Unified AI System Design

Create a project structure and architecture design for a system that integrates RAG, RL, Persona modeling, and graph-based orchestration. Include: 1. Directory structure 2. Key interfaces and abstract classes 3. Component interaction diagram 4. Required dependencies ``` unified_ai_system/ │ ├── src/ │ ├── __init__.py │ │ │ ├── rag/ │ │ ├── __init__.py │ │ ├── retriever.py │ │ ├── document_store.py │ │ ├── embeddings.py │ │ └── generator.py │ │ │ ├── rl/ │ │ ├── __init__.py │ │ ├── agent.py │ │ ├── environment.py │ │ ├── policy.py │ │ └── world_model.py │ │ │ ├── persona/ │ │ ├── __init__.py │ │ ├── schemas.py │ │ ├── manager.py │ │ └── adapter.py │ │ │ ├── orchestrator/ │ │ ├── __init__.py │ │ ├── graph.py │ │ ├── nodes.py │ │ └── state_manager.py │ │ │ ├── core/ │ │ ├── __init__.py │ │ ├── interfaces.py │ │ ├── config.py │ │ └── exceptions.py │ │ │ └── utils/ │ ├── __init__.py │ ├── logging.py │ └── metrics.py │ ├── tests/ │ ├── __init__.py │ ├── test_rag/ │ ├── test_rl/ │ ├── test_persona/ │ └── test_orchestrator/ │ ├── config/ │ ├── default.yaml │ └── personas/ │ ├── examples/ │ └── basic_usage.py │ ├── requirements.txt ├── setup.py └── README.md ``` # src/core/interfaces.py from abc import ABC, abstractmethod from typing import Dict, Any, List, Optional from pydantic import BaseModel class Document(BaseModel): """Base document structure for RAG""" id: str content: str metadata: Dict[str, Any] class State(BaseModel): """System state representation""" context: Dict[str, Any] history: List[Dict[str, Any]] metadata: Dict[str, Any] class IRetriever(ABC): """Interface for document retrieval""" @abstractmethod def retrieve(self, query: str, k: int = 3) -> List[Document]: pass class IGenerator(ABC): """Interface for text generation""" @abstractmethod def generate(self, prompt: str, context: List[Document]) -> str: pass class IRLAgent(ABC): """Interface for RL agent""" @abstractmethod def select_action(self, state: State) -> str: pass @abstractmethod def update(self, state: State, action: str, reward: float, next_state: State): pass class IPersona(ABC): """Interface for persona management""" @abstractmethod def adapt_response(self, response: str, context: Dict[str, Any]) -> str: pass @abstractmethod def validate_constraints(self, response: str) -> bool: pass class IOrchestrator(ABC): """Interface for workflow orchestration""" @abstractmethod def process_step(self, state: State) -> State: pass @abstractmethod def add_node(self, node_id: str, component: Any): pass @abstractmethod def add_edge(self, from_node: str, to_node: str, condition: Optional[callable] = None): pass class IStateManager(ABC): """Interface for state management""" @abstractmethod def update_state(self, state: State, updates: Dict[str, Any]) -> State: pass @abstractmethod def get_current_state(self) -> State: pass class IWorldModel(ABC): """Interface for RL world model""" @abstractmethod def predict(self, state: State, action: str) -> Tuple[State, float, bool]: pass @abstractmethod def update_model(self, state: State, action: str, next_state: State): pass # Custom exceptions class UnifiedAIException(Exception): """Base exception for the unified AI system""" pass class RAGException(UnifiedAIException): """RAG-specific exceptions""" pass class RLException(UnifiedAIException): """RL-specific exceptions""" pass ``` ``` graph TD User[User Input] --> Orchestrator Orchestrator --> RAG Orchestrator --> RL Orchestrator --> Persona subgraph RAG[RAG Pipeline] Retriever --> DocumentStore Retriever --> Generator end subgraph RL[RL System] Agent --> WorldModel Agent --> Policy end subgraph Persona[Persona System] PersonaManager --> SchemaValidator PersonaManager --> ResponseAdapter end RAG --> StateManager RL --> StateManager Persona --> StateManager StateManager --> Orchestrator Orchestrator --> Output[Final Output] ``` ``` # requirements.txt # Core Dependencies pydantic>=2.0.0 networkx>=2.8.0 PyYAML>=6.0.0 numpy>=1.21.0 torch>=2.0.0 # RAG Dependencies faiss-cpu>=1.7.0 # or faiss-gpu for GPU support sentence-transformers>=2.2.0 elasticsearch>=7.17.0 # optional, for document storage # RL Dependencies gymnasium>=0.26.0 stable-baselines3>=2.0.0 # Persona Dependencies jsonschema>=4.17.0 # Utility Dependencies loguru>=0.7.0 pytest>=7.0.0 python-dotenv>=1.0.0 # API and Service Dependencies (optional) fastapi>=0.100.0 uvicorn>=0.22.0 redis>=4.5. # config/default.yaml system: name: "unified-ai-system" version: "1.0.0" log_level: "INFO" rag: retriever: type: "faiss" top_k: 3 similarity_threshold: 0.7 generator: model: "gpt-3.5-turbo" max_tokens: 500 temperature: 0.7 rl: agent: type: "hierarchical" learning_rate: 0.001 gamma: 0.99 world_model: type: "probabilistic" update_frequency: 100 persona: validation_schema: "config/personas/schema.json" default_persona: "neutral" adaptation_strength: 0.8 orchestrator: max_steps: 10 timeout_seconds: 30 retry_attempts: 3 ``` 2. **RAG Pipeline Implementation** ``` Provide implementation code for a RAG (Retrieval-Augmented Generation) pipeline that: 1. Handles document retrieval 2. Implements relevance ranking 3. Integrates with a language model 4. Includes error handling and logging ``` 3. **RL Framework Development** ``` Create a hierarchical Reinforcement Learning framework that: 1. Implements state and action spaces 2. Defines reward functions 3. Creates policy management 4. Handles model-based predictions ``` ``` # src/rl/types.py from typing import Dict, List, Any, Tuple, Optional from dataclasses import dataclass from enum import Enum import numpy as np from pydantic import BaseModel class StateType(BaseModel): """Representation of the system state""" context: Dict[str, Any] current_goal: str subgoals: List[str] steps_taken: int metadata: Dict[str, Any] class ActionType(Enum): """Enumeration of possible actions""" RETRIEVE = "retrieve" GENERATE = "generate" REFINE = "refine" FINALIZE = "finalize" REQUEST_MORE_INFO = "request_more_info" @dataclass class Experience: """Storage class for agent experiences""" state: StateType action: ActionType reward: float next_state: StateType done: bool ``` ``` # src/rl/world_model.py from typing import Tuple import torch import torch.nn as nn import torch.nn.functional as F class WorldModel(nn.Module): """Neural network-based world model for state prediction""" def __init__(self, state_dim: int, action_dim: int, hidden_dim: int = 128): super().__init__() self.encoder = nn.Sequential( nn.Linear(state_dim + action_dim, hidden_dim), nn.ReLU(), nn.Linear(hidden_dim, hidden_dim), nn.ReLU() ) self.state_predictor = nn.Linear(hidden_dim, state_dim) self.reward_predictor = nn.Linear(hidden_dim, 1) def forward(self, state: torch.Tensor, action: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: x = torch.cat([state, action], dim=-1) hidden = self.encoder(x) next_state = self.state_predictor(hidden) reward = self.reward_predictor(hidden) return next_state, reward def predict(self, state: StateType, action: ActionType) -> Tuple[StateType, float]: """Predict next state and reward for a given state-action pair""" state_tensor = self.state_to_tensor(state) action_tensor = self.action_to_tensor(action) with torch.no_grad(): next_state_tensor, reward = self.forward(state_tensor, action_tensor) return self.tensor_to_state(next_state_tensor), reward.item() ``` ``` # src/rl/policy.py import torch import torch.nn as nn from typing import List, Dict class HierarchicalPolicy: def update(self, experiences: List[Experience]): """Update policies based on collected experiences""" meta_experiences = self._extract_meta_experiences(experiences) self.meta_policy.update(meta_experiences) # Update sub-policies for each subgoal for subgoal in self.sub_policies: subgoal_experiences = [ exp for exp in experiences if exp.state.current_goal == subgoal ] if subgoal_experiences: self.sub_policies[subgoal].update(subgoal_experiences) def _extract_meta_experiences(self, experiences: List[Experience]) -> List[Experience]: """Extract high-level transitions for meta-policy learning""" meta_experiences = [] current_episode = [] for exp in experiences: current_episode.append(exp) if exp.done: # Create meta-transition from episode if len(current_episode) > 0: meta_exp = Experience( state=current_episode[0].state, action=current_episode[0].state.current_goal, reward=sum(e.reward for e in current_episode), next_state=current_episode[-1].next_state, done=True ) meta_experiences.append(meta_exp) current_episode = [] return meta_experiences class MetaPolicy(nn.Module): """High-level policy for subgoal selection""" def __init__(self, state_dim: int, n_subgoals: int): super().__init__() self.network = nn.Sequential( nn.Linear(state_dim, 128), nn.ReLU(), nn.Linear(128, 128), nn.ReLU(), nn.Linear(128, n_subgoals) ) self.optimizer = torch.optim.Adam(self.parameters(), lr=1e-3) def forward(self, state: torch.Tensor) -> torch.Tensor: return self.network(state) def select_action(self, state: StateType) -> str: state_tensor = self._preprocess_state(state) with torch.no_grad(): q_values = self.forward(state_tensor) action = torch.argmax(q_values).item() return str(action) def update(self, experiences: List[Experience]): if not experiences: return states = torch.stack([self._preprocess_state(exp.state) for exp in experiences]) actions = torch.tensor([int(exp.action) for exp in experiences]) rewards = torch.tensor([exp.reward for exp in experiences]) next_states = torch.stack([self._preprocess_state(exp.next_state) for exp in experiences]) dones = torch.tensor([exp.done for exp in experiences]) # Q-learning update current_q = self.forward(states).gather(1, actions.unsqueeze(1)) next_q = self.forward(next_states).max(1)[0].detach() target_q = rewards + (1 - dones) * 0.99 * next_q loss = F.smooth_l1_loss(current_q.squeeze(), target_q) self.optimizer.zero_grad() loss.backward() self.optimizer.step() def _preprocess_state(self, state: StateType) -> torch.Tensor: # Convert state to tensor representation # This would need to be implemented based on your specific state structure pass # src/rl/policy.py (continued) class SubPolicy(nn.Module): """Low-level policy for action selection within a subgoal""" def __init__(self, state_dim: int, action_dim: int): super().__init__() self.network = nn.Sequential( nn.Linear(state_dim, 128), nn.ReLU(), nn.Linear(128, 128), nn.ReLU(), nn.Linear(128, action_dim) ) self.optimizer = torch.optim.Adam(self.parameters(), lr=1e-3) def forward(self, state: torch.Tensor) -> torch.Tensor: return self.network(state) def select_action(self, state: StateType) -> ActionType: state_tensor = self._preprocess_state(state) with torch.no_grad(): q_values = self.forward(state_tensor) action_idx = torch.argmax(q_values).item() return ActionType(action_idx) def update(self, experiences: List[Experience]): if not experiences: return states = torch.stack([self._preprocess_state(exp.state) for exp in experiences]) actions = torch.tensor([exp.action.value for exp in experiences]) rewards = torch.tensor([exp.reward for exp in experiences]) next_states = torch.stack([self._preprocess_state(exp.next_state) for exp in experiences]) dones = torch.tensor([exp.done for exp in experiences]) current_q = self.forward(states).gather(1, actions.unsqueeze(1)) next_q = self.forward(next_states).max(1)[0].detach() target_q = rewards + (1 - dones) * 0.99 * next_q loss = F.smooth_l1_loss(current_q.squeeze(), target_q) self.optimizer.zero_grad() loss.backward() self.optimizer.step() def _preprocess_state(self, state: StateType) -> torch.Tensor: # Implementation similar to MetaPolicy pass # src/rl/rewards.py from typing import Dict, Any from .types import StateType, ActionType # src/rl/rewards.py (continued) class RewardFunction: def _is_task_completed(self, state: StateType) -> bool: """Check if the current task is completed""" return len(state.subgoals) == 0 and state.steps_taken > 0 def _calculate_information_gain(self, state: StateType, next_state: StateType) -> float: """Calculate the information gain between states""" # Compare context sizes or specific metrics current_info = len(state.context) next_info = len(next_state.context) # Normalize information gain return max(0, (next_info - current_info) / max(current_info, 1)) def get_intermediate_reward(self, state: StateType, subgoal: str) -> float: """Calculate intermediate reward for subgoal completion""" if subgoal in state.subgoals and subgoal not in state.context.get('completed_subgoals', []): return self.weights.get('subgoal_completion', 0.5) return 0.0 # src/rl/agent.py from typing import List, Dict, Any, Optional import numpy as np from collections import deque from .types import StateType, ActionType, Experience from .policy import HierarchicalPolicy from .world_model import WorldModel from .rewards import RewardFunction # src/rl/agent.py (continued) class HierarchicalRLAgent: def _train(self): """Train the agent using experiences from the buffer""" if len(self.buffer) < self.batch_size: return # Sample batch of experiences batch = np.random.choice(self.buffer, size=self.batch_size, replace=False) # Update policies self.policy.update(batch) # Optionally perform imagination-based updates using world model if self.config.get('use_imagination', True): self._imagine_and_update() def _imagine_and_update(self, n_steps: int = 5): """Use world model to imagine trajectories and update policy""" # Sample initial states from buffer initial_states = np.random.choice(self.buffer, size=self.batch_size) initial_states = [exp.state for exp in initial_states] imagined_experiences = [] for init_state in initial_states: current_state = init_state trajectory = [] for _ in range(n_steps): # Select action using current policy action = self.select_action(current_state) # Use world model to predict outcome next_state, predicted_reward = self.world_model.predict(current_state, action) # Calculate reward using reward function reward = self.reward_function.calculate_reward( current_state, action, next_state, {} ) # Store imagined experience exp = Experience(current_state, action, reward, next_state, False) trajectory.append(exp) current_state = next_state imagined_experiences.extend(trajectory) # Update policy with imagined experiences if imagined_experiences: self.policy.update(imagined_experiences) def save(self, path: str): """Save agent's components to disk""" save_dict = { 'policy_state': self.policy.state_dict(), 'world_model_state': self.world_model.state_dict(), 'config': self.config, 'steps': self.steps } torch.save(save_dict, path) def load(self, path: str): """Load agent's components from disk""" checkpoint = torch.load(path) self.policy.load_state_dict(checkpoint['policy_state']) self.world_model.load_state_dict(checkpoint['world_model_state']) self.config = checkpoint['config'] self.steps = checkpoint['steps'] # src/rl/evaluation.py from typing import List, Dict, Any from dataclasses import dataclass import numpy as np @dataclass class EpisodeMetrics: """Metrics for evaluating episode performance""" total_reward: float episode_length: int completed_subgoals: int average_information_gain: float success: bool class RLEvaluator: def evaluate_episode(self, experiences: List[Experience]) -> EpisodeMetrics: """Calculate metrics for a single episode""" total_reward = sum(exp.reward for exp in experiences) episode_length = len(experiences) # Count completed subgoals completed_subgoals = len(set( exp.state.context.get('completed_subgoals', []) for exp in experiences )) # Calculate average information gain info_gains = [] for i in range(len(experiences)-1): current_info = len(experiences[i].state.context) next_info = len(experiences[i+1].state.context) info_gain = max(0, (next_info - current_info) / max(current_info, 1)) info_gains.append(info_gain) avg_information_gain = np.mean(info_gains) if info_gains else 0.0 # Determine success (all subgoals completed and positive reward) success = completed_subgoals > 0 and total_reward > 0 return EpisodeMetrics( total_reward=total_reward, episode_length=episode_length, completed_subgoals=completed_subgoals, average_information_gain=avg_information_gain, success=success ) def evaluate_agent(self, agent: HierarchicalRLAgent, env, n_episodes: int = 10) -> Dict[str, float]: """Evaluate agent performance over multiple episodes""" metrics_list = [] for _ in range(n_episodes): experiences = self._run_evaluation_episode(agent, env) metrics = self.evaluate_episode(experiences) metrics_list.append(metrics) # Aggregate metrics return { 'avg_reward': np.mean([m.total_reward for m in metrics_list]), 'avg_episode_length': np.mean([m.episode_length for m in metrics_list]), 'avg_completed_subgoals': np.mean([m.completed_subgoals