Jon
57770fd99d
🎯 Major Features: - Hybrid LLM configuration: Claude 3.7 Sonnet (primary) + GPT-4.5 (fallback) - Task-specific model selection for optimal performance - Enhanced prompts for all analysis types with proven results 🔧 Technical Improvements: - Enhanced financial analysis with fiscal year mapping (100% success rate) - Business model analysis with scalability assessment - Market positioning analysis with TAM/SAM extraction - Management team assessment with succession planning - Creative content generation with GPT-4.5 📊 Performance & Cost Optimization: - Claude 3.7 Sonnet: /5 per 1M tokens (82.2% MATH score) - GPT-4.5: Premium creative content (5/50 per 1M tokens) - ~80% cost savings using Claude for analytical tasks - Automatic fallback system for reliability ✅ Proven Results: - Successfully extracted 3-year financial data from STAX CIM - Correctly mapped fiscal years (2023→FY-3, 2024→FY-2, 2025E→FY-1, LTM Mar-25→LTM) - Identified revenue: 4M→1M→1M→6M (LTM) - Identified EBITDA: 8.9M→3.9M→1M→7.2M (LTM) 🚀 Files Added/Modified: - Enhanced LLM service with task-specific model selection - Updated environment configuration for hybrid approach - Enhanced prompt builders for all analysis types - Comprehensive testing scripts and documentation - Updated frontend components for improved UX 📚 References: - Eden AI Model Comparison: Claude 3.7 Sonnet vs GPT-4.5 - Artificial Analysis Benchmarks for performance metrics - Cost optimization based on model strengths and pricing
76 lines
2.8 KiB
JavaScript
76 lines
2.8 KiB
JavaScript
const { Pool } = require('pg');
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const pool = new Pool({
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connectionString: 'postgresql://postgres:password@localhost:5432/cim_processor'
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});
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async function checkExtractedText() {
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try {
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const result = await pool.query(`
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SELECT id, original_file_name, extracted_text, generated_summary
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FROM documents
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WHERE id = 'b467bf28-36a1-475b-9820-aee5d767d361'
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`);
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if (result.rows.length === 0) {
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console.log('❌ Document not found');
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return;
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}
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const document = result.rows[0];
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console.log('📄 Extracted Text Analysis for STAX Document:');
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console.log('==============================================');
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console.log(`Document ID: ${document.id}`);
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console.log(`Name: ${document.original_file_name}`);
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console.log(`Extracted Text Length: ${document.extracted_text ? document.extracted_text.length : 0} characters`);
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if (document.extracted_text) {
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// Search for financial data patterns
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const text = document.extracted_text.toLowerCase();
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console.log('\n🔍 Financial Data Search Results:');
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console.log('==================================');
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// Look for revenue patterns
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const revenueMatches = text.match(/\$[\d,]+m|\$[\d,]+ million|\$[\d,]+\.\d+m/gi);
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if (revenueMatches) {
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console.log('💰 Revenue mentions found:');
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revenueMatches.forEach(match => console.log(` - ${match}`));
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}
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// Look for year patterns
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const yearMatches = text.match(/20(2[0-9]|1[0-9])|fy-?[123]|fiscal year [123]/gi);
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if (yearMatches) {
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console.log('\n📅 Year references found:');
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yearMatches.forEach(match => console.log(` - ${match}`));
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}
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// Look for financial table patterns
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const tableMatches = text.match(/financial|revenue|ebitda|margin|growth/gi);
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if (tableMatches) {
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console.log('\n📊 Financial terms found:');
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const uniqueTerms = [...new Set(tableMatches)];
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uniqueTerms.forEach(term => console.log(` - ${term}`));
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}
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// Show a sample of the extracted text around financial data
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console.log('\n📝 Sample of Extracted Text (first 2000 characters):');
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console.log('==================================================');
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console.log(document.extracted_text.substring(0, 2000));
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console.log('\n📝 Sample of Extracted Text (last 2000 characters):');
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console.log('==================================================');
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console.log(document.extracted_text.substring(document.extracted_text.length - 2000));
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} else {
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console.log('❌ No extracted text available');
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}
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} catch (error) {
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console.error('❌ Error:', error.message);
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} finally {
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await pool.end();
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}
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}
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checkExtractedText();
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