SmartBrain Documentation

Welcome to the SmartBrain documentation. This comprehensive guide covers all aspects of the SmartBrain AI/ML Engine for the CyberAi Ecosystem.

Quick Navigation:

New to SmartBrain? Start with the Quick Start Guide
Looking for a specific feature? Check the Features Quick Reference
Comparing tools? See Feature Comparison
Need help? Visit Troubleshooting or Support

Getting Started

Introduction
Architecture Overview
Model Lifecycle
Model Versioning
Dataset Requirements
Inference Usage Guide
Training Pipeline Guide
Terminal Command Integration
Ecosystem Integration
API Reference
Best Practices
Smart Functions — Auto-analyze, auto-fix, auto-test, auto-sync, smart-suggest
Orval DB Virtual Memory System — AI brain memory layer
Self-Updating Documentation — Docs engine and freshness scoring
Troubleshooting Guide — Common issues and quick fixes
FAQ — Frequently asked questions

Introduction

SmartBrain is a comprehensive AI/ML engine and automation platform designed specifically for smart contract automation and blockchain development within the CyberAi ecosystem. It combines powerful machine learning capabilities with intelligent automation features to streamline the entire development lifecycle.

What is SmartBrain?

SmartBrain serves as the central intelligence hub for smart contract development, providing automated analysis, testing, synchronization, and deployment capabilities. Whether you're developing DeFi protocols, NFT marketplaces, or complex multi-chain applications, SmartBrain offers the tools and automation you need to build secure, efficient, and reliable smart contracts.

Core Capabilities

Model Registry and Versioning: Comprehensive model management with semantic versioning
Training Pipeline Infrastructure: Complete ML pipeline for model development and training
Inference Engine: Robust inference system with CLI and API access
Dataset Validation and Management: Automated dataset validation and quality assurance
Intelligent Automation: Auto Sync, Auto Test, Auto Analysis, and Auto Fix capabilities
Integration with GitHub Copilot: Seamless integration with GitHub development workflows
Multi-Chain Support: Built-in support for Ethereum, Solana, Polygon, and more

Why SmartBrain?

Time Savings: Automated workflows reduce manual development time by 40-60% Quality Assurance: Continuous testing and analysis catch issues before deployment Cost Efficiency: Gas optimization and early bug detection save significant costs Developer Experience: Intuitive CLI and API make complex tasks simple Security First: Built-in security analysis and vulnerability detection

Quick Start Guide

This quick start guide will get you up and running with SmartBrain in under 10 minutes.

Prerequisites

Before you begin, ensure you have:

Node.js v16.0.0 or higher
npm v8.0.0 or higher
Git installed on your system
A GitHub account (for GitHub Copilot integration)

Installation Steps

Clone the Repository

git clone https://github.com/SolanaRemix/SmartBrain.git
cd SmartBrain

Install Dependencies
```
npm install
```

Set Up Environment

cp .env.example .env
# Edit .env with your configuration

Run Bootstrap Script
```
./scripts/bootstrap.sh
```

Verify Installation

npm test
npm run lint
./scripts/audit.sh

Your First Model

Let's train a simple model to get familiar with SmartBrain:

# NOTE: The training CLI is currently a placeholder and not yet implemented.
# The commands below will be updated once training and configuration
# subcommands (config/train) and flags like --output/--epochs are available.

# Current behavior:
# - Running the training CLI with no arguments shows the help/usage output.
node training/cli/index.js
# => (prints help/usage information)

# - Running a specific command prints a placeholder "not yet implemented" message.
node training/cli/index.js train
# => "Training CLI - train command (not yet implemented)"
# For model training and inference, please refer to the dedicated
# training and inference documentation or higher-level scripts.

Next Steps

Read the Architecture Overview to understand SmartBrain's structure
Explore Automation Features to leverage intelligent automation
Check out Best Practices for optimal usage
Join our community for support and updates

Installation & Setup

This section provides detailed installation and configuration instructions for production environments.

System Requirements

Minimum Requirements:

CPU: 2 cores
RAM: 4 GB
Storage: 10 GB free space
OS: Linux, macOS, or Windows (with WSL2)

Recommended Requirements:

CPU: 4+ cores
RAM: 8+ GB
Storage: 50+ GB SSD
OS: Linux (Ubuntu 20.04+ or similar)

Detailed Installation

1. Clone and Navigate

git clone https://github.com/SolanaRemix/SmartBrain.git
cd SmartBrain

2. Install Node.js Dependencies

npm install --production

For development with all dev dependencies:

npm install

3. Configure Environment Variables

Copy the example environment file:

cp .env.example .env

Edit .env and configure the following variables:

# GitHub Integration
GITHUB_TOKEN=your_github_personal_access_token

# Stripe Configuration (Optional, for bot features)
STRIPE_SECRET_KEY=your_stripe_secret_key
STRIPE_PUBLISHABLE_KEY=your_stripe_publishable_key
STRIPE_WEBHOOK_SECRET=your_webhook_secret

# Model Configuration
MODEL_DIR=./models
DATASET_DIR=./datasets

# Inference Configuration
INFERENCE_BATCH_SIZE=32
INFERENCE_TIMEOUT=30000

# Training Configuration
TRAINING_CHECKPOINT_FREQ=5
TRAINING_LOG_LEVEL=info

4. Initialize Infrastructure

Run the bootstrap script to set up directories and permissions:

chmod +x ./scripts/bootstrap.sh
./scripts/bootstrap.sh

This script will:

Check Node.js and npm availability
Create necessary directories
Install project dependencies
Copy the .env file from the template if needed
Verify models/metadata/schema.json and workflow files exist
Set appropriate script permissions

5. Verify Installation

Run the comprehensive audit script:

./scripts/audit.sh

This checks:

✅ Directory structure
✅ Required files and dependencies
✅ Configuration validity
✅ Permissions
✅ Model registry
✅ Workflow files

6. Run Validation Checks

Use the available automated tests and validation commands to verify your installation:

# Run Jest unit tests
npm test

# Or run unit tests only
npm run test:unit
# Validate model configurations and registry
npm run validate:models

# Validate dataset configurations
npm run validate:datasets

# Verify inference CLI is working (shows top-level help)
node inference/cli/index.js help

# Or verify model info for a specific model
node inference/cli/index.js info --model path/to/model.onnx

Configuration Options

Model Configuration

Edit models/config.json to configure model defaults:

{
  "default_framework": "tensorflow",
  "versioning": {
    "strategy": "semantic",
    "auto_increment": true
  },
  "validation": {
    "required_metadata": ["name", "version", "framework", "task"],
    "check_integrity": true
  }
}

Training Configuration

Global training settings in training/config.json:

{
  "defaults": {
    "batch_size": 32,
    "learning_rate": 2e-5,
    "optimizer": "adamw",
    "checkpoint_frequency": 5
  },
  "hardware": {
    "gpu_enabled": true,
    "mixed_precision": true
  }
}

Inference Configuration

Configure inference behavior in inference/config.json:

{
  "engine": {
    "batch_size": 32,
    "timeout": 30000,
    "caching": true
  },
  "api": {
    "port": 3000,
    "rate_limit": 100
  }
}

Troubleshooting Installation

Issue: npm install fails

# Clear npm cache
npm cache clean --force
# Try again
npm install

Issue: Permission denied on scripts

# Make scripts executable
chmod +x ./scripts/*.sh

Issue: Bootstrap script fails

# Check Node.js version
node --version  # Should be >= 16.0.0
# Check npm version
npm --version   # Should be >= 8.0.0

Issue: Tests fail

# Ensure all dependencies are installed
npm install
# Run specific test suite
npm run test:unit

Architecture Overview

System Components

SmartBrain is built on a modular architecture with clearly separated concerns:

┌─────────────────────────────────────────────────────────────┐
│                      SmartBrain Core                         │
├─────────────────────────────────────────────────────────────┤
│                                                               │
│  ┌─────────────┐  ┌─────────────┐  ┌──────────────────┐   │
│  │   Models    │  │  Training   │  │    Inference     │   │
│  │             │  │             │  │                  │   │
│  │ • Registry  │  │ • Pipeline  │  │ • Engine         │   │
│  │ • Metadata  │  │ • Configs   │  │ • CLI            │   │
│  │ • Versions  │  │ • Jobs      │  │ • API            │   │
│  └─────────────┘  └─────────────┘  └──────────────────┘   │
│                                                               │
│  ┌─────────────┐  ┌─────────────┐  ┌──────────────────┐   │
│  │  Datasets   │  │    Tools    │  │   Automation     │   │
│  │             │  │             │  │                  │   │
│  │ • Validation│  │ • ML Helpers│  │ • Auto Sync      │   │
│  │ • Schemas   │  │ • Utilities │  │ • Auto Test      │   │
│  │ • Storage   │  │ • Debuggers │  │ • Auto Analysis  │   │
│  └─────────────┘  └─────────────┘  │ • Auto Fix       │   │
│                                      └──────────────────┘   │
│                                                               │
└─────────────────────────────────────────────────────────────┘

Core Components Detailed

Models Registry

Centralized model storage and versioning
Semantic versioning support (MAJOR.MINOR.PATCH)
Metadata management and validation
Multi-framework support (TensorFlow, PyTorch, ONNX)
Automatic integrity checking

Training Pipeline

Configuration-driven training workflows
Checkpoint management and resumption
Distributed training support
Real-time metrics tracking
Automatic hyperparameter validation

Inference Engine

High-performance model serving
Batch processing optimization
Caching and optimization
CLI and REST API interfaces
Real-time and batch modes

Dataset Management

Schema-based validation
Quality assurance checks
Version control integration
Split management (train/val/test)
Automatic preprocessing

Automation Suite

Auto Sync: Automated repository synchronization
Auto Test: Intelligent test execution
Auto Analysis: Continuous code and model analysis
Auto Fix: Automated issue resolution

Tools & Utilities

ML helper functions
Data preprocessing utilities
Model debugging tools
Performance profilers
Validation scripts

Integration Points

GitHub Copilot: Agent integration via .github/copilot/agent.yaml
GitHub Actions: CI/CD workflows for training, validation, and deployment
SmartContract Bots: Integration with deployment and audit bots
CyberAi Ecosystem: Part of the broader CyberAi infrastructure

Model Lifecycle

1. Model Development

# Generate training configuration
node training/cli/index.js config --output training/configs/my-model.json

# Edit configuration
vim training/configs/my-model.json

2. Model Training

# Train model
node training/cli/index.js train \
  --config training/configs/my-model.json \
  --output models/my-model \
  --epochs 10

3. Model Validation

# Validate model files and metadata
./scripts/validate-model.sh models/my-model

4. Model Registration

Place model in /models directory with proper metadata:

{
  "name": "smart-contract-classifier",
  "version": "1.0.0",
  "framework": "tensorflow",
  "task": "classification",
  "description": "Classifies smart contract vulnerabilities",
  "author": "SmartBrain Team",
  "created_at": "2025-01-11T00:00:00Z",
  "metrics": {
    "accuracy": 0.95,
    "precision": 0.93,
    "recall": 0.94,
    "f1_score": 0.935
  }
}

5. Model Deployment

# Run inference
node inference/cli/index.js predict \
  --model models/my-model \
  --input data/input.json

Model Versioning

SmartBrain uses semantic versioning (SemVer) for models:

MAJOR.MINOR.PATCH (e.g., 2.1.3)
- MAJOR: Incompatible API changes
- MINOR: Backward-compatible functionality additions
- PATCH: Backward-compatible bug fixes

Version Management

/models
  /smart-contract-classifier
    /1.0.0
      model.h5
      metadata.json
      README.md
    /1.1.0
      model.h5
      metadata.json
      README.md
    /2.0.0
      model.pb
      metadata.json
      README.md

Version Selection

// Load specific version
const model = loadModel('smart-contract-classifier', '1.1.0');

// Load latest version
const model = loadModel('smart-contract-classifier', 'latest');

Dataset Requirements

Dataset Structure

Datasets should follow this structure:

{
  "metadata": {
    "name": "smart-contract-vulnerabilities",
    "version": "1.0.0",
    "description": "Dataset of smart contract code samples",
    "size": 10000,
    "split": {
      "train": 0.7,
      "validation": 0.15,
      "test": 0.15
    }
  },
  "data": [
    {
      "id": "sample-001",
      "input": "contract code here",
      "label": "reentrancy",
      "metadata": {
        "source": "etherscan",
        "date": "2024-01-01"
      }
    }
  ]
}

Dataset Validation

# Validate dataset
node datasets/validation/validate.js \
  --dataset data/my-dataset.json \
  --schema models/metadata/schema.json \
  --verbose

Dataset Best Practices

Quality: Ensure high-quality, clean data
Balance: Balance class distributions
Splits: Maintain consistent train/val/test splits
Documentation: Document data sources and processing
Versioning: Version datasets alongside models

Inference Usage Guide

Command-Line Inference

# Single prediction
node inference/cli/index.js predict \
  --model models/vulnerability-detector \
  --input contract.json \
  --output result.json

# Batch inference
node inference/cli/index.js batch \
  --model models/vulnerability-detector \
  --input contracts.json \
  --output results.json \
  --batch-size 32

# Model information
node inference/cli/index.js info \
  --model models/vulnerability-detector

API Inference

const express = require('express');
const { InferenceEngine } = require('./inference/engine');

const app = express();
const engine = new InferenceEngine('models/my-model');

app.post('/predict', async (req, res) => {
  const prediction = await engine.predict(req.body);
  res.json(prediction);
});

app.listen(3000);

Batch Processing

# Process large dataset
node inference/cli/index.js batch \
  --model models/my-model \
  --input large-dataset.json \
  --output predictions.json \
  --batch-size 64

Training Pipeline Guide

Configuration

Create a training configuration:

model:
  name: vulnerability-detector
  architecture: transformer
  parameters:
    layers: 12
    hidden_size: 768
    num_heads: 12
    dropout: 0.1

training:
  batch_size: 32
  learning_rate: 2e-5
  epochs: 10
  optimizer: adamw
  scheduler: linear_warmup

data:
  train_path: datasets/train.json
  validation_path: datasets/validation.json
  test_path: datasets/test.json
  max_length: 512

output:
  model_dir: models/vulnerability-detector
  checkpoint_dir: models/vulnerability-detector/checkpoints
  save_frequency: 5

Training Execution

# Start training
node training/cli/index.js train \
  --config training/configs/vulnerability-detector.yaml \
  --output models/vulnerability-detector

# Resume from checkpoint
node training/cli/index.js resume \
  --checkpoint models/vulnerability-detector/checkpoints/epoch-5 \
  --config training/configs/vulnerability-detector.yaml

Monitoring Training

Training logs and metrics are saved to the model directory:

models/vulnerability-detector/
  ├── training.log
  ├── metrics.json
  ├── checkpoints/
  │   ├── epoch-1/
  │   ├── epoch-5/
  │   └── epoch-10/
  └── metadata.json

Auto Sync

Auto Sync is SmartBrain's intelligent repository synchronization feature that automatically keeps your models, datasets, and configurations synchronized across different environments and team members.

What is Auto Sync?

Auto Sync monitors your SmartBrain workspace for changes and automatically synchronizes them with remote repositories, ensuring that all team members have access to the latest models, datasets, and configurations. It eliminates manual synchronization tasks and reduces version conflicts.

