mkdir dynamic_persona_moe_rag
cd dynamic_persona_moe_rag
python3 -m venv venv

# Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
*$py.class

# Environments
.env
.venv
env/
venv/
ENV/

src/
├── core/           # Main orchestration and interfaces
├── graph/          # Dynamic knowledge graph implementation
├── personas/       # Persona lifecycle and storage
├── agents/         # Specialized AI agents
├── evaluation/     # Scoring and metrics
└── storage/        # Persistence and snapshots

configs/            # YAML configuration files
scripts/            # Pipeline execution
data/               # Input/output data

# Global system parameters
max_iterations:  # Maximum number of iterations for the pipeline
batch_size:  # Batch size for processing
log_level:  # Logging level (DEBUG, INFO, etc.)
enable_caching:  # Whether to enable caching

# Pruning and promotion thresholds
pruning_threshold:  # Threshold for pruning personas
promotion_threshold:  # Threshold for promoting personas
demotion_threshold:  # Threshold for demoting personas
activation_threshold:  # Threshold for activating personas

# Local model configuration
model_name:  # Name of the Ollama model to use
temperature:  # Temperature for generation
max_tokens:  # Maximum tokens to generate
api_endpoint:  # Ollama API endpoint (usually localhost)

{
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "type": "object",
  "properties": {
    "persona_id": {
      "type": "string",
      "description": "Unique identifier for the persona"
    },
    "traits": {
      "type": "object",
      "patternProperties": {
        "^.*$": {
          "type": "integer",
          "minimum": 1,
          "maximum": 9,
          "description": "Trait value between 1 and 9"
        }
      },
      "description": "Object containing trait names as keys and numeric values 1-9 as values"
    },
    "expertise": {
      "type": "array",
      "items": {
        "type": "string"
      },
      "description": "Array of strings representing areas of expertise"
    },
    "activation_cost": {
      "type": "number",
      "description": "Float representing the cost to activate this persona"
    },
    "historical_performance": {
      "type": "object",
      "description": "Object containing historical performance metrics"
    },
    "metadata": {
      "type": "object",
      "description": "Object containing additional metadata"
    }
  },
  "required": ["persona_id", "traits", "expertise", "activation_cost", "historical_performance", "metadata"]
}

class DynamicKnowledgeGraph:
    """
    A dynamic graph that constructs nodes and edges on-demand for a single query.
    """

    def __init__(self):
        self.nodes = {}
        self.edges = []

    def add_node(self, node_id, node_data):
        """Lazily construct a node when needed."""
        pass

    def add_edge(self, source_id, target_id, edge_data):
        """Create an edge on-demand between nodes."""
        pass

class Node:
    """Represents a node in the dynamic knowledge graph."""
    def __init__(self, node_id, data=None):
        self.node_id = node_id
        self.data = data or {}
        self.edges = []

class Edge:
    """Represents an edge in the dynamic knowledge graph."""
    def __init__(self, source_node, target_node, data=None):
        self.source = source_node
        self.target = target_node
        self.data = data or {}

from abc import ABC, abstractmethod

class PersonaTraversalInterface(ABC):
    """
    Abstract base class defining the interface for persona traversal.
    """

    @abstractmethod
    def evaluate_node_relevance(self, persona, node):
        """
        Evaluate how relevant a graph node is to a given persona.
        Returns: float (relevance score between 0 and 1)
        """
        pass

    @abstractmethod
    def decide_traversal(self, current_node, available_nodes, persona):
        """
        Decide which nodes to traverse to next based on persona evaluation.
        Returns: list (nodes to traverse to next)
        """
        pass

class MoeOrchestrator:
    """
    Orchestrates the mixture-of-experts RAG system.
    """

    def expansion_phase(self):
        """Expansion phase: Generate diverse outputs from active personas."""
        pass

    def evaluation_phase(self):
        """Evaluation phase: Score and rank the generated outputs."""
        pass

    def pruning_phase(self):
        """Pruning phase: Remove underperforming personas and promote high performers."""
        pass

def score_relevance(output, query):
    """Score the relevance of an output to the input query."""
    return 0.0

def score_consistency(output, reference_outputs):
    """Score the consistency of an output with reference outputs."""
    return 0.0

def score_novelty(output, existing_outputs):
    """Score the novelty of an output compared to existing outputs."""
    return 0.0

def score_entity_grounding(output, entities):
    """Score how well the output is grounded in the provided entities."""
    return 0.0

def calculate_average_score(scores):
    """Calculate the average of a list of scores."""
    return 0.0

def calculate_weighted_score(scores, weights):
    """Calculate a weighted average of scores."""
    return 0.0

def aggregate_persona_performance(persona_scores):
    """Aggregate performance metrics for a persona across multiple evaluations."""
    return {}

def evaluate_pruning_thresholds(persona_performance, thresholds):
    """
    Threshold-based demotion: Personas below certain performance metrics
    are demoted from active to stable, stable to experimental, experimental to pruned.
    """
    return 'keep'

def move_persona_to_folder(persona_id, current_folder, target_folder):
    """
    Folder-based archival: Move personas to appropriate archival folders
    instead of deleting them.
    """
    pass

def synthesize_persona_context(persona_outputs, graph_context):
    """Synthesize context from multiple persona outputs and graph traversal."""
    return ""

def send_prompt_to_ollama(synthesized_context, query, ollama_client):
    """Send the final prompt to the local Ollama model."""
    return ""

def load_persona_from_file(filepath):
    """Load a persona JSON file from disk."""
    return {}

def save_persona_to_file(persona_data, filepath):
    """Save persona data to a JSON file."""
    pass

def save_graph_snapshot(graph, query_id, timestamp):
    """Save a snapshot of the current graph state."""
    pass

def main():
    # 1. Input ingestion
    input_query = ""

    # 2. Entity construction
    entities = {}

    # 3. Graph creation
    graph = None

    # 4. Persona traversal loop
    traversal_outputs = []

    # 5. Scoring and pruning
    scores = []

    # 6. Final Ollama inference
    final_response = ""