How to Build an Intelligent Email Thread Importance Ranker
Develop a cutting-edge email management tool that uses machine learning to analyze and rank email threads by importance. This project will help users efficiently manage their inboxes, save time, and ensure critical communications are never missed.
Learn2Vibe AI
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What do you want to build?
Simple Summary
An intelligent system that automatically ranks email threads based on importance, helping users prioritize their inbox and focus on what matters most.
Product Requirements Document (PRD)
Goals:
- Create an intelligent system to automatically rank email threads by importance
- Improve user productivity by prioritizing important emails
- Reduce time spent managing inboxes
Target Audience:
- Professionals with high email volume
- Business users
- Anyone looking to optimize their email workflow
Key Features:
- Email thread importance ranking algorithm
- User-friendly interface to display ranked threads
- Customizable importance criteria
- Integration with popular email providers
- Real-time updates as new emails arrive
- Analytics dashboard for email management insights
User Requirements:
- Easy setup and integration with existing email accounts
- Intuitive interface for viewing ranked threads
- Ability to customize importance criteria
- Quick access to top-ranked threads
- Option to manually adjust rankings
- Mobile app for on-the-go email management
User Flows
-
Email Account Integration:
- User signs up for the service
- User authorizes access to their email account
- System syncs and analyzes existing email threads
-
Viewing Ranked Threads:
- User opens the application
- System displays a list of email threads ranked by importance
- User can sort, filter, or search through ranked threads
-
Customizing Importance Criteria:
- User navigates to settings
- User selects and weights various importance factors
- System applies new criteria and re-ranks threads
Technical Specifications
Frontend:
- React for web application
- React Native for mobile app
Backend:
- Node.js with Express.js
- PostgreSQL for data storage
- Redis for caching
Machine Learning:
- TensorFlow or PyTorch for developing the ranking algorithm
- Natural Language Processing (NLP) libraries for text analysis
API:
- RESTful API for communication between frontend and backend
- GraphQL for more complex data queries
Authentication:
- JWT for secure user authentication
- OAuth2 for email provider integration
API Endpoints
- POST /api/auth/register
- POST /api/auth/login
- GET /api/threads
- GET /api/threads/:id
- PUT /api/threads/:id/rank
- GET /api/settings
- PUT /api/settings
- GET /api/analytics
Database Schema
Users:
- id (PK)
- password_hash
- created_at
- updated_at
EmailAccounts:
- id (PK)
- user_id (FK)
- provider
- access_token
- refresh_token
- created_at
- updated_at
Threads:
- id (PK)
- email_account_id (FK)
- subject
- importance_score
- last_updated
- created_at
- updated_at
Settings:
- id (PK)
- user_id (FK)
- importance_criteria (JSON)
- created_at
- updated_at
File Structure
/src
/components
Header.js
Footer.js
ThreadList.js
ThreadItem.js
ImportanceMeter.js
/pages
Home.js
Login.js
Register.js
Settings.js
Analytics.js
/api
auth.js
threads.js
settings.js
/utils
rankingAlgorithm.js
emailIntegration.js
/styles
global.css
components.css
/public
/assets
logo.svg
icons/
/server
/routes
/controllers
/models
/middleware
/ml
trainModel.py
predictImportance.py
README.md
package.json
Implementation Plan
-
Project Setup (1 week)
- Set up development environment
- Initialize Git repository
- Create project structure
-
Backend Development (3 weeks)
- Implement user authentication
- Develop email integration services
- Create API endpoints
- Set up database and models
-
Machine Learning Model (4 weeks)
- Collect and preprocess training data
- Develop and train importance ranking model
- Implement model integration with backend
-
Frontend Development (3 weeks)
- Create responsive UI components
- Implement user flows
- Integrate with backend API
-
Testing and Refinement (2 weeks)
- Conduct unit and integration tests
- Perform user testing and gather feedback
- Refine UI/UX based on feedback
-
Deployment and Launch (1 week)
- Set up production environment
- Deploy application
- Monitor performance and fix issues
Deployment Strategy
- Use containerization (Docker) for consistent environments
- Deploy backend to a scalable cloud platform (e.g., AWS ECS or Google Cloud Run)
- Host frontend on a CDN for fast global access
- Utilize a managed database service for PostgreSQL
- Implement CI/CD pipeline using GitHub Actions or GitLab CI
- Set up monitoring and logging with tools like Prometheus and ELK stack
- Use blue-green deployment for zero-downtime updates
- Implement automated backups and disaster recovery procedures
Design Rationale
- React and React Native chosen for cross-platform compatibility and rich ecosystem
- Node.js backend for JavaScript consistency across stack and high performance
- PostgreSQL for robust relational data storage with JSON support for flexibility
- Machine learning integration to provide intelligent, adaptive ranking
- RESTful API for simplicity, with GraphQL option for future complex data requirements
- Containerization and cloud deployment for scalability and ease of management
- Emphasis on user customization to cater to diverse email management needs
- Mobile app inclusion to support on-the-go productivity