612 lines
26 KiB
Markdown
612 lines
26 KiB
Markdown
# Knowledge Graph Builder
|
|
|
|
## Advanced Knowledge Graph Construction for Enhanced BMAD System
|
|
|
|
The Knowledge Graph Builder creates comprehensive, interconnected knowledge representations that capture relationships between code, concepts, patterns, decisions, and outcomes across all development activities.
|
|
|
|
### Knowledge Graph Architecture
|
|
|
|
#### Multi-Dimensional Knowledge Representation
|
|
```yaml
|
|
knowledge_graph_structure:
|
|
node_types:
|
|
concept_nodes:
|
|
- code_concepts: "Functions, classes, modules, patterns"
|
|
- domain_concepts: "Business logic, requirements, features"
|
|
- technical_concepts: "Architectures, technologies, frameworks"
|
|
- process_concepts: "Workflows, methodologies, practices"
|
|
- team_concepts: "Roles, skills, collaboration patterns"
|
|
|
|
artifact_nodes:
|
|
- code_artifacts: "Files, components, libraries, APIs"
|
|
- documentation_artifacts: "READMEs, specs, comments"
|
|
- decision_artifacts: "ADRs, meeting notes, rationale"
|
|
- test_artifacts: "Test cases, scenarios, coverage data"
|
|
- deployment_artifacts: "Configs, scripts, environments"
|
|
|
|
relationship_nodes:
|
|
- dependency_relationships: "Uses, imports, calls, inherits"
|
|
- semantic_relationships: "Similar to, implements, abstracts"
|
|
- temporal_relationships: "Before, after, during, triggers"
|
|
- causality_relationships: "Causes, prevents, enables, blocks"
|
|
- collaboration_relationships: "Authored by, reviewed by, approved by"
|
|
|
|
context_nodes:
|
|
- project_contexts: "Project phases, milestones, goals"
|
|
- team_contexts: "Team structure, skills, availability"
|
|
- technical_contexts: "Environment, constraints, limitations"
|
|
- business_contexts: "Requirements, priorities, deadlines"
|
|
- quality_contexts: "Standards, criteria, metrics"
|
|
|
|
edge_types:
|
|
structural_edges:
|
|
- composition: "Part of, contains, includes"
|
|
- inheritance: "Extends, implements, derives from"
|
|
- association: "Uses, references, calls"
|
|
- aggregation: "Composed of, made from, built with"
|
|
|
|
semantic_edges:
|
|
- similarity: "Similar to, related to, analogous to"
|
|
- classification: "Type of, instance of, category of"
|
|
- transformation: "Converts to, maps to, becomes"
|
|
- equivalence: "Same as, alias for, identical to"
|
|
|
|
temporal_edges:
|
|
- sequence: "Followed by, preceded by, concurrent with"
|
|
- causality: "Causes, results in, leads to"
|
|
- lifecycle: "Created, modified, deprecated, removed"
|
|
- versioning: "Previous version, next version, variant of"
|
|
|
|
contextual_edges:
|
|
- applicability: "Used in, applies to, relevant for"
|
|
- constraint: "Requires, depends on, limited by"
|
|
- optimization: "Improves, enhances, optimizes"
|
|
- conflict: "Conflicts with, incompatible with, blocks"
|
|
```
|
|
|
|
#### Knowledge Graph Construction Engine
|
|
```python
|
|
import networkx as nx
|
|
from sklearn.feature_extraction.text import TfidfVectorizer
|
|
from sklearn.metrics.pairwise import cosine_similarity
|
|
import spacy
|
|
from transformers import AutoTokenizer, AutoModel
|
|
import torch
|
|
|
|
class KnowledgeGraphBuilder:
|
|
"""
|
|
Advanced knowledge graph construction for development activities
|
|
"""
|
|
|
|
def __init__(self):
|
|
self.graph = nx.MultiDiGraph()
|
|
self.nlp = spacy.load("en_core_web_sm")
|
|
self.embedder = AutoModel.from_pretrained("microsoft/codebert-base")
|
|
self.tokenizer = AutoTokenizer.from_pretrained("microsoft/codebert-base")
|
|
self.vectorizer = TfidfVectorizer(max_features=1000, stop_words='english')
|
|
|
|
# Initialize knowledge extractors
|
|
self.code_extractor = CodeKnowledgeExtractor()
|
|
self.conversation_extractor = ConversationKnowledgeExtractor()
