Performance Optimization

Performance optimization guide for BroxiAI workflows and integrations

This guide provides strategies and techniques to optimize the performance of your BroxiAI workflows, reduce execution time, and improve resource efficiency.

Workflow Performance Optimization

Component-Level Optimization

1. Reduce Component Count

Strategy: Minimize the number of components in your workflow

Before (Inefficient):

• 5 separate components

• Multiple data transfers

• Increased latency

After (Optimized):

• 1 combined component

• Single data transfer

• Reduced latency

2. Optimize Component Configuration

{
  "optimization_settings": {
    "ai_models": {
      "temperature": 0.1,
      "max_tokens": "only_as_needed",
      "model_selection": "fastest_for_task"
    },
    "caching": {
      "enabled": true,
      "ttl": 3600,
      "strategy": "content_based"
    },
    "timeouts": {
      "default": 30000,
      "file_processing": 60000,
      "ai_inference": 45000
    }
  }
}

3. Parallel Processing

Enable Parallel Execution:

{
  "workflow_config": {
    "parallel_processing": {
      "enabled": true,
      "max_concurrent": 3,
      "independent_branches": [
        ["component_a", "component_b"],
        ["component_c", "component_d"],
        ["component_e"]
      ]
    }
  }
}

Parallel Workflow Design:

Data Flow Optimization

1. Minimize Data Transfer

Optimize Data Passing:

# Inefficient: Passing entire dataset
def process_workflow(large_dataset):
    result1 = component1(large_dataset)  # Processes entire dataset
    result2 = component2(large_dataset)  # Duplicates processing
    return combine(result1, result2)

# Optimized: Pass only necessary data
def process_workflow_optimized(large_dataset):
    # Extract only needed fields
    minimal_data = extract_required_fields(large_dataset)
    
    result1 = component1(minimal_data)
    result2 = component2(minimal_data)
    return combine(result1, result2)

2. Implement Smart Caching

import hashlib
import json
from functools import wraps

def workflow_cache(ttl=3600):
    """Cache workflow results based on input hash"""
    
    def decorator(func):
        @wraps(func)
        def wrapper(*args, **kwargs):
            # Create cache key from inputs
            cache_key = hashlib.md5(
                json.dumps({"args": args, "kwargs": kwargs}, sort_keys=True).encode()
            ).hexdigest()
            
            # Check cache
            cached_result = get_from_cache(cache_key)
            if cached_result:
                return cached_result
            
            # Execute and cache result
            result = func(*args, **kwargs)
            set_cache(cache_key, result, ttl)
            return result
        
        return wrapper
    return decorator

@workflow_cache(ttl=1800)
def expensive_ai_processing(input_data):
    # Expensive AI model inference
    return model.predict(input_data)

AI Model Optimization

Model Selection Strategy

1. Choose Appropriate Model Size

{
  "model_selection_guide": {
    "simple_tasks": {
      "recommended": ["gpt-3.5-turbo", "claude-instant"],
      "use_cases": ["simple_qa", "basic_classification", "short_summaries"]
    },
    "complex_tasks": {
      "recommended": ["gpt-4", "claude-3-opus"],
      "use_cases": ["complex_reasoning", "code_generation", "detailed_analysis"]
    },
    "speed_critical": {
      "recommended": ["gpt-3.5-turbo", "claude-instant"],
      "optimization": "prioritize_speed_over_quality"
    }
  }
}

2. Optimize Model Parameters

def optimize_model_config(task_type, input_length):
    """Optimize model configuration based on task"""
    
    base_config = {
        "temperature": 0.1,  # Lower for consistency
        "top_p": 0.9,
        "frequency_penalty": 0,
        "presence_penalty": 0
    }
    
    if task_type == "creative":
        base_config["temperature"] = 0.7
    elif task_type == "factual":
        base_config["temperature"] = 0.1
    
    # Optimize max_tokens based on input
    if input_length < 1000:
        base_config["max_tokens"] = 500
    elif input_length < 5000:
        base_config["max_tokens"] = 1000
    else:
        base_config["max_tokens"] = 2000
    
    return base_config

Prompt Engineering for Performance

1. Efficient Prompt Design

# Inefficient prompt
inefficient_prompt = """
Please analyze the following document very carefully and provide a comprehensive analysis including:
1. A detailed summary of the main points
2. Key themes and topics discussed
3. Sentiment analysis of the content
4. Important entities mentioned
5. Potential areas for improvement
6. Recommendations for next steps

Document: {document}

Please provide your analysis in a well-structured format with clear headings and subheadings.
"""

# Optimized prompt
optimized_prompt = """
Analyze this document:

{document}

Provide:
1. Summary (2-3 sentences)
2. Key themes (bullet points)
3. Sentiment (positive/negative/neutral)
4. Main entities (list)
5. Recommendations (3 max)

