# MyDocManager

## Overview

MyDocManager is a real-time document processing application that automatically detects files in a monitored directory,
processes them asynchronously, and stores the results in a database. The application uses a modern microservices
architecture with Redis for task queuing and MongoDB for data persistence.

## Architecture

### Technology Stack

- **Backend API**: FastAPI (Python 3.12)
- **Task Processing**: Celery with Redis broker
- **Document Processing**: EasyOCR, PyMuPDF, python-docx, pdfplumber
- **Database**: MongoDB
- **Frontend**: React
- **Containerization**: Docker & Docker Compose
- **File Monitoring**: Python watchdog library

### Services Architecture

    ┌─────────────────┐    ┌─────────────┐    ┌─────────────┐    ┌─────────────┐    ┌─────────────┐
    │   Frontend      │    │ file-       │    │    Redis    │    │   Worker    │    │  MongoDB    │
    │   (React)       │◄──►│ processor   │───►│  (Broker)   │◄──►│  (Celery)   │───►│ (Results)   │
    │                 │    │ (FastAPI +  │    │             │    │             │    │             │
    │                 │    │ watchdog)   │    │             │    │             │    │             │
    └─────────────────┘    └─────────────┘    └─────────────┘    └─────────────┘    └─────────────┘

### Docker Services

1. **file-processor**: FastAPI + real-time file monitoring + Celery task dispatch
2. **worker**: Celery workers for document processing (OCR, text extraction)
3. **redis**: Message broker for Celery tasks
4. **mongodb**: Final database for processing results
5. **frontend**: React interface for monitoring and file access

## Data Flow

1. **File Detection**: Watchdog monitors target directory in real-time
2. **Task Creation**: FastAPI creates Celery task for each detected file
3. **Task Processing**: Worker processes document (OCR, text extraction)
4. **Result Storage**: Processed data stored in MongoDB
5. **Monitoring**: React frontend displays processing status and results

## Document Processing Capabilities

### Supported File Types

- **PDF**: Direct text extraction + OCR for scanned documents
- **Word Documents**: .docx text extraction
- **Images**: OCR text recognition (JPG, PNG, etc.)

### Processing Libraries

- **EasyOCR**: Modern OCR engine (80+ languages, deep learning-based)
- **PyMuPDF**: PDF text extraction and manipulation
- **python-docx**: Word document processing
- **pdfplumber**: Advanced PDF text extraction

## Development Environment

### Container-Based Development

The application is designed for container-based development with hot-reload capabilities:

- Source code mounted as volumes for real-time updates
- All services orchestrated via Docker Compose
- Development and production parity

### Key Features

- **Real-time Processing**: Immediate file detection and processing
- **Horizontal Scaling**: Multiple workers can be added easily
- **Fault Tolerance**: Celery provides automatic retry mechanisms
- **Monitoring**: Built-in task status tracking
- **Hot Reload**: Development changes reflected instantly in containers

### Docker Services

1. **file-processor**: FastAPI + real-time file monitoring + Celery task dispatch
2. **worker**: Celery workers for document processing (OCR, text extraction)
3. **redis**: Message broker for Celery tasks
4. **mongodb**: Final database for processing results
5. **frontend**: React interface for monitoring and file access

## Project Structure

```
MyDocManager/
├── docker-compose.yml
├── src/
│   ├── file-processor/
│   │   ├── Dockerfile
│   │   ├── requirements.txt
│   │   ├── app/
│   │   │   ├── main.py
│   │   │   ├── file_watcher.py
│   │   │   ├── celery_app.py
│   │   │   ├── config/
│   │   │   │   ├── __init__.py
│   │   │   │   └── settings.py              # JWT, MongoDB config
│   │   │   ├── models/
│   │   │   │   ├── __init__.py
│   │   │   │   ├── user.py                  # User Pydantic models
│   │   │   │   └── auth.py                  # Auth Pydantic models
│   │   │   ├── database/
│   │   │   │   ├── __init__.py
│   │   │   │   ├── connection.py            # MongoDB connection
│   │   │   │   └── repositories/
│   │   │   │       ├── __init__.py
│   │   │   │       └── user_repository.py   # User CRUD operations
│   │   │   ├── services/
│   │   │   │   ├── __init__.py
│   │   │   │   ├── auth_service.py          # JWT & password logic
│   │   │   │   ├── user_service.py          # User business logic
│   │   │   │   └── init_service.py          # Admin creation at startup
│   │   │   ├── api/
│   │   │   │   ├── __init__.py
│   │   │   │   ├── dependencies.py          # Auth dependencies
│   │   │   │   └── routes/
│   │   │   │       ├── __init__.py
│   │   │   │       ├── auth.py              # Authentication routes
│   │   │   │       └── users.py             # User management routes
│   │   │   └── utils/
│   │   │       ├── __init__.py
│   │   │       ├── security.py             # Password utilities
│   │   │       └── exceptions.py           # Custom exceptions
│   ├── worker/
│   │   ├── Dockerfile
│   │   ├── requirements.txt
│   │   └── tasks/
│   └── frontend/
│       ├── Dockerfile
│       ├── package.json
│       └── src/
├── tests/
│   ├── file-processor/
│   │   ├── test_auth/
│   │   ├── test_users/
│   │   └── test_services/
│   └── worker/
├── volumes/
│   └── watched_files/
└── README.md
```