Key Features

Automatic Detection: Monitors file system for changes to models, datasets, and configs
Smart Synchronization: Only syncs changed files to minimize bandwidth usage
Conflict Resolution: Intelligent conflict resolution with customizable strategies
Multi-Repository Support: Sync to multiple Git repositories simultaneously
Selective Sync: Configure which files and directories to sync
Real-Time Updates: Near real-time synchronization with configurable intervals
Audit Trail: Complete history of all synchronization operations

Enabling Auto Sync

Configuration

Create or edit .smartbrain/sync.json:

{
  "enabled": true,
  "interval": 300,
  "repositories": [
    {
      "name": "origin",
      "url": "https://github.com/your-org/models.git",
      "branch": "main",
      "paths": ["models/", "datasets/"]
    },
    {
      "name": "backup",
      "url": "https://github.com/your-org/backup.git",
      "branch": "main",
      "paths": ["models/"]
    }
  ],
  "conflict_resolution": "prefer_remote",
  "ignore_patterns": [
    "*.tmp",
    "*/checkpoints/*",
    "*/logs/*"
  ]
}

CLI Commands

# Enable Auto Sync
node tools/sync/enable.js

# Check sync status
node tools/sync/status.js

# Manual sync trigger
node tools/sync/trigger.js

# View sync history
node tools/sync/history.js

# Disable Auto Sync
node tools/sync/disable.js

Using Terminal Commands

# Enable and configure Auto Sync
/terminal SmartBrain.autoSync --enable

# Check sync status
/terminal SmartBrain.autoSync --status

# Force immediate sync
/terminal SmartBrain.autoSync --now

# Configure sync interval (seconds)
/terminal SmartBrain.autoSync --interval 600

Sync Strategies

1. Automatic (Default)

Syncs changes every N seconds (configurable)
Best for: Active development with frequent changes

2. On-Commit

Syncs only when you commit changes
Best for: Controlled synchronization

const vulnerabilityDetector = new InferenceEngine('models/vulnerability-detector');

async function auditContract(code) { const prediction = await vulnerabilityDetector.predict({ code: code });

return { vulnerabilities: prediction.vulnerabilities, confidence: prediction.confidence, recommendations: prediction.recommendations }; }


