# Semantic Understanding Engine ## Deep Semantic Analysis and Intent Understanding for Enhanced BMAD System The Semantic Understanding Engine provides sophisticated semantic analysis capabilities that understand the meaning, intent, and context behind code, documentation, and development activities, enabling more intelligent and context-aware assistance. ### Semantic Analysis Architecture #### Multi-Modal Semantic Understanding Framework ```yaml semantic_analysis_architecture: understanding_domains: code_semantics: - structural_semantics: "Understanding code structure and relationships" - functional_semantics: "Understanding what code does and how" - intentional_semantics: "Understanding developer intent behind code" - behavioral_semantics: "Understanding code behavior and side effects" - evolutionary_semantics: "Understanding how code meaning changes over time" natural_language_semantics: - requirement_semantics: "Understanding requirement specifications" - documentation_semantics: "Understanding technical documentation" - conversation_semantics: "Understanding development discussions" - comment_semantics: "Understanding code comments and annotations" - query_semantics: "Understanding developer queries and requests" cross_modal_semantics: - code_to_language: "Understanding relationships between code and descriptions" - language_to_code: "Understanding how descriptions map to code" - multimodal_consistency: "Ensuring consistency across modalities" - semantic_bridging: "Bridging semantic gaps between modalities" - contextual_disambiguation: "Resolving ambiguity using context" domain_semantics: - business_domain: "Understanding business logic and rules" - technical_domain: "Understanding technical concepts and patterns" - architectural_domain: "Understanding system architecture semantics" - process_domain: "Understanding development process semantics" - team_domain: "Understanding team collaboration semantics" analysis_techniques: symbolic_analysis: - abstract_syntax_trees: "Structural code analysis" - control_flow_graphs: "Code execution flow analysis" - data_flow_analysis: "Data movement and transformation analysis" - dependency_graphs: "Code dependency relationship analysis" - semantic_networks: "Concept relationship networks" statistical_analysis: - distributional_semantics: "Meaning from usage patterns" - co_occurrence_analysis: "Semantic relationships from co-occurrence" - frequency_analysis: "Semantic importance from frequency" - clustering_analysis: "Semantic grouping and categorization" - dimensionality_reduction: "Semantic space compression" neural_analysis: - transformer_models: "Deep contextual understanding" - attention_mechanisms: "Focus on semantically important parts" - embeddings: "Dense semantic representations" - sequence_modeling: "Temporal semantic understanding" - multimodal_fusion: "Cross-modal semantic integration" knowledge_based_analysis: - ontology_reasoning: "Formal semantic reasoning" - rule_based_inference: "Logical semantic deduction" - knowledge_graph_traversal: "Semantic relationship exploration" - concept_hierarchies: "Hierarchical semantic understanding" - semantic_matching: "Semantic similarity and equivalence" understanding_capabilities: intent_recognition: - development_intent: "What developer wants to accomplish" - code_purpose_intent: "Why code was written this way" - modification_intent: "What changes are trying to achieve" - architectural_intent: "Intended system design and structure" - optimization_intent: "Intended improvements and optimizations" context_awareness: - project_context: "Understanding within project scope" - temporal_context: "Understanding time-dependent semantics" - team_context: "Understanding within team dynamics" - domain_context: "Understanding within business domain" - technical_context: "Understanding within technical constraints" ambiguity_resolution: - lexical_disambiguation: "Resolving word meaning ambiguity" - syntactic_disambiguation: "Resolving structural ambiguity" - semantic_disambiguation: "Resolving meaning ambiguity" - pragmatic_disambiguation: "Resolving usage context ambiguity" - reference_resolution: "Resolving what entities refer to" ``` #### Semantic Understanding Engine Implementation ```python import ast import re import spacy