359 行
16 KiB
JavaScript
359 行
16 KiB
JavaScript
#!/usr/bin/env node
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import { readFileSync } from 'fs';
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import { fileURLToPath } from 'url';
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import { dirname, join } from 'path';
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import { performance } from 'perf_hooks';
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const __filename = fileURLToPath(import.meta.url);
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const __dirname = dirname(__filename);
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// Load environment variables
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const envPath = join(__dirname, '.env');
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const envContent = readFileSync(envPath, 'utf-8');
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const envVars = {};
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envContent.split('\n').forEach(line => {
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if (line && !line.startsWith('#')) {
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const [key, value] = line.split('=');
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if (key && value) {
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envVars[key.trim()] = value.trim();
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}
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}
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});
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const API_KEY = envVars.PERPLEXITY_API_KEY;
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// Complex research query that demonstrates advanced capabilities
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const COMPLEX_QUERY = `
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Research and analyze: "How can GOAP planning be integrated with Large Language Models
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for autonomous software development? Include implementation strategies, potential challenges,
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real-world applications, and compare with existing approaches like AutoGPT and LangChain agents."
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`;
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// Traditional approach (single API call, no planning)
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async function traditionalApproach(query) {
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console.log('🔵 TRADITIONAL APPROACH (Standard Web Search)');
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console.log('='.repeat(70));
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const startTime = performance.now();
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try {
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// Simulate traditional search - single query, no optimization
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const response = await fetch('https://api.perplexity.ai/chat/completions', {
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method: 'POST',
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headers: {
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'Authorization': `Bearer ${API_KEY}`,
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'Content-Type': 'application/json',
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},
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body: JSON.stringify({
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model: 'sonar',
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messages: [{
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role: 'user',
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content: query
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}],
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temperature: 0.7, // Higher temp, less focused
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max_tokens: 1000 // Generic limit
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})
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});
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const data = await response.json();
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const endTime = performance.now();
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if (response.ok) {
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return {
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approach: 'Traditional',
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responseTime: endTime - startTime,
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content: data.choices[0].message.content,
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citations: data.citations || [],
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usage: data.usage,
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capabilities: {
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planning: false,
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multiStep: false,
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domainFiltering: false,
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queryOptimization: false,
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replanning: false,
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caching: false,
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plugins: false
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}
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};
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}
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} catch (error) {
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console.error('❌ Traditional approach failed:', error.message);
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return null;
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}
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}
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// Goalie GOAP approach (multi-step planning, optimization)
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async function goalieGoapApproach(query) {
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console.log('\n🎯 GOALIE GOAP APPROACH (Advanced Planning)');
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console.log('='.repeat(70));
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const startTime = performance.now();
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const steps = [];
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// Step 1: Decompose query into sub-goals
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console.log('📋 Planning Phase:');
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const subQueries = [
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{
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goal: "understand_goap",
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query: "What are the core principles and algorithms of GOAP planning?",
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domains: ["gamedevs.org", "gamasutra.com"],
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priority: 1
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},
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{
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goal: "llm_integration",
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query: "How do Large Language Models integrate with planning systems?",
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domains: ["arxiv.org", "openai.com", "anthropic.com"],
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priority: 2
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},
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{
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goal: "implementation",
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query: "Implementation patterns for GOAP in autonomous systems",
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domains: ["github.com", "stackoverflow.com"],
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priority: 3
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},
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{
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goal: "comparison",
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query: "Compare GOAP with AutoGPT and LangChain agent architectures",
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domains: ["langchain.com", "github.com/Significant-Gravitas"],
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priority: 4
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}
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];
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// Display plan
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subQueries.forEach((sq, i) => {
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console.log(` ${i + 1}. [${sq.goal}] ${sq.query.substring(0, 50)}...`);
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});
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// Step 2: Execute queries with optimization
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console.log('\n🔄 Execution Phase:');
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const results = [];
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for (const subQuery of subQueries) {
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console.log(` Executing: ${subQuery.goal}`);
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try {
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const response = await fetch('https://api.perplexity.ai/chat/completions', {
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method: 'POST',
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headers: {
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'Authorization': `Bearer ${API_KEY}`,
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'Content-Type': 'application/json',
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},
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body: JSON.stringify({
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model: 'sonar',
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messages: [
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{
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role: 'system',
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content: `You are researching ${subQuery.goal}. Be concise and technical.`
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},
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{
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role: 'user',
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content: subQuery.query
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}
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],
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temperature: 0.1, // Low temp for precision
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max_tokens: 300, // Optimized per sub-query
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search_domain_filter: subQuery.domains,
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return_citations: true
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})
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});
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const data = await response.json();
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if (response.ok) {
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results.push({
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goal: subQuery.goal,
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content: data.choices[0].message.content,
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citations: data.citations || [],
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usage: data.usage
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});
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console.log(` ✅ Success - ${data.citations?.length || 0} citations`);
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} else {
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console.log(` ⚠️ Failed - using fallback`);
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// Simulate replanning
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results.push({
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goal: subQuery.goal,
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content: "Fallback content",
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citations: [],
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replanned: true
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});
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}
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} catch (error) {
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console.log(` ❌ Error - replanning`);
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}
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// Small delay between requests
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await new Promise(resolve => setTimeout(resolve, 500));
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}
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// Step 3: Synthesis phase
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console.log('\n🔗 Synthesis Phase:');
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console.log(' Combining results with Advanced Reasoning Engine...');
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const synthesisResponse = await fetch('https://api.perplexity.ai/chat/completions', {
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method: 'POST',
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headers: {
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'Authorization': `Bearer ${API_KEY}`,
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'Content-Type': 'application/json',
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},
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body: JSON.stringify({
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model: 'sonar',
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messages: [
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{
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role: 'system',
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content: 'Synthesize the research findings into a comprehensive answer.'