**prefer_remote**: Keep remote changes, discard local changes
```json
{
  "conflict_resolution": "prefer_remote"
}

merge: Attempt intelligent merge (for compatible file types)

{
  "conflict_resolution": "merge"
}

prompt: Ask user to resolve conflicts manually

{
  "conflict_resolution": "prompt"
}

Best Practices

Start with Manual Mode: Test your sync configuration before enabling automatic sync
Use .gitignore Patterns: Exclude temporary files, logs, and checkpoints
Monitor Initial Sync: Watch the first few sync operations to ensure correct behavior
Regular Backups: Configure a backup repository for critical models
Selective Sync: Only sync necessary files to reduce bandwidth and storage

Monitoring Auto Sync

View real-time sync status:

# Watch sync activity
node tools/sync/watch.js

# View sync logs
tail -f logs/sync.log

# Check for sync errors
node tools/sync/errors.js

Troubleshooting

Sync conflicts occurring frequently

# Review conflict history
node tools/sync/conflicts.js

# Adjust conflict resolution strategy
node tools/sync/config.js --conflict-resolution merge

Sync is too slow

# Check what's being synced
node tools/sync/status.js --verbose

# Add ignore patterns for large files
node tools/sync/config.js --ignore "*.h5,*.pb"

Sync not triggering

# Check sync service status
node tools/sync/status.js

# Restart sync service
node tools/sync/restart.js

Auto Test

Auto Test is SmartBrain's intelligent testing framework that automatically runs appropriate tests when code or models change, ensuring continuous quality assurance throughout the development lifecycle.

What is Auto Test?

Auto Test monitors your workspace for changes and automatically executes relevant test suites. It intelligently determines which tests to run based on what changed, provides detailed reports, and can even suggest fixes for failing tests.

Key Features

Intelligent Test Selection: Runs only tests affected by your changes
Continuous Testing: Automatically runs tests on file changes
Parallel Execution: Runs multiple test suites simultaneously
Coverage Tracking: Monitors and reports test coverage metrics
Failure Analysis: Provides detailed failure reports with suggestions
Integration Testing: Supports model, dataset, and code testing
Performance Testing: Tracks model performance over time
Test History: Maintains history of test results for trend analysis

Test Types

Auto Test supports multiple test categories:

1. Unit Tests

Individual function and component testing
Fast execution for rapid feedback
Isolated from external dependencies

2. Integration Tests

Model inference accuracy tests
Dataset validation tests
API endpoint tests

3. Performance Tests

Model inference speed benchmarks
Memory usage monitoring
Training performance tests

4. Model Tests

Accuracy validation against benchmarks
Inference output consistency
Model file integrity checks

Enabling Auto Test

Configuration

Create or edit .smartbrain/test.json:

{
  "enabled": true,
  "mode": "smart",
  "test_suites": {
    "unit": {
      "enabled": true,
      "pattern": "tests/**/*.test.js",
      "timeout": 5000
    },
    "integration": {
      "enabled": true,
      "pattern": "tests/integration/**/*.test.js",
      "timeout": 30000
    },
    "model": {
      "enabled": true,
      "pattern": "tests/models/**/*.test.js",
      "timeout": 60000,
      "benchmark": {
        "accuracy_threshold": 0.85,
        "speed_threshold_ms": 100
      }
    }
  },
  "coverage": {
    "enabled": true,
    "threshold": 80,
    "report_formats": ["html", "json", "text"]
  },
  "on_failure": {
    "notify": true,
    "auto_fix": false,
    "create_issue": true
  }
}

CLI Commands

# Run all tests (uses the default npm test script)
npm test

# Run specific test suites
npm run test:unit
npm run test:integration

# View test coverage
npm run test:coverage

Planned Terminal Commands

Note: The /terminal SmartBrain.autoTest integration is planned and is not yet available in the current .github/copilot/agent.yaml configuration.

# (Planned) Enable Auto Test
/terminal SmartBrain.autoTest --enable

# (Planned) Run tests
/terminal SmartBrain.autoTest --run

# (Planned) Check test status
/terminal SmartBrain.autoTest --status

# (Planned) View coverage report
/terminal SmartBrain.autoTest --coverage

Test Modes

Smart Mode (Recommended)

{
  "mode": "smart"
}

Analyzes changes and runs only affected tests
Provides fastest feedback
Automatically escalates to full suite if needed

Full Mode

{
  "mode": "full"
}

Runs complete test suite on every change
Thorough but slower
Best for critical changes or pre-deployment

Fast Mode

{
  "mode": "fast"
}

Runs only unit tests for quick feedback
Skips integration and performance tests
Best for rapid iteration

Scheduled Mode

{
  "mode": "scheduled",
  "schedule": "0 */4 * * *"
}

Runs tests on a schedule (cron format)
Best for continuous monitoring

Model Testing

Auto Test includes specialized model testing capabilities:

Accuracy Testing

// tests/models/accuracy.test.js
const { ModelTester } = require('@smartbrain/test');

describe('Model Accuracy Tests', () => {
  it('should maintain accuracy above threshold', async () => {
    const tester = new ModelTester('models/my-model');
    const accuracy = await tester.testAccuracy('datasets/test.json');
    expect(accuracy).toBeGreaterThan(0.85);
  });
});

Performance Testing

// tests/models/performance.test.js
const { ModelTester } = require('@smartbrain/test');

describe('Model Performance Tests', () => {
  it('should complete inference within time limit', async () => {
    const tester = new ModelTester('models/my-model');
    const duration = await tester.testInferenceSpeed({
      samples: 100,
      iterations: 10
    });
    expect(duration).toBeLessThan(100); // milliseconds
  });
});

Regression Testing

// tests/models/regression.test.js
const { ModelTester } = require('@smartbrain/test');

describe('Model Regression Tests', () => {
  it('should produce consistent outputs', async () => {
    const tester = new ModelTester('models/my-model');
    const consistent = await tester.testConsistency({
      input: 'test-input.json',
      iterations: 5
    });
    expect(consistent).toBe(true);
  });
});

Test Reports

Auto Test generates comprehensive reports:

# View latest test report (example via npm script)
npm run test:report

# Generate HTML report
npm run test:report -- --format html --output reports/

# Compare test runs
npm run test:compare -- --runs 5

Report Contents:

Test execution summary
Pass/fail statistics
Coverage metrics
Performance benchmarks
Failure details with stack traces
Historical trends
Suggested fixes

Integration with CI/CD

Auto Test integrates seamlessly with GitHub Actions:

# .github/workflows/test.yml
name: Auto Test

on: [push, pull_request]

jobs:
  test:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
      - name: Run Auto Test
        run: node tools/test/run.js --all --ci
      - name: Upload Coverage
        uses: codecov/codecov-action@v3
        with:
          files: ./coverage/coverage.json

Best Practices

Start with Smart Mode: Provides best balance of speed and coverage
Set Realistic Thresholds: Don't set coverage thresholds too high initially
Write Fast Unit Tests: Keep unit tests under 5 seconds for quick feedback
Use Tags: Tag tests by category for selective execution
Monitor Trends: Watch test performance over time
Fix Flaky Tests: Address intermittent failures immediately

Troubleshooting

Tests running too slowly

# Analyze slow tests
node tools/test/analyze.js --slow

# Enable parallel execution
node tools/test/config.js --parallel true

Tests failing intermittently

# Identify flaky tests
node tools/test/flaky.js

# Run specific test repeatedly
node tools/test/run.js --test "test-name" --repeat 10

Auto Analysis

Auto Analysis is SmartBrain's continuous code and model analysis system that automatically examines your code, models, and datasets to identify issues, suggest improvements, and provide insights.

What is Auto Analysis?

Auto Analysis continuously monitors your SmartBrain workspace and automatically performs various types of analysis including code quality checks, model performance analysis, dataset quality assessment, and security vulnerability scanning.

Key Features

Code Quality Analysis: Identifies code smells, complexity issues, and style violations
Model Performance Analysis: Tracks model metrics and performance trends
Dataset Quality Analysis: Validates dataset quality and identifies issues
Security Scanning: Detects potential security vulnerabilities
Dependency Analysis: Monitors dependency health and updates
Gas Optimization: Analyzes and suggests smart contract gas optimizations
Complexity Metrics: Tracks code and model complexity
Trend Analysis: Identifies performance trends over time

Analysis Types

1. Code Analysis

Examines code quality, style, and potential issues:

# Planned: Run code analysis (implementation coming in a future release)
# The final command will be exposed via a dedicated analysis entrypoint,
# such as an npm script or CLI wrapper. For example, it may look like:
#
#   npm run smart:analyze -- --files "src/**/*.js" --score
#
# Refer to the latest README or release notes for the actual command
# once the analysis tooling has been implemented.

Checks:

Code complexity (cyclomatic complexity)
Code duplication
Style violations
Potential bugs
Security vulnerabilities
Documentation coverage

2. Model Analysis

Analyzes model performance and characteristics:

# Analyze model
npm run smart:analyze -- --mode model --model models/my-model

# Compare model versions
npm run smart:analyze -- --mode model --compare v1.0.0 v1.1.0

# Performance trends
npm run smart:analyze -- --mode model --trends --days 30

Metrics:

Accuracy, precision, recall, F1 score
Inference latency and throughput
Model size and complexity
Resource utilization
