JAEGIS Natural Language Processing Engine

Advanced NLP Engine for Automatic Workflow Path Determination

Engine Overview

This advanced NLP engine analyzes any natural language input to automatically determine the optimal JAEGIS workflow path without manual selection, enabling seamless automatic workflow initiation based on intelligent input analysis.

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🧠 INTELLIGENT INPUT ANALYSIS FRAMEWORK

Advanced NLP Processing Architecture

class JAEGISNaturalLanguageEngine:
    def __init__(self):
        """
        Advanced NLP engine for automatic workflow path determination
        """
        self.nlp_models = {
            'intent_classifier': self.initialize_intent_classifier(),
            'complexity_analyzer': self.initialize_complexity_analyzer(),
            'domain_detector': self.initialize_domain_detector(),
            'workflow_router': self.initialize_workflow_router()
        }
        
        self.workflow_patterns = {
            'documentation_mode_patterns': [
                'create documentation', 'generate specs', 'write requirements',
                'design architecture', 'create checklist', 'document system',
                'specification for', 'requirements document', 'technical documentation'
            ],
            'full_development_mode_patterns': [
                'build application', 'develop system', 'create project',
                'implement solution', 'code application', 'full development',
                'complete implementation', 'build from scratch', 'develop and deploy'
            ],
            'configuration_mode_patterns': [
                'configure system', 'adjust parameters', 'customize workflow',
                'optimize settings', 'modify configuration', 'tune performance',
                'change settings', 'update configuration', 'personalize system'
            ],
            'analysis_mode_patterns': [
                'analyze', 'evaluate', 'assess', 'review', 'examine',
                'investigate', 'study', 'research', 'audit', 'validate'
            ]
        }
        
        print("🧠 Natural Language Processing Engine: INITIALIZED")
        print("   ✅ Intent classification: READY")
        print("   ✅ Complexity analysis: READY")
        print("   ✅ Domain detection: READY")
        print("   ✅ Workflow routing: READY")
    
    def analyze_input_and_route_workflow(self, user_input):
        """
        Analyze natural language input and automatically route to optimal workflow
        """
        print(f"🔍 Analyzing input: '{user_input[:100]}...'")
        
        # Phase 1: Intent Classification
        intent_analysis = self.classify_intent(user_input)
        
        # Phase 2: Complexity Assessment
        complexity_analysis = self.assess_complexity(user_input)
        
        # Phase 3: Domain Detection
        domain_analysis = self.detect_domain(user_input)
        
        # Phase 4: Context Analysis
        context_analysis = self.analyze_context(user_input)
        
        # Phase 5: Workflow Routing Decision
        routing_decision = self.make_routing_decision(
            intent_analysis, complexity_analysis, domain_analysis, context_analysis
        )
        
        print(f"🎯 Workflow routing decision: {routing_decision['selected_workflow']}")
        print(f"   📊 Confidence: {routing_decision['confidence']}%")
        print(f"   🔧 Configuration: {routing_decision['configuration']}")
        
        return routing_decision
    
    def classify_intent(self, user_input):
        """
        Classify user intent from natural language input
        """
        input_lower = user_input.lower()
        intent_scores = {}
        
        # Documentation Mode Intent Detection
        doc_score = sum(1 for pattern in self.workflow_patterns['documentation_mode_patterns'] 
                       if pattern in input_lower)
        intent_scores['documentation_mode'] = doc_score
        
        # Full Development Mode Intent Detection
        dev_score = sum(1 for pattern in self.workflow_patterns['full_development_mode_patterns'] 
                       if pattern in input_lower)
        intent_scores['full_development_mode'] = dev_score
        
        # Configuration Mode Intent Detection
        config_score = sum(1 for pattern in self.workflow_patterns['configuration_mode_patterns'] 
                          if pattern in input_lower)
        intent_scores['configuration_mode'] = config_score
        
        # Analysis Mode Intent Detection
        analysis_score = sum(1 for pattern in self.workflow_patterns['analysis_mode_patterns'] 
                            if pattern in input_lower)
        intent_scores['analysis_mode'] = analysis_score
        
        # Determine primary intent
        primary_intent = max(intent_scores, key=intent_scores.get) if any(intent_scores.values()) else 'general_inquiry'
        
        return {
            'primary_intent': primary_intent,
            'intent_scores': intent_scores,
            'confidence': max(intent_scores.values()) * 20 if intent_scores.values() else 50
        }
    
    def assess_complexity(self, user_input):
        """
        Assess complexity level of the request
        """
        complexity_indicators = {
            'high_complexity': [
                'enterprise', 'scalable', 'production', 'comprehensive', 'complete system',
                'full stack', 'microservices', 'distributed', 'cloud native', 'multi-tier'
            ],
            'medium_complexity': [
                'application', 'system', 'platform', 'framework', 'integration',
                'database', 'api', 'workflow', 'automation', 'dashboard'
            ],
            'low_complexity': [
                'simple', 'basic', 'quick', 'small', 'prototype',
                'demo', 'example', 'test', 'minimal', 'lightweight'
            ]
        }
        
        input_lower = user_input.lower()
        complexity_scores = {}
        
        for level, indicators in complexity_indicators.items():
            score = sum(1 for indicator in indicators if indicator in input_lower)
            complexity_scores[level] = score
        