for m in metrics_list]), 'avg_information_gain': np.mean([m.average_information_gain for m in metrics_list]), 'success_rate': np.mean([m.success for m in metrics_list]), } def _run_evaluation_episode(self, agent: HierarchicalRLAgent, env) -> List[Experience]: """Run a single evaluation episode""" experiences = [] state = env.reset() done = False while not done: action = agent.select_action(state) next_state, reward, done, _ = env.step(action) exp = Experience(state, action, reward, next_state, done) experiences.append(exp) state = next_state return experiences # src/rl/training.py import logging from typing import Dict, Any, List from tqdm import tqdm logger = logging.getLogger(__name__) class RLTrainer: def __init__(self, agent: HierarchicalRLAgent, env, evaluator: RLEvaluator, config: Dict[str, Any]): # ... (previous initialization code) self.best_reward = float('-inf') self.metrics_history = [] def train(self): """Main training loop""" logger.info("Starting training...") for episode in tqdm(range(self.n_episodes)): # Run training episode episode_experiences = self._run_training_episode() # Update agent with experiences for exp in episode_experiences: self.agent.update(exp.state, exp.action, exp.reward, exp.next_state, exp.done) # Periodic evaluation if episode % self.eval_frequency == 0: metrics = self.evaluator.evaluate_agent(self.agent, self.env) self.metrics_history.append(metrics) # Log metrics logger.info(f"Episode {episode}/{self.n_episodes}") for metric_name, value in metrics.items(): logger.info(f"{metric_name}: {value:.4f}") # Save best model if metrics['avg_reward'] > self.best_reward: self.best_reward = metrics['avg_reward'] self._save_checkpoint(f"{self.save_path}/best_model.pt") # Periodic saving if episode % self.save_frequency == 0: self._save_checkpoint(f"{self.save_path}/checkpoint_{episode}.pt") logger.info("Training completed!") return self.metrics_history def _run_training_episode(self) -> List[Experience]: """Run a single training episode""" experiences = [] state = self.env.reset() done = False episode_steps = 0 max_steps = self.config.get('max_episode_steps', 1000) while not done and episode_steps < max_steps: # Select and execute action action = self.agent.select_action(state) next_state, reward, done, _ = self.env.step(action) # Store experience exp = Experience(state, action, reward, next_state, done) experiences.append(exp) state = next_state episode_steps += 1 return experiences def _save_checkpoint(self, path: str): """Save training checkpoint""" self.agent.save(path) # Save additional training state training_state = { 'metrics_history': self.metrics_history, 'best_reward': self.best_reward, 'config': self.config } torch.save(training_state, f"{path}_training_state.pt") ```python # src/rl/main.py import argparse import yaml import logging from pathlib import Path from .agent import HierarchicalRLAgent from .evaluation import RLEvaluator from .training import RLTrainer from .environment import create_environment # You would need to implement this def setup_logging(config): logging.basicConfig( level=config.get('log_level', 'INFO'), format='%(asctime)s - %(name)s - %(levelname)s - %(message)s' ) # src/rl/main.py (continued) def main(): parser = argparse.ArgumentParser(description='Train RL agent') parser.add_argument('--config', type=str, required=True, help='Path to config file') parser.add_argument('--checkpoint', type=str, help='Path to checkpoint to resume from') args = parser.parse_args() # Load config with open(args.config, 'r') as f: config = yaml.safe_load(f) # Setup logging setup_logging(config) logger = logging.getLogger(__name__) # Create save directory save_path = Path(config['training']['save_path']) save_path.mkdir(parents=True, exist_ok=True) # Create environment env = create_environment(config['environment']) # Initialize agent agent = HierarchicalRLAgent(config['agent']) # Load checkpoint if specified if args.checkpoint: logger.info(f"Loading checkpoint from {args.checkpoint}") agent.load(args.checkpoint) # Initialize evaluator evaluator = RLEvaluator(config['evaluation']) # Initialize trainer trainer = RLTrainer(agent, env, evaluator, config['training']) # Start training try: metrics_history = trainer.train() # Save final results final_metrics = evaluator.evaluate_agent(agent, env, n_episodes=config['evaluation']['final_eval_episodes']) logger.info("Final Evaluation Results:") for metric_name, value in final_metrics.items(): logger.info(f"{metric_name}: {value:.4f}") except KeyboardInterrupt: logger.info("Training interrupted by user") except Exception as e: logger.error(f"Error during training: {str(e)}") raise finally: # Save final model agent.save(save_path / "final_model.pt") logger.info("Training completed") if __name__ == "__main__": main() ```python # config/rl_config.yaml environment: type: "unified_ai_env" max_steps: 1000 observation_space: type: "dict" context_dim: 256 metadata_dim: 64 action_space: type: "discrete" n_actions: 5 agent: state_dim: 320 # context_dim + metadata_dim n_subgoals: 4 world_model_hidden_dim: 256 buffer_size: 100000 batch_size: 64 update_frequency: 100 use_imagination: true reward_weights: task_completion: 1.0 step_penalty: -0.1 information_gain: 0.5 user_satisfaction: 2.0 training: n_episodes: 10000 eval_frequency: 100 save_frequency: 500 save_path: "checkpoints" max_episode_steps: 1000 learning_rate: 0.001 gamma: 0.99 evaluation: n_episodes: 10 final_eval_episodes: 100 metrics: - "avg_reward" - "success_rate" - "avg_completed_subgoals" - "avg_information_gain" logging: level: "INFO" save_path: "logs" tensorboard: true ``` ``` # src/rl/environment.py from typing import Dict, Any, Tuple import gymnasium as gym from gymnasium import spaces import numpy as np class UnifiedAIEnvironment(gym.Env): def _calculate_reward(self, action_result: Dict[str, Any]) -> float: """Calculate reward based on action results""" reward = 0.0 # Base reward for successful actions if action_result['success']: reward += 0.5 # Reward for information gain reward += action_result['info_gain'] * 2.0 # Reward for completing subgoals if action_result['subgoal_completed']: reward += 2.0 # Extra reward for completing all subgoals if not self.subgoals: reward += 5.0 # Step penalty to encourage efficiency reward -= 0.1 return reward def _is_terminated(self) -> bool: """Check if episode should terminate""" # Episode ends if all subgoals are completed if not self.subgoals: return True # Episode ends if we've reached max steps if self.steps >= self.max_steps: return True return False def _encode_task(self, task: Dict[str, Any]) -> np.ndarray: """Encode task information into a vector""" # Define task type encodings task_type_encoding = { 'information_retrieval': [1, 0, 0], 'query_answering': [0, 1, 0], 'planning': [0, 0, 1] } # Define difficulty encodings difficulty_encoding = { 'easy': [1, 0, 0], 'medium': [0, 1, 0], 'hard': [0, 0, 1] } # Combine encodings encoding = np.concatenate([ task_type_encoding[task['type']], difficulty_encoding[task['difficulty']] ]) return encoding def _encode_subgoals(self) -> np.ndarray: """Encode subgoals status into a vector""" # Create a fixed-size encoding for subgoals max_subgoals = 5 # Maximum number of subgoals we support encoding = np.zeros(max_subgoals) # Mark remaining subgoals for i, subgoal in enumerate(self.subgoals): if i < max_subgoals: encoding[i] = 1.0 return encoding def render(self): """Render the environment state""" print("\n=== Environment State ===") print(f"Task: {self.current_task}") print(f"Remaining Subgoals: {self.subgoals}") print(f"Steps: {self.steps}/{self.max_steps}") print("========================\n") # src/rl/utils.py from typing import Dict, Any, List import numpy as np class StateEncoder: def decode_state(self, encoded_state: np.ndarray) -> Dict[str, np.ndarray]: """Decode flat vector back into state dictionary""" context = encoded_state[:self.context_dim] metadata = encoded_state[self.context_dim:] return { 'context': context, 'metadata': metadata } class ExperienceBuffer: """Utility class for managing experience replay buffer""" def __init__(self, max_size: int = 100000): self.max_size = max_size self.buffer = [] self.position = 0 def push(self, experience: Experience): """Add experience to buffer""" if len(self.buffer) < self.max_size: self.buffer.append(experience) else: self.buffer[self.position] = experience self.position = (self.position + 1) % self.max_size def sample(self, batch_size: int) -> List[Experience]: """Sample a batch of experiences""" return random.sample(self.buffer, min(batch_size, len(self.buffer))) def __len__(self): return len(self.buffer) class MetricsTracker: """Utility class for tracking and logging metrics""" def __init__(self, log_dir: str): self.log_dir = Path(log_dir) self.log_dir.mkdir(parents=True, exist_ok=True) self.metrics = defaultdict(list) # Setup tensorboard writer self.writer = SummaryWriter(log_dir) def update(self, metrics: Dict[str, float], step: int): """Update metrics""" for name, value in metrics.items(): self.metrics[name].append(value) self.writer.add_scalar(name, value, step) def get_average(self, metric_name: str, window: int = 100) -> float: """Get moving average of a metric""" values = self.metrics[metric_name] if not values: return 0.0 return np.mean(values[-window:]) def save(self): """Save metrics to disk""" metrics_path = self.log_dir / 'metrics.json' with open(metrics_path, 'w') as f: json.dump(self.metrics, f) def close(self): """Cleanup""" self.writer.close() class ActionManager: def is_valid_action(self, action: int, state: Dict[str, Any]) -> bool: """Check if action is valid in current state""" if action not in range(self.action_space): return False action_type = ActionType(action) # Check action-specific conditions if action_type == ActionType.FINALIZE: # Can't finalize if no subgoals return len(state.get('subgoals', [])) > 0 if action_type == ActionType.REFINE: # Can't refine without context return np.any(state['context'] != 0) if action_type == ActionType.GENERATE: # Need some context to generate return np.any(state['context'] != 0) # RETRIEVE and REQUEST_MORE_INFO are always valid return True def get_valid_actions(self, state: Dict[str, Any]) -> List[int]: """Get list of valid actions for current state""" return [action for action in range(self.action_space) if self.is_valid_action(action, state)] class DebugLogger: """Utility class for detailed debugging and logging""" def __init__(self, log_dir: str, enabled: bool = True): self.enabled = enabled if enabled: self.log_dir = Path(log_dir) self.log_dir.mkdir(parents=True, exist_ok=True) self.episode_logs = [] # Setup logging logging.basicConfig( filename=self.log_dir / 'debug.log', level=logging.DEBUG, format='%(asctime)s - %(levelname)s - %(message)s' ) def log_step(self, step_data: Dict[str, Any]): """Log detailed step information""" if not self.enabled: return self.episode_logs.append(step_data) logging.debug(f"Step {len(self.episode_logs)}: {step_data}") def log_episode(self, episode_num: int): """Save episode logs to file""" if not self.enabled: return episode_path = self.log_dir / f"episode_{episode_num}.json" with open(episode_path, 'w') as f: json.dump(self.episode_logs, f, indent=2) self.episode_logs = [] def log_error(self, error: Exception, context: Dict[str, Any]): """Log error with context""" if not self.enabled: return logging.error(f"Error: {str(error)}", exc_info=True) logging.error(f"Context: {context}") class PerformanceMonitor: def get_statistics(self) -> Dict[str, float]: """Get performance statistics""" return { 'avg_step_time': np.mean(self.step_times), 'max_step_time': np.max(self.step_times), 'min_step_time': np.min(self.step_times), 'avg_memory_usage_mb': np.mean(self.memory_usage), 'max_memory_usage_mb': np.max(self.memory_usage), 'total_runtime': time.time() - self.start_time, 'total_steps': len(self.step_times) } def reset(self): """Reset monitoring stats""" self.step_times = [] self.memory_usage = [] self.start_time = time.time() class SystemMonitor: def _check_warnings(self, step_data: Dict[str, Any]) -> List[Dict[str, Any]]: """Check for warning conditions in step data""" warnings = [] # Check step execution time step_time = self.performance_monitor.step_times[-1] if step_time > self.warning_thresholds['step_time']: warnings.append({ 'type': 'performance', 'message': f'Step time ({step_time:.2f}s) exceeded threshold', 'severity': 'warning' }) # Check memory usage memory_usage = self.performance_monitor.memory_usage[-1] if memory_usage > self.warning_thresholds['memory_usage']: warnings.append({ 'type': 'resource', 'message': f'Memory usage ({memory_usage:.2f}MB) exceeded threshold', 'severity': 'warning' }) # Check reward if step_data['reward'] < self.warning_thresholds['reward_min']: warnings.append({ 'type': 'training', 'message': f'Low reward ({step_data["reward"]:.2f}) detected', 'severity': 'info' }) return warnings def _handle_warnings(self, warnings: List[Dict[str, Any]]): """Handle detected warnings""" for warning in warnings: self.debug_logger.log_step({ 'event': 'warning', 'warning': warning, 'episode': self.current_episode, 'timestamp': time.time() }) # Emit metrics for monitoring self.metrics_tracker.update({ f'warnings_{warning["type"]}': 1 }, self.current_episode) def get_training_summary(self) -> Dict[str, Any]: """Get comprehensive training summary""" return { 'performance': self.performance_monitor.get_statistics(), 'metrics': { name: self.metrics_tracker.get_average(name) for name in self.metrics_tracker.metrics.keys() }, 'episodes_completed': self.current_episode, 'warnings_total': sum( len(self.metrics_tracker.metrics.get(f'warnings_{wtype}', [])) for wtype in ['performance', 'resource', 'training'] ) } class ModelCheckpointer: def load_best_checkpoint(self, metric_name: str = 'episode_reward') -> Optional[Dict[str, Any]]: """Load checkpoint with best metric value""" if not self.checkpoints: return None best_checkpoint = None best_metric_value = float('-inf') for checkpoint_path in self.checkpoints: checkpoint = torch.load(checkpoint_path) metric_value = checkpoint['metrics'].get(metric_name, float('-inf')) if metric_value > best_metric_value: best_metric_value = metric_value best_checkpoint = checkpoint return best_checkpoint def get_checkpoint_history(self) -> List[Dict[str, Any]]: """Get history of checkpoint metrics""" history = [] for checkpoint_path in self.checkpoints: checkpoint = torch.load(checkpoint_path) history.append({ 'episode': checkpoint['episode'], 'metrics': checkpoint['metrics'], 'timestamp': checkpoint['timestamp'] }) return history class ModelAnalyzer: """Utility for analyzing model behavior and performance""" def __init__(self, config: Dict[str, Any]): self.config = config self.action_manager = ActionManager(config) self.state_encoder = StateEncoder(config) def analyze_episode(self, experiences: List[Experience]) -> Dict[str, Any]: """Analyze episode behavior patterns""" analysis = { 'action_distribution': defaultdict(int), 'reward_statistics': { 'mean': np.mean([exp.reward for exp in experiences]), 'std': np.std([exp.reward for exp in experiences]), 'min': min(exp.reward for exp in experiences), 'max': max(exp.reward for exp in experiences) }, 'subgoal_completion': self._analyze_subgoal_completion(experiences), 'state_transitions': self._analyze_state_transitions(experiences) } # Analyze action patterns for exp in experiences: analysis['action_distribution'][exp.action] += 1 return analysis def _analyze_subgoal_completion(self, experiences: List[Experience]) -> Dict[str, Any]: """Analyze subgoal completion patterns""" completed_subgoals = [] completion_times = [] current_subgoal_start = 0 for i, exp in enumerate(experiences): if 'subgoal_completed' in exp.info: completed_subgoals.append(exp.info['subgoal_completed']) completion_times.append(i - current_subgoal_start) current_subgoal_start = i return { 'completed_subgoals': completed_subgoals, 'avg_completion_time': np.mean(completion_times) if completion_times else 0, 'completion_rate': len(completed_subgoals) / len(experiences) } def _analyze_state_transitions(self, experiences: List[Experience]) -> Dict[str, Any]: """Analyze state transition patterns""" state_changes = [] for i in range(len(experiences) - 1): current_state = self.state_encoder.encode_state(experiences[i].state) next_state = self.state_encoder.encode_state(experiences[i+1].state) state_change = np.linalg.norm(next_state - current_state) state_changes.append(state_change) return { 'avg_state_change': np.mean(state_changes), 'max_state_change': np.max(state_changes), 'state_stability': np.std(state_changes) } finish the utils.py

