SCRIBE Developer Documentation
SCRIBE Resonance AI System - Documentation
Documentation
Technical Reference
SCRIBE Developer Documentation
Developer Overview
This guide provides comprehensive information for developers working with the SCRIBE Resonance AI System, including setup, architecture, contribution guidelines, and advanced development topics.
️ Development Environment Setup
Prerequisites
- Python 3.13+
- Git
- Virtual environment support
Quick Setup
# Clone the repository
git clone <repository-url>
cd scribe
# Setup development environment
./deploy.sh
# Activate virtual environment
source scribe_env/bin/activate
# Run tests
python3 validate_system.py
Development Dependencies
# Install development dependencies
pip install -r requirements.txt
pip install pytest black flake8 mypy
Project Structure
scribe/
├ src/ # Source code
│ ├ core/ # System orchestration
│ ├ emitter/ # Audio signal generation
│ ├ listener/ # Audio capture
│ ├ processing/ # Signal analysis
│ ├ ai/ # AI interpretation
│ ├ feedback/ # Learning system
│ ├ chat/ # User interface
│ ├ api/ # REST API
│ ├ monitoring/ # Analytics
│ └ utils/ # Utilities
├ docs/ # Documentation
├ tests/ # Test files
├ scripts/ # Utility scripts
├ main.py # Entry point
├ requirements.txt # Dependencies
└ README.html.html # Project info
️ Architecture Overview
Component Architecture
The SCRIBE system follows a modular, event-driven architecture:
System Controller (Core)
├ Emission Engine (Audio Output)
├ Listening Module (Audio Input)
├ Signal Processing (Feature Extraction)
├ AI Interpreter (Pattern Recognition)
├ Feedback Loop (Learning System)
└ Chat Interface (User Interaction)
Data Flow
Signal Generation → Audio Capture → Signal Processing → AI Interpretation → User Interface
Core Components
System Controller
from core.system_controller import ScribeSystemController
from utils.config import Config
# Initialize system
config = Config()
system = ScribeSystemController(config)
# Start system
await system.start()
# Perform scan
result = await system.perform_resonance_scan()
# Stop system
await system.stop()
Audio Components
from emitter.mock_audio import MockResonanceEmissionEngine, SignalConfig
from listener.mock_capture import MockMicroListeningModule
# Create components
emitter = MockResonanceEmissionEngine(config)
listener = MockMicroListeningModule(config)
# Initialize
await emitter.initialize()
await listener.initialize()
# Generate signal
signal_config = SignalConfig(signal_type='sine', frequency=440.0, duration=2.0)
emitted = await emitter.emit_signals([signal_config])
# Capture response
response = await listener.capture_response(duration=2.0)
Signal Processing
from processing.fft_analyzer import SignalProcessingLayer
# Create processor
processor = SignalProcessingLayer(config)
await processor.initialize()
# Analyze signal
results = await processor.analyze_signal(audio_data, emitted_signals)
# Access features
features = results['features']
fft_data = results['frequency_domain']
AI Interpretation
from ai.interpreter import ResonanceInterpretationEngine
# Create interpreter
interpreter = ResonanceInterpretationEngine(config)
await interpreter.initialize()
# Interpret results
interpretation = await interpreter.interpret_resonance(features, signal_history)
# Get insights
insights = interpretation['insights']
confidence = interpretation['confidence_scores']
Testing
Running Tests
# System validation
python3 validate_system.py
# Basic tests
python3 test_system.py
# Component tests
python3 -m pytest tests/
Test Structure
tests/
├ test_core/
├ test_emitter/
├ test_listener/
├ test_processing/
├ test_ai/
├ test_feedback/
└ test_chat/
Writing Tests
import pytest
from core.system_controller import ScribeSystemController
from utils.config import Config
@pytest.mark.asyncio
async def test_system_controller():
config = Config()
system = ScribeSystemController(config)
await system.start()
assert system.is_running
result = await system.perform_resonance_scan()
assert 'interpretation' in result
await system.stop()
assert not system.is_running
Adding New Components
1. Create Component Structure
mkdir src/new_component
touch src/new_component/__init__.py
touch src/new_component/main.py
2. Implement Component Interface
# src/new_component/main.py
import asyncio
import logging
from typing import Dict, Any, Optional
class NewComponent:
def __init__(self, config):
self.config = config
self.logger = logging.getLogger(__name__)
self.is_initialized = False
async def initialize(self):
"""Initialize component"""
self.logger.info("Initializing new component")
self.is_initialized = True
async def cleanup(self):
"""Cleanup resources"""
self.is_initialized = False
self.logger.info("New component cleaned up")
async def get_status(self) -> Dict[str, Any]:
"""Get component status"""
return {
'initialized': self.is_initialized,
'component': 'new_component'
}
3. Update System Controller
# src/core/system_controller.py
from new_component.main import NewComponent
class ScribeSystemController:
def __init__(self, config):