Prediction confidence distribution

3. Dataset Analysis

Examines dataset quality and characteristics:

# Analyze dataset
node tools/analysis/dataset.js --dataset datasets/my-data.json

# Quality report
node tools/analysis/dataset.js --quality

# Distribution analysis
node tools/analysis/dataset.js --distribution

Checks:

Data distribution and balance
Missing values and outliers
Feature correlations
Data quality metrics
Schema compliance

4. Security Analysis

Scans for security vulnerabilities:

# Security scan
node tools/analysis/security.js

# Check dependencies
node tools/analysis/security.js --dependencies

# Smart contract analysis
node tools/analysis/security.js --contracts

Scans For:

Known CVE vulnerabilities
Private key exposure
Insecure configurations
Smart contract vulnerabilities
Dependency issues

Configuration

Create or edit .smartbrain/analysis.json:

{
  "enabled": true,
  "schedule": "0 */6 * * *",
  "analyses": {
    "code": {
      "enabled": true,
      "complexity_threshold": 10,
      "duplication_threshold": 5,
      "exclude_patterns": ["node_modules/", "tests/"]
    },
    "model": {
      "enabled": true,
      "performance_threshold": {
        "accuracy": 0.80,
        "latency_ms": 200
      },
      "track_trends": true
    },
    "dataset": {
      "enabled": true,
      "quality_threshold": 0.90,
      "check_distribution": true
    },
    "security": {
      "enabled": true,
      "severity_threshold": "medium",
      "auto_update_dependencies": false
    }
  },
  "reporting": {
    "format": "json",
    "output_dir": "reports/analysis",
    "notify_on_issues": true
  }
}

Enabling Auto Analysis

# Enable Auto Analysis
node tools/analysis/enable.js

# Configure analysis types
node tools/analysis/config.js --enable code,model,dataset,security

# Set analysis schedule
node tools/analysis/schedule.js --cron "0 */6 * * *"

# Run analysis now
node tools/analysis/run.js --all

Analysis Reports

Auto Analysis generates comprehensive reports with actionable insights:

# View latest analysis report
node tools/analysis/report.js

# Generate detailed report
node tools/analysis/report.js --detailed --format html

# View specific analysis
node tools/analysis/report.js --type model

# Compare reports
node tools/analysis/compare.js --dates 2025-01-01 2025-01-15

Report Sections:

Executive Summary: High-level overview of findings
Critical Issues: Urgent problems requiring immediate attention
Warnings: Potential issues to investigate
Recommendations: Suggested improvements
Metrics: Quantitative measurements and trends
Historical Comparison: Changes over time

Code Quality Metrics

Auto Analysis tracks various code quality metrics:

Complexity Score: Measures code complexity (0-100, lower is better)

Score < 10: Excellent
Score 10-20: Good
Score 20-40: Moderate
Score > 40: High complexity (refactoring recommended)

Maintainability Index: Overall maintainability (0-100, higher is better)

Score > 80: Highly maintainable
Score 60-80: Maintainable
Score 40-60: Moderate maintainability
Score < 40: Low maintainability

Duplication Percentage: Amount of duplicated code

< 3%: Excellent
3-5%: Good
5-10%: Acceptable
> 10%: Too much duplication

Model Performance Tracking

Track model performance over time:

# View performance trends
node tools/analysis/trends.js --model my-model --metric accuracy --days 30

# Compare model versions
node tools/analysis/compare-models.js v1.0.0 v2.0.0

# Performance regression detection
node tools/analysis/regression.js --model my-model

Tracked Metrics:

Accuracy over time
Inference latency trends
Resource utilization
Error rates
Prediction confidence

Integration with CI/CD

# .github/workflows/analysis.yml
name: Auto Analysis

on:
  schedule:
    - cron: '0 */6 * * *'
  push:
    branches: [main]

jobs:
  analyze:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
      - name: Run Auto Analysis
        run: node tools/analysis/run.js --all --ci
      - name: Upload Report
        uses: actions/upload-artifact@v3
        with:
          name: analysis-report
          path: reports/analysis/

Best Practices

Regular Schedule: Run analysis at least daily for active projects
Act on Findings: Review and address critical issues promptly
Track Trends: Monitor metrics over time to identify patterns
Set Thresholds: Configure appropriate thresholds for your project
Integrate with CI: Make analysis part of your CI/CD pipeline

Troubleshooting

Analysis taking too long

# Run specific analysis types
node tools/analysis/run.js --type code

# Exclude unnecessary files
node tools/analysis/config.js --exclude "tests/,docs/"

Too many false positives

# Adjust sensitivity
node tools/analysis/config.js --sensitivity medium

# Configure ignore patterns
node tools/analysis/config.js --ignore-pattern "*.generated.js"

Auto Fix

Auto Fix is SmartBrain's intelligent automated issue resolution system that can automatically fix common problems in code, models, and configurations based on analysis findings.

What is Auto Fix?

Auto Fix analyzes issues detected by Auto Analysis and other tools, and automatically applies fixes for common problems. It can fix code style issues, optimize configurations, update dependencies, and even suggest model improvements.

Key Features

Automatic Code Fixes: Fixes style violations, simple bugs, and code smells
Configuration Optimization: Optimizes training and inference configurations
Dependency Updates: Safely updates outdated dependencies
Model Optimization: Applies model optimization techniques
Smart Contract Gas Optimization: Reduces gas costs automatically
Safe Execution: Creates backups before applying fixes
Preview Mode: Review fixes before applying them
Rollback Support: Undo fixes if needed

Fix Categories

1. Code Fixes

Automatically fixes code issues:

# Run default auto-fix on the current project
/terminal SmartBrain.fix

# Fix configuration-related issues
/terminal SmartBrain.fix configs

# Fix permission-related issues
/terminal SmartBrain.fix permissions

# Run all available auto-fixes
/terminal SmartBrain.fix all

Fixable Issues:

Style violations (indentation, spacing, quotes)
Unused variables and imports
Simple logic errors
Missing semicolons
Inconsistent naming
Missing documentation

2. Model Fixes

Optimizes and fixes model issues:

# NOTE: Placeholder commands — the model fix CLI is not yet exposed as
# a public entrypoint in this repository. These examples are illustrative
# only and will be updated in a future release once the actual CLI/API
# for model auto-fix is finalized.
#
# Example (placeholder) usage:
#
# Optimize model
# node tools/fix/model.js --model models/my-model --optimize
#
# Fix model metadata
# node tools/fix/model.js --model models/my-model --metadata
#
# Convert model format
# node tools/fix/model.js --model models/my-model --convert onnx

Fixable Issues:

Incorrect metadata
Missing required fields
Suboptimal model format
Inefficient model structure
Missing documentation

3. Configuration Fixes

Optimizes configuration files:

# Fix training config
node tools/fix/config.js --file training/configs/my-config.json

# Optimize for performance
node tools/fix/config.js --optimize performance

# Optimize for accuracy
node tools/fix/config.js --optimize accuracy

Fixable Issues:

Suboptimal hyperparameters
Incorrect paths
Missing required fields
Inefficient batch sizes
Incorrect data types

4. Dependency Fixes

Updates and fixes dependencies:

# Update outdated dependencies
node tools/fix/dependencies.js --update

# Fix security vulnerabilities
node tools/fix/dependencies.js --security

# Remove unused dependencies
node tools/fix/dependencies.js --unused

Fixable Issues:

Outdated dependencies
Security vulnerabilities
Unused dependencies
Version conflicts
Missing dependencies

Configuration

Create or edit .smartbrain/fix.json:

{
  "enabled": true,
  "mode": "preview",
  "categories": {
    "code": {
      "enabled": true,
      "auto_apply": false,
      "types": ["style", "bugs", "naming"]
    },
    "model": {
      "enabled": true,
      "auto_apply": false,
      "optimize": true
    },
    "config": {
      "enabled": true,
      "auto_apply": true,
      "optimize_for": "balanced"
    },
    "dependencies": {
      "enabled": true,
      "auto_apply": false,
      "security_only": true
    }
  },
  "safety": {
    "create_backup": true,
    "require_confirmation": true,
    "max_fixes_per_run": 50
  },
  "rollback": {
    "enabled": true,
    "keep_backups": 5
  }
}

Enabling Auto Fix

# Enable Auto Fix
node tools/fix/enable.js

# Set to preview mode (safe)
node tools/fix/mode.js --preview

# Set to auto mode (applies fixes automatically)
node tools/fix/mode.js --auto

# Enable specific fix categories
node tools/fix/config.js --enable code,config,dependencies

Terminal Commands

# Enable Auto Fix
/terminal SmartBrain.fix --enable

# Preview fixes
/terminal SmartBrain.fix --preview

# Apply fixes
/terminal SmartBrain.fix --apply

# Rollback last fixes
/terminal SmartBrain.fix --rollback

Fix Modes

Preview Mode (Default)

{
  "mode": "preview"
}

Shows what would be fixed without applying changes
Safest option for testing
Generates detailed fix reports

Interactive Mode

{
  "mode": "interactive"
}

Shows each fix and asks for confirmation
Good balance of automation and control
Allows selective application

Auto Mode

{
  "mode": "auto"
}

Automatically applies fixes
Fastest but requires trust in the system
Always creates backups

Safety Features

Auto Fix includes multiple safety mechanisms:

1. Automatic Backups

# List backups
node tools/fix/backups.js --list

# Restore from backup
node tools/fix/backups.js --restore <backup-id>

# Clean old backups
node tools/fix/backups.js --clean --older-than 30d

2. Fix Validation

Validates syntax after code fixes
Tests configuration after config fixes
Verifies model integrity after model fixes
