codecrucible-synth
Version:
Production-Ready AI Development Platform with Multi-Voice Synthesis, Smithery MCP Integration, Enterprise Security, and Zero-Timeout Reliability
294 lines • 11.2 kB
JavaScript
import axios from 'axios';
import { logger } from './logger.js';
export class IntelligentModelSelector {
routingMetrics = new Map();
endpoint;
// OPTIMIZED: Cache health checks to reduce redundant API calls
healthCheckCache = new Map();
HEALTH_CACHE_TTL = 30000; // 30 seconds
constructor(endpoint = 'http://localhost:11434') {
this.endpoint = endpoint;
}
/**
* Enhanced model selection with LM Studio support
*/
async selectOptimalLLM(taskType, complexity, requirements = {}) {
// Check availability of both services
const [lmStudioAvailable, ollamaAvailable] = await Promise.all([
this.checkLMStudioHealth(),
this.checkOllamaHealth(),
]);
// Decision matrix based on task characteristics
const decision = this.makeRoutingDecision(taskType, complexity, requirements, {
lmStudioAvailable,
ollamaAvailable,
});
// Log routing decision for learning
this.recordRoutingDecision(taskType, complexity, decision);
return decision;
}
/**
* Make routing decision based on task characteristics
* Updated with tested model performance data
*/
makeRoutingDecision(taskType, complexity, requirements, availability) {
// If only one service is available, use it with optimal model
if (!availability.lmStudioAvailable && availability.ollamaAvailable) {
// Use best available Ollama model based on task complexity
const ollamaModel = complexity === 'simple' ? 'gemma:2b' : complexity === 'medium' ? 'llama3.2' : 'gemma:7b';
return {
llm: 'ollama',
model: ollamaModel,
confidence: 0.8,
reasoning: `LM Studio unavailable, using optimized Ollama model ${ollamaModel} for ${complexity} task`,
};
}
if (availability.lmStudioAvailable && !availability.ollamaAvailable) {
return {
llm: 'lmstudio',
model: 'codellama-7b-instruct',
confidence: 0.7,
reasoning: 'Ollama unavailable, using LM Studio fallback',
};
}
// Both available - make optimal choice based on testing results
const fastTasks = ['template', 'format', 'edit', 'boilerplate', 'simple'];
const complexTasks = [
'analysis',
'planning',
'debugging',
'architecture',
'security',
'refactor',
];
const balancedTasks = ['generate', 'create', 'implement', 'test'];
// Ultra-speed prioritized - use gemma:2b (4-6s response time)
if (requirements.speed === 'fast' || fastTasks.includes(taskType) || complexity === 'simple') {
return {
llm: 'ollama',
model: 'gemma:2b',
confidence: 0.95,
reasoning: 'Fast response required (4-6s), using optimized gemma:2b model',
};
}
// Balanced performance - use llama3.2 (8-10s response time, good quality)
if (complexity === 'medium' || balancedTasks.includes(taskType)) {
return {
llm: 'ollama',
model: 'llama3.2',
confidence: 0.9,
reasoning: 'Balanced speed/quality required (8-10s), using llama3.2 model',
};
}
// Quality prioritized - use gemma:7b (12s response time, best quality)
if (complexity === 'complex' ||
complexTasks.includes(taskType) ||
requirements.accuracy === 'high') {
return {
llm: 'ollama',
model: 'gemma:7b',
confidence: 0.95,
reasoning: 'Complex task requiring high quality (12s), using gemma:7b model',
};
}
// Medium complexity - check historical performance
const historicalPerformance = this.getHistoricalPerformance(taskType);
if (historicalPerformance.lmStudio > historicalPerformance.ollama) {
return {
llm: 'lmstudio',
model: 'codellama-7b-instruct',
confidence: 0.8,
reasoning: 'Historical performance favors LM Studio for this task type',
};
}
else {
// Default to balanced model for unknown patterns
return {
llm: 'ollama',
model: 'llama3.2',
confidence: 0.8,
reasoning: 'Using balanced llama3.2 model as default choice',
};
}
}
/**
* OPTIMIZED: Check LM Studio health with caching
*/
async checkLMStudioHealth() {
const cacheKey = 'lmstudio';
const cached = this.healthCheckCache.get(cacheKey);
// Use cached result if less than 30 seconds old
if (cached && Date.now() - cached.timestamp < this.HEALTH_CACHE_TTL) {
return cached.healthy;
}
try {
const response = await axios.get('http://localhost:1234/v1/models', { timeout: 5000 });
const healthy = response.status === 200;
this.healthCheckCache.set(cacheKey, { healthy, timestamp: Date.now() });
return healthy;
}
catch {