Below is an example of a comprehensive project structure and initial architecture design for a unified AI system that integrates Retrieval-Augmented Generation (RAG), Reinforcement Learning (RL), Persona modeling, and a graph-based orchestrator. This solution also includes an example of the key interfaces and abstract classes, a component interaction diagram, required dependencies, along with sample configurations, and partial implementation details for RAG and RL components. --- ### 1. Directory Structure A suggested directory structure is outlined below. This structure separates different functionalities into modules, ensures testability, and keeps related code together: ``` unified_ai_system/ │ ├── src/ │ ├── __init__.py │ │ │ ├── rag/ │ │ ├── __init__.py │ │ ├── retriever.py │ │ ├── document_store.py │ │ ├── embeddings.py │ │ └── generator.py │ │ │ ├── rl/ │ │ ├── __init__.py │ │ ├── agent.py │ │ ├── environment.py │ │ ├── policy.py │ │ ├── training.py │ │ ├── evaluation.py │ │ ├── rewards.py │ │ ├── types.py │ │ └── world_model.py │ │ │ ├── persona/ │ │ ├── __init__.py │ │ ├── schemas.py │ │ ├── manager.py │ │ └── adapter.py │ │ │ ├── orchestrator/ │ │ ├── __init__.py │ │ ├── graph.py │ │ ├── nodes.py │ │ └── state_manager.py │ │ │ ├── core/ │ │ ├── __init__.py │ │ ├── interfaces.py │ │ ├── config.py │ │ └── exceptions.py │ │ │ └── utils/ │ ├── __init__.py │ ├── logging.py │ ├── metrics.py │ └── utils.py │ ├── tests/ │ ├── __init__.py │ ├── test_rag/ │ ├── test_rl/ │ ├── test_persona/ │ └── test_orchestrator/ │ ├── config/ │ ├── default.yaml │ ├── rl_config.yaml │ └── personas/ │ └── schema.json │ ├── examples/ │ └── basic_usage.py │ ├── requirements.txt ├── setup.py └── README.md ``` --- ### 2. Key Interfaces and Abstract Classes The `core/interfaces.py` file defines the main interfaces for each subsystem (RAG, RL, Persona, Orchestrator): ```python # src/core/interfaces.py from abc import ABC, abstractmethod from typing import Dict, Any, List, Optional, Tuple from pydantic import BaseModel class Document(BaseModel): """Base document structure for RAG""" id: str content: str metadata: Dict[str, Any] class State(BaseModel): """System state representation""" context: Dict[str, Any] history: List[Dict[str, Any]] metadata: Dict[str, Any] class IRetriever(ABC): @abstractmethod def retrieve(self, query: str, k: int = 3) -> List[Document]: pass class IGenerator(ABC): @abstractmethod def generate(self, prompt: str, context: List[Document]) -> str: pass class IRLAgent(ABC): @abstractmethod def select_action(self, state: State) -> str: pass @abstractmethod def update(self, state: State, action: str, reward: float, next_state: State): pass class IPersona(ABC): @abstractmethod def adapt_response(self, response: str, context: Dict[str, Any]) -> str: pass @abstractmethod def validate_constraints(self, response: str) -> bool: pass class IOrchestrator(ABC): @abstractmethod def process_step(self, state: State) -> State: pass @abstractmethod def add_node(self, node_id: str, component: Any): pass @abstractmethod def add_edge(self, from_node: str, to_node: str, condition: Optional[callable] = None): pass class IStateManager(ABC): @abstractmethod def update_state(self, state: State, updates: Dict[str, Any]) -> State: pass @abstractmethod def get_current_state(self) -> State: pass class IWorldModel(ABC): @abstractmethod def predict(self, state: State, action: str) -> Tuple[State, float, bool]: pass @abstractmethod def update_model(self, state: State, action: str, next_state: State): pass class UnifiedAIException(Exception): """Base exception for the unified AI system""" pass class RAGException(UnifiedAIException): """RAG-specific exceptions""" pass class RLException(UnifiedAIException): """RL-specific exceptions""" pass ``` --- ### 3. Component Interaction Diagram Below is a conceptual high-level interaction diagram using Mermaid syntax: ```mermaid graph TD User[User Input] --> Orchestrator Orchestrator --> RAG Orchestrator --> RL Orchestrator --> Persona subgraph RAG[RAG Pipeline] Retriever --> DocumentStore Retriever --> Generator end subgraph RL[RL System] Agent --> WorldModel Agent --> Policy end subgraph Persona[Persona System] PersonaManager --> SchemaValidator PersonaManager --> ResponseAdapter end RAG --> StateManager RL --> StateManager Persona --> StateManager StateManager --> Orchestrator Orchestrator --> Output[Final Output] ``` --- ### 4. Required Dependencies A `requirements.txt` file listing core dependencies: ```txt # Core Dependencies pydantic>=2.0.0 networkx>=2.8.0 PyYAML>=6.0.0 numpy>=1.21.0 torch>=2.0.0 # RAG Dependencies faiss-cpu>=1.7.0 # or faiss-gpu for GPU support sentence-transformers>=2.2.0 elasticsearch>=7.17.0 # optional, for document storage # RL Dependencies gymnasium>=0.26.0 stable-baselines3>=2.0.0 # Persona Dependencies jsonschema>=4.17.0 # Utility Dependencies loguru>=0.7.0 pytest>=7.0.0 python-dotenv>=1.0.0 # API and Service Dependencies (optional) fastapi>=0.100.0 uvicorn>=0.22.0 redis>=4.5.0 ``` Configuration files are placed in `config/`, including `default.yaml`, `rl_config.yaml`, and persona schemas. --- ### RAG Pipeline Implementation (Example) Below is an example snippet of the RAG pipeline code. This includes retrieval, ranking, and generation with integrated error handling and logging. The example is partial and would need to be complemented with real logic and model loading: ```python # src/rag/retriever.py import logging from typing import List from ..core.interfaces import IRetriever, Document, RAGException logger = logging.getLogger(__name__) class FaissRetriever(IRetriever): def __init__(self, index, embeddings, top_k=3): self.index = index self.embeddings = embeddings self.top_k = top_k def retrieve(self, query: str, k: int = None) -> List[Document]: k = k or self.top_k try: query_vec = self.embeddings.embed_text(query) D, I = self.index.search(query_vec, k) docs = self._get_documents_from_ids(I) ranked_docs = self._rank_documents(query, docs) return ranked_docs except Exception as e: logger.error(f"Retrieval error: {str(e)}") raise RAGException("Failed to retrieve documents") def _get_documents_from_ids(self, ids: List[int]) -> List[Document]: # Implementation depends on DocumentStore pass def _rank_documents(self, query: str, docs: List[Document]) -> List[Document]: # Implement relevance scoring/reranking return docs ``` ```python # src/rag/generator.py import logging from typing import List from ..core.interfaces import IGenerator, Document, RAGException logger = logging.getLogger(__name__) class LMGenerator(IGenerator): def __init__(self, model, max_tokens=500, temperature=0.7): self.model = model self.max_tokens = max_tokens self.temperature = temperature def generate(self, prompt: str, context: List[Document]) -> str: try: context_text = " ".join([doc.content for doc in context]) full_prompt = f"{prompt}\n\nContext:\n{context_text}" response = self.model.generate( prompt=full_prompt, max_tokens=self.max_tokens, temperature=self.temperature ) return response.strip() except Exception as e: logger.error(f"Generation error: {str(e)}") raise RAGException("Failed to generate response") ``` --- ### RL Framework Development (Example) **Hierarchical RL Components:** - **State and Action Spaces:** Defined via `types.py` and `environment.py`. - **Reward Functions:** Implemented in `rewards.py`. - **Policy Management:** Implemented in `policy.py`. - **Model-Based Predictions:** `world_model.py` handles predictive modeling. (See the provided code snippets in the prompt for a detailed RL setup.) --- ### Finishing `utils.py` Below is a completed `utils.py` file that includes missing imports, references, and final touches. It contains multiple utility classes for logging, metrics, checkpoints, debugging, and analyzing the training process. ```python # src/rl/utils.py import json import time import random import logging import numpy as np from pathlib import Path from collections import defaultdict from typing import Dict, Any, List, Optional from torch.utils.tensorboard import SummaryWriter from .types import Experience from .types import StateType, ActionType class StateEncoder: def __init__(self, config: Dict[str, Any]): self.context_dim = config['environment']['observation_space']['context_dim'] self.metadata_dim = config['environment']['observation_space']['metadata_dim'] def encode_state(self, state: StateType) -> np.ndarray: # Encode the state into a numpy array # Just a placeholder: would depend on state definition context_vec = np.zeros(self.context_dim) metadata_vec = np.zeros(self.metadata_dim) return np.concatenate([context_vec, metadata_vec]) def decode_state(self, encoded_state: np.ndarray) -> Dict[str, np.ndarray]: context = encoded_state[:self.context_dim] metadata = encoded_state[self.context_dim:] return { 'context': context, 'metadata': metadata } class ExperienceBuffer: """Utility class for managing experience replay buffer""" def __init__(self, max_size: int = 100000): self.max_size = max_size self.buffer = [] self.position = 0 def push(self, experience: Experience): """Add experience to buffer""" if len(self.buffer) < self.max_size: self.buffer.append(experience) else: self.buffer[self.position] = experience self.position = (self.position + 1) % self.max_size def sample(self, batch_size: int) -> List[Experience]: """Sample a batch of experiences""" return random.sample(self.buffer, min(batch_size, len(self.buffer))) def __len__(self): return len(self.buffer) class MetricsTracker: """Utility class for tracking and logging metrics""" def __init__(self, log_dir: str): self.log_dir = Path(log_dir) self.log_dir.mkdir(parents=True, exist_ok=True) self.metrics = defaultdict(list) self.writer = SummaryWriter(log_dir) def update(self, metrics: Dict[str, float], step: int): """Update metrics""" for name, value in metrics.items(): self.metrics[name].append(value) self.writer.add_scalar(name, value, step) def get_average(self, metric_name: str, window: int = 100) -> float: """Get moving average of a metric""" values = self.metrics[metric_name] if not values: return 0.0 return np.mean(values[-window:]) def save(self): """Save metrics to disk""" metrics_path = self.log_dir / 'metrics.json' with open(metrics_path, 'w') as f: json.dump({k: v for k, v in self.metrics.items()}, f, indent=2) def close(self): """Cleanup""" self.writer.close() class ActionManager: def __init__(self, config: Dict[str, Any]): self.action_space = config['environment']['action_space']['n_actions'] def is_valid_action(self, action: int, state: Dict[str, Any]) -> bool: """Check if action is valid in current state""" if action not in range(self.action_space): return False action_type = ActionType(action) # Example conditions, adjust based on actual logic if action_type == ActionType.FINALIZE: return len(state.get('subgoals', [])) == 0 if action_type == ActionType.REFINE: return len(state['context']) > 0 if action_type == ActionType.GENERATE: return len(state['context']) > 0 # RETRIEVE and REQUEST_MORE_INFO are always valid return True def get_valid_actions(self, state: Dict[str, Any]) -> List[int]: """Get list of valid actions for current state""" return [action for action in range(self.action_space) if self.is_valid_action(action, state)] class DebugLogger: """Utility class for detailed debugging and logging""" def __init__(self, log_dir: str, enabled: bool = True): self.enabled = enabled self.episode_logs = [] if enabled: self.log_dir = Path(log_dir) self.log_dir.mkdir(parents=True, exist_ok=True) logging.basicConfig( filename=self.log_dir / 'debug.log', level=logging.DEBUG, format='%(asctime)s - %(levelname)s - %(message)s' ) def log_step(self, step_data: Dict[str, Any]): if not self.enabled: return self.episode_logs.append(step_data) logging.debug(f"Step {len(self.episode_logs)}: {step_data}") def log_episode(self, episode_num: int): if not self.enabled: return episode_path = self.log_dir / f"episode_{episode_num}.json" with open(episode_path, 'w') as f: json.dump(self.episode_logs, f, indent=2) self.episode_logs = [] def log_error(self, error: Exception, context: Dict[str, Any]): if not self.enabled: return logging.error(f"Error: {str(error)}", exc_info=True) logging.error(f"Context: {context}") class PerformanceMonitor: def __init__(self): self.step_times = [] self.memory_usage = [] self.start_time = time.time() def record_step_time(self, step_time: float): self.step_times.append(step_time) def record_memory_usage(self, mem_usage: float): self.memory_usage.append(mem_usage) def get_statistics(self) -> Dict[str, float]: if not self.step_times: return {} return { 'avg_step_time': np.mean(self.step_times), 'max_step_time': np.max(self.step_times), 'min_step_time': np.min(self.step_times), 'avg_memory_usage_mb': np.mean(self.memory_usage) if self.memory_usage else 0, 'max_memory_usage_mb': np.max(self.memory_usage) if self.memory_usage else 0, 'total_runtime': time.time() - self.start_time, 'total_steps': len(self.step_times) } def reset(self): self.step_times = [] self.memory_usage = [] self.start_time = time.time() class SystemMonitor: def __init__(self, debug_logger: DebugLogger, metrics_tracker: MetricsTracker, performance_monitor: PerformanceMonitor, warning_thresholds: Dict[str, float]): self.debug_logger = debug_logger self.metrics_tracker = metrics_tracker self.performance_monitor = performance_monitor self.warning_thresholds = warning_thresholds self.current_episode = 0 def start_episode(self, episode_number: int): self.current_episode = episode_number def _check_warnings(self, step_data: Dict[str, Any]) -> List[Dict[str, Any]]: warnings = [] if self.performance_monitor.step_times: step_time = self.performance_monitor.step_times[-1] if step_time > self.warning_thresholds.get('step_time', float('inf')): warnings.append({ 'type': 'performance', 'message': f'Step time ({step_time:.2f}s) exceeded threshold', 'severity': 'warning' }) if self.performance_monitor.memory_usage: memory_usage = self.performance_monitor.memory_usage[-1] if memory_usage > self.warning_thresholds.get('memory_usage', float('inf')): warnings.append({ 'type': 'resource', 'message': f'Memory usage ({memory_usage:.2f}MB) exceeded threshold', 'severity': 'warning' }) if step_data.get('reward', 0) < self.warning_thresholds.get('reward_min', -float('inf')): warnings.append({ 'type': 'training', 'message': f'Low reward ({step_data["reward"]:.2f}) detected', 'severity': 'info' }) return warnings def _handle_warnings(self, warnings: List[Dict[str, Any]]): for warning in warnings: self.debug_logger.log_step({ 'event': 'warning', 'warning': warning, 'episode': self.current_episode, 'timestamp': time.time() }) self.metrics_tracker.update({ f'warnings_{warning["type"]}': 1.0 }, self.current_episode) def process_step(self, step_data: Dict[str, Any]): warnings = self._check_warnings(step_data) self._handle_warnings(warnings) def get_training_summary(self) -> Dict[str, Any]: performance_stats = self.performance_monitor.get_statistics() return { 'performance': performance_stats, 'metrics': { name: self.metrics_tracker.get_average(name) for name in self.metrics_tracker.metrics.keys() }, 'episodes_completed': self.current_episode, 'warnings_total': sum( len(self.metrics_tracker.metrics.get(f'warnings_{wtype}', [])) for wtype in ['performance', 'resource', 'training'] ) } class ModelCheckpointer: def __init__(self): self.checkpoints = [] def save_checkpoint(self, path: str, data: Dict[str, Any]): torch.save(data, path) self.checkpoints.append(path) def load_best_checkpoint(self, metric_name: str = 'episode_reward') -> Optional[Dict[str, Any]]: if not self.checkpoints: return None best_checkpoint = None best_metric_value = float('-inf') for checkpoint_path in self.checkpoints: checkpoint = torch.load(checkpoint_path) metric_value = checkpoint['metrics'].get(metric_name, float('-inf')) if metric_value > best_metric_value: best_metric_value = metric_value best_checkpoint = checkpoint return best_checkpoint def get_checkpoint_history(self) -> List[Dict[str, Any]]: history = [] for checkpoint_path in self.checkpoints: checkpoint = torch.load(checkpoint_path) history.append({ 'episode': checkpoint['episode'], 'metrics': checkpoint['metrics'], 'timestamp': checkpoint['timestamp'] }) return history class ModelAnalyzer: """Utility for analyzing model behavior and performance""" def __init__(self, config: Dict[str, Any]): self.config = config self.action_manager = ActionManager(config) self.state_encoder = StateEncoder(config) def analyze_episode(self, experiences: List[Experience]) -> Dict[str, Any]: analysis = { 'action_distribution': defaultdict(int), 'reward_statistics': { 'mean': float(np.mean([exp.reward for exp in experiences])) if experiences else 0, 'std': float(np.std([exp.reward for exp in experiences])) if experiences else 0, 'min': float(min(exp.reward for exp in experiences)) if experiences else 0, 'max': float(max(exp.reward for exp in experiences)) if experiences else 0 }, 'subgoal_completion': self._analyze_subgoal_completion(experiences), 'state_transitions': self._analyze_state_transitions(experiences) } for exp in experiences: analysis['action_distribution'][exp.action] += 1 return analysis def _analyze_subgoal_completion(self, experiences: List[Experience]) -> Dict[str, Any]: completed_subgoals = [] completion_times = [] current_subgoal_start = 0 for i, exp in enumerate(experiences): # Assuming exp.info is available and contains subgoal info if hasattr(exp, 'info') and 'subgoal_completed' in getattr(exp, 'info', {}): completed_subgoals.append(exp.info['subgoal_completed']) completion_times.append(i - current_subgoal_start) current_subgoal_start = i return { 'completed_subgoals': completed_subgoals, 'avg_completion_time': float(np.mean(completion_times)) if completion_times else 0.0, 'completion_rate': len(completed_subgoals) / len(experiences) if experiences else 0.0 } def _analyze_state_transitions(self, experiences: List[Experience]) -> Dict[str, Any]: state_changes = [] for i in range(len(experiences) - 1): current_state = self.state_encoder.encode_state(experiences[i].state) next_state = self.state_encoder.encode_state(experiences[i+1].state) state_change = np.linalg.norm(next_state - current_state) state_changes.append(state_change) return { 'avg_state_change': float(np.mean(state_changes)) if state_changes else 0.0, 'max_state_change': float(np.max(state_changes)) if state_changes else 0.0, 'state_stability': float(np.std(state_changes)) if state_changes else 0.0 } ``` --- **Note:** The provided code snippets are illustrative and will require adaptation to the real system's logic, data formats, and integrations. The placeholders, such as methods not fully implemented and pseudo-logic for encoding states or retrieving documents, should be replaced with actual application logic and model integrations. This architecture and code structure give you a starting point to implement a complex, unified AI system that merges RAG, RL, Persona modeling, and orchestration.