## Authentication & User Management

### Security Features

- **JWT Authentication**: Stateless authentication with 24-hour token expiration
- **Password Security**: bcrypt hashing with automatic salting
- **Role-Based Access**: Admin and User roles with granular permissions
- **Protected Routes**: All user management APIs require valid authentication
- **Auto Admin Creation**: Default admin user created on first startup

### User Roles

- **Admin**: Full access to user management (create, read, update, delete users)
- **User**: Limited access (view own profile, access document processing features)

### Authentication Flow

1. **Login**: User provides credentials → Server validates → Returns JWT token
2. **API Access**: Client includes JWT in Authorization header
3. **Token Validation**: Server verifies token signature and expiration
4. **Role Check**: Server validates user permissions for requested resource

### User Management APIs

```
POST /auth/login              # Generate JWT token
GET  /users                   # List all users (admin only)
POST /users                   # Create new user (admin only)
PUT  /users/{user_id}         # Update user (admin only)
DELETE /users/{user_id}       # Delete user (admin only)
GET  /users/me                # Get current user profile (authenticated users)
```

### Useful Service URLs

- **FastAPI API**: http://localhost:8000
- **FastAPI Docs**: http://localhost:8000/docs
- **Health Check**: http://localhost:8000/health
- **Redis**: localhost:6379
- **MongoDB**: localhost:27017

### Testing Commands

```bash
# Test FastAPI health
curl http://localhost:8000/health

# Test Celery task dispatch
curl -X POST http://localhost:8000/test-task \
  -H "Content-Type: application/json" \
  -d '{"message": "Hello from test!"}'

# Monitor Celery tasks
docker-compose logs -f worker
```

## Default Admin User

On first startup, the application automatically creates a default admin user:

- **Username**: `admin`
- **Password**: `admin`
- **Role**: `admin`
- **Email**: `admin@mydocmanager.local`
  **⚠️ Important**: Change the default admin password immediately after first login in production environments.

## File Processing Architecture

### Document Processing Flow

1. **File Detection**: Watchdog monitors `/volumes/watched_files/` directory in real-time
2. **Task Creation**: File watcher creates Celery task for each detected file
3. **Document Processing**: Celery worker processes the document and extracts content
4. **Database Storage**: Processed data stored in MongoDB collections

### MongoDB Collections Design

#### Files Collection

Stores file metadata and extracted content:

```json
{
  "_id": "ObjectId",
  "filename": "document.pdf",
  "filepath": "/watched_files/document.pdf",
  "file_type": "pdf",
  "mime_type": "application/pdf",
  "file_size": 2048576,
  "content": "extracted text content...",
  "encoding": "utf-8",
  "extraction_method": "direct_text",
  // direct_text, ocr, hybrid
  "metadata": {
    "page_count": 15,
    // for PDFs
    "word_count": 250,
    // for text files  
    "image_dimensions": {
      // for images
      "width": 1920,
      "height": 1080
    }
  },
  "detected_at": "2024-01-15T10:29:00Z",
  "file_hash": "sha256_hash_value"
}
```

#### Processing Jobs Collection

Tracks processing status and lifecycle:

```json
{
  "_id": "ObjectId",
  "file_id": "reference_to_files_collection",
  "status": "completed",
  // pending, processing, completed, failed
  "task_id": "celery_task_uuid",
  "created_at": "2024-01-15T10:29:00Z",
  "started_at": "2024-01-15T10:29:30Z",
  "completed_at": "2024-01-15T10:30:00Z",
  "error_message": null
}
```

### Supported File Types (Initial Implementation)

- **Text Files** (`.txt`): Direct content reading
- **PDF Documents** (`.pdf`): Text extraction via PyMuPDF/pdfplumber
- **Word Documents** (`.docx`): Content extraction via python-docx

### File Processing Architecture Decisions

#### Watchdog Implementation

- **Choice**: Dedicated observer thread (Option A)
- **Rationale**: Standard approach, clean separation of concerns
- **Implementation**: Watchdog observer runs in separate thread from FastAPI

#### Task Dispatch Strategy

- **Choice**: Direct Celery task creation from file watcher
- **Rationale**: Minimal latency, straightforward flow
- **Implementation**: File detected → Immediate Celery task dispatch

#### Data Storage Strategy

- **Choice**: Separate collections for files and processing status
- **Rationale**: Clean separation of file data vs processing lifecycle
- **Benefits**:
    - Better query performance
    - Clear data model boundaries
    - Easy processing status tracking

#### Content Storage Location

- **Choice**: Store extracted content in `files` collection
- **Rationale**: Content is intrinsic property of the file
- **Benefits**: Single query to get file + content, simpler data model

### Implementation Order

1. ✅ Pydantic models for MongoDB collections
2. ✅ Repository layer for data access (files + processing_jobs)
3. ✅ Celery tasks for document processing
4. ✅ Watchdog file monitoring implementation
5. ✅ FastAPI integration and startup coordination

