# AI-Powered Optimization

> Module: Intelligent optimization suggestions using Context7
> Complexity: Advanced
> Time: 20+ minutes
> Dependencies: Context7 MCP, asyncio

## Core Implementation

### Intelligent Optimizer

```python
from typing import Dict, List, Any
import asyncio

class IntelligentOptimizer:
    """Optimizer that uses AI to suggest the best optimizations."""

    def __init__(self, context7_client=None):
        self.context7 = context7_client
        self.optimization_history = []
        self.performance_models = {}

    async def get_ai_optimization_suggestions(
        self, bottlenecks: List[PerformanceBottleneck],
        codebase_context: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Get AI-powered optimization suggestions using Context7."""

        if not self.context7:
            return self._get_rule_based_suggestions(bottlenecks)

        # Get latest performance optimization patterns
        try:
            optimization_patterns = await self.context7.get_library_docs(
                context7_library_id="/performance/python-profiling",
                topic="advanced performance optimization patterns 2025",
                tokens=5000
            )

            # Get algorithm complexity patterns
            algorithm_patterns = await self.context7.get_library_docs(
                context7_library_id="/algorithms/python",
                topic="algorithm optimization big-O complexity reduction",
                tokens=3000
            )

            # Generate AI suggestions
            ai_suggestions = await self._generate_ai_suggestions(
                bottlenecks, optimization_patterns, algorithm_patterns, codebase_context
            )

            return ai_suggestions

        except Exception as e:
            print(f"AI optimization failed: {e}")
            return self._get_rule_based_suggestions(bottlenecks)

    async def _generate_ai_suggestions(
        self, bottlenecks: List[PerformanceBottleneck],
        opt_patterns: Dict, algo_patterns: Dict, context: Dict
    ) -> Dict[str, Any]:
        """Generate AI-powered optimization suggestions."""

        suggestions = {
            'algorithm_improvements': [],
            'data_structure_optimizations': [],
            'concurrency_improvements': [],
            'caching_strategies': [],
            'io_optimizations': []
        }

        for bottleneck in bottlenecks:
            # Analyze bottleneck characteristics
            if bottleneck.bottleneck_type == "cpu":
                # Check for algorithmic improvements
                if "O(" in bottleneck.description or any(
                    keyword in bottleneck.description.lower()
                    for keyword in ["loop", "iteration", "search", "sort"]
                ):
                    improvement = self._suggest_algorithm_improvement(
                        bottleneck, algo_patterns
                    )
                    suggestions['algorithm_improvements'].append(improvement)

                # Check for concurrency opportunities
                if bottleneck.metrics.get('call_count', 0) > 1000:
                    concurrency = self._suggest_concurrency_improvement(bottleneck)
                    suggestions['concurrency_improvements'].append(concurrency)

            elif bottleneck.bottleneck_type == "memory":
                # Suggest data structure optimizations
                data_structure = self._suggest_data_structure_improvement(
                    bottleneck, opt_patterns
                )
                suggestions['data_structure_optimizations'].append(data_structure)

        return suggestions

    def _suggest_algorithm_improvement(
        self, bottleneck: PerformanceBottleneck, algo_patterns: Dict
    ) -> Dict[str, Any]:
        """Suggest algorithmic improvements based on Context7 patterns."""

        # Analyze function name and code to identify algorithm type
        function_name = bottleneck.function_name.lower()

        suggestions = []

        if any(keyword in function_name for keyword in ["search", "find"]):
            suggestions.extend([
                "Consider using binary search for sorted data",
                "Implement hash-based lookup for O(1) average case",
                "Use trie structures for prefix searches"
            ])

        elif any(keyword in function_name for keyword in ["sort", "order"]):
            suggestions.extend([
                "Consider using Timsort (Python's built-in sort)",
                "Use radix sort for uniform integer data",
                "Implement bucket sort for uniformly distributed data"
            ])