Develop a persona-based interaction module that: 1. Defines persona schemas using Pydantic 2. Implements persona validation 3. Creates response adaptation logic 4. Handles persona management

Below is a sample implementation of a persona-based interaction module that utilizes Pydantic for schema definitions, includes persona validation using JSON schemas or Pydantic validation, creates logic for adapting responses to a persona’s style and constraints, and handles persona lifecycle and management. --- ### Overview This module focuses on how different personas affect responses generated by the system. A persona defines specific conversational characteristics such as tone, formality level, domain knowledge requirements, and any constraints (e.g., do not mention certain topics, always maintain a specific style). Key functionalities: 1. **Persona Schemas**: Persona definitions using Pydantic models. 2. **Persona Validation**: Ensures personas conform to defined standards and that responses adhere to persona constraints. 3. **Response Adaptation**: Adjusts generated responses according to the active persona’s stylistic and content guidelines. 4. **Persona Management**: Allows switching, updating, and persisting personas. The code below can be placed in `src/persona/`. --- ### Directory Structure ``` src/persona/ ├── __init__.py ├── schemas.py ├── adapter.py └── manager.py ``` --- ### Persona Schemas (schemas.py) We define a flexible schema using Pydantic. Each persona can have: - **name**: Persona name (e.g., "enthusiastic_helper") - **description**: A textual description of the persona’s role and characteristics. - **style**: Stylistic guidelines (e.g., tone, formality). - **constraints**: Hard constraints on output content. - **validation_rules**: Rules to validate responses (e.g., prohibited keywords, required keywords). ```python # src/persona/schemas.py from pydantic import BaseModel, Field, validator from typing import List, Optional, Dict, Any import re class PersonaStyle(BaseModel): tone: str = Field(..., description="Tone of the persona, e.g., 'friendly', 'professional', 'witty'") formality: str = Field(..., description="Level of formality: 'formal', 'informal', etc.") domain_knowledge: Optional[str] = Field(None, description="Domain expertise area") class PersonaConstraints(BaseModel): prohibited_phrases: List[str] = Field(default_factory=list, description="List of phrases the persona should never use") max_length: Optional[int] = Field(None, description="Maximum length of the response in characters or tokens") required_keywords: List[str] = Field(default_factory=list, description="List of keywords that must be included if applicable") @validator('prohibited_phrases', each_item=True) def no_empty_phrases(cls, phrase): if not phrase.strip(): raise ValueError("Prohibited phrase cannot be empty.") return phrase class PersonaValidationRules(BaseModel): regex_checks: Dict[str, str] = Field(default_factory=dict, description="Key: rule name, Value: regex pattern to verify presence/absence") min_length: Optional[int] = Field(None, description="Minimum length of the response") max_length: Optional[int] = Field(None, description="Maximum length of the response") class PersonaSchema(BaseModel): name: str = Field(..., description="Unique identifier for the persona") description: str = Field(..., description="Brief summary of the persona's purpose and style") style: PersonaStyle constraints: PersonaConstraints validation_rules: PersonaValidationRules @validator('name') def name_cannot_be_empty(cls, v): if not v.strip(): raise ValueError("Persona name cannot be empty.") return v ``` --- ### Persona Validation (within adapter.py and manager.py) Validation can occur in two ways: 1. **Schema-level validation**: Handled automatically by Pydantic when creating or updating personas. 2. **Response-level validation**: Ensures that generated responses conform to persona constraints and validation rules. We integrate a validation method into the `adapter.py` module that checks a response against the persona’s constraints and rules. ```python # src/persona/adapter.py import re from typing import Dict, Any from .schemas import PersonaSchema class PersonaAdapter: def __init__(self, persona: PersonaSchema): self.persona = persona def validate_response(self, response: str) -> bool: # Check prohibited phrases for phrase in self.persona.constraints.prohibited_phrases: if phrase.lower() in response.lower(): return False # Check required length constraints if self.persona.constraints.max_length is not None and len(response) > self.persona.constraints.max_length: return False # Check validation rules: length if self.persona.validation_rules.min_length is not None and len(response) < self.persona.validation_rules.min_length: return False if self.persona.validation_rules.max_length is not None and len(response) > self.persona.validation_rules.max_length: return False # Check required keywords for kw in self.persona.constraints.required_keywords: if kw.lower() not in response.lower(): return False # Regex checks for rule_name, pattern in self.persona.validation_rules.regex_checks.items(): # If the regex pattern is expected to be found and isn't, return False. if not re.search(pattern, response): return False return True def adapt_response(self, raw_response: str, context: Dict[str, Any]) -> str: """ Adapt the raw response according to the persona's style and constraints. Potential adaptations could include: - Adjusting tone and formality - Adding domain-specific jargon - Ensuring compliance with constraints (e.g., removing prohibited phrases) """ adapted_response = raw_response # Example adaptation: ensure tone by adding an introductory phrase if tone is friendly # In a real scenario, you might leverage NLG techniques or prompt engineering. if self.persona.style.tone.lower() == 'friendly' and not adapted_response.lower().startswith("hey"): adapted_response = "Hey there! " + adapted_response # Check for prohibited phrases and remove them for phrase in self.persona.constraints.prohibited_phrases: adapted_response = re.sub(re.escape(phrase), "****", adapted_response, flags=re.IGNORECASE) # Ensure required keywords are present; if not, we might append them at the end or re-generate for kw in self.persona.constraints.required_keywords: if kw.lower() not in adapted_response.lower(): adapted_response += f" {kw}" # If response too long, truncate (naive approach) if self.persona.constraints.max_length is not None: adapted_response = adapted_response[:self.persona.constraints.max_length] # Additional logic could involve changing formality, adding domain knowledge hints, etc. return adapted_response.strip() ``` --- ### Persona Management (manager.py) The manager handles loading, updating, switching, and persisting personas. For simplicity, this example uses a dictionary to store personas in memory. In a real application, this could be replaced with database operations or file storage. ```python # src/persona/manager.py import json from typing import Dict, Any, Optional from pathlib import Path from .schemas import PersonaSchema from .adapter import PersonaAdapter from pydantic import ValidationError class PersonaManager: def __init__(self, persona_dir: str = "config/personas"): self.persona_dir = Path(persona_dir) self.persona_dir.mkdir(parents=True, exist_ok=True) self.personas: Dict[str, PersonaSchema] = {} self.active_persona: Optional[PersonaSchema] = None def load_persona(self, name: str) -> PersonaSchema: persona_path = self.persona_dir / f"{name}.json" if not persona_path.exists(): raise FileNotFoundError(f"Persona file {persona_path} not found") with open(persona_path, 'r') as f: data = json.load(f) try: persona = PersonaSchema(**data) self.personas[name] = persona return persona except ValidationError as e: raise ValueError(f"Invalid persona configuration: {e}") def save_persona(self, persona: PersonaSchema): persona_path = self.persona_dir / f"{persona.name}.json" with open(persona_path, 'w') as f: json.dump(persona.dict(), f, indent=2) self.personas[persona.name] = persona def create_persona(self, persona_data: Dict[str, Any]) -> PersonaSchema: try: persona = PersonaSchema(**persona_data) self.save_persona(persona) return persona except ValidationError as e: raise ValueError(f"Error creating persona: {e}") def switch_persona(self, name: str): if name not in self.personas: # Attempt to load if not already loaded self.load_persona(name) self.active_persona = self.personas[name] def get_active_persona(self) -> Optional[PersonaSchema]: return self.active_persona def adapt_response(self, response: str, context: Dict[str, Any]) -> str: if not self.active_persona: raise ValueError("No active persona selected") adapter = PersonaAdapter(self.active_persona) adapted_response = adapter.adapt_response(response, context) # Validate final response if not adapter.validate_response(adapted_response): # In a real scenario, you might attempt to re-generate or refine the response. # Here we raise an error. raise ValueError("Adapted response does not comply with persona constraints.") return adapted_response ``` --- ### Example Usage ```python # examples/basic_usage.py from src.persona.manager import PersonaManager # Sample persona definition example_persona_data = { "name": "enthusiastic_helper", "description": "An enthusiastic assistant that is always friendly and helpful", "style": { "tone": "friendly", "formality": "informal", "domain_knowledge": "general" }, "constraints": { "prohibited_phrases": ["unfortunately", "I can't do that"], "max_length": 200, "required_keywords": ["help", "glad"] }, "validation_rules": { "regex_checks": { "must_mention_hello": "hello" }, "min_length": 20, "max_length": 200 } } manager = PersonaManager() persona = manager.create_persona(example_persona_data) # Switch to the newly created persona manager.switch_persona("enthusiastic_helper") # Generate a raw response from a model (hypothetical) raw_response = "Hello! I am here to assist you with whatever you need." context = {"user_query": "How do I bake a cake?"} adapted_response = manager.adapt_response(raw_response, context) print("Adapted Response:", adapted_response) ``` In this example, the adapter and manager ensure the response includes the required phrases ("help", "glad"), meets length constraints, and does not include prohibited phrases. It also starts with a friendly greeting due to the persona’s tone. If the response fails validation, an exception is raised. --- This solution forms a flexible blueprint for persona-based interaction. Real-world complexity might involve integrating with a language model to re-generate responses that fail validation, using more sophisticated NLG techniques for adaptation, and persisting/loading personas from external databases or services.

Implement a graph-based orchestration system using networkx that: 1. Creates agent nodes and edges 2. Manages workflow transitions 3. Handles conditional routing 4. Implements state management

Below is an example of a graph-based orchestration system implemented using NetworkX. The goal is to illustrate how to: 1. Create agent nodes and edges. 2. Manage workflow transitions (e.g., move from one node to the next). 3. Handle conditional routing based on custom logic. 4. Integrate state management to track the workflow's current position, contextual data, and history. This example uses the interfaces defined earlier (in previous discussions) and shows how you might implement them in the `orchestrator` directory. --- ### Directory Structure For reference: ``` src/orchestrator/ ├── __init__.py ├── graph.py ├── nodes.py └── state_manager.py ``` ### Dependencies Make sure to have `networkx` installed: ```bash pip install networkx ``` --- ### nodes.py Define node types and logic. Nodes represent steps or tasks in the workflow. They might be associated with a particular agent, a persona adaptation step, a retrieval action, etc. ```python # src/orchestrator/nodes.py from typing import Callable, Any, Dict from abc import ABC, abstractmethod class OrchestrationNode(ABC): """Base class for any orchestration node.""" def __init__(self, node_id: str): self.node_id = node_id @abstractmethod def process(self, state: Dict[str, Any]) -> Dict[str, Any]: """Process the state and return updated state.""" pass class AgentNode(OrchestrationNode): """A node that represents an RL agent step or a persona adaptation step.""" def __init__(self, node_id: str, agent_fn: Callable[[Dict[str, Any]], Dict[str, Any]]): super().__init__(node_id) self.agent_fn = agent_fn def process(self, state: Dict[str, Any]) -> Dict[str, Any]: # agent_fn could be a function that takes state and returns updated state updated_state = self.agent_fn(state) return updated_state class DecisionNode(OrchestrationNode): """A node that makes a decision and sets a flag or modifies state for conditional routing.""" def __init__(self, node_id: str, decision_fn: Callable[[Dict[str, Any]], Dict[str, Any]]): super().__init__(node_id) self.decision_fn = decision_fn def process(self, state: Dict[str, Any]) -> Dict[str, Any]: updated_state = self.decision_fn(state) return updated_state ``` --- ### state_manager.py Implements the `IStateManager` interface to update and retrieve the current state. The state includes workflow position and any contextual data. ```python # src/orchestrator/state_manager.py from typing import Dict, Any from ..core.interfaces import IStateManager, State class StateManager(IStateManager): def __init__(self): self._state = State(context={}, history=[], metadata={}) def update_state(self, state: State, updates: Dict[str, Any]) -> State: new_context = {**state.context, **updates.get('context', {})} new_history = state.history + updates.get('history', []) new_metadata = {**state.metadata, **updates.get('metadata', {})} updated_state = State( context=new_context, history=new_history, metadata=new_metadata ) self._state = updated_state return updated_state def get_current_state(self) -> State: return self._state ``` --- ### graph.py Implements the orchestrator using NetworkX. This orchestrator: - Adds nodes and edges to a directed graph. - Allows conditional edges (i.e., edges that require a certain condition function to return True before following that path). - Steps through the workflow by processing the current node and then determining which edge to follow next based on conditions and the updated state. ```python # src/orchestrator/graph.py import networkx as nx from typing import Any, Dict, Optional, Callable, List from ..core.interfaces import IOrchestrator, State from .state_manager import StateManager from .nodes import OrchestrationNode class ConditionalEdge: """Represents an edge with an optional condition.""" def __init__(self, from_node: str, to_node: str, condition: Optional[Callable[[Dict[str, Any]], bool]] = None): self.from_node = from_node self.to_node = to_node self.condition = condition class GraphOrchestrator(IOrchestrator): def __init__(self, state_manager: StateManager): self.graph = nx.DiGraph() self.state_manager = state_manager self.current_node: Optional[str] = None self.edges: List[ConditionalEdge] = [] def add_node(self, node_id: str, component: Any): # component should be an instance of OrchestrationNode or similar self.graph.add_node(node_id, component=component) if self.current_node is None: self.current_node = node_id # Start from the first added node if not specified def add_edge(self, from_node: str, to_node: str, condition: Optional[Callable[[Dict[str, Any]], bool]] = None): self.graph.add_edge(from_node, to_node) self.edges.append(ConditionalEdge(from_node, to_node, condition)) def set_start_node(self, node_id: str): if node_id in self.graph.nodes: self.current_node = node_id else: raise ValueError(f"Start node {node_id} not in graph") def _select_next_node(self, state_dict: Dict[str, Any]) -> Optional[str]: """Select the next node based on conditions.""" successors = list(self.graph.successors(self.current_node)) if not successors: # No successors means we reached an end node return None # Check if there are conditional edges and pick the one that matches valid_successors = [] for edge in self.edges: if edge.from_node == self.current_node: if edge.condition is None or edge.condition(state_dict): valid_successors.append(edge.to_node) # For simplicity, if multiple valid successors, just pick the first # A more sophisticated approach could pick based on priority return valid_successors[0] if valid_successors else None def process_step(self, state: State) -> State: if self.current_node is None: raise ValueError("No current node set in the orchestrator") component: OrchestrationNode = self.graph.nodes[self.current_node]['component'] # Process current node updated_state_dict = component.process(state.context) # Update global state new_state = self.state_manager.update_state(state, { 'context': updated_state_dict, 'history': [{'node': self.current_node, 'updates': updated_state_dict}] }) # Move to next node if any next_node = self._select_next_node(new_state.context) self.current_node = next_node return new_state ``` --- ### Example Usage Below is an example demonstrating how to set up the orchestrator with some nodes, edges, conditions, and run through a workflow. ```python # examples/graph_orchestration_example.py import sys sys.path.append('.') # Ensure current directory is on the path from src.orchestrator.graph import GraphOrchestrator from src.orchestrator.state_manager import StateManager from src.orchestrator.nodes import AgentNode, DecisionNode from src.core.interfaces import State # Dummy agent functions def greet_agent_fn(state_dict): # Just add a greeting to the context state_dict['message'] = "Hello! I am here to help." return state_dict def persona_adapt_fn(state_dict): # Adapt the message according to a persona - here just append text state_dict['message'] += " (Adapted with friendly persona)" return state_dict def decision_fn(state_dict): # Decide which path to take based on context user_need_help = state_dict.get('user_need_help', False) state_dict['decision'] = 'help_path' if user_need_help else 'no_help_path' return state_dict def help_agent_fn(state_dict): # Provide some help state_dict['message'] += " Here's the help you requested!" return state_dict def no_help_agent_fn(state_dict): # If user doesn't need help state_dict['message'] += " It seems you don't need help." return state_dict def help_condition(context): return context.get('decision') == 'help_path' def no_help_condition(context): return context.get('decision') == 'no_help_path' # Setup orchestrator sm = StateManager() orchestrator = GraphOrchestrator(sm) # Add nodes orchestrator.add_node("greet_node", AgentNode("greet_node", greet_agent_fn)) orchestrator.add_node("persona_node", AgentNode("persona_node", persona_adapt_fn)) orchestrator.add_node("decision_node", DecisionNode("decision_node", decision_fn)) orchestrator.add_node("help_node", AgentNode("help_node", help_agent_fn)) orchestrator.add_node("no_help_node", AgentNode("no_help_node", no_help_agent_fn)) # Add edges orchestrator.add_edge("greet_node", "persona_node") orchestrator.add_edge("persona_node", "decision_node") orchestrator.add_edge("decision_node", "help_node", condition=help_condition) orchestrator.add_edge("decision_node", "no_help_node", condition=no_help_condition) # Set start node orchestrator.set_start_node("greet_node") # Initial state initial_state = State(context={"user_need_help": True}, history=[], metadata={}) # Run workflow steps until no next node current_state = initial_state while orchestrator.current_node is not None: current_state = orchestrator.process_step(current_state) print("Final State Context:", current_state.context) print("Workflow History:", current_state.history) ``` **What the example does:** - Starts at `greet_node`, adds a greeting to the state. - Moves to `persona_node`, adapts the greeting. - Moves to `decision_node`, decides the next path based on `user_need_help`. - If `user_need_help` is True, it moves to `help_node`; otherwise, `no_help_node`. - The process continues until no more edges exist. - Prints the final context and the workflow history, showing how the state evolved at each step. --- **In Summary:** This example shows how to: - Use NetworkX to create a directed graph of workflow steps (nodes). - Implement logic within nodes to transform state. - Add conditional transitions (edges) that depend on the current context. - Manage a global state using a StateManager that conforms to the `IStateManager` interface. - Step through the graph, updating and tracking state at each node. This serves as a blueprint that can be extended or integrated with the rest of the AI system (RAG, RL, Persona) to orchestrate complex multi-step workflows.

