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
passBenefits:
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 resultsBenefits:
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:
Document Processing: Factory and Template Method patterns for handling multiple formats
AI Integration: Adapter pattern for flexible AI model integration
User Experience: Observer pattern for real-time feedback
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.
📁 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.
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