🧠 SemantiCore

🚀 Transform unstructured data into structured semantic layers for LLMs, Agents, RAG systems, and Knowledge Graphs.

📖 Documentation • 🚀 Quick Start • 💡 Examples • 🤝 Community • 🔧 API Reference

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🌟 What is SemantiCore?

SemantiCore bridges the gap between raw unstructured data and intelligent AI systems by providing a comprehensive toolkit for semantic extraction, schema generation, and knowledge representation. Built for developers creating AI agents, RAG systems, and intelligent applications that need to understand meaning, not just text.

"The missing piece between your data and AI" - Transform messy, unstructured information into clean, schema-compliant semantic layers that power next-generation AI applications.

🎯 Why Choose SemantiCore?

Modern AI systems require structured, semantically rich data to perform effectively. SemantiCore solves the fundamental challenge of converting unstructured information into intelligent, actionable knowledge:

🤖 Intelligent Agents Type-safe, validated input/output schemas Multi-agent orchestration with semantic routing Real-time decision making capabilities	🔍 Enhanced RAG Systems Semantic chunking with context preservation Multi-modal embedding support Intelligent context compression
🕸️ Knowledge Graphs Automated entity and relationship extraction Temporal modeling and evolution tracking Multi-format export (Neo4j, RDF, Cypher)	🛠️ LLM Tool Integration Semantic contracts for reliable operation Context engineering and optimization Memory management systems

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✨ Core Capabilities

🧠 Advanced Semantic Processing

• Multi-layer Understanding: Lexical, syntactic, semantic, and pragmatic analysis
• Entity & Relationship Extraction: Named entities, relationships, and complex event detection
• Context Preservation: Maintain semantic context across document boundaries
• Domain Adaptation: Specialized processing for cybersecurity, finance, healthcare, research
• Temporal Analysis: Time-aware semantic understanding and event sequencing

🎯 LLM Optimization & Integration

• Context Engineering: Intelligent context compression and enhancement for LLMs
• Prompt Optimization: Semantic-aware prompt engineering and optimization
• Memory Management: Episodic, semantic, and procedural memory systems
• Multi-Model Support: OpenAI, Anthropic, Google Gemini, Hugging Face, local models
• Token Efficiency: Smart token usage optimization for cost reduction

🕸️ Knowledge Graph Construction

• Automated Construction: Build knowledge graphs from unstructured data
• Graph Databases: Neo4j, KuzuDB, ArangoDB, Amazon Neptune integration
• Semantic Reasoning: Inductive, deductive, and abductive reasoning capabilities
• Temporal Modeling: Time-aware relationships and evolution tracking
• Graph Analytics: Centrality analysis, community detection, path finding

📊 Vector & Embedding Excellence

• Contextual Embeddings: Semantic embeddings with preserved context
• Vector Stores: Pinecone, Milvus, Weaviate, Chroma, FAISS integration
• Hybrid Search: Combine semantic and keyword search strategies
• Embedding Models: OpenAI, Cohere, Sentence Transformers, custom models
• Semantic Similarity: Advanced similarity metrics and clustering

🔗 Ontology & Schema Generation

• Automated Ontology Creation: Generate OWL/RDF ontologies from data
• Schema Evolution: Dynamic schema adaptation and versioning
• Standard Compliance: Schema.org, FIBO, domain-specific ontologies
• Multi-format Export: OWL, RDF, JSON-LD, Turtle formats
• Type Safety: Strong typing with Pydantic models

🤖 Agent Integration & Orchestration

• Semantic Routing: Intelligent request routing based on semantic understanding
• Agent Orchestration: Coordinate multiple AI agents with shared semantic context
• Framework Integration: LangChain, LlamaIndex, CrewAI, AutoGen compatibility
• Real-time Processing: Stream processing for live data semantic analysis
• Multi-Agent Systems: Collaborative AI agent ecosystems

🏗️ Enterprise-Ready Features

• Scalability: Horizontal scaling with distributed processing
• Security: End-to-end encryption and privacy protection
• Monitoring: Comprehensive observability and metrics
• Compliance: SOC2, GDPR, HIPAA compliance support
• High Availability: 99.9% uptime SLA with redundancy

