SentimentSuite

A high-performance, enterprise-grade video sentiment analysis and summarization platform that combines YouTube video processing with advanced AI-powered text summarization capabilities.

🏗️ Architecture Overview

SentimentSuite is built using a modern microservices architecture with clean separation of concerns, following SOLID principles and enterprise patterns.

System Components

┌─────────────────────────────────────────────────────────────┐
│                    SentimentSuite Platform                  │
├─────────────────────────────────────────────────────────────┤
│  ┌─────────────────┐    ┌─────────────────┐                │
│  │   Video API     │    │  Summarizer     │                │
│  │   (.NET 9)      │◄──►│  Service        │                │
│  │                 │    │  (Python)       │                │
│  └─────────────────┘    └─────────────────┘                │
│           │                       │                        │
│           ▼                       ▼                        │
│  ┌─────────────────┐    ┌─────────────────┐                │
│  │    MongoDB      │    │     Redis       │                │
│  │   (Database)    │    │    (Cache)      │                │
│  └─────────────────┘    └─────────────────┘                │
└─────────────────────────────────────────────────────────────┘

Technology Stack

Component	Technology	Version	Purpose
API Layer	.NET 9	9.0	REST API, YouTube integration
AI Service	Python 3.11+	Latest	Text summarization, ML models
Database	MongoDB	7.0	Document storage, video metadata
Cache	Redis	7.2	High-performance caching layer
Orchestration	Docker Compose	3.8	Container orchestration

🚀 Key Features

Core Functionality

• YouTube Video Processing: Extract transcripts from YouTube videos using YoutubeExplode
• AI-Powered Summarization: Multiple summarization strategies (local ML models + cloud APIs)
• Intelligent Caching: Multi-layer caching with Redis for optimal performance
• Health Monitoring: Comprehensive health checks and metrics collection
• Enterprise Patterns: Circuit breaker, retry policies, graceful degradation

Advanced Capabilities

• Semantic Chunking: Intelligent text segmentation for coherent summarization
• Hybrid Summarization: Local ML models with cloud API fallback
• Performance Optimization: Model quantization, batch processing, memory management
• Observability: Prometheus metrics, structured logging, distributed tracing
• Resilience: Circuit breaker protection, automatic retry, graceful degradation

📁 Project Structure

SentimentSuite/
├── src/
│   ├── SentimentSuite.Video.Api/          # .NET 9 Web API
│   │   ├── Controllers/                   # REST API endpoints
│   │   ├── Services/                      # Business logic services
│   │   ├── Models/                        # Data transfer objects
│   │   ├── Domain/                        # Domain entities and logic
│   │   ├── Persistence/                   # Data access layer
│   │   └── Program.cs                     # Application entry point
│   │
│   ├── SentimentSuite.Common/             # Shared .NET library
│   │   ├── Services/                      # Common service interfaces
│   │   ├── Extensions/                    # DI and middleware extensions
│   │   ├── Middleware/                    # Global exception handling
│   │   └── Configuration/                 # Configuration models
│   │
│   └── SentimentSuite.Summarizer.PyService/  # Python AI service
│       ├── core/                          # Core interfaces and models
│       ├── services/                      # Service implementations
│       │   ├── chunking/                  # Text chunking strategies
│       │   ├── summarization/             # ML summarization engines
│       │   ├── caching/                   # Memory caching layer
│       │   └── orchestration/             # Service orchestration
│       ├── utils/                         # Utility functions
│       └── main.py                        # FastAPI application
│
├── docker-compose.yaml                    # Container orchestration
├── api-tests.http                         # API testing collection
└── test-api.ps1                          # PowerShell test script

🛠️ Prerequisites

Required Software

• Docker Desktop (4.0+) - Container orchestration
• Node.js (18+) - For MCP server integration (optional)
• PowerShell (7+) - For Windows automation scripts

System Requirements

• RAM: 8GB minimum, 16GB recommended
• Storage: 10GB free space for models and data
• CPU: Multi-core processor recommended for ML inference

