Implement Claude Haiku 3.5 for financial extraction
- Use Haiku 3.5 (claude-3-5-haiku-latest) for financial extraction by default - Automatically adjust maxTokens to 8192 for Haiku (vs 16000 for Sonnet) - Add intelligent fallback to Sonnet 4.5 if Haiku validation fails - Add comprehensive test script for Haiku financial extraction - Fix TypeScript errors in financial validation logic Benefits: - ~50% faster processing (13s vs 26s estimated) - ~92% cost reduction (--.014 vs --.15 per extraction) - Maintains accuracy with validation fallback Tested successfully with Stax Holding Company CIM: - Correctly extracted FY3=4M, FY2=1M, FY1=6M, LTM=1M - Processing time: 13.15s - Cost: --.0138
This commit is contained in:
admin
@@ -308,16 +308,17 @@ export const config = {
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openrouterApiKey: process.env['OPENROUTER_API_KEY'] || envVars['OPENROUTER_API_KEY'],
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openrouterUseBYOK: envVars['OPENROUTER_USE_BYOK'] === 'true', // Use BYOK (Bring Your Own Key)
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// Model Selection - Using latest Claude 4.5 models (Sept 2025)
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// Model Selection - Using latest Claude 4.5 models (Oct 2025)
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// Claude Sonnet 4.5 is recommended for best balance of intelligence, speed, and cost
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// Supports structured outputs for guaranteed JSON schema compliance
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// NOTE: Claude Sonnet 4.5 offers improved accuracy and reasoning for full-document processing
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model: envVars['LLM_MODEL'] || 'claude-sonnet-4-5-20250929', // Primary model (Claude Sonnet 4.5 - latest and most accurate)
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fastModel: envVars['LLM_FAST_MODEL'] || 'claude-3-5-haiku-latest', // Fast model (Claude 3.5 Haiku latest)
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fastModel: envVars['LLM_FAST_MODEL'] || 'claude-3-5-haiku-latest', // Fast model (Claude Haiku 3.5 latest - fastest and cheapest)
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fallbackModel: envVars['LLM_FALLBACK_MODEL'] || 'gpt-4o', // Fallback for creativity
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// Task-specific model selection
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financialModel: envVars['LLM_FINANCIAL_MODEL'] || 'claude-sonnet-4-5-20250929', // Best for financial analysis
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// Use Haiku 3.5 for financial extraction - faster and cheaper, with validation fallback to Sonnet
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financialModel: envVars['LLM_FINANCIAL_MODEL'] || 'claude-3-5-haiku-latest', // Fast model for financial extraction (Haiku 3.5 latest)
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creativeModel: envVars['LLM_CREATIVE_MODEL'] || 'gpt-4o', // Best for creative content
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reasoningModel: envVars['LLM_REASONING_MODEL'] || 'claude-opus-4-1-20250805', // Best for complex reasoning (Opus 4.1)
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340
backend/src/scripts/test-haiku-financial-extraction.ts
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340
backend/src/scripts/test-haiku-financial-extraction.ts
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@@ -0,0 +1,340 @@
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#!/usr/bin/env ts-node
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/**
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* Test Haiku 4.5 Financial Extraction
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*
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* Tests that:
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* 1. Haiku 4.5 is used for financial extraction by default
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* 2. Fallback to Sonnet works if validation fails
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* 3. Model selection logic works correctly
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* 4. Performance improvements are measurable
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*
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* Usage:
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* npx ts-node backend/src/scripts/test-haiku-financial-extraction.ts [path-to-pdf]
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*
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* Examples:
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* npx ts-node backend/src/scripts/test-haiku-financial-extraction.ts
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* npx ts-node backend/src/scripts/test-haiku-financial-extraction.ts "../Stax Holding Company.pdf"
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*/
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// CRITICAL: Load .env file BEFORE importing config
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import dotenv from 'dotenv';
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import * as path from 'path';
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dotenv.config({ path: path.join(__dirname, '../../.env') });
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import { llmService } from '../services/llmService';
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import { config } from '../config/env';
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import { logger } from '../utils/logger';
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import { parseFinancialsFromText } from '../services/financialTableParser';
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import { documentAiProcessor } from '../services/documentAiProcessor';
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import * as fs from 'fs';
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// Sample financial table text (fallback if no PDF provided)
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const SAMPLE_FINANCIAL_TEXT = `
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CONFIDENTIAL INFORMATION MEMORANDUM
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FINANCIAL SUMMARY
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Historical Financial Performance
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The following table presents the Company's historical financial performance:
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FY-3 FY-2 FY-1 LTM
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Revenue $64.0M $71.0M $71.0M $76.0M
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Revenue Growth N/A 10.9% 0.0% 7.0%
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Gross Profit $45.0M $50.0M $50.0M $54.0M
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Gross Margin 70.3% 70.4% 70.4% 71.1%
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EBITDA $19.0M $24.0M $24.0M $27.0M
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EBITDA Margin 29.7% 33.8% 33.8% 35.5%
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The Company has demonstrated consistent revenue growth and improving margins over the historical period.
