Dynamic Persona MoE RAG - Building a Sovereign Synthetic Intelligence System
Dynamic Persona MoE RAG - Building a Sovereign Synthetic Intelligence System
Date: January 25, 2026
Author: Daniel Kliewer
Introduction
In an era where artificial intelligence is increasingly centralized in the hands of a few tech giants, the need for sovereign, local-first AI systems has never been more critical. This blog post explores the implementation of a Dynamic Persona Mixture-of-Experts Retrieval-Augmented Generation (MoE RAG) system - a sophisticated architecture that transforms large, heterogeneous corpuses into grounded, attributable, and conversationally explorable intelligence while maintaining complete data sovereignty.
This system represents a paradigm shift from traditional "Artificial Intelligence" - which implies a hollow imitation of human cognition - toward Synthetic Intelligence: an engineered, deterministic, and human-constrained system designed for high-integrity knowledge synthesis.
The Problem with Current AI Systems
Before diving into the solution, let's examine the fundamental issues with current AI approaches:
1. Centralization and Surveillance
Most AI systems rely on cloud-based infrastructure, exposing sensitive data to third-party surveillance and creating single points of failure. For sectors like healthcare, legal, and defense, this is unacceptable.
2. Hallucination and Unaccountability
Current RAG systems are fundamentally limited by their reliance on opaque cloud infrastructure, static model weights, and probabilistic generation that prone to hallucination. When an AI "hallucinates," it's not a bug - it's an architectural failure.
3. Lack of Determinism
Traditional systems produce different outputs for identical inputs, making them unsuitable for high-integrity environments where reproducibility is paramount.
4. Static Personas
Most systems treat "personas" as static text prompts, failing to capture the dynamic, evolving nature of human expertise and perspective.
The Solution: Dynamic Persona MoE RAG
Our system addresses these challenges through a sophisticated architecture that separates Intelligence (the LLM) from Identity (the Persona Lens). This separation enables air-gapped security, deterministic reasoning, and the creation of evolving, autonomous personas that adapt to new information through explicit heuristic feedback loops.
System Architecture Overview
┌─────────────────┐ ┌──────────────────┐ ┌─────────────────┐
│ Input Query │───▶│ Entity Constructor│───▶│ Dynamic Graph │
└─────────────────┘ └──────────────────┘ └─────────────────┘
│ │
▼ ▼
┌─────────────────┐ ┌──────────────────┐ ┌─────────────────┐
│ Persona Store │◀───│ MoE Orchestrator │◀───│ Graph Traversal │
└─────────────────┘ └──────────────────┘ └─────────────────┘
│ │
▼ ▼
┌─────────────────┐ ┌──────────────────┐ ┌─────────────────┐
│ Ollama LLM │◀───│ Evaluation & │◀───│ Graph Snapshots │
│ (Local) │ │ Scoring │ │ & Persistence │
└─────────────────┘ └──────────────────┘ └─────────────────┘
Core Components
1. Entity Constructor Agent
The Entity Constructor Agent serves as the system's eyes and ears, extracting meaningful entities and relationships from input text. This component implements both sophisticated NLP techniques (using spaCy when available) and robust fallback mechanisms using regex patterns.
Pythonclass EntityConstructorAgent: def extract_entities(self, text: str) -> Dict[str, List[str]]: """Extract entities from input text.""" entities = defaultdict(list) # Use spaCy if available if self.nlp: doc = self.nlp(text) for ent in doc.ents: entity_type = ent.label_.lower() entity_text = ent.text.strip() if entity_text and len(entity_text) > 1: entities[entity_type].append(entity_text) # Fallback to regex-based extraction entities.update(self._extract_with_regex(text)) return dict(entities)
The agent extracts various entity types including:
- Named Entities: People, organizations, locations
- Technical Entities: Dates, numbers, percentages
- Communication Entities: Emails, URLs, phone numbers
- Conceptual Entities: Key phrases and proper nouns
2. Dynamic Knowledge Graph
Unlike traditional vector stores that flatten semantic relationships, our Dynamic Knowledge Graph represents knowledge as explicit, traversable relationships between entities. Built using NetworkX, this graph is constructed on-demand for each query, ensuring relevance and preventing state pollution.
