AI Integration with TuskLang and Ruby

🧠 Intelligence Meets Innovation

TuskLang enables sophisticated AI integration for Ruby applications, providing natural language processing, computer vision, recommendation systems, and AI model management. Build applications that leverage the power of artificial intelligence to solve complex problems.

🚀 Quick Start: AI Platform Setup

Basic AI Configuration

config/ai.tsk
[ai]
enabled: @env("AI_ENABLED", "true")
platform: @env("AI_PLATFORM", "openai") # openai, azure, google, custom
api_key: @env.secure("AI_API_KEY")
model_cache: @env("AI_MODEL_CACHE", "true")
rate_limiting: @env("AI_RATE_LIMITING", "true")
[natural_language_processing]
enabled: @env("NLP_ENABLED", "true")
language_detection: @env("LANGUAGE_DETECTION_ENABLED", "true")
sentiment_analysis: @env("SENTIMENT_ANALYSIS_ENABLED", "true")
text_generation: @env("TEXT_GENERATION_ENABLED", "true")
translation: @env("TRANSLATION_ENABLED", "true")
[computer_vision]
enabled: @env("COMPUTER_VISION_ENABLED", "true")
image_recognition: @env("IMAGE_RECOGNITION_ENABLED", "true")
object_detection: @env("OBJECT_DETECTION_ENABLED", "true")
face_recognition: @env("FACE_RECOGNITION_ENABLED", "true")
ocr: @env("OCR_ENABLED", "true")
[recommendation_systems]
enabled: @env("RECOMMENDATION_SYSTEMS_ENABLED", "true")
collaborative_filtering: @env("COLLABORATIVE_FILTERING_ENABLED", "true")
content_based: @env("CONTENT_BASED_ENABLED", "true")
hybrid: @env("HYBRID_RECOMMENDATIONS_ENABLED", "true")

AI Platform Implementation

lib/ai_platform.rb
require 'tusk'
require 'redis'
require 'json'
require 'securerandom'
require 'net/http'
require 'uri'
class AIPlatform
  def initialize(config_path = 'config/ai.tsk')
    @config = Tusk.load(config_path)
    @redis = Redis.new(url: @config['redis']['url'])
    @models = {}
    @cache = {}
    setup_ai_platform
  end
  def process_text(text, task_type, options = {})
    return { success: false, error: 'AI disabled' } unless @config['ai']['enabled'] == 'true'
    # Check cache first
    cache_key = generate_cache_key(text, task_type, options)
    cached_result = get_cached_result(cache_key)
    return cached_result if cached_result
    case task_type
    when 'sentiment_analysis'
      result = perform_sentiment_analysis(text, options)
    when 'language_detection'
      result = perform_language_detection(text, options)
    when 'text_generation'
      result = perform_text_generation(text, options)
    when 'translation'
      result = perform_translation(text, options)
    when 'summarization'
      result = perform_summarization(text, options)
    when 'classification'
      result = perform_text_classification(text, options)
    else
      return { success: false, error: "Unknown task type: #{task_type}" }
    end
    # Cache result
    cache_result(cache_key, result) if result[:success] && @config['ai']['model_cache'] == 'true'
    result
  end
  def process_image(image_data, task_type, options = {})
    return { success: false, error: 'Computer vision disabled' } unless @config['computer_vision']['enabled'] == 'true'
    # Check cache first
    cache_key = generate_image_cache_key(image_data, task_type, options)
    cached_result = get_cached_result(cache_key)
    return cached_result if cached_result
    case task_type
    when 'image_recognition'
      result = perform_image_recognition(image_data, options)
    when 'object_detection'
      result = perform_object_detection(image_data, options)
    when 'face_recognition'
      result = perform_face_recognition(image_data, options)
    when 'ocr'
      result = perform_ocr(image_data, options)
    when 'image_classification'
      result = perform_image_classification(image_data, options)
    else
      return { success: false, error: "Unknown task type: #{task_type}" }
    end
    # Cache result
    cache_result(cache_key, result) if result[:success] && @config['ai']['model_cache'] == 'true'
    result
  end
  def generate_recommendations(user_id, item_type, options = {})
    return { success: false, error: 'Recommendation systems disabled' } unless @config['recommendation_systems']['enabled'] == 'true'
    recommendations = []
    # Collaborative filtering
    if @config['recommendation_systems']['collaborative_filtering'] == 'true'
      cf_recommendations = generate_collaborative_recommendations(user_id, item_type, options)
      recommendations.concat(cf_recommendations)
    end
    # Content-based filtering
    if @config['recommendation_systems']['content_based'] == 'true'
      cb_recommendations = generate_content_based_recommendations(user_id, item_type, options)
      recommendations.concat(cb_recommendations)
    end
    # Hybrid recommendations
    if @config['recommendation_systems']['hybrid'] == 'true'
      hybrid_recommendations = generate_hybrid_recommendations(user_id, item_type, options)
      recommendations.concat(hybrid_recommendations)
    end
    # Remove duplicates and rank
    unique_recommendations = remove_duplicate_recommendations(recommendations)
    ranked_recommendations = rank_recommendations(unique_recommendations, user_id)
    {
      success: true,
      user_id: user_id,
      item_type: item_type,
      recommendations: ranked_recommendations,
      total_count: ranked_recommendations.length
    }
  end
  def train_custom_model(model_config)
    model_id = SecureRandom.uuid
    model = {
      id: model_id,
      name: model_config[:name],
      type: model_config[:type],
      config: model_config,
      status: 'training',
      created_at: Time.now.iso8601,
      training_data: model_config[:training_data] || []
    }
    @models[model_id] = model
    @redis.hset('ai_models', model_id, model.to_json)
    # Start training process
    Thread.new { train_model_async(model_id) }
    {
      success: true,
      model_id: model_id,
      status: 'training_started'
    }
  end
  def get_model_status(model_id)
    return nil unless @models[model_id]
    model = @models[model_id]
    
