List[str]
    budget_limit: float
    deadline: Optional[int]  # seconds

class AIOrchestrator:
    """
    Intelligent AI service orchestration and routing.
    Core component of the meta-platform architecture.
    """
    
    def __init__(self):
        self.services = self._initialize_services()
        self.service_health = {}
        self.rate_limiters = {}
        self.circuit_breakers = {}
        self.session = None
        
    def _initialize_services(self) -> Dict[str, Dict[str, AIServiceConfig]]:
        """Initialize AI service configurations."""
        
        return {
            ContentCategory.ACADEMIC_PAPERS.value: {
                "paperpal": AIServiceConfig(
                    name="paperpal",
                    api_endpoint="https://api.paperpal.com/v1/generate",
                    auth_type="oauth2",
                    credentials={"client_id": "airdocs_client", "scope": "research"},
                    rate_limit=10,  # 10 requests per minute
                    timeout=30,
                    retry_attempts=3,
                    quality_score=96,
                    specialization="academic_writing",
                    cost_per_request=0.0,  # Free tier
                    free_credits=10
                ),
                "jenni_ai": AIServiceConfig(
                    name="jenni_ai",
                    api_endpoint="https://api.jenni.ai/v1/research",
                    auth_type="oauth2",
                    credentials={"client_id": "airdocs_client", "scope": "research_papers"},
                    rate_limit=8,
                    timeout=25,
                    retry_attempts=3,
                    quality_score=94,
                    specialization="research_papers",
                    cost_per_request=0.0,
                    free_credits=15
                )
            },
            
            ContentCategory.PRESENTATIONS.value: {
                "genspark": AIServiceConfig(
                    name="genspark",
                    api_endpoint="https://api.genspark.ai/v1/presentations",
                    auth_type="oauth2",
                    credentials={"client_id": "airdocs_client", "scope": "presentations"},
                    rate_limit=15,
                    timeout=20,
                    retry_attempts=3,
                    quality_score=95,
                    specialization="executive_presentations",
                    cost_per_request=0.0,
                    free_credits=50
                ),
                "manus": AIServiceConfig(
                    name="manus",
                    api_endpoint="https://api.manus.ai/v1/generate",
                    auth_type="oauth2",
                    credentials={"client_id": "airdocs_client", "scope": "business_presentations"},
                    rate_limit=12,
                    timeout=25,
                    retry_attempts=3,
                    quality_score=93,
                    specialization="business_presentations",
                    cost_per_request=0.0,
                    free_credits=30
                )
            },
            
            ContentCategory.BUSINESS_REPORTS.value: {
                "pitchbook_ai": AIServiceConfig(
                    name="pitchbook_ai",
                    api_endpoint="https://api.pitchbook.com/v1/analysis",
                    auth_type="oauth2",
                    credentials={"client_id": "airdocs_client", "scope": "market_analysis"},
                    rate_limit=6,
                    timeout=45,
                    retry_attempts=3,
                    quality_score=96,
                    specialization="market_analysis",
                    cost_per_request=0.0,
                    free_credits=15
                )
            }
        }
    
    async def route_request(self, context: RequestContext) -> Dict[str, Any]:
        """
        Intelligent routing of requests to optimal AI service.
        Core orchestration logic.
        """
        
        logger.info(f"🎯 Routing request for {context.content_category.value}")
        
        # 1. Get available services for content category
        available_services = await self._get_available_services(context.content_category)
        
        if not available_services:
            return await self._handle_no_services_available(context)
        
        # 2. Select optimal service based on requirements
        selected_service = await self._select_optimal_service(available_services, context)
        
        # 3. Execute request with selected service
        result = await self._execute_request(selected_service, context)
        
        # 4. Post-process and standardize output
        standardized_result = await self._standardize_output(result, selected_service)
        
        return standardized_result
    
    async def _get_available_services(self, category: ContentCategory) -> List[AIServiceConfig]:
        """Get healthy, available services for content category."""
        
        category_services = self.services.get(category.value, {})
        available_services = []
        
        for service_name, config in category_services.items():
            # Check service health
            health_status = await self._check_service_health(config)
            
            if health_status == ServiceStatus.HEALTHY:
                # Check rate limits
                if await self._check_rate_limit(service_name):
                    # Check credits availability
                    if await self._check_credits_available(service_name):
                        available_services.append(config)
        
        return available_services
    
    async def _select_optimal_service(self, services: List[AIServiceConfig], 
                                    context: RequestContext) -> AIServiceConfig:
        """Select optimal service based on requirements and constraints."""
        
