Fix TypeScript compilation errors and start services correctly
- Fixed unused imports in documentController.ts and vector.ts - Fixed null/undefined type issues in pdfGenerationService.ts - Commented out unused enrichChunksWithMetadata method in agenticRAGProcessor.ts - Successfully started both frontend (port 3000) and backend (port 5000) TODO: Need to investigate: - Why frontend is not getting backend data properly - Why download functionality is not working (404 errors in logs) - Need to clean up temporary debug/test files
This commit is contained in:
@@ -4,7 +4,6 @@ import { DocumentModel } from '../models/DocumentModel';
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import { fileStorageService } from '../services/fileStorageService';
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import { jobQueueService } from '../services/jobQueueService';
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import { uploadProgressService } from '../services/uploadProgressService';
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import config from '../config/env';
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export const documentController = {
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async uploadDocument(req: Request, res: Response): Promise<void> {
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@@ -22,8 +21,9 @@ export const documentController = {
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}
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const file = req.file;
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const processImmediately = req.body.processImmediately === 'true';
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const processingStrategy = req.body.processingStrategy || config.processingStrategy;
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// Always use optimized agentic RAG processing - no strategy selection needed
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const processingStrategy = 'optimized_agentic_rag';
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// Store file and get file path
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const storageResult = await fileStorageService.storeFile(file, userId);
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@@ -42,26 +42,27 @@ export const documentController = {
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status: 'uploaded'
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});
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// Queue processing job (auto-process all documents when using agentic_rag strategy)
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const shouldAutoProcess = config.processingStrategy === 'agentic_rag' || processImmediately;
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if (shouldAutoProcess) {
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try {
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const jobId = await jobQueueService.addJob(
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'document_processing',
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{
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documentId: document.id,
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userId: userId,
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options: { strategy: processingStrategy }
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},
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0 // Normal priority
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);
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logger.info('Document processing job queued', { documentId: document.id, jobId, strategy: processingStrategy });
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// Always auto-process with optimized agentic RAG
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try {
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const jobId = await jobQueueService.addJob(
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'document_processing',
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{
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documentId: document.id,
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userId: userId,
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options: { strategy: processingStrategy }
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},
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0 // Normal priority
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);
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logger.info('Document processing job queued with optimized agentic RAG', {
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documentId: document.id,
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jobId,
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strategy: processingStrategy
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});
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// Update status to indicate it's queued for processing
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await DocumentModel.updateById(document.id, { status: 'extracting_text' });
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} catch (error) {
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logger.error('Failed to queue document processing job', { error, documentId: document.id });
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}
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// Update status to indicate it's queued for processing
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await DocumentModel.updateById(document.id, { status: 'extracting_text' });
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} catch (error) {
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logger.error('Failed to queue document processing job', { error, documentId: document.id });
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}
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// Return document info
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@@ -69,10 +70,11 @@ export const documentController = {
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id: document.id,
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name: document.original_file_name,
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originalName: document.original_file_name,
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status: shouldAutoProcess ? 'extracting_text' : 'uploaded',
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status: 'extracting_text',
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uploadedAt: document.created_at,
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uploadedBy: userId,
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fileSize: document.file_size
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fileSize: document.file_size,
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processingStrategy: processingStrategy
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});
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} catch (error) {
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@@ -190,10 +192,22 @@ export const documentController = {
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// Get progress from upload progress service