    {
      model_id: model_id,
      name: model[:name],
      type: model[:type],
      status: model[:status],
      created_at: model[:created_at],
      training_progress: get_training_progress(model_id),
      performance_metrics: get_model_performance(model_id)
    }
  end
  def deploy_model(model_id, deployment_config = {})
    return { success: false, error: 'Model not found' } unless @models[model_id]
    model = @models[model_id]
    return { success: false, error: 'Model not ready' } unless model[:status] == 'trained'
    deployment = {
      id: SecureRandom.uuid,
      model_id: model_id,
      endpoint: generate_model_endpoint(model_id),
      config: deployment_config,
      status: 'deploying',
      created_at: Time.now.iso8601
    }
    @redis.hset('model_deployments', deployment[:id], deployment.to_json)
    # Deploy model
    Thread.new { deploy_model_async(deployment[:id]) }
    {
      success: true,
      deployment_id: deployment[:id],
      endpoint: deployment[:endpoint]
    }
  end
  def get_ai_statistics
    {
      total_models: @models.length,
      trained_models: @models.count { |_, model| model[:status] == 'trained' },
      deployed_models: get_deployed_models_count,
      total_requests: get_total_requests,
      cache_hit_rate: get_cache_hit_rate,
      average_response_time: get_average_response_time
    }
  end
  private
  def setup_ai_platform
    # Initialize AI platform components
  end
  def perform_sentiment_analysis(text, options)
    begin
      # Implementation for sentiment analysis
      sentiment_score = analyze_sentiment(text)
      
      {
        success: true,
        text: text,
        sentiment: get_sentiment_label(sentiment_score),
        score: sentiment_score,
        confidence: calculate_confidence(sentiment_score)
      }
    rescue => e
      {
        success: false,
        error: "Sentiment analysis failed: #{e.message}"
      }
    end
  end
  def perform_language_detection(text, options)
    begin
      # Implementation for language detection
      detected_language = detect_language(text)
      
      {
        success: true,
        text: text,
        language: detected_language[:language],
        confidence: detected_language[:confidence]
      }
    rescue => e
      {
        success: false,
        error: "Language detection failed: #{e.message}"
      }
    end
  end
  def perform_text_generation(prompt, options)
    begin
      # Implementation for text generation
      generated_text = generate_text(prompt, options)
      