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🚀 Quick Start

📦 Installation Options

🐍 Python Installation

# Basic installation
pip install semanticore

# Full installation with all dependencies
pip install "semanticore[all]"

# Specific integrations
pip install "semanticore[openai,neo4j,pinecone]"

# Development installation
git clone https://github.com/yourusername/semanticore.git
cd semanticore
pip install -e ".[dev]"

🐳 Docker Installation

# Pull and run SemantiCore
docker run -p 8000:8000 semanticore/semanticore:latest

# With custom configuration
docker run -v ./config:/app/config semanticore/semanticore:latest

# Docker Compose for full stack
curl -O https://raw.githubusercontent.com/semanticore/semanticore/main/docker-compose.yml
docker-compose up -d

☸️ Kubernetes Deployment

# Deploy to Kubernetes
kubectl apply -f https://raw.githubusercontent.com/semanticore/semanticore/main/k8s/

# Helm installation
helm repo add semanticore https://charts.semanticore.io
helm install my-semanticore semanticore/semanticore

⚡ 30-Second Demo

from semanticore import SemantiCore

# Initialize with your preferred providers
core = SemantiCore(
    llm_provider="openai",
    embedding_model="text-embedding-3-large",
    vector_store="pinecone",
    graph_db="neo4j"
)

# Transform unstructured text into semantic knowledge
text = """
Tesla reported Q4 2024 earnings with $25.2B revenue, a 15% increase year-over-year.
CEO Elon Musk highlighted the success of the Model Y and expansion in the Chinese market.
The company plans to launch three new models in 2025, including the long-awaited Cybertruck.
"""

# Extract semantic information
result = core.extract_semantics(text)

print("Entities:", result.entities)
# [Entity(name="Tesla", type="ORGANIZATION"), Entity(name="Elon Musk", type="PERSON")]

print("Relationships:", result.relationships) 
# [Relation(subject="Tesla", predicate="reported", object="Q4 2024 earnings")]

print("Events:", result.events)
# [Event(type="EARNINGS_REPORT", date="Q4 2024", amount="$25.2B")]

# Generate knowledge graph
knowledge_graph = core.build_knowledge_graph(text)
print("Graph nodes:", len(knowledge_graph.nodes))
print("Graph edges:", len(knowledge_graph.edges))

🎬 Interactive Demo

from semanticore import SemantiCore

# Initialize the core engine
core = SemantiCore()

# Extract semantic information from text
text = """
OpenAI released GPT-4 in March 2023, which significantly improved 
reasoning capabilities over GPT-3.5. The model was trained using 
reinforcement learning from human feedback (RLHF).
"""

# One-line semantic extraction
result = core.extract(text)

print(result.entities)     # [Entity(name="OpenAI", type="ORGANIZATION"), ...]
print(result.relations)    # [Relation(subject="OpenAI", predicate="released", object="GPT-4"), ...]
print(result.schema)       # Auto-generated Pydantic schema
print(result.metadata)     # Enriched contextual information

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🧩 Core Features Deep Dive

🧠 Semantic Extraction Engine

Advanced NLP pipeline that extracts meaningful structure from unstructured data:

from semanticore.extract import EntityExtractor, RelationExtractor, TopicClassifier

# Named Entity Recognition with custom models
extractor = EntityExtractor(
    model="en_core_web_trf",  # spaCy model
    custom_labels=["MALWARE", "THREAT_ACTOR", "VULNERABILITY"]
)

entities = extractor.extract("APT29 used FrostBite malware against critical infrastructure")

# Relation and Triple Extraction
rel_extractor = RelationExtractor(llm_provider="openai")
relations = rel_extractor.extract_relations(text, entities)

# Topic Classification and Categorization
classifier = TopicClassifier()
topics = classifier.classify(text, categories=["cybersecurity", "technology", "politics"])

🧱 Dynamic Schema Generation

Automatically generate type-safe schemas from extracted data:

from semanticore.schema import SchemaGenerator, validate_data

# Generate Pydantic models from extracted entities
generator = SchemaGenerator()
schema = generator.from_entities(entities)

# Export to various formats
schema.to_pydantic()    # Python Pydantic model
schema.to_json_schema() # JSON Schema
schema.to_yaml()        # YAML Schema
schema.to_typescript()  # TypeScript interfaces

# Validate new data against generated schema
is_valid = validate_data(new_data, schema)

🔌 Universal Connectors

Seamlessly connect to any data source:

from semanticore.connectors import FileConnector, WebConnector, APIConnector

# File processing (PDF, DOCX, CSV, JSON, Markdown)
file_conn = FileConnector()
documents = file_conn.load("./documents/*.pdf")
semantic_docs = core.process_documents(documents)