import networkx as nx import numpy as np from transformers import AutoTokenizer, AutoModel, pipeline from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.metrics.pairwise import cosine_similarity from sklearn.decomposition import LatentDirichletAllocation import torch import torch.nn.functional as F from typing import Dict, List, Any, Optional, Tuple, Union from dataclasses import dataclass from collections import defaultdict import asyncio from datetime import datetime @dataclass class SemanticContext: """ Represents semantic context for understanding """ project_context: Dict[str, Any] temporal_context: Dict[str, Any] team_context: Dict[str, Any] domain_context: Dict[str, Any] technical_context: Dict[str, Any] @dataclass class SemanticUnderstanding: """ Represents the result of semantic analysis """ primary_intent: str confidence_score: float semantic_concepts: List[str] relationships: List[Tuple[str, str, str]] # (entity1, relation, entity2) ambiguities: List[Dict[str, Any]] context_factors: List[str] recommendations: List[str] class SemanticUnderstandingEngine: """ Advanced semantic understanding and analysis engine """ def __init__(self, config=None): self.config = config or { 'semantic_similarity_threshold': 0.7, 'intent_confidence_threshold': 0.8, 'max_ambiguity_candidates': 5, 'context_window_size': 512 } # Initialize NLP components self.nlp = spacy.load("en_core_web_sm") self.code_bert = AutoModel.from_pretrained("microsoft/codebert-base") self.code_tokenizer = AutoTokenizer.from_pretrained("microsoft/codebert-base") # Initialize specialized analyzers self.code_semantic_analyzer = CodeSemanticAnalyzer(self.config) self.language_semantic_analyzer = LanguageSemanticAnalyzer(self.config) self.intent_recognizer = IntentRecognizer(self.config) self.context_analyzer = ContextAnalyzer(self.config) self.ambiguity_resolver = AmbiguityResolver(self.config) # Semantic knowledge base self.concept_ontology = ConceptOntology() self.semantic_patterns = SemanticPatternLibrary() # Cross-modal understanding self.multimodal_fusion = MultimodalSemanticFusion(self.config) async def analyze_semantic_understanding(self, input_data, context=None): """ Perform comprehensive semantic analysis of input data """ analysis_session = { 'session_id': generate_uuid(), 'input_data': input_data, 'context': context, 'understanding_results': {}, 'semantic_insights': {}, 'recommendations': [] } # Determine input type and prepare for analysis input_analysis = await self.analyze_input_type(input_data) analysis_session['input_analysis'] = input_analysis # Create semantic context semantic_context = await self.create_semantic_context(context, input_data) analysis_session['semantic_context'] = semantic_context # Perform domain-specific semantic analysis understanding_tasks = [] if input_analysis['has_code']: understanding_tasks.append( self.analyze_code_semantics(input_data, semantic_context) ) if input_analysis['has_natural_language']: understanding_tasks.append( self.analyze_language_semantics(input_data, semantic_context) ) if input_analysis['is_multimodal']: understanding_tasks.append( self.analyze_multimodal_semantics(input_data, semantic_context) ) # Execute analyses in parallel understanding_results = await asyncio.gather(*understanding_tasks) # Integrate results integrated_understanding = await self.integrate_semantic_analyses( understanding_results, semantic_context ) analysis_session['understanding_results'] = integrated_understanding # Recognize primary intent primary_intent = await self.intent_recognizer.recognize_intent( integrated_understanding, semantic_context ) analysis_session['primary_intent'] = primary_intent # Resolve ambiguities disambiguation_results = await self.ambiguity_resolver.resolve_ambiguities( integrated_understanding, semantic_context ) analysis_session['disambiguation_results'] = disambiguation_results # Generate semantic insights semantic_insights = await self.generate_semantic_insights( integrated_understanding, primary_intent, disambiguation_results, semantic_context ) analysis_session['semantic_insights'] = semantic_insights # Generate recommendations recommendations = await self.generate_semantic_recommendations( semantic_insights, semantic_context ) analysis_session['recommendations'] = recommendations