Add DocumentProcessorModule and DocumentProcessorService for medical document processing

- Introduced DocumentProcessorModule in backend/src/modules/document-processor.module.ts to encapsulate the document processing logic.
- Implemented DocumentProcessorService in backend/src/services/document-processor.service.ts, integrating AWS Textract for text extraction and AWS Bedrock for medical analysis.
- Added unit tests for DocumentProcessorService in backend/src/services/document-processor.service.spec.ts to ensure functionality and error handling.
- Updated app.module.ts to include DocumentProcessorModule, enhancing the application's capability to process medical documents efficiently.

Author: adamrefaey

backend/src/app.module.ts

@@ -12,6 +12,7 @@ import { ReportsModule } from './reports/reports.module';
 import { HealthController } from './health/health.controller';
 import { AuthMiddleware } from './auth/auth.middleware';
 import { TextractModule } from './modules/textract.module';
+import { DocumentProcessorModule } from './modules/document-processor.module';
 
 @Module({
   imports: [
@@ -21,6 +22,7 @@ import { TextractModule } from './modules/textract.module';
     }),
     ReportsModule,
     TextractModule,
+    DocumentProcessorModule,
   ],
   controllers: [AppController, HealthController, PerplexityController, UserController],
   providers: [AppService, AwsSecretsService, AwsBedrockService, PerplexityService],

backend/src/modules/document-processor.module.ts

@@ -0,0 +1,13 @@
+import { Module } from '@nestjs/common';
+import { DocumentProcessorService } from '../services/document-processor.service';
+import { ConfigModule } from '@nestjs/config';
+import { AwsTextractService } from '../services/aws-textract.service';
+import { AwsBedrockService } from '../services/aws-bedrock.service';
+
+@Module({
+  imports: [ConfigModule],
+  controllers: [],
+  providers: [DocumentProcessorService, AwsTextractService, AwsBedrockService],
+  exports: [DocumentProcessorService],
+})
+export class DocumentProcessorModule {}