Provide code to integrate all components (RAG, RL, Persona, Orchestrator) including: 1. Interface implementations 2. State management 3. Error handling 4. Event logging

Below is an example of a unified integration of the previously discussed components—RAG, RL, Persona, and Orchestrator—into a single cohesive workflow. This example demonstrates: 1. **Interface Implementations:** Uses previously defined interfaces (`IRetriever`, `IGenerator`, `IRLAgent`, `IPersona`, `IOrchestrator`, `IStateManager`, `IWorldModel`) and their concrete classes. 2. **State Management:** Integrates with `StateManager` to maintain and update the workflow’s global state. 3. **Error Handling:** Uses try/except blocks, custom exceptions, and logging for robust error handling. 4. **Event Logging:** Demonstrates logging events at each step of the workflow, including metrics and debug information. **Note:** This is a conceptual integration. Some methods are placeholders and would need real implementations (e.g., loading actual models, performing retrieval with a configured document store, RL agent training logic, etc.). The code assumes that you have the previously discussed modules implemented and accessible under `src/`. --- ### Directory Structure Recap ``` unified_ai_system/ │ ├── src/ │ ├── rag/ │ ├── rl/ │ ├── persona/ │ ├── orchestrator/ │ ├── core/ │ └── utils/ │ ├── config/ ├── examples/ │ └── main_integration_example.py └── requirements.txt ``` **Files like `interfaces.py`, `manager.py` for persona, `retriever.py`, `generator.py` for RAG, `agent.py` for RL, `graph.py` for orchestrator, and `state_manager.py` are assumed to be present as described previously.** --- ### High-Level Integration We will create a main script (`main_integration_example.py`) that: - Loads configuration. - Sets up logging. - Initializes the Persona Manager, RAG components (Retriever and Generator), RL Agent, and Orchestrator. - Builds a workflow graph that uses these components. - Runs a single user query through the orchestrated pipeline. - Logs all events and handles errors. --- ### Example Persona, RAG, RL, and Orchestrator Integration **Key Steps:** 1. **Initialize Logging and Config:** Use `loguru` or Python’s built-in logging. 2. **Load Persona and Set Active Persona:** Use `PersonaManager` to load a persona that will adapt system responses. 3. **Initialize RAG Components:** Create a `FaissRetriever` and `LMGenerator` (from previous examples) to handle retrieval-augmented generation. 4. **Initialize RL Agent:** Create an RL agent that might decide how to refine the response or which path to take next. 5. **Create Orchestrator Graph:** The orchestrator is built from `AgentNode`, `DecisionNode`, or custom nodes. For example: - `persona_node`: Adapts response to persona style. - `retrieval_node`: Runs the RAG retrieval step. - `generation_node`: Generates a draft response using RAG. - `rl_node`: Uses the RL agent to decide if we finalize or refine further. - `final_node`: Outputs the final response. 6. **Run the Workflow:** Pass initial user query state through the orchestrator until no next node is available. 7. **Error Handling and Logging:** Wrap main logic in try/except, log errors and events, and record metrics. --- ### Example Code: `main_integration_example.py` ```python # examples/main_integration_example.py import sys sys.path.append('.') # Ensure project root is on path import logging from pathlib import Path import yaml from src.core.interfaces import State, UnifiedAIException from src.utils.logging import setup_logging # Assume a logging setup function from src.persona.manager import PersonaManager from src.rag.retriever import FaissRetriever from src.rag.generator import LMGenerator from src.rl.agent import HierarchicalRLAgent from src.orchestrator.graph import GraphOrchestrator from src.orchestrator.nodes import AgentNode, DecisionNode from src.orchestrator.state_manager import StateManager # Placeholder agent functions, conditions, and node logic def persona_adapt_fn(context): # Placeholder: persona adaptation done externally via PersonaManager # Just log that persona adaptation will happen later context['persona_adapted'] = False return context def retrieval_fn(context, retriever): query = context.get('user_query', '') docs = retriever.retrieve(query) context['retrieved_docs'] = [doc.dict() for doc in docs] return context def generation_fn(context, generator): query = context.get('user_query', '') docs = context.get('retrieved_docs', []) # Convert docs back to Document objects if needed from src.core.interfaces import Document doc_objs = [Document(**d) for d in docs] response = generator.generate(prompt=query, context=doc_objs) context['draft_response'] = response return context def persona_adapter_fn(context, persona_manager): # Adapt the draft response according to the active persona # Uses persona_manager.adapt_response draft = context.get('draft_response', '') adapted = persona_manager.adapt_response(draft, context) context['final_response'] = adapted context['persona_adapted'] = True return context def rl_decision_fn(context, rl_agent): # Example: use RL agent to decide if we should finalize or refine # For simplicity, always finalize context['rl_decision'] = 'finalize' return context def finalize_condition(context): return context.get('rl_decision') == 'finalize' def refine_condition(context): return context.get('rl_decision') == 'refine' # Specialized node classes to integrate components class PersonaNode(AgentNode): def __init__(self, node_id: str, persona_manager): def agent_fn(ctx): return persona_adapter_fn(ctx, persona_manager) super().__init__(node_id, agent_fn) class RetrievalNode(AgentNode): def __init__(self, node_id: str, retriever): def agent_fn(ctx): return retrieval_fn(ctx, retriever) super().__init__(node_id, agent_fn) class GenerationNode(AgentNode): def __init__(self, node_id: str, generator): def agent_fn(ctx): return generation_fn(ctx, generator) super().__init__(node_id, agent_fn) class RLDecisionNode(AgentNode): def __init__(self, node_id: str, rl_agent): def agent_fn(ctx): return rl_decision_fn(ctx, rl_agent) super().__init__(node_id, agent_fn) if __name__ == "__main__": # Load config config_path = Path("config/default.yaml") with open(config_path, 'r') as f: config = yaml.safe_load(f) # Setup logging setup_logging(config) # Assume this sets up logging and log levels logger = logging.getLogger(__name__) try: # Initialize Persona persona_manager = PersonaManager(persona_dir="config/personas") # Load an example persona persona = persona_manager.create_persona({ "name": "enthusiastic_helper", "description": "An enthusiastic assistant that is always friendly and helpful", "style": { "tone": "friendly", "formality": "informal", "domain_knowledge": "general" }, "constraints": { "prohibited_phrases": ["unfortunately", "I can't do that"], "max_length": 200, "required_keywords": ["help", "glad"] }, "validation_rules": { "regex_checks": { "must_mention_hello": "hello" }, "min_length": 20, "max_length": 200 } }) persona_manager.switch_persona("enthusiastic_helper") # Initialize RAG components # Placeholder: Load FAISS index and embeddings index = None # Replace with actual faiss index embeddings = None # Replace with actual embedding model retriever = FaissRetriever(index=index, embeddings=embeddings, top_k=3) # Placeholder: language model generator lm_model = None # Replace with actual model instance generator = LMGenerator(model=lm_model, max_tokens=200, temperature=0.7) # Initialize RL agent # Placeholder: RL agent initialization with config rl_agent = HierarchicalRLAgent(config={'agent':{}, 'world_model':{}}) # Initialize Orchestrator state_manager = StateManager() orchestrator = GraphOrchestrator(state_manager) # Add nodes # Suppose the workflow: # 1. retrieval_node: Retrieve docs # 2. generation_node: Generate draft response # 3. persona_node: Adapt response to persona # 4. rl_node: RL decision to finalize or refine # 5. final_node: End workflow retrieval_node = RetrievalNode("retrieval_node", retriever) generation_node = GenerationNode("generation_node", generator) persona_node = PersonaNode("persona_node", persona_manager) rl_node = RLDecisionNode("rl_node", rl_agent) # Final node: we can just use a generic AgentNode that does nothing but end def final_fn(ctx): logger.info("Finalizing response.") return ctx final_node = AgentNode("final_node", final_fn) orchestrator.add_node("retrieval_node", retrieval_node) orchestrator.add_node("generation_node", generation_node) orchestrator.add_node("persona_node", persona_node) orchestrator.add_node("rl_node", rl_node) orchestrator.add_node("final_node", final_node) # Add edges orchestrator.add_edge("retrieval_node", "generation_node") orchestrator.add_edge("generation_node", "persona_node") orchestrator.add_edge("persona_node", "rl_node") orchestrator.add_edge("rl_node", "final_node", condition=finalize_condition) # If we had a refine path: # orchestrator.add_edge("rl_node", "retrieval_node", condition=refine_condition) orchestrator.set_start_node("retrieval_node") # Initial user query initial_state = State(context={"user_query": "Hello! Can you help me bake a chocolate cake?"}, history=[], metadata={}) # Run the workflow current_state = initial_state while orchestrator.current_node is not None: logger.info(f"Processing node: {orchestrator.current_node}") current_state = orchestrator.process_step(current_state) # Once done, print the final state logger.info(f"Final State: {current_state.context}") logger.info("Workflow History:") for h in current_state.history: logger.info(h) except UnifiedAIException as e: logger.error(f"Unified AI system error: {str(e)}", exc_info=True) except Exception as e: logger.error(f"Unexpected error: {str(e)}", exc_info=True) ``` --- ### What This Code Does: - **Logging and Config:** Uses `setup_logging(config)` to configure logging. Logs each node's processing step. - **Persona Manager:** Loads a persona and sets it as active. - **RAG Components:** Initializes a `FaissRetriever` and `LMGenerator` (as placeholders) to handle retrieval and generation. - **RL Agent:** Creates a `HierarchicalRLAgent` (stubbed config). - **Orchestrator:** - Adds nodes: - `retrieval_node` retrieves documents. - `generation_node` generates a draft response using retrieved docs. - `persona_node` adapts the draft response according to the persona. - `rl_node` uses the RL agent to decide finalization or refinement. - `final_node` finalizes. - Adds edges with conditions. - Runs the workflow until no next node is found. - **State Management:** Uses the `StateManager` to keep track of the global state and updates it at each step. - **Error Handling:** Wraps the main logic in a try/except block, logs custom exceptions (`UnifiedAIException`) and any unexpected errors. - **Event Logging:** Logs node transitions, final state, and workflow history. In a full system, you would integrate `MetricsTracker` and `DebugLogger` from previous code for even more granular event logging and metrics. **Note:** This is a high-level integration example. Actual methods (like retrieval, generation, RL decision-making) need real implementations. The code provides a framework for integrating all components, ensuring that each step is logged, errors are handled, and state changes are managed. --- This integration serves as a template for building a complex, production-ready system, showing how to tie together the various pieces into a coherent orchestration pipeline.

Create a comprehensive testing framework including: 1. Unit tests for each component 2. Integration tests 3. Performance tests 4. Mock implementations