return analysis_session async def analyze_code_semantics(self, input_data, semantic_context): """ Analyze semantic meaning of code """ code_semantics = { 'structural_semantics': {}, 'functional_semantics': {}, 'intentional_semantics': {}, 'behavioral_semantics': {} } # Extract code from input data code_content = self.extract_code_content(input_data) if not code_content: return code_semantics # Analyze structural semantics structural_analysis = await self.code_semantic_analyzer.analyze_structural_semantics( code_content, semantic_context ) code_semantics['structural_semantics'] = structural_analysis # Analyze functional semantics functional_analysis = await self.code_semantic_analyzer.analyze_functional_semantics( code_content, semantic_context ) code_semantics['functional_semantics'] = functional_analysis # Analyze intentional semantics intentional_analysis = await self.code_semantic_analyzer.analyze_intentional_semantics( code_content, semantic_context ) code_semantics['intentional_semantics'] = intentional_analysis # Analyze behavioral semantics behavioral_analysis = await self.code_semantic_analyzer.analyze_behavioral_semantics( code_content, semantic_context ) code_semantics['behavioral_semantics'] = behavioral_analysis return code_semantics async def analyze_language_semantics(self, input_data, semantic_context): """ Analyze semantic meaning of natural language """ language_semantics = { 'entity_semantics': {}, 'relationship_semantics': {}, 'intent_semantics': {}, 'context_semantics': {} } # Extract natural language from input data text_content = self.extract_text_content(input_data) if not text_content: return language_semantics # Analyze entity semantics entity_analysis = await self.language_semantic_analyzer.analyze_entity_semantics( text_content, semantic_context ) language_semantics['entity_semantics'] = entity_analysis # Analyze relationship semantics relationship_analysis = await self.language_semantic_analyzer.analyze_relationship_semantics( text_content, semantic_context ) language_semantics['relationship_semantics'] = relationship_analysis # Analyze intent semantics intent_analysis = await self.language_semantic_analyzer.analyze_intent_semantics( text_content, semantic_context ) language_semantics['intent_semantics'] = intent_analysis # Analyze context semantics context_analysis = await self.language_semantic_analyzer.analyze_context_semantics( text_content, semantic_context ) language_semantics['context_semantics'] = context_analysis return language_semantics async def create_semantic_context(self, context, input_data): """ Create comprehensive semantic context for analysis """ semantic_context = SemanticContext( project_context={}, temporal_context={}, team_context={}, domain_context={}, technical_context={} ) if context: # Extract project context semantic_context.project_context = await self.context_analyzer.extract_project_context( context, input_data ) # Extract temporal context semantic_context.temporal_context = await self.context_analyzer.extract_temporal_context( context, input_data ) # Extract team context semantic_context.team_context = await self.context_analyzer.extract_team_context( context, input_data ) # Extract domain context semantic_context.domain_context = await self.context_analyzer.extract_domain_context( context, input_data ) # Extract technical context semantic_context.technical_context = await self.context_analyzer.extract_technical_context( context, input_data ) return semantic_context class CodeSemanticAnalyzer: """ Specialized analyzer for code semantics """ def __init__(self, config): self.config = config self.ast_analyzer = ASTSemanticAnalyzer() self.pattern_matcher = CodePatternMatcher() async def analyze_structural_semantics(self, code_content, semantic_context): """ Analyze the structural semantic meaning of code """ structural_semantics = { 'hierarchical_structure': {}, 'modular_relationships': {}, 'dependency_semantics': {}, 'composition_patterns': {} } try: # Parse code into AST tree = ast.parse(code_content) # Analyze hierarchical structure hierarchical_analysis = await self.ast_analyzer.analyze_hierarchy(tree) structural_semantics['hierarchical_structure'] = hierarchical_analysis # Analyze modular relationships modular_analysis = await