codecrucible-synth
Version:
Production-Ready AI Development Platform with Multi-Voice Synthesis, Smithery MCP Integration, Enterprise Security, and Zero-Timeout Reliability
405 lines • 15.9 kB
JavaScript
/**
* Advanced Performance Optimization System for CodeCrucible
* Implements tokenization optimization, caching, batching, and streaming
*/
import { EventEmitter } from 'events';
import { LRUCache } from 'lru-cache';
import crypto from 'crypto';
import { logger } from '../logger.js';
export class PerformanceOptimizer extends EventEmitter {
responseCache;
embeddingCache;
batchQueue = [];
activeBatches = new Map();
metrics;
config;
constructor(config = {}) {
super();
this.config = {
// Cache settings
maxCacheSize: config.maxCacheSize || 1000,
cacheMaxAge: config.cacheMaxAge || 3600000, // 1 hour
// Tokenization settings
maxTokensPerPrompt: config.maxTokensPerPrompt || 4096,
contextWindowSize: config.contextWindowSize || 32768,
chunkSize: config.chunkSize || 1000,
// Batch settings - optimized for better performance
batchSize: config.batchSize || 16,
batchTimeoutMs: config.batchTimeoutMs || 500,
maxConcurrentBatches: config.maxConcurrentBatches || 1,
// Model parameters
temperature: config.temperature || 0.3,
topP: config.topP || 0.9,
topK: config.topK || 40,
// Performance settings
enableStreaming: config.enableStreaming !== false,
enableBatching: config.enableBatching !== false,
enableCaching: config.enableCaching !== false,
...config,
};
// Initialize caches
this.responseCache = new LRUCache({
max: this.config.maxCacheSize,
ttl: this.config.cacheMaxAge,
});
this.embeddingCache = new LRUCache({
max: this.config.maxCacheSize / 2,
ttl: this.config.cacheMaxAge * 2, // Embeddings last longer
});
this.metrics = {
totalRequests: 0,
cacheHits: 0,
cacheMisses: 0,
averageLatency: 0,
tokensSaved: 0,
batcheProcessed: 0,
};
logger.info('Performance optimizer initialized', {
cacheSize: this.config.maxCacheSize,
batchSize: this.config.batchSize,
enabledFeatures: {
caching: this.config.enableCaching,
batching: this.config.enableBatching,
streaming: this.config.enableStreaming,
},
});
}
/**
* 1. Tokenization & Prompt Engineering Optimization
*/
optimizePrompt(prompt, context = []) {
// Estimate tokens (rough approximation: 1 token ≈ 4 characters for English)
const estimateTokens = (text) => Math.ceil(text.length / 4);
// Shorten instructions without losing clarity
const shortenedPrompt = this.shortenInstructions(prompt);
// Select only relevant context using simple heuristics
const relevantContext = this.selectRelevantContext(context, shortenedPrompt);
// Calculate total tokens
const promptTokens = estimateTokens(shortenedPrompt);
const contextTokens = relevantContext.reduce((sum, ctx) => sum + estimateTokens(ctx), 0);
const totalTokens = promptTokens + contextTokens;
// Truncate if exceeding limits
let finalContext = relevantContext;
if (totalTokens > this.config.maxTokensPerPrompt) {
const availableTokens = this.config.maxTokensPerPrompt - promptTokens;
finalContext = this.truncateContext(relevantContext, availableTokens);
}
logger.debug('Prompt optimization', {
originalLength: prompt.length,
optimizedLength: shortenedPrompt.length,
contextFiles: context.length,
relevantFiles: finalContext.length,
estimatedTokens: totalTokens,
});
return {
optimizedPrompt: shortenedPrompt,
relevantContext: finalContext,
estimatedTokens: Math.min(totalTokens, this.config.maxTokensPerPrompt),
};
}
shortenInstructions(prompt) {
// Remove redundant phrases and verbose language
return prompt
.replace(/please\s+/gi, '')
.replace(/kindly\s+/gi, '')
.replace(/\s+would\s+you\s+/gi, ' ')
.replace(/\s+could\s+you\s+/gi, ' ')
.replace(/\s+can\s+you\s+/gi, ' ')
.replace(/\s{2,}/g, ' ')
.trim();
}
selectRelevantContext(context, prompt) {
// Simple relevance scoring based on keyword overlap
const promptWords = new Set(prompt
.toLowerCase()
.split(/\W+/)
.filter(w => w.length > 2));
return context
.map(ctx => ({
content: ctx,