        # Determine complexity level
        if complexity_scores['high_complexity'] > 0:
            complexity_level = 'high'
        elif complexity_scores['medium_complexity'] > 0:
            complexity_level = 'medium'
        elif complexity_scores['low_complexity'] > 0:
            complexity_level = 'low'
        else:
            # Default based on input length and technical terms
            word_count = len(user_input.split())
            technical_terms = ['system', 'application', 'platform', 'solution', 'architecture']
            tech_score = sum(1 for term in technical_terms if term in input_lower)
            
            if word_count > 50 or tech_score > 2:
                complexity_level = 'medium'
            else:
                complexity_level = 'low'
        
        return {
            'complexity_level': complexity_level,
            'complexity_scores': complexity_scores,
            'word_count': len(user_input.split()),
            'estimated_effort': self.estimate_effort(complexity_level)
        }
    
    def detect_domain(self, user_input):
        """
        Detect domain/industry context from input
        """
        domain_keywords = {
            'software_development': [
                'software', 'application', 'code', 'programming', 'development',
                'api', 'database', 'frontend', 'backend', 'framework'
            ],
            'business_analysis': [
                'business', 'requirements', 'process', 'workflow', 'analysis',
                'stakeholder', 'user story', 'business case', 'roi', 'strategy'
            ],
            'data_science': [
                'data', 'analytics', 'machine learning', 'ai', 'model',
                'dataset', 'visualization', 'statistics', 'prediction', 'algorithm'
            ],
            'infrastructure': [
                'infrastructure', 'cloud', 'deployment', 'server', 'network',
                'security', 'monitoring', 'devops', 'kubernetes', 'docker'
            ],
            'design_ux': [
                'design', 'user experience', 'interface', 'ui', 'ux',
                'wireframe', 'prototype', 'usability', 'user journey', 'mockup'
            ]
        }
        
        input_lower = user_input.lower()
        domain_scores = {}
        
        for domain, keywords in domain_keywords.items():
            score = sum(1 for keyword in keywords if keyword in input_lower)
            domain_scores[domain] = score
        
        primary_domain = max(domain_scores, key=domain_scores.get) if any(domain_scores.values()) else 'general'
        
        return {
            'primary_domain': primary_domain,
            'domain_scores': domain_scores,
            'multi_domain': sum(1 for score in domain_scores.values() if score > 0) > 1
        }
    
    def analyze_context(self, user_input):
        """
        Analyze contextual factors for workflow optimization
        """
        context_factors = {
            'urgency_indicators': ['urgent', 'asap', 'immediately', 'quickly', 'fast', 'rush'],
            'quality_indicators': ['high quality', 'production ready', 'enterprise grade', 'robust', 'reliable'],
            'collaboration_indicators': ['team', 'collaborate', 'stakeholders', 'review', 'feedback'],
            'iteration_indicators': ['iterate', 'improve', 'enhance', 'optimize', 'refine', 'update']
        }
        
        input_lower = user_input.lower()
        context_analysis = {}
        
        for factor, indicators in context_factors.items():
            score = sum(1 for indicator in indicators if indicator in input_lower)
            context_analysis[factor] = score > 0
        
        return context_analysis
    
    def make_routing_decision(self, intent_analysis, complexity_analysis, domain_analysis, context_analysis):
        """
        Make intelligent workflow routing decision based on all analysis factors
        """
        # Decision matrix based on analysis results
        decision_factors = {
            'intent_weight': 0.4,
            'complexity_weight': 0.3,
            'domain_weight': 0.2,
            'context_weight': 0.1
        }
        
        # Calculate workflow scores
        workflow_scores = {
            'documentation_mode': 0,
            'full_development_mode': 0,
            'hybrid_mode': 0
        }
        
        # Intent-based scoring
        if intent_analysis['primary_intent'] == 'documentation_mode':
            workflow_scores['documentation_mode'] += 80 * decision_factors['intent_weight']
        elif intent_analysis['primary_intent'] == 'full_development_mode':
            workflow_scores['full_development_mode'] += 80 * decision_factors['intent_weight']
        else:
            workflow_scores['hybrid_mode'] += 60 * decision_factors['intent_weight']
        
        # Complexity-based scoring
        if complexity_analysis['complexity_level'] == 'high':
            workflow_scores['full_development_mode'] += 70 * decision_factors['complexity_weight']
            workflow_scores['documentation_mode'] += 60 * decision_factors['complexity_weight']
        elif complexity_analysis['complexity_level'] == 'medium':
            workflow_scores['hybrid_mode'] += 80 * decision_factors['complexity_weight']
        else:
            workflow_scores['documentation_mode'] += 70 * decision_factors['complexity_weight']
        