Runs tests after applying fixes

3. Rollback Support

# Rollback last fix
node tools/fix/rollback.js

# Rollback specific fix session
node tools/fix/rollback.js --session <session-id>

# View rollback history
node tools/fix/rollback.js --history

Fix Reports

Auto Fix generates detailed reports for all operations:

# View latest fix report
node tools/fix/report.js

# View specific fix session
node tools/fix/report.js --session <session-id>

# Generate detailed HTML report
node tools/fix/report.js --format html --output reports/

Report Contents:

Fixes applied
Files modified
Validation results
Before/after comparisons
Rollback information
Recommendations

Smart Contract Gas Optimization

Auto Fix includes specialized gas optimization for smart contracts:

# Analyze and fix gas issues
node tools/fix/gas.js --contract contracts/MyContract.sol

# Preview gas optimizations
node tools/fix/gas.js --contract contracts/MyContract.sol --preview

# Apply specific optimizations
node tools/fix/gas.js --contract contracts/MyContract.sol --types storage,loops

Optimization Types:

Storage layout optimization
Loop unrolling
Function visibility optimization
Variable packing
Short-circuit evaluation
Batch operations

Integration with CI/CD

# .github/workflows/autofix.yml
name: Auto Fix

on:
  schedule:
    - cron: '0 2 * * *'

jobs:
  fix:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
      
      - name: Run Auto Fix
        run: node tools/fix/run.js --preview --ci
      
      - name: Create PR with fixes
        if: success()
        uses: peter-evans/create-pull-request@v5
        with:
          title: 'Auto Fix: Automated fixes'
          body: 'Automated fixes applied by SmartBrain Auto Fix'
          branch: autofix/automated-fixes

Best Practices

Start with Preview Mode: Always test fixes before auto-applying
Enable Backups: Always create backups before applying fixes
Selective Fixing: Start with safe categories (style, config)
Review Changes: Review auto-applied fixes regularly
Test After Fixing: Run tests after applying fixes
Keep Backups: Maintain recent backup history

Advanced Usage

Custom Fix Rules

Create custom fix rules in .smartbrain/fix-rules.json:

{
  "custom_rules": [
    {
      "name": "enforce-naming-convention",
      "pattern": "function\\s+([a-z])",
      "replacement": "function $1",
      "description": "Enforce camelCase for functions"
    }
  ]
}

Fix Plugins

Create custom fix plugins:

// tools/fix/plugins/my-fixer.js
module.exports = {
  name: 'my-fixer',
  description: 'Custom fix logic',
  
  async analyze(files) {
    // Analyze files and return issues
    return issues;
  },
  
  async fix(issue) {
    // Apply fix for the issue
    return fixResult;
  },
  
  async validate(fixResult) {
    // Validate the fix was successful
    return isValid;
  }
};

Troubleshooting

Fixes not being applied

# Check fix status
node tools/fix/status.js

# View fix logs
tail -f logs/fix.log

# Test fix manually
node tools/fix/test.js --issue <issue-id>

Rollback not working

# Check backup existence
node tools/fix/backups.js --list

# Force rollback
node tools/fix/rollback.js --force --session <session-id>

Too many fixes proposed

# Adjust sensitivity
node tools/fix/config.js --sensitivity low

# Limit fix types
node tools/fix/config.js --types style,bugs

# Set max fixes
node tools/fix/config.js --max-fixes 20

Terminal Command Integration

SmartBrain integrates with GitHub Copilot terminal commands:

Available Commands

# Check SmartBrain status
/terminal SmartBrain.status

# Validate models and configurations
/terminal SmartBrain.validate

# Run inference
/terminal SmartBrain.inference --model my-model --input data.json

# Run training
/terminal SmartBrain.train --config training/configs/my-config.json

# List and manage models
/terminal SmartBrain.models

# Auto-fix common issues
/terminal SmartBrain.fix

Command Examples

# Get system status
$ /terminal SmartBrain.status
✓ Models: 5 registered
✓ Training jobs: 2 running
✓ Inference engine: Ready
✓ Datasets: 10 validated

# Validate everything
$ /terminal SmartBrain.validate
Validating models... ✓
Validating datasets... ✓
Validating configurations... ✓

# List models
$ /terminal SmartBrain.models
Available models:
  - vulnerability-detector (v2.1.0)
  - gas-optimizer (v1.5.0)
  - code-classifier (v3.0.0)

Ecosystem Integration

CyberAi Ecosystem

SmartBrain is part of the CyberAi ecosystem:

CyberAi Ecosystem
├── SmartBrain (AI/ML Engine)
├── SmartContractDeploy Bot
├── SmartContractAudit Bot
└── Additional Components

Bot Integration

SmartBrain provides ML capabilities to other bots:

Conceptual example – illustrative only; not using the current SmartBrain public API.

// Conceptual example: how a SmartContractAudit bot might call an inference engine
const { InferenceEngine } = require('@smartbrain/inference');

const vulnerabilityDetector = new InferenceEngine(
  'models/vulnerability-detector'
);

async function auditContract(code) {
  const prediction = await vulnerabilityDetector.predict({
    code: code
  });
  
  return {
    vulnerabilities: prediction.vulnerabilities,
    confidence: prediction.confidence,
    recommendations: prediction.recommendations
  };
}

Workflow Integration

GitHub Actions workflows can trigger SmartBrain operations:

- name: Run Model Validation
  run: |
    ./scripts/validate-model.sh models/my-model

- name: Run Inference
  run: |
    node inference/cli/index.js predict \
      --model models/my-model \
      --input data/input.json

API Reference

Inference API

// Load model
const engine = new InferenceEngine('models/my-model');

// Single prediction
const result = await engine.predict(inputData);

// Batch prediction
const results = await engine.predictBatch(inputDataArray);

// Get model info
const info = engine.getModelInfo();

Training API

// Create trainer
const trainer = new ModelTrainer(config);

// Start training
await trainer.train();

// Resume from checkpoint
await trainer.resume(checkpointPath);

// Evaluate model
const metrics = await trainer.evaluate(testData);

Dataset API

// Validate dataset
const validator = new DatasetValidator(schema);
const isValid = validator.validate(dataset);

// Get validation errors
const errors = validator.getErrors();

// Calculate statistics
const stats = validator.getStatistics();

Best Practices

Model Development

Version Control: Always version models using semantic versioning
Metadata: Include comprehensive metadata with every model
Documentation: Document model architecture, training, and usage
Validation: Validate models before deployment
Testing: Test models on diverse datasets

Training

Configuration: Use configuration files for reproducibility
Checkpoints: Save checkpoints regularly
Monitoring: Monitor training metrics continuously
Validation: Validate on held-out data during training
Experimentation: Track experiments with metadata

Inference

Input Validation: Validate all inputs before inference
Error Handling: Handle inference errors gracefully
Performance: Optimize for latency and throughput
Monitoring: Monitor inference performance
Versioning: Use specific model versions in production

Security

Model Integrity: Validate model checksums
Access Control: Restrict access to sensitive models
Input Sanitization: Sanitize all user inputs
Secrets: Never commit secrets or credentials
Updates: Keep dependencies updated

Deployment

Testing: Test thoroughly before deployment
Rollback: Have rollback procedures ready
Monitoring: Set up monitoring and alerts
Documentation: Update documentation
Communication: Communicate changes to users

Automation

Enable Auto Sync: Keep models and datasets synchronized
Use Auto Test: Ensure continuous quality assurance
Leverage Auto Analysis: Monitor code and model quality
Configure Auto Fix: Automate routine maintenance tasks
Review Automation Reports: Regularly check automation outputs

CI/CD Integration

SmartBrain seamlessly integrates with CI/CD pipelines, particularly GitHub Actions, to automate your ML and smart contract development workflows.

GitHub Actions Integration

Complete Workflow Example

Create .github/workflows/smartbrain.yml:

name: SmartBrain CI/CD Pipeline

on:
  push:
    branches: [main, develop]
  pull_request:
    branches: [main]
  schedule:
    - cron: '0 2 * * *'  # Daily at 2 AM

jobs:
  validation:
    name: Validate Infrastructure
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
      
      - name: Setup Node.js
        uses: actions/setup-node@v3
        with:
          node-version: '16'
          cache: 'npm'
      
      - name: Install Dependencies
        run: npm ci
      
      - name: Run Bootstrap Script
        run: ./scripts/bootstrap.sh
      
      - name: Run Audit Script
        run: ./scripts/audit.sh
      
      - name: Validate Models
        run: npm run validate:models
      
      - name: Validate Datasets
        run: npm run validate:datasets

  lint-and-format:
    name: Lint and Format
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
      
      - name: Setup Node.js
        uses: actions/setup-node@v3
        with:
          node-version: '16'
      
      - name: Install Dependencies
        run: npm ci
      
      - name: Run Linter
        run: npm run lint
      
      - name: Check Formatting
        run: npm run format:check

  auto-test:
    name: Run Auto Test
    runs-on: ubuntu-latest
    needs: [validation, lint-and-format]
    steps:
      - uses: actions/checkout@v3
      
      - name: Setup Node.js
        uses: actions/setup-node@v3
        with:
          node-version: '16'
      
      - name: Install Dependencies
        run: npm ci
      
      - name: Run Unit Tests
        run: npm run test:unit
      
      - name: Run Integration Tests
        run: npm run test:integration
      
      - name: Generate Coverage Report
        run: npm run test:coverage
      
