"""
AI-powered query optimization for Zenoo RPC.

This module provides intelligent query analysis and optimization
suggestions to improve performance and efficiency.
"""

import logging
from typing import Any, Dict, List, Optional, TYPE_CHECKING

if TYPE_CHECKING:
    from ..core.ai_client import AIClient
    from ...client import ZenooClient

logger = logging.getLogger(__name__)


class AIQueryOptimizer:
    """AI-powered query optimization and analysis.
    
    This optimizer analyzes query patterns, performance metrics,
    and provides intelligent suggestions for improving query efficiency.
    
    Features:
    - Query performance analysis
    - Optimization recommendations
    - Caching strategy suggestions
    - Index recommendations
    - Batch processing suggestions
    
    Example:
        >>> optimizer = AIQueryOptimizer(ai_client, zenoo_client)
        >>> 
        >>> # Analyze query performance
        >>> stats = {
        ...     "execution_time": 2.5,
        ...     "record_count": 10000,
        ...     "model": "res.partner"
        ... }
        >>> suggestions = await optimizer.analyze_performance(stats)
    """
    
    def __init__(self, ai_client: "AIClient", zenoo_client: "ZenooClient"):
        """Initialize the query optimizer.
        
        Args:
            ai_client: AI client for optimization analysis
            zenoo_client: Zenoo client for query context
        """
        self.ai_client = ai_client
        self.zenoo_client = zenoo_client
    
    async def analyze_performance(self, query_stats: Dict[str, Any]) -> List[str]:
        """Analyze query performance and suggest optimizations.
        
        Args:
            query_stats: Dictionary with performance statistics
            
        Returns:
            List of optimization suggestions
        """
        schema = {
            "type": "object",
            "properties": {
                "suggestions": {
                    "type": "array",
                    "items": {"type": "string"},
                    "description": "List of specific optimization suggestions"
                },
                "priority": {
                    "type": "string",
                    "enum": ["low", "medium", "high", "critical"],
                    "description": "Priority level for optimization"
                },
                "estimated_improvement": {
                    "type": "string",
                    "description": "Estimated performance improvement percentage"
                },
                "implementation_effort": {
                    "type": "string",
                    "enum": ["low", "medium", "high"],
                    "description": "Effort required to implement suggestions"
                }
            },
            "required": ["suggestions", "priority"]
        }
        
        prompt = f"""Analyze this Odoo query performance and provide optimization suggestions:

Performance Statistics:
{self._format_stats(query_stats)}

Consider these optimization strategies:
1. Query structure and domain filters
2. Field selection optimization
3. Pagination and limiting
4. Caching strategies
5. Index recommendations
6. Batch processing opportunities
7. Relationship loading optimization

Provide specific, actionable suggestions with realistic impact estimates."""
        
        response = await self.ai_client.complete_structured(
            prompt=prompt,
            schema=schema,
            system=self._get_optimization_system_prompt(),
            temperature=0.2
        )
        
        return response["suggestions"]
    
    async def suggest_caching_strategy(
        self,
        query_info: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Suggest optimal caching strategy for a query.
        
        Args:
            query_info: Information about the query
            
        Returns:
            Dictionary with caching recommendations
        """
        schema = {
            "type": "object",
            "properties": {
                "should_cache": {
                    "type": "boolean",
                    "description": "Whether this query should be cached"
                },
                "cache_ttl": {
                    "type": "integer",
                    "description": "Recommended TTL in seconds"
                },
                "cache_key_strategy": {
                    "type": "string",
                    "description": "Strategy for generating cache keys"
                },
                "invalidation_triggers": {
                    "type": "array",
                    "items": {"type": "string"},
                    "description": "Events that should invalidate the cache"
                },
                "reasoning": {
                    "type": "string",
                    "description": "Explanation of the caching recommendation"
                }
            },
            "required": ["should_cache", "reasoning"]
        }
        
        prompt = f"""Analyze this query and recommend a caching strategy:

Query Information:
{self._format_query_info(query_info)}

Consider:
1. Query frequency and patterns
2. Data volatility and update frequency
3. Query complexity and execution time
4. Memory usage implications
5. Cache invalidation requirements

Provide specific caching recommendations with reasoning."""
        
        response = await self.ai_client.complete_structured(
            prompt=prompt,
            schema=schema,
            system=self._get_caching_system_prompt(),
            temperature=0.1
        )
        
        return response
    
    async def optimize_domain(self, domain: List, model: str) -> Dict[str, Any]:
        """Optimize domain filters for better performance.
        