        # Scoring algorithm for service selection
        scored_services = []
        
        for service in services:
            score = 0
            
            # Quality score (40% weight)
            quality_weight = 0.4
            quality_score = (service.quality_score / 100) * quality_weight
            score += quality_score
            
            # Cost optimization (30% weight)
            cost_weight = 0.3
            if service.cost_per_request == 0:  # Free tier
                cost_score = cost_weight
            else:
                cost_score = max(0, cost_weight - (service.cost_per_request * 10))
            score += cost_score
            
            # Performance (20% weight)
            performance_weight = 0.2
            performance_score = (1 / max(service.timeout, 1)) * performance_weight * 100
            score += performance_score
            
            # Specialization match (10% weight)
            specialization_weight = 0.1
            # Simple keyword matching for specialization
            if context.content_category.value in service.specialization:
                score += specialization_weight
            
            scored_services.append((service, score))
        
        # Select service with highest score
        best_service = max(scored_services, key=lambda x: x[1])
        
        logger.info(f"✅ Selected {best_service[0].name} (score: {best_service[1]:.3f})")
        
        return best_service[0]
    
    async def _execute_request(self, service: AIServiceConfig, 
                             context: RequestContext) -> Dict[str, Any]:
        """Execute request to selected AI service with retry logic."""
        
        if not self.session:
            self.session = aiohttp.ClientSession()
        
        # Prepare request payload
        payload = await self._prepare_request_payload(service, context)
        
        # Execute with retry logic
        for attempt in range(service.retry_attempts):
            try:
                logger.info(f"🔄 Attempt {attempt + 1} for {service.name}")
                
                async with self.session.post(
                    service.api_endpoint,
                    json=payload,
                    timeout=aiohttp.ClientTimeout(total=service.timeout),
                    headers=await self._get_auth_headers(service)
                ) as response:
                    
                    if response.status == 200:
                        result = await response.json()
                        logger.info(f"✅ Success from {service.name}")
                        return {
                            "success": True,
                            "service": service.name,
                            "data": result,
                            "attempt": attempt + 1
                        }
                    
                    elif response.status == 429:  # Rate limited
                        logger.warning(f"⚠️ Rate limited by {service.name}")
                        await asyncio.sleep(2 ** attempt)  # Exponential backoff
                        continue
                    
                    else:
                        logger.error(f"❌ HTTP {response.status} from {service.name}")
                        if attempt == service.retry_attempts - 1:
                            return {
                                "success": False,
                                "service": service.name,
                                "error": f"HTTP {response.status}",
                                "attempt": attempt + 1
                            }
            
            except asyncio.TimeoutError:
                logger.error(f"⏰ Timeout for {service.name}")
                if attempt == service.retry_attempts - 1:
                    return {
                        "success": False,
                        "service": service.name,
                        "error": "timeout",
                        "attempt": attempt + 1
                    }
            
            except Exception as e:
                logger.error(f"💥 Error with {service.name}: {str(e)}")
                if attempt == service.retry_attempts - 1:
                    return {
                        "success": False,
                        "service": service.name,
                        "error": str(e),
                        "attempt": attempt + 1
                    }
        
        return {"success": False, "service": service.name, "error": "max_retries_exceeded"}
    
    async def _prepare_request_payload(self, service: AIServiceConfig, 
                                     context: RequestContext) -> Dict[str, Any]:
        """Prepare service-specific request payload."""
        
        # Base payload structure
        payload = {
            "prompt": context.prompt,
            "quality_level": context.quality_level,
            "output_formats": context.output_formats
        }
        
        # Service-specific customizations
        if service.name == "paperpal":
            payload.update({
                "citation_style": "apa",
                "academic_level": "research",
                "word_count_target": 2000
            })
        
        elif service.name == "genspark":
            payload.update({
                "presentation_type": "executive",
                "slide_count": 10,
                "include_charts": True
            })
        
        elif service.name == "pitchbook_ai":
            payload.update({
                "analysis_type": "market_research",
                "include_financials": True,
                "data_sources": ["public_filings", "market_data"]
            })
        
        return payload
    
    async def _get_auth_headers(self, service: AIServiceConfig) -> Dict[str, str]:
        """Get authentication headers for service."""
        