|
|
self.decision_extractor = DecisionKnowledgeExtractor()
|
|
self.pattern_extractor = PatternKnowledgeExtractor()
|
|
|
|
async def build_knowledge_graph(self, data_sources):
|
|
"""
|
|
Build comprehensive knowledge graph from multiple data sources
|
|
"""
|
|
construction_session = {
|
|
'session_id': generate_uuid(),
|
|
'data_sources': data_sources,
|
|
'extraction_results': {},
|
|
'graph_statistics': {},
|
|
'quality_metrics': {}
|
|
}
|
|
|
|
# Extract knowledge from different sources
|
|
for source_type, source_data in data_sources.items():
|
|
if source_type == 'codebase':
|
|
extraction_result = await self.extract_code_knowledge(source_data)
|
|
elif source_type == 'conversations':
|
|
extraction_result = await self.extract_conversation_knowledge(source_data)
|
|
elif source_type == 'documentation':
|
|
extraction_result = await self.extract_documentation_knowledge(source_data)
|
|
elif source_type == 'decisions':
|
|
extraction_result = await self.extract_decision_knowledge(source_data)
|
|
elif source_type == 'patterns':
|
|
extraction_result = await self.extract_pattern_knowledge(source_data)
|
|
else:
|
|
extraction_result = await self.extract_generic_knowledge(source_data)
|
|
|
|
construction_session['extraction_results'][source_type] = extraction_result
|
|
|
|
# Add extracted knowledge to graph
|
|
await self.integrate_knowledge_into_graph(extraction_result)
|
|
|
|
# Build relationships between knowledge nodes
|
|
await self.construct_knowledge_relationships()
|
|
|
|
# Validate and optimize graph structure
|
|
graph_validation = await self.validate_knowledge_graph()
|
|
construction_session['quality_metrics'] = graph_validation
|
|
|
|
# Generate graph statistics
|
|
construction_session['graph_statistics'] = await self.generate_graph_statistics()
|
|
|
|
return construction_session
|
|
|
|
async def extract_code_knowledge(self, codebase_data):
|
|
"""
|
|
Extract knowledge from codebase using AST analysis and semantic understanding
|
|
"""
|
|
code_knowledge = {
|
|
'functions': [],
|
|
'classes': [],
|
|
'modules': [],
|
|
'dependencies': [],
|
|
'patterns': [],
|
|
'relationships': []
|
|
}
|
|
|
|
for file_path, file_content in codebase_data.items():
|
|
# Parse code using AST
|
|
ast_analysis = await self.code_extractor.parse_code_ast(file_content, file_path)
|
|
|
|
# Extract semantic embeddings
|
|
code_embeddings = await self.generate_code_embeddings(file_content)
|
|
|
|
# Identify code entities
|
|
entities = await self.code_extractor.identify_code_entities(ast_analysis)
|
|
|
|
# Extract patterns
|
|
patterns = await self.code_extractor.extract_code_patterns(ast_analysis)
|
|
|
|
# Build dependency graph
|
|
dependencies = await self.code_extractor.extract_dependencies(ast_analysis)
|
|
|
|
code_knowledge['functions'].extend(entities['functions'])
|
|
code_knowledge['classes'].extend(entities['classes'])
|
|
code_knowledge['modules'].append({
|
|
'path': file_path,
|
|
'content': file_content,
|
|
'embeddings': code_embeddings,
|
|
'ast': ast_analysis
|
|
})
|
|
code_knowledge['dependencies'].extend(dependencies)
|
|
code_knowledge['patterns'].extend(patterns)
|
|
|
|
# Analyze cross-file relationships
|
|
cross_file_relationships = await self.analyze_cross_file_relationships(code_knowledge)
|
|
code_knowledge['relationships'] = cross_file_relationships
|
|
|
|
return code_knowledge
|
|
|
|
async def extract_conversation_knowledge(self, conversation_data):
|
|
"""
|
|
Extract knowledge from development conversations and discussions
|
|
"""
|
|
conversation_knowledge = {
|
|
'concepts_discussed': [],
|
|
'decisions_made': [],
|
|
'problems_identified': [],
|
|
'solutions_proposed': [],
|
|
'consensus_reached': [],
|
|
'action_items': []