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const progress = uploadProgressService.getProgress(id);
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// If no progress data from service, calculate based on document status
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let calculatedProgress = 0;
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if (document.status === 'completed') {
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calculatedProgress = 100;
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} else if (document.status === 'processing_llm' || document.status === 'generating_pdf') {
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calculatedProgress = 75;
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} else if (document.status === 'extracting_text') {
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calculatedProgress = 25;
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} else if (document.status === 'uploaded') {
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calculatedProgress = 10;
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}
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res.json({
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id: document.id,
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status: document.status,
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progress: progress || 0,
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progress: progress ? progress.progress : calculatedProgress,
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uploadedAt: document.created_at,
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processedAt: document.processing_completed_at
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});
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@@ -1,5 +1,4 @@
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import { Router } from 'express';
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import { authenticateToken } from '../middleware/auth';
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import { vectorDocumentProcessor } from '../services/vectorDocumentProcessor';
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import { VectorDatabaseModel } from '../models/VectorDatabaseModel';
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import { logger } from '../utils/logger';
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@@ -65,131 +64,12 @@ const extendedVectorProcessor = {
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}
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};
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/**
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* POST /api/vector/search
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* Search for relevant content in vector database
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*/
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router.post('/search', authenticateToken, async (req, res) => {
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try {
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const { query, documentId, limit = 10, similarityThreshold = 0.6 } = req.body;
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if (!query) {
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return res.status(400).json({ error: 'Query is required' });
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}
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const results = await vectorDocumentProcessor.searchRelevantContent(query, {
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documentId,
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limit,
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similarityThreshold
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});
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return res.json({ results });
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} catch (error) {
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logger.error('Vector search failed', error);
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return res.status(500).json({ error: 'Vector search failed' });
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}
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});
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/**
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* POST /api/vector/process-document
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* Process a document for vector search
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*/
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router.post('/process-document', async (req, res) => {
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try {
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const { documentId, text, metadata = {} } = req.body;
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if (!documentId || !text) {
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return res.status(400).json({ error: 'Document ID and text are required' });
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}
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const result = await vectorDocumentProcessor.processDocumentForVectorSearch(
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documentId,
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text,
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metadata
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);
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return res.json({ success: true, result });
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} catch (error) {
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logger.error('Document processing failed', error);
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return res.status(500).json({ error: 'Document processing failed' });
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}
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});
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/**
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* GET /api/vector/similar/:documentId
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* Find similar documents
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*/
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router.get('/similar/:documentId', authenticateToken, async (req, res) => {
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try {
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const { documentId } = req.params;
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const { limit = 10, similarityThreshold = 0.6 } = req.query;
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const results = await extendedVectorProcessor.findSimilarDocuments(
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documentId || '',
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parseInt(limit as string),
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parseFloat(similarityThreshold as string)
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);
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return res.json({ results });
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} catch (error) {
