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Agentic Architecture Pattern: Performance Optimization

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    """
Pattern 174: Performance Optimization Pattern with LangChain/LangGraph

Purpose: Optimize agent performance through profiling, tuning, and LLM-powered insights.
"""

from typing import Dict, Any, List, Optional, Callable, TypedDict
from dataclasses import dataclass, field
from datetime import datetime, timedelta
import time
from functools import wraps
from enum import Enum
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI
from langgraph.graph import StateGraph, END
import statistics


class OptimizationStrategy(Enum):
    """Performance optimization strategies."""
    CACHING = "caching"
    BATCHING = "batching"
    PARALLELIZATION = "parallelization"
    LAZY_LOADING = "lazy_loading"
    INDEX_OPTIMIZATION = "index_optimization"


class BottleneckType(Enum):
    """Types of performance bottlenecks."""
    CPU_BOUND = "cpu_bound"
    IO_BOUND = "io_bound"
    MEMORY_BOUND = "memory_bound"
    NETWORK_BOUND = "network_bound"
    DATABASE_BOUND = "database_bound"


class ProfileLevel(Enum):
    """Profiling detail levels."""
    BASIC = "basic"
    DETAILED = "detailed"
    COMPREHENSIVE = "comprehensive"


class PriorityLevel(Enum):
    """Optimization priority levels."""
    LOW = 1
    MEDIUM = 2
    HIGH = 3
    CRITICAL = 4


@dataclass
class PerformanceProfile:
    """Performance profile for an operation."""
    operation: str
    execution_count: int = 0
    total_time: float = 0.0
    min_time: float = float('inf')
    max_time: float = 0.0
    errors: int = 0
    last_execution: Optional[datetime] = None
    
    def avg_time(self) -> float:
        """Average execution time."""
        return self.total_time / self.execution_count if self.execution_count > 0 else 0.0
    
    def record(self, duration: float, error: bool = False):
        """Record an execution."""
        self.execution_count += 1
        self.total_time += duration
        self.min_time = min(self.min_time, duration)
        self.max_time = max(self.max_time, duration)
        self.last_execution = datetime.now()
        if error:
            self.errors += 1


@dataclass
class Bottleneck:
    """Identified performance bottleneck."""
    operation: str
    bottleneck_type: BottleneckType
    severity: float  # 0.0 to 1.0
    impact: str
    measured_time_ms: float
    expected_time_ms: float


@dataclass
class OptimizationSuggestion:
    """Performance optimization suggestion."""
    operation: str
    strategy: OptimizationStrategy
    issue: str
    suggestion: str
    priority: PriorityLevel
    expected_improvement: str


@dataclass
class OptimizationResult:
    """Result of applying an optimization."""
    operation: str
    strategy: OptimizationStrategy
    before_avg_ms: float
    after_avg_ms: float
    improvement_percent: float
    success: bool


@dataclass
class PerformanceReport:
    """Comprehensive performance report."""
    total_operations: int
    total_execution_time_ms: float
    avg_execution_time_ms: float
    bottlenecks: List[Bottleneck]
    suggestions: List[OptimizationSuggestion]
    top_slowest_operations: List[str]


class OptimizationState(TypedDict):
    """State for optimization workflow."""
    profiles: Dict[str, Dict[str, Any]]
    bottlenecks: List[Dict[str, Any]]
    analysis: str
    suggestions: List[str]
    strategies: List[str]


class PerformanceOptimizer:
    """
    Advanced performance optimizer with LangChain/LangGraph integration.
    """
    
    def __init__(self, model_name: str = "gpt-4", temperature: float = 0.3):
        self.llm = ChatOpenAI(model=model_name, temperature=temperature)
        self.profiles: Dict[str, PerformanceProfile] = {}
        self.bottlenecks: List[Bottleneck] = []
        self.optimizations: List[OptimizationResult] = []
        self.workflow = self._build_optimization_workflow()
        
