Your SIM-ONE Framework has a sophisticated RAG system that aligns perfectly with the Five Laws:
Current Components:
Current Limitations:
Instead of bloating the MVLM, create a dedicated Semantic Encoding Protocol (SEP) within your framework:
# New Protocol: code/mcp_server/protocols/sep/semantic_encoding_protocol.py
class SemanticEncodingProtocol(BaseProtocol):
"""
Lightweight transformer encoder for semantic embeddings.
Maintains SIM-ONE principles by keeping encoding separate from generation.
"""
def __init__(self):
# Use a small, efficient transformer encoder
# Options: sentence-transformers/all-MiniLM-L6-v2 (22MB)
# sentence-transformers/all-distilroberta-v1 (82MB)
self.encoder = self._load_lightweight_encoder()
self.cache = EmbeddingCache(max_size=10000)
def encode_text(self, text: str) -> np.ndarray:
"""Generate semantic embeddings while respecting energy stewardship."""
# Check cache first (Law 4: Energy Stewardship)
cache_key = hashlib.md5(text.encode()).hexdigest()
if cache_key in self.cache:
return self.cache[cache_key]
# Generate embedding
embedding = self.encoder.encode(text)
self.cache[cache_key] = embedding
return embedding
1. Technical Documentation Corpus
# code/mcp_server/rag_sources/technical_knowledge.py
TECHNICAL_SOURCES = {
'ai_research': {
'papers': 'arXiv AI/ML papers (2020-2024)',
'size': '~500K documents',
'update_frequency': 'weekly',
'embedding_model': 'scientific-text-encoder'
},
'software_engineering': {
'docs': 'Python, JavaScript, system design patterns',
'size': '~100K documents',
'relevance': 'High for protocol development'
}
}
2. Philosophical and Ethical Knowledge
PHILOSOPHICAL_SOURCES = {
'ethics_corpus': {
'content': 'Philosophical texts on AI ethics, governance',
'relevance': 'Critical for Law 3 (Truth Foundation)',
'sources': ['Stanford Encyclopedia', 'Ethics papers', 'Governance frameworks']
},
'cognitive_science': {
'content': 'Research on cognition, decision-making, reasoning',
'relevance': 'Supports cognitive governance principles'
}
}
3. Domain-Specific Knowledge Bases
DOMAIN_SOURCES = {
'business_intelligence': {
'content': 'Business processes, decision frameworks, strategy',
'size': '~200K documents',
'use_case': 'Enterprise protocol applications'
},
'scientific_knowledge': {
'content': 'Peer-reviewed research across disciplines',
'quality': 'High (peer-reviewed only)',
'relevance': 'Truth validation and fact-checking'
}
}
# code/mcp_server/rag_manager/enhanced_rag_manager.py
class EnhancedRAGManager:
"""
Multi-tier RAG system respecting SIM-ONE architectural principles.
"""
def __init__(self):
self.tiers = {
'memory': MemoryRAGTier(), # Personal/session memory
'knowledge': KnowledgeRAGTier(), # Curated knowledge bases
'web': WebRAGTier(), # Real-time web search
'contextual': ContextualRAGTier() # Cross-protocol context
}
self.semantic_encoder = SemanticEncodingProtocol()
self.governance = RAGGovernanceProtocol()
async def retrieve_context(self, query: str, context: Dict) -> str:
"""
Governed retrieval respecting Five Laws.
"""
# Law 2: Cognitive Governance - validate query
validated_query = await self.governance.validate_query(query, context)
# Law 1: Architectural Intelligence - coordinate tiers
results = await self._coordinate_retrieval_tiers(validated_query, context)
# Law 3: Truth Foundation - validate and rank results
validated_results = await self.governance.validate_results(results)
# Law 5: Deterministic Reliability - consistent formatting
return self._format_context(validated_results)
Memory Tier Enhancement:
class MemoryRAGTier:
"""Enhanced memory retrieval with semantic understanding."""
async def retrieve(self, query: str, context: Dict) -> List[Dict]:
# Use semantic encoder for better matching
query_embedding = self.semantic_encoder.encode_text(query)
# Enhanced scoring with semantic similarity
memories = await self.memory_manager.semantic_search(
query_embedding,
context,
scoring_factors={
'semantic_similarity': 0.4,
'emotional_salience': 0.2,
'rehearsal_count': 0.15,
'recency': 0.15,
'actor_relevance': 0.1
}
)
return memories
Knowledge Tier Implementation:
class KnowledgeRAGTier:
"""Curated knowledge base retrieval."""
def __init__(self):
self.knowledge_bases = {
'technical': TechnicalKnowledgeBase(),
'philosophical': PhilosophicalKnowledgeBase(),
'domain_specific': DomainKnowledgeBase()
}
async def retrieve(self, query: str, context: Dict) -> List[Dict]:
# Determine relevant knowledge bases
relevant_bases = self._select_knowledge_bases(query, context)
# Parallel retrieval with governance
results = await asyncio.gather(*[
kb.search(query, context) for kb in relevant_bases
])
return self._merge_and_rank_results(results)
# code/mcp_server/protocols/critic/enhanced_critic_rag.py
class CriticRAGEnhancer:
"""Specialized RAG for fact-checking and validation."""
async def fact_check_retrieval(self, claim: str) -> Dict:
# Retrieve from high-authority sources
sources = await self.rag_manager.retrieve_context(
claim,
context={
'priority_sources': ['peer_reviewed', 'authoritative'],
'fact_check_mode': True,
'confidence_threshold': 0.8
}
)
return {
'supporting_evidence': sources['supporting'],
'contradicting_evidence': sources['contradicting'],
'confidence_score': sources['confidence'],
'source_authority': sources['authority_scores']
}
# code/mcp_server/protocols/ideator/enhanced_ideator_rag.py
class IdeatorRAGEnhancer:
"""Creative and innovative context retrieval."""
async def creative_context_retrieval(self, topic: str) -> Dict:
# Retrieve diverse perspectives and creative examples
context = await self.rag_manager.retrieve_context(
topic,
context={
'diversity_mode': True,
'creative_sources': ['innovation_cases', 'cross_domain'],
'perspective_variety': True
}
)
return context
Law 1 (Architectural Intelligence):
Law 2 (Cognitive Governance):
Law 3 (Truth Foundation):
Law 4 (Energy Stewardship):
Law 5 (Deterministic Reliability):
This enhancement maintains your architectural purity while significantly improving RAG capabilities through framework-level improvements rather than model bloat.