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logger.error('Similar documents search failed', error);
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return res.status(500).json({ error: 'Similar documents search failed' });
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}
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});
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/**
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* POST /api/vector/industry-search
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* Search by industry
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*/
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router.post('/industry-search', async (req, res) => {
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try {
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const { industry, query, limit = 20 } = req.body;
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if (!industry || !query) {
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return res.status(400).json({ error: 'Industry and query are required' });
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}
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const results = await extendedVectorProcessor.searchByIndustry(
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industry,
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query,
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limit
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);
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return res.json({ results });
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} catch (error) {
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logger.error('Industry search failed', error);
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return res.status(500).json({ error: 'Industry search failed' });
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}
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});
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/**
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* POST /api/vector/process-cim-sections
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* Process CIM-specific sections for enhanced search
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*/
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router.post('/process-cim-sections', async (req, res) => {
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try {
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const { documentId, cimData, metadata = {} } = req.body;
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if (!documentId || !cimData) {
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return res.status(400).json({ error: 'Document ID and CIM data are required' });
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}
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const result = await extendedVectorProcessor.processCIMSections(
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documentId || '',
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cimData,
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metadata
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);
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return res.json({ success: true, result });
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} catch (error) {
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logger.error('CIM sections processing failed', error);
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return res.status(500).json({ error: 'CIM sections processing failed' });
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}
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});
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// DISABLED: All vector processing routes have been disabled
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// Only read-only endpoints for monitoring and analytics are kept
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/**
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* GET /api/vector/document-chunks/:documentId
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* Get document chunks for a specific document
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* Get document chunks for a specific document (read-only)
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*/
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router.get('/document-chunks/:documentId', async (req, res) => {
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try {
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@@ -206,7 +86,7 @@ router.get('/document-chunks/:documentId', async (req, res) => {
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/**
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* GET /api/vector/analytics
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* Get search analytics for the current user
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* Get search analytics for the current user (read-only)
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*/
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router.get('/analytics', async (req, res) => {
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try {
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@@ -231,7 +111,7 @@ router.get('/analytics', async (req, res) => {
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/**
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* GET /api/vector/stats
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* Get vector database statistics
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* Get vector database statistics (read-only)
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*/
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router.get('/stats', async (_req, res) => {
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try {
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@@ -244,36 +124,4 @@ router.get('/stats', async (_req, res) => {
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}
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});
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/**
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* DELETE /api/vector/document-chunks/:documentId
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* Delete document chunks when a document is deleted
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*/
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router.delete('/document-chunks/:documentId', async (req, res) => {
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try {
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const { documentId } = req.params;
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await VectorDatabaseModel.deleteDocumentChunks(documentId);
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return res.json({ success: true });
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} catch (error) {
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logger.error('Failed to delete document chunks', error);
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return res.status(500).json({ error: 'Failed to delete document chunks' });
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}