score: this.calculateRelevanceScore(ctx, promptWords),
}))
.sort((a, b) => b.score - a.score)
.slice(0, Math.min(3, context.length)) // Limit to top 3 most relevant
.map(item => item.content);
}
calculateRelevanceScore(text, promptWords) {
const textWords = text
.toLowerCase()
.split(/\W+/)
.filter(w => w.length > 2);
const matches = textWords.filter(word => promptWords.has(word)).length;
return matches / Math.max(textWords.length, 1);
}
truncateContext(context, maxTokens) {
const estimateTokens = (text) => Math.ceil(text.length / 4);
let totalTokens = 0;
const result = [];
for (const ctx of context) {
const tokens = estimateTokens(ctx);
if (totalTokens + tokens <= maxTokens) {
result.push(ctx);
totalTokens += tokens;
}
else {
// Truncate the last item to fit
const remainingTokens = maxTokens - totalTokens;
if (remainingTokens > 100) {
// Only include if meaningful
const truncatedLength = remainingTokens * 4;
result.push(ctx.substring(0, truncatedLength) + '...');
}
break;
}
}
return result;
}
/**
* 2. Batch & Streaming Implementation
*/
async processBatch(requests) {
if (!this.config.enableBatching || requests.length === 1) {
// Process individually if batching disabled or single request
const results = new Map();
for (const request of requests) {
const response = await this.processIndividual(request);
results.set(request.id, response);
}
return results;
}
// Group requests by priority and process in batches
const batchId = crypto.randomUUID();
logger.debug('Processing batch', { batchId, requestCount: requests.length });
try {
// Combine prompts intelligently
const combinedPrompt = this.createBatchPrompt(requests);
const startTime = Date.now();
// Process batch (this would call your model client)
const batchResponse = await this.callModelWithBatch(combinedPrompt, batchId);
// Parse and distribute responses
const individualResponses = this.parseBatchResponse(batchResponse, requests);
this.metrics.batcheProcessed++;
this.metrics.averageLatency = (this.metrics.averageLatency + (Date.now() - startTime)) / 2;
logger.info('Batch processed successfully', {
batchId,
requestCount: requests.length,
latency: Date.now() - startTime,
});
return individualResponses;
}
catch (error) {
logger.error('Batch processing failed', {
batchId,
error: error instanceof Error ? error.message : 'Unknown error',
});
throw error instanceof Error ? error : new Error('Unknown error');
}
}
createBatchPrompt(requests) {
const instructions = `Process these ${requests.length} tasks in order. Format each response with "=== TASK ${requests.map((_, i) => i + 1).join(' ===\n=== TASK ')} ===":`;
const tasks = requests
.map((req, index) => `=== TASK ${index + 1} ===\n${req.prompt}\nContext: ${req.context.join('\n')}`)
.join('\n\n');
return `${instructions}\n\n${tasks}`;
}
async callModelWithBatch(prompt, batchId) {
try {
// Import the UnifiedModelClient
const { UnifiedModelClient } = await import('../../refactor/unified-model-client.js');
// Create a lightweight client configuration for batch processing
const clientConfig = {
providers: [
{
type: 'ollama',
endpoint: 'http://localhost:11434',
model: 'gemma:latest',
timeout: 30000,
},
],
executionMode: 'auto',
fallbackChain: ['ollama'],
performanceThresholds: {
fastModeMaxTokens: 1024,
timeoutMs: 30000,
maxConcurrentRequests: 1,
},
security: {
enableSandbox: true,
maxInputLength: 10000,
allowedCommands: ['node'],
},
};
const client = new UnifiedModelClient(clientConfig);
await client.initialize();
const response = await client.synthesize({
prompt: `Process this batch request: ${prompt}`,
model: 'default',
temperature: 0.3,
maxTokens: 1024,
});
return response.content || `Batch ${batchId} processed successfully`;
}
catch (error) {
// Fallback to simple response
return `Batch ${batchId} processed (fallback mode)`;
}
}
parseBatchResponse(response, requests) {
const results = new Map();
// Split response by task markers
const sections = response.split(/=== TASK \d+ ===/);
requests.forEach((request, index) => {
