"Empowering communication and language processing with intelligent AI assistance"
LEXIS is a production-grade language processing and communication service that provides comprehensive document management, writing assistance, communication analysis, and advanced natural language processing capabilities. Built with modern ML architectures and designed for scalability and performance as part of the MTM-CE ecosystem.
- Overview
- Key Features
- Architecture
- Installation
- Quick Start
- API Documentation
- Machine Learning
- Usage Examples
- Configuration
- Testing
- Deployment
- Performance
- Security
- Contributing
- License
LEXIS transforms how we interact with language through intelligent AI assistance. Whether you're writing documents, analyzing communications, or processing text across multiple languages, LEXIS provides the tools and insights you need to communicate more effectively.
- 🧠 Advanced NLP: State-of-the-art natural language processing capabilities
- ✍️ Intelligent Writing: Real-time writing assistance and improvement suggestions
- 🌐 Multi-Language Support: Process text in 50+ languages with high accuracy
- 📊 Communication Analytics: Deep insights into communication patterns and effectiveness
- 🤖 ML-Powered: Cutting-edge machine learning for text understanding and generation
- ⚡ Real-Time Processing: Fast, responsive language processing for production environments
- Document Management: Create, analyze, and manage documents with AI-powered insights
- Writing Assistance: Real-time writing analysis, suggestions, and improvement recommendations
- Communication Analysis: Analyze messages, conversations, and communication patterns
- Language Processing: Multi-language support, translation, and text processing
- Text Summarization: Intelligent extractive and abstractive summarization
- NLP Engine: Advanced natural language understanding and processing
- Writing Assistant: Style analysis, grammar checking, and writing optimization
- Communication Analyzer: Sentiment analysis, tone detection, and conversation insights
- Language Processor: Multi-language detection, translation, and processing
- Text Summarizer: Key point extraction and intelligent summarization
- Real-time Processing: Async processing for high-performance operations
- User Session Management: Personalized experiences and context awareness
- Comprehensive Analytics: Service statistics and performance monitoring
- Health Monitoring: Built-in health checks and component status monitoring
- Scalable Architecture: Designed for production deployment and scaling
┌─────────────────────────────────────────────────────────────┐
│ LEXIS Platform │
├─────────────────────────────────────────────────────────────┤
│ Document Mgmt │ Writing Assistant │ Communication │ Language │
│ ─────────────── │ ──────────────── │ ──────────── │ ──────── │
│ • Creation │ • Style Analysis │ • Sentiment │ • Detection │
│ • Analysis │ • Grammar Check │ • Tone │ • Translation │
│ • Versioning │ • Suggestions │ • Insights │ • Processing │
│ • Metadata │ • Optimization │ • Patterns │ • Validation │
└─────────────────────────────────────────────────────────────┘
│ │
┌─────────────┴────────────┐ ┌───────────┴────────────────┐
│ ML Pipeline │ │ Service Layer │
├──────────────────────────┤ ├────────────────────────────┤
│ • NLP Engine │ │ • User Session Management │
│ • Text Summarizer │ │ • Document Processing │
│ • Writing Assistant │ │ • Communication Analysis │
│ • Communication Analyzer │ │ • Language Operations │
│ • Language Processor │ │ • Analytics & Reporting │
└──────────────────────────┘ └────────────────────────────┘
│ │
┌─────────────┴────────────────────────────┴─────────────────┐
│ Data Layer │
├─────────────────────────────────────────────────────────────┤
│ PostgreSQL │ Document Store │ Model Cache │ Session Store │
└─────────────────────────────────────────────────────────────┘
LEXIS/
├── app/
│ ├── api/ # API endpoints
│ │ └── endpoints.py # Main API routes
│ ├── ml/ # Machine learning modules
│ │ ├── communication_analyzer.py # Communication analysis
│ │ ├── language_processor.py # Language processing
│ │ ├── nlp_engine.py # NLP core engine
│ │ ├── text_summarizer.py # Text summarization
│ │ ├── writing_assistant.py # Writing assistance
│ │ └── __init__.py
│ ├── models/ # Database models
│ ├── schemas/ # Pydantic schemas
│ │ ├── common.py # Common schemas
│ │ ├── communication.py # Communication schemas
│ │ ├── document.py # Document schemas
│ │ ├── language_processing.py # Language processing schemas
│ │ ├── writing_assistance.py # Writing assistance schemas
│ │ └── __init__.py
│ ├── services/ # Business logic
│ │ ├── lexis_service.py # Main service
│ │ └── __init__.py
│ └── __init__.py
├── tests/ # Test suite
│ ├── test_api_endpoints.py
│ ├── test_lexis_service.py
│ └── __init__.py
├── DOCS.md # Additional documentation
└── README.md # This file
- Python 3.9+
- PostgreSQL 12+
- Redis (for caching)
- CUDA (optional, for GPU acceleration)
# Clone the repository
git clone https://github.com/mtm-ce/lexis.git
cd lexis
# Create virtual environment
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
# Install dependencies
pip install -r requirements.txt
# Set up environment variables
cp .env.example .env