        # Statistics
        self.stats = {
            "total_operations_profiled": 0,
            "total_optimizations_applied": 0,
            "bottlenecks_identified": 0,
            "avg_improvement_percent": 0.0,
            "strategies_used": {strategy: 0 for strategy in OptimizationStrategy}
        }
    
    def _build_optimization_workflow(self) -> StateGraph:
        """Build LangGraph workflow for performance optimization."""
        workflow = StateGraph(OptimizationState)
        
        # Add nodes
        workflow.add_node("analyze_profiles", self._analyze_profiles_node)
        workflow.add_node("identify_bottlenecks", self._identify_bottlenecks_node)
        workflow.add_node("generate_insights", self._generate_insights_node)
        workflow.add_node("recommend_strategies", self._recommend_strategies_node)
        workflow.add_node("prioritize_optimizations", self._prioritize_optimizations_node)
        
        # Define edges
        workflow.set_entry_point("analyze_profiles")
        workflow.add_edge("analyze_profiles", "identify_bottlenecks")
        workflow.add_edge("identify_bottlenecks", "generate_insights")
        workflow.add_edge("generate_insights", "recommend_strategies")
        workflow.add_edge("recommend_strategies", "prioritize_optimizations")
        workflow.add_edge("prioritize_optimizations", END)
        
        return workflow.compile()
    
    def _analyze_profiles_node(self, state: OptimizationState) -> OptimizationState:
        """Analyze performance profiles."""
        profiles_data = {}
        
        for operation, profile in self.profiles.items():
            profiles_data[operation] = {
                "execution_count": profile.execution_count,
                "avg_time_ms": profile.avg_time() * 1000,
                "min_time_ms": profile.min_time * 1000,
                "max_time_ms": profile.max_time * 1000,
                "error_rate": (profile.errors / profile.execution_count * 100) 
                             if profile.execution_count > 0 else 0.0
            }
        
        state["profiles"] = profiles_data
        return state
    
    def _identify_bottlenecks_node(self, state: OptimizationState) -> OptimizationState:
        """Identify performance bottlenecks."""
        profiles = state.get("profiles", {})
        bottlenecks_data = []
        
        # Identify slow operations
        if profiles:
            avg_times = [p["avg_time_ms"] for p in profiles.values()]
            if len(avg_times) > 1:
                mean_time = statistics.mean(avg_times)
                stdev_time = statistics.stdev(avg_times)
                
                for operation, profile in profiles.items():
                    avg_time = profile["avg_time_ms"]
                    
                    # Operation is significantly slower than average
                    if avg_time > mean_time + 2 * stdev_time:
                        bottlenecks_data.append({
                            "operation": operation,
                            "avg_time_ms": avg_time,
                            "deviation": avg_time - mean_time,
                            "severity": min((avg_time - mean_time) / mean_time, 1.0)
                        })
        
        state["bottlenecks"] = bottlenecks_data
        return state
    
    def _generate_insights_node(self, state: OptimizationState) -> OptimizationState:
        """Generate insights using LLM."""
        profiles = state.get("profiles", {})
        bottlenecks = state.get("bottlenecks", [])
        
        # Use LLM to analyze performance data
        prompt = ChatPromptTemplate.from_template(
            "Analyze the following performance data and provide insights:\n"
            "Performance Profiles: {profiles_summary}\n"
            "Identified Bottlenecks: {bottlenecks_summary}\n\n"
            "Provide detailed insights about performance patterns and issues."
        )
        
        chain = prompt | self.llm
        response = chain.invoke({
            "profiles_summary": f"{len(profiles)} operations profiled",
            "bottlenecks_summary": f"{len(bottlenecks)} bottlenecks identified"
        })
        
        state["analysis"] = str(response.content)
        return state
    
    def _recommend_strategies_node(self, state: OptimizationState) -> OptimizationState:
        """Recommend optimization strategies using LLM."""
        bottlenecks = state.get("bottlenecks", [])
        analysis = state.get("analysis", "")
        
        suggestions = []
        
        if bottlenecks:
            # Use LLM to recommend strategies
            prompt = ChatPromptTemplate.from_template(
                "Based on the performance analysis, recommend specific optimization strategies:\n"
                "Analysis: {analysis}\n"
                "Bottlenecks: {bottlenecks}\n\n"
                "Provide 3-5 specific, actionable optimization strategies."
            )
            
            chain = prompt | self.llm
            response = chain.invoke({
                "analysis": analysis,
                "bottlenecks": str(bottlenecks)
            })
            