### Processing Pipeline Features

- **Duplicate Detection**: SHA256 hashing prevents reprocessing same files
- **Error Handling**: Failed processing tracked with error messages
- **Status Tracking**: Real-time processing status via `processing_jobs` collection
- **Extensible Metadata**: Flexible metadata storage per file type
- **Multiple Extraction Methods**: Support for direct text, OCR, and hybrid approaches

## Key Implementation Notes

### Python Standards

- **Style**: PEP 8 compliance
- **Documentation**: Google/NumPy docstring format
- **Naming**: snake_case for variables and functions
- **Testing**: pytest with test_i_can_xxx / test_i_cannot_xxx patterns

### Security Best Practices

- **Password Storage**: Never store plain text passwords, always use bcrypt hashing
- **JWT Secrets**: Use strong, randomly generated secret keys in production
- **Token Expiration**: 24-hour expiration with secure signature validation
- **Role Validation**: Server-side role checking for all protected endpoints

### Dependencies Management

- **Package Manager**: pip (standard)
- **External Dependencies**: Listed in each service's requirements.txt
- **Standard Library First**: Prefer standard library when possible

### Testing Strategy

- All code must be testable
- Unit tests for each authentication and user management function
- Integration tests for complete authentication flow
- Tests validated before implementation

### Critical Architecture Decisions Made

1. **JWT Authentication**: Simple token-based auth with 24-hour expiration
2. **Role-Based Access**: Admin/User roles for granular permissions
3. **bcrypt Password Hashing**: Industry-standard password security
4. **MongoDB User Storage**: Centralized user management in main database
5. **Auto Admin Creation**: Automatic setup for first-time deployment
6. **Single FastAPI Service**: Handles both API and file watching with authentication
7. **Celery with Redis**: Chosen over other async patterns for scalability
8. **EasyOCR Preferred**: Selected over Tesseract for modern OCR needs
9. **Container Development**: Hot-reload setup required for development workflow
10. **Dedicated Watchdog Observer**: Thread-based file monitoring for reliability
11. **Separate MongoDB Collections**: Files and processing jobs stored separately
12. **Content in Files Collection**: Extracted content stored with file metadata
13. **Direct Task Dispatch**: File watcher directly creates Celery tasks
14. **SHA256 Duplicate Detection**: Prevents reprocessing identical files

### Development Process Requirements

1. **Collaborative Validation**: All options must be explained before coding
2. **Test-First Approach**: Test cases defined and validated before implementation
3. **Incremental Development**: Start simple, extend functionality progressively
4. **Error Handling**: Clear problem explanation required before proposing fixes

### Next Implementation Steps

1. ✅ Create docker-compose.yml with all services => Done
2. ✅ Define user management and authentication architecture => Done
3. ✅ Implement user models and authentication services =>
    1. models/user.py => Done
    2. models/auth.py => Done
    3. database/repositories/user_repository.py => Done
4. ✅ Add automatic admin user creation if it does not exists => Done
5. **IN PROGRESS**: Implement file processing pipeline =>
    1. Create Pydantic models for files and processing_jobs collections
    2. Implement repository layer for file and processing job data access
    3. Create Celery tasks for document processing (.txt, .pdf, .docx)
    4. Implement Watchdog file monitoring with dedicated observer
    5. Integrate file watcher with FastAPI startup
6. Create protected API routes for user management
7. Build React monitoring interface with authentication

## Annexes

### Docker Commands Reference

#### Initial Setup & Build

```bash
# Build and start all services (first time)
docker-compose up --build

# Build and start in background
docker-compose up --build -d

# Build specific service
docker-compose build file-processor
docker-compose build worker
```

#### Development Workflow

```bash
# Start all services
docker-compose up

# Start in background (detached mode)
docker-compose up -d

# Stop all services
docker-compose down

# Stop and remove volumes (⚠️ deletes MongoDB data)
docker-compose down -v

# Restart specific service
docker-compose restart file-processor
docker-compose restart worker
docker-compose restart redis
docker-compose restart mongodb
```

#### Monitoring & Debugging

```bash
# View logs of all services
docker-compose logs

# View logs of specific service
docker-compose logs file-processor
docker-compose logs worker
docker-compose logs redis
docker-compose logs mongodb

# Follow logs in real-time
docker-compose logs -f
docker-compose logs -f worker

# View running containers
docker-compose ps

# Execute command in running container
docker-compose exec file-processor bash
docker-compose exec worker bash
docker-compose exec mongodb mongosh
```

#### Service Management

```bash
# Start only specific services
docker-compose up redis mongodb file-processor

# Stop specific service
docker-compose stop worker
docker-compose stop file-processor

# Remove stopped containers
docker-compose rm

# Scale workers (multiple instances)
docker-compose up --scale worker=3
```

### Hot-Reload Configuration

- **file-processor**: Hot-reload enabled via `--reload` flag
    - Code changes in `src/file-processor/app/` automatically restart FastAPI
- **worker**: No hot-reload (manual restart required for stability)
    - Code changes in `src/worker/tasks/` require: `docker-compose restart worker`