Below is a proposed testing framework structure and examples for a comprehensive suite of tests covering unit, integration, and performance tests, as well as mock implementations. This framework uses `pytest` for test execution and `unittest.mock` for mocking. Additionally, it illustrates how to structure tests to keep them organized and maintainable. **Key Points:** - **Unit Tests:** Test individual modules and classes in isolation. - **Integration Tests:** Test how components interact with each other. - **Performance Tests:** Measure performance and ensure the system meets performance criteria. - **Mock Implementations:** Create stand-ins for external systems or heavy dependencies. - **Directory Structure:** Keep test code separate from production code, mirroring the `src/` hierarchy. --- ### Directory Structure ``` unified_ai_system/ ├── src/ │ ├── core/ │ ├── rag/ │ ├── rl/ │ ├── persona/ │ ├── orchestrator/ │ └── utils/ │ ├── tests/ │ ├── __init__.py │ ├── unit/ │ │ ├── test_core.py │ │ ├── test_rag.py │ │ ├── test_rl.py │ │ ├── test_persona.py │ │ ├── test_orchestrator.py │ │ └── test_utils.py │ │ │ ├── integration/ │ │ ├── test_full_workflow.py │ │ └── test_rag_persona_integration.py │ │ │ ├── performance/ │ │ └── test_performance.py │ │ │ ├── mocks/ │ │ ├── mock_retriever.py │ │ ├── mock_generator.py │ │ ├── mock_rl_agent.py │ │ ├── mock_persona_manager.py │ │ └── mock_orchestrator.py │ │ │ └── conftest.py │ ├── config/ └── requirements.txt ``` - `unit/`: Contains unit tests for each module. - `integration/`: Contains integration tests that test multiple components together. - `performance/`: Contains performance tests, possibly using pytest-benchmark or custom timing logic. - `mocks/`: Contains mock classes or fixtures for dependencies. - `conftest.py`: Pytest configuration file for shared fixtures and setup. --- ### Mock Implementations (Example in `tests/mocks/`) Mock classes simulate actual components without requiring heavy dependencies (e.g., external services, large models): ```python # tests/mocks/mock_retriever.py from typing import List from src.core.interfaces import Document, IRetriever class MockRetriever(IRetriever): def retrieve(self, query: str, k: int = 3) -> List[Document]: # Return a fixed set of documents for testing return [ Document(id="doc1", content="Test Document 1", metadata={"score": 0.9}), Document(id="doc2", content="Test Document 2", metadata={"score": 0.8}) ] ``` ```python # tests/mocks/mock_generator.py from src.core.interfaces import IGenerator, Document class MockGenerator(IGenerator): def generate(self, prompt: str, context: List[Document]) -> str: # Return a fixed response to ensure predictable testing return "Mock response based on given context." ``` ```python # tests/mocks/mock_rl_agent.py from src.core.interfaces import IRLAgent, State class MockRLAgent(IRLAgent): def select_action(self, state: State) -> str: return "finalize" # Always choose to finalize for predictable tests def update(self, state: State, action: str, reward: float, next_state: State): pass ``` ```python # tests/mocks/mock_persona_manager.py from typing import Dict, Any from src.persona.manager import PersonaManager class MockPersonaManager(PersonaManager): def adapt_response(self, response: str, context: Dict[str, Any]) -> str: # Return response unchanged for testing, or add a tag for verification return response + " [PersonaAdapted]" ``` ```python # tests/mocks/mock_orchestrator.py from src.orchestrator.graph import GraphOrchestrator from src.orchestrator.state_manager import StateManager from src.core.interfaces import State class MockOrchestrator(GraphOrchestrator): def __init__(self): super().__init__(StateManager()) # Setup a trivial graph or a known configuration for predictable tests def run_test_workflow(self, initial_state: State) -> State: # Simplified workflow execution current_state = initial_state while self.current_node is not None: current_state = self.process_step(current_state) return current_state ``` --- ### Unit Tests (Examples in `tests/unit/`) Focus on testing individual classes and methods in isolation. Use mocks or stubs where necessary. ```python # tests/unit/test_rag.py import pytest from unittest.mock import MagicMock from src.core.interfaces import Document from src.rag.retriever import FaissRetriever @pytest.fixture def mock_index(): # Mock index object index = MagicMock() # Mock return values from search index.search.return_value = ([[0.1,0.2]], [[1,2]]) return index @pytest.fixture def mock_embeddings(): # Mock embeddings embeddings = MagicMock() embeddings.embed_text.return_value = [0.1, 0.1, 0.2] return embeddings def test_faiss_retriever(mock_index, mock_embeddings): retriever = FaissRetriever(index=mock_index, embeddings=mock_embeddings, top_k=2) docs = retriever.retrieve("test query") assert len(docs) == 2 # Expecting 2 documents # Additional assertions about docs content once implemented ``` ```python # tests/unit/test_persona.py import pytest from pydantic import ValidationError from src.persona.schemas import PersonaSchema, PersonaStyle, PersonaConstraints, PersonaValidationRules def test_persona_schema_validation(): persona_data = { "name": "test_persona", "description": "A test persona", "style": { "tone": "friendly", "formality": "informal", "domain_knowledge": "general" }, "constraints": { "prohibited_phrases": ["badword"], "max_length": 100, "required_keywords": ["hello"] }, "validation_rules": { "regex_checks": { "must_mention_hello": "hello" }, "min_length": 10, "max_length": 200 } } persona = PersonaSchema(**persona_data) assert persona.name == "test_persona" # Test invalid persona persona_data["name"] = " " # invalid with pytest.raises(ValidationError): PersonaSchema(**persona_data) ``` --- ### Integration Tests (Examples in `tests/integration/`) Integration tests ensure components work together. Here we can use the mock implementations to isolate certain parts while ensuring the orchestration flow works end-to-end. ```python # tests/integration/test_full_workflow.py import pytest from src.core.interfaces import State from tests.mocks.mock_retriever import MockRetriever from tests.mocks.mock_generator import MockGenerator from tests.mocks.mock_persona_manager import MockPersonaManager from tests.mocks.mock_rl_agent import MockRLAgent from tests.mocks.mock_orchestrator import MockOrchestrator @pytest.fixture def persona_manager(): pm = MockPersonaManager(persona_dir="config/personas") # Assume persona is already loaded or create a mock persona pm.active_persona = pm.create_persona({ "name": "test_persona", "description": "Testing persona", "style": { "tone": "friendly", "formality": "informal", "domain_knowledge": "general" }, "constraints": { "prohibited_phrases": [], "max_length": 200, "required_keywords": [] }, "validation_rules": { "regex_checks": {}, "min_length": 0, "max_length": 200 } }) return pm def test_full_workflow_integration(persona_manager): retriever = MockRetriever() generator = MockGenerator() rl_agent = MockRLAgent() # In a real scenario, set up a GraphOrchestrator with these components orchestrator = MockOrchestrator() # Add nodes and edges as done in the main code and use mocks initial_state = State(context={"user_query": "Test query?"}, history=[], metadata={}) final_state = orchestrator.run_test_workflow(initial_state) # Assert final state has expected keys assert "final_response" in final_state.context or "draft_response" in final_state.context ``` --- ### Performance Tests (Examples in `tests/performance/`) Use `pytest-benchmark` or custom timing. Performance tests measure speed and resource usage. ```python # tests/performance/test_performance.py import pytest import time from tests.mocks.mock_retriever import MockRetriever @pytest.mark.performance def test_retriever_performance(benchmark): retriever = MockRetriever() def run_retrieval(): retriever.retrieve("performance test query") # benchmark will run run_retrieval multiple times and measure performance benchmark(run_retrieval) ``` **To run performance tests:** ```bash pytest --benchmark-only tests/performance/ ``` --- ### conftest.py A `conftest.py` can hold shared fixtures or configurations. For example, set environment variables, global logging, or fixtures that all tests can use. ```python # tests/conftest.py import pytest import logging @pytest.fixture(scope="session", autouse=True) def setup_logging(): # Configure global logging for tests if needed logging.basicConfig(level=logging.INFO) logging.info("Global test setup complete.") ``` --- ### Running Tests **Run all tests:** ```bash pytest tests/ ``` **Run only unit tests:** ```bash pytest tests/unit/ ``` **Run only integration tests:** ```bash pytest tests/integration/ ``` **Run only performance tests:** ```bash pytest --benchmark-only tests/performance/ ``` **Run tests with coverage:** ```bash pytest --cov=src --cov-report=term-missing tests/ ``` --- ### Summary This testing framework provides: - **Unit tests:** Validate individual components in isolation. - **Integration tests:** Ensure multiple components work correctly together. - **Performance tests:** Benchmark the system’s performance. - **Mock implementations:** Provide controlled and predictable test conditions without relying on external resources or heavy dependencies. This structure and the provided examples can be extended and adapted to the specifics of the system’s implementation and testing requirements.

Provide optimization strategies and implementations for: 1. Performance improvements 2. Scaling considerations 3. Resource management 4. Caching mechanisms

Below are various optimization strategies and example implementations that address performance improvements, scaling, resource management, and caching mechanisms within a unified AI system that integrates RAG, RL, Persona modeling, and Orchestrator components. These strategies are illustrative and can be adapted to your specific implementation and environment. --- ### 1. Performance Improvements **Strategies:** - **Batching Operations:** For retrieval or generation, process multiple queries in a batch rather than one-by-one. This reduces overhead and can leverage vectorization. - **Asynchronous I/O:** Use async frameworks or concurrency to handle multiple retrievals or persona adaptations in parallel, especially if they involve network calls. - **Model Optimization:** - Use efficient model inference techniques like quantization, pruning, or distillation. - Cache embeddings or frequently accessed documents. - **Lazy Loading and Caching of Dependencies:** Load models and indexes only when needed and reuse them. **Example Implementation (Batching):** ```python # Example: Batched retrieval in RAG retriever def batched_retrieve(self, queries: List[str], k: int = 3) -> List[List[Document]]: """Retrieve documents for multiple queries at once.""" query_vectors = self.embeddings.embed_texts(queries) # Embed multiple queries at once D, I = self.index.search(query_vectors, k) # Convert indices to documents for each query results = [] for i, indices in enumerate(I): docs = self._get_documents_from_ids(indices) ranked_docs = self._rank_documents(queries[i], docs) results.append(ranked_docs) return results ``` --- ### 2. Scaling Considerations **Strategies:** - **Horizontal Scaling:** - Run multiple instances of RAG servers behind a load balancer. - Use a distributed computing framework (like Ray or Dask) to parallelize RL training steps. - **Vertical Scaling:** - Increase computational resources (GPU/TPU) for heavy tasks (model inference or RL simulation). - **Sharding and Partitioning:** - For document stores, partition the document index across multiple nodes for faster retrieval. - **Microservices Architecture:** - Break the system into microservices: a persona service, a retrieval service, an RL decision service. Communicate over RPC or message queues. **Example Implementation (Distributed RL Training with Ray):** ```python # Example: Using Ray to scale RL training import ray ray.init() @ray.remote class RLWorker: def __init__(self, agent_config): self.agent = HierarchicalRLAgent(agent_config) def run_episode(self, env_config): # Run an episode and return experiences return run_episode_in_env(self.agent, env_config) workers = [RLWorker.remote(config) for _ in range(10)] results = ray.get([w.run_episode.remote(env_config) for w in workers]) # Aggregate results, update agent/policy in a central coordinator ``` --- ### 3. Resource Management **Strategies:** - **Graceful Shutdown & Checkpointing:** Regularly save model states and indices to handle resource failures. - **GPU/CPU Resource Allocation:** Assign specific GPU resources to heavy tasks (e.g., generation) and use CPU for lighter tasks (e.g., persona adaptation). - **Memory Management:** - Use memory-mapped files for large indices. - Limit the batch size based on available GPU memory. - **Monitoring and Autoscaling:** - Integrate monitoring tools (Prometheus, Grafana) to track CPU, GPU, memory usage. - Automatically scale the number of service instances based on load. **Example Implementation (GPU Assignment):** ```python # Example: Assign GPU resource to generation and CPU for persona import torch class ResourceManagedGenerator: def __init__(self, model): self.model = model.to("cuda") # Move model to GPU def generate(self, prompt, context): with torch.cuda.device(0): return self.model.generate(prompt, context) class CPUOnlyPersonaAdapter: def adapt(self, response, persona): # Persona adaptation on CPU only (light-weight) return persona.apply_rules(response) ``` --- ### 4. Caching Mechanisms **Strategies:** - **Result Caching for RAG and Persona Adaptation:** Cache frequently retrieved documents and their embeddings. If the same query repeats, return cached results. - **Response Caching:** If a user asks the same question multiple times, return the cached final response. - **Memory Caches and Distributed Caches:** - Use `functools.lru_cache` for in-process caching of small results. - Use Redis or Memcached for distributed caching across multiple service instances. - **Cache Invalidations and TTLs:** - Set a time-to-live (TTL) for cached entries so that stale data is eventually refreshed. - **Partial Caching:** Cache intermediate steps (retrieved docs, embeddings) and final responses. The orchestrator can skip already computed steps if the context hasn’t changed. **Example Implementation (Redis Caching):** ```python import redis import json from typing import List class RedisCache: def __init__(self, host='localhost', port=6379, db=0): self.client = redis.Redis(host=host, port=port, db=db) def set(self, key: str, value: dict, ttl: int = 300): self.client.setex(key, ttl, json.dumps(value)) def get(self, key: str) -> dict: data = self.client.get(key) return json.loads(data) if data else None # Using caching in RAG retriever class CachingRetriever(FaissRetriever): def __init__(self, index, embeddings, cache: RedisCache, top_k=3): super().__init__(index, embeddings, top_k) self.cache = cache def retrieve(self, query: str, k: int = 3) -> List[Document]: cache_key = f"retrieve:{query}:{k}" cached = self.cache.get(cache_key) if cached: return [Document(**d) for d in cached] docs = super().retrieve(query, k) self.cache.set(cache_key, [d.dict() for d in docs], ttl=600) return docs ``` --- ### Additional Considerations - **Profiling and Benchmarking:** - Use tools like `line_profiler`, `py-spy`, or `cProfile` to identify bottlenecks. - Use `pytest-benchmark` to track performance regressions. - **Async and Parallelism:** - Leverage `asyncio` or threading for I/O-bound tasks (like retrieval). - Use multiprocessing or distributed frameworks for CPU/GPU-bound tasks. - **Event-Driven Architecture:** - Use message queues (e.g., RabbitMQ, Kafka) to decouple components and scale horizontally. - **Observability:** - Add metrics (response time, cache hit rates) and logs to identify performance issues. --- **In Summary:** The above strategies and code snippets are starting points for implementing performance optimization, scaling approaches, resource management techniques, and caching systems in a unified AI stack. Tailoring these strategies to the specific workload, infrastructure, and application constraints will yield the best results.

Create deployment configuration including: 1. Dockerfile 2. Docker-compose setup 3. Environment configurations 4. Deployment instructions

Below is an example of how you might configure a deployment for the unified AI system. The provided configuration includes: 1. **Dockerfile:** Builds an image that installs dependencies, sets up the Python environment, and copies the source code. 2. **Docker-compose:** Defines services, networking, and dependencies. This may include a web service, a worker service for RL or indexing, and a cache service like Redis. 3. **Environment configurations:** Uses `.env` files to separate environment-specific configurations (like development vs. production). 4. **Deployment instructions:** Explains how to build, run, and test the system locally and how to deploy to a production environment. These configurations are examples and will likely need to be adjusted to match your exact project structure, dependencies, and infrastructure requirements. --- ### 1. Dockerfile Assume the following structure: - `requirements.txt` in the project root - `setup.py` in the project root to handle installation (optional) - `src/` directory containing all source code - `config/` directory containing YAML configuration files **Example `Dockerfile`:** ```dockerfile # Use an official Python runtime as a parent image FROM python:3.10-slim # Set the working directory in the container WORKDIR /app # Install system dependencies RUN apt-get update && apt-get install -y \ build-essential \ libfaiss-dev \ && rm -rf /var/lib/apt/lists/* # Copy requirements COPY requirements.txt /app/requirements.txt # Install Python dependencies RUN pip install --no-cache-dir --upgrade pip && \ pip install --no-cache-dir -r requirements.txt # Copy the rest of the application code COPY . /app # Set environment variables ENV PYTHONUNBUFFERED 1 ENV APP_ENV=production # Expose port (if running a web server) EXPOSE 8000 # Run the application entrypoint (e.g., uvicorn for FastAPI) CMD ["uvicorn", "src.api.app:app", "--host", "0.0.0.0", "--port", "8000"] ``` **Notes:** - Adjust system packages as needed (e.g., `libfaiss-dev` if required for FAISS). - If you have a GPU-based environment or need CUDA, start from a CUDA-enabled base image. - Adjust the `CMD` if your application runs differently (e.g., a script that starts orchestrator services). --- ### 2. Docker-Compose Setup A `docker-compose.yaml` can orchestrate multiple services: - **app**: The main service running the orchestrated system. - **redis**: Cache service for caching. - **worker**: Optional background worker for RL training or batch indexing. **Example `docker-compose.yaml`:** ```yaml version: "3.9" services: app: build: . container_name: unified_ai_app env_file: - .env ports: - "8000:8000" depends_on: - redis command: uvicorn src.api.app:app --host 0.0.0.0 --port 8000 environment: APP_ENV: ${APP_ENV:-development} redis: image: redis:6.2-alpine container_name: unified_ai_redis command: ["redis-server", "--appendonly", "yes"] ports: - "6379:6379" worker: build: . container_name: unified_ai_worker env_file: - .env depends_on: - redis command: python src/rl/main.py --config config/rl_config.yaml environment: APP_ENV: ${APP_ENV:-development} ``` **Notes:** - Adjust services and commands to suit your setup. - The `worker` service could run RL training or background tasks. - You can add a `depends_on` relationship to ensure `redis` starts before the `app` and `worker`. --- ### 3. Environment Configurations Use a `.env` file to store environment variables. You might have multiple `.env` files, e.g. `.env.dev` for development and `.env.prod` for production. **Example `.env` file:** ``` APP_ENV=development LOG_LEVEL=debug DATABASE_URL=postgresql://user:password@db:5432/unified_ai REDIS_URL=redis://redis:6379/0 ``` **Notes:** - Ensure `.env` is not committed to version control if it contains sensitive information. - Use `.env.template` as a safe, non-sensitive example. --- ### 4. Deployment Instructions **Local Development:** 1. **Build the image:** ```bash docker-compose build ``` 2. **Run the services:** ```bash docker-compose up ``` This starts `app`, `redis`, and `worker` services. The API should be accessible at `http://localhost:8000`. 3. **Stop the services:** ```bash docker-compose down ``` 4. **Iterate rapidly:** - Update code locally. - Rebuild the image with `docker-compose build`. - Restart `docker-compose up`. **Testing:** - Run tests inside the container: ```bash docker-compose run --rm app pytest tests/ ``` **Production Deployment:** For production, consider these steps: 1. **Use a Production `.env` file:** - Copy `.env.prod` to `.env` or specify `env_file: .env.prod` in `docker-compose.yaml`. - Set `APP_ENV=production` and stricter log levels. 2. **Build and Push Image to a Registry:** ```bash docker build -t yourregistry/unified_ai_system:latest . docker push yourregistry/unified_ai_system:latest ``` 3. **Deploy with Docker Compose on Server:** - Ensure you have the `.env` and `docker-compose.yaml` on the server. - Pull the image from the registry: ```bash docker pull yourregistry/unified_ai_system:latest ``` - Start services: ```bash docker-compose up -d ``` 4. **Scaling Services:** - To scale the `app` service: ```bash docker-compose up -d --scale app=3 ``` - Use a reverse proxy (e.g., Nginx, Traefik) in front of multiple `app` instances. 5. **Monitoring and Logging:** - Integrate logging with ELK stack or cloud logging. - Add Prometheus/Grafana for monitoring performance. **Kubernetes Deployment (Optional):** - If using Kubernetes, create `Deployment`, `Service`, and `Ingress` YAML files. - Use a CI/CD pipeline (GitHub Actions, GitLab CI) to build, push images, and apply manifests to your cluster. - Integrate secrets via Kubernetes Secrets and config through ConfigMaps. --- **In Summary:** The provided `Dockerfile`, `docker-compose.yaml`, `.env` example, and instructions outline a baseline approach to containerizing and deploying the unified AI system. Adjust these examples according to the actual code structure, services required, and infrastructure constraints.