        Args:
            domain: Odoo domain filter
            model: Model name
            
        Returns:
            Dictionary with optimized domain and explanations
        """
        schema = {
            "type": "object",
            "properties": {
                "optimized_domain": {
                    "type": "array",
                    "description": "Optimized domain filter"
                },
                "improvements": {
                    "type": "array",
                    "items": {"type": "string"},
                    "description": "List of improvements made"
                },
                "performance_impact": {
                    "type": "string",
                    "description": "Expected performance impact"
                },
                "explanation": {
                    "type": "string",
                    "description": "Explanation of optimizations"
                }
            },
            "required": ["optimized_domain", "improvements", "explanation"]
        }
        
        prompt = f"""Optimize this Odoo domain filter for better performance:

Model: {model}
Current Domain: {domain}

Optimization strategies:
1. Reorder conditions by selectivity (most selective first)
2. Use indexed fields when possible
3. Optimize date/datetime filters
4. Combine related conditions
5. Use appropriate operators (=, in, ilike, etc.)
6. Avoid expensive operations in loops

Provide an optimized domain with explanations."""
        
        response = await self.ai_client.complete_structured(
            prompt=prompt,
            schema=schema,
            system=self._get_domain_optimization_prompt(),
            temperature=0.1
        )
        
        return response
    
    def _format_stats(self, stats: Dict[str, Any]) -> str:
        """Format performance statistics for AI analysis."""
        formatted = []
        for key, value in stats.items():
            formatted.append(f"  {key}: {value}")
        return "\n".join(formatted)
    
    def _format_query_info(self, query_info: Dict[str, Any]) -> str:
        """Format query information for AI analysis."""
        formatted = []
        for key, value in query_info.items():
            formatted.append(f"  {key}: {value}")
        return "\n".join(formatted)
    
    def _get_optimization_system_prompt(self) -> str:
        """Get system prompt for performance optimization."""
        return """You are a database and Odoo performance optimization expert.

Your expertise includes:
- Odoo ORM performance characteristics
- Database indexing and query optimization
- Caching strategies and patterns
- Memory management and resource optimization
- Batch processing and pagination techniques

When analyzing performance:
1. Focus on high-impact, practical optimizations
2. Consider the specific Odoo context and constraints
3. Provide realistic performance estimates
4. Balance performance with maintainability
5. Consider both immediate and long-term optimizations

Always provide specific, actionable recommendations."""
    
    def _get_caching_system_prompt(self) -> str:
        """Get system prompt for caching strategy."""
        return """You are a caching strategy expert for web applications and databases.

Your expertise includes:
- Cache invalidation patterns
- TTL optimization strategies
- Memory usage optimization
- Cache key design patterns
- Performance vs. consistency trade-offs

When recommending caching:
1. Consider data volatility and update patterns
2. Balance cache hit rates with memory usage
3. Design appropriate invalidation strategies
4. Consider cache warming and preloading
5. Account for distributed caching scenarios

Provide practical, implementable caching strategies."""
    
    def _get_domain_optimization_prompt(self) -> str:
        """Get system prompt for domain optimization."""
        return """You are an Odoo domain filter optimization expert.

Your expertise includes:
- Odoo ORM query patterns and performance
- Database indexing and selectivity
- Filter ordering and optimization
- Operator selection and efficiency
- Field access patterns in Odoo

When optimizing domains:
1. Order filters by selectivity (most selective first)
2. Use indexed fields when available
3. Choose optimal operators for each field type
4. Combine related conditions efficiently
5. Consider the underlying database structure

Provide optimized domains with clear explanations."""