|
|
}
|
|
|
|
for conversation in conversation_data:
|
|
# Extract key concepts using NLP
|
|
concepts = await self.conversation_extractor.extract_concepts(conversation)
|
|
|
|
# Identify decision points
|
|
decisions = await self.conversation_extractor.identify_decisions(conversation)
|
|
|
|
# Extract problems and solutions
|
|
problem_solution_pairs = await self.conversation_extractor.extract_problem_solutions(conversation)
|
|
|
|
# Identify consensus and disagreements
|
|
consensus_analysis = await self.conversation_extractor.analyze_consensus(conversation)
|
|
|
|
# Extract actionable items
|
|
action_items = await self.conversation_extractor.extract_action_items(conversation)
|
|
|
|
conversation_knowledge['concepts_discussed'].extend(concepts)
|
|
conversation_knowledge['decisions_made'].extend(decisions)
|
|
conversation_knowledge['problems_identified'].extend(problem_solution_pairs['problems'])
|
|
conversation_knowledge['solutions_proposed'].extend(problem_solution_pairs['solutions'])
|
|
conversation_knowledge['consensus_reached'].extend(consensus_analysis['consensus'])
|
|
conversation_knowledge['action_items'].extend(action_items)
|
|
|
|
return conversation_knowledge
|
|
|
|
async def construct_knowledge_relationships(self):
|
|
"""
|
|
Build sophisticated relationships between knowledge nodes
|
|
"""
|
|
relationship_types = [
|
|
'semantic_similarity',
|
|
'functional_dependency',
|
|
'temporal_sequence',
|
|
'causal_relationship',
|
|
'compositional_relationship',
|
|
'collaborative_relationship'
|
|
]
|
|
|
|
relationship_results = {}
|
|
|
|
for relationship_type in relationship_types:
|
|
if relationship_type == 'semantic_similarity':
|
|
relationships = await self.build_semantic_relationships()
|
|
elif relationship_type == 'functional_dependency':
|
|
relationships = await self.build_functional_dependencies()
|
|
elif relationship_type == 'temporal_sequence':
|
|
relationships = await self.build_temporal_relationships()
|
|
elif relationship_type == 'causal_relationship':
|
|
relationships = await self.build_causal_relationships()
|
|
elif relationship_type == 'compositional_relationship':
|
|
relationships = await self.build_compositional_relationships()
|
|
elif relationship_type == 'collaborative_relationship':
|
|
relationships = await self.build_collaborative_relationships()
|
|
|
|
relationship_results[relationship_type] = relationships
|
|
|
|
# Add relationships to graph
|
|
for relationship in relationships:
|
|
self.graph.add_edge(
|
|
relationship['source'],
|
|
relationship['target'],
|
|
relationship_type=relationship_type,
|
|
weight=relationship['strength'],
|
|
metadata=relationship['metadata']
|
|
)
|
|
|
|
return relationship_results
|
|
|
|
async def build_semantic_relationships(self):
|
|
"""
|
|
Build relationships based on semantic similarity
|
|
"""
|
|
semantic_relationships = []
|
|
|
|
# Get all nodes with textual content
|
|
text_nodes = [node for node, data in self.graph.nodes(data=True)
|
|
if 'text_content' in data]
|
|
|
|
# Generate embeddings for all text content
|
|
embeddings = {}
|
|
for node in text_nodes:
|
|
text_content = self.graph.nodes[node]['text_content']
|
|
embedding = await self.generate_text_embeddings(text_content)
|
|
embeddings[node] = embedding
|
|
|
|
# Calculate pairwise similarities
|
|
for i, node1 in enumerate(text_nodes):
|
|
for node2 in text_nodes[i+1:]:
|
|
similarity = cosine_similarity(
|
|
embeddings[node1].reshape(1, -1),
|
|
embeddings[node2].reshape(1, -1)
|
|
)[0][0]
|
|
|
|
if similarity > 0.7: # High similarity threshold
|
|
semantic_relationships.append({
|
|
'source': node1,
|
|
'target': node2,
|
|
'strength': similarity,
|
|
'metadata': {
|
|
'similarity_score': similarity,
|
|
'relationship_basis': 'semantic_content'
