Resume-Matcher System Design

Project Overview

Resume-Matcher is an open-source AI-powered resume optimization tool that helps job seekers improve their resumes by analyzing ATS compatibility, suggesting keyword optimizations, and providing match scores against job descriptions. The project demonstrates several design patterns in AI-driven document processing applications.

Repository: https://github.com/srbhr/Resume-Matcher Analysis Date: 2025-08-10 Focus: Design patterns in AI-powered document analysis and optimization systems

Project Architecture

Technology Stack

• Backend: FastAPI (Python 3.12+)

• Frontend: Next.js 15+ (TypeScript)

• AI Processing: Ollama (Local LLM)

• Database: SQLite

• Styling: Tailwind CSS

Core Features

• ✅ AI-powered resume analysis

• ✅ ATS compatibility checking

• ✅ Keyword optimization suggestions

• ✅ Match scoring between resume and job description

• ✅ Local processing (privacy-focused)

• ✅ Real-time feedback and recommendations

Design Patterns Identified

1. Strategy Pattern 🎯

Implementation: Multiple analysis strategies for different document types and optimization approaches

Evidence:

• Different analysis algorithms for various resume formats

• Multiple keyword extraction strategies

• Various scoring mechanisms for ATS compatibility

Conceptual Implementation:

class AnalysisStrategy(ABC):
    @abstractmethod
    def analyze_document(self, resume, job_description):
        pass

class ATSCompatibilityStrategy(AnalysisStrategy):
    def analyze_document(self, resume, job_description):
        # ATS-specific analysis logic
        return self.check_formatting_compatibility(resume)

class KeywordOptimizationStrategy(AnalysisStrategy):
    def analyze_document(self, resume, job_description):
        # Keyword matching and optimization
        return self.extract_missing_keywords(resume, job_description)

class ResumeAnalyzer:
    def __init__(self, strategy: AnalysisStrategy):
        self.strategy = strategy
    
    def analyze(self, resume, job_description):
        return self.strategy.analyze_document(resume, job_description)

Benefits:

• Easy to add new analysis methods

• Different optimization approaches for different user needs

• Flexible scoring algorithms

2. Template Method Pattern 📋

Implementation: Standardized document processing pipeline

Evidence:

• Consistent workflow for resume analysis regardless of format

• Standard steps: parse → analyze → score → recommend

• Common validation and formatting steps

Conceptual Implementation:

class DocumentProcessor(ABC):
    def process_resume(self, resume_file, job_description):
        # Template method defining standard workflow
        parsed_resume = self.parse_document(resume_file)
        validated_data = self.validate_content(parsed_resume)
        analysis_results = self.analyze_content(validated_data, job_description)
        score = self.calculate_match_score(analysis_results)
        recommendations = self.generate_recommendations(analysis_results)
        
        return self.format_results(score, recommendations)
    
    def parse_document(self, resume_file):
        # Common parsing logic
        pass
    
    @abstractmethod
    def analyze_content(self, resume_data, job_description):
        # Subclass-specific analysis
        pass
    
    def calculate_match_score(self, analysis_results):
        # Default scoring implementation
        pass

Benefits:

• Consistent processing workflow

• Easy to extend for new document types

• Standardized output format

3. Factory Pattern 🏭

Implementation: Document parser creation based on file type

Evidence:

• Multiple document format support (PDF, DOCX, TXT)

• Dynamic parser selection based on file extension

• Centralized parser instantiation

Conceptual Implementation:

class DocumentParserFactory:
    _parsers = {
        '.pdf': PDFParser,
        '.docx': DocxParser,
        '.txt': TextParser,
        '.doc': DocParser
    }
    
    @classmethod
    def create_parser(cls, file_extension):
        parser_class = cls._parsers.get(file_extension.lower())
        if not parser_class:
            raise ValueError(f"Unsupported file type: {file_extension}")
        return parser_class()
    
    @classmethod
    def parse_resume(cls, file_path):
        file_extension = Path(file_path).suffix
        parser = cls.create_parser(file_extension)
        return parser.parse(file_path)

Benefits:

• Easy addition of new file format support

• Centralized parser management

• Clean separation of parsing logic

4. Observer Pattern 👁️

Implementation: Real-time UI updates during analysis

Evidence:

• Live progress indicators during processing

• Real-time recommendations as analysis progresses

• Dynamic UI updates based on analysis results

Conceptual Implementation:

class AnalysisObserver(ABC):
    @abstractmethod
    def on_analysis_start(self, document_id):
        pass
    
    @abstractmethod
    def on_progress_update(self, document_id, progress, message):
        pass
    