            # Parse suggestions
            suggestions = [
                line.strip()
                for line in str(response.content).split('\n')
                if line.strip() and len(line.strip()) > 10
            ][:5]
        
        state["suggestions"] = suggestions
        return state
    
    def _prioritize_optimizations_node(self, state: OptimizationState) -> OptimizationState:
        """Prioritize optimization strategies."""
        suggestions = state.get("suggestions", [])
        bottlenecks = state.get("bottlenecks", [])
        
        # Map common strategies
        strategies = []
        
        for bottleneck in bottlenecks:
            severity = bottleneck.get("severity", 0.0)
            if severity > 0.7:
                strategies.append(f"CRITICAL: Optimize '{bottleneck['operation']}'")
            elif severity > 0.4:
                strategies.append(f"HIGH: Improve '{bottleneck['operation']}'")
        
        # Add LLM suggestions
        strategies.extend(suggestions)
        
        state["strategies"] = strategies[:10]  # Top 10 strategies
        return state
    
    def profile_operation(self, operation: str) -> Callable:
        """Decorator to profile an operation."""
        def decorator(func: Callable) -> Callable:
            @wraps(func)
            def wrapper(*args, **kwargs):
                start_time = time.time()
                error_occurred = False
                
                try:
                    result = func(*args, **kwargs)
                    return result
                except Exception as e:
                    error_occurred = True
                    raise e
                finally:
                    duration = time.time() - start_time
                    
                    # Record profile
                    if operation not in self.profiles:
                        self.profiles[operation] = PerformanceProfile(operation=operation)
                    
                    self.profiles[operation].record(duration, error_occurred)
                    self.stats["total_operations_profiled"] += 1
            
            return wrapper
        return decorator
    
    def record_execution(
        self,
        operation: str,
        duration_ms: float,
        error: bool = False
    ):
        """Manually record an operation execution."""
        if operation not in self.profiles:
            self.profiles[operation] = PerformanceProfile(operation=operation)
        
        self.profiles[operation].record(duration_ms / 1000.0, error)
        self.stats["total_operations_profiled"] += 1
    
    def identify_bottleneck(
        self,
        operation: str,
        bottleneck_type: BottleneckType,
        measured_time_ms: float,
        expected_time_ms: float
    ) -> Bottleneck:
        """Manually identify a bottleneck."""
        severity = min((measured_time_ms - expected_time_ms) / expected_time_ms, 1.0)
        
        bottleneck = Bottleneck(
            operation=operation,
            bottleneck_type=bottleneck_type,
            severity=max(0.0, severity),
            impact=f"{measured_time_ms - expected_time_ms:.2f}ms slower than expected",
            measured_time_ms=measured_time_ms,
            expected_time_ms=expected_time_ms
        )
        
        self.bottlenecks.append(bottleneck)
        self.stats["bottlenecks_identified"] += 1
        
        return bottleneck
    
    def apply_optimization(
        self,
        operation: str,
        strategy: OptimizationStrategy,
        before_avg_ms: float,
        after_avg_ms: float
    ) -> OptimizationResult:
        """Record an applied optimization."""
        improvement = ((before_avg_ms - after_avg_ms) / before_avg_ms * 100) if before_avg_ms > 0 else 0.0
        
        result = OptimizationResult(
            operation=operation,
            strategy=strategy,
            before_avg_ms=before_avg_ms,
            after_avg_ms=after_avg_ms,
            improvement_percent=improvement,
            success=improvement > 0
        )
        
        self.optimizations.append(result)
        self.stats["total_optimizations_applied"] += 1
        self.stats["strategies_used"][strategy] += 1
        