|
|
}
|
|
})
|
|
|
|
return semantic_relationships
|
|
|
|
async def generate_code_embeddings(self, code_content):
|
|
"""
|
|
Generate embeddings for code content using CodeBERT
|
|
"""
|
|
# Tokenize code
|
|
tokens = self.tokenizer(
|
|
code_content,
|
|
return_tensors="pt",
|
|
truncation=True,
|
|
max_length=512,
|
|
padding=True
|
|
)
|
|
|
|
# Generate embeddings
|
|
with torch.no_grad():
|
|
outputs = self.embedder(**tokens)
|
|
embeddings = outputs.last_hidden_state.mean(dim=1).squeeze()
|
|
|
|
return embeddings.numpy()
|
|
|
|
async def generate_text_embeddings(self, text_content):
|
|
"""
|
|
Generate embeddings for natural language text
|
|
"""
|
|
# Use TF-IDF for text embeddings (can be replaced with more advanced models)
|
|
tfidf_matrix = self.vectorizer.fit_transform([text_content])
|
|
return tfidf_matrix.toarray()[0]
|
|
```
|
|
|
|
#### Knowledge Quality Assessment
|
|
```python
|
|
class KnowledgeQualityAssessor:
|
|
"""
|
|
Assess and maintain quality of knowledge in the graph
|
|
"""
|
|
|
|
def __init__(self):
|
|
self.quality_metrics = {}
|
|
self.validation_rules = {}
|
|
self.quality_thresholds = {
|
|
'completeness': 0.8,
|
|
'consistency': 0.9,
|
|
'accuracy': 0.85,
|
|
'currency': 0.7,
|
|
'relevance': 0.75
|
|
}
|
|
|
|
async def assess_knowledge_quality(self, knowledge_graph):
|
|
"""
|
|
Comprehensive quality assessment of knowledge graph
|
|
"""
|
|
quality_assessment = {
|
|
'overall_score': 0.0,
|
|
'dimension_scores': {},
|
|
'quality_issues': [],
|
|
'improvement_recommendations': []
|
|
}
|
|
|
|
# Assess different quality dimensions
|
|
dimension_assessments = {}
|
|
|
|
# Completeness - how complete is the knowledge
|
|
completeness_score = await self.assess_completeness(knowledge_graph)
|
|
dimension_assessments['completeness'] = completeness_score
|
|
|
|
# Consistency - how consistent is the knowledge
|
|
consistency_score = await self.assess_consistency(knowledge_graph)
|
|
dimension_assessments['consistency'] = consistency_score
|
|
|
|
# Accuracy - how accurate is the knowledge
|
|
accuracy_score = await self.assess_accuracy(knowledge_graph)
|
|
dimension_assessments['accuracy'] = accuracy_score
|
|
|
|
# Currency - how up-to-date is the knowledge
|
|
currency_score = await self.assess_currency(knowledge_graph)
|
|
dimension_assessments['currency'] = currency_score
|
|
|
|
# Relevance - how relevant is the knowledge
|
|
relevance_score = await self.assess_relevance(knowledge_graph)
|
|
dimension_assessments['relevance'] = relevance_score
|
|
|
|
# Calculate overall quality score
|
|
overall_score = sum(dimension_assessments.values()) / len(dimension_assessments)
|
|
|
|
quality_assessment.update({
|
|
'overall_score': overall_score,
|
|
'dimension_scores': dimension_assessments,
|
|
'quality_issues': await self.identify_quality_issues(dimension_assessments),
|
|
'improvement_recommendations': await self.generate_improvement_recommendations(dimension_assessments)
|
|
})
|
|
|
|
return quality_assessment
|
|
|
|
async def assess_completeness(self, knowledge_graph):
|
|
"""
|
|
Assess how complete the knowledge representation is
|
|
"""
|
|
completeness_metrics = {
|
|
'node_coverage': 0.0,
|
|
'relationship_coverage': 0.0,
|
|
'domain_coverage': 0.0,
|
|
'temporal_coverage': 0.0
|
|
}
|
|
|
|
# Analyze node coverage
|
|
total_nodes = knowledge_graph.number_of_nodes()
|
|
nodes_with_complete_data = sum(1 for node, data in knowledge_graph.nodes(data=True)
|
|
if self.is_node_complete(data))
|
|
completeness_metrics['node_coverage'] = nodes_with_complete_data / total_nodes if total_nodes > 0 else 0
|
|
|
|
# Analyze relationship coverage
|
|
total_possible_relationships = total_nodes * (total_nodes - 1) # Directed graph
|
|