        elif "nested" in function_name or bottleneck.metrics.get('per_call_time', 0) > 0.1:
            suggestions.extend([
                "Look for O(n²) nested loops to optimize",
                "Consider dynamic programming for overlapping subproblems",
                "Use memoization to avoid repeated calculations"
            ])

        return {
            'bottleneck': bottleneck.function_name,
            'suggestions': suggestions,
            'estimated_improvement': "30-90% depending on algorithm",
            'implementation_complexity': "medium to high"
        }

    def _suggest_concurrency_improvement(
        self, bottleneck: PerformanceBottleneck
    ) -> Dict[str, Any]:
        """Suggest concurrency improvements."""

        return {
            'bottleneck': bottleneck.function_name,
            'suggestions': [
                "Implement multiprocessing for CPU-bound tasks",
                "Use threading for I/O-bound operations",
                "Consider asyncio for concurrent I/O operations",
                "Use concurrent.futures for thread/process pool execution"
            ],
            'estimated_improvement': "2-8x speedup on multi-core systems",
            'implementation_complexity': "medium"
        }

    def _suggest_data_structure_improvement(
        self, bottleneck: PerformanceBottleneck, opt_patterns: Dict
    ) -> Dict[str, Any]:
        """Suggest data structure optimizations."""

        return {
            'bottleneck': bottleneck.function_name,
            'suggestions': [
                "Use generators instead of lists for large datasets",
                "Implement lazy loading for expensive data structures",
                "Use memoryviews or numpy arrays for numerical data",
                "Consider using collections.deque for queue operations",
                "Use set/dict for O(1) lookups instead of list searches"
            ],
            'estimated_improvement': "30-80% memory reduction",
            'implementation_complexity': "low to medium"
        }

    def _get_rule_based_suggestions(
        self, bottlenecks: List[PerformanceBottleneck]
    ) -> Dict[str, Any]:
        """Generate rule-based optimization suggestions."""

        suggestions = {
            'algorithm_improvements': [],
            'data_structure_optimizations': [],
            'concurrency_improvements': [],
            'caching_strategies': [],
            'io_optimizations': []
        }

        for bottleneck in bottlenecks:
            if bottleneck.bottleneck_type == "cpu":
                if bottleneck.metrics.get('call_count', 0) > 1000:
                    suggestions['caching_strategies'].append({
                        'bottleneck': bottleneck.function_name,
                        'suggestions': [
                            "Implement functools.lru_cache decorator",
                            "Add custom memoization for expensive operations",
                            "Cache database query results"
                        ]
                    })

            elif bottleneck.bottleneck_type == "memory":
                suggestions['data_structure_optimizations'].append({
                    'bottleneck': bottleneck.function_name,
                    'suggestions': [
                        "Use generators (yield) instead of lists",
                        "Implement lazy evaluation patterns",
                        "Use __slots__ for classes with many instances"
                    ]
                })

        return suggestions
```

## Usage Examples

```python
# Get AI-powered optimization suggestions
optimizer = IntelligentOptimizer(context7_client=context7)
ai_suggestions = await optimizer.get_ai_optimization_suggestions(
    bottlenecks,
    codebase_context={'project_type': 'web_api', 'language': 'python'}
)

print("AI Optimization Suggestions:")
for category, items in ai_suggestions.items():
    if items:
        print(f"\n{category.replace('_', ' ').title()}:")
        for item in items:
            print(f"  - {item['bottleneck']}")
            for suggestion in item['suggestions']:
                print(f"    • {suggestion}")
```

## Best Practices

1. **Context7 Integration**: Use latest documentation for up-to-date patterns
2. **Hybrid Approach**: Combine AI suggestions with rule-based heuristics
3. **Codebase Context**: Provide project context for better recommendations
4. **Learning System**: Track optimization history for continuous improvement
5. **Validation**: Always validate AI suggestions with performance tests

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Related: [Optimization Plan](./optimization-plan.md) | [Profiler Core](./profiler-core.md)