Generate comprehensive documentation including: 1. API documentation 2. Usage examples 3. Configuration guides 4. Troubleshooting instructions

Below is an example of comprehensive documentation for the unified AI system, covering API documentation, usage examples, configuration guides, and troubleshooting instructions. This example can be adapted and expanded based on your actual implementation details. --- # Unified AI System Documentation ## Overview The Unified AI System is a modular framework that integrates **Retrieval-Augmented Generation (RAG)**, **Reinforcement Learning (RL)**, **Persona modeling**, and a **Graph-based Orchestrator** to deliver context-aware, adaptive, and optimized AI-driven interactions. The system provides: - **Retrieval-Augmented Generation (RAG):** Uses external knowledge sources to enhance text generation responses. - **Hierarchical Reinforcement Learning (RL):** Guides decision-making, subgoals, and task completion strategies. - **Persona Modeling:** Adapts system responses to defined personas with constraints, tone, and style. - **Orchestrator (Workflow Management):** Coordinates complex workflows using a graph-based approach, allowing conditional transitions and dynamic execution paths. This documentation includes details on the API endpoints, examples of usage, configuration instructions, and troubleshooting tips. --- ## 1. API Documentation ### Overview The system exposes a RESTful API (using FastAPI or a similar framework) for receiving user queries, retrieving contextual knowledge, generating responses, and managing personas and RL workflows. **Base URL:** `http://localhost:8000/` (adjust based on your deployment) **Common Response Format:** ```json { "status": "success", "data": {}, "message": "" } ``` - `status`: "success" or "error" - `data`: Response data (varies per endpoint) - `message`: Additional info (errors, hints, or empty on success) ### Endpoints #### `POST /query` **Description:** Submit a user query to the system. The orchestrator will handle the workflow, persona adaptation, and RAG to produce a final response. **Request:** ```json { "query": "How do I bake a chocolate cake?", "persona": "enthusiastic_helper" } ``` **Response:** ```json { "status": "success", "data": { "final_response": "Hey there! I'm glad to help. To bake a chocolate cake, first preheat your oven to 350°F..." }, "message": "" } ``` #### `GET /personas` **Description:** Retrieve a list of available personas. **Response:** ```json { "status": "success", "data": [ { "name": "enthusiastic_helper", "description": "An enthusiastic assistant that is always friendly and helpful" }, { "name": "neutral_expert", "description": "A neutral-tone expert who provides detailed, factual responses" } ], "message": "" } ``` #### `POST /personas` **Description:** Create a new persona. **Request:** ```json { "name": "formal_advisor", "description": "A formal advisor persona", "style": { "tone": "formal", "formality": "high", "domain_knowledge": "finance" }, "constraints": { "prohibited_phrases": ["slang"], "max_length": 200, "required_keywords": ["please"] }, "validation_rules": { "regex_checks": { "must_mention_hello": "hello" }, "min_length": 50, "max_length": 200 } } ``` **Response:** ```json { "status": "success", "data": { "name": "formal_advisor", "description": "A formal advisor persona" }, "message": "Persona created successfully." } ``` #### `GET /status` **Description:** Check system health and status of services (RAG, RL, persona DB, orchestrator). **Response:** ```json { "status": "success", "data": { "rag": "ok", "rl": "ok", "persona": "ok", "orchestrator": "ok" }, "message": "" } ``` --- ## 2. Usage Examples ### Basic Query **Goal:** Ask a cooking-related question and get a persona-adapted response. **Steps:** 1. Start the API server (e.g., `docker-compose up`). 2. Send a `POST /query` request with `query` and `persona`. **Example (curl):** ```bash curl -X POST http://localhost:8000/query \ -H "Content-Type: application/json" \ -d '{"query":"How do I bake a chocolate cake?", "persona":"enthusiastic_helper"}' ``` **Expected Response:** ```json { "status": "success", "data": { "final_response": "Hey there! I'm glad to help. To bake a chocolate cake, first preheat your oven..." }, "message": "" } ``` ### Changing Personas **Goal:** Use a different persona to adapt the response. **Steps:** 1. Create a new persona (`POST /personas`) or use an existing one. 2. Send the same query but with a different persona. **Example:** ```bash curl -X POST http://localhost:8000/query \ -H "Content-Type: application/json" \ -d '{"query":"How do I bake a chocolate cake?", "persona":"formal_advisor"}' ``` **Expected Response:** ```json { "status": "success", "data": { "final_response": "Hello. It would be my pleasure to assist. Begin by preheating your oven to 350°F..." }, "message": "" } ``` ### RL-Driven Refinement If the RL system decides the response needs refining, it may trigger additional retrieval or persona adaptation steps internally, all transparent to the user. --- ## 3. Configuration Guides ### YAML Configuration Files **`config/default.yaml`**: ```yaml system: name: "unified-ai-system" version: "1.0.0" log_level: "INFO" rag: retriever: type: "faiss" top_k: 3 generator: model: "gpt-3.5-turbo" max_tokens: 500 temperature: 0.7 rl: agent: type: "hierarchical" learning_rate: 0.001 gamma: 0.99 persona: default_persona: "enthusiastic_helper" orchestrator: max_steps: 10 timeout_seconds: 30 retry_attempts: 3 ``` **`config/rl_config.yaml`**: Configure RL training parameters: ```yaml environment: type: "unified_ai_env" max_steps: 1000 agent: state_dim: 320 n_subgoals: 4 training: n_episodes: 10000 eval_frequency: 100 ``` ### Setting Environment Variables Use `.env` files to specify environment-specific variables: **`.env` (example)**: ``` APP_ENV=development LOG_LEVEL=debug REDIS_URL=redis://redis:6379/0 DATABASE_URL=postgresql://user:password@db:5432/unified_ai ``` ### Docker and Docker-Compose Refer to the provided `Dockerfile` and `docker-compose.yaml` for building and running the system in containers: **Build and run:** ```bash docker-compose build docker-compose up ``` The API should be available at `http://localhost:8000`. --- ## 4. Troubleshooting Instructions ### Common Issues & Solutions 1. **No Response or Long Delays:** - **Check Logs:** Run `docker-compose logs app` and check for errors. - **Verify Services:** Ensure `redis` and other dependencies are running. ```bash docker-compose ps ``` - **Increase Timeout:** Adjust `orchestrator.timeout_seconds` in config if complex queries take long. 2. **Persona Validation Errors:** - Ensure persona definitions meet schema requirements (e.g., no empty names). - Check `prohibited_phrases` and `validation_rules` formatting. 3. **Model Not Loaded Errors:** - Verify that the model files or environment variables (e.g., `MODEL_PATH`) are correct. - Ensure GPU dependencies are installed if using GPU inference. 4. **RL Agent Not Updating:** - Check RL training logs (if running a separate RL training worker). - Confirm that the RL agent configuration in `rl_config.yaml` matches your environment. 5. **Cache and Performance Issues:** - Verify Redis is running (`redis-cli PING`). - Check cache hit rates by enabling debug logs: `APP_ENV=development LOG_LEVEL=debug`. ### Logs and Monitoring - Use `docker-compose logs -f app` to follow logs. - Integrate tools like Prometheus/Grafana for performance monitoring. - Check CPU/GPU usage on the host machine or cluster. ### When to Reach Out for Help - If you encounter repeated exceptions not covered in the logs or documentation, consider: - Posting an issue in the project's issue tracker with detailed steps to reproduce. - Providing environment details (Docker versions, OS, hardware specs). --- ## Additional Resources - **Developer Guides:** Check `src/` docstrings and inline comments for developer-oriented details. - **Performance Optimization:** See the optimization guide (e.g., caching, batching) from previous documentation. - **Scaling and Deployment:** Follow best practices from Kubernetes or cloud provider documentation if scaling beyond Docker Compose. --- **End of Documentation**

Provide a complete working example that: 1. Demonstrates all components 2. Shows typical workflow 3. Includes error handling 4. Demonstrates configuration

Below is a comprehensive working example that ties together the previously discussed components—RAG (Retriever and Generator), RL agent, Persona management, and the Orchestrator—showing a typical workflow, error handling, and configuration usage. This is a conceptual example and may need adaptation to run as-is. It is intended to provide a template illustrating how the various pieces fit together. **Key Features Demonstrated:** - Loading configuration from YAML files. - Initializing Persona Manager, RAG retriever/generator, RL agent, and the Orchestrator. - Running a user query through the orchestrated pipeline. - Error handling with try/except and custom exceptions. - Logging and metrics. **Assumptions:** - The directory structure and code outlined in previous answers are present. - A FastAPI endpoint is provided to simulate a user query. - A minimal RL agent, retriever, generator, and persona manager implementation are available as previously discussed. - A `config/default.yaml` and `config/rl_config.yaml` are present. - You have a `requirements.txt` that includes FastAPI, uvicorn, pydantic, yaml, networkx, redis, faiss-cpu, torch, etc. --- ### Directory Structure (Recap) ``` unified_ai_system/ ├── src/ │ ├── core/interfaces.py │ ├── core/config.py │ ├── core/exceptions.py │ ├── rag/retriever.py │ ├── rag/generator.py │ ├── rl/agent.py │ ├── rl/world_model.py │ ├── persona/manager.py │ ├── orchestrator/graph.py │ ├── orchestrator/nodes.py │ ├── orchestrator/state_manager.py │ ├── utils/logging.py │ ├── utils/metrics.py │ └── api/app.py │ ├── config/ │ ├── default.yaml │ └── rl_config.yaml │ ├── requirements.txt └── README.md ``` **Note:** `app.py` will host a FastAPI application demonstrating a user query endpoint. --- ### Example Configuration Files **`config/default.yaml`**: ```yaml system: name: "unified-ai-system" version: "1.0.0" log_level: "INFO" rag: retriever: type: "faiss" top_k: 3 similarity_threshold: 0.7 generator: model: "gpt-3.5-turbo" max_tokens: 200 temperature: 0.7 rl: agent: type: "hierarchical" learning_rate: 0.001 gamma: 0.99 persona: default_persona: "enthusiastic_helper" orchestrator: max_steps: 10 timeout_seconds: 30 retry_attempts: 3 ``` **`config/rl_config.yaml`**: ```yaml environment: type: "unified_ai_env" max_steps: 1000 agent: state_dim: 320 n_subgoals: 4 training: n_episodes: 10000 eval_frequency: 100 ``` --- ### Code for `app.py` (The Entry Point) ```python # src/api/app.py import logging from fastapi import FastAPI, HTTPException from pydantic import BaseModel from typing import Optional import yaml from pathlib import Path from ..persona.manager import PersonaManager from ..rag.retriever import FaissRetriever from ..rag.generator import LMGenerator from ..rl.agent import HierarchicalRLAgent from ..orchestrator.graph import GraphOrchestrator from ..orchestrator.nodes import AgentNode, DecisionNode from ..orchestrator.state_manager import StateManager from ..core.interfaces import State, UnifiedAIException from ..core.exceptions import RAGException, RLException from ..utils.logging import setup_logging # Load config config_path = Path("config/default.yaml") with open(config_path, 'r') as f: config = yaml.safe_load(f) setup_logging(config) logger = logging.getLogger(__name__) # Initialize Persona Manager persona_manager = PersonaManager(persona_dir="config/personas") # Create a default persona (if not already created) try: persona_manager.create_persona({ "name": "enthusiastic_helper", "description": "An enthusiastic assistant that is always friendly and helpful", "style": { "tone": "friendly", "formality": "informal", "domain_knowledge": "general" }, "constraints": { "prohibited_phrases": ["unfortunately", "I can't do that"], "max_length": 200, "required_keywords": ["help", "glad"] }, "validation_rules": { "regex_checks": { "must_mention_hello": "hello" }, "min_length": 20, "max_length": 200 } }) except: # Persona might already exist, ignore pass # Switch to default persona persona_manager.switch_persona(config['persona']['default_persona']) # Initialize RAG components # Placeholder: Load FAISS index and embeddings index = None # In real scenario, load your FAISS index embeddings = None # Load embeddings model retriever = FaissRetriever(index=index, embeddings=embeddings, top_k=config['rag']['retriever']['top_k']) lm_model = None # Load or connect to an LLM API generator = LMGenerator(model=lm_model, max_tokens=config['rag']['generator']['max_tokens'], temperature=config['rag']['generator']['temperature']) # Initialize RL agent rl_agent = HierarchicalRLAgent(config=config['rl']) # Orchestrator setup state_manager = StateManager() orchestrator = GraphOrchestrator(state_manager) # Define node functions def retrieval_fn(ctx): query = ctx.get('user_query', '') if not query: raise ValueError("No user_query provided.") docs = retriever.retrieve(query) ctx['retrieved_docs'] = [d.dict() for d in docs] return ctx def generation_fn(ctx): from ..core.interfaces import Document docs = [Document(**d) for d in ctx.get('retrieved_docs', [])] query = ctx.get('user_query', '') resp = generator.generate(query, docs) ctx['draft_response'] = resp return ctx def persona_adapt_fn(ctx): draft = ctx.get('draft_response', '') adapted = persona_manager.adapt_response(draft, ctx) ctx['final_response'] = adapted return ctx def rl_decision_fn(ctx): # For simplicity, let's always finalize here ctx['rl_decision'] = 'finalize' return ctx def finalize_condition(ctx): return ctx.get('rl_decision') == 'finalize' # Create orchestrator nodes retrieval_node = AgentNode("retrieval_node", retrieval_fn) generation_node = AgentNode("generation_node", generation_fn) persona_node = AgentNode("persona_node", persona_adapt_fn) rl_node = AgentNode("rl_node", rl_decision_fn) def final_fn(ctx): # Just log finalization logger.info("Finalizing response.") return ctx final_node = AgentNode("final_node", final_fn) # Add nodes to orchestrator orchestrator.add_node("retrieval_node", retrieval_node) orchestrator.add_node("generation_node", generation_node) orchestrator.add_node("persona_node", persona_node) orchestrator.add_node("rl_node", rl_node) orchestrator.add_node("final_node", final_node) # Add edges orchestrator.add_edge("retrieval_node", "generation_node") orchestrator.add_edge("generation_node", "persona_node") orchestrator.add_edge("persona_node", "rl_node") orchestrator.add_edge("rl_node", "final_node", condition=finalize_condition) orchestrator.set_start_node("retrieval_node") # FastAPI app app = FastAPI(title="Unified AI System") class QueryRequest(BaseModel): query: str persona: Optional[str] = None @app.post("/query") def handle_query(request: QueryRequest): try: if request.persona: persona_manager.switch_persona(request.persona) initial_state = State( context={"user_query": request.query}, history=[], metadata={} ) current_state = initial_state while orchestrator.current_node is not None: logger.info(f"Processing node: {orchestrator.current_node}") current_state = orchestrator.process_step(current_state) final_resp = current_state.context.get('final_response', current_state.context.get('draft_response', '')) if not final_resp: raise ValueError("No final response generated.") return { "status": "success", "data": {"final_response": final_resp}, "message": "" } except UnifiedAIException as e: logger.error(f"Unified AI system error: {str(e)}", exc_info=True) raise HTTPException(status_code=500, detail=str(e)) except (RAGException, RLException) as e: logger.error(f"Specialized error: {str(e)}", exc_info=True) raise HTTPException(status_code=500, detail=str(e)) except Exception as e: logger.error(f"Unexpected error: {str(e)}", exc_info=True) raise HTTPException(status_code=500, detail="Internal server error") ``` --- ### Running the Example 1. **Install dependencies:** ```bash pip install -r requirements.txt ``` 2. **Run the application:** ```bash uvicorn src.api.app:app --host 0.0.0.0 --port 8000 ``` 3. **Make a test query:** ```bash curl -X POST http://localhost:8000/query -H "Content-Type: application/json" -d '{"query":"How do I bake a chocolate cake?"}' ``` **Expected Response (Mocked):** ```json { "status": "success", "data": { "final_response": "Hey there! I'm glad to help. To bake a chocolate cake..." }, "message": "" } ``` --- ### Error Handling Demonstration - If the `query` field is missing: ```bash curl -X POST http://localhost:8000/query -H "Content-Type: application/json" -d '{}' ``` **Expected Response:** ```json { "detail": [ { "loc": ["body", "query"], "msg": "field required", "type": "value_error.missing" } ] } ``` - If the persona does not exist: ```bash curl -X POST http://localhost:8000/query -H "Content-Type: application/json" -d '{"query":"Help me with math?", "persona":"unknown_persona"}' ``` The server would log an error and return a 500 error. In a real system, you’d handle this gracefully by creating or falling back to a default persona. --- ### Configuration Demonstration - Modify `config/default.yaml` to adjust `log_level` or `rag` parameters. - Add or change personas via `persona_manager.create_persona(...)`. - Change RL behavior by updating `config/rl_config.yaml` and reinitializing the RL agent. Restart the application after changes to configuration files to apply updates. --- ### Summary This example provides a cohesive demonstration of: - Initializing all components (Persona, RAG, RL, Orchestrator). - Running a typical query workflow. - Handling errors and returning standardized responses. - Using configuration files to manage settings. - Logging and extensibility (e.g., adding new nodes, new personas). It serves as a template from which you can build a full production system.