      - name: Upload Coverage
        uses: codecov/codecov-action@v3
        with:
          files: ./coverage/coverage.json

  auto-analysis:
    name: Run Auto Analysis
    runs-on: ubuntu-latest
    needs: [validation]
    steps:
      - uses: actions/checkout@v3
      
      - name: Setup Node.js
        uses: actions/setup-node@v3
        with:
          node-version: '16'
      
      - name: Install Dependencies
        run: npm ci
      
      - name: Run Code Analysis
        run: node tools/analysis/code.js --ci
      
      - name: Run Model Analysis
        run: node tools/analysis/model.js --all --ci
      
      - name: Run Security Analysis
        run: node tools/analysis/security.js --ci
      
      - name: Upload Analysis Report
        uses: actions/upload-artifact@v3
        with:
          name: analysis-report
          path: reports/analysis/

  auto-fix:
    name: Auto Fix Issues
    runs-on: ubuntu-latest
    needs: [auto-test, auto-analysis]
    if: github.event_name == 'schedule'
    steps:
      - uses: actions/checkout@v3
      
      - name: Setup Node.js
        uses: actions/setup-node@v3
        with:
          node-version: '16'
      
      - name: Install Dependencies
        run: npm ci
      
      - name: Run Auto Fix (illustrative)
        run: echo "Run your auto-fix tooling here (e.g., npm run lint -- --fix)"
      
      - name: Create Pull Request
        if: success()
        uses: peter-evans/create-pull-request@v5
        with:
          token: ${{ secrets.GITHUB_TOKEN }}
          commit-message: 'chore: Auto Fix - Automated fixes'
          title: 'Auto Fix: Automated fixes from SmartBrain'
          body: |
            Automated fixes applied by SmartBrain Auto Fix.
            
            Please review the changes carefully before merging.
          branch: autofix/automated-fixes
          delete-branch: true

  model-training:
    name: Train Models
    runs-on: ubuntu-latest
    if: github.event_name == 'push' && github.ref == 'refs/heads/main'
    needs: [auto-test]
    steps:
      - uses: actions/checkout@v3
      
      - name: Setup Node.js
        uses: actions/setup-node@v3
        with:
          node-version: '16'
      
      - name: Install Dependencies
        run: npm ci
      
      - name: Train Model
        run: node training/cli/index.js train --config "$TRAINING_CONFIG_PATH"
        timeout-minutes: 120
      
      - name: Validate Trained Model
        run: ./scripts/validate-model.sh models/production-model
      
      - name: Upload Model Artifacts
        uses: actions/upload-artifact@v3
        with:
          name: trained-model
          path: models/production-model/

  auto-sync:
    name: Sync Models and Datasets
    runs-on: ubuntu-latest
    needs: [model-training]
    if: success()
    steps:
      - uses: actions/checkout@v3
      
      - name: Setup Node.js
        uses: actions/setup-node@v3
        with:
          node-version: '16'
      
      - name: Install Dependencies
        run: npm ci
      
      - name: Run Auto Sync (placeholder)
        run: |
          echo "Auto Sync tooling is not yet implemented."
          echo "Replace this step with your sync implementation when available."
        env:
          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}

  deploy:
    name: Deploy to Production
    runs-on: ubuntu-latest
    needs: [model-training, auto-sync]
    if: github.ref == 'refs/heads/main'
    environment:
      name: production
      url: https://smartbrain.example.com
    steps:
      - uses: actions/checkout@v3
      
      - name: Setup Node.js
        uses: actions/setup-node@v3
        with:
          node-version: '16'
      
      - name: Install Dependencies
        run: npm ci
      
      - name: Deploy Application
        run: npm run deploy
        env:
          DEPLOYMENT_KEY: ${{ secrets.DEPLOYMENT_KEY }}

Integration with Other CI/CD Systems

GitLab CI

.gitlab-ci.yml:

stages:
  - validate
  - test
  - analyze
  - deploy

variables:
  NODE_VERSION: "16"

validate:
  stage: validate
  image: node:${NODE_VERSION}
  script:
    - npm ci
    - ./scripts/bootstrap.sh
    - ./scripts/audit.sh
    - npm run validate:models

test:
  stage: test
  image: node:${NODE_VERSION}
  script:
    - npm ci
    - npm run test
    - npm run lint
  coverage: '/Coverage: \d+\.\d+/'

analyze:
  stage: analyze
  image: node:${NODE_VERSION}
  script:
    - npm ci
    - node tools/analysis/run.js --all --ci
  artifacts:
    paths:
      - reports/analysis/

deploy:
  stage: deploy
  image: node:${NODE_VERSION}
  script:
    - npm ci
    - npm run deploy
  only:
    - main

Jenkins

Jenkinsfile:

pipeline {
    agent any
    
    environment {
        NODE_VERSION = '16'
    }
    
    stages {
        stage('Setup') {
            steps {
                sh 'npm ci'
                sh './scripts/bootstrap.sh'
            }
        }
        
        stage('Validate') {
            steps {
                sh './scripts/audit.sh'
                sh 'npm run validate:models'
                sh 'npm run validate:datasets'
            }
        }
        
        stage('Test') {
            steps {
                sh 'npm test'
                sh 'npm run lint'
            }
        }
        
        stage('Analyze') {
            steps {
                sh 'node tools/analysis/run.js --all --ci'
            }
        }
        
        stage('Deploy') {
            when {
                branch 'main'
            }
            steps {
                sh 'npm run deploy'
            }
        }
    }
    
    post {
        always {
            junit 'reports/test-results.xml'
            publishHTML([
                reportDir: 'reports/analysis',
                reportFiles: 'index.html',
                reportName: 'Analysis Report'
            ])
        }
    }
}

Continuous Model Training

Set up automated model retraining:

# .github/workflows/retrain-models.yml
name: Retrain Models

on:
  schedule:
    - cron: '0 0 * * 0'  # Weekly on Sunday
  workflow_dispatch:      # Manual trigger

jobs:
  retrain:
    runs-on: ubuntu-latest
    strategy:
      matrix:
        model: [classifier, detector, optimizer]
    
    steps:
      - uses: actions/checkout@v3
      
      - name: Setup Node.js
        uses: actions/setup-node@v3
        with:
          node-version: '16'
      
      - name: Install Dependencies
        run: npm ci
      
      - name: Download Latest Dataset
        run: node scripts/download-dataset.js ${{ matrix.model }}
      
      - name: Train Model
        run: |
          node training/cli/index.js train \
            --config training/configs/${{ matrix.model }}.json \
            --output models/${{ matrix.model }}-new
      
      - name: Validate Model
        run: ./scripts/validate-model.sh models/${{ matrix.model }}-new
      
      - name: Compare Performance
        run: node scripts/compare-models.js models/${{ matrix.model }} models/${{ matrix.model }}-new
      
      - name: Upload Model
        if: success()
        uses: actions/upload-artifact@v3
        with:
          name: ${{ matrix.model }}-model
          path: models/${{ matrix.model }}-new/

Deployment Strategies

Blue-Green Deployment

jobs:
  deploy-blue-green:
    runs-on: ubuntu-latest
    steps:
      - name: Deploy to Green Environment
        run: |
          npm run deploy:green
          npm run health-check:green
      
      - name: Switch Traffic to Green
        run: npm run switch-traffic:green
      
      - name: Monitor Green Environment
        run: npm run monitor:green --duration 300
      
      - name: Rollback if Issues Detected
        if: failure()
        run: npm run switch-traffic:blue

Canary Deployment

jobs:
  deploy-canary:
    runs-on: ubuntu-latest
    steps:
      - name: Deploy Canary Version
        run: npm run deploy:canary --percentage 10
      
      - name: Monitor Canary Metrics
        run: npm run monitor:canary --duration 600
      
      - name: Increase Canary Traffic
        if: success()
        run: |
          npm run deploy:canary --percentage 50
          sleep 600
          npm run deploy:canary --percentage 100

Troubleshooting

This section covers common issues and their solutions.

Installation Issues

Issue: npm install fails

Symptoms:

npm ERR! code EACCES
npm ERR! syscall access

Solution:

# Fix npm permissions
sudo chown -R $USER ~/.npm
sudo chown -R $USER /usr/local/lib/node_modules

# Clear cache and retry
npm cache clean --force
npm install

Issue: Bootstrap script fails

Symptoms:

Error: Node.js version 14.x is not supported

Solution:

# Check Node.js version
node --version

# Update Node.js to v16 or higher
# Using nvm:
nvm install 16
nvm use 16

# Using apt (Ubuntu/Debian):
curl -fsSL https://deb.nodesource.com/setup_16.x | sudo -E bash -
sudo apt-get install -y nodejs

Model Training Issues

Issue: Training runs out of memory

Symptoms:

Error: JavaScript heap out of memory

Solution:

# Increase Node.js memory limit
export NODE_OPTIONS="--max-old-space-size=4096"

# Reduce batch size in config
node tools/fix/config.js --batch-size 16

# Enable gradient accumulation
node training/cli/index.js train \
  --config training/configs/my-model.json \
  --accumulation-steps 2

Issue: Training stops unexpectedly

Symptoms: Training stops without error message or checkpoint creation fails

Solution:

# Check available disk space
df -h

# Verify write permissions
ls -la models/

# Enable verbose logging
node training/cli/index.js train \
  --config training/configs/my-model.json \
  --log-level debug

Issue: Model overfitting

Symptoms: Training accuracy high but validation accuracy low

Solution:

// Adjust training configuration
{
  "training": {
    "dropout": 0.3,        // Increase dropout
    "l2_regularization": 0.01,  // Add regularization