backend/src/services/README.md

@@ -0,0 +1,217 @@
+# Document Processor Service
+
+This service integrates AWS Textract for text extraction and AWS Bedrock for medical analysis to process medical documents.
+
+## Overview
+
+The Document Processor Service provides a unified interface for processing medical documents through a two-step approach:
+
+1. Extract text from medical documents (images or PDFs) using AWS Textract
+2. Analyze the extracted text using AWS Bedrock (Claude) to provide structured medical information
+
+## Components
+
+The integration consists of the following components:
+
+1. **DocumentProcessorService**: Main service that orchestrates the document processing workflow
+2. **AwsTextractService**: Extracts text, tables, and form data from medical documents 
+3. **AwsBedrockService**: Analyzes medical text using Claude model to extract structured information
+4. **DocumentProcessorController**: Exposes HTTP endpoints for document upload and processing
+
+## Data Models
+
+### ProcessedDocumentResult
+
+The result of document processing includes:
+
+```typescript
+export interface ProcessedDocumentResult {
+  extractedText: ExtractedTextResult;
+  analysis: MedicalDocumentAnalysis;
+  processingMetadata: {
+    processingTimeMs: number;
+    fileType: string;
+    fileSize: number;
+  };
+}
+```
+
+### ExtractedTextResult
+
+The raw text extraction from Textract:
+
+```typescript
+export interface ExtractedTextResult {
+  rawText: string;
+  lines: string[];
+  tables: Array<{
+    rows: string[][];
+  }>;
+  keyValuePairs: Array<{
+    key: string;
+    value: string;
+  }>;
+}
+```
+
+### MedicalDocumentAnalysis
+
+The structured medical information from Bedrock:
+
+```typescript
+export interface MedicalDocumentAnalysis {
+  keyMedicalTerms: Array<{ term: string; definition: string }>;
+  labValues: Array<{
+    name: string;
+    value: string;
+    unit: string;
+    normalRange: string;
+    isAbnormal: boolean;
+  }>;
+  diagnoses: Array<{ condition: string; details: string; recommendations: string }>;
+  metadata: {
+    isMedicalReport: boolean;
+    confidence: number;
+    missingInformation: string[];
+  };
+}
+```
+
+## API Endpoints
+
+### Process a Document
+
+```
+POST /api/document-processor/analyze
+```
+
+**Request Format:**
+- Content-Type: `multipart/form-data`
+- Body: Form with a file upload field named `file`
+- Authorization: Bearer token required
+
+**Example Request:**
+```bash
+curl -X POST \
+  "http://localhost:3000/api/document-processor/analyze" \
+  -H "Authorization: Bearer YOUR_JWT_TOKEN" \
+  -H "Content-Type: multipart/form-data" \
+  -F "file=@/path/to/medical_report.pdf"
+```
+
+**Response:**
+```json
+{
+  "extractedText": {
+    "rawText": "BLOOD TEST RESULTS\nPatient: John Doe\nHemoglobin: 14.2 g/dL (Normal: 13.5-17.5)",
+    "lines": ["BLOOD TEST RESULTS", "Patient: John Doe", "Hemoglobin: 14.2 g/dL (Normal: 13.5-17.5)"],
+    "tables": [],
+    "keyValuePairs": [
+      { "key": "Patient", "value": "John Doe" },
+      { "key": "Hemoglobin", "value": "14.2 g/dL (Normal: 13.5-17.5)" }
+    ]
+  },
+  "analysis": {
+    "keyMedicalTerms": [
+      { "term": "Hemoglobin", "definition": "Oxygen-carrying protein in red blood cells" }
+    ],
+    "labValues": [
+      {
+        "name": "Hemoglobin",
+        "value": "14.2",
+        "unit": "g/dL",
+        "normalRange": "13.5-17.5",
+        "isAbnormal": false
+      }
+    ],
+    "diagnoses": [],
+    "metadata": {
+      "isMedicalReport": true,
+      "confidence": 0.95,
+      "missingInformation": []
+    }
+  },
+  "processingMetadata": {
+    "processingTimeMs": 2345,
+    "fileType": "application/pdf",
+    "fileSize": 12345
+  }
+}
+```
+
+## Usage from Code
+
+```typescript
+// Inject the service
+constructor(private readonly documentProcessorService: DocumentProcessorService) {}
+
+// Process a document
+async processReport(fileBuffer: Buffer, fileType: string, userId: string) {
+  try {
+    const result = await this.documentProcessorService.processDocument(
+      fileBuffer,
+      fileType,
+      userId
+    );
+    
+    // Use the structured medical data
+    const labValues = result.analysis.labValues;
+    const abnormalValues = labValues.filter(lab => lab.isAbnormal);
+    
+    return result;
+  } catch (error) {
+    console.error('Error processing medical document:', error);
+    throw error;
+  }
+}
+```
+
+## Rate Limiting
+
+Both services implement rate limiting based on user ID:
+- AWS Textract: 10 document requests per minute by default (configurable)
+- AWS Bedrock: 20 model invocations per minute by default (configurable)
+
+## Batch Processing
+
+The service supports batch processing of multiple documents:
+
+```typescript
+const results = await documentProcessorService.processBatch(
+  [
+    { buffer: fileBuffer1, type: fileType1 },
+    { buffer: fileBuffer2, type: fileType2 }
+  ],
+  userId
+);
+```
+
+## Configuration
+
+Configure the services through environment variables:
+
+```bash
+# AWS Region
+AWS_REGION=us-east-1
+
+# AWS Credentials (if not using IAM roles)
+AWS_ACCESS_KEY_ID=your-access-key
+AWS_SECRET_ACCESS_KEY=your-secret-key
+
+# AWS Bedrock 
+AWS_BEDROCK_MODEL=us.anthropic.claude-3-7-sonnet-20250219-v1:0
+AWS_BEDROCK_MAX_TOKENS=2048
+AWS_BEDROCK_REQUESTS_PER_MINUTE=20
+
+# AWS Textract
+AWS_TEXTRACT_MAX_BATCH_SIZE=10
+AWS_TEXTRACT_DOCS_PER_MINUTE=10
+```
+
+## Future Enhancements
+
+Planned future enhancements:
+- Support for multi-page PDF processing using async APIs
+- Enhanced lab report detection and categorization
+- Integration with medical terminology databases
+- OCR preprocessing for low-quality images 

backend/src/services/aws-bedrock.service.spec.ts

@@ -215,7 +215,7 @@ describe('AwsBedrockService', () => {
 
       // Verify the invokeBedrock was called with the correct prompt
       expect(service['invokeBedrock']).toHaveBeenCalled();
-      const prompt = (service['invokeBedrock'] as jest.Mock).mock.calls[0][0];
+      const prompt = (service['invokeBedrock'] as any).mock.calls[0][0];
       expect(prompt).toContain('Please analyze this medical document carefully');
     });
 
@@ -238,7 +238,7 @@ describe('AwsBedrockService', () => {
 
       // Verify the invokeBedrock was called with the correct prompt
       expect(service['invokeBedrock']).toHaveBeenCalled();
-      const prompt = (service['invokeBedrock'] as jest.Mock).mock.calls[0][0];
+      const prompt = (service['invokeBedrock'] as any).mock.calls[0][0];
       expect(prompt).toContain('Please analyze this medical document carefully');
     });
 

backend/src/services/aws-textract.service.spec.ts

@@ -202,7 +202,7 @@ describe('AwsTextractService', () => {
 
     it('should handle rate limiting by user ID', async () => {
       // Mock rate limiter to reject the request
-      (service['rateLimiter'].tryRequest as jest.Mock).mockReturnValueOnce(false);
+      (service['rateLimiter'].tryRequest as any).mockReturnValueOnce(false);
 
       // Use a test user ID
       const userId = 'rate-limited-user';