Skip to content

Taskv1.md

Latest commit

 

History

History
254 lines (202 loc) · 6.91 KB

Taskv1.md

File metadata and controls

254 lines (202 loc) · 6.91 KB

MDUS System - MVP Task Breakdown (Taskv1)

Product Overview

Multi-Modal Document Understanding System (MDUS) - Proof of Concept MVP for automated document processing using AI-powered computer vision and NLP with Docker deployment.

MVP Architecture (Docker-Based)

Core Components

  • AI Processing Service: LayoutLMv3 + Donut models in Docker containers
  • API Service: FastAPI backend in Docker container
  • Web Interface: React frontend served via Docker
  • Database: PostgreSQL in Docker container
  • Redis Cache: Redis container for session management
  • File Storage: Local Docker volumes for document storage

MVP Task Breakdown

TASK 1: Docker Environment Setup

Effort: 1 week Priority: Critical

Description: Set up Docker-based development and deployment environment

Acceptance Criteria:

  • Docker Compose configuration for all services
  • Local development environment with hot-reload
  • Environment variables configuration
  • Docker networking between services
  • Volume mounts for persistent storage
  • Basic monitoring with Docker logs

Deliverables:

  • docker-compose.yml file
  • Dockerfile for each service
  • .env configuration files
  • Development setup documentation

TASK 2: Basic AI Model Integration

Effort: 2 weeks Priority: Critical

Description: Integrate core AI models for document processing

Acceptance Criteria:

  • LayoutLMv3 model containerized and running
  • Basic OCR processing with Donut
  • Document classification (invoice, receipt, form, contract)
  • Key-value pair extraction
  • Processing pipeline handling single documents
  • Basic error handling and logging

Deliverables:

  • AI processing service Docker container
  • Model inference pipeline
  • Basic document type classification
  • Key information extraction functionality

TASK 3: REST API Development

Effort: 1 week Priority: Critical

Description: Create minimal API for document upload and processing

Acceptance Criteria:

  • FastAPI application in Docker container
  • Document upload endpoint
  • Processing status endpoint
  • Results retrieval endpoint
  • Basic authentication
  • API documentation with Swagger
  • Error handling and validation

Deliverables:

  • FastAPI service with core endpoints
  • API documentation
  • Basic request/response models
  • Authentication middleware

TASK 4: Database Setup

Effort: 3 days Priority: High

Description: Set up PostgreSQL database for storing processing results

Acceptance Criteria:

  • PostgreSQL container configuration
  • Database schema for documents and results
  • Database migrations setup
  • Connection pooling configuration
  • Basic data models and relationships

Deliverables:

  • PostgreSQL Docker service
  • Database schema and migrations
  • Data access layer
  • Connection configuration

TASK 5: Basic Web Interface

Effort: 1 week Priority: High

Description: Simple React web interface for document upload and results viewing

Acceptance Criteria:

  • React application containerized
  • Document upload interface
  • Processing status display
  • Results visualization
  • Basic responsive design
  • Integration with backend API

Deliverables:

  • React frontend Docker container
  • Upload interface component
  • Results display component
  • Basic styling and layout

TASK 6: File Storage & Processing Pipeline

Effort: 3 days Priority: High

Description: Set up file storage and basic processing workflow

Acceptance Criteria:

  • Docker volume for file storage
  • File upload and storage handling
  • Processing queue with Redis
  • Basic workflow: upload → process → store results
  • File cleanup and management

Deliverables:

  • File storage configuration
  • Processing workflow
  • Redis queue setup
  • File management utilities

TASK 7: Integration & Testing -- start here using data-scientist

Effort: 3 days Priority: Medium

Description: End-to-end integration testing of all components

Acceptance Criteria:

  • All Docker services communicate properly
  • End-to-end document processing workflow
  • Basic integration tests
  • Performance testing with sample documents
  • Error scenario handling

Deliverables:

  • Integration test suite
  • Sample test documents
  • Performance benchmarks
  • Error handling verification

Docker Services Configuration

Services Overview

services:
  # AI Processing Service
  ai-processor:
    - LayoutLMv3 model
    - Donut OCR model
    - Python ML environment
    
  # API Backend
  api-backend:
    - FastAPI application
    - Authentication
    - File handling
    
  # Web Frontend
  web-frontend:
    - React application
    - Nginx server
    
  # Database
  postgres:
    - PostgreSQL database
    - Persistent volumes
    
  # Cache & Queue
  redis:
    - Session storage
    - Processing queue

MVP Success Criteria

Technical Performance

  • Processing Time: <60 seconds per document
  • Accuracy: >90% for key-value extraction
  • Uptime: 99% during development testing
  • Container Startup: <30 seconds for all services

Functional Requirements

  • Support for PDF, PNG, JPG document formats
  • Process 3 document types: invoices, receipts, forms
  • Extract 5-10 key fields per document type
  • Basic web interface for upload and results
  • RESTful API for programmatic access

Resource Requirements

Development Environment

  • Docker: Latest stable version
  • Memory: 8GB RAM minimum (16GB recommended)
  • Storage: 20GB for models and data
  • CPU: Multi-core processor (GPU optional for faster processing)

Deployment

  • Single Server: 4 CPU cores, 16GB RAM, 100GB storage
  • Network: Standard internet connection
  • OS: Linux (Ubuntu 20.04+ recommended) or Windows with WSL2

Timeline Summary

Total MVP Development Time: 6-7 weeks

  • Week 1: Docker setup and environment configuration
  • Week 2-3: AI model integration and containerization
  • Week 4: API development and database setup
  • Week 5: Web interface development
  • Week 6: Integration, testing, and documentation
  • Week 7: Bug fixes and optimization

Deliverables

Code Deliverables

  • Complete Docker Compose setup
  • AI processing service with containerized models
  • FastAPI backend service
  • React frontend application
  • Database schema and migrations
  • Integration tests and documentation

Documentation

  • Setup and installation guide
  • API documentation
  • User guide for web interface
  • Troubleshooting guide
  • Architecture overview

Next Steps (Post-MVP)

  1. Performance optimization
  2. Additional document types support
  3. Enhanced UI/UX
  4. Batch processing capabilities
  5. Advanced error handling and monitoring
  6. Security enhancements
  7. Scalability improvements

This MVP provides a solid foundation for demonstrating the core MDUS functionality while keeping complexity minimal and focusing on Docker-based local deployment.