SCRIBE System Components

SCRIBE Resonance AI System - Documentation

Documentation
Technical Reference

SCRIBE System Components

Component Overview

The SCRIBE system consists of 7 core components, each with specific responsibilities and well-defined interfaces. This section provides detailed documentation for each component.

Component Structure

src/
├ 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

System Controller

File: src/core/system_controller.py

Purpose: Central orchestration and component coordination

Key Methods:

  • start() - Initialize all components
  • stop() - Cleanup and shutdown
  • perform_resonance_scan() - Execute complete scan cycle
  • get_system_status() - Return system health status
  • get_scan_history() - Retrieve scan history

Dependencies:

  • All core components
  • Configuration system
  • Logging framework

Usage Example:

from core.system_controller import ScribeSystemController
from utils.config import Config

config = Config()
system = ScribeSystemController(config)

await system.start()
result = await system.perform_resonance_scan()
await system.stop()

Resonance Emission Engine

Files: src/emitter/

Purpose: Generate controlled acoustic signals for environmental probing

Real Engine: tone_generator.py

Signal Types:

  • Sine waves (single frequency)
  • Frequency sweeps (20Hz - 20kHz)
  • Pulse bursts
  • Harmonic stacks

Key Methods:

  • emit_signals() - Generate audio signals
  • initialize() - Setup audio hardware
  • cleanup() - Release resources

Mock Engine: mock_audio.py

Purpose: Fallback for testing without audio hardware

Features:

  • Simulated signal generation
  • Realistic timing and metadata
  • No hardware dependencies

️ Micro Listening Module

Files: src/listener/

Purpose: Capture environmental acoustic responses

Real Module: mic_capture.py

Features:

  • High-fidelity audio capture
  • Real-time processing
  • Multi-device support

Mock Module: mock_capture.py

Purpose: Fallback for testing without audio hardware

Features:

  • Simulated audio capture
  • Realistic response generation
  • Configurable noise and reflections

Signal Processing Layer

File: src/processing/fft_analyzer.py

Purpose: Extract meaningful features from audio signals

Analysis Types:

  • FFT (Fast Fourier Transform)
  • Spectrogram analysis
  • Envelope detection
  • Resonance peak extraction
  • Harmonic analysis
  • Noise analysis

Key Methods:

  • analyze_signal() - Complete signal analysis
  • compute_fft() - FFT computation
  • _analyze_spectrogram() - Spectrogram generation
  • _analyze_resonance_peaks() - Peak detection

Output Features:

  • Time domain features (RMS, peak, zero crossings)
  • Frequency domain features (dominant frequencies, spectral centroid)
  • Resonance peaks and Q-factors
  • Harmonic content and ratios
  • Envelope characteristics

AI Interpretation Engine

File: src/ai/interpreter.py

Purpose: Intelligent pattern recognition and interpretation

Approaches:

  • Rule-based analysis
  • Machine learning pattern matching
  • Anomaly detection
  • Confidence scoring

Key Methods:

  • interpret_resonance() - Main interpretation function
  • _rule_based_analysis() - Apply expert rules
  • _ml_pattern_matching() - ML-based recognition
  • _detect_anomalies() - Anomaly detection

Output Categories:

  • Material identification
  • Environment classification
  • State assessment
  • Anomaly detection
  • Confidence scores

Feedback Loop System

File: src/feedback/learning_system.py

Purpose: Continuous learning and adaptation from user feedback

Features:

  • User feedback integration
  • Pattern adaptation
  • Learning insights
  • Performance tracking
  • Database storage

Key Methods:

  • store_scan_result() - Store scan data
  • add_user_feedback() - Process user corrections
  • get_learning_insights() - Learning analytics
  • adapt_patterns() - Update recognition patterns

Database Schema:

  • Scan results storage
  • User feedback tracking
  • Pattern adaptations
  • Performance metrics

Chat Interface

File: src/chat/interface.py

Purpose: Natural language user interaction

Features:

  • Command processing
  • Natural language queries
  • Real-time responses
  • Context awareness

Supported Commands:

  • /scan - Perform resonance analysis
  • /status - Check system health
  • /help - Show available commands
  • /history - View scan history
  • /feedback - Provide corrections

Natural Language Examples:

  • "What did you detect?"
  • "Is this environment stable?"
  • "Compare this scan to previous"
  • "What changed?"

REST API

File: src/api/main.py

Purpose: HTTP interface for external integration

Endpoints:

  • GET / - API information
  • POST /scan - Perform scan
  • GET /status - System status
  • GET /scans - Scan history
  • POST /feedback - User feedback
  • GET /learning/insights - Learning analytics

Features:

  • FastAPI framework
  • Automatic documentation
  • Request validation
  • Error handling

Monitoring & Analytics

File: src/monitoring/analytics.py

Purpose: Real-time system monitoring and performance tracking

Features:

  • Prometheus metrics
  • Performance tracking
  • System health monitoring
  • Alert generation

Metrics Collected:

  • Scan count and duration
  • Confidence scores
  • Error rates
  • Resource usage
  • User interaction patterns

Utilities

Configuration: src/utils/config.py

Purpose: System configuration management

Classes:

  • AudioConfig - Audio settings
  • ProcessingConfig - Signal processing parameters
  • AIConfig - AI model settings
  • DatabaseConfig - Database configuration

Logging: src/utils/logger.py

Purpose: Centralized logging system

Features:

  • Console and file logging
  • Rotating log files
  • Component-specific loggers
  • Error tracking

Component Integration

Data Flow

Emission Engine → Listening Module → Signal Processing → AI Interpreter → Feedback Loop → Chat Interface

Communication Patterns

  • Async/Await: Non-blocking operations
  • Event-driven: Component notifications
  • Status polling: Health checks
  • Error propagation: Graceful failure handling

Dependencies

  • Core: All components depend on system controller
  • Audio: Emission and listener are independent
  • Processing: Depends on audio components
  • AI: Depends on processing results
  • Feedback: Depends on AI interpretation
  • Interface: Depends on all components

️ Component Development

Adding New Components

  1. Create component directory
  2. Implement required interfaces
  3. Add to system controller
  4. Update configuration
  5. Add tests
  6. Update documentation

Component Interfaces

  • initialize() - Setup component
  • cleanup() - Release resources
  • get_status() - Health check
  • Error handling and logging

Best Practices

  • Async/await for I/O operations
  • Proper error handling
  • Comprehensive logging
  • Configuration flexibility
  • Mock implementations for testing

Last Updated: 2026-05-06
Component Version: 1.0.0
Status: Production Ready