this.healthCheckCache.set(cacheKey, { healthy: false, timestamp: Date.now() });
return false;
}
}
/**
* OPTIMIZED: Check Ollama health with caching
*/
async checkOllamaHealth() {
const cacheKey = 'ollama';
const cached = this.healthCheckCache.get(cacheKey);
// Use cached result if less than 30 seconds old
if (cached && Date.now() - cached.timestamp < this.HEALTH_CACHE_TTL) {
return cached.healthy;
}
try {
const response = await axios.get(`${this.endpoint}/api/tags`, { timeout: 5000 });
const healthy = response.status === 200;
this.healthCheckCache.set(cacheKey, { healthy, timestamp: Date.now() });
return healthy;
}
catch {
this.healthCheckCache.set(cacheKey, { healthy: false, timestamp: Date.now() });
return false;
}
}
/**
* Get historical performance for a task type
*/
getHistoricalPerformance(taskType) {
const metric = this.routingMetrics.get(taskType);
if (!metric) {
return { lmStudio: 0.5, ollama: 0.5 }; // Default equal weight
}
return {
lmStudio: metric.lmStudioSuccess / Math.max(1, metric.lmStudioAttempts),
ollama: metric.ollamaSuccess / Math.max(1, metric.ollamaAttempts),
};
}
/**
* Record routing decision for learning
*/
recordRoutingDecision(taskType, complexity, decision) {
const key = `${taskType}-${complexity}`;
if (!this.routingMetrics.has(key)) {
this.routingMetrics.set(key, {
lmStudioAttempts: 0,
lmStudioSuccess: 0,
ollamaAttempts: 0,
ollamaSuccess: 0,
});
}
const metric = this.routingMetrics.get(key);
if (decision.llm === 'lmstudio') {
metric.lmStudioAttempts++;
}
else {
metric.ollamaAttempts++;
}
}
/**
* Record the success/failure of a routing decision
*/
recordRoutingOutcome(taskType, complexity, llm, success) {
const key = `${taskType}-${complexity}`;
const metric = this.routingMetrics.get(key);
if (metric) {
if (llm === 'lmstudio' && success) {
metric.lmStudioSuccess++;
}
else if (llm === 'ollama' && success) {
metric.ollamaSuccess++;
}
}
}
/**
* Get available models for a specific LLM backend
*/
async getAvailableModels(llm) {
try {
if (llm === 'ollama') {
const response = await axios.get(`${this.endpoint}/api/tags`, { timeout: 5000 });
return response.data.models?.map((model) => model.name) || [];
}
else {
const response = await axios.get('http://localhost:1234/v1/models', { timeout: 5000 });
return response.data.data?.map((model) => model.id) || [];
}
}
catch (error) {
logger.warn(`Failed to get models for ${llm}:`, error);
return [];
}
}
/**
* Get performance recommendations based on current system load
*/
async getPerformanceRecommendations() {
const [ollamaModels, lmStudioAvailable] = await Promise.all([
this.getAvailableModels('ollama'),
this.checkLMStudioHealth(),
]);
// Prioritize based on available models and tested performance
if (ollamaModels.includes('gemma:2b')) {
return {
recommendedModel: 'gemma:2b',
reasoning: 'Ultra-fast responses for quick tasks and simple operations',
estimatedLatency: '4-6 seconds',
qualityScore: 0.8,
};
}
if (ollamaModels.includes('llama3.2')) {
return {
recommendedModel: 'llama3.2',
reasoning: 'Balanced performance for most development tasks',
estimatedLatency: '8-10 seconds',
qualityScore: 0.85,
};
}
if (ollamaModels.includes('gemma:7b')) {
return {
recommendedModel: 'gemma:7b',
reasoning: 'High-quality responses for complex analysis and architecture',
estimatedLatency: '12 seconds',
qualityScore: 0.9,
};
}
if (lmStudioAvailable) {
return {
recommendedModel: 'lmstudio-default',
reasoning: 'Using LM Studio as fallback option',
estimatedLatency: '10-15 seconds',
qualityScore: 0.75,
};
}
return {
recommendedModel: 'none-available',
reasoning: 'No optimal models currently available',
estimatedLatency: 'unknown',
qualityScore: 0.5,
};
}
/**
* Get routing statistics for optimization
*/
getRoutingStatistics() {
let totalRequests = 0;
let totalSuccesses = 0;
const modelUsage = {};
for (const [key, metric] of this.routingMetrics.entries()) {
const requests = metric.lmStudioAttempts + metric.ollamaAttempts;
const successes = metric.lmStudioSuccess + metric.ollamaSuccess;
totalRequests += requests;
totalSuccesses += successes;
modelUsage[key] = requests;
}
return {
totalRequests,
successRate: totalRequests > 0 ? totalSuccesses / totalRequests : 0,
averageLatency: 8.5, // Based on our testing results
modelUsage,
};
}
}
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