        # Update average improvement
        if self.optimizations:
            self.stats["avg_improvement_percent"] = statistics.mean(
                opt.improvement_percent for opt in self.optimizations
            )
        
        return result
    
    def analyze_performance(self) -> PerformanceReport:
        """Analyze performance using workflow."""
        initial_state: OptimizationState = {
            "profiles": {},
            "bottlenecks": [],
            "analysis": "",
            "suggestions": [],
            "strategies": []
        }
        
        # Run workflow
        result = self.workflow.invoke(initial_state)
        
        # Create report
        total_ops = len(self.profiles)
        total_time = sum(p.total_time for p in self.profiles.values()) * 1000
        avg_time = total_time / total_ops if total_ops > 0 else 0.0
        
        # Get top slowest operations
        sorted_profiles = sorted(
            self.profiles.items(),
            key=lambda x: x[1].avg_time(),
            reverse=True
        )
        top_slowest = [op for op, _ in sorted_profiles[:5]]
        
        # Convert suggestions to proper format
        suggestions = [
            OptimizationSuggestion(
                operation="general",
                strategy=OptimizationStrategy.CACHING,
                issue="Performance improvement needed",
                suggestion=sugg,
                priority=PriorityLevel.MEDIUM,
                expected_improvement="TBD"
            )
            for sugg in result["suggestions"]
        ]
        
        return PerformanceReport(
            total_operations=total_ops,
            total_execution_time_ms=total_time,
            avg_execution_time_ms=avg_time,
            bottlenecks=self.bottlenecks,
            suggestions=suggestions,
            top_slowest_operations=top_slowest
        )
    
    def get_profile(self, operation: str) -> Optional[PerformanceProfile]:
        """Get profile for an operation."""
        return self.profiles.get(operation)
    
    def get_statistics(self) -> Dict[str, Any]:
        """Get optimization statistics."""
        return {
            "total_operations_profiled": self.stats["total_operations_profiled"],
            "unique_operations": len(self.profiles),
            "total_optimizations_applied": self.stats["total_optimizations_applied"],
            "bottlenecks_identified": self.stats["bottlenecks_identified"],
            "avg_improvement_percent": round(self.stats["avg_improvement_percent"], 2),
            "strategies_used": {
                strategy.value: count
                for strategy, count in self.stats["strategies_used"].items()
                if count > 0
            },
            "successful_optimizations": len([o for o in self.optimizations if o.success])
        }


class PerformanceOptimizationPattern:
    """
    Comprehensive performance optimization pattern coordinator with LangChain/LangGraph.
    """
    
    def __init__(self, model_name: str = "gpt-4"):
        self.optimizer = PerformanceOptimizer(model_name=model_name)
    
    def profile(self, operation: str) -> Callable:
        """Profile an operation using decorator."""
        return self.optimizer.profile_operation(operation)
    
    def record(
        self,
        operation: str,
        duration_ms: float,
        error: bool = False
    ):
        """Record operation execution."""
        self.optimizer.record_execution(operation, duration_ms, error)
    
    def identify_bottleneck(
        self,
        operation: str,
        bottleneck_type: BottleneckType,
        measured_ms: float,
        expected_ms: float
    ) -> Bottleneck:
        """Identify a performance bottleneck."""
        return self.optimizer.identify_bottleneck(
            operation, bottleneck_type, measured_ms, expected_ms
        )
    
    def optimize(
        self,
        operation: str,
        strategy: OptimizationStrategy,
        before_ms: float,
        after_ms: float
    ) -> OptimizationResult:
        """Record an optimization."""
        return self.optimizer.apply_optimization(
            operation, strategy, before_ms, after_ms
        )
    
    def analyze(self) -> Dict[str, Any]:
        """Analyze performance."""
        report = self.optimizer.analyze_performance()
        return {
            "total_operations": report.total_operations,
            "total_time_ms": report.total_execution_time_ms,
            "avg_time_ms": report.avg_execution_time_ms,
            "bottlenecks_count": len(report.bottlenecks),
            "suggestions_count": len(report.suggestions),
            "top_slowest": report.top_slowest_operations
        }
    
    def get_statistics(self) -> Dict[str, Any]:
        """Get statistics."""
        return self.optimizer.get_statistics()



if __name__ == "__main__":
    print("=== Performance Optimization Pattern with LangChain/LangGraph Demo ===\n")
    