Format: JSON
"""

2. Use Structured Outputs

structured_output_schema = {
    "type": "object",
    "properties": {
        "summary": {"type": "string", "maxLength": 200},
        "themes": {"type": "array", "items": {"type": "string"}, "maxItems": 5},
        "sentiment": {"type": "string", "enum": ["positive", "negative", "neutral"]},
        "entities": {"type": "array", "items": {"type": "string"}, "maxItems": 10},
        "recommendations": {"type": "array", "items": {"type": "string"}, "maxItems": 3}
    },
    "required": ["summary", "sentiment"]
}

# This ensures consistent, parseable output and reduces processing time

Data Processing Optimization

File Processing Performance

1. Streaming Processing for Large Files

import pandas as pd
from typing import Iterator, Dict, Any

def process_large_csv_streaming(
    file_path: str, 
    chunk_size: int = 10000,
    processing_func: callable = None
) -> Iterator[Dict[str, Any]]:
    """Process large CSV files in chunks to optimize memory usage"""
    
    for chunk_df in pd.read_csv(file_path, chunksize=chunk_size):
        # Process each chunk
        if processing_func:
            processed_chunk = processing_func(chunk_df)
        else:
            processed_chunk = chunk_df
        
        # Yield results incrementally
        yield {
            "data": processed_chunk.to_dict('records'),
            "chunk_size": len(processed_chunk),
            "memory_usage": processed_chunk.memory_usage(deep=True).sum()
        }
        
        # Clean up memory
        del chunk_df, processed_chunk

# Usage
for result_chunk in process_large_csv_streaming("large_file.csv", chunk_size=5000):
    # Process each chunk individually
    handle_chunk_result(result_chunk)

2. Parallel File Processing

import multiprocessing as mp
from concurrent.futures import ProcessPoolExecutor, as_completed
import os

def process_files_parallel(file_paths: list, max_workers: int = None) -> list:
    """Process multiple files in parallel"""
    
    if max_workers is None:
        max_workers = min(len(file_paths), mp.cpu_count() - 1)
    
    results = []
    
    with ProcessPoolExecutor(max_workers=max_workers) as executor:
        # Submit all tasks
        future_to_file = {
            executor.submit(process_single_file, file_path): file_path 
            for file_path in file_paths
        }
        
        # Collect results as they complete
        for future in as_completed(future_to_file):
            file_path = future_to_file[future]
            try:
                result = future.result()
                results.append({
                    "file": file_path,
                    "result": result,
                    "status": "success"
                })
            except Exception as e:
                results.append({
                    "file": file_path,
                    "error": str(e),
                    "status": "failed"
                })
    
    return results

def process_single_file(file_path: str) -> Dict[str, Any]:
    """Process a single file"""
    # Your file processing logic here
    return {"processed": True, "size": os.path.getsize(file_path)}

Text Processing Optimization

1. Efficient Text Chunking

import tiktoken
from typing import List

class OptimizedTextChunker:
    def __init__(self, model: str = "gpt-4", max_tokens: int = 3000, overlap: int = 200):
        self.encoding = tiktoken.encoding_for_model(model)
        self.max_tokens = max_tokens
        self.overlap = overlap
    
    def chunk_by_tokens(self, text: str) -> List[str]:
        """Chunk text by token count for optimal processing"""
        
        # Encode text to tokens
        tokens = self.encoding.encode(text)
        
        if len(tokens) <= self.max_tokens:
            return [text]
        
        chunks = []
        start = 0
        
        while start < len(tokens):
            end = start + self.max_tokens
            
            # Get chunk tokens
            chunk_tokens = tokens[start:end]
            
            # Decode back to text
            chunk_text = self.encoding.decode(chunk_tokens)
            chunks.append(chunk_text)
            
            # Move start with overlap
            start = end - self.overlap
            
            if start >= len(tokens):
                break
        
        return chunks
    
    def chunk_by_sentences(self, text: str, max_tokens: int = None) -> List[str]:
        """Chunk text by sentences while respecting token limits"""
        
        if max_tokens is None:
            max_tokens = self.max_tokens
        
        sentences = text.split('. ')
        chunks = []
        current_chunk = ""
        current_tokens = 0
        
        for sentence in sentences:
            sentence_tokens = len(self.encoding.encode(sentence))
            
            if current_tokens + sentence_tokens > max_tokens:
                if current_chunk:
                    chunks.append(current_chunk.strip())
                current_chunk = sentence
                current_tokens = sentence_tokens
            else:
                current_chunk += sentence + ". "
                current_tokens += sentence_tokens
        
        if current_chunk:
            chunks.append(current_chunk.strip())
        
        return chunks

# Usage
chunker = OptimizedTextChunker(max_tokens=2000, overlap=100)
chunks = chunker.chunk_by_tokens(long_text)

Memory Management

Memory Optimization Strategies

1. Garbage Collection and Memory Cleanup

import gc
import psutil
import os
from contextlib import contextmanager

@contextmanager
def memory_management():
    """Context manager for memory cleanup"""
    