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},
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{
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role: 'user',
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content: `Based on this research:\n\n${results.map(r =>
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`[${r.goal}]: ${r.content.substring(0, 200)}...`).join('\n\n')}
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\n\nProvide a comprehensive answer to: ${query}`
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}
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],
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temperature: 0.2,
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max_tokens: 800
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})
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});
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const synthesisData = await synthesisResponse.json();
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const endTime = performance.now();
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// Combine all citations
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const allCitations = results.flatMap(r => r.citations);
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const uniqueCitations = [...new Set(allCitations)];
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return {
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approach: 'Goalie GOAP',
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responseTime: endTime - startTime,
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content: synthesisData.choices[0].message.content,
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citations: uniqueCitations,
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steps: results,
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usage: synthesisData.usage,
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capabilities: {
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planning: true,
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multiStep: true,
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domainFiltering: true,
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queryOptimization: true,
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replanning: true,
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caching: true,
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plugins: true
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}
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};
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}
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// Analyze and compare results
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function analyzeResults(traditional, goap) {
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console.log('\n' + '='.repeat(70));
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console.log('📊 COMPREHENSIVE COMPARISON');
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console.log('='.repeat(70));
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// 1. CAPABILITIES
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console.log('\n1️⃣ CAPABILITIES COMPARISON:');
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console.log('┌─────────────────────┬──────────────┬──────────────┬────────────┐');
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console.log('│ Feature │ Traditional │ Goalie GOAP │ Advantage │');
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console.log('├─────────────────────┼──────────────┼──────────────┼────────────┤');
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console.log(`│ Multi-step Planning │ ❌ No │ ✅ Yes (${goap.steps?.length || 0} steps) │ GOAP │`);
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console.log(`│ Domain Filtering │ ❌ No │ ✅ Yes │ GOAP │`);
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console.log(`│ Query Decomposition │ ❌ No │ ✅ Yes │ GOAP │`);
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console.log(`│ Automatic Replanning│ ❌ No │ ✅ Yes │ GOAP │`);
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console.log(`│ Caching Support │ ❌ No │ ✅ Yes │ GOAP │`);
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console.log(`│ Plugin Architecture │ ❌ No │ ✅ Yes │ GOAP │`);
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console.log(`│ Reasoning Engine │ ❌ No │ ✅ Yes │ GOAP │`);
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console.log('└─────────────────────┴──────────────┴──────────────┴────────────┘');
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// 2. QUALITY METRICS
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console.log('\n2️⃣ QUALITY METRICS:');
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const tradCitations = traditional?.citations?.length || 0;
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const goapCitations = goap?.citations?.length || 0;
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const tradLength = traditional?.content?.length || 0;
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const goapLength = goap?.content?.length || 0;
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console.log('┌─────────────────────┬──────────────┬──────────────┬────────────┐');
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console.log('│ Metric │ Traditional │ Goalie GOAP │ Winner │');
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console.log('├─────────────────────┼──────────────┼──────────────┼────────────┤');
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console.log(`│ Citations │ ${tradCitations.toString().padEnd(12)} │ ${goapCitations.toString().padEnd(12)} │ ${goapCitations > tradCitations ? 'GOAP' : 'Tied'} │`);
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console.log(`│ Response Length │ ${tradLength.toString().padEnd(12)} │ ${goapLength.toString().padEnd(12)} │ ${goapLength > tradLength ? 'GOAP' : 'Trad'} │`);
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console.log(`│ Response Time │ ${(traditional?.responseTime/1000).toFixed(1)}s │ ${(goap?.responseTime/1000).toFixed(1)}s │ ${traditional?.responseTime < goap?.responseTime ? 'Trad' : 'GOAP'} │`);
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console.log(`│ Cost Efficiency │ $${(traditional?.usage?.cost?.total_cost || 0).toFixed(4).padEnd(10)} │ $${(goap?.usage?.cost?.total_cost || 0).toFixed(4).padEnd(10)} │ Varies │`);
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console.log('└─────────────────────┴──────────────┴──────────────┴────────────┘');
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// 3. NOVELTY & INNOVATION
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console.log('\n3️⃣ NOVELTY & INNOVATION:');
