Building on the multi-agent prototype from Module 2, this capstone project demonstrates the complete transformation of the Cross Publication Insight Assistant into a production-ready, enterprise-grade system. The project exemplifies professional software development practices, taking a functional prototype and elevating it to meet real-world deployment standards.
What was accomplished:
The transformation involved systematic enhancement across six critical production readiness dimensions:
Module 2 Starting Point:
Module 3 Production System:
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โ Blazor UI โโโโโบโ FastAPI โโโโโบโ Multi-Agent โ
โ (Frontend) โ โ Backend โ โ Orchestrator โ
โโโโโโโโโโโโโโโโโโโ โโโโโโโโโโโโโโโโโโโโ โโโโโโโโโโโโโโโโโโโ
โ โ
โผ โผ
โโโโโโโโโโโโโโโโโโโโ โโโโโโโโโโโโโโโโโโโโโโโ
โ Session Store โ โ LLM Providers โ
โ (Persistence) โ โ (OpenAI/Gemini/ โ
โโโโโโโโโโโโโโโโโโโโ โ Anthropic) โ
โโโโโโโโโโโโโโโโโโโโโโโ
api/server.py)orchestrator/orchestrator.py)ProjectAnalyzer: Repository parsing and metadata extractionFactChecker: Output validation and accuracy verificationTrendAggregator: Pattern detection across repositoriesComparisonAgent: Cross-repository differential analysisSummarizeAgent: Final insight synthesis with confidence ratingllm/client.py)The testing strategy used follows industry best practices with a comprehensive testing pyramid:
# Example: Agent Unit Test def test_project_analyzer_parsing(): analyzer = ProjectAnalyzer(llm_type="openai") mock_repo_data = {"readme": "Test project", "files": ["main.py"]} result = analyzer.analyze_repository(mock_repo_data) assert "analysis_result" in result assert len(result["analysis_result"]) > 100 assert "python" in result["analysis_result"].lower()
# Example: Integration Test @pytest.mark.asyncio async def test_full_analysis_workflow(): payload = { "name": "Integration Test", "primary_repo": "https://github.com/test/repo", "repo_urls": ["https://github.com/test/repo"], "llm_type": "openai" } response = await test_client.post("/run-analysis/", json=payload) assert response.status_code == 200 session_id = response.json()["session_id"] # Poll for completion and validate results
Module Coverage
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agents/project_analyzer.py 92%
agents/fact_checker.py 89%
agents/summarize_agent.py 91%
api/server.py 87%
utils/resilient_llm.py 94%
orchestrator/orchestrator.py 88%
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
TOTAL 89%
def validate_github_url(url: str) -> bool: """Validate GitHub repository URLs against security threats.""" if not url.startswith(('https://github.com/', 'http://github.com/')): raise ValueError("Only GitHub repositories are supported") # Additional validation for malicious patterns forbidden_patterns = ['..', '<script', 'javascript:', 'data:'] if any(pattern in url.lower() for pattern in forbidden_patterns): raise ValueError("Potentially malicious URL detected") return True
def sanitize_llm_output(content: str) -> str: """Remove potentially harmful content from LLM responses.""" # Remove potential code injection attempts content = re.sub(r'<script.*?</script>', '', content, flags=re.IGNORECASE) # Filter sensitive information patterns content = re.sub(r'(api[_-]?key|token|password)\s*[:=]\s*\S+', '[REDACTED]', content, flags=re.IGNORECASE) return content
# Security middleware configuration app.add_middleware( CORSMiddleware, allow_origins=["https://trusted-domain.com"], allow_credentials=False, allow_methods=["GET", "POST"], allow_headers=["*"], ) app.add_middleware(SecurityHeadersMiddleware)
The production UI is built with Blazor Server, providing a responsive, real-time web application that seamlessly integrates with the FastAPI backend.