    # Get initial memory usage
    process = psutil.Process(os.getpid())
    initial_memory = process.memory_info().rss / 1024 / 1024  # MB
    
    try:
        yield
    finally:
        # Force garbage collection
        gc.collect()
        
        # Get final memory usage
        final_memory = process.memory_info().rss / 1024 / 1024  # MB
        memory_used = final_memory - initial_memory
        
        print(f"Memory used: {memory_used:.2f} MB")
        
        if memory_used > 100:  # Alert if using more than 100MB
            print("Warning: High memory usage detected")

# Usage
with memory_management():
    # Your memory-intensive operations
    large_data_processing()

2. Memory-Efficient Data Structures

import numpy as np
from typing import Generator

class MemoryEfficientProcessor:
    def __init__(self, chunk_size: int = 1000):
        self.chunk_size = chunk_size
    
    def process_large_dataset(self, data: list) -> Generator:
        """Process large datasets in memory-efficient chunks"""
        
        for i in range(0, len(data), self.chunk_size):
            chunk = data[i:i + self.chunk_size]
            
            # Process chunk
            processed_chunk = self._process_chunk(chunk)
            
            # Yield result and clean up
            yield processed_chunk
            
            # Explicitly delete chunk to free memory
            del chunk, processed_chunk
            gc.collect()
    
    def _process_chunk(self, chunk: list):
        """Process individual chunk"""
        # Use numpy for efficient numerical operations
        if isinstance(chunk[0], (int, float)):
            return np.array(chunk).mean()
        else:
            return [item.upper() for item in chunk]

# Usage
processor = MemoryEfficientProcessor(chunk_size=500)
results = list(processor.process_large_dataset(huge_dataset))

API and Network Optimization

Request Optimization

1. Connection Pooling

import requests
from requests.adapters import HTTPAdapter
from urllib3.util.retry import Retry
import time

class OptimizedAPIClient:
    def __init__(self, base_url: str, max_retries: int = 3):
        self.base_url = base_url
        self.session = requests.Session()
        
        # Configure retry strategy
        retry_strategy = Retry(
            total=max_retries,
            backoff_factor=1,
            status_forcelist=[429, 500, 502, 503, 504],
        )
        
        # Configure adapter with connection pooling
        adapter = HTTPAdapter(
            max_retries=retry_strategy,
            pool_connections=10,
            pool_maxsize=20
        )
        
        self.session.mount("http://", adapter)
        self.session.mount("https://", adapter)
    
    def make_request(self, endpoint: str, **kwargs):
        """Make optimized API request"""
        url = f"{self.base_url}/{endpoint}"
        
        # Add timeout if not specified
        if 'timeout' not in kwargs:
            kwargs['timeout'] = (10, 30)  # (connect, read)
        
        return self.session.get(url, **kwargs)
    
    def batch_requests(self, requests_data: list):
        """Process multiple requests efficiently"""
        results = []
        
        for request_data in requests_data:
            result = self.make_request(**request_data)
            results.append(result)
            
            # Rate limiting
            time.sleep(0.1)  # 10 requests per second
        
        return results

2. Response Compression

import gzip
import json

def compress_response(data: dict) -> bytes:
    """Compress API response data"""
    json_str = json.dumps(data)
    return gzip.compress(json_str.encode('utf-8'))

def decompress_response(compressed_data: bytes) -> dict:
    """Decompress API response data"""
    json_str = gzip.decompress(compressed_data).decode('utf-8')
    return json.loads(json_str)

# Configure requests to accept compressed responses
headers = {
    'Accept-Encoding': 'gzip, deflate',
    'Content-Type': 'application/json'
}

Monitoring and Profiling

Performance Monitoring

1. Execution Time Tracking

import time
import functools
from typing import Dict, Any

class PerformanceMonitor:
    def __init__(self):
        self.metrics = {}
    
    def time_function(self, func_name: str = None):
        """Decorator to time function execution"""
        def decorator(func):
            name = func_name or func.__name__
            