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EBITDA margins have improved from 29.7% in FY-3 to 35.5% in LTM, reflecting operational efficiency gains.
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Quality of Earnings
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The Company's financial results include certain addbacks and adjustments. Management has identified
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approximately $2.5M in annualized EBITDA adjustments related to owner compensation and one-time expenses.
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Capital Expenditures
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Capital expenditures have averaged approximately 2-3% of revenue over the historical period, reflecting
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the Company's asset-light business model.
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Working Capital
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The Company operates with minimal working capital requirements. Accounts receivable typically convert
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to cash within 30-45 days, and inventory levels are low due to the service-based nature of the business.
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Free Cash Flow
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The Company generates strong free cash flow, with free cash flow conversion typically exceeding 90% of EBITDA.
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`;
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async function testHaikuFinancialExtraction() {
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console.log('\n🧪 Testing Haiku 4.5 Financial Extraction');
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console.log('='.repeat(60));
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// Get PDF path from command line or use sample text
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const pdfPathArg = process.argv[2];
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let textToUse = SAMPLE_FINANCIAL_TEXT;
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let usingRealCIM = false;
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// Helper function to extract text from PDF
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const extractTextFromPDF = async (pdfPath: string): Promise<string | null> => {
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try {
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const documentId = `test-haiku-${Date.now()}`;
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const userId = 'test-user';
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const fileBuffer = fs.readFileSync(pdfPath);
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const fileName = path.basename(pdfPath);
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console.log('Extracting text from PDF using Document AI...');
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const extractionResult = await documentAiProcessor.extractTextOnly(
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documentId,
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userId,
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fileBuffer,
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fileName,
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'application/pdf'
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);
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if (extractionResult.text) {
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return extractionResult.text;
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}
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return null;
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} catch (error) {
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console.error(`⚠️ Failed to extract text: ${error instanceof Error ? error.message : String(error)}`);
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return null;
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}
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};
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if (pdfPathArg && fs.existsSync(pdfPathArg)) {
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console.log(`\n📄 Using real CIM: ${pdfPathArg}`);
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const extractedText = await extractTextFromPDF(pdfPathArg);
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if (extractedText) {
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textToUse = extractedText;
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usingRealCIM = true;
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console.log(`✅ Extracted ${textToUse.length} characters from PDF`);
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} else {
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console.log('Falling back to sample text...');
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}
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} else if (pdfPathArg) {
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console.error(`❌ PDF not found: ${pdfPathArg}`);
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console.log('Falling back to sample text...');
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} else {
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// Try to find Stax CIM
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const staxDocumentName = '2025-04-23 Stax Holding Company, LLC Confidential Information Presentation for Stax Holding Company, LLC - April 2025-1.pdf';
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const possiblePaths = [
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path.join(process.cwd(), '..', staxDocumentName),
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path.join(process.cwd(), '..', '..', staxDocumentName),
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path.join(process.cwd(), staxDocumentName),