# Web scraping and RSS feeds
web_conn = WebConnector()
pages = web_conn.scrape_urls(["https://example.com/news"])
web_semantics = core.extract_from_web(pages)

# REST API integration
api_conn = APIConnector(base_url="https://api.example.com")
api_data = api_conn.fetch("/endpoints")
structured_data = core.structure_api_response(api_data)

🧪 Validation & Quality Assurance

Ensure data quality and consistency across your pipeline:

from semanticore.validation import SchemaValidator, ConsistencyChecker, QualityMetrics

# Schema validation
validator = SchemaValidator(schema)
validation_result = validator.validate(data)

if not validation_result.is_valid:
    print(f"Validation errors: {validation_result.errors}")

# Consistency checking across multiple extractions
checker = ConsistencyChecker()
consistency_score = checker.check_consistency([result1, result2, result3])

# Quality metrics and confidence scoring
metrics = QualityMetrics()
quality_report = metrics.assess(extraction_result)
print(f"Extraction confidence: {quality_report.confidence}")

📐 Intelligent Chunking & Embedding

RAG-optimized document processing with semantic awareness:

from semanticore.vectorizer import SemanticChunker, EmbeddingEngine

# Semantic-aware chunking
chunker = SemanticChunker(
    chunk_size=512,
    overlap=50,
    respect_boundaries=True,  # Don't split entities/relations
    add_metadata=True
)

chunks = chunker.chunk_document(document, semantic_info=result)

# Multi-modal embedding support
embedder = EmbeddingEngine(
    provider="sentence-transformers",  # or "openai", "huggingface"
    model="all-MiniLM-L6-v2"
)

embedded_chunks = embedder.embed_chunks(chunks)

# Direct vector database integration
from semanticore.vector_stores import FAISSStore, PineconeStore

store = FAISSStore()
store.add_embeddings(embedded_chunks)

📚 Knowledge Graph Export

Transform extracted semantics into graph databases:

from semanticore.kg import Neo4jExporter, RDFExporter, KuzuExporter

# Neo4j export with Cypher generation
neo4j_exporter = Neo4jExporter(
    uri="bolt://localhost:7687",
    user="neo4j",
    password="password"
)

# Create nodes and relationships
neo4j_exporter.export_entities(entities)
neo4j_exporter.export_relations(relations)

# RDF triple export
rdf_exporter = RDFExporter(format="turtle")
triples = rdf_exporter.to_triples(entities, relations)

# Query the generated knowledge graph
from semanticore.kg.query import GraphQuerier

querier = GraphQuerier(neo4j_exporter)
results = querier.cypher("MATCH (n:ORGANIZATION)-[r:RELEASED]->(m:PRODUCT) RETURN n, r, m")

📡 Semantic Routing

Intelligently route queries and tasks to appropriate handlers:

from semanticore.routing import SemanticRouter, IntentClassifier

# Set up routing rules
router = SemanticRouter()

# Intent-based routing
router.add_intent_route("question_answering", qa_agent)
router.add_intent_route("data_extraction", extraction_pipeline)
router.add_intent_route("summarization", summary_agent)

# Keyword and pattern-based routing
router.add_keyword_route(["threat", "malware", "vulnerability"], security_agent)
router.add_pattern_route(r"CVE-\d{4}-\d+", vulnerability_lookup)

# LLM-powered semantic routing
router.add_semantic_route(
    description="Handle complex analytical queries about financial data",
    handler=financial_analysis_agent,
    examples=["What's the trend in quarterly revenue?", "Analyze the risk factors"]
)

# Route incoming requests
query = "What are the latest cybersecurity threats targeting healthcare?"
handler = router.route(query)
response = handler.process(query)

---

🔧 Integration Examples

🤖 LLM Provider Integration

from semanticore.llm import LLMProvider

# OpenAI Integration
openai_provider = LLMProvider(
    provider="openai",
    model="gpt-4-turbo",
    api_key="your-openai-key"
)

# Anthropic Integration
anthropic_provider = LLMProvider(
    provider="anthropic", 
    model="claude-3-opus-20240229",
    api_key="your-anthropic-key"
)

