Sema Translation API - Complete Documentation

Welcome to the comprehensive documentation for the Sema Translation API - an enterprise-grade translation service supporting 200+ languages with custom HuggingFace models and a focus on African languages.

📚 Documentation Overview

This documentation covers all aspects of the Sema Translation API, from custom model implementation to advanced deployment scenarios and future application ideas.

🚀 Core Documentation

Custom Models Implementation

Essential Reading - Detailed documentation of how we implemented custom HuggingFace models:

Unified sematech/sema-utils repository structure
CTranslate2 optimization for 2-4x faster inference
Model loading pipeline and caching strategy
Performance benchmarks and monitoring
Model update and versioning process

API Capabilities

Complete overview of enhanced API features:

55+ African languages (updated from 23)
Server-side performance timing
Language detection with confidence scores
Comprehensive language metadata system

Future Considerations

Roadmap and application ideas:

Authentication & user management with Supabase
Database integration and caching strategies
Document translation and real-time streaming
Innovative application ideas (chatbots, education, government services)

Deployment Architecture

Infrastructure and deployment details:

HuggingFace Spaces deployment process
Performance characteristics and resource requirements
Monitoring with Prometheus and structured logging
CI/CD pipeline and scaling considerations

📖 Additional Documentation

Project Overview

High-level project introduction and goals

API Reference

Complete endpoint documentation with examples

🌟 Key Achievements & Features

Custom HuggingFace Models Integration

Unified Repository: sematech/sema-utils containing all models
Optimized Performance: CTranslate2 INT8 quantization (75% size reduction)
Automatic Updates: HuggingFace Hub integration with version management
Enterprise Caching: Intelligent model caching and loading strategies

Enhanced African Language Support

55+ African Languages: Complete FLORES-200 African language coverage
Regional Distribution: West, East, Southern, Central, and North Africa
Multiple Scripts: Latin, Arabic, Ethiopic, Tifinagh support
Cultural Context: Native names and regional information

Performance & Monitoring

Server-Side Timing: Request performance tracking in headers and responses
Prometheus Metrics: Comprehensive monitoring and analytics
Request Tracking: Unique request IDs for debugging
Health Monitoring: System status and model availability checks

🔧 Technical Implementation Highlights

Model Architecture

Custom HuggingFace Models (sematech/sema-utils)
├── Translation: NLLB-200 3.3B (CTranslate2 optimized)
├── Language Detection: FastText LID.176
├── Tokenization: SentencePiece
└── Language Database: FLORES-200 complete

Performance Metrics

Model Size: 2.5GB (optimized from 6.6GB)
Inference Speed: 0.2-2.5 seconds depending on text length
Memory Usage: ~3.2GB for models, 50-100MB per request
Language Detection: 0.01-0.05 seconds with 99%+ accuracy

API Enhancements

Request Timing: Server-side performance measurement
Language Metadata: Complete language information system
Error Handling: Comprehensive validation and error responses
Rate Limiting: 60 requests/minute with graceful degradation

🚀 Quick Start Examples

Basic Translation with Timing

curl -v -X POST "https://sematech-sema-api.hf.space/api/v1/translate" \
  -H "Content-Type: application/json" \
  -d '{"text": "Habari ya asubuhi", "target_language": "eng_Latn"}'

# Response includes timing information:
# X-Response-Time: 1.234s
# X-Request-ID: 550e8400-e29b-41d4-a716-446655440000

African Languages Discovery

# Get all 55+ African languages
curl "https://sematech-sema-api.hf.space/api/v1/languages/african"

# Search for specific African languages
curl "https://sematech-sema-api.hf.space/api/v1/languages/search?q=Akan"
curl "https://sematech-sema-api.hf.space/api/v1/languages/search?q=Bambara"

Language Detection with Confidence

curl -X POST "https://sematech-sema-api.hf.space/api/v1/detect-language" \
  -H "Content-Type: application/json" \
  -d '{"text": "Habari ya asubuhi"}'

# Returns: detected language, confidence score, timing information

🎯 Application Use Cases

1. Multilingual Chatbot Implementation

async def process_user_input(user_text):
    # 1. Detect language
    detection = await detect_language(user_text)
    
    # 2. Decide processing flow
    if detection.is_english:
        response = await llm_chat(user_text)
    else:
        # Translate → Process → Translate back
        english_input = await translate(user_text, "eng_Latn")
        english_response = await llm_chat(english_input)
        response = await translate(english_response, detection.detected_language)
    
    return response

2. African News Platform

Aggregate news from multiple African countries
Translate between African languages
Provide summaries in user's preferred language

3. Educational Platform

Interactive language learning with African languages
Cultural context and pronunciation guides
Progress tracking across multiple languages

4. Government Services

Multilingual official document translation
Emergency notifications in local languages
Citizen services in preferred languages

📊 API Statistics & Metrics

Language Coverage

Total Languages: 200+ (FLORES-200 complete)
African Languages: 55+ (updated from 23)
Writing Scripts: Latin, Arabic, Ethiopic, Tifinagh, Cyrillic, Han, etc.
Geographic Regions: Comprehensive global coverage

Performance Benchmarks

Translation Speed: 0.2-2.5s depending on text length
Language Detection: 0.01-0.05s with 99%+ accuracy
Model Efficiency: 75% size reduction with maintained quality
Concurrent Handling: Linear scaling with available resources

Quality Metrics

BLEU Scores: Industry-standard translation quality
African Languages: Specialized cultural context preservation
Uptime: 99.9% target availability
Error Rate: <1% under normal load

🔮 Future Roadmap

Immediate (3-6 months)

User authentication and usage tracking
Database integration with PostgreSQL
Redis caching for improved performance
Advanced monitoring dashboards

Medium-term (6-12 months)

Document translation with formatting preservation
Real-time translation streaming via WebSocket
Domain-specific models (medical, legal, technical)
Mobile SDK development

Long-term (1-2 years)

AI-powered translation ecosystem
Enterprise integration platform
African language research contributions
Voice-to-voice translation capabilities

🛠️ Development & Deployment

Local Development

# Clone and setup
git clone https://github.com/lewiskimaru/sema.git
cd sema/backend/sema-api

# Install dependencies
pip install -r requirements.txt

# Run locally
uvicorn app.main:app --reload --host 0.0.0.0 --port 8000

Testing

# Run comprehensive tests
python tests/test_african_languages_update.py
python tests/test_performance_timing.py
python tests/simple_test.py

Deployment

Platform: HuggingFace Spaces
Auto-deployment: Git integration
Model Updates: Automatic from sematech/sema-utils
Monitoring: Prometheus metrics and health checks

📞 Support & Resources

Documentation Links

Live API: https://sematech-sema-api.hf.space
Interactive Docs: https://sematech-sema-api.hf.space/ (Swagger UI)
Health Status: https://sematech-sema-api.hf.space/health
Metrics: https://sematech-sema-api.hf.space/metrics

Model Repository

HuggingFace: https://huggingface.co/sematech/sema-utils
Model Documentation: Comprehensive model usage and optimization guides
Version History: Track model updates and improvements

Community & Support

GitHub Repository: Complete source code and issue tracking
Model Contributions: Community-driven improvements
Research Collaboration: Academic partnerships for African language research

The Sema Translation API represents a significant advancement in African language technology, combining custom HuggingFace models with enterprise-grade infrastructure to serve diverse global communities.

Documentation last updated: June 2024 | API Version: 2.0.0