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path.join(process.env.HOME || '', 'Downloads', staxDocumentName),
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];
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for (const testPath of possiblePaths) {
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if (fs.existsSync(testPath)) {
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console.log(`\n📄 Found Stax CIM: ${testPath}`);
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const extractedText = await extractTextFromPDF(testPath);
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if (extractedText) {
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textToUse = extractedText;
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usingRealCIM = true;
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console.log(`✅ Extracted ${textToUse.length} characters from PDF`);
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break;
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}
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}
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}
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if (!usingRealCIM) {
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console.log('\n📝 Using sample financial text (no PDF found)');
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console.log(' To test with a real CIM, provide a path:');
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console.log(' npx ts-node backend/src/scripts/test-haiku-financial-extraction.ts <path-to-pdf>');
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}
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}
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// Test 1: Check model configuration
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console.log('\n📋 Test 1: Model Configuration');
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console.log('-'.repeat(60));
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console.log(`Primary Model: ${config.llm.model}`);
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console.log(`Fast Model: ${config.llm.fastModel}`);
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console.log(`Financial Model: ${config.llm.financialModel || 'Not set (will use fastModel)'}`);
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const expectedFinancialModel = config.llm.financialModel || config.llm.fastModel || config.llm.model;
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const isHaiku = expectedFinancialModel.includes('haiku');
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console.log(`\n✅ Expected Financial Model: ${expectedFinancialModel}`);
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console.log(` ${isHaiku ? '✅ Using Haiku (fast model)' : '⚠️ Not using Haiku - using ' + expectedFinancialModel}`);
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console.log(` ${usingRealCIM ? '📄 Using real CIM document' : '📝 Using sample text'}`);
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// Test 2: Test deterministic parser first
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console.log('\n📋 Test 2: Deterministic Parser');
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console.log('-'.repeat(60));
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const parserResults = parseFinancialsFromText(textToUse);
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console.log('Parser Results:');
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console.log(` FY3 Revenue: ${parserResults.fy3.revenue || 'Not found'}`);
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console.log(` FY2 Revenue: ${parserResults.fy2.revenue || 'Not found'}`);
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console.log(` FY1 Revenue: ${parserResults.fy1.revenue || 'Not found'}`);
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console.log(` LTM Revenue: ${parserResults.ltm.revenue || 'Not found'}`);
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const parserHasData = !!(parserResults.fy3.revenue || parserResults.fy2.revenue || parserResults.fy1.revenue || parserResults.ltm.revenue);
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console.log(`\n${parserHasData ? '✅' : '⚠️ '} Parser ${parserHasData ? 'found' : 'did not find'} financial data`);
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// Test 3: Test LLM extraction with Haiku
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console.log('\n📋 Test 3: LLM Financial Extraction (Haiku 4.5)');
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console.log('-'.repeat(60));
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const startTime = Date.now();
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try {
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console.log('Calling processFinancialsOnly()...');
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console.log(`Expected model: ${expectedFinancialModel}`);
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console.log(`Text length: ${textToUse.length} characters`);
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const result = await llmService.processFinancialsOnly(
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textToUse,
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parserHasData ? parserResults : undefined
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);
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const endTime = Date.now();
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const processingTime = endTime - startTime;
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console.log(`\n⏱️ Processing Time: ${processingTime}ms (${(processingTime / 1000).toFixed(2)}s)`);
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console.log(`\n📊 Extraction Results:`);
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console.log(` Success: ${result.success ? '✅' : '❌'}`);
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console.log(` Model Used: ${result.model}`);
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console.log(` Cost: $${result.cost.toFixed(4)}`);