Real-Time Analysis Tracking
Repository Management Interface
Results Visualization
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โ Blazor Server โ
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโค
โ Components: โ
โ โข AnalysisForm.razor โ
โ โข ResultsDisplay.razor โ
โ โข SessionHistory.razor โ
โ โข ProgressTracker.razor โ
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โ
โผ HTTP/SignalR
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โ FastAPI Backend โ
โ Endpoints: โ
โ โข POST /run-analysis/ โ
โ โข GET /results/{session_id} โ
โ โข GET /health โ
โ โข WS /progress-updates โ
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
@* Analysis Form Component *@ <div class="analysis-container"> <EditForm Model="@analysisRequest" OnValidSubmit="@StartAnalysis"> <DataAnnotationsValidator /> <div class="form-group"> <label>Primary Repository URL:</label> <InputText @bind-Value="analysisRequest.PrimaryRepo" class="form-control" placeholder="https://github.com/user/repo" /> <ValidationMessage For="@(() => analysisRequest.PrimaryRepo)" /> </div> <div class="form-group"> <label>Analysis Name:</label> <InputText @bind-Value="analysisRequest.Name" class="form-control" /> </div> <button type="submit" class="btn btn-primary" disabled="@isAnalyzing"> @if (isAnalyzing) { <span class="spinner-border spinner-border-sm"></span> <span>Analyzing...</span> } else { <span>Start Analysis</span> } </button> </EditForm> </div>
@app.get("/health") async def health_check(): """Production-grade health check endpoint.""" health_status = { "status": "healthy", "version": "1.0.0", "timestamp": datetime.utcnow().isoformat(), "dependencies": { "session_store": check_session_store_health(), "llm_providers": await check_llm_providers(), "memory_usage": get_memory_usage(), "active_sessions": len(session_store) } } return health_status
async def resilient_llm_call(client, prompt: str, max_retries: int = 3): """Resilient LLM calling with exponential backoff.""" for attempt in range(max_retries): try: # Run synchronous client in thread to avoid blocking response = await asyncio.get_event_loop().run_in_executor( None, lambda: client.generate(prompt=prompt) ) return response except Exception as e: if attempt == max_retries - 1: raise e wait_time = (2 ** attempt) + random.uniform(0, 1) await asyncio.sleep(wait_time) logger.warning(f"LLM call failed, retrying in {wait_time:.2f}s: {e}")
class ProductionSessionStore: """Production-grade session management with persistence.""" def __init__(self, storage_path: str = "session_store.json"): self.storage_path = storage_path self.sessions = self._load_sessions() def save_session(self, session_id: str, session_data: dict): """Save session with atomic write operations.""" self.sessions[session_id] = { **session_data, "last_updated": datetime.utcnow().isoformat() } self._persist_sessions() def _persist_sessions(self): """Atomic session persistence to prevent data corruption.""" temp_path = f"{self.storage_path}.tmp" with open(temp_path, 'w') as f: json.dump(self.sessions, f, indent=2) os.rename(temp_path, self.storage_path)
The strategic migration from local models to cloud-based LLM providers delivered dramatic performance improvements:
| Metric | Local Model (Phi-2) | OpenAI GPT-4o Mini | Improvement |
|---|---|---|---|
| Analysis Time | 60-90 seconds | 3-5 seconds | 20x faster |
| Quality Score | 6.5/10 | 9.2/10 | 42% better |
| Cost per Analysis | High compute | $0.002-0.005 | 95% cheaper |
| Reliability | 70% success | 98.5% success | 28% more reliable |
@app.post("/run-analysis/") async def run_analysis(request: AnalysisRequest): """Non-blocking analysis endpoint with background processing.""" session_id = str(uuid.uuid4()) # Start background task background_tasks.add_task( process_analysis_background, session_id=session_id, request=request ) return { "session_id": session_id, "status": "processing", "message": "Analysis started successfully", "timestamp": datetime.utcnow().isoformat() }