        # Domain-based scoring
        if domain_analysis['multi_domain']:
            workflow_scores['full_development_mode'] += 60 * decision_factors['domain_weight']
        
        # Context-based scoring
        if context_analysis.get('urgency_indicators'):
            workflow_scores['documentation_mode'] += 50 * decision_factors['context_weight']
        if context_analysis.get('quality_indicators'):
            workflow_scores['full_development_mode'] += 60 * decision_factors['context_weight']
        
        # Select optimal workflow
        selected_workflow = max(workflow_scores, key=workflow_scores.get)
        confidence = workflow_scores[selected_workflow]
        
        # Generate configuration recommendations
        configuration = self.generate_configuration_recommendations(
            intent_analysis, complexity_analysis, domain_analysis, context_analysis
        )
        
        return {
            'selected_workflow': selected_workflow,
            'confidence': min(100, max(50, int(confidence))),
            'workflow_scores': workflow_scores,
            'configuration': configuration,
            'analysis_summary': {
                'intent': intent_analysis['primary_intent'],
                'complexity': complexity_analysis['complexity_level'],
                'domain': domain_analysis['primary_domain'],
                'context_factors': context_analysis
            }
        }
    
    def generate_configuration_recommendations(self, intent_analysis, complexity_analysis, domain_analysis, context_analysis):
        """
        Generate intelligent configuration recommendations based on analysis
        """
        config_recommendations = {
            'agent_activation_frequency': 80,  # Default
            'quality_validation_intensity': 85,  # Default
            'deep_web_research_frequency': 70,  # Default
            'task_decomposition_depth': 75,  # Default
            'real_time_monitoring_frequency': 80,  # Default
            'cross_validation_frequency': 75   # Default
        }
        
        # Adjust based on complexity
        if complexity_analysis['complexity_level'] == 'high':
            config_recommendations['agent_activation_frequency'] = 95
            config_recommendations['quality_validation_intensity'] = 95
            config_recommendations['task_decomposition_depth'] = 90
        elif complexity_analysis['complexity_level'] == 'low':
            config_recommendations['agent_activation_frequency'] = 60
            config_recommendations['task_decomposition_depth'] = 60
        
        # Adjust based on context
        if context_analysis.get('urgency_indicators'):
            config_recommendations['deep_web_research_frequency'] = 50
            config_recommendations['cross_validation_frequency'] = 60
        
        if context_analysis.get('quality_indicators'):
            config_recommendations['quality_validation_intensity'] = 100
            config_recommendations['cross_validation_frequency'] = 90
        
        return config_recommendations
    
    def estimate_effort(self, complexity_level):
        """
        Estimate effort based on complexity level
        """
        effort_estimates = {
            'low': '1-3 hours',
            'medium': '4-8 hours',
            'high': '8-20 hours'
        }
        
        return effort_estimates.get(complexity_level, '4-8 hours')

Automatic Workflow Initiation Protocol

class AutomaticWorkflowInitiator:
    def __init__(self, nlp_engine):
        """
        Automatic workflow initiation based on NLP analysis
        """
        self.nlp_engine = nlp_engine
        self.workflow_engines = {
            'documentation_mode': DocumentationModeEngine(),
            'full_development_mode': FullDevelopmentModeEngine(),
            'hybrid_mode': HybridModeEngine()
        }
        
    def process_natural_language_input(self, user_input):
        """
        Process natural language input and automatically initiate workflow
        """
        print("🚀 Processing natural language input for automatic workflow initiation...")
        
        # Analyze input and determine optimal workflow
        routing_decision = self.nlp_engine.analyze_input_and_route_workflow(user_input)
        
        # Apply configuration recommendations
        self.apply_configuration(routing_decision['configuration'])
        
        # Initiate selected workflow automatically
        workflow_result = self.initiate_workflow(
            routing_decision['selected_workflow'],
            user_input,
            routing_decision
        )
        
        return workflow_result
    
    def initiate_workflow(self, workflow_type, user_input, routing_decision):
        """
        Automatically initiate the selected workflow
        """
        print(f"🔄 Initiating {workflow_type} workflow automatically...")
        
        workflow_engine = self.workflow_engines[workflow_type]
        
        # Execute workflow with automatic progression
        workflow_result = workflow_engine.execute_automatic_workflow(
            user_input=user_input,
            routing_decision=routing_decision,
            auto_progression=True,
            validation_active=True
        )
        
        return workflow_result
    
    def apply_configuration(self, configuration_recommendations):
        """
        Apply intelligent configuration recommendations
        """
        print("⚙️ Applying intelligent configuration recommendations...")
        
        for parameter, value in configuration_recommendations.items():
            self.update_system_parameter(parameter, value)
        
        print("   ✅ Configuration applied successfully")

This advanced NLP engine provides intelligent automatic workflow path determination through comprehensive input analysis, enabling seamless workflow initiation without manual selection while optimizing system configuration based on detected requirements and context.