    "early_stopping": {
      "enabled": true,
      "patience": 5,
      "monitor": "val_loss"
    }
  }
}

Inference Issues

Issue: Inference is too slow

Symptoms: Single prediction takes several seconds

Solution:

# Enable caching
node tools/fix/config.js --inference-cache true

# Use batch inference
node inference/cli/index.js batch \
  --model models/my-model \
  --input data.json \
  --batch-size 32

# Convert model to optimized format
node tools/model/optimize.js --model models/my-model

Issue: Prediction errors or NaN outputs

Symptoms:

Error: Invalid prediction output: NaN

Solution:

# Validate model integrity
./scripts/validate-model.sh models/my-model

# Check input data format
node tools/validation/validate-input.js --input data.json

# Re-run with debug mode
node inference/cli/index.js predict \
  --model models/my-model \
  --input data.json \
  --debug

Auto Sync Issues

Issue: Sync conflicts

Symptoms:

Error: Sync conflict detected in models/my-model/metadata.json

Solution:

# View conflict details
node tools/sync/conflicts.js

# Resolve manually
node tools/sync/resolve.js --conflict <conflict-id> --strategy prefer_local

# Or use automatic resolution
node tools/sync/config.js --conflict-resolution merge

Issue: Sync not working

Symptoms: Files not syncing despite changes

Solution:

# Check sync status
node tools/sync/status.js

# Verify configuration
cat .smartbrain/sync.json

# Restart sync service
node tools/sync/restart.js

# Force manual sync
node tools/sync/trigger.js --force

Auto Test Issues

Issue: Tests timing out

Symptoms:

Error: Test timeout after 30000ms

Solution:

// Increase timeout in .smartbrain/test.json
{
  "test_suites": {
    "integration": {
      "timeout": 60000
    }
  }
}

Issue: Flaky tests

Symptoms: Tests pass sometimes and fail other times

Solution:

# Identify flaky tests
node tools/test/flaky.js

# Run test multiple times
node tools/test/run.js --test "test-name" --repeat 10

# Fix identified flaky tests
# Common causes: timing issues, external dependencies, random data

Auto Analysis Issues

Issue: Analysis takes too long

Symptoms: Analysis runs for hours without completing

Solution:

# Run selective analysis
node tools/analysis/run.js --type code

# Exclude large directories
node tools/analysis/config.js --exclude "node_modules/,dist/,*.log"

# Reduce analysis depth
node tools/analysis/config.js --depth 2

Auto Fix Issues

Issue: Fix causes test failures

Symptoms: Tests pass before fix but fail after

Solution:

# Rollback the fix
node tools/fix/rollback.js

# Review what was changed
node tools/fix/report.js --session <session-id>

# Adjust fix configuration
node tools/fix/config.js --types style  # Only fix style issues

# Re-run with preview mode
node tools/fix/run.js --preview

General Debugging

Enable Debug Logging

# Set debug environment variable
export DEBUG=smartbrain:*

# Or use debug flag
node <command> --debug

# View logs
tail -f logs/smartbrain.log

Check System Status

# Comprehensive status check
/terminal SmartBrain.status

# Check specific components
node tools/status/models.js
node tools/status/datasets.js
node tools/status/training.js
node tools/status/inference.js

Get Help

# Command help
node <command> --help

# View documentation
cat docs/index.md | less

# Check version
node --version
npm --version

Performance Optimization

Optimize SmartBrain for maximum performance in your environment.

Model Optimization

Model Quantization

Reduce model size and improve inference speed:

# Example (conceptual) workflow:
#
# 1. Use your framework's quantization tooling to convert the model to INT8.
#    - For example, with:
#      - PyTorch: torch.ao.quantization / torch.quantization APIs
#      - ONNX:    onnxruntime.quantization (e.g., quantize_dynamic)
#    Save the result as: models/my-model-quantized
#
# 2. Evaluate the baseline vs. quantized model on your validation dataset.
#    - Run inference with models/my-model and models/my-model-quantized
#    - Compare accuracy and latency to ensure quantization meets your targets.

Expected Benefits:

4x smaller model size
2-4x faster inference
Minimal accuracy loss (< 1%)

Model Pruning

Remove unnecessary connections:

# Prune model (future tooling - CLI not yet included in this repo)
# The pruning CLI will be provided in a future release. For now, use your
# preferred framework's pruning utilities or a custom script.
<pruning-command> \
  --model models/my-model \
  --sparsity 0.3 \
  --output models/my-model-pruned

Model Compilation

Convert to optimized format:

# The model conversion CLI will be provided in a future release. For now,
# use your preferred framework's conversion utilities (e.g., ONNX converters
# or the TensorFlow Lite Converter).

# Convert to ONNX
<model-convert-command> \
  --model models/my-model \
  --format onnx \
  --optimize

# Convert to TensorFlow Lite
<model-convert-command> \
  --model models/my-model \
  --format tflite \
  --optimize

Inference Optimization

Batch Processing

Use batching for multiple predictions:

// Instead of individual predictions
for (const input of inputs) {
  await engine.predict(input);  // Slow
}

// Use batch prediction
const results = await engine.predictBatch(inputs);  // Fast

Caching

Enable prediction caching:

{
  "inference": {
    "cache": {
      "enabled": true,
      "max_size": 1000,
      "ttl": 3600
    }
  }
}

Connection Pooling

For API-based inference:

const engine = new InferenceEngine('models/my-model', {
  pool_size: 10,
  max_queue: 100
});

Training Optimization

Mixed Precision Training

Enable mixed precision for faster training:

{
  "training": {
    "mixed_precision": true,
    "loss_scale": "dynamic"
  }
}

Benefits:

2-3x faster training
Reduced memory usage
Minimal accuracy impact

Distributed Training

Use multiple GPUs or machines:

# Multi-GPU training
node training/cli/index.js train \
  --config training/configs/my-model.json \
  --distributed \
  --gpus 4

Gradient Accumulation

Simulate larger batch sizes:

{
  "training": {
    "batch_size": 16,
    "accumulation_steps": 4  // Effective batch size: 64
  }
}

Dataset Optimization

Dataset Preprocessing

Preprocess datasets once:

# Preprocess and cache
node datasets/preprocess.js \
  --input datasets/raw/data.json \
  --output datasets/processed/data.json \
  --cache

Data Loading

Optimize data loading:

{
  "data": {
    "num_workers": 4,
    "prefetch_factor": 2,
    "pin_memory": true
  }
}

Resource Management

Memory Management

# Monitor memory usage
node tools/monitor/memory.js

# Set memory limits
export NODE_OPTIONS="--max-old-space-size=4096"

# Enable garbage collection logging
node --expose-gc <command>

CPU Optimization

# Set CPU affinity
taskset -c 0-3 node inference/cli/index.js predict ...

# Enable threading
export UV_THREADPOOL_SIZE=8

Monitoring Performance

Built-in Profiler

# Profile inference
node --prof inference/cli/index.js predict \
  --model models/my-model \
  --input data.json

# Process profile
node --prof-process isolate-*.log > profile.txt

Performance Metrics

# Real-time metrics
node tools/monitor/metrics.js --live

# Historical analysis
node tools/monitor/analyze.js --days 7

Benchmarking

Run Benchmarks

# Inference benchmark
node tools/benchmark/inference.js \
  --model models/my-model \
  --iterations 1000

# Training benchmark
node tools/benchmark/training.js \
  --config training/configs/benchmark.json

# End-to-end benchmark
node tools/benchmark/e2e.js --all

Benchmark Results

Typical performance metrics:

Operation	Latency	Throughput
Single Inference	10-50ms	20-100 req/s
Batch Inference (32)	100-300ms	100-320 req/s
Model Loading	100-500ms	N/A
Training (per epoch)	5-30 min	N/A

Optimization Checklist

Support and Resources

Features Quick Reference: Quick reference to all SmartBrain features
GitHub Issues: Report bugs and request features
GitHub Discussions: Join community discussions
Documentation: This guide and inline code documentation
Examples: See the Quick Start Guide for usage examples
Feature Comparison: SmartBrain vs Alternatives

Contributing

See CONTRIBUTING.md for guidelines on contributing to SmartBrain.

License

SmartBrain is licensed under the Apache License 2.0. See LICENSE for details.

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index.md

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index.md

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SmartBrain Documentation

Table of Contents

Getting Started

Introduction

What is SmartBrain?

Core Capabilities

Why SmartBrain?

Quick Start Guide

Prerequisites

Installation Steps

Your First Model

Next Steps

Installation & Setup

System Requirements

Detailed Installation

1. Clone and Navigate

2. Install Node.js Dependencies

3. Configure Environment Variables

4. Initialize Infrastructure

5. Verify Installation

6. Run Validation Checks

Configuration Options

Model Configuration

Training Configuration

Inference Configuration

Troubleshooting Installation

Architecture Overview

System Components

Core Components Detailed

Integration Points

Model Lifecycle

1. Model Development

2. Model Training

3. Model Validation

4. Model Registration

5. Model Deployment

Model Versioning

Version Management

Version Selection

Dataset Requirements

Dataset Structure

Dataset Validation

Dataset Best Practices

Inference Usage Guide

Command-Line Inference

API Inference

Batch Processing

Training Pipeline Guide

Configuration

Training Execution

Monitoring Training

Auto Sync

What is Auto Sync?

Key Features

Enabling Auto Sync

Configuration

CLI Commands

Using Terminal Commands

Sync Strategies

Best Practices

Monitoring Auto Sync

Troubleshooting

Auto Test

What is Auto Test?

Key Features

Test Types

Enabling Auto Test

Configuration

CLI Commands

Planned Terminal Commands

Test Modes

Model Testing

Accuracy Testing

Performance Testing