      {
        success: true,
        prompt: prompt,
        generated_text: generated_text,
        tokens_used: calculate_tokens(prompt, generated_text)
      }
    rescue => e
      {
        success: false,
        error: "Text generation failed: #{e.message}"
      }
    end
  end
  def perform_translation(text, options)
    begin
      source_lang = options[:source_language] || 'auto'
      target_lang = options[:target_language] || 'en'
      
      # Implementation for translation
      translated_text = translate_text(text, source_lang, target_lang)
      
      {
        success: true,
        original_text: text,
        translated_text: translated_text,
        source_language: source_lang,
        target_language: target_lang
      }
    rescue => e
      {
        success: false,
        error: "Translation failed: #{e.message}"
      }
    end
  end
  def perform_summarization(text, options)
    begin
      max_length = options[:max_length] || 150
      
      # Implementation for summarization
      summary = summarize_text(text, max_length)
      
      {
        success: true,
        original_text: text,
        summary: summary,
        original_length: text.length,
        summary_length: summary.length
      }
    rescue => e
      {
        success: false,
        error: "Summarization failed: #{e.message}"
      }
    end
  end
  def perform_text_classification(text, options)
    begin
      categories = options[:categories] || ['positive', 'negative', 'neutral']
      
      # Implementation for text classification
      classification = classify_text(text, categories)
      
      {
        success: true,
        text: text,
        category: classification[:category],
        confidence: classification[:confidence],
        all_scores: classification[:scores]
      }
    rescue => e
      {
        success: false,
        error: "Text classification failed: #{e.message}"
      }
    end
  end
  def perform_image_recognition(image_data, options)
    begin
      # Implementation for image recognition
      recognition_result = recognize_image(image_data)
      
      {
        success: true,
        image_data: image_data,
        objects: recognition_result[:objects],
        confidence: recognition_result[:confidence]
      }
    rescue => e
      {
        success: false,
        error: "Image recognition failed: #{e.message}"
      }
    end
  end
  def perform_object_detection(image_data, options)
    begin
      # Implementation for object detection
      detection_result = detect_objects(image_data)
      
      {
        success: true,
        image_data: image_data,
        objects: detection_result[:objects],
        bounding_boxes: detection_result[:bounding_boxes],
        confidence: detection_result[:confidence]
      }
    rescue => e
      {
        success: false,
        error: "Object detection failed: #{e.message}"
      }
    end
  end
  def perform_face_recognition(image_data, options)
    begin
      # Implementation for face recognition
      face_result = recognize_faces(image_data)
      
      {
        success: true,
        image_data: image_data,
        faces: face_result[:faces],
        landmarks: face_result[:landmarks],
        confidence: face_result[:confidence]
      }
    rescue => e
      {
        success: false,
        error: "Face recognition failed: #{e.message}"
      }
    end
  end
  def perform_ocr(image_data, options)
    begin
      # Implementation for OCR
      ocr_result = extract_text_from_image(image_data)
      
      {
        success: true,
        image_data: image_data,
        extracted_text: ocr_result[:text],
        confidence: ocr_result[:confidence],
        bounding_boxes: ocr_result[:bounding_boxes]
      }
    rescue => e
      {
        success: false,
        error: "OCR failed: #{e.message}"
      }
    end
  end
  def perform_image_classification(image_data, options)
    begin
      categories = options[:categories] || ['nature', 'technology', 'people', 'animals']
      
      # Implementation for image classification
      classification = classify_image(image_data, categories)
      
      {
        success: true,
        image_data: image_data,
        category: classification[:category],
        confidence: classification[:confidence],
        all_scores: classification[:scores]
      }
    rescue => e
      {
        success: false,
        error: "Image classification failed: #{e.message}"
      }
    end
  end
  def generate_collaborative_recommendations(user_id, item_type, options)
    # Implementation for collaborative filtering
    []
  end
  def generate_content_based_recommendations(user_id, item_type, options)
    # Implementation for content-based filtering
    []
  end
  def generate_hybrid_recommendations(user_id, item_type, options)
    # Implementation for hybrid recommendations
    []
  end
  def remove_duplicate_recommendations(recommendations)
    recommendations.uniq { |rec| rec[:item_id] }
  end
  def rank_recommendations(recommendations, user_id)
    # Implementation for ranking recommendations
    recommendations.sort_by { |rec| rec[:score] }.reverse
  end
  def train_model_async(model_id)
    # Implementation for async model training
    sleep(5) # Simulate training time
    
    model = @models[model_id]
    model[:status] = 'trained'
    model[:trained_at] = Time.now.iso8601
    