# Google Gemini Integration
gemini_provider = LLMProvider(
    provider="google",
    model="gemini-pro",
    api_key="your-google-key"
)

# Hugging Face Integration
hf_provider = LLMProvider(
    provider="huggingface",
    model="mistralai/Mistral-7B-Instruct-v0.1",
    api_key="your-hf-key"
)

# Local Model Integration
local_provider = LLMProvider(
    provider="local",
    model_path="/path/to/model",
    device="cuda"
)

# Use with SemantiCore
core = SemantiCore(llm_provider=openai_provider)

🕸️ Knowledge Graph Database Integration

from semanticore.graph import GraphDatabase

# Neo4j Integration
neo4j_db = GraphDatabase(
    provider="neo4j",
    uri="bolt://localhost:7687",
    username="neo4j",
    password="password"
)

# KuzuDB Integration (Embedded Graph Database)
kuzu_db = GraphDatabase(
    provider="kuzu",
    database_path="/path/to/kuzu/db"
)

# ArangoDB Integration
arango_db = GraphDatabase(
    provider="arangodb",
    host="localhost",
    port=8529,
    username="root",
    password="password"
)

# Amazon Neptune Integration
neptune_db = GraphDatabase(
    provider="neptune",
    endpoint="your-neptune-endpoint.amazonaws.com",
    port=8182,
    region="us-east-1"
)

# Build knowledge graph
from semanticore import SemantiCore

core = SemantiCore(graph_db=neo4j_db)
documents = ["doc1.txt", "doc2.txt", "doc3.txt"]

# Automatically extract entities and relationships, build graph
knowledge_graph = core.build_knowledge_graph_from_documents(documents)
print(f"Created graph with {knowledge_graph.node_count} nodes and {knowledge_graph.edge_count} edges")

📊 Vector Store Integration

from semanticore.vector import VectorStore

# Pinecone Integration
pinecone_store = VectorStore(
    provider="pinecone",
    api_key="your-pinecone-key",
    environment="us-west1-gcp",
    index_name="semanticore-index"
)

# Milvus Integration
milvus_store = VectorStore(
    provider="milvus",
    host="localhost",
    port=19530,
    collection_name="semantic_embeddings"
)

# Weaviate Integration
weaviate_store = VectorStore(
    provider="weaviate",
    url="http://localhost:8080",
    class_name="SemanticChunk"
)

# Chroma Integration
chroma_store = VectorStore(
    provider="chroma",
    persist_directory="/path/to/chroma/db",
    collection_name="documents"
)

# FAISS Integration (Local)
faiss_store = VectorStore(
    provider="faiss",
    index_path="/path/to/faiss/index",
    dimension=1536
)

# Use with SemantiCore for RAG
core = SemantiCore(
    vector_store=pinecone_store,
    embedding_model="text-embedding-3-large"
)

# Semantic chunking and embedding
chunks = core.semantic_chunk_documents(documents)
embeddings = core.embed_chunks(chunks)
vector_store.store_embeddings(chunks, embeddings)

# Semantic search
query = "What are the latest AI developments?"
results = core.semantic_search(query, top_k=5)

🔗 Framework Integration

# LangChain Integration
from semanticore.integrations.langchain import SemanticChain
from langchain.chains import ConversationalRetrievalChain

semantic_chain = SemanticChain(
    semanticore_instance=core,
    retriever_type="semantic",
    context_engineering=True
)

langchain_chain = ConversationalRetrievalChain(
    retriever=semantic_chain.as_retriever(),
    memory=semantic_chain.get_memory(),
    return_source_documents=True
)

# LlamaIndex Integration
from semanticore.integrations.llamaindex import SemanticIndex
from llama_index import VectorStoreIndex

semantic_index = SemanticIndex(
    semanticore_instance=core,
    enable_semantic_routing=True
)

llama_index = VectorStoreIndex.from_vector_store(
    semantic_index.get_vector_store()
)

# CrewAI Integration
from semanticore.integrations.crewai import SemanticCrew
from crewai import Agent, Task, Crew

# Create semantic-aware agents
researcher = Agent(
    role='Research Analyst',
    goal='Analyze semantic patterns in data',
    backstory='Expert in semantic data analysis',
    semantic_memory=core.get_semantic_memory()
)

writer = Agent(
    role='Content Writer',
    goal='Create semantic-rich content',
    backstory='Specialist in semantic content creation',
    semantic_memory=core.get_semantic_memory()
)