self.ast_analyzer.analyze_modules(tree) structural_semantics['modular_relationships'] = modular_analysis # Analyze dependency semantics dependency_analysis = await self.ast_analyzer.analyze_dependencies(tree) structural_semantics['dependency_semantics'] = dependency_analysis # Identify composition patterns composition_analysis = await self.pattern_matcher.identify_composition_patterns(tree) structural_semantics['composition_patterns'] = composition_analysis except SyntaxError as e: structural_semantics['error'] = f"Syntax error in code: {str(e)}" return structural_semantics async def analyze_functional_semantics(self, code_content, semantic_context): """ Analyze what the code functionally does """ functional_semantics = { 'primary_functions': [], 'side_effects': [], 'data_transformations': [], 'control_flow_semantics': {} } try: tree = ast.parse(code_content) # Identify primary functions primary_functions = await self.identify_primary_functions(tree) functional_semantics['primary_functions'] = primary_functions # Identify side effects side_effects = await self.identify_side_effects(tree) functional_semantics['side_effects'] = side_effects # Analyze data transformations data_transformations = await self.analyze_data_transformations(tree) functional_semantics['data_transformations'] = data_transformations # Analyze control flow semantics control_flow = await self.analyze_control_flow_semantics(tree) functional_semantics['control_flow_semantics'] = control_flow except SyntaxError as e: functional_semantics['error'] = f"Syntax error in code: {str(e)}" return functional_semantics async def analyze_intentional_semantics(self, code_content, semantic_context): """ Analyze the intent behind the code """ intentional_semantics = { 'design_intent': {}, 'optimization_intent': {}, 'maintenance_intent': {}, 'feature_intent': {} } # Analyze comments and docstrings for intent clues intent_clues = await self.extract_intent_clues(code_content) # Analyze naming patterns for intent naming_intent = await self.analyze_naming_intent(code_content) # Analyze structural patterns for design intent design_intent = await self.analyze_design_intent_patterns(code_content) # Combine analyses intentional_semantics['design_intent'] = design_intent intentional_semantics['intent_clues'] = intent_clues intentional_semantics['naming_intent'] = naming_intent return intentional_semantics async def extract_intent_clues(self, code_content): """ Extract intent clues from comments and docstrings """ intent_clues = { 'explicit_intents': [], 'implicit_intents': [], 'design_rationale': [], 'todo_items': [] } # Extract comments comment_pattern = r'#\s*(.+?)(?:\n|$)' comments = re.findall(comment_pattern, code_content) # Extract docstrings docstring_pattern = r'"""(.*?)"""' docstrings = re.findall(docstring_pattern, code_content, re.DOTALL) # Analyze comments for intent keywords intent_keywords = { 'explicit': ['todo', 'fix', 'hack', 'temporary', 'optimize'], 'design': ['because', 'reason', 'purpose', 'goal', 'intent'], 'improvement': ['improve', 'enhance', 'refactor', 'cleanup'] } for comment in comments: comment_lower = comment.lower() # Check for explicit intents for keyword in intent_keywords['explicit']: if keyword in comment_lower: intent_clues['explicit_intents'].append({ 'keyword': keyword, 'text': comment, 'confidence': 0.8 }) # Check for design rationale for keyword in intent_keywords['design']: if keyword in comment_lower: intent_clues['design_rationale'].append({ 'keyword': keyword, 'text': comment, 'confidence': 0.7 }) return intent_clues class LanguageSemanticAnalyzer: """ Specialized analyzer for natural language semantics """ def __init__(self, config): self.config = config self.nlp = spacy.load("en_core_web_sm") self.entity_linker = EntityLinker() self.relation_extractor = RelationExtractor() async def analyze_entity_semantics(self, text_content, semantic_context): """ Analyze entities and their semantic roles """ entity_semantics = { 'named_entities': [], 'concept_entities': [], 'technical_entities': [], 'relationship_entities': [] } # Process text with spaCy doc = self.nlp(text_content) # Extract named entities for ent in doc.ents: entity_info = { 'text': ent.text, 'label': ent.label_, 'start': ent.start_char, 'end': ent.end_char, 