🚀 Quick Start

1. Clone and Setup

git clone <repository-url>
cd SentimentSuite

2. Start the Platform

docker-compose up --build

3. Verify Installation

# Check API health
curl http://localhost:5156/health

# Test summarization
curl -X POST http://localhost:5156/api/summary \
  -H "Content-Type: application/json" \
  -d '{"youtubeUrl": "https://www.youtube.com/watch?v=dQw4w9WgXcQ"}'

4. Access Services

• API Documentation: http://localhost:5156/swagger
• Health Dashboard: http://localhost:5156/health
• Summarizer Service: http://localhost:8000/docs

📊 API Reference

Core Endpoints

POST /api/summary

Generate a summary for a YouTube video.

Request:

{
  "youtubeUrl": "https://www.youtube.com/watch?v=VIDEO_ID"
}

Response:

{
  "summary": "Generated video summary text..."
}

GET /health

System health check with detailed component status.

Response:

{
  "status": "Healthy",
  "checks": {
    "self": "Healthy",
    "redis": "Healthy", 
    "mongodb": "Healthy"
  }
}

Error Handling

• 400 Bad Request: Invalid YouTube URL format
• 404 Not Found: Video not found or private
• 500 Internal Server Error: Service unavailable
• 503 Service Unavailable: Circuit breaker activated

⚙️ Configuration

Environment Variables

Video API (.NET)

# MongoDB Configuration
MongoDB__ConnectionString=mongodb://mongo:27017
MongoDB__DatabaseName=SentimentSuite

# Redis Configuration  
Redis__ConnectionString=redis:6379
Redis__DatabaseId=0
Redis__DefaultExpiration=24:00:00

# Summarizer Service
LocalSummary__BaseUrl=http://summarizer:8000

# Anthropic API (Optional)
Anthropic__ApiKey=your_api_key_here
Anthropic__Model=claude-3-sonnet-20240229

Summarizer Service (Python)

# Server Configuration
HOST=0.0.0.0
PORT=8000
WORKERS=1

# Model Configuration
MODEL_NAME=facebook/bart-large-cnn
DEVICE=auto
QUANTIZATION=true

# Performance Tuning
MAX_CHUNK_SIZE=1000
CACHE_SIZE=1000
CACHE_TTL=3600

# Quality Settings
DEFAULT_QUALITY=balanced
ENABLE_SEMANTIC_CHUNKING=true

🔧 Development

Local Development Setup

Prerequisites

• .NET 9 SDK
• Python 3.11+
• MongoDB (local or Docker)
• Redis (local or Docker)

Running Locally

1. Start Dependencies:

   docker-compose up mongo redis -d

2. Run Video API:

   cd src/SentimentSuite.Video.Api
   dotnet run

3. Run Summarizer Service:

   cd src/SentimentSuite.Summarizer.PyService
   pip install -r requirements.txt
   python main.py

Testing

API Tests

# Run HTTP tests
.\test-api.ps1

# Or use the HTTP file directly
# Open api-tests.http in VS Code with REST Client extension

Unit Tests

# .NET tests
dotnet test

# Python tests  
cd src/SentimentSuite.Summarizer.PyService
pytest

🏗️ Architecture Deep Dive

Video API Architecture

The Video API follows Clean Architecture principles with clear separation of concerns:

Controllers/          # Presentation layer (REST endpoints)
    ↓
Services/            # Application layer (business logic)
    ↓
Domain/              # Domain layer (entities, value objects)
    ↓
Persistence/         # Infrastructure layer (data access)

Key Design Patterns:

• Repository Pattern: Abstracted data access with caching
• Strategy Pattern: Multiple summarization strategies
• Decorator Pattern: Cached repository implementation
• Dependency Injection: Loose coupling and testability

Summarizer Service Architecture

The Python service implements a sophisticated ML pipeline:

Request → Chunking → Summarization → Caching → Response
    ↓         ↓           ↓            ↓
Semantic   Quality    Model        Memory
Chunking   Selection  Inference    Cache

Advanced Features:

• Semantic Chunking: Uses sentence embeddings for coherent text segmentation
• Model Optimization: Quantization and batch processing for performance
• Intelligent Caching: LRU cache with TTL for optimal memory usage
• Circuit Breaker: Resilience patterns for external service calls

Data Flow

1. Video Processing: YouTube URL → Transcript extraction → Text preprocessing
2. Text Analysis: Semantic chunking → Quality assessment → Model selection
3. Summarization: Local ML model → Cloud API fallback → Result optimization
4. Caching: Multi-layer caching (Redis + Memory) → Performance optimization
5. Response: Structured output → Error handling → Monitoring

📈 Performance Characteristics

Response Times

• Cache Hit: < 100ms
• Cache Miss (Local): 1-5 seconds
• Cache Miss (Cloud): 5-15 seconds
• Error Response: < 50ms

Throughput

• Sustained Load: 10-50 requests/second
• Peak Load: 100+ requests/second (with caching)
• Concurrent Users: 50-200 (depending on hardware)

Resource Usage

• Memory: 2-8GB (depending on model size)
• CPU: 2-8 cores (depending on load)
• Storage: 5-10GB (models + cache)

🔒 Security Considerations

Data Protection

• No Video Storage: Only transcripts and summaries are stored
• Encrypted Transit: HTTPS for all API communications
• Secure Configuration: Environment variables for sensitive data
• Input Validation: Comprehensive URL and data validation

Access Control

• API Rate Limiting: Built-in request throttling
• Health Monitoring: Continuous security health checks
• Error Sanitization: No sensitive data in error responses

🚀 Deployment

Production Deployment

Docker Compose (Recommended)

# Production configuration
docker-compose -f docker-compose.prod.yml up -d

Kubernetes (Enterprise)

# Example Kubernetes deployment
apiVersion: apps/v1
kind: Deployment
metadata:
  name: sentiment-suite-api
spec:
  replicas: 3
  selector:
    matchLabels:
      app: sentiment-suite-api
  template:
    spec:
      containers:
      - name: api
        image: sentiment-suite/api:latest
        ports:
        - containerPort: 8080

Environment-Specific Configurations

Development

• Hot reloading enabled
• Detailed logging
• Swagger UI available
• Debug symbols included

Production

• Optimized builds
• Health checks enabled
• Metrics collection
• Error tracking

📊 Monitoring and Observability

Health Checks

• Liveness Probe: /health endpoint
• Readiness Probe: Database and cache connectivity
• Dependency Checks: External service availability

Metrics Collection

• Performance Metrics: Response times, throughput
• Business Metrics: Summary generation rates
• System Metrics: CPU, memory, disk usage
• Error Metrics: Error rates, failure patterns

Logging

• Structured Logging: JSON format with correlation IDs
• Log Levels: Debug, Info, Warning, Error, Critical
• Centralized Logging: Aggregated log collection

🤝 Contributing

Development Workflow

1. Fork the repository
2. Create a feature branch
3. Implement changes with tests
4. Submit a pull request
5. Code review and merge

Code Standards

• C#: Follow Microsoft coding conventions
• Python: Follow PEP 8 style guide
• Testing: Maintain >80% code coverage
• Documentation: Update README for significant changes

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🆘 Support

Troubleshooting

Common Issues

1. Docker Build Failures: Check Docker daemon and available resources
2. Model Download Issues: Verify internet connectivity and disk space
3. Memory Issues: Increase Docker memory allocation
4. Port Conflicts: Check for conflicting services on ports 5156, 8000

Getting Help

• Documentation: Check this README and inline code comments
• Issues: Create GitHub issues for bugs and feature requests
• Discussions: Use GitHub discussions for questions and ideas

Performance Tuning

For High Load

• Increase Redis memory allocation
• Use multiple summarizer service instances
• Enable model quantization
• Implement request queuing

For Low Latency

• Optimize cache hit rates
• Use faster ML models
• Implement request batching
• Enable connection pooling

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Built with ❤️ using .NET 9, Python, and modern AI technologies