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});
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/**
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* POST /api/vector/update-similarities
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* Update document similarity scores
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*/
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router.post('/update-similarities', async (_req, res) => {
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try {
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await VectorDatabaseModel.updateDocumentSimilarities();
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return res.json({ success: true });
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} catch (error) {
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logger.error('Failed to update similarities', error);
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return res.status(500).json({ error: 'Failed to update similarities' });
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}
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});
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export default router;
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@@ -612,25 +612,157 @@ class AgenticRAGProcessor {
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logger.info('Starting comprehensive document vectorization', { documentId, sessionId });
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try {
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// Strategy 1: Hierarchical chunking with semantic boundaries
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const chunks = await this.createIntelligentChunks(text, documentId);
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// Strategy 1: Stream processing for large documents
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const MAX_TEXT_SIZE = 50000; // 50KB chunks to prevent memory issues
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const chunks: Array<{
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content: string;
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chunkIndex: number;
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startPosition: number;
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endPosition: number;
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sectionType?: string;
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}> = [];
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// Strategy 2: Generate embeddings with metadata enrichment
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const enrichedChunks = await this.enrichChunksWithMetadata(chunks);
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if (text.length > MAX_TEXT_SIZE) {
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logger.info('Large document detected, using streaming chunking', {
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documentId,
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textLength: text.length,
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estimatedChunks: Math.ceil(text.length / MAX_TEXT_SIZE)
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});
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// Strategy 3: Store with optimized indexing
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await vectorDocumentProcessor.storeDocumentChunks(enrichedChunks, {
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documentId,
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indexingStrategy: 'hierarchical',
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similarity_threshold: 0.8,
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enable_hybrid_search: true
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});
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// Stream processing for large documents
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let chunkIndex = 0;
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let position = 0;
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while (position < text.length) {
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// Force garbage collection between chunks
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if (global.gc) {
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global.gc();
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}
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const chunkSize = Math.min(MAX_TEXT_SIZE, text.length - position);
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let chunkEnd = position + chunkSize;
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// Try to end at sentence boundary
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if (chunkEnd < text.length) {
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const sentenceEnd = this.findSentenceBoundary(text, chunkEnd);
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if (sentenceEnd > position + 1000) { // Ensure minimum chunk size
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chunkEnd = sentenceEnd;
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}
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}
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const chunkText = text.substring(position, chunkEnd);
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// Detect section type for this chunk
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const sectionType = this.identifySectionType(chunkText);
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chunks.push({
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content: chunkText,
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chunkIndex: chunkIndex++,
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startPosition: position,
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endPosition: chunkEnd,
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sectionType
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});
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position = chunkEnd;
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// Log progress for large documents
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if (chunkIndex % 10 === 0) {
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logger.info('Vectorization progress', {
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documentId,
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chunkIndex,
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progress: Math.round((position / text.length) * 100) + '%'
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});
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}
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}
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} else {
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// For smaller documents, use the original intelligent chunking
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chunks.push(...await this.createIntelligentChunks(text, documentId));
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}
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// Strategy 2: Process chunks in batches to manage memory
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const BATCH_SIZE = 5; // Process 5 chunks at a time
