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.
🧠 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
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.
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