Pythonclass DynamicKnowledgeGraph: def __init__(self): self.graph = nx.DiGraph() # Use NetworkX for robust graph operations self.nodes = {} # Cache for Node objects self.edges = [] # Cache for Edge objects self.query_context = None self._is_active = False def add_node(self, node_id: str, node_data: Dict[str, Any]) -> Node: """Lazily construct a node when needed.""" if node_id in self.nodes: return self.nodes[node_id] # Create NetworkX node with metadata node_attributes = { 'id': node_id, 'data': node_data, 'timestamp': self._get_timestamp(), 'query_id': self.query_context['query_id'] } self.graph.add_node(node_id, **node_attributes) # Create and cache Node object node = Node(node_id, node_data) self.nodes[node_id] = node return node
The graph supports sophisticated operations including:
- Pathfinding: Shortest path algorithms for logical reasoning
- Centrality Analysis: Identifying key entities in the knowledge network
- Subgraph Extraction: Focusing on specific domains of knowledge
- Relationship Traversal: Following semantic connections between concepts
3. Persona Store
The Persona Store manages the lifecycle of digital personas - the system's "experts" that provide diverse perspectives on queries. Personas are stored as validated JSON files with strict schemas ensuring consistency and reliability.
JSON{ "persona_id": "analytical_thinker", "name": "Analytical Thinker", "description": "A methodical and detail-oriented analyst who focuses on logical reasoning and evidence-based conclusions.", "traits": { "analytical_rigor": 0.9, "evidence_based": 0.8, "skepticism": 0.7, "objectivity": 0.8, "thoroughness": 0.9 }, "expertise": ["data_analysis", "research", "problem_solving", "critical_thinking"], "activation_cost": 0.3, "historical_performance": { "total_queries": 0, "average_score": 0.0, "last_used": null, "success_rate": 0.0 }, "metadata": { "created_at": "2026-01-25T10:00:00Z", "updated_at": "2026-01-25T10:00:00Z", "version": "1.0", "status": "active" } }
Personas progress through a sophisticated lifecycle:
- Experimental: Newly created or modified personas being tested
- Active: Proven performers participating in inference
- Stable: Reliable performers, quick to activate
- Pruned: Underperforming personas, archived for potential recovery
4. MoE Orchestrator
The MoE Orchestrator serves as the system's conductor, coordinating the complex interplay between personas, graphs, and evaluation. It implements the core Mixture-of-Experts algorithm with three distinct phases:
Phase 1: Expansion
The orchestrator activates relevant personas and has them traverse the knowledge graph to generate diverse perspectives on the query.
Pythondef expansion_phase(self, query: str, entities: Dict[str, Any]) -> List[Dict[str, Any]]: """Expansion phase: Generate diverse outputs from active personas.""" if not self.active_personas: return [] # Create dynamic knowledge graph for this query self.graph = DynamicKnowledgeGraph() self.current_query_id = f"query_{int(time.time())}" self.graph.start_query(self.current_query_id, query) # Build graph from entities self._build_graph_from_entities(entities) # Generate outputs from each persona persona_outputs = [] for persona in self.active_personas: try: output = self._generate_persona_output(persona, query, entities) persona_outputs.append({ 'persona_id': persona['persona_id'], 'output': output, 'timestamp': time.time() }) except Exception as e: self.logger.error(f"Failed to generate output for persona {persona['persona_id']}: {e}") self.outputs = persona_outputs return persona_outputs
Phase 2: Evaluation
Each persona's output is rigorously evaluated using multiple criteria:
- Relevance: How well the output addresses the query
- Consistency: Alignment with reference outputs and established knowledge
- Novelty: Contribution of new insights or perspectives
- Grounding: Connection to provided entities and factual accuracy
Phase 3: Pruning
Based on performance metrics, the system automatically manages the persona population:
- Promotion: High-performing experimental personas become active
- Demotion: Underperforming active personas move to stable status
- Pruning: Consistently poor performers are archived
- Activation: Stable personas are reactivated when needed
5. Persona Traversal System
The Persona Traversal System implements different cognitive strategies that personas use to navigate the knowledge graph:
Analytical Traversal
Focuses on logical connections and evidence-based reasoning:
Pythonclass AnalyticalTraversal(PersonaTraversalInterface): def evaluate_node_relevance(self, persona: Dict[str, Any], node: Node) -> float: analytical_rigor = persona.get('traits', {}).get('analytical_rigor', 0.5) evidence_weight = persona.get('traits', {}).get('evidence_based', 0.5) node_relevance = node.data.get('relevance_score', 0.5) weighted_relevance = ( node_relevance * analytical_rigor * 0.6 + evidence_weight * 0.4 ) return min(max(weighted_relevance, 0.0), 1.0)
Creative Traversal
Emphasizes novel connections and lateral thinking:
Pythonclass CreativeTraversal(PersonaTraversalInterface): def decide_traversal(self, current_node: Node, available_nodes: List[Node], persona: Dict[str, Any]) -> List[Node]: # Add randomness for creative exploration import random creative_boost = random.uniform(0, 0.3) * persona.get('traits', {}).get('creativity', 0.5) # Return more candidates for creative exploration return [node for node, score in node_scores[:5]]
Pragmatic Traversal
Prioritizes efficiency and practical outcomes:
Pythonclass PragmaticTraversal(PersonaTraversalInterface): def evaluate_node_relevance(self, persona: Dict[str, Any], node: Node) -> float: practicality = persona.get('traits', {}).get('practicality', 0.5) efficiency = persona.get('traits', {}).get('efficiency', 0.5) utility_score = node.data.get('utility_score', 0.5) weighted_relevance = ( utility_score * practicality * 0.7 + efficiency * 0.3 ) return min(max(weighted_relevance, 0.0), 1.0)
6. Evaluation and Scoring Framework
The Evaluation Framework implements sophisticated multi-criteria scoring that goes beyond simple similarity metrics:
Relevance Scoring
Uses TF-IDF cosine similarity with non-linear transformations to emphasize high similarity:
Pythondef score_relevance(self, output: str, query_id: str) -> float: documents = [query_text, output] tfidf_matrix = self.vectorizer.fit_transform(documents) similarity = cosine_similarity(tfidf_matrix[0:1], tfidf_matrix[1:2])[0][0] # Apply non-linear transformation to emphasize high similarity relevance_score = math.tanh(similarity * 3.0) return max(0.0, min(1.0, relevance_score))
Consistency Scoring
Measures alignment with reference outputs while penalizing high variance:
Pythondef score_consistency(self, output: str, query_id: str, persona_id: str) -> float: # Calculate similarity with each reference similarities = [] for ref_output in reference_outputs: documents = [output, ref_output] tfidf_matrix = self.vectorizer.fit_transform(documents) similarity = cosine_similarity(tfidf_matrix[0:1], tfidf_matrix[1:2])[0][0] similarities.append(similarity) # Use median similarity to reduce outlier impact median_similarity = np.median(similarities) # Apply consistency penalty for high variance if len(similarities) > 1: variance_penalty = np.var(similarities) * 0.5 consistency_score = max(0.0, median_similarity - variance_penalty) else: consistency_score = median_similarity return max(0.0, min(1.0, consistency_score))
Novelty Scoring
Rewards genuinely novel content while detecting creative elements:
Pythondef score_novelty(self, output: str, query_id: str, persona_id: str) -> float: # Calculate dissimilarity with existing outputs dissimilarities = [] for existing_output in existing_outputs: documents = [output, existing_output] tfidf_matrix = self.vectorizer.fit_transform(documents) similarity = cosine_similarity(tfidf_matrix[0:1], tfidf_matrix[1:2])[0][0] dissimilarity = 1.0 - similarity dissimilarities.append(dissimilarity) # Use maximum dissimilarity to reward truly novel content novelty_score = max(dissimilarities) # Apply novelty bonus for creative elements novelty_bonus = self._calculate_creative_bonus(output) novelty_score = min(1.0, novelty_score + novelty_bonus * 0.2) return max(0.0, min(1.0, novelty_score))
Grounding Scoring
Ensures outputs are connected to provided entities and minimizes hallucinations:
Pythondef score_entity_grounding(self, output: str, query_id: str) -> float: entities = self._extract_entities_from_query(query_id) # Count entity mentions in output entity_mentions = 0 for entity_type, entity_list in entities.items(): for entity in entity_list: mentions = len(re.findall(r'\b' + re.escape(entity.lower()) + r'\b', output.lower())) if mentions > 0: entity_mentions += 1 # Calculate grounding score entity_coverage = entity_mentions / len(entities) # Apply grounding penalty for hallucinations hallucination_penalty = self._detect_hallucinations(output, entities) grounding_score = max(0.0, entity_coverage - hallucination_penalty) return max(0.0, min(1.0, grounding_score))