# Create semantic crew
semantic_crew = SemanticCrew(
    agents=[researcher, writer],
    semantic_coordination=True,
    knowledge_sharing=True
)

---

🎯 Use Cases & Examples

🔐 Cybersecurity Threat Intelligence

from semanticore.domains.cyber import ThreatIntelExtractor

# Specialized cybersecurity extraction
threat_extractor = ThreatIntelExtractor()
threat_report = """
APT29 (Cozy Bear) launched a sophisticated spear-phishing campaign 
targeting US government agencies using a previously unknown malware 
variant called FrostBite. The attack exploited CVE-2024-1234 in 
Microsoft Exchange servers.
"""

intel = threat_extractor.extract(threat_report)
print(intel.threat_actors)    # ["APT29", "Cozy Bear"]
print(intel.malware)          # ["FrostBite"]
print(intel.vulnerabilities)  # ["CVE-2024-1234"]
print(intel.attack_patterns)  # ["spear-phishing", "server exploitation"]

# Export to STIX format for threat intelligence platforms
stix_bundle = intel.to_stix()

🧬 Biomedical Research Assistant

from semanticore.domains.biomedical import BiomedicalExtractor

bio_extractor = BiomedicalExtractor()
research_text = """
The study investigated the efficacy of remdesivir in treating COVID-19 
patients. Results showed a 31% reduction in recovery time compared to 
placebo (p<0.001). Side effects included nausea in 12% of patients.
"""

bio_data = bio_extractor.extract(research_text)
print(bio_data.drugs)         # ["remdesivir"]
print(bio_data.conditions)    # ["COVID-19"]
print(bio_data.outcomes)      # ["31% reduction in recovery time"]
print(bio_data.side_effects)  # ["nausea"]

# Generate structured clinical data
clinical_schema = bio_data.to_clinical_schema()

📊 Financial Document Analysis

from semanticore.domains.finance import FinancialExtractor

fin_extractor = FinancialExtractor()
earnings_report = """
Q4 2024 revenue increased 15% YoY to $2.3B, driven by strong performance 
in the cloud computing segment. Operating margin improved to 23.5% from 
21.2% in the prior year. The company announced a $1B share buyback program.
"""

financial_data = fin_extractor.extract(earnings_report)
print(financial_data.metrics)     # {"revenue": "$2.3B", "margin": "23.5%"}
print(financial_data.periods)     # ["Q4 2024"]
print(financial_data.events)      # ["$1B share buyback program"]

# Export to financial analysis tools
financial_json = financial_data.to_standardized_json()

---

🎯 Advanced Features

🧠 Multi-Domain Semantic Processing

from semanticore.domains import CybersecurityProcessor, FinanceProcessor, HealthcareProcessor

# Cybersecurity semantic processing
cyber_processor = CybersecurityProcessor(
    threat_intelligence_feeds=["misp", "stix"],
    ontology="cybersecurity.owl",
    enable_threat_hunting=True
)

# Process security incidents
incident_report = """
APT29 exploited CVE-2024-1234 in Microsoft Exchange to deploy Cobalt Strike.
The attack used spear-phishing emails with malicious attachments.
"""

cyber_analysis = cyber_processor.analyze(incident_report)
print("Threat Actors:", cyber_analysis.threat_actors)
print("Vulnerabilities:", cyber_analysis.vulnerabilities)
print("Attack Techniques:", cyber_analysis.mitre_techniques)

# Financial semantic processing
finance_processor = FinanceProcessor(
    market_data_sources=["yahoo", "alpha_vantage"],
    ontology="finance.owl",
    enable_sentiment_analysis=True
)

# Healthcare semantic processing
health_processor = HealthcareProcessor(
    medical_ontologies=["snomed", "icd10"],
    enable_drug_interaction_detection=True
)

🎯 Context Engineering for RAG

from semanticore.context import ContextEngineer

# Advanced context engineering
context_engineer = ContextEngineer(
    max_context_length=128000,
    compression_strategy="semantic_preservation",
    relevance_scoring=True
)

# Optimize context for specific queries
query = "How can we improve cloud security against APT attacks?"
documents = load_security_documents()