    # Note: This demo shows the structure. In production, you'd need:
    # - export OPENAI_API_KEY="your-key-here"
    # - Actual LLM calls would be made
    
    print("Scenario 1: Operation Profiling")
    print("-" * 50)
    optimizer = PerformanceOptimizationPattern(model_name="gpt-4")
    
    # Record multiple operations
    operations = [
        ("database_query", 45.5, False),
        ("api_call", 120.3, False),
        ("data_processing", 230.8, False),
        ("cache_lookup", 5.2, False),
        ("database_query", 52.1, False),
        ("api_call", 115.7, False),
        ("data_processing", 245.2, False),
        ("database_query", 48.9, True),  # Error
    ]
    
    print("Recording operation executions:")
    for operation, duration, error in operations:
        optimizer.record(operation, duration, error)
        status = "ERROR" if error else "OK"
        print(f"  {operation}: {duration:.1f}ms [{status}]")
    
    print("\nScenario 2: Bottleneck Identification")
    print("-" * 50)
    
    # Identify bottlenecks
    bottlenecks = [
        ("data_processing", BottleneckType.CPU_BOUND, 240.0, 100.0),
        ("api_call", BottleneckType.NETWORK_BOUND, 118.0, 50.0),
        ("database_query", BottleneckType.DATABASE_BOUND, 50.0, 20.0),
    ]
    
    print("Identifying bottlenecks:")
    for operation, bt_type, measured, expected in bottlenecks:
        bottleneck = optimizer.identify_bottleneck(
            operation, bt_type, measured, expected
        )
        print(f"  {operation} ({bt_type.value}):")
        print(f"    Measured: {measured:.1f}ms, Expected: {expected:.1f}ms")
        print(f"    Severity: {bottleneck.severity:.2f}, Impact: {bottleneck.impact}")
    
    print("\nScenario 3: Optimization Application")
    print("-" * 50)
    
    # Apply optimizations
    optimizations = [
        ("data_processing", OptimizationStrategy.PARALLELIZATION, 240.0, 85.0),
        ("api_call", OptimizationStrategy.CACHING, 118.0, 12.0),
        ("database_query", OptimizationStrategy.INDEX_OPTIMIZATION, 50.0, 18.0),
    ]
    
    print("Applying optimizations:")
    for operation, strategy, before, after in optimizations:
        result = optimizer.optimize(operation, strategy, before, after)
        print(f"  {operation} ({strategy.value}):")
        print(f"    Before: {before:.1f}ms, After: {after:.1f}ms")
        print(f"    Improvement: {result.improvement_percent:.1f}%")
        print(f"    Success: {result.success}")
    
    print("\nScenario 4: Performance Analysis")
    print("-" * 50)
    
    print("Analyzing performance with LLM insights...")
    analysis = optimizer.analyze()
    
    print(f"\nAnalysis Results:")
    print(f"  Total Operations: {analysis['total_operations']}")
    print(f"  Total Execution Time: {analysis['total_time_ms']:.1f}ms")
    print(f"  Average Time per Operation: {analysis['avg_time_ms']:.1f}ms")
    print(f"  Bottlenecks Identified: {analysis['bottlenecks_count']}")
    print(f"  Optimization Suggestions: {analysis['suggestions_count']}")
    
    if analysis['top_slowest']:
        print(f"\nTop Slowest Operations:")
        for i, operation in enumerate(analysis['top_slowest'], 1):
            print(f"    {i}. {operation}")
    
    print("\nScenario 5: Using Profile Decorator")
    print("-" * 50)
    
    # Example of using decorator
    @optimizer.profile("heavy_computation")
    def heavy_computation(n: int) -> int:
        """Simulate heavy computation."""
        result = 0
        for i in range(n):
            result += i * i
        return result
    
    print("Profiling decorated function:")
    for i in range(3):
        result = heavy_computation(1000)
        print(f"  Iteration {i+1}: computed {result}")
    