7. Ollama Integration
The Ollama Interface provides local LLM inference with deterministic configuration, ensuring complete data sovereignty:
Pythonclass OllamaInterface: def __init__(self, config: Dict[str, Any]): self.config = config self.api_endpoint = config.get('api_endpoint', 'http://localhost:11434') self.model_name = config.get('model_name', 'llama3.2') self.temperature = config.get('temperature', 0.1) # Low temperature for determinism self.seed = config.get('seed', 42) # Fixed seed for reproducibility self.max_tokens = config.get('max_tokens', 2000) def generate_response(self, prompt: str, system_prompt: Optional[str] = None) -> str: # Build the request payload with deterministic parameters payload = { "model": self.model_name, "messages": [ {"role": "system", "content": system_prompt}, {"role": "user", "content": prompt} ], "options": { "temperature": self.temperature, "seed": self.seed, "num_predict": self.max_tokens }, "stream": False } # Make the API call response = requests.post(f"{self.api_endpoint}/api/chat", json=payload) return response.json()['message']['content']
8. Graph Snapshots and Persistence
The Graph Snapshot Manager provides persistent storage and analysis of graph states, enabling system debugging, performance analysis, and knowledge preservation:
Pythonclass GraphSnapshotManager: def save_snapshot(self, graph, query_id: str, scores: List[Dict[str, Any]], metadata: Optional[Dict[str, Any]] = None) -> bool: snapshot_data = { 'query_id': query_id, 'timestamp': datetime.utcnow().isoformat(), 'graph_data': self._serialize_graph(graph), 'scores': scores, 'metadata': metadata or {}, 'graph_stats': self._get_graph_stats(graph) } # Save compressed snapshot with gzip.open(filepath, 'wt', encoding='utf-8') as f: json.dump(snapshot_data, f, indent=2, ensure_ascii=False) return True
Key Innovations and Advantages
1. Persona as Constraints, Not Prompts
Traditional systems treat personas as text prompts that are concatenated to the input. Our system implements personas as weighted constraint vectors that deterministically shape model behavior:
Pythondef _build_persona_prompt(self, persona: Dict[str, Any], query: str, context: str) -> str: traits = persona.get('traits', {}) system_prompt = f"You are a {persona.get('name', 'specialist')} with the following traits: " trait_descriptions = [] for trait_name, trait_value in traits.items(): trait_descriptions.append(f"{trait_name} ({trait_value:.2f})") system_prompt += ", ".join(trait_descriptions) + ". " system_prompt += persona.get('description', 'You are an expert in your field.') user_prompt = f"Context: {context}\n\nQuery: {query}\n\nPlease provide a response based on the context and your expertise." return f"{system_prompt}\n\n{user_prompt}"
2. Query-Scoped Graphs
Unlike persistent knowledge graphs that accumulate noise and become unwieldy, our system builds query-scoped graphs that are constructed fresh for each query. This ensures:
- Relevance: Only entities and relationships relevant to the current query are included
- Performance: Graphs remain manageable in size
- Accuracy: No state pollution from unrelated queries
- Security: No persistent storage of sensitive relationships
3. Auditable Persona Evolution
Persona evolution follows bounded update functions with explicit audit trails:
Pythondef update_persona_performance(self, persona_id: str, score: float) -> bool: # Load current persona data persona_data = self.load_persona_from_file(persona_file) # Update performance metrics performance = persona_data['historical_performance'] performance['total_queries'] += 1 performance['last_used'] = datetime.utcnow().isoformat() + 'Z' # Calculate new average score old_avg = performance['average_score'] total_queries = performance['total_queries'] new_avg = ((old_avg * (total_queries - 1)) + score) / total_queries performance['average_score'] = new_avg # Update metadata timestamp persona_data['metadata']['updated_at'] = datetime.utcnow().isoformat() + 'Z' # Save updated persona return self.save_persona_to_file(persona_data, persona_file)
4. Multi-Strategy Cognitive Processing
The system implements different cognitive strategies that personas use to process information:
- Analytical: Logical, evidence-based reasoning
- Creative: Novel connections and lateral thinking
- Pragmatic: Efficiency and practical outcomes
This multi-strategy approach ensures comprehensive analysis from multiple perspectives, similar to how human experts with different backgrounds would approach the same problem.