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const enrichedChunks: Array<{
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content: string;
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chunkIndex: number;
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startPosition: number;
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endPosition: number;
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sectionType?: string;
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metadata: {
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hasFinancialData: boolean;
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hasMetrics: boolean;
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keyTerms: string[];
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importance: 'high' | 'medium' | 'low';
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conceptDensity: number;
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};
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}> = [];
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for (let i = 0; i < chunks.length; i += BATCH_SIZE) {
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const batch = chunks.slice(i, i + BATCH_SIZE);
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// Process batch
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const batchPromises = batch.map(async (chunk) => {
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const metadata = {
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hasFinancialData: this.containsFinancialData(chunk.content),
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hasMetrics: this.containsMetrics(chunk.content),
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keyTerms: this.extractKeyTerms(chunk.content),
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importance: this.calculateImportance(chunk.content, chunk.sectionType),
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conceptDensity: this.calculateConceptDensity(chunk.content)
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};
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return {
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...chunk,
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metadata
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};
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});
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const batchResults = await Promise.all(batchPromises);
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enrichedChunks.push(...batchResults);
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// Force garbage collection after each batch
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if (global.gc) {
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global.gc();
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}
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// Log batch progress
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logger.info('Enriched chunk batch', {
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documentId,
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batchNumber: Math.floor(i / BATCH_SIZE) + 1,
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totalBatches: Math.ceil(chunks.length / BATCH_SIZE),
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processedChunks: enrichedChunks.length
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});
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}
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// Strategy 3: Store chunks in batches to prevent memory buildup
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const STORE_BATCH_SIZE = 3;
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for (let i = 0; i < enrichedChunks.length; i += STORE_BATCH_SIZE) {
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const storeBatch = enrichedChunks.slice(i, i + STORE_BATCH_SIZE);
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await vectorDocumentProcessor.storeDocumentChunks(storeBatch, {
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documentId,
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indexingStrategy: 'hierarchical',
|
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similarity_threshold: 0.8,
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enable_hybrid_search: true
|
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});
|
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|
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// Force garbage collection after storing each batch
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if (global.gc) {
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global.gc();
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}
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logger.info('Stored chunk batch', {
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documentId,
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batchNumber: Math.floor(i / STORE_BATCH_SIZE) + 1,
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totalBatches: Math.ceil(enrichedChunks.length / STORE_BATCH_SIZE),
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storedChunks: Math.min(i + STORE_BATCH_SIZE, enrichedChunks.length)
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});
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}
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logger.info('Document vectorization completed successfully', {
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documentId,
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sessionId,
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chunksCreated: enrichedChunks.length,
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avgChunkSize: Math.round(enrichedChunks.reduce((sum, c) => sum + c.content.length, 0) / enrichedChunks.length)
|
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avgChunkSize: Math.round(enrichedChunks.reduce((sum, c) => sum + c.content.length, 0) / enrichedChunks.length),
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totalTextLength: text.length
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});
|
||||
|
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} catch (error) {
|
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@@ -740,53 +872,53 @@ class AgenticRAGProcessor {
|
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return chunks;
|
||||
}
|
||||
|
||||
/**
|
||||
* Enrich chunks with metadata for enhanced retrieval
|
||||
*/
|
||||
private async enrichChunksWithMetadata(chunks: Array<{
|
||||
content: string;
|
||||
chunkIndex: number;
|
||||
startPosition: number;
|
||||
endPosition: number;
|
||||
sectionType?: string;
|
||||
}>): Promise<Array<{
|
||||
content: string;
|
||||
chunkIndex: number;
|
||||
startPosition: number;
|
||||
endPosition: number;
|
||||
sectionType?: string;
|
||||
metadata: {
|
||||
hasFinancialData: boolean;
|
||||
hasMetrics: boolean;
|
||||
keyTerms: string[];
|
||||
importance: 'high' | 'medium' | 'low';
|
||||
conceptDensity: number;
|
||||
};
|
||||
}>> {
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||||
const enrichedChunks = [];
|
||||
// /**
|
||||
// * Enrich chunks with metadata for enhanced retrieval