    # Get profile for decorated function
    profile = optimizer.optimizer.get_profile("heavy_computation")
    if profile:
        print(f"\nProfile for 'heavy_computation':")
        print(f"  Executions: {profile.execution_count}")
        print(f"  Avg Time: {profile.avg_time()*1000:.2f}ms")
        print(f"  Min Time: {profile.min_time*1000:.2f}ms")
        print(f"  Max Time: {profile.max_time*1000:.2f}ms")
    
    print("\nScenario 6: Statistics")
    print("-" * 50)
    
    stats = optimizer.get_statistics()
    print(f"Total Operations Profiled: {stats['total_operations_profiled']}")
    print(f"Unique Operations: {stats['unique_operations']}")
    print(f"Optimizations Applied: {stats['total_optimizations_applied']}")
    print(f"Bottlenecks Identified: {stats['bottlenecks_identified']}")
    print(f"Average Improvement: {stats['avg_improvement_percent']:.1f}%")
    print(f"Successful Optimizations: {stats['successful_optimizations']}")
    
    if stats['strategies_used']:
        print(f"\nOptimization Strategies Used:")
        for strategy, count in stats['strategies_used'].items():
            print(f"  {strategy}: {count}")
    
    print("\n" + "=" * 50)
    print("Performance optimization pattern demonstration complete!")
    print("=" * 50)

Agentic Architecture Pattern: Performance Optimization

Pattern 174: 174 Performance Optimization – Line-by-Line Explanation

Overview

Pattern 174: Performance Optimization Pattern with LangChain/LangGraph

Purpose: Optimize agent performance through profiling, tuning, and LLM-powered insights.

Code Structure Analysis

File Information

  • Pattern Number: 174
  • Pattern Name: 174 Performance Optimization
  • File Path: patterns\quality_performance\174_performance_optimization.py
  • Total Lines: 648

Detailed Code Explanation

Module Docstring

"""
Pattern 174: Performance Optimization Pattern with LangChain/LangGraph

Purpose: Optimize agent performance through profiling, tuning, and LLM-powered insights.
"""

Explanation: This module docstring describes the purpose and key features of the 174 Performance Optimization pattern.

Imports Section

from typing import Dict, Any, List, Optional, Callable, TypedDict
from dataclasses import dataclass, field
from datetime import datetime, timedelta
import time
from functools import wraps
from enum import Enum
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI
from langgraph.graph import StateGraph, END
import statistics  # First 20 imports shown

Explanation: The module imports necessary dependencies: – from typing import Dict, Any, List, Optional, Callable, TypedDict: Type hints for better code documentation – from dataclasses import dataclass, field: Data class decorators for clean data structures – from datetime import datetime, timedelta: Date and time utilities – from enum import Enum: Enumeration support for fixed sets of values – from langchain_core.prompts import ChatPromptTemplate: LangChain framework integration – from langchain_openai import ChatOpenAI: LangChain framework integration – from langgraph.graph import StateGraph, END: LangGraph for workflow orchestration

Enumerations

OptimizationStrategy (Line 19)

class OptimizationStrategy(Enum):
    CACHING
    BATCHING
    PARALLELIZATION
    LAZY_LOADING
    INDEX_OPTIMIZATION

Purpose: Performance optimization strategies.

Values: – CACHING: Represents caching – BATCHING: Represents batching – PARALLELIZATION: Represents parallelization – LAZY_LOADING: Represents lazy loading – INDEX_OPTIMIZATION: Represents index optimization

BottleneckType (Line 28)

class BottleneckType(Enum):
    CPU_BOUND
    IO_BOUND
    MEMORY_BOUND
    NETWORK_BOUND
    DATABASE_BOUND

Purpose: Types of performance bottlenecks.

Values: – CPU_BOUND: Represents cpu bound – IO_BOUND: Represents io bound – MEMORY_BOUND: Represents memory bound – NETWORK_BOUND: Represents network bound – DATABASE_BOUND: Represents database bound

ProfileLevel (Line 37)

class ProfileLevel(Enum):
    BASIC
    DETAILED
    COMPREHENSIVE

Purpose: Profiling detail levels.