5. Hallucination Control
The system implements multiple layers of hallucination control:
- Structural Constraints: Explicit entity grounding requirements
- Provenance Tracking: Every output is traceable to specific graph nodes
- Multi-Criteria Evaluation: Grounding is explicitly scored
- Contextual Validation: Outputs are validated against provided context
Use Cases and Applications
1. Secure Intelligence Analysis
For sectors where data cannot leave the premise (legal, medical, defense), this system offers a "SCIF-in-a-box" solution:
Bash# Secure analysis of sensitive documents python3 scripts/run_pipeline.py --input classified_documents.txt --air-gapped-mode
2. Research and Development
Researchers can ingest terabytes of academic papers and use specialized personas to identify connections and generate hypotheses:
Bash# Create domain-specific personas python3 scripts/run_pipeline.py --input research_corpus.json --create-personas --domain "quantum_computing"
3. Business Intelligence
Companies can analyze market data, competitor information, and internal reports without exposing sensitive information to external services:
Bash# Business analysis with multiple expert personas python3 scripts/run_pipeline.py --input market_analysis.json --multi-expert-mode
4. Personal Knowledge Management
Individuals can create digital twins that evolve with their thinking and provide personalized insights:
Bash# Create a personalized digital assistant python3 scripts/run_pipeline.py --input personal_notes.json --create-digital-twin
Performance and Scalability
The system is designed for local-first performance while maintaining scalability:
Memory Management
- Query-scoped graphs prevent memory accumulation
- Compressed snapshots minimize storage requirements
- Efficient persona storage using JSON with validation
Processing Efficiency
- Parallel persona processing during expansion phase
- Optimized graph algorithms using NetworkX
- Caching strategies for frequently accessed data
Scalability Considerations
- Modular architecture allows component scaling
- Configuration-driven thresholds enable performance tuning
- Monitoring and logging for performance analysis
Security and Privacy
Air-Gapped Operation
The system operates entirely offline, with no external network dependencies:
YAML# System configuration air_gapped_mode: true enable_caching: true deterministic_mode: true
Data Sovereignty
All data processing occurs on local hardware, ensuring complete control over sensitive information.
Auditability
Every system operation is logged and traceable, enabling compliance with regulatory requirements.
Future Enhancements
1. Multi-Modal Personas
Support for different input/output modalities (text, audio, image, video) to handle diverse data types.
2. Federated Learning
Distributed persona training across multiple systems while maintaining privacy.
3. Hierarchical Graphs
Multi-level graph representations for complex domain knowledge.
4. Real-Time Adaptation
Continuous learning during inference cycles for dynamic environments.
5. Advanced Evaluation Metrics
Integration with external knowledge bases for enhanced validation.
Conclusion
The Dynamic Persona MoE RAG system represents a significant advancement in local-first, sovereign AI systems. By separating intelligence from identity and implementing sophisticated persona-based reasoning, the system provides a robust alternative to centralized AI services.
Key achievements include:
- Complete data sovereignty through air-gapped operation
- Deterministic outputs ensuring reproducibility and trust
- Sophisticated persona management enabling diverse perspectives
- Robust hallucination control maintaining factual accuracy
- Comprehensive evaluation frameworks ensuring quality and reliability
This system demonstrates that it's possible to build powerful, intelligent systems without sacrificing privacy, security, or control. As the demand for sovereign AI solutions grows, architectures like this will become increasingly important for organizations and individuals who cannot afford to compromise on data sovereignty.
The codebase is available and ready for use, testing, and contribution. Whether you're working in healthcare, legal, defense, research, or simply value your digital sovereignty, this system provides a foundation for building intelligent applications that respect your privacy and maintain your control over sensitive information.