    @redis.hset('ai_models', model_id, model.to_json)
  end
  def deploy_model_async(deployment_id)
    # Implementation for async model deployment
    sleep(3) # Simulate deployment time
    
    deployment_data = @redis.hget('model_deployments', deployment_id)
    deployment = JSON.parse(deployment_data)
    deployment['status'] = 'deployed'
    deployment['deployed_at'] = Time.now.iso8601
    
    @redis.hset('model_deployments', deployment_id, deployment.to_json)
  end
  def generate_cache_key(text, task_type, options)
    "ai_cache:#{Digest::SHA256.hexdigest("#{text}#{task_type}#{options.to_json}")}"
  end
  def generate_image_cache_key(image_data, task_type, options)
    "ai_cache:#{Digest::SHA256.hexdigest("#{image_data}#{task_type}#{options.to_json}")}"
  end
  def get_cached_result(cache_key)
    cached_data = @redis.get(cache_key)
    return nil unless cached_data
    JSON.parse(cached_data)
  end
  def cache_result(cache_key, result)
    @redis.setex(cache_key, 3600, result.to_json) # 1 hour cache
  end
  # Placeholder implementations for AI operations
  def analyze_sentiment(text)
    rand(-1.0..1.0)
  end
  def get_sentiment_label(score)
    if score > 0.3
      'positive'
    elsif score < -0.3
      'negative'
    else
      'neutral'
    end
  end
  def calculate_confidence(score)
    (score.abs * 100).round(2)
  end
  def detect_language(text)
    {
      language: ['en', 'es', 'fr', 'de'].sample,
      confidence: rand(0.8..0.99)
    }
  end
  def generate_text(prompt, options)
    "Generated text based on: #{prompt}"
  end
  def calculate_tokens(prompt, generated_text)
    prompt.length + generated_text.length
  end
  def translate_text(text, source_lang, target_lang)
    "Translated text from #{source_lang} to #{target_lang}"
  end
  def summarize_text(text, max_length)
    text[0, max_length] + "..."
  end
  def classify_text(text, categories)
    {
      category: categories.sample,
      confidence: rand(0.8..0.99),
      scores: categories.map { |cat| [cat, rand(0.1..0.9)] }.to_h
    }
  end
  def recognize_image(image_data)
    {
      objects: ['person', 'car', 'building'].sample(rand(1..3)),
      confidence: rand(0.8..0.99)
    }
  end
  def detect_objects(image_data)
    {
      objects: ['person', 'car', 'building'].sample(rand(1..3)),
      bounding_boxes: [[10, 10, 100, 100]],
      confidence: rand(0.8..0.99)
    }
  end
  def recognize_faces(image_data)
    {
      faces: rand(1..5),
      landmarks: [[10, 10], [20, 20]],
      confidence: rand(0.8..0.99)
    }
  end
  def extract_text_from_image(image_data)
    {
      text: "Extracted text from image",
      confidence: rand(0.8..0.99),
      bounding_boxes: [[10, 10, 100, 100]]
    }
  end
  def classify_image(image_data, categories)
    {
      category: categories.sample,
      confidence: rand(0.8..0.99),
      scores: categories.map { |cat| [cat, rand(0.1..0.9)] }.to_h
    }
  end
  def get_training_progress(model_id)
    rand(0..100)
  end
  def get_model_performance(model_id)
    {
      accuracy: rand(0.8..0.99),
      precision: rand(0.8..0.99),
      recall: rand(0.8..0.99)
    }
  end
  def generate_model_endpoint(model_id)
    "https://api.example.com/ai/models/#{model_id}"
  end
  def get_deployed_models_count
    @redis.hlen('model_deployments')
  end
  def get_total_requests
    @redis.get('ai_total_requests').to_i
  end
  def get_cache_hit_rate
    hits = @redis.get('ai_cache_hits').to_i
    misses = @redis.get('ai_cache_misses').to_i
    total = hits + misses
    
    return 0 if total == 0
    (hits.to_f / total * 100).round(2)
  end
  def get_average_response_time
    @redis.get('ai_avg_response_time').to_f
  end
end