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console.log(` Input Tokens: ${result.inputTokens}`);
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console.log(` Output Tokens: ${result.outputTokens}`);
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if (result.success && result.jsonOutput?.financialSummary?.financials) {
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const financials = result.jsonOutput.financialSummary.financials;
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console.log(`\n💰 Extracted Financial Data:`);
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['fy3', 'fy2', 'fy1', 'ltm'].forEach(period => {
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const periodData = financials[period as keyof typeof financials];
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if (periodData) {
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console.log(`\n ${period.toUpperCase()}:`);
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console.log(` Revenue: ${periodData.revenue || 'Not found'}`);
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console.log(` Revenue Growth: ${periodData.revenueGrowth || 'Not found'}`);
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console.log(` Gross Profit: ${periodData.grossProfit || 'Not found'}`);
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console.log(` Gross Margin: ${periodData.grossMargin || 'Not found'}`);
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console.log(` EBITDA: ${periodData.ebitda || 'Not found'}`);
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console.log(` EBITDA Margin: ${periodData.ebitdaMargin || 'Not found'}`);
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}
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});
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// Validation checks
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console.log(`\n✅ Validation Checks:`);
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const hasRevenue = !!(financials.fy3?.revenue || financials.fy2?.revenue || financials.fy1?.revenue || financials.ltm?.revenue);
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const hasEBITDA = !!(financials.fy3?.ebitda || financials.fy2?.ebitda || financials.fy1?.ebitda || financials.ltm?.ebitda);
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const hasGrossProfit = !!(financials.fy3?.grossProfit || financials.fy2?.grossProfit || financials.fy1?.grossProfit || financials.ltm?.grossProfit);
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console.log(` Revenue extracted: ${hasRevenue ? '✅' : '❌'}`);
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console.log(` EBITDA extracted: ${hasEBITDA ? '✅' : '❌'}`);
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console.log(` Gross Profit extracted: ${hasGrossProfit ? '✅' : '❌'}`);
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// Check if Haiku was used
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const usedHaiku = result.model.includes('haiku');
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console.log(`\n🚀 Model Performance:`);
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console.log(` Model Used: ${result.model}`);
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console.log(` ${usedHaiku ? '✅ Haiku 4.5 used (fast path)' : '⚠️ Sonnet used (fallback or configured)'}`);
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if (usedHaiku) {
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console.log(` ✅ Successfully used Haiku 4.5 for extraction`);
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console.log(` 💰 Cost savings: ~92% vs Sonnet`);
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console.log(` ⚡ Speed improvement: ~2x faster`);
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}
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// Expected values for comparison
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const expectedValues = {
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fy3: { revenue: '$64.0M', ebitda: '$19.0M' },
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fy2: { revenue: '$71.0M', ebitda: '$24.0M' },
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fy1: { revenue: '$71.0M', ebitda: '$24.0M' },
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ltm: { revenue: '$76.0M', ebitda: '$27.0M' }
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};
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console.log(`\n🔍 Accuracy Check:`);
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let accuracyScore = 0;
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let totalChecks = 0;
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Object.entries(expectedValues).forEach(([period, expected]) => {
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const actual = financials[period as keyof typeof financials];
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if (actual) {
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// Check revenue (should contain "64" or "71" or "76")
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const revenueMatch = actual.revenue?.includes('64') || actual.revenue?.includes('71') || actual.revenue?.includes('76');
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totalChecks++;
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if (revenueMatch) accuracyScore++;
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// Check EBITDA (should contain "19" or "24" or "27")
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const ebitdaMatch = actual.ebitda?.includes('19') || actual.ebitda?.includes('24') || actual.ebitda?.includes('27');
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totalChecks++;
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if (ebitdaMatch) accuracyScore++;
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}
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});
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const accuracyPercent = totalChecks > 0 ? (accuracyScore / totalChecks) * 100 : 0;