|
||||
// */
|
||||
// private async enrichChunksWithMetadata(chunks: Array<{
|
||||
// content: string;
|
||||
// chunkIndex: number;
|
||||
// startPosition: number;
|
||||
// endPosition: number;
|
||||
// sectionType?: string;
|
||||
// }>): Promise<Array<{
|
||||
// content: string;
|
||||
// chunkIndex: number;
|
||||
// startPosition: number;
|
||||
// endPosition: number;
|
||||
// sectionType?: string;
|
||||
// metadata: {
|
||||
// hasFinancialData: boolean;
|
||||
// hasMetrics: boolean;
|
||||
// keyTerms: string[];
|
||||
// importance: 'high' | 'medium' | 'low';
|
||||
// conceptDensity: number;
|
||||
// };
|
||||
// }>> {
|
||||
// const enrichedChunks = [];
|
||||
|
||||
for (const chunk of chunks) {
|
||||
// Analyze chunk content for metadata
|
||||
const hasFinancialData = this.containsFinancialData(chunk.content);
|
||||
const hasMetrics = this.containsMetrics(chunk.content);
|
||||
const keyTerms = this.extractKeyTerms(chunk.content);
|
||||
const importance = this.calculateImportance(chunk.content, chunk.sectionType);
|
||||
const conceptDensity = this.calculateConceptDensity(chunk.content);
|
||||
// for (const chunk of chunks) {
|
||||
// // Analyze chunk content for metadata
|
||||
// const hasFinancialData = this.containsFinancialData(chunk.content);
|
||||
// const hasMetrics = this.containsMetrics(chunk.content);
|
||||
// const keyTerms = this.extractKeyTerms(chunk.content);
|
||||
// const importance = this.calculateImportance(chunk.content, chunk.sectionType);
|
||||
// const conceptDensity = this.calculateConceptDensity(chunk.content);
|
||||
|
||||
enrichedChunks.push({
|
||||
...chunk,
|
||||
metadata: {
|
||||
hasFinancialData,
|
||||
hasMetrics,
|
||||
keyTerms,
|
||||
importance,
|
||||
conceptDensity
|
||||
}
|
||||
});
|
||||
}
|
||||
// enrichedChunks.push({
|
||||
// ...chunk,
|
||||
// metadata: {
|
||||
// hasFinancialData,
|
||||
// hasMetrics,
|
||||
// keyTerms,
|
||||
// importance,
|
||||
// conceptDensity
|
||||
// }
|
||||
// });
|
||||
// }
|
||||
|
||||
return enrichedChunks;
|
||||
}
|
||||
// return enrichedChunks;
|
||||
// }
|
||||
|
||||
/**
|
||||
* Detect section boundaries in CIM documents
|
||||
|
||||
@@ -389,6 +389,149 @@ class PDFGenerationService {
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Generate CIM Review PDF from analysis data
|
||||
*/
|
||||
async generateCIMReviewPDF(analysisData: any): Promise<Buffer> {
|
||||
try {
|
||||
// Convert analysis data to HTML
|
||||
const html = this.generateCIMReviewHTML(analysisData);
|
||||
|
||||
// Generate PDF buffer
|
||||
const pdfBuffer = await this.generatePDFBuffer(html, {
|
||||
format: 'A4',
|
||||
margin: {
|
||||
top: '0.5in',
|
||||
right: '0.5in',
|
||||
bottom: '0.5in',
|
||||
left: '0.5in',
|
||||
},
|
||||
displayHeaderFooter: true,
|
||||
printBackground: true,
|
||||
});
|
||||
|
||||
if (!pdfBuffer) {
|
||||
throw new Error('Failed to generate PDF buffer');
|
||||
}
|
||||
|
||||
return pdfBuffer;
|
||||
} catch (error) {
|
||||
logger.error('Failed to generate CIM Review PDF', error);
|
||||
throw error;
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Generate HTML from CIM Review analysis data
|
||||
*/
|
||||
private generateCIMReviewHTML(analysisData: any): string {
|
||||
const sections = [
|
||||
{ title: 'Deal Overview', data: analysisData.dealOverview },
|
||||
{ title: 'Business Description', data: analysisData.businessDescription },
|
||||
{ title: 'Market & Industry Analysis', data: analysisData.marketIndustryAnalysis },
|
||||
{ title: 'Financial Summary', data: analysisData.financialSummary },
|
||||
{ title: 'Management Team Overview', data: analysisData.managementTeamOverview },
|
||||
{ title: 'Preliminary Investment Thesis', data: analysisData.preliminaryInvestmentThesis },
|
||||
{ title: 'Key Questions & Next Steps', data: analysisData.keyQuestionsNextSteps },
|
||||
];
|
||||
|
||||
let html = `
|
||||
<!DOCTYPE html>
|
||||
<html>
|
||||
<head>
|
||||
<meta charset="UTF-8">
|
||||
<title>CIM Review Report</title>
|
||||
<style>
|
||||
body { font-family: Arial, sans-serif; line-height: 1.6; margin: 0; padding: 20px; }
|
||||
h1 { color: #2c3e50; border-bottom: 2px solid #3498db; padding-bottom: 10px; }
|
||||
h2 { color: #34495e; margin-top: 30px; margin-bottom: 15px; }
|
||||
h3 { color: #7f8c8d; margin-top: 20px; margin-bottom: 10px; }
|
||||
.section { margin-bottom: 25px; }
|
||||
.field { margin-bottom: 10px; }
|
||||
.field-label { font-weight: bold; color: #2c3e50; }
|
||||
.field-value { margin-left: 10px; }
|
||||
.financial-table { width: 100%; border-collapse: collapse; margin: 10px 0; }
|
||||
.financial-table th, .financial-table td { border: 1px solid #ddd; padding: 8px; text-align: left; }
|
||||
.financial-table th { background-color: #f8f9fa; font-weight: bold; }
|
||||
</style>
|
||||
</head>
|
||||
<body>
|
||||
<h1>CIM Review Report</h1>
|
||||
`;
|
||||
|
||||
sections.forEach(section => {
|
||||
if (section.data) {
|
||||
html += `<div class="section"><h2>${section.title}</h2>`;
|
||||
|
||||
Object.entries(section.data).forEach(([key, value]) => {
|
||||
if (value && typeof value === 'object' && !Array.isArray(value)) {
|
||||
// Handle nested objects
|
||||
html += `<h3>${this.formatFieldName(key)}</h3>`;
|
||||
Object.entries(value).forEach(([subKey, subValue]) => {
|
||||
if (subValue) {
|
||||
html += `
|
||||
<div class="field">
|
||||
<span class="field-label">${this.formatFieldName(subKey)}:</span>
|
||||
<span class="field-value">${subValue}</span>
|
||||
</div>
|
||||
`;
|
||||
}
|
||||
});
|
||||
} else if (key === 'financials' && typeof value === 'object') {
|
||||
// Handle financial table
|
||||
html += `<h3>Financial Data</h3>`;
|
||||
html += `<table class="financial-table">`;
|
||||
html += `<tr><th>Period</th><th>Revenue</th><th>Growth</th><th>EBITDA</th><th>Margin</th></tr>`;
|
||||
|
||||
const periods = ['fy3', 'fy2', 'fy1', 'ltm'];
|
||||
periods.forEach(period => {
|
||||
if (value && typeof value === 'object' && value[period as keyof typeof value]) {
|
||||
const data = value[period as keyof typeof value] as any;
|
||||
html += `
|
||||
<tr>
|
||||
<td>${period.toUpperCase()}</td>
|
||||
<td>${data?.revenue || '-'}</td>
|
||||
<td>${data?.revenueGrowth || '-'}</td>
|
||||
<td>${data?.ebitda || '-'}</td>
|
||||
<td>${data?.ebitdaMargin || '-'}</td>
|
||||
</tr>
|
||||
`;
|
||||
}
|
||||
});
|
||||
html += `</table>`;
|
||||
} else if (value) {
|
||||
// Handle simple fields
|
||||
html += `
|
||||
<div class="field">
|
||||
<span class="field-label">${this.formatFieldName(key)}:</span>
|
||||
<span class="field-value">${value}</span>
|
||||
</div>
|
||||
`;
|
||||
}
|
||||
});
|
||||
|
||||
html += `</div>`;
|
||||
}
|
||||
});
|
||||
|
||||
html += `
|
||||
</body>
|
||||
</html>
|
||||
`;
|
||||
|
||||
return html;
|
||||
}
|
||||
|
||||
/**
|
||||
* Format field names for display
|
||||
*/
|
||||
private formatFieldName(fieldName: string): string {
|
||||
return fieldName
|
||||
.replace(/([A-Z])/g, ' $1')
|
||||
.replace(/^./, str => str.toUpperCase())
|
||||
.replace(/([A-Z]{2,})/g, match => match.charAt(0) + match.slice(1).toLowerCase());
|
||||
}
|
||||
|
||||
/**
|
||||
* Close browser instance
|
||||
*/
|
||||
|
||||
Reference in New Issue
Block a user