    @abstractmethod
    def on_analysis_complete(self, document_id, results):
        pass

class UIUpdateObserver(AnalysisObserver):
    def on_progress_update(self, document_id, progress, message):
        # Update progress bar and status message
        self.update_ui_progress(progress, message)
    
    def on_analysis_complete(self, document_id, results):
        # Display final results and recommendations
        self.display_results(results)

class ResumeAnalysisEngine:
    def __init__(self):
        self.observers = []
    
    def add_observer(self, observer):
        self.observers.append(observer)
    
    def notify_progress(self, document_id, progress, message):
        for observer in self.observers:
            observer.on_progress_update(document_id, progress, message)

Benefits:

• Responsive user interface

• Decoupled UI logic from analysis logic

• Easy to add new notification types

5. Command Pattern 🔧

Implementation: Analysis operations and recommendations

Evidence:

• Different types of analysis operations

• Undo/redo capability for recommendations

• Batch processing of multiple resumes

Conceptual Implementation:

class AnalysisCommand(ABC):
    @abstractmethod
    def execute(self, document_data):
        pass
    
    @abstractmethod
    def undo(self):
        pass

class KeywordAnalysisCommand(AnalysisCommand):
    def __init__(self, job_description):
        self.job_description = job_description
        self.previous_state = None
    
    def execute(self, resume_data):
        self.previous_state = resume_data.copy()
        return self.analyze_keywords(resume_data, self.job_description)
    
    def undo(self):
        return self.previous_state

class ATSCheckCommand(AnalysisCommand):
    def execute(self, resume_data):
        return self.check_ats_compatibility(resume_data)

class AnalysisInvoker:
    def __init__(self):
        self.commands = []
        self.history = []
    
    def execute_analysis(self, commands, document_data):
        results = {}
        for command in commands:
            result = command.execute(document_data)
            results[command.__class__.__name__] = result
            self.history.append(command)
        return results

Benefits:

• Flexible analysis pipeline

• Easy to add new analysis types

• Support for operation history and undo

6. Facade Pattern 🎭

Implementation: Simplified API interface for complex analysis system

Evidence:

• Simple REST API endpoints hiding complex analysis logic

• Unified interface for different analysis types

• Clean separation between frontend and backend complexity

Conceptual Implementation:

class ResumeMatcherFacade:
    def __init__(self):
        self.parser_factory = DocumentParserFactory()
        self.analyzer = ResumeAnalyzer()
        self.scorer = MatchScorer()
        self.recommender = RecommendationEngine()
    
    def analyze_resume(self, resume_file, job_description):
        """Simplified interface for complete resume analysis"""
        # Parse document
        resume_data = self.parser_factory.parse_resume(resume_file)
        
        # Run analysis
        analysis_results = self.analyzer.analyze(resume_data, job_description)
        
        # Calculate score
        match_score = self.scorer.calculate_score(analysis_results)
        
        # Generate recommendations
        recommendations = self.recommender.generate_recommendations(analysis_results)
        
        return {
            'score': match_score,
            'analysis': analysis_results,
            'recommendations': recommendations
        }
    
    def quick_score(self, resume_file, job_description):
        """Simplified interface for quick scoring"""
        resume_data = self.parser_factory.parse_resume(resume_file)
        return self.scorer.quick_score(resume_data, job_description)

Benefits:

• Simple API for complex operations

• Hides implementation complexity from clients

• Easy to use and understand interface

7. Builder Pattern 🏗️

Implementation: Flexible recommendation generation

Evidence:

• Customizable recommendation types

• Different recommendation formats for different user needs

• Step-by-step recommendation building

Conceptual Implementation:

class RecommendationBuilder:
    def __init__(self):
        self.recommendations = {}
    
    def add_keyword_recommendations(self, missing_keywords):
        self.recommendations['keywords'] = {
            'type': 'keyword_optimization',
            'missing_keywords': missing_keywords,
            'priority': 'high'
        }
        return self
    
    def add_formatting_recommendations(self, formatting_issues):
        self.recommendations['formatting'] = {
            'type': 'ats_formatting',
            'issues': formatting_issues,
            'priority': 'medium'
        }
        return self
    
    def add_content_recommendations(self, content_suggestions):
        self.recommendations['content'] = {
            'type': 'content_improvement',
            'suggestions': content_suggestions,
            'priority': 'low'
        }
        return self
    
    def build(self):
        return RecommendationReport(self.recommendations)

# Usage
recommendations = (RecommendationBuilder()
    .add_keyword_recommendations(['Python', 'Machine Learning'])
    .add_formatting_recommendations(['Use bullet points'])
    .build())

Benefits:

• Flexible recommendation creation

• Easy to customize recommendation types

• Clear, readable recommendation building process

8. Adapter Pattern 🔌

Implementation: Integration with different AI models and services

Evidence:

• Ollama integration for local AI processing

• Different LLM model support

• Unified interface for various AI services

Conceptual Implementation:

class LLMAdapter(ABC):
    @abstractmethod
    def analyze_text(self, text, context):
        pass

class OllamaAdapter(LLMAdapter):
    def __init__(self, model_name):
        self.model_name = model_name
        self.client = OllamaClient()
    
    def analyze_text(self, text, context):
        prompt = self.create_analysis_prompt(text, context)
        response = self.client.generate(self.model_name, prompt)
        return self.parse_response(response)

class OpenAIAdapter(LLMAdapter):
    def __init__(self, api_key, model):
        self.client = OpenAI(api_key=api_key)
        self.model = model
    
    def analyze_text(self, text, context):
        response = self.client.chat.completions.create(
            model=self.model,
            messages=[{"role": "user", "content": f"Analyze: {text} Context: {context}"}]
        )
        return response.choices[0].message.content

class AIAnalysisService:
    def __init__(self, llm_adapter: LLMAdapter):
        self.llm = llm_adapter
    
    def analyze_resume_content(self, resume_text, job_description):
        return self.llm.analyze_text(resume_text, job_description)

Benefits:

• Easy integration with different AI services

• Consistent interface across different models

• Flexibility to switch between local and cloud AI

Architecture Analysis

Microservices Architecture

• Frontend Service: Next.js application for user interface

• Backend Service: FastAPI for business logic and AI processing

• Data Service: SQLite for storing analysis results and user data

Local-First Design

• Privacy-focused approach with local AI processing

• No external API dependencies for core functionality

• Offline capability for resume analysis

Modular Component Design

• Clear separation between parsing, analysis, scoring, and recommendation modules

• Plugin-like architecture for adding new features

• Configurable analysis pipeline

Real-World Benefits Demonstrated

1. Privacy and Security

• Problem: Sensitive resume data being sent to external services

• Solution: Local AI processing with Ollama

• Benefit: Complete data privacy and offline capability

2. Flexible Analysis Pipeline

• Problem: Different users need different types of analysis

• Solution: Strategy pattern for multiple analysis approaches

• Benefit: Customizable analysis based on user needs

3. Multi-Format Support

• Problem: Users have resumes in different formats

• Solution: Factory pattern for document parsing

• Benefit: Seamless support for PDF, DOCX, TXT, and other formats

4. Real-Time User Feedback

• Problem: Long analysis times without user feedback

• Solution: Observer pattern for progress updates

• Benefit: Responsive UI with live progress indicators

Learning Opportunities

1. Local AI Integration

• How to integrate local LLM models (Ollama) in applications

• Balancing privacy with functionality

• Offline-first AI application design

2. Document Processing Pipeline

• Multi-format document parsing strategies

• Text extraction and preprocessing techniques

• Structured data extraction from unstructured documents

3. Full-Stack AI Applications

• Frontend-backend integration for AI applications

• Real-time progress updates and streaming responses

• State management in AI-powered UIs

4. User-Centric Design

• Building intuitive interfaces for complex AI functionality

• Progressive disclosure of analysis results

• Actionable recommendations and guidance

Comparison with Our Pattern Examples

Similarities

• Strategy Pattern: Both projects use strategy for different AI approaches

• Factory Pattern: Dynamic object creation based on runtime conditions

• Template Method: Standardized processing pipelines

Differences

• Domain Focus: Resume-Matcher is domain-specific (HR/recruiting)

• Local Processing: Emphasis on privacy and offline capability

• User Interface: Full-stack application vs. notebook examples

• Document Processing: Specialized for document analysis and optimization

What We Can Learn

• Privacy-First AI: How to build AI applications that respect user privacy

• Domain-Specific Optimization: Tailoring general patterns for specific use cases

• Full-Stack Integration: Connecting design patterns across frontend and backend

• User Experience: Making complex AI analysis accessible and actionable

Conclusion

Resume-Matcher demonstrates excellent application of design patterns in a real-world AI-powered application. The project shows how classic patterns can be adapted for:

1. Document Processing: Factory and Template Method patterns for handling multiple formats

2. AI Integration: Adapter pattern for flexible AI model integration

3. User Experience: Observer pattern for real-time feedback

4. System Architecture: Facade pattern for clean API design

Key Takeaway: Design patterns enable building sophisticated AI applications that are maintainable, extensible, and user-friendly while addressing real-world concerns like privacy and performance.

The project serves as an excellent example of how to combine multiple design patterns to create a cohesive, professional AI application that solves genuine user problems in the job search and recruitment space.

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📁 Project Structure Analysis

📋 Resume-Matcher Detailed Project Structure - Comprehensive analysis of Resume-Matcher's architecture, directory organization, and pattern implementation mapping across the entire codebase.