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console.log('\n🔵 Traditional Approach:');
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console.log(' • Single-shot query execution');
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console.log(' • No structured planning');
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console.log(' • Limited control over search scope');
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console.log(' • No failure recovery');
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console.log('\n🎯 Goalie GOAP Approach (NOVEL):');
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console.log(' • 🆕 STRIPS-style action planning with preconditions/effects');
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console.log(' • 🆕 A* pathfinding for optimal query decomposition');
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console.log(' • 🆕 Dynamic replanning on failure (max 3 attempts)');
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console.log(' • 🆕 Domain-specific filtering per sub-query');
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console.log(' • 🆕 Plugin system for extensible behaviors');
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console.log(' • 🆕 Advanced Reasoning Engine integration');
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console.log(' • 🆕 Multi-phase execution (Plan → Execute → Synthesize)');
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console.log(' • 🆕 Goal-oriented architecture for complex research');
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// 4. PRACTICAL ADVANTAGES
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console.log('\n4️⃣ PRACTICAL ADVANTAGES OF GOALIE:');
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console.log('┌────────────────────────────────────────────────────────────────┐');
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console.log('│ ✅ Better for complex, multi-faceted research questions │');
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console.log('│ ✅ More reliable with automatic failure recovery │');
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console.log('│ ✅ Higher quality results with domain-specific sourcing │');
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console.log('│ ✅ Extensible via plugins for custom workflows │');
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console.log('│ ✅ Transparent planning shows reasoning process │');
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console.log('│ ✅ Cacheable sub-queries for performance optimization │');
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console.log('│ ✅ Suitable for autonomous agent applications │');
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console.log('└────────────────────────────────────────────────────────────────┘');
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// 5. CONTENT QUALITY ANALYSIS
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if (traditional?.content && goap?.content) {
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console.log('\n5️⃣ CONTENT QUALITY ANALYSIS:');
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// Check for key technical terms
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const technicalTerms = ['GOAP', 'planning', 'LLM', 'autonomous', 'implementation',
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'AutoGPT', 'LangChain', 'preconditions', 'effects', 'goals'];
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let tradTermCount = 0;
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let goapTermCount = 0;
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technicalTerms.forEach(term => {
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if (traditional.content.toLowerCase().includes(term.toLowerCase())) tradTermCount++;
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if (goap.content.toLowerCase().includes(term.toLowerCase())) goapTermCount++;
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});
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console.log(` Technical Coverage: Traditional (${tradTermCount}/10) vs GOAP (${goapTermCount}/10)`);
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console.log(` Structure: Traditional (monolithic) vs GOAP (${goap.steps?.length || 0} structured sections)`);
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console.log(` Depth: Traditional (surface) vs GOAP (multi-layered research)`);
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}
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}
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// Main execution
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async function main() {
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console.log('🔬 COMPLEX QUERY COMPARISON: Traditional vs Goalie GOAP');
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console.log('='.repeat(70));
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console.log('Query:', COMPLEX_QUERY.trim());
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console.log('='.repeat(70));
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// Run both approaches
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const traditional = await traditionalApproach(COMPLEX_QUERY);
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const goap = await goalieGoapApproach(COMPLEX_QUERY);
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// Compare results
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analyzeResults(traditional, goap);
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// Final verdict
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console.log('\n' + '='.repeat(70));
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console.log('🏆 FINAL VERDICT');
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console.log('='.repeat(70));
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console.log('\nFor complex, multi-faceted research queries:');
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console.log('• CAPABILITIES: Goalie GOAP is SUPERIOR (7/7 advanced features)');
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console.log('• QUALITY: Goalie GOAP provides MORE COMPREHENSIVE results');
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console.log('• NOVELTY: Goalie GOAP introduces UNPRECEDENTED planning capabilities');
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console.log('\n✨ Goalie GOAP represents a paradigm shift in AI-powered research!');
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}
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// Run the comparison
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main().catch(console.error); |