Values: – BASIC: Represents basic – DETAILED: Represents detailed – COMPREHENSIVE: Represents comprehensive

PriorityLevel (Line 44)

class PriorityLevel(Enum):
    LOW
    MEDIUM
    HIGH
    CRITICAL

Purpose: Optimization priority levels.

Values: – LOW: Represents low – MEDIUM: Represents medium – HIGH: Represents high – CRITICAL: Represents critical

Classes

OptimizationStrategy (Line 19)

Description: Performance optimization strategies.

BottleneckType (Line 28)

Description: Types of performance bottlenecks.

ProfileLevel (Line 37)

Description: Profiling detail levels.

PriorityLevel (Line 44)

Description: Optimization priority levels.

PerformanceProfile (Line 53)

Description: Performance profile for an operation.

Methods:

avg_time (Line 63)
def avg_time(self):
    """Average execution time."""

Purpose: Average execution time.

Parameters: – self: Instance reference

record (Line 67)
def record(self, duration, error):
    """Record an execution."""

Purpose: Record an execution.

Parameters: – self: Instance reference – duration: Parameter for duration – error: Parameter for error

Bottleneck (Line 79)

Description: Identified performance bottleneck.

OptimizationSuggestion (Line 90)

Description: Performance optimization suggestion.

OptimizationResult (Line 101)

Description: Result of applying an optimization.

PerformanceReport (Line 112)

Description: Comprehensive performance report.

OptimizationState (Line 122)

Description: State for optimization workflow.

PerformanceOptimizer (Line 131)

Description: Advanced performance optimizer with LangChain/LangGraph integration.

Methods:

__init__ (Line 136)
def __init__(self, model_name, temperature):
    """No method docstring."""

Purpose: No method docstring.

Parameters: – self: Instance reference – model_name: LLM model to use (e.g., “gpt-4”) – temperature: Randomness in LLM responses (0.0-1.0)

_build_optimization_workflow (Line 152)
def _build_optimization_workflow(self):
    """Build LangGraph workflow for performance optimization."""

Purpose: Build LangGraph workflow for performance optimization.

Parameters: – self: Instance reference

_analyze_profiles_node (Line 173)
def _analyze_profiles_node(self, state):
    """Analyze performance profiles."""

Purpose: Analyze performance profiles.

Parameters: – self: Instance reference – state: Current workflow state

_identify_bottlenecks_node (Line 190)
def _identify_bottlenecks_node(self, state):
    """Identify performance bottlenecks."""

Purpose: Identify performance bottlenecks.

Parameters: – self: Instance reference – state: Current workflow state

_generate_insights_node (Line 217)
def _generate_insights_node(self, state):
    """Generate insights using LLM."""

Purpose: Generate insights using LLM.

Parameters: – self: Instance reference – state: Current workflow state

_recommend_strategies_node (Line 239)
def _recommend_strategies_node(self, state):
    """Recommend optimization strategies using LLM."""

Purpose: Recommend optimization strategies using LLM.

Parameters: – self: Instance reference – state: Current workflow state

_prioritize_optimizations_node (Line 271)
def _prioritize_optimizations_node(self, state):
    """Prioritize optimization strategies."""

Purpose: Prioritize optimization strategies.

Parameters: – self: Instance reference – state: Current workflow state

profile_operation (Line 292)
def profile_operation(self, operation):
    """Decorator to profile an operation."""

Purpose: Decorator to profile an operation.

Parameters: – self: Instance reference – operation: Parameter for operation

record_execution (Line 319)
def record_execution(self, operation, duration_ms, error):
    """Manually record an operation execution."""

Purpose: Manually record an operation execution.

Parameters: – self: Instance reference – operation: Parameter for operation – duration_ms: Parameter for duration_ms – error: Parameter for error

identify_bottleneck (Line 332)
def identify_bottleneck(self, operation, bottleneck_type, measured_time_ms, expected_time_ms):
    """Manually identify a bottleneck."""

Purpose: Manually identify a bottleneck.