        if service.auth_type == "oauth2":
            # In production, this would handle OAuth token refresh
            return {
                "Authorization": f"Bearer {service.credentials.get('access_token', 'mock_token')}",
                "Content-Type": "application/json"
            }
        
        elif service.auth_type == "api_key":
            return {
                "X-API-Key": service.credentials.get("api_key", "mock_key"),
                "Content-Type": "application/json"
            }
        
        return {"Content-Type": "application/json"}
    
    async def _standardize_output(self, result: Dict[str, Any], 
                                service: AIServiceConfig) -> Dict[str, Any]:
        """Standardize output format across all services."""
        
        if not result.get("success"):
            return result
        
        # Extract content from service-specific response format
        raw_data = result["data"]
        
        # Standardized response format
        standardized = {
            "success": True,
            "content": {
                "text": self._extract_text_content(raw_data, service.name),
                "metadata": {
                    "service": service.name,
                    "quality_score": service.quality_score,
                    "specialization": service.specialization,
                    "generation_time": raw_data.get("generation_time", 0),
                    "word_count": self._count_words(raw_data),
                    "timestamp": time.time()
                }
            },
            "routing_info": {
                "selected_service": service.name,
                "attempt_count": result.get("attempt", 1),
                "cost": service.cost_per_request
            }
        }
        
        return standardized
    
    def _extract_text_content(self, raw_data: Dict[str, Any], service_name: str) -> str:
        """Extract text content from service-specific response."""
        
        # Service-specific content extraction
        if service_name == "paperpal":
            return raw_data.get("academic_paper", {}).get("content", "")
        elif service_name == "genspark":
            return raw_data.get("presentation", {}).get("slides_content", "")
        elif service_name == "pitchbook_ai":
            return raw_data.get("report", {}).get("analysis", "")
        
        # Generic extraction
        return raw_data.get("content", raw_data.get("text", str(raw_data)))
    
    def _count_words(self, raw_data: Dict[str, Any]) -> int:
        """Count words in generated content."""
        content = str(raw_data)
        return len(content.split())
    
    async def _check_service_health(self, service: AIServiceConfig) -> ServiceStatus:
        """Check health status of AI service."""
        # Simplified health check - in production would ping health endpoint
        return ServiceStatus.HEALTHY
    
    async def _check_rate_limit(self, service_name: str) -> bool:
        """Check if service is within rate limits."""
        # Simplified rate limiting - in production would use Redis
        return True
    
    async def _check_credits_available(self, service_name: str) -> bool:
        """Check if service has available credits."""
        # Simplified credit check - in production would check database
        return True
    
    async def _handle_no_services_available(self, context: RequestContext) -> Dict[str, Any]:
        """Handle case when no services are available."""
        
        logger.warning(f"⚠️ No services available for {context.content_category.value}")
        
        return {
            "success": False,
            "error": "no_services_available",
            "message": f"No AI services currently available for {context.content_category.value}",
            "fallback_suggestion": "Please try again later or contact support"
        }

# Example usage and testing
async def test_meta_platform():
    """Test the meta-platform orchestration."""
    
    orchestrator = AIOrchestrator()
    
    # Test academic paper request
    context = RequestContext(
        user_id="test_user",
        content_category=ContentCategory.ACADEMIC_PAPERS,
        prompt="Analyze the impact of AI on healthcare delivery",
        quality_level=3,
        output_formats=["pdf", "docx"],
        budget_limit=5.0,
        deadline=300
    )
    
    result = await orchestrator.route_request(context)
    print(f"Academic Paper Result: {result}")
    
    # Test presentation request
    context.content_category = ContentCategory.PRESENTATIONS
    result = await orchestrator.route_request(context)
    print(f"Presentation Result: {result}")

class ReliabilityManager:
    """
    Manages reliability, performance, and failover for the meta-platform.
    Ensures 99.9% uptime despite third-party service dependencies.
    """

    def __init__(self):
        self.circuit_breakers = {}
        self.health_monitors = {}
        self.performance_metrics = {}
        self.failover_chains = self._initialize_failover_chains()

    def _initialize_failover_chains(self) -> Dict[str, List[str]]:
        """Initialize failover chains for each content category."""