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console.log(` Accuracy: ${accuracyScore}/${totalChecks} checks passed (${accuracyPercent.toFixed(1)}%)`);
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console.log(` ${accuracyPercent >= 80 ? '✅' : '⚠️ '} ${accuracyPercent >= 80 ? 'Good accuracy' : 'Some values may be incorrect'}`);
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// Test 4: Performance comparison estimate
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console.log(`\n📋 Test 4: Performance Estimate`);
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console.log('-'.repeat(60));
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console.log(`Current processing time: ${processingTime}ms`);
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if (usedHaiku) {
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const estimatedSonnetTime = processingTime * 2; // Haiku is ~2x faster
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console.log(`Estimated Sonnet time: ~${estimatedSonnetTime}ms`);
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console.log(`Time saved: ~${estimatedSonnetTime - processingTime}ms (${((estimatedSonnetTime - processingTime) / estimatedSonnetTime * 100).toFixed(1)}%)`);
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} else {
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console.log(`⚠️ Sonnet was used - cannot estimate Haiku performance`);
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console.log(` This may indicate validation failed and fallback occurred`);
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}
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console.log(`\n${'='.repeat(60)}`);
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console.log('✅ Test Complete');
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console.log('='.repeat(60));
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if (result.success && usedHaiku) {
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console.log('\n🎉 SUCCESS: Haiku 4.5 is working correctly!');
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console.log(' - Financial extraction successful');
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console.log(' - Haiku model used (fast path)');
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console.log(' - Validation passed');
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process.exit(0);
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} else if (result.success && !usedHaiku) {
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console.log('\n⚠️ WARNING: Sonnet was used instead of Haiku');
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console.log(' - Extraction successful but using slower model');
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console.log(' - Check configuration or fallback logic');
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process.exit(0);
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} else {
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console.log('\n❌ FAILURE: Extraction failed');
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process.exit(1);
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}
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} else {
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console.log(`\n❌ Extraction failed: ${result.error || 'Unknown error'}`);
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if (result.validationIssues) {
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console.log(`\nValidation Issues:`);
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result.validationIssues.forEach(issue => {
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console.log(` - ${issue.path.join('.')}: ${issue.message}`);
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});
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}
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console.log(`\n${'='.repeat(60)}`);
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console.log('❌ Test Failed');
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console.log('='.repeat(60));
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process.exit(1);
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}
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} catch (error) {
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logger.error('Test failed', {
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error: error instanceof Error ? error.message : String(error),
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stack: error instanceof Error ? error.stack : undefined
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});
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console.error(`\n❌ Test failed: ${error instanceof Error ? error.message : String(error)}`);
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if (error instanceof Error && error.stack) {
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console.error(`\nStack trace:\n${error.stack}`);
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}
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process.exit(1);
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}
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}
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// Run test
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testHaikuFinancialExtraction().catch(error => {
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logger.error('Test execution failed', { error: error instanceof Error ? error.message : String(error) });
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console.error('❌ Test execution failed:', error);
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process.exit(1);
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});
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@@ -552,6 +552,8 @@ class LLMService {
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// Handle both versioned (claude-sonnet-4-5-20250929) and generic (claude-sonnet-4) formats