# ... existing code ...
self.new_component = NewComponent(config)
async def start(self):
# ... existing code ...
await self.new_component.initialize()
async def stop(self):
# ... existing code ...
await self.new_component.cleanup()
4. Add Tests
# tests/test_new_component/test_main.py
import pytest
from new_component.main import NewComponent
from utils.config import Config
@pytest.mark.asyncio
async def test_new_component():
config = Config()
component = NewComponent(config)
await component.initialize()
assert component.is_initialized
status = await component.get_status()
assert status['initialized']
await component.cleanup()
assert not component.is_initialized
Configuration
Configuration Classes
# src/utils/config.py
@dataclass
class NewComponentConfig:
parameter1: float = 1.0
parameter2: str = "default"
enabled: bool = True
@dataclass
class Config:
# ... existing configs ...
new_component: NewComponentConfig = field(default_factory=NewComponentConfig)
Using Configuration
from utils.config import Config
config = Config()
param1 = config.new_component.parameter1
enabled = config.new_component.enabled
API Development
Adding New Endpoints
# src/api/main.py
from fastapi import APIRouter
router = APIRouter(prefix="/new-component", tags=["new-component"])
@router.post("/action")
async def new_component_action(
request: NewComponentRequest,
system: ScribeSystemController = Depends(get_system_controller)
):
"""Perform new component action"""
try:
result = await system.new_component.perform_action(request.dict())
return {"status": "success", "result": result}
except Exception as e:
raise HTTPException(status_code=500, detail=str(e))
# Add router to app
app.include_router(router)
API Models
# src/api/main.py
from pydantic import BaseModel, Field
class NewComponentRequest(BaseModel):
parameter1: float = Field(..., description="Parameter 1")
parameter2: str = Field(..., description="Parameter 2")
class NewComponentResponse(BaseModel):
status: str
result: Dict[str, Any]
timestamp: str
Monitoring and Analytics
Adding Metrics
# src/monitoring/analytics.py
class AnalyticsEngine:
def __init__(self, config):
# ... existing metrics ...
self.new_component_counter = Counter('new_component_actions_total', 'Total new component actions')
self.new_component_duration = Histogram('new_component_action_duration_seconds', 'Action duration')
async def record_new_component_action(self, action_type: str, duration: float):
"""Record new component action"""
self.new_component_counter.labels(action_type=action_type).inc()
self.new_component_duration.observe(duration)
Custom Analytics
# src/new_component/analytics.py
from monitoring.analytics import AnalyticsEngine
class NewComponentAnalytics:
def __init__(self, analytics: AnalyticsEngine):
self.analytics = analytics
self.action_count = 0
self.total_duration = 0.0
async def record_action(self, action_type: str, duration: float):
"""Record action with custom analytics"""
self.action_count += 1
self.total_duration += duration
await self.analytics.record_new_component_action(action_type, duration)
def get_stats(self) -> Dict[str, Any]:
"""Get custom statistics"""
return {
'action_count': self.action_count,
'avg_duration': self.total_duration / max(1, self.action_count),
'total_duration': self.total_duration
}
Debugging
Logging
import logging
logger = logging.getLogger(__name__)
class NewComponent:
async def perform_action(self, params):
logger.info(f"Starting action with params: {params}")
try:
result = await self._do_action(params)
logger.info(f"Action completed successfully: {result}")
return result
except Exception as e:
logger.error(f"Action failed: {e}", exc_info=True)
raise
Debug Mode
# src/utils/config.py
@dataclass
class Config:
debug_mode: bool = False
# Usage
if config.debug_mode:
logging.basicConfig(level=logging.DEBUG)
Performance Profiling
import time
import functools
def profile_function(func):
@functools.wraps(func)
async def wrapper(*args, **kwargs):
start_time = time.time()
result = await func(*args, **kwargs)
duration = time.time() - start_time
logger.info(f"{func.__name__} took {duration:.3f}s")
return result
return wrapper
@profile_function
async def expensive_operation(self):
# ... operation ...
pass
Code Style and Standards
Python Style Guide
- Follow PEP 8
- Use type hints
- Document all public methods
- Use async/await for I/O operations
Example Code Style
from typing import Dict, List, Any, Optional
import logging
import asyncio
logger = logging.getLogger(__name__)
class ExampleComponent:
"""Example component for SCRIBE system.