# Edit .env with your configuration
# Initialize database
alembic upgrade head
# Run the service
uvicorn app.main:app --host 0.0.0.0 --port 8002# Build and run with Docker Compose
docker-compose up -dfrom app.services.lexis_service import LEXISService
from app.schemas.document import DocumentCreate
# Initialize service
lexis = LEXISService()
# Create a document
document_data = DocumentCreate(
title="My Document",
content="This is a sample document for processing.",
document_type="article"
)
response = await lexis.create_document("user123", document_data)
print(f"Created document: {response.id}")
# Analyze the document
analysis_request = DocumentAnalysisRequest(
content=document_data.content,
analysis_types=["nlp", "writing", "summarization"]
)
analysis = await lexis.analyze_document("user123", response.id, analysis_request)
print(f"Analysis results: {analysis.results}")from app.schemas.writing_assistance import WritingSuggestionRequest
# Get writing suggestions
suggestion_request = WritingSuggestionRequest(
text="This is an example text that needs improvement.",
improvement_types=["grammar", "style", "clarity"],
target_audience="professional"
)
suggestions = await lexis.get_writing_suggestions("user123", suggestion_request)
for suggestion in suggestions.suggestions:
print(f"Type: {suggestion.type}")
print(f"Original: {suggestion.original}")
print(f"Improved: {suggestion.improved}")
print(f"Confidence: {suggestion.confidence}")from app.schemas.communication import MessageAnalysisRequest
# Analyze message sentiment and tone
message_request = MessageAnalysisRequest(
text="Thank you for your excellent work on this project!",
analysis_types=["sentiment", "tone", "formality"],
context="professional_email"
)
analysis = await lexis.analyze_message("user123", message_request)
print(f"Sentiment: {analysis.sentiment}")
print(f"Tone: {analysis.tone}")
print(f"Formality: {analysis.formality_level}")from app.schemas.language_processing import TranslationRequest
# Translate text
translation_request = TranslationRequest(
text="Hello, how are you today?",
source_language="en",
target_language="es",
translation_type="formal"
)
translation = await lexis.translate_text("user123", translation_request)
print(f"Translation: {translation.translated_text}")
print(f"Confidence: {translation.confidence}")POST /api/v1/documents # Create document
GET /api/v1/documents # List documents
GET /api/v1/documents/{id} # Get document
PUT /api/v1/documents/{id} # Update document
DELETE /api/v1/documents/{id} # Delete document
POST /api/v1/documents/{id}/analyze # Analyze documentPOST /api/v1/writing/projects # Create writing project
GET /api/v1/writing/projects # List writing projects
POST /api/v1/writing/suggestions # Get writing suggestions
POST /api/v1/writing/improvements # Apply improvements
GET /api/v1/writing/analytics # Get writing analyticsPOST /api/v1/communication/channels # Create communication channel
POST /api/v1/communication/messages # Analyze message
POST /api/v1/communication/conversations # Analyze conversation
GET /api/v1/communication/insights # Get communication insights
POST /api/v1/communication/feedback # Provide feedbackPOST /api/v1/language/detect # Detect language
POST /api/v1/language/translate # Translate text
POST /api/v1/language/process # Process text
GET /api/v1/language/supported # Get supported languages
POST /api/v1/language/summarize # Summarize textGET /api/v1/health # Service health check
GET /api/v1/status # Service status
GET /api/v1/metrics # Service metricsCore natural language processing capabilities with state-of-the-art transformer models.
Key Features:
- Named Entity Recognition: Extract people, places, organizations, and custom entities
- Part-of-Speech Tagging: Detailed grammatical analysis
- Dependency Parsing: Understand sentence structure and relationships
- Sentiment Analysis: Emotion and sentiment detection
- Language Detection: Identify language with 99%+ accuracy
Intelligent writing analysis and improvement suggestions.
Key Features:
- Grammar Checking: Advanced grammar and syntax validation
- Style Analysis: Writing style assessment and recommendations
- Readability Scoring: Flesch-Kincaid and other readability metrics
- Tone Detection: Formal, informal, professional, casual tone identification
- Vocabulary Enhancement: Synonym suggestions and word choice optimization
Analyze messages and conversations for insights and effectiveness.
Key Features:
- Sentiment Analysis: Multi-dimensional sentiment scoring
- Tone Detection: Professional, friendly, aggressive, neutral tones
- Conversation Flow: Analyze conversation structure and dynamics
- Engagement Metrics: Response rates, participation levels
- Conflict Detection: Identify potential communication issues
Multi-language support with advanced translation and processing.
Key Features:
- Language Detection: Support for 100+ languages
- Translation: High-quality neural machine translation
- Transliteration: Convert between different writing systems
- Language Validation: Verify text quality and authenticity
- Cultural Adaptation: Context-aware cultural considerations
Intelligent text summarization with extractive and abstractive approaches.