'semantic_type': self.classify_entity_semantics(ent) } entity_semantics['named_entities'].append(entity_info) # Extract technical entities technical_entities = await self.extract_technical_entities(text_content) entity_semantics['technical_entities'] = technical_entities # Extract concept entities concept_entities = await self.extract_concept_entities(text_content, semantic_context) entity_semantics['concept_entities'] = concept_entities return entity_semantics async def analyze_relationship_semantics(self, text_content, semantic_context): """ Analyze semantic relationships between entities """ relationship_semantics = { 'explicit_relationships': [], 'implicit_relationships': [], 'causal_relationships': [], 'temporal_relationships': [] } # Extract explicit relationships explicit_rels = await self.relation_extractor.extract_explicit_relations(text_content) relationship_semantics['explicit_relationships'] = explicit_rels # Infer implicit relationships implicit_rels = await self.relation_extractor.infer_implicit_relations( text_content, semantic_context ) relationship_semantics['implicit_relationships'] = implicit_rels # Extract causal relationships causal_rels = await self.relation_extractor.extract_causal_relations(text_content) relationship_semantics['causal_relationships'] = causal_rels # Extract temporal relationships temporal_rels = await self.relation_extractor.extract_temporal_relations(text_content) relationship_semantics['temporal_relationships'] = temporal_rels return relationship_semantics def classify_entity_semantics(self, entity): """ Classify the semantic type of an entity """ semantic_mappings = { 'PERSON': 'agent', 'ORG': 'organization', 'PRODUCT': 'artifact', 'EVENT': 'process', 'DATE': 'temporal', 'TIME': 'temporal', 'MONEY': 'resource', 'PERCENT': 'metric' } return semantic_mappings.get(entity.label_, 'unknown') class IntentRecognizer: """ Recognizes intent from semantic analysis results """ def __init__(self, config): self.config = config self.intent_patterns = { 'information_seeking': [ 'what', 'how', 'why', 'when', 'where', 'explain', 'describe' ], 'problem_solving': [ 'fix', 'solve', 'resolve', 'debug', 'troubleshoot' ], 'implementation': [ 'implement', 'create', 'build', 'develop', 'code' ], 'optimization': [ 'optimize', 'improve', 'enhance', 'faster', 'better' ], 'analysis': [ 'analyze', 'review', 'examine', 'evaluate', 'assess' ] } async def recognize_intent(self, semantic_understanding, context): """ Recognize primary intent from semantic understanding """ intent_scores = defaultdict(float) # Analyze language semantics for intent keywords if 'language_semantics' in semantic_understanding: lang_semantics = semantic_understanding['language_semantics'] for intent_type, keywords in self.intent_patterns.items(): for keyword in keywords: if any(keyword in str(analysis).lower() for analysis in lang_semantics.values()): intent_scores[intent_type] += 1.0 # Analyze code semantics for implementation intent if 'code_semantics' in semantic_understanding: code_semantics = semantic_understanding['code_semantics'] # Check for implementation patterns if code_semantics.get('functional_semantics', {}).get('primary_functions'): intent_scores['implementation'] += 2.0 # Check for optimization patterns if any('optimization' in str(analysis).lower() for analysis in code_semantics.get('intentional_semantics', {}).values()): intent_scores['optimization'] += 1.5 # Determine primary intent if intent_scores: primary_intent = max(intent_scores.items(), key=lambda x: x[1]) confidence = min(primary_intent[1] / sum(intent_scores.values()), 1.0) return { 'intent': primary_intent[0], 'confidence': confidence, 'all_scores': dict(intent_scores) } return { 'intent': 'unknown', 'confidence': 0.0, 'all_scores': {} } class AmbiguityResolver: """ Resolves semantic ambiguities using context and knowledge """ def __init__(self, config): self.config = config async def resolve_ambiguities(self, semantic_understanding, context): """ Resolve identified ambiguities in semantic understanding """ disambiguation_results = { 'resolved_ambiguities': [], 'remaining_ambiguities': [], 'confidence_scores': {} } # Identify potential ambiguities ambiguities = await self.identify_ambiguities(semantic_understanding) # Resolve each ambiguity using context for ambiguity in