actual_relationships = knowledge_graph.number_of_edges()
|
|
completeness_metrics['relationship_coverage'] = min(actual_relationships / total_possible_relationships, 1.0) if total_possible_relationships > 0 else 0
|
|
|
|
# Analyze domain coverage
|
|
domains_represented = set(data.get('domain', 'unknown') for node, data in knowledge_graph.nodes(data=True))
|
|
expected_domains = {'code', 'architecture', 'business', 'process', 'team'}
|
|
completeness_metrics['domain_coverage'] = len(domains_represented.intersection(expected_domains)) / len(expected_domains)
|
|
|
|
# Analyze temporal coverage
|
|
nodes_with_timestamps = sum(1 for node, data in knowledge_graph.nodes(data=True)
|
|
if 'timestamp' in data)
|
|
completeness_metrics['temporal_coverage'] = nodes_with_timestamps / total_nodes if total_nodes > 0 else 0
|
|
|
|
return sum(completeness_metrics.values()) / len(completeness_metrics)
|
|
|
|
async def assess_consistency(self, knowledge_graph):
|
|
"""
|
|
Assess consistency of knowledge representation
|
|
"""
|
|
consistency_issues = []
|
|
|
|
# Check for conflicting information
|
|
conflicts = await self.detect_knowledge_conflicts(knowledge_graph)
|
|
consistency_issues.extend(conflicts)
|
|
|
|
# Check for naming inconsistencies
|
|
naming_issues = await self.detect_naming_inconsistencies(knowledge_graph)
|
|
consistency_issues.extend(naming_issues)
|
|
|
|
# Check for relationship inconsistencies
|
|
relationship_issues = await self.detect_relationship_inconsistencies(knowledge_graph)
|
|
consistency_issues.extend(relationship_issues)
|
|
|
|
# Calculate consistency score
|
|
total_nodes = knowledge_graph.number_of_nodes()
|
|
consistency_score = max(0, 1 - (len(consistency_issues) / total_nodes)) if total_nodes > 0 else 1
|
|
|
|
return consistency_score
|
|
```
|
|
|
|
#### Knowledge Curation Engine
|
|
```python
|
|
class KnowledgeCurationEngine:
|
|
"""
|
|
Automated knowledge curation and maintenance
|
|
"""
|
|
|
|
def __init__(self):
|
|
self.curation_rules = {}
|
|
self.quality_assessor = KnowledgeQualityAssessor()
|
|
self.update_scheduler = UpdateScheduler()
|
|
|
|
async def curate_knowledge_continuously(self, knowledge_graph):
|
|
"""
|
|
Continuously curate and improve knowledge quality
|
|
"""
|
|
curation_session = {
|
|
'session_id': generate_uuid(),
|
|
'curation_actions': [],
|
|
'quality_improvements': {},
|
|
'optimization_results': {}
|
|
}
|
|
|
|
# Identify curation opportunities
|
|
curation_opportunities = await self.identify_curation_opportunities(knowledge_graph)
|
|
|
|
# Execute curation actions
|
|
for opportunity in curation_opportunities:
|
|
curation_action = await self.execute_curation_action(
|
|
opportunity,
|
|
knowledge_graph
|
|
)
|
|
curation_session['curation_actions'].append(curation_action)
|
|
|
|
# Optimize knowledge structure
|
|
optimization_results = await self.optimize_knowledge_structure(knowledge_graph)
|
|
curation_session['optimization_results'] = optimization_results
|
|
|
|
# Assess quality improvements
|
|
quality_improvements = await self.assess_quality_improvements(knowledge_graph)
|
|
curation_session['quality_improvements'] = quality_improvements
|
|
|
|
return curation_session
|
|
|
|
async def identify_curation_opportunities(self, knowledge_graph):
|
|
"""
|
|
Identify opportunities for knowledge curation
|
|
"""
|
|
opportunities = []
|
|
|
|
# Identify duplicate or near-duplicate nodes
|
|
duplicates = await self.identify_duplicate_knowledge(knowledge_graph)
|
|
for duplicate_set in duplicates:
|
|
opportunities.append({
|
|
'type': 'merge_duplicates',
|
|
'nodes': duplicate_set,
|
|
'priority': 'high',
|
|
'expected_improvement': 'consistency'
|
|
})
|
|
|
|
# Identify orphaned nodes
|
|
orphaned_nodes = await self.identify_orphaned_nodes(knowledge_graph)