        return {
            "academic_papers": [
                "paperpal",      # Primary (highest quality)
                "jenni_ai",      # Secondary
                "scispace",      # Tertiary
                "gpt-4o",        # Generic fallback
                "claude-3-opus"  # Final fallback
            ],
            "presentations": [
                "genspark",      # Primary
                "manus",         # Secondary
                "gamma_app",     # Tertiary
                "gpt-4o",        # Generic fallback
                "claude-3-opus"  # Final fallback
            ],
            "business_reports": [
                "pitchbook_ai",  # Primary
                "cb_insights",   # Secondary
                "analyst_ai",    # Tertiary
                "gpt-4o",        # Generic fallback
                "claude-3-opus"  # Final fallback
            ]
        }

    async def execute_with_failover(self, category: str, context: RequestContext) -> Dict[str, Any]:
        """Execute request with automatic failover through service chain."""

        failover_chain = self.failover_chains.get(category, [])

        for i, service_name in enumerate(failover_chain):
            try:
                logger.info(f"🔄 Trying {service_name} (attempt {i+1}/{len(failover_chain)})")

                # Check circuit breaker
                if self._is_circuit_open(service_name):
                    logger.warning(f"⚡ Circuit breaker open for {service_name}")
                    continue

                # Attempt service call
                result = await self._call_service(service_name, context)

                if result.get("success"):
                    # Record success
                    self._record_success(service_name)

                    # Add failover metadata
                    result["failover_info"] = {
                        "attempt_number": i + 1,
                        "service_used": service_name,
                        "is_primary": i == 0,
                        "fallback_level": "primary" if i == 0 else "secondary" if i < 3 else "generic"
                    }

                    return result

                else:
                    # Record failure
                    self._record_failure(service_name, result.get("error"))
                    continue

            except Exception as e:
                logger.error(f"💥 Service {service_name} failed: {str(e)}")
                self._record_failure(service_name, str(e))
                continue

        # All services failed
        return {
            "success": False,
            "error": "all_services_failed",
            "message": "All AI services in failover chain are currently unavailable",
            "attempted_services": failover_chain
        }

    def _is_circuit_open(self, service_name: str) -> bool:
        """Check if circuit breaker is open for service."""

        breaker = self.circuit_breakers.get(service_name, {})

        if not breaker:
            return False

        # Simple circuit breaker logic
        failure_rate = breaker.get("failure_rate", 0)
        last_failure = breaker.get("last_failure", 0)

        # Open circuit if failure rate > 50% and recent failures
        if failure_rate > 0.5 and (time.time() - last_failure) < 300:  # 5 minutes
            return True

        return False

    def _record_success(self, service_name: str):
        """Record successful service call."""

        if service_name not in self.circuit_breakers:
            self.circuit_breakers[service_name] = {
                "success_count": 0,
                "failure_count": 0,
                "failure_rate": 0,
                "last_success": time.time()
            }

        breaker = self.circuit_breakers[service_name]
        breaker["success_count"] += 1
        breaker["last_success"] = time.time()

        # Update failure rate
        total_calls = breaker["success_count"] + breaker["failure_count"]
        breaker["failure_rate"] = breaker["failure_count"] / max(total_calls, 1)

    def _record_failure(self, service_name: str, error: str):
        """Record failed service call."""

        if service_name not in self.circuit_breakers:
            self.circuit_breakers[service_name] = {
                "success_count": 0,
                "failure_count": 0,
                "failure_rate": 0,
                "last_failure": time.time()
            }

        breaker = self.circuit_breakers[service_name]
        breaker["failure_count"] += 1
        breaker["last_failure"] = time.time()

        # Update failure rate
        total_calls = breaker["success_count"] + breaker["failure_count"]
        breaker["failure_rate"] = breaker["failure_count"] / max(total_calls, 1)

        logger.warning(f"⚠️ {service_name} failure rate: {breaker['failure_rate']:.2%}")

class ValueAddProcessor:
    """
    Provides unique value-add services beyond simple API routing.
    This is where AIRDOCS differentiates from basic API aggregation.
    """

    def __init__(self):
        self.quality_validators = {}
        self.format_converters = {}
        self.enhancement_engines = {}

    async def enhance_output(self, raw_result: Dict[str, Any],
                           context: RequestContext) -> Dict[str, Any]:
        """Apply value-add enhancements to AI-generated content."""

        if not raw_result.get("success"):
            return raw_result

        enhanced_result = raw_result.copy()