# Intelligent context compression
optimized_context = context_engineer.optimize_context(
    query=query,
    documents=documents,
    preserve_entities=True,
    maintain_relationships=True,
    compression_ratio=0.3  # 70% reduction while preserving meaning
)

print(f"Context compressed from {len(documents)} to {len(optimized_context)} tokens")
print(f"Semantic preservation: {context_engineer.preservation_score:.2%}")

🔄 Real-time Semantic Processing

from semanticore.streaming import SemanticStreamProcessor

# Real-time semantic processing
stream_processor = SemanticStreamProcessor(
    input_streams=["kafka://events", "websocket://feeds"],
    processing_pipeline=[
        "entity_extraction",
        "relationship_detection", 
        "ontology_mapping",
        "knowledge_graph_update"
    ],
    batch_size=100,
    processing_interval="5s"
)

# Process streaming data
async for semantic_event in stream_processor.process():
    if semantic_event.confidence > 0.8:
        # Update knowledge graph
        core.update_knowledge_graph(semantic_event)
        
        # Trigger alerts if needed
        if semantic_event.importance == "critical":
            await alert_system.send_alert(semantic_event)

🔀 Semantic Routing & Orchestration

from semanticore.routing import SemanticRouter

# Multi-dimensional semantic routing
router = SemanticRouter(
    routing_dimensions=["intent", "domain", "complexity", "urgency"],
    agents={
        "security_analyst": SecurityAgent(),
        "data_scientist": DataScienceAgent(),
        "business_analyst": BusinessAgent()
    }
)

# Route queries to appropriate agents
query = "Analyze the security implications of our latest data breach"
routed_agent = router.route_query(query)
response = routed_agent.process(query)

---

🔧 Advanced Configuration

🎛️ Custom Model Integration

from semanticore.models import CustomLLMProvider

# Integrate your own models
class MyCustomLLM(CustomLLMProvider):
    def __init__(self, model_path):
        self.model = load_model(model_path)
    
    def extract_entities(self, text):
        return self.model.predict(text)

# Use custom model in SemantiCore
core = SemantiCore(llm_provider=MyCustomLLM("./my_model"))

🏗️ Pipeline Customization

from semanticore.pipeline import Pipeline, Step

# Build custom processing pipeline
pipeline = Pipeline([
    Step("preprocess", text_cleaner),
    Step("extract_entities", entity_extractor),
    Step("extract_relations", relation_extractor),
    Step("enrich_metadata", metadata_enricher),
    Step("validate", schema_validator),
    Step("export", knowledge_graph_exporter)
])

# Process data through pipeline
results = pipeline.run(input_data)

⚙️ Configuration Management

# semanticore.yaml
extractors:
  entity:
    model: "en_core_web_trf"
    confidence_threshold: 0.8
  relation:
    llm_provider: "openai"
    model: "gpt-4"
    
schema:
  auto_generate: true
  validation_level: "strict"
  
export:
  formats: ["json", "rdf", "cypher"]
  knowledge_graph:
    provider: "neo4j"
    batch_size: 1000

# Load configuration
from semanticore.config import load_config
config = load_config("semanticore.yaml")
core = SemantiCore(config=config)

---

🏗️ Architecture

SemantiCore follows a modular, cloud-native architecture designed for scalability and extensibility:

graph TB
    subgraph "🌐 API Gateway"
        A[REST API] --> B[GraphQL API]
        B --> C[WebSocket API]
    end
    
    subgraph "🧠 Core Engine"
        D[Semantic Processor] --> E[Schema Generator]
        E --> F[Validator]
        F --> G[Router]
    end
    
    subgraph "🔌 Connectors"
        H[File Connector]
        I[Web Connector]
        J[API Connector]
        K[Database Connector]
    end
    
    subgraph "🔍 Extractors"
        L[NER Extractor]
        M[Relation Extractor]
        N[Topic Classifier]
        O[LLM Extractor]
    end
    
    subgraph "📊 Storage Layer"
        P[Vector Stores]
        Q[Graph Databases]
        R[Document Stores]
        S[Cache Layer]
    end
    
    subgraph "🚀 Export Layer"
        T[Knowledge Graphs]
        U[Embeddings]
        V[Schemas]
        W[APIs]
    end
    
    A --> D
    D --> L
    D --> M
    D --> N
    D --> O
    
    H --> D
    I --> D
    J --> D
    K --> D
    
    G --> P
    G --> Q
    G --> R
    G --> S
    
    F --> T
    F --> U
    F --> V
    F --> W

---