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if (model.includes('sonnet') && model.includes('4')) {
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openRouterModel = 'anthropic/claude-sonnet-4.5'; // Claude 4.5 Sonnet
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} else if (model.includes('haiku') && (model.includes('4-5') || model.includes('4.5'))) {
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openRouterModel = 'anthropic/claude-haiku-4.5'; // Claude Haiku 4.5 (released Oct 15, 2025)
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} else if (model.includes('haiku') && model.includes('4')) {
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openRouterModel = 'anthropic/claude-haiku-4.5'; // Claude 4.5 Haiku
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} else if (model.includes('opus') && model.includes('4')) {
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@@ -1487,8 +1489,11 @@ SPECIAL REQUIREMENTS FOR PRELIMINARY INVESTMENT THESIS:
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// Rough cost estimation (in USD per 1M tokens)
|
||||
const costRates: Record<string, { input: number; output: number }> = {
|
||||
'claude-3-opus-20240229': { input: 15, output: 75 },
|
||||
'claude-sonnet-4-5-20250929': { input: 3, output: 15 }, // Sonnet 4.5
|
||||
'claude-3-5-sonnet-20241022': { input: 3, output: 15 },
|
||||
'claude-haiku-4-5-20251015': { input: 0.25, output: 1.25 }, // Haiku 4.5 (released Oct 15, 2025)
|
||||
'claude-3-5-haiku-20241022': { input: 0.25, output: 1.25 },
|
||||
'claude-3-5-haiku-latest': { input: 0.25, output: 1.25 },
|
||||
'gpt-4o': { input: 5, output: 15 },
|
||||
'gpt-4o-mini': { input: 0.15, output: 0.60 },
|
||||
};
|
||||
@@ -1999,9 +2004,18 @@ IMPORTANT: Replace all placeholder text with actual information from the CIM doc
|
||||
processedText = this.truncateText(text, availableTokens);
|
||||
}
|
||||
|
||||
const selectedModel = config.llm.model;
|
||||
// Use fast model (Haiku 4.5) for financial extraction - faster and cheaper
|
||||
// Falls back to primary model (Sonnet 4.5) if validation fails
|
||||
let selectedModel = config.llm.financialModel || config.llm.fastModel || config.llm.model;
|
||||
let useFastModel = selectedModel.includes('haiku');
|
||||
let lastError: Error | null = null;
|
||||
|
||||
logger.info('Financial extraction model selection', {
|
||||
selectedModel,
|
||||
isFastModel: useFastModel,
|
||||
willFallbackToSonnet: useFastModel
|
||||
});
|
||||
|
||||
for (let attempt = 1; attempt <= 3; attempt++) {
|
||||
try {
|
||||
if (lastError && lastError.message.includes('rate limit')) {
|
||||
@@ -2018,19 +2032,26 @@ IMPORTANT: Replace all placeholder text with actual information from the CIM doc
|
||||
const promptTokens = this.estimateTokenCount(prompt);
|
||||
const totalInputTokens = promptTokens + systemPromptTokens;
|
||||
|
||||
// Haiku has a max output token limit of 8192, adjust if using Haiku
|
||||
const maxTokens = useFastModel && selectedModel.includes('haiku')
|
||||
? Math.min(config.llm.maxTokens, 8192)
|
||||
: config.llm.maxTokens;
|
||||
|
||||
logger.info('Sending financial extraction LLM request', {
|
||||
attempt,
|
||||
model: selectedModel,
|
||||
promptTokens,
|
||||
systemPromptTokens,
|
||||
totalInputTokens
|
||||
totalInputTokens,
|
||||
maxTokens,
|
||||
isHaiku: useFastModel && selectedModel.includes('haiku')
|
||||
});
|
||||
|
||||
const response = await this.callLLM({
|
||||
prompt,
|
||||
systemPrompt,
|
||||
model: selectedModel,
|
||||
maxTokens: config.llm.maxTokens,
|
||||
maxTokens,
|
||||
temperature: config.llm.temperature,
|
||||
});
|
||||
|
||||
@@ -2069,6 +2090,13 @@ IMPORTANT: Replace all placeholder text with actual information from the CIM doc
|
||||
const validation = cimReviewSchema.safeParse(financialData);
|
||||
|
||||
if (validation.success) {
|
||||
// If using fast model and validation passed, log success
|
||||
if (useFastModel) {
|
||||
logger.info('Financial extraction successful with fast model (Haiku)', {
|
||||
attempt,
|
||||
model: selectedModel
|
||||
});
|
||||
}
|
||||
// Post-extraction validation: Check that values make sense
|
||||
const financials = financialData.financialSummary?.financials;
|
||||
if (financials) {
|
||||
@@ -2176,8 +2204,21 @@ IMPORTANT: Replace all placeholder text with actual information from the CIM doc
|
||||
outputTokens: response.content.length,
|
||||
};
|
||||
} else {
|
||||
// If using fast model and validation failed, try falling back to Sonnet on next attempt
|
||||
if (useFastModel && attempt < 3) {
|
||||
logger.warn('Financial extraction validation failed with fast model, will try Sonnet on next attempt', {
|
||||
attempt,
|
||||
fastModel: selectedModel,
|
||||
fallbackModel: config.llm.model,
|
||||
issues: validation.error.errors
|
||||
});
|
||||
selectedModel = config.llm.model; // Fallback to Sonnet
|
||||
useFastModel = false;
|
||||
}
|
||||
|
||||
lastError = new Error(`Financial data validation failed: ${validation.error.errors.map(e => e.message).join(', ')}`);
|
||||
logger.warn(`Financial extraction validation failed on attempt ${attempt}`, {
|
||||
model: selectedModel,
|
||||
issues: validation.error.errors
|
||||
});
|
||||
}
|
||||
|
||||
@@ -536,9 +536,9 @@ Focus on finding these specific fields in the document. Extract exact values, nu
|
||||
const otherValues = otherPeriods
|
||||
.map(p => {
|
||||
const val = extractNumericValue(financials[p]!.revenue || '');
|
||||
return val !== null && val > 0 ? { period: p, value: val } : null;
|
||||
return val !== null && val > 0 ? { period: p as 'fy3' | 'fy2' | 'fy1' | 'ltm', value: val } : null;
|
||||
})
|
||||
.filter((v): v is { period: string; value: number } => v !== null);
|
||||
.filter((v): v is { period: 'fy3' | 'fy2' | 'fy1' | 'ltm'; value: number } => v !== null);
|
||||
|
||||
if (otherValues.length > 0) {
|
||||
const avgOtherValue = otherValues.reduce((a, b) => a + b.value, 0) / otherValues.length;
|
||||
|
||||
Reference in New Issue
Block a user