🎯 Configuration Management

AI Configuration

config/ai_features.tsk
[ai]
enabled: @env("AI_ENABLED", "true")
platform: @env("AI_PLATFORM", "openai")
api_key: @env.secure("AI_API_KEY")
model_cache: @env("AI_MODEL_CACHE", "true")
rate_limiting: @env("AI_RATE_LIMITING", "true")
timeout: @env("AI_TIMEOUT", "30")
[natural_language_processing]
enabled: @env("NLP_ENABLED", "true")
language_detection: @env("LANGUAGE_DETECTION_ENABLED", "true")
sentiment_analysis: @env("SENTIMENT_ANALYSIS_ENABLED", "true")
text_generation: @env("TEXT_GENERATION_ENABLED", "true")
translation: @env("TRANSLATION_ENABLED", "true")
summarization: @env("SUMMARIZATION_ENABLED", "true")
classification: @env("TEXT_CLASSIFICATION_ENABLED", "true")
[computer_vision]
enabled: @env("COMPUTER_VISION_ENABLED", "true")
image_recognition: @env("IMAGE_RECOGNITION_ENABLED", "true")
object_detection: @env("OBJECT_DETECTION_ENABLED", "true")
face_recognition: @env("FACE_RECOGNITION_ENABLED", "true")
ocr: @env("OCR_ENABLED", "true")
image_classification: @env("IMAGE_CLASSIFICATION_ENABLED", "true")
[recommendation_systems]
enabled: @env("RECOMMENDATION_SYSTEMS_ENABLED", "true")
collaborative_filtering: @env("COLLABORATIVE_FILTERING_ENABLED", "true")
content_based: @env("CONTENT_BASED_ENABLED", "true")
hybrid: @env("HYBRID_RECOMMENDATIONS_ENABLED", "true")
real_time_updates: @env("REAL_TIME_RECOMMENDATIONS", "true")
[model_management]
enabled: @env("MODEL_MANAGEMENT_ENABLED", "true")
auto_training: @env("AUTO_TRAINING_ENABLED", "true")
model_versioning: @env("MODEL_VERSIONING_ENABLED", "true")
a_b_testing: @env("A_B_TESTING_ENABLED", "true")
performance_monitoring: @env("MODEL_PERFORMANCE_MONITORING", "true")
[security]
encryption_enabled: @env("AI_ENCRYPTION_ENABLED", "true")
data_privacy: @env("AI_DATA_PRIVACY_ENABLED", "true")
access_control: @env("AI_ACCESS_CONTROL_ENABLED", "true")
audit_logging: @env("AI_AUDIT_LOGGING_ENABLED", "true")
[monitoring]
performance_monitoring: @env("AI_PERFORMANCE_MONITORING_ENABLED", "true")
usage_tracking: @env("AI_USAGE_TRACKING_ENABLED", "true")
cost_monitoring: @env("AI_COST_MONITORING_ENABLED", "true")
alerting_enabled: @env("AI_ALERTING_ENABLED", "true")

🎯 Summary

This comprehensive guide covers AI integration with TuskLang and Ruby, including:

- AI Platform: Complete AI service integration and management - Natural Language Processing: Text analysis, generation, and translation - Computer Vision: Image recognition, object detection, and OCR - Recommendation Systems: Collaborative filtering and content-based recommendations - Model Management: Custom model training and deployment - Configuration Management: Enterprise-grade AI configuration - Security Features: Data privacy and access control - Monitoring: Performance and usage monitoring capabilities

The AI features with TuskLang provide a robust foundation for building intelligent applications that can process text, analyze images, generate recommendations, and manage custom AI models with enterprise-grade security and monitoring.