This component demonstrates proper coding standards.
Attributes:
config: Component configuration
is_initialized: Whether component is initialized
"""
def __init__(self, config: Dict[str, Any]) -> None:
"""Initialize the component.
Args:
config: Component configuration dictionary
"""
self.config = config
self.is_initialized = False
async def initialize(self) -> None:
"""Initialize the component asynchronously."""
try:
logger.info("Initializing component")
# ... initialization logic ...
self.is_initialized = True
logger.info("Component initialized successfully")
except Exception as e:
logger.error(f"Failed to initialize component: {e}")
raise
async def process_data(self, data: List[Any]) -> Dict[str, Any]:
"""Process input data and return results.
Args:
data: Input data to process
Returns:
Processing results
Raises:
ValueError: If data is invalid
"""
if not data:
raise ValueError("Data cannot be empty")
# ... processing logic ...
return {"processed": True, "count": len(data)}
Continuous Integration
GitHub Actions Example
# .github/workflows/ci.yml
name: CI
on: [push, pull_request]
jobs:
test:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v2
- name: Set up Python
uses: actions/setup-python@v2
with:
python-version: 3.13
- name: Install dependencies
run: |
python -m pip install --upgrade pip
pip install -r requirements.txt
- name: Run tests
run: python3 validate_system.py
- name: Run linting
run: |
pip install flake8 black
flake8 src/
black --check src/
Documentation
Code Documentation
- Use docstrings for all classes and methods
- Include parameter and return type documentation
- Provide usage examples
API Documentation
- Update API docs when adding endpoints
- Include request/response examples
- Document error conditions
Wiki Updates
- Update component documentation
- Add new features to user guide
- Update architecture diagrams
Contributing
Pull Request Process
- Fork the repository
- Create feature branch
- Make changes with tests
- Update documentation
- Submit pull request
Code Review Checklist
- [ ] Code follows style guidelines
- [ ] Tests are included and passing
- [ ] Documentation is updated
- [ ] No breaking changes
- [ ] Performance impact considered
Issue Reporting
- Use GitHub issues for bug reports
- Include reproduction steps
- Provide system information
- Attach relevant logs
Advanced Topics
Custom Signal Processing
import numpy as np
from scipy import signal
class CustomProcessor:
def custom_filter(self, audio_data: np.ndarray, cutoff: float) -> np.ndarray:
"""Apply custom Butterworth filter."""
nyquist = 0.5 * self.sample_rate
normal_cutoff = cutoff / nyquist
b, a = signal.butter(4, normal_cutoff, btype='low', analog=False)
filtered = signal.filtfilt(b, a, audio_data)
return filtered
Machine Learning Integration
from sklearn.ensemble import RandomForestClassifier
import joblib
class MLModel:
def __init__(self, model_path: str):
self.model_path = model_path
self.model = None
async def load_model(self):
"""Load trained model."""
self.model = joblib.load(self.model_path)
async def predict(self, features: np.ndarray) -> Dict[str, float]:
"""Make predictions."""
if self.model is None:
await self.load_model()
probabilities = self.model.predict_proba([features])[0]
classes = self.model.classes_
return dict(zip(classes, probabilities))
Real-time Processing
import asyncio
from collections import deque
class RealTimeProcessor:
def __init__(self, buffer_size: int = 1024):
self.buffer = deque(maxlen=buffer_size)
self.processing = False
async def add_data(self, data: np.ndarray):
"""Add data to buffer and trigger processing."""
self.buffer.extend(data)
if not self.processing and len(self.buffer) >= self.buffer_size // 2:
self.processing = True
asyncio.create_task(self._process_buffer())
async def _process_buffer(self):
"""Process buffered data."""
while len(self.buffer) >= self.buffer_size // 2:
chunk = [self.buffer.popleft() for _ in range(self.buffer_size // 2)]
await self._process_chunk(chunk)
self.processing = False
External Resources
- Python Documentation
- AsyncIO Documentation
- NumPy Documentation
- FastAPI Documentation
- Pydantic Documentation
Last Updated: 2026-05-06
Developer Guide Version: 1.0.0
Status: Production Ready