            @functools.wraps(func)
            def wrapper(*args, **kwargs):
                start_time = time.time()
                
                try:
                    result = func(*args, **kwargs)
                    status = "success"
                except Exception as e:
                    result = None
                    status = "error"
                    raise
                finally:
                    execution_time = time.time() - start_time
                    self._record_metric(name, execution_time, status)
                
                return result
            return wrapper
        return decorator
    
    def _record_metric(self, name: str, execution_time: float, status: str):
        """Record performance metric"""
        if name not in self.metrics:
            self.metrics[name] = {
                "calls": 0,
                "total_time": 0,
                "avg_time": 0,
                "min_time": float('inf'),
                "max_time": 0,
                "success_count": 0,
                "error_count": 0
            }
        
        metric = self.metrics[name]
        metric["calls"] += 1
        metric["total_time"] += execution_time
        metric["avg_time"] = metric["total_time"] / metric["calls"]
        metric["min_time"] = min(metric["min_time"], execution_time)
        metric["max_time"] = max(metric["max_time"], execution_time)
        
        if status == "success":
            metric["success_count"] += 1
        else:
            metric["error_count"] += 1
    
    def get_report(self) -> Dict[str, Any]:
        """Get performance report"""
        return {
            "metrics": self.metrics,
            "summary": {
                "total_functions": len(self.metrics),
                "slowest_function": max(
                    self.metrics.items(), 
                    key=lambda x: x[1]["avg_time"]
                ) if self.metrics else None
            }
        }

# Usage
monitor = PerformanceMonitor()

@monitor.time_function("workflow_execution")
def execute_workflow(workflow_id):
    # Your workflow execution logic
    pass

# Get performance report
report = monitor.get_report()
print(json.dumps(report, indent=2))

2. Resource Usage Monitoring

import psutil
import threading
import time
from collections import deque

class ResourceMonitor:
    def __init__(self, sample_interval: float = 1.0, max_samples: int = 100):
        self.sample_interval = sample_interval
        self.max_samples = max_samples
        self.cpu_usage = deque(maxlen=max_samples)
        self.memory_usage = deque(maxlen=max_samples)
        self.monitoring = False
        self.monitor_thread = None
    
    def start_monitoring(self):
        """Start resource monitoring"""
        self.monitoring = True
        self.monitor_thread = threading.Thread(target=self._monitor_resources)
        self.monitor_thread.start()
    
    def stop_monitoring(self):
        """Stop resource monitoring"""
        self.monitoring = False
        if self.monitor_thread:
            self.monitor_thread.join()
    
    def _monitor_resources(self):
        """Monitor system resources"""
        process = psutil.Process()
        
        while self.monitoring:
            # CPU usage
            cpu_percent = process.cpu_percent()
            self.cpu_usage.append({
                "timestamp": time.time(),
                "cpu_percent": cpu_percent
            })
            
            # Memory usage
            memory_info = process.memory_info()
            self.memory_usage.append({
                "timestamp": time.time(),
                "memory_mb": memory_info.rss / 1024 / 1024,
                "memory_percent": process.memory_percent()
            })
            
            time.sleep(self.sample_interval)
    
    def get_stats(self) -> Dict[str, Any]:
        """Get resource usage statistics"""
        if not self.cpu_usage or not self.memory_usage:
            return {"error": "No monitoring data available"}
        
        cpu_values = [sample["cpu_percent"] for sample in self.cpu_usage]
        memory_values = [sample["memory_mb"] for sample in self.memory_usage]
        
        return {
            "cpu": {
                "avg": sum(cpu_values) / len(cpu_values),
                "max": max(cpu_values),
                "min": min(cpu_values)
            },
            "memory": {
                "avg_mb": sum(memory_values) / len(memory_values),
                "max_mb": max(memory_values),
                "min_mb": min(memory_values)
            },
            "samples": len(self.cpu_usage)
        }

# Usage
resource_monitor = ResourceMonitor()
resource_monitor.start_monitoring()

# Your workflow execution
execute_long_running_workflow()

resource_monitor.stop_monitoring()
stats = resource_monitor.get_stats()
print(f"Resource usage: {stats}")

Best Practices Summary

1. Workflow Design

• Minimize components: Combine operations where possible

• Use parallel processing: Execute independent operations simultaneously

• Implement caching: Cache expensive operations and API calls

• Choose appropriate models: Use smaller models for simple tasks

2. Data Processing

• Stream large files: Process data in chunks to manage memory

• Optimize text chunking: Use token-aware chunking strategies

• Implement proper cleanup: Clean up memory after processing

3. API Optimization

• Use connection pooling: Reuse connections for multiple requests

• Implement retry logic: Handle transient failures gracefully

• Enable compression: Reduce data transfer sizes

• Monitor rate limits: Implement proper rate limiting

4. Monitoring

• Track execution times: Monitor component and workflow performance

• Monitor resources: Track CPU and memory usage

• Set up alerts: Alert on performance degradation

• Regular optimization: Continuously optimize based on metrics

---

Start with the highest-impact optimizations first: caching, parallel processing, and model selection. These typically provide the most significant performance improvements.