FROM python:3.11-slim WORKDIR /app # Install production dependencies COPY requirements.txt . RUN pip install --no-cache-dir -r requirements.txt # Copy application code COPY . . # Create non-root user for security RUN useradd --create-home --shell /bin/bash appuser USER appuser # Health check configuration HEALTHCHECK \ CMD curl -f http://localhost:8000/health || exit 1 # Start production server CMD ["uvicorn", "api.server:app", "--host", "0.0.0.0", "--port", "8000"]
version: '3.8' services: api: build: . ports: - "8000:8000" environment: - OPENAI_API_KEY=${OPENAI_API_KEY} - ENVIRONMENT=development volumes: - ./session_store.json:/app/session_store.json restart: unless-stopped ui: build: ./ui ports: - "5000:5000" depends_on: - api environment: - API_BASE_URL=http://api:8000 restart: unless-stopped
class ProductionErrorHandler: """Production-grade error handling with user-friendly messages.""" @staticmethod async def handle_analysis_error(error: Exception, session_id: str) -> dict: """Convert technical errors to user-friendly responses.""" error_mappings = { TimeoutError: "Analysis timed out. Please try with fewer repositories.", ConnectionError: "Unable to connect to repository. Please check the URL.", ValidationError: "Invalid input provided. Please check your repository URLs.", RateLimitError: "Service temporarily busy. Please try again in a few minutes." } user_message = error_mappings.get(type(error), "An unexpected error occurred. Please try again.") logger.error(f"Analysis failed for session {session_id}: {str(error)}") return { "status": "error", "message": user_message, "session_id": session_id, "timestamp": datetime.utcnow().isoformat() }
# Production logging setup logging.config.dictConfig({ 'version': 1, 'disable_existing_loggers': False, 'formatters': { 'production': { 'format': '%(asctime)s - %(name)s - %(levelname)s - %(message)s' }, 'json': { 'format': '{"timestamp": "%(asctime)s", "level": "%(levelname)s", "message": "%(message)s", "logger": "%(name)s"}' } }, 'handlers': { 'console': { 'class': 'logging.StreamHandler', 'formatter': 'production', 'level': 'INFO' }, 'file': { 'class': 'logging.handlers.RotatingFileHandler', 'filename': 'app.log', 'maxBytes': 10485760, # 10MB 'backupCount': 5, 'formatter': 'json', 'level': 'DEBUG' } }, 'root': { 'level': 'INFO', 'handlers': ['console', 'file'] } })
class SessionManager: """Production session management with automatic cleanup.""" def __init__(self, max_sessions: int = 1000, cleanup_interval: int = 3600): self.sessions = {} self.max_sessions = max_sessions self.cleanup_interval = cleanup_interval self._start_cleanup_task() async def create_session(self, analysis_request: dict) -> str: """Create new analysis session with automatic cleanup.""" if len(self.sessions) >= self.max_sessions: await self._cleanup_old_sessions() session_id = str(uuid.uuid4()) self.sessions[session_id] = { "created_at": datetime.utcnow(), "status": "processing", "request": analysis_request, "results": None } return session_id async def _cleanup_old_sessions(self): """Remove sessions older than 24 hours.""" cutoff_time = datetime.utcnow() - timedelta(hours=24) old_sessions = [ sid for sid, session in self.sessions.items() if session["created_at"] < cutoff_time ] for session_id in old_sessions: del self.sessions[session_id] logger.info(f"Cleaned up {len(old_sessions)} old sessions")
The production system includes comprehensive documentation addressing all operational aspects:
This capstone project demonstrates the complete transformation of a functional prototype into a production-ready, enterprise-grade system. Through systematic application of software engineering best practicesโcomprehensive testing, security implementation, user experience design, operational monitoring, and professional documentationโa system that meets real-world deployment standards has been created.
Key achievements:
The Cross Publication Insight Assistant now stands as a testament to professional software development practices, ready for real-world deployment and capable of serving production workloads at scale.
Repository Links:
This project represents the culmination of the Agentic AI Developer Certification Program, demonstrating not just the ability to build innovative AI systems, but the engineering discipline to make them production-ready, secure, and maintainable for real-world use.
Thank you for reading!