plan.md

CV Matcher Project Plan

Project Overview

CV Matcher is a tool designed to match job descriptions with candidate CVs/resumes using advanced text analysis and matching algorithms.

Project Structure

cv_matcher/
├── README.md           # Project documentation and setup instructions
├── Dockerfile         # Container configuration
├── requirements.txt   # Python dependencies
├── src/
│   ├── __init__.py
│   ├── main.py       # Application entry point
│   ├── api/          # API endpoints
│   ├── core/         # Core matching logic
│   ├── models/       # Data models
│   └── utils/        # Utility functions
├── tests/            # Test suite
└── docs/             # Additional documentation

Technical Stack

• Backend: Python 3.12+
• API Framework: FastAPI
• Text Processing: MarkItDown
• ML/AI: Pydantic AI
• Docker: Containerization
• Testing: pytest

Core Features

1. CV Processing

   • PDF/DOCX parsing
   • Text extraction
   • Structure recognition
   • Skills extraction
   • Experience mapping

2. Job Description Processing

   • Requirements extraction
   • Skills identification
   • Experience level detection
   • Must-have vs. nice-to-have classification

3. Matching Engine

   • Skills matching
   • Experience matching
   • Education matching
   • Scoring system
   • Ranking algorithm

4. API Endpoints

   • Upload CV
   • Submit job description
   • Get matching results
   • Manage candidates
   • Manage job postings

Project Class Diagram

classDiagram
    class AIClient {
        -_model
        -_model_settings
        -_cache_dir
        +run(prompt, max_tokens, system_prompt, result_type, use_cache)
    }

    class ResumeAnalysisService {
        -client: AIClient
        -analyzer: JobAnalyzer
        -markitdown: MarkItDown
        -console: Console
        +process_files(resume_path, job_desc_path)
        +analyze_resume(resume_text, job_description)
        +show_analysis_result(result)
    }

    class JobAnalyzer {
        -client: AIClient
        -_resume_processor: ResumeProcessor
        -_criteria_evaluator: CriteriaEvaluator
        -_red_flag_analyzer: RedFlagAnalyzer
        +unify_resume(resume_text)
        +extract_job_requirements(job_description)
        +match_resume(resume_text, job_description, job_requirements)
    }

    class ResumeProcessor {
        -client: AIClient
        +unify_resume(resume_text)
        +get_website(resume_text)
    }

    class CriteriaEvaluator {
        -client: AIClient
        +evaluate_criterion(criterion, resume_text, job_requirements)
    }

    class RedFlagAnalyzer {
        +analyze(criteria)
    }

    class DetailedMatchResult {
        +overall_score: int
        +criteria_scores: List[ScoringCriterion]
        +match_reasons: str
        +red_flags: Dict[str, List[str]]
        +website: Optional[str]
    }

    class ScoringCriterion {
        +name: str
        +key: str
        +weight: int
        +description: str
        +factors: List[str]
        +score: Optional[int]
    }

    class JobRequirements {
        +required_experience_years: int
        +required_education_level: str
        +required_skills: List[str]
        +optional_skills: List[str]
        +certifications_preferred: List[str]
        +soft_skills: List[str]
        +keywords_to_match: List[str]
        +location: Location
        +emphasis: Emphasis
    }

    ResumeAnalysisService --> AIClient
    ResumeAnalysisService --> JobAnalyzer
    JobAnalyzer --> ResumeProcessor
    JobAnalyzer --> CriteriaEvaluator
    JobAnalyzer --> RedFlagAnalyzer
    JobAnalyzer --> AIClient
    ResumeProcessor --> AIClient
    CriteriaEvaluator --> AIClient
    JobAnalyzer ..> DetailedMatchResult
    JobAnalyzer ..> JobRequirements
    DetailedMatchResult --> ScoringCriterion

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Implementation Plan

Phase 1: Setup and Basic Structure

1. Initialize project structure
2. Set up development environment
3. Configure Docker
4. Implement basic API structure
5. Set up database models

Phase 2: Core Processing

1. Implement CV parsing
2. Implement job description parsing
3. Create basic text processing utilities
4. Set up initial matching logic

Phase 3: Advanced Features

1. Implement ML-based matching
2. Add scoring system
3. Create ranking algorithm
4. Implement advanced filters

Phase 4: API and Integration

1. Complete REST API endpoints
2. Add authentication
3. Implement rate limiting
4. Add API documentation

Phase 5: Testing and Deployment

1. Write unit tests
2. Add integration tests
3. Set up CI/CD
4. Prepare production deployment

Security Considerations

• Implement JWT authentication
• Secure file uploads
• Rate limiting
• Input validation
• Data encryption
• CORS configuration
• Environment variables for sensitive data

Monitoring and Logging

• Request logging
• Error tracking
• Performance metrics
• API usage statistics
• System health monitoring

Development Guidelines

1. Follow PEP 8 style guide
2. Write comprehensive docstrings
3. Maintain test coverage >80%
4. Use type hints
5. Regular code reviews
6. Version control best practices

Deployment Strategy

1. Docker-based deployment
2. Scalable architecture
3. Load balancing
4. Monitoring setup
5. CI/CD pipeline

Future Enhancements

1. AI-powered matching improvements
2. Resume parsing accuracy
3. Additional file format support
4. Advanced analytics