        # 1. Quality validation and scoring
        quality_analysis = await self._validate_quality(raw_result, context)
        enhanced_result["quality_analysis"] = quality_analysis

        # 2. Multi-format conversion
        format_outputs = await self._convert_formats(raw_result, context.output_formats)
        enhanced_result["format_outputs"] = format_outputs

        # 3. Content enhancement
        enhanced_content = await self._enhance_content(raw_result, context)
        enhanced_result["enhanced_content"] = enhanced_content

        # 4. Metadata enrichment
        metadata = await self._enrich_metadata(raw_result, context)
        enhanced_result["enriched_metadata"] = metadata

        return enhanced_result

    async def _validate_quality(self, result: Dict[str, Any],
                              context: RequestContext) -> Dict[str, Any]:
        """Validate and score content quality."""

        content = result.get("content", {}).get("text", "")

        quality_metrics = {
            "word_count": len(content.split()),
            "readability_score": self._calculate_readability(content),
            "completeness_score": self._assess_completeness(content, context),
            "accuracy_score": self._estimate_accuracy(content),
            "professional_score": self._assess_professionalism(content)
        }

        # Overall quality score (weighted average)
        overall_score = (
            quality_metrics["readability_score"] * 0.2 +
            quality_metrics["completeness_score"] * 0.3 +
            quality_metrics["accuracy_score"] * 0.3 +
            quality_metrics["professional_score"] * 0.2
        )

        return {
            "overall_score": overall_score,
            "metrics": quality_metrics,
            "recommendations": self._generate_quality_recommendations(quality_metrics)
        }

    async def _convert_formats(self, result: Dict[str, Any],
                             formats: List[str]) -> Dict[str, str]:
        """Convert content to multiple output formats."""

        content = result.get("content", {}).get("text", "")
        format_outputs = {}

        for format_type in formats:
            if format_type == "pdf":
                format_outputs["pdf"] = await self._convert_to_pdf(content)
            elif format_type == "docx":
                format_outputs["docx"] = await self._convert_to_docx(content)
            elif format_type == "pptx":
                format_outputs["pptx"] = await self._convert_to_pptx(content)
            elif format_type == "html":
                format_outputs["html"] = await self._convert_to_html(content)

        return format_outputs

    async def _enhance_content(self, result: Dict[str, Any],
                             context: RequestContext) -> Dict[str, Any]:
        """Apply content enhancements and optimizations."""

        content = result.get("content", {}).get("text", "")

        enhancements = {
            "grammar_check": await self._check_grammar(content),
            "fact_verification": await self._verify_facts(content),
            "citation_validation": await self._validate_citations(content),
            "style_consistency": await self._check_style_consistency(content),
            "brand_alignment": await self._align_with_brand(content, context)
        }

        return enhancements

    def _calculate_readability(self, content: str) -> float:
        """Calculate readability score (simplified)."""
        words = content.split()
        sentences = content.split('.')

        if not sentences or not words:
            return 0.0

        avg_words_per_sentence = len(words) / len(sentences)

        # Simplified readability score (0-100)
        if avg_words_per_sentence < 15:
            return 90.0
        elif avg_words_per_sentence < 25:
            return 75.0
        else:
            return 60.0

    def _assess_completeness(self, content: str, context: RequestContext) -> float:
        """Assess content completeness based on requirements."""

        # Simple completeness check based on word count
        word_count = len(content.split())

        if context.content_category == ContentCategory.ACADEMIC_PAPERS:
            target_words = 2000
        elif context.content_category == ContentCategory.PRESENTATIONS:
            target_words = 500
        else:
            target_words = 1000

        completeness = min(word_count / target_words, 1.0) * 100
        return completeness

    def _estimate_accuracy(self, content: str) -> float:
        """Estimate content accuracy (simplified)."""
        # In production, this would use fact-checking APIs
        return 85.0  # Placeholder

    def _assess_professionalism(self, content: str) -> float:
        """Assess professional tone and language."""
        # Simple professionalism check
        professional_indicators = [
            "analysis", "research", "findings", "recommendations",
            "methodology", "conclusions", "objectives", "strategy"
        ]

        content_lower = content.lower()
        matches = sum(1 for indicator in professional_indicators if indicator in content_lower)

        professionalism_score = min(matches * 10, 100)
        return professionalism_score

if __name__ == "__main__":
    asyncio.run(test_meta_platform())