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Add initial backend services for AI OCR
Introduces a NestJS-based backend for document upload and OCR management, including modules for document handling, database stubs, queue stubs, and OCR stubs. Adds REST API endpoints, DTO validation, file storage logic, and test scripts for upload functionality. Includes configuration, documentation, and testing instructions.
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‎apps/README.md‎

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# AI OCR Dashboard
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Backend services for government document processing and OCR management.
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## Project Structure
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This is a monorepo containing:
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- **Backend Services** (`apps/backend-services`) - NestJS API services for document processing
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## Features
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- 📄 Document Upload & Processing
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- 🔍 **OCR (Optical Character Recognition)** - Real-time text extraction from images and PDFs
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- Image OCR processing with word-level bounding boxes
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- Multi-page PDF OCR with per-page analysis
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- Confidence scoring for extracted text
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## Tech Stack
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### Backend Services
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- **NestJS** - Node.js framework
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- **Fastify** - HTTP server
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- **TypeScript** - Type Safety
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## Getting Started
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### Prerequisites
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- Node.js 22+ (recommended)
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- npm 9+
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### Installation
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1. Install root dependencies:
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```bash
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npm install
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```
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2. Install backend services dependencies:
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```bash
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cd apps/backend-services && npm install
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```
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### Development
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#### Start Backend Services
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```bash
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npm run dev
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# or
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cd apps/backend-services && npm run start:dev
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```
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### Available Scripts
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**Root Level:**
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- `npm run dev` - Start backend services
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- `npm run build` - Build backend services
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**Backend Services:**
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- `cd apps/backend-services && npm run start:dev` - Start development server
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- `cd apps/backend-services && npm run build` - Build for production
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- `cd apps/backend-services && npm run start:prod` - Start production server
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## Project Structure
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```
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ai-ocr/
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├── apps/
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│ └── backend-services/ # NestJS backend services
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│ ├── src/
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│ │ ├── modules/ # Feature modules
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│ │ ├── common/ # Shared utilities
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│ │ └── main.ts # Application entry point
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│ └── package.json # Backend services dependencies
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└── package.json # Root workspace configuration
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```
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## Development Notes
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- Backend services handle document uploads via REST API
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- Files are stored to local filesystem with UUID-based naming
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- Database API and message queue integrations are stubbed and ready for implementation
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- See `apps/backend-services/README.md` for detailed documentation
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## License
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MIT
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‎apps/backend-services/.gitignore‎

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# compiled output
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/dist
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/node_modules
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# Logs
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logs
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*.log
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npm-debug.log*
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pnpm-debug.log*
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yarn-debug.log*
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yarn-error.log*
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lerna-debug.log*
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# OS
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.DS_Store
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# Tests
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/coverage
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/.nyc_output
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# IDEs and editors
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/.idea
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.project
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.classpath
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.c9/
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*.launch
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.settings/
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*.sublime-workspace
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# IDE - VSCode
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.vscode/*
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!.vscode/settings.json
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!.vscode/tasks.json
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!.vscode/launch.json
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!.vscode/extensions.json
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# Environment
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.env
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.env.local
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.env.*.local
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# Storage
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/storage
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# Build info
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*.tsbuildinfo
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‎apps/backend-services/README.md‎

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# Backend Services
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NestJS backend services for the AI OCR pipeline. Handles document uploads via REST API, stores files to local filesystem, and integrates with database API and message queue (stubbed).
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## Features
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- REST API endpoint for document uploads (base64-encoded files)
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- Local filesystem storage with UUID-based naming
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- Stubbed database API integration
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- Stubbed RabbitMQ message queue integration
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- File type validation
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- Comprehensive error handling and logging
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## Prerequisites
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- Node.js 22+
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- npm 9+
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## Setup
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### 1. Install Dependencies
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```bash
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npm install
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```
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### 2. Environment Configuration
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Create a `.env` file in the backend services directory:
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```env
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PORT=3002
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NODE_ENV=development
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FRONTEND_URL=http://localhost:5173
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DATABASE_API_URL=http://localhost:3001/api/documents
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STORAGE_PATH=./storage/documents
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RABBITMQ_URL=amqp://localhost:5672
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RABBITMQ_EXCHANGE=document_upload
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RABBITMQ_ROUTING_KEY=document.uploaded
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```
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### 3. Run the Service
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```bash
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# Development mode
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npm run start:dev
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# Production mode
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npm run build
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npm run start:prod
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```
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## API Endpoints
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### POST /api/upload
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Upload a document with base64-encoded file data.
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**Request Body:**
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```json
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{
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"title": "Document Title",
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"file": "base64-encoded-file-data",
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"file_type": "pdf|image|scan",
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"original_filename": "document.pdf",
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"metadata": {
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"key": "value"
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}
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}
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```
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**Response:**
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```json
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{
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"success": true,
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"document": {
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"id": "doc_1234567890_abc123",
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"title": "Document Title",
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"original_filename": "document.pdf",
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"file_type": "pdf",
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"file_size": 1024,
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"status": "pending",
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"created_at": "2024-01-01T00:00:00.000Z"
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}
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}
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```
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## Architecture
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- **Framework**: NestJS with Fastify
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- **Database**: Stubbed API client (ready for HTTP client integration)
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- **Message Queue**: Stubbed RabbitMQ interface (ready for amqplib integration)
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- **File Storage**: Local filesystem (can be upgraded to S3/object storage)
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## Testing
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See [TESTING.md](./TESTING.md) for comprehensive testing instructions.
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### Quick Test
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1. **Start the service:**
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```bash
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npm run start:dev
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```
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2. **Use the test script:**
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```bash
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# Using bash script
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./test-upload.sh path/to/your/file.pdf
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# Using Node.js script
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node test-upload.js path/to/your/file.pdf
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```
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3. **Or use cURL:**
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```bash
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FILE_BASE64=$(base64 -i yourfile.pdf)
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curl -X POST http://localhost:3002/api/upload \
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-H "Content-Type: application/json" \
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-d "{
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\"title\": \"Test Document\",
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\"file\": \"$FILE_BASE64\",
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\"file_type\": \"pdf\",
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\"original_filename\": \"yourfile.pdf\"
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}"
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```
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## Development
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The service uses stubbed implementations for:
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- Database operations (API calls logged, ready for HTTP client)
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- RabbitMQ message publishing (logged, ready for amqplib)
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Replace the stubbed implementations when ready to integrate with actual services.
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