|
|
for node in orphaned_nodes:
|
|
opportunities.append({
|
|
'type': 'connect_orphaned',
|
|
'node': node,
|
|
'priority': 'medium',
|
|
'expected_improvement': 'completeness'
|
|
})
|
|
|
|
# Identify outdated knowledge
|
|
outdated_nodes = await self.identify_outdated_knowledge(knowledge_graph)
|
|
for node in outdated_nodes:
|
|
opportunities.append({
|
|
'type': 'update_outdated',
|
|
'node': node,
|
|
'priority': 'high',
|
|
'expected_improvement': 'currency'
|
|
})
|
|
|
|
# Identify missing relationships
|
|
missing_relationships = await self.identify_missing_relationships(knowledge_graph)
|
|
for relationship in missing_relationships:
|
|
opportunities.append({
|
|
'type': 'add_relationship',
|
|
'relationship': relationship,
|
|
'priority': 'medium',
|
|
'expected_improvement': 'completeness'
|
|
})
|
|
|
|
return sorted(opportunities, key=lambda x: self.priority_score(x['priority']), reverse=True)
|
|
|
|
async def execute_curation_action(self, opportunity, knowledge_graph):
|
|
"""
|
|
Execute a specific curation action
|
|
"""
|
|
action_result = {
|
|
'opportunity': opportunity,
|
|
'action_taken': '',
|
|
'success': False,
|
|
'impact': {}
|
|
}
|
|
|
|
try:
|
|
if opportunity['type'] == 'merge_duplicates':
|
|
result = await self.merge_duplicate_nodes(opportunity['nodes'], knowledge_graph)
|
|
action_result['action_taken'] = 'merged_duplicate_nodes'
|
|
action_result['impact'] = result
|
|
|
|
elif opportunity['type'] == 'connect_orphaned':
|
|
result = await self.connect_orphaned_node(opportunity['node'], knowledge_graph)
|
|
action_result['action_taken'] = 'connected_orphaned_node'
|
|
action_result['impact'] = result
|
|
|
|
elif opportunity['type'] == 'update_outdated':
|
|
result = await self.update_outdated_knowledge(opportunity['node'], knowledge_graph)
|
|
action_result['action_taken'] = 'updated_outdated_knowledge'
|
|
action_result['impact'] = result
|
|
|
|
elif opportunity['type'] == 'add_relationship':
|
|
result = await self.add_missing_relationship(opportunity['relationship'], knowledge_graph)
|
|
action_result['action_taken'] = 'added_missing_relationship'
|
|
action_result['impact'] = result
|
|
|
|
action_result['success'] = True
|
|
|
|
except Exception as e:
|
|
action_result['error'] = str(e)
|
|
action_result['success'] = False
|
|
|
|
return action_result
|
|
```
|
|
|
|
### Knowledge Management Commands
|
|
|
|
```bash
|
|
# Knowledge graph construction
|
|
bmad knowledge build --sources "codebase,conversations,documentation"
|
|
bmad knowledge extract --from-conversations --session-id "uuid"
|
|
bmad knowledge index --codebase-path "src/" --include-dependencies
|
|
|
|
# Knowledge graph querying and exploration
|
|
bmad knowledge search --semantic "authentication patterns"
|
|
bmad knowledge explore --concept "microservices" --depth 3
|
|
bmad knowledge relationships --between "UserAuth" "DatabaseConnection"
|
|
|
|
# Knowledge quality management
|
|
bmad knowledge assess --quality-dimensions "completeness,consistency,accuracy"
|
|
bmad knowledge curate --auto-fix --quality-threshold 0.8
|
|
bmad knowledge validate --check-conflicts --suggest-merges
|
|
|
|
# Knowledge graph optimization
|
|
bmad knowledge optimize --structure --remove-duplicates
|
|
bmad knowledge update --refresh-outdated --source "recent-conversations"
|
|
bmad knowledge export --format "graphml" --include-metadata
|
|
```
|
|
|
|
This Knowledge Graph Builder creates a sophisticated, multi-dimensional knowledge representation that captures not just information, but the complex relationships and contexts that make knowledge truly useful for development teams. The system continuously learns, curates, and optimizes the knowledge graph to maintain high quality and relevance. |