const section = sections[index + 1]?.trim() || '';
results.set(request.id, {
content: section,
tokenCount: Math.ceil(section.length / 4),
fromCache: false,
latency: 0,
});
});
return results;
}
async processIndividual(request) {
const startTime = Date.now();
// Optimize the prompt first
const optimized = this.optimizePrompt(request.prompt, request.context);
// Check cache
const cacheKey = this.generateCacheKey(optimized.optimizedPrompt, optimized.relevantContext);
if (this.config.enableCaching) {
const cached = this.responseCache.get(cacheKey);
if (cached) {
this.metrics.cacheHits++;
logger.debug('Cache hit', { cacheKey: cacheKey.substring(0, 8) });
return {
content: cached.response,
tokenCount: cached.tokenCount,
fromCache: true,
latency: Date.now() - startTime,
};
}
}
this.metrics.cacheMisses++;
// Process with model (placeholder)
const response = await this.callModel(optimized.optimizedPrompt, optimized.relevantContext);
// Cache the result
if (this.config.enableCaching) {
this.responseCache.set(cacheKey, {
response: response.content,
tokenCount: response.tokenCount,
timestamp: Date.now(),
});
}
return {
...response,
fromCache: false,
latency: Date.now() - startTime,
};
}
async callModel(prompt, context) {
// Placeholder for actual model call
const content = `Generated response for: ${prompt.substring(0, 50)}...`;
return {
content,
tokenCount: Math.ceil(content.length / 4),
fromCache: false,
latency: 0,
};
}
/**
* 3. Caching & Memoization
*/
generateCacheKey(prompt, context) {
const combined = prompt + '|||' + context.join('|||');
return crypto.createHash('sha256').update(combined).digest('hex');
}
clearCache() {
this.responseCache.clear();
this.embeddingCache.clear();
logger.info('Performance caches cleared');
}
/**
* 4. Streaming Support
*/
async *streamResponse(prompt, context = []) {
if (!this.config.enableStreaming) {
// Fallback to non-streaming
const response = await this.processIndividual({
id: crypto.randomUUID(),
prompt,
context,
priority: 1,
});
yield {
id: crypto.randomUUID(),
chunk: response.content,
complete: true,
metadata: { fromCache: response.fromCache, tokenCount: response.tokenCount },
};
return;
}
// Implement actual streaming here
const optimized = this.optimizePrompt(prompt, context);
const responseId = crypto.randomUUID();
// Simulate streaming chunks
const chunks = this.simulateStreamingChunks(optimized.optimizedPrompt);
for (let i = 0; i < chunks.length; i++) {
yield {
id: responseId,
chunk: chunks[i],
complete: i === chunks.length - 1,
metadata: i === chunks.length - 1
? {
totalTokens: optimized.estimatedTokens,
optimizations: 'prompt_shortened,context_filtered',
}
: undefined,
};
// Small delay to simulate network latency
await new Promise(resolve => setTimeout(resolve, 10));
}
}
simulateStreamingChunks(prompt) {
// This would integrate with actual streaming model client
const response = `Optimized response for: ${prompt}`;
const chunkSize = 20;
const chunks = [];
for (let i = 0; i < response.length; i += chunkSize) {
chunks.push(response.substring(i, i + chunkSize));
}
return chunks;
}
/**
* Performance Monitoring
*/
getMetrics() {
return {
...this.metrics,
cacheHitRatio: this.metrics.totalRequests > 0 ? this.metrics.cacheHits / this.metrics.totalRequests : 0,
cacheSize: this.responseCache.size,
embeddingCacheSize: this.embeddingCache.size,
};
}
/**
* Configuration Management
*/
updateConfig(updates) {
this.config = { ...this.config, ...updates };
logger.info('Performance optimizer config updated', updates);
}
/**
* Fast Mode - Minimal Initialization
*/
enableFastMode() {
this.config = {
...this.config,
temperature: 0.1, // More deterministic
maxTokensPerPrompt: 2048, // Smaller context
batchSize: 1, // No batching
enableStreaming: false, // Simpler processing
cacheMaxAge: 7200000, // Longer cache life
};
logger.info('Fast mode enabled - optimized for minimal latency');
}
}
export default PerformanceOptimizer;
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