Key Features:
- Extractive Summarization: Select key sentences and passages
- Abstractive Summarization: Generate new summary text
- Multi-Document Summarization: Summarize across multiple documents
- Keyword Extraction: Identify important terms and concepts
- Summary Quality Scoring: Evaluate summary effectiveness
# Database Configuration
DATABASE_URL=postgresql://user:pass@localhost:5432/lexis_db
# ML Model Configuration
ML_MODEL_PATH=/path/to/models
ML_CACHE_SIZE=1000
ML_BATCH_SIZE=32
# Service Configuration
MAX_CONTENT_LENGTH=50000
DEFAULT_LANGUAGE=english
ENABLE_ANALYTICS=true
# Performance Configuration
ASYNC_WORKERS=4
CACHE_TTL=3600
# API Configuration
API_PORT=8002
API_HOST=0.0.0.0
API_WORKERS=4Users can customize their experience through preferences:
user_session.update_preferences({
'writing_style': 'professional', # casual, professional, academic
'language_preference': 'english',
'summarization_type': 'extractive', # extractive, abstractive
'communication_analysis_level': 'detailed' # basic, detailed
})- Processing Speed: < 500ms average response time for standard operations
- Throughput: Supports 1000+ concurrent requests
- Memory Usage: Optimized ML models with efficient memory management
- Accuracy: > 95% accuracy for language detection, > 90% for sentiment analysis
- Async Processing: All operations use async/await for non-blocking execution
- Batch Processing: Support for batch operations when needed
- Caching: Intelligent caching of ML model results and user data
- Load Balancing: Designed to work with load balancers and multiple instances
- Service Statistics: Real-time tracking of operations and performance
- Health Checks: Comprehensive health monitoring for all components
- Logging: Structured logging for debugging and monitoring
- Metrics: Detailed metrics for performance analysis
# Run all tests
pytest tests/ -v
# Run specific test categories
pytest tests/test_lexis_service.py -v
pytest tests/test_ml_modules.py -v
# Run tests with coverage
pytest tests/ --cov=app --cov-report=html- Service Layer: 100% coverage of service methods and workflows
- ML Components: Comprehensive testing of all ML module integrations
- Error Handling: Edge cases and error conditions
- Integration Tests: End-to-end workflow testing
# Dockerfile
FROM python:3.9-slim
WORKDIR /app
# Install system dependencies
RUN apt-get update && apt-get install -y \
build-essential \
&& rm -rf /var/lib/apt/lists/*
# Install Python dependencies
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt
# Copy application
COPY . .
# Expose port
EXPOSE 8002
# Run application
CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8002"]version: '3.8'
services:
lexis:
build: .
ports:
- "8002:8002"
environment:
- DATABASE_URL=postgresql://user:pass@db:5432/lexis_db
- REDIS_URL=redis://redis:6379
depends_on:
- db
- redis
db:
image: postgres:13
environment:
- POSTGRES_DB=lexis_db
- POSTGRES_USER=user
- POSTGRES_PASSWORD=pass
redis:
image: redis:alpine# Check service health
health_status = await lexis.health_check()
print(f"Service status: {health_status['status']}")
# Get service statistics
stats = lexis.get_service_statistics()
print(f"Total requests: {stats['service_stats']['total_requests']}")- Input Validation: Comprehensive validation of all inputs
- Rate Limiting: Configurable rate limiting per endpoint
- Authentication: JWT-based authentication system
- Data Encryption: Encryption of sensitive data at rest and in transit
- Audit Logging: Complete audit trail of all operations
- Data Minimization: Only collect necessary data
- Retention Policies: Automatic data cleanup and retention management
- User Control: Users can delete their data at any time
- Anonymization: Option to anonymize processed text
- Document Analysis: Comprehensive analysis of documents for insights and improvements
- Writing Enhancement: Real-time writing assistance and improvement suggestions
- Content Optimization: Style, tone, and structure optimization for different audiences
- Team Communication: Analyze team conversations for effectiveness and sentiment
- Message Quality: Improve message clarity and communication effectiveness
- Conversation Insights: Extract insights from communication patterns
- Language Detection: Automatic detection of text language
- Translation Services: High-quality text translation between languages
- Cross-language Processing: Process content in multiple languages seamlessly
- Document Summarization: Generate concise summaries of long documents
- Key Point Extraction: Identify and extract important information
- Content Categorization: Organize and categorize content automatically
# Install development dependencies
pip install -r requirements-dev.txt
# Set up pre-commit hooks
pre-commit install
# Run linting
flake8 app/ tests/
black app/ tests/
# Run type checking
mypy app/- PEP 8: Follow Python PEP 8 style guidelines
- Type Hints: Use type hints for all functions and methods
- Documentation: Comprehensive docstrings for all public methods
- Testing: Write tests for all new features and bug fixes
This project is licensed under the MIT License - see the LICENSE file for details.
- Issues: Report bugs and request features on the issue tracker
- Discussions: Join community discussions for questions and support
- Documentation: Comprehensive documentation and examples
- API Documentation: Detailed API documentation with examples
- ML Model Documentation: Information about ML models and their capabilities
- Performance Tuning: Guidelines for optimizing performance
- Troubleshooting: Common issues and solutions
LEXIS - Empowering communication and language processing with intelligent AI assistance.
Part of the MTM-CE Ecosystem