ambiguities: resolution = await self.resolve_single_ambiguity(ambiguity, context) if resolution['confidence'] > self.config['intent_confidence_threshold']: disambiguation_results['resolved_ambiguities'].append(resolution) else: disambiguation_results['remaining_ambiguities'].append(ambiguity) return disambiguation_results async def identify_ambiguities(self, semantic_understanding): """ Identify potential ambiguities in semantic understanding """ ambiguities = [] # Check for multiple possible intents if 'language_semantics' in semantic_understanding: intent_semantics = semantic_understanding['language_semantics'].get('intent_semantics', {}) if len(intent_semantics.get('possible_intents', [])) > 1: ambiguities.append({ 'type': 'intent_ambiguity', 'candidates': intent_semantics['possible_intents'], 'context': 'multiple_intents_detected' }) # Check for ambiguous entity references if 'language_semantics' in semantic_understanding: entity_semantics = semantic_understanding['language_semantics'].get('entity_semantics', {}) for entity in entity_semantics.get('named_entities', []): if entity.get('ambiguous', False): ambiguities.append({ 'type': 'entity_reference_ambiguity', 'entity': entity['text'], 'candidates': entity.get('candidates', []), 'context': 'ambiguous_entity_reference' }) return ambiguities async def resolve_single_ambiguity(self, ambiguity, context): """ Resolve a single ambiguity using available context """ resolution = { 'ambiguity_type': ambiguity['type'], 'original_candidates': ambiguity.get('candidates', []), 'resolved_value': None, 'confidence': 0.0, 'resolution_method': 'context_based' } if ambiguity['type'] == 'intent_ambiguity': # Use context to determine most likely intent resolution = await self.resolve_intent_ambiguity(ambiguity, context) elif ambiguity['type'] == 'entity_reference_ambiguity': # Use context to determine most likely entity reference resolution = await self.resolve_entity_ambiguity(ambiguity, context) return resolution ``` ### Semantic Analysis Commands ```bash # Semantic understanding and analysis bmad semantic analyze --input "code-file.py" --context "project-requirements" bmad semantic understand --query "implement user authentication" --deep-analysis bmad semantic extract --concepts --from "documentation/" --relationships # Intent recognition and disambiguation bmad semantic intent --recognize --from "user-query" --confidence-threshold 0.8 bmad semantic disambiguate --ambiguous-terms --use-context bmad semantic clarify --unclear-requirements --suggest-interpretations # Cross-modal semantic analysis bmad semantic bridge --code-to-language --explain "function-implementation" bmad semantic consistency --check --across "code,docs,comments" bmad semantic map --requirements-to-code --show-gaps # Semantic insights and recommendations bmad semantic insights --generate --focus "intent-code-alignment" bmad semantic recommend --improvements --based-on-semantics bmad semantic export --understanding --format "knowledge-graph" ``` [{"content": "Phase 1: Core Intelligence Foundation - COMPLETED \u2705", "status": "completed", "priority": "high", "id": "phase1"}, {"content": "Phase 2: LLM Integration and Knowledge Management - COMPLETED \u2705", "status": "completed", "priority": "high", "id": "phase2"}, {"content": "Create LLM Abstraction Framework", "status": "completed", "priority": "high", "id": "2.1"}, {"content": "Implement Knowledge Management Core", "status": "completed", "priority": "high", "id": "2.2"}, {"content": "Build Cross-Project Learning System", "status": "completed", "priority": "high", "id": "2.3"}, {"content": "Develop Advanced Memory Architecture", "status": "completed", "priority": "high", "id": "2.4"}, {"content": "Create Universal Workflow Engine", "status": "completed", "priority": "high", "id": "2.5"}, {"content": "Implement Knowledge Discovery Platform", "status": "completed", "priority": "high", "id": "2.6"}, {"content": "Build Semantic Analysis Engine", "status": "completed", "priority": "high", "id": "2.7"}, {"content": "Phase 3: Advanced Intelligence and Claude Code Integration", "status": "pending", "priority": "medium", "id": "phase3"}, {"content": "Phase 4: Self-Optimization and Enterprise Features", "status": "pending", "priority": "medium", "id": "phase4"}]