Parameters: – self: Instance reference – operation: Parameter for operation – bottleneck_type: Parameter for bottleneck_type – measured_time_ms: Parameter for measured_time_ms – expected_time_ms: Parameter for expected_time_ms

apply_optimization (Line 356)
def apply_optimization(self, operation, strategy, before_avg_ms, after_avg_ms):
    """Record an applied optimization."""

Purpose: Record an applied optimization.

Parameters: – self: Instance reference – operation: Parameter for operation – strategy: Parameter for strategy – before_avg_ms: Parameter for before_avg_ms – after_avg_ms: Parameter for after_avg_ms

analyze_performance (Line 387)
def analyze_performance(self):
    """Analyze performance using workflow."""

Purpose: Analyze performance using workflow.

Parameters: – self: Instance reference

get_profile (Line 435)
def get_profile(self, operation):
    """Get profile for an operation."""

Purpose: Get profile for an operation.

Parameters: – self: Instance reference – operation: Parameter for operation

get_statistics (Line 439)
def get_statistics(self):
    """Get optimization statistics."""

Purpose: Get optimization statistics.

Parameters: – self: Instance reference

PerformanceOptimizationPattern (Line 456)

Description: Comprehensive performance optimization pattern coordinator with LangChain/LangGraph.

Methods:

__init__ (Line 461)
def __init__(self, model_name):
    """No method docstring."""

Purpose: No method docstring.

Parameters: – self: Instance reference – model_name: LLM model to use (e.g., “gpt-4”)

profile (Line 464)
def profile(self, operation):
    """Profile an operation using decorator."""

Purpose: Profile an operation using decorator.

Parameters: – self: Instance reference – operation: Parameter for operation

record (Line 468)
def record(self, operation, duration_ms, error):
    """Record operation execution."""

Purpose: Record operation execution.

Parameters: – self: Instance reference – operation: Parameter for operation – duration_ms: Parameter for duration_ms – error: Parameter for error

identify_bottleneck (Line 477)
def identify_bottleneck(self, operation, bottleneck_type, measured_ms, expected_ms):
    """Identify a performance bottleneck."""

Purpose: Identify a performance bottleneck.

Parameters: – self: Instance reference – operation: Parameter for operation – bottleneck_type: Parameter for bottleneck_type – measured_ms: Parameter for measured_ms – expected_ms: Parameter for expected_ms

optimize (Line 489)
def optimize(self, operation, strategy, before_ms, after_ms):
    """Record an optimization."""

Purpose: Record an optimization.

Parameters: – self: Instance reference – operation: Parameter for operation – strategy: Parameter for strategy – before_ms: Parameter for before_ms – after_ms: Parameter for after_ms

analyze (Line 501)
def analyze(self):
    """Analyze performance."""

Purpose: Analyze performance.

Parameters: – self: Instance reference

get_statistics (Line 513)
def get_statistics(self):
    """Get statistics."""

Purpose: Get statistics.

Parameters: – self: Instance reference

Design Patterns Used

  • Strategy Pattern: Different execution strategies for various scenarios
  • State Pattern: Managing different states in the workflow
  • Chain of Responsibility: Sequential processing through workflow nodes
  • Observer Pattern: Tracking and monitoring pattern execution
  • Factory Pattern: Creating instances with proper configuration

Usage Example

# Initialize the pattern
pattern = OptimizationStrategy(name="example", model_name="gpt-4")

# Configure and execute
result = pattern.execute(data={"key": "value"})

# Check results
print(result)

Key Concepts

  • LLM Integration: Uses LangChain/OpenAI for intelligent decision-making
  • Workflow Orchestration: LangGraph manages execution flow through nodes
  • State Management: TypedDict defines workflow state structure
  • Error Handling: Comprehensive error handling and fallback mechanisms
  • Extensibility: Designed for easy customization and extension

Extension Points

This pattern can be extended by: 1. Adding custom workflow nodes 2. Implementing additional validation logic 3. Integrating with external services 4. Customizing LLM prompts and responses 5. Adding monitoring and logging capabilities

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