Implement monitoring and logging systems that: 1. Track component performance 2. Log system events 3. Monitor resource usage 4. Generate alerts

Below is an example of how you might implement comprehensive monitoring and logging systems into the unified AI system. The solution focuses on four main areas: 1. **Tracking Component Performance:** Use metrics (e.g., Prometheus) to measure latency, throughput, cache hit rates, RL step times, etc. 2. **Logging System Events:** Use Python’s `logging` library with `loguru` or `structlog` for structured logs. Integrate logs with a central logging service (e.g., ELK stack or cloud logging). 3. **Monitoring Resource Usage:** Track CPU, memory, GPU usage via Prometheus exporters or Python system monitoring packages. 4. **Generate Alerts:** Integrate alerting via Prometheus alert rules, or send notifications through Slack, email, or PagerDuty using Python code hooks. This example provides conceptual code snippets and configurations. In a real environment, you might have dedicated scripts, service manifests, and CI/CD integrations. --- ### Directory Structure ``` unified_ai_system/ ├── src/ │ ├── core/ │ ├── rag/ │ ├── rl/ │ ├── persona/ │ ├── orchestrator/ │ ├── utils/ │ │ ├── logging.py │ │ ├── metrics.py │ │ ├── monitoring.py │ │ └── alerts.py │ └── api/ │ └── app.py │ ├── prometheus/ │ ├── prometheus.yml │ └── alert_rules.yml └── docker-compose.yml ``` **Note:** - `prometheus/` contains Prometheus configuration and alerting rules. - `utils/` has the utilities for logging, metrics, monitoring, and alerts. --- ### 1. Tracking Component Performance Use Prometheus metrics to track performance. `metrics.py` could define custom Prometheus metrics and expose them through a FastAPI endpoint. **Key metrics:** - **Response latency** of the `/query` endpoint. - **Cache hit rate** for RAG retrieval. - **RL decision latency** for RL agent steps. - **Number of orchestrator steps per query**. ```python # src/utils/metrics.py from prometheus_client import Counter, Histogram # Track number of queries processed queries_total = Counter('unified_ai_queries_total', 'Total number of queries processed') # Track latency of queries query_latency = Histogram('unified_ai_query_latency_seconds', 'Latency of query processing in seconds') # Track persona adaptation success/failure persona_adaptations = Counter('unified_ai_persona_adaptations_total', 'Count of persona adaptations', ['status']) # Track RL decision latency rl_decision_latency = Histogram('unified_ai_rl_decision_latency_seconds', 'Time taken by RL agent to decide actions') ``` **Integration in `app.py`:** ```python # src/api/app.py (snippet) from fastapi import Request from ..utils.metrics import queries_total, query_latency, persona_adaptations, rl_decision_latency import time @app.post("/query") def handle_query(request: QueryRequest): start_time = time.time() queries_total.inc() try: # ... handle query (as previously shown) final_resp = current_state.context.get('final_response') query_latency.observe(time.time() - start_time) persona_adaptations.labels(status="success").inc() return { "status": "success", "data": {"final_response": final_resp}, "message": "" } except Exception as e: persona_adaptations.labels(status="failure").inc() query_latency.observe(time.time() - start_time) raise ``` **Prometheus Endpoint:** Add a `/metrics` endpoint to expose metrics: ```python # src/api/app.py from prometheus_client import generate_latest, CONTENT_TYPE_LATEST from fastapi.responses import Response @app.get("/metrics") def metrics_endpoint(): return Response(generate_latest(), media_type=CONTENT_TYPE_LATEST) ``` --- ### 2. Logging System Events Use Python’s `logging` library or `loguru`. Here’s an example with the standard `logging` library and a custom `setup_logging` function: ```python # src/utils/logging.py import logging import sys def setup_logging(config): log_level = config['system'].get('log_level', 'INFO') logging.basicConfig( level=log_level, format='%(asctime)s [%(levelname)s] %(name)s: %(message)s', handlers=[logging.StreamHandler(sys.stdout)] ) ``` **Usage in `app.py`:** ```python # src/api/app.py from ..utils.logging import setup_logging logger = logging.getLogger(__name__) @app.post("/query") def handle_query(request: QueryRequest): logger.info("Received query: %s", request.query) # ... rest of the logic logger.info("Query processed successfully.") return response ``` **Structured Logging:** You can integrate `structlog` or `loguru` for JSON logs: ```python # Using structlog (optional) import structlog struct_logger = structlog.get_logger() struct_logger.info("query_received", query=request.query) ``` Logs can be collected by Docker’s stdout and integrated with ELK or OpenSearch for indexing and visualization. --- ### 3. Monitoring Resource Usage Use Prometheus node exporters or Python libraries (e.g., `psutil`) to track CPU/memory usage. A simple Python-based approach: ```python # src/utils/monitoring.py import psutil from prometheus_client import Gauge cpu_usage = Gauge('unified_ai_cpu_usage_percent', 'CPU usage percentage') memory_usage = Gauge('unified_ai_memory_usage_bytes', 'Memory usage in bytes') def update_resource_metrics(): cpu_usage.set(psutil.cpu_percent()) mem = psutil.virtual_memory() memory_usage.set(mem.used) ``` Schedule `update_resource_metrics()` using a background thread or async task: ```python # src/api/app.py import threading, time from ..utils.monitoring import update_resource_metrics def metrics_updater(): while True: update_resource_metrics() time.sleep(5) threading.Thread(target=metrics_updater, daemon=True).start() ``` For GPU usage, integrate libraries like `nvidia-ml-py` if running on GPU machines. **Prometheus Node Exporter:** For more robust monitoring, run a Node Exporter container: ```yaml # docker-compose.yml snippet node_exporter: image: prom/node-exporter expose: - "9100" # Node exporter provides host metrics, to be scraped by Prometheus ``` --- ### 4. Generate Alerts Set up Prometheus alerts and integrate with Slack or email. For example, create `prometheus/alert_rules.yml`: ```yaml groups: - name: unified_ai_alerts rules: - alert: HighCPUUsage expr: unified_ai_cpu_usage_percent > 80 for: 1m labels: severity: warning annotations: summary: "High CPU usage detected" description: "CPU usage > 80% for 1 minute" - alert: QueryLatencyHigh expr: histogram_quantile(0.95, sum(rate(unified_ai_query_latency_seconds_bucket[5m])) by (le)) > 2 for: 5m labels: severity: critical annotations: summary: "High query latency" description: "95th percentile latency exceeds 2 seconds" ``` **Prometheus Configuration (`prometheus.yml`):** ```yaml global: scrape_interval: 15s scrape_configs: - job_name: 'unified_ai' static_configs: - targets: ['app:8000'] # assuming the app exposes /metrics rule_files: - 'alert_rules.yml' ``` **Alertmanager Integration:** Use Alertmanager with Prometheus to send alerts to Slack: ```yaml # alertmanager.yml route: receiver: 'slack-notifications' receivers: - name: 'slack-notifications' slack_configs: - channel: '#alerts' send_resolved: true text: "Alert: {{ .CommonLabels.alertname }}\n{{ .CommonAnnotations.description }}" ``` **Launching with Docker-Compose:** ```yaml version: '3.9' services: app: build: . ports: - "8000:8000" # The app exposes /metrics prometheus: image: prom/prometheus volumes: - ./prometheus/prometheus.yml:/etc/prometheus/prometheus.yml - ./prometheus/alert_rules.yml:/etc/prometheus/alert_rules.yml command: --config.file=/etc/prometheus/prometheus.yml ports: - "9090:9090" alertmanager: image: prom/alertmanager volumes: - ./prometheus/alertmanager.yml:/etc/alertmanager/alertmanager.yml ports: - "9093:9093" node_exporter: image: prom/node-exporter ports: - "9100:9100" ``` Run `docker-compose up` and access Prometheus at `http://localhost:9090`. Check alerts at `http://localhost:9090/alerts`. --- ### Additional Integration for Alerts in Code If you want Python code to generate alerts (e.g., send Slack messages when certain conditions occur), integrate a Slack webhook: ```python # src/utils/alerts.py import requests SLACK_WEBHOOK_URL = "https://hooks.slack.com/services/XXX/YYY/ZZZ" def send_slack_alert(message: str): payload = {"text": message} requests.post(SLACK_WEBHOOK_URL, json=payload) def check_system_health(context): # If certain conditions are met (like no persona adaptation possible) if 'error' in context: send_slack_alert(f"Error encountered in workflow: {context['error']}") ``` Call `check_system_health()` in orchestrator steps when errors occur to trigger immediate alerts outside of Prometheus. --- ### Summary **What we have covered:** - **Performance Tracking:** Implemented Prometheus metrics, integrated with code. - **Logging System Events:** Standard logging with adjustable log levels, can be integrated with ELK. - **Monitor Resource Usage:** Used Prometheus node exporter or `psutil` to gather CPU/memory usage. - **Generate Alerts:** Configured Prometheus alerts, integrated Alertmanager to send Slack or email notifications. This approach provides a robust and flexible monitoring and alerting framework for the unified AI system in production, helping maintain reliability, performance, and stability.

Analyze this program in the attached file, and create a program that integrates all of the code into one cohesive program where the code is analyzed and the parts that are not needed are excluded and a final version is created. Write a guide to make this program, if you can not complete it all in one prompt provide the follow up prompts which will complete it.

Based on the document's content and your request, I'll guide you on creating a program to integrate all components into a cohesive system. This program will analyze the code, exclude unnecessary parts, and produce a final optimized version. ### **Steps to Develop the Integrated Program** 1. **Code Analysis and Selection**: - Read and categorize the components from the document into functional units like RAG, RL, Persona, and Orchestrator. - Identify dependencies and interfaces used across the system. - Exclude unused or redundant modules. 2. **Create Unified Architecture**: - Consolidate interfaces into a central repository. - Align overlapping functionalities (e.g., state management shared by RL and Orchestrator). - Standardize logging, metrics, and error handling across components. 3. **Build the Final System**: - Implement key integrations: - RAG: Retrieval-Augmented Generation system with optimized embeddings and retriever logic. - RL: Hierarchical Reinforcement Learning with a focus on subgoal management. - Persona: Persona-based response adaptation. - Orchestrator: Graph-based workflow management. - Include error handling, state management, and logging. 4. **Testing**: - Write unit, integration, and performance tests. - Create mock environments for RL and RAG to simulate workflows. 5. **Documentation**: - Provide detailed usage instructions, configuration guides, and troubleshooting steps. --- ### **Follow-Up Prompts to Complete the Task** If the integration is complex, here are detailed prompts to use: 1. **Step 1: Code Analysis**: - _"Identify and categorize the main components of the system based on functionality (RAG, RL, Persona, Orchestrator). List their inputs, outputs, and dependencies."_ - _"Extract interfaces and classes shared across components and determine overlaps or redundancies."_ 2. **Step 2: Consolidate Architecture**: - _"Design a unified architecture that combines shared functionalities like logging, state management, and metrics across RAG, RL, Persona, and Orchestrator."_ - _"Define a single entry point for initializing the system and orchestrating workflows."_ 3. **Step 3: Implement Integration**: - _"Write the main program to integrate RAG, RL, Persona, and Orchestrator. Ensure seamless data flow and error handling."_ - _"Optimize dependencies and remove redundant code, focusing on performance and maintainability."_ 4. **Step 4: Testing and Validation**: - _"Develop a comprehensive testing framework with unit, integration, and performance tests. Include mock implementations for RL environments and RAG queries."_ - _"Write validation scripts to test persona adherence, state transitions, and graph workflows."_ 5. **Step 5: Documentation**: - _"Create detailed API documentation and usage examples for the integrated system."_ - _"Write a troubleshooting guide addressing common issues in configuration, performance, and dependencies."_ --- ### **Expected Output** The resulting system should include: - A unified project directory with consolidated modules. - An optimized main program integrating all components. - Configurations for deployment (e.g., Docker, YAML files). - Comprehensive tests for validation and performance. - Detailed documentation. Let me know if you'd like to start with any specific step or focus on a particular aspect, such as optimization, testing, or integration logic!

Steps to Develop the Integrated Program Code Analysis and Selection: Read and categorize the components from the document into functional units like RAG, RL, Persona, and Orchestrator. Identify dependencies and interfaces used across the system. Exclude unused or redundant modules. Create Unified Architecture: Consolidate interfaces into a central repository. Align overlapping functionalities (e.g., state management shared by RL and Orchestrator). Standardize logging, metrics, and error handling across components. Build the Final System: Implement key integrations: RAG: Retrieval-Augmented Generation system with optimized embeddings and retriever logic. RL: Hierarchical Reinforcement Learning with a focus on subgoal management. Persona: Persona-based response adaptation. Orchestrator: Graph-based workflow management. Include error handling, state management, and logging. Testing: Write unit, integration, and performance tests. Create mock environments for RL and RAG to simulate workflows. Documentation: Provide detailed usage instructions, configuration guides, and troubleshooting steps. Follow-Up Prompts to Complete the Task If the integration is complex, here are detailed prompts to use: Step 1: Code Analysis: "Identify and categorize the main components of the system based on functionality (RAG, RL, Persona, Orchestrator). List their inputs, outputs, and dependencies." "Extract interfaces and classes shared across components and determine overlaps or redundancies." Step 2: Consolidate Architecture: "Design a unified architecture that combines shared functionalities like logging, state management, and metrics across RAG, RL, Persona, and Orchestrator." "Define a single entry point for initializing the system and orchestrating workflows." Step 3: Implement Integration: "Write the main program to integrate RAG, RL, Persona, and Orchestrator. Ensure seamless data flow and error handling." "Optimize dependencies and remove redundant code, focusing on performance and maintainability." Step 4: Testing and Validation: "Develop a comprehensive testing framework with unit, integration, and performance tests. Include mock implementations for RL environments and RAG queries." "Write validation scripts to test persona adherence, state transitions, and graph workflows." Step 5: Documentation: "Create detailed API documentation and usage examples for the integrated system." "Write a troubleshooting guide addressing common issues in configuration, performance, and dependencies." Expected Output The resulting system should include: A unified project directory with consolidated modules. An optimized main program integrating all components. Configurations for deployment (e.g., Docker, YAML files). Comprehensive tests for validation and performance. Detailed documentation.

To integrate the components described and optimize the program for its intended purpose, here are the structured steps and guidelines to create a cohesive program. This process will include code integration, optimization, and a step-by-step guide to ensure the system works as a single, streamlined entity. --- ### **Step 1: Analyze and Categorize Components** 1. **Categorize by Functionality:** - **Retrieval-Augmented Generation (RAG):** Handles document retrieval and response generation. - **Reinforcement Learning (RL):** Implements hierarchical learning for decision-making. - **Persona Module:** Defines schemas, validates personas, and adapts responses. - **Orchestrator:** Manages workflows and transitions via a graph-based architecture. 2. **Extract Common Functionalities:** - **State Management:** Shared by RL, Orchestrator, and Persona modules. - **Logging and Metrics:** Used across all components for debugging and performance tracking. 3. **Exclude Redundant Code:** - Remove overlapping state management implementations in RL and Orchestrator. - Consolidate logging and validation logic into shared utilities. --- ### **Step 2: Define Unified Architecture** - **Central Interfaces Repository:** Combine shared interfaces into a `core/interfaces.py`. - **Single Entry Point:** Define a `main.py` to initialize and manage all components. - **Standardized Logging and Metrics:** Use a unified logging framework (`loguru`) and shared metrics tracker. --- ### **Step 3: Implement the Final Program** #### **Program Structure** ``` unified_ai_system/ │ ├── src/ │ ├── rag/ │ │ ├── retriever.py │ │ ├── generator.py │ │ │ ├── rl/ │ │ ├── agent.py │ │ ├── environment.py │ │ ├── policy.py │ │ │ ├── persona/ │ │ ├── schemas.py │ │ ├── adapter.py │ │ │ ├── orchestrator/ │ │ ├── graph.py │ │ ├── state_manager.py │ │ │ ├── core/ │ │ ├── interfaces.py │ │ ├── config.py │ │ │ ├── utils/ │ │ ├── logging.py │ │ ├── metrics.py │ │ └── main.py ``` #### **Key Integration Steps** 1. **Initialize RAG, RL, Persona, and Orchestrator in `main.py`.** ```python from src.rag.retriever import FaissRetriever from src.rag.generator import LMGenerator from src.rl.agent import HierarchicalRLAgent from src.persona.adapter import PersonaAdapter from src.orchestrator.graph import WorkflowGraph from src.utils.logging import setup_logger def main(): # Setup logging logger = setup_logger() # Initialize components retriever = FaissRetriever(...) generator = LMGenerator(...) agent = HierarchicalRLAgent(...) persona = PersonaAdapter(...) # Create orchestrator workflow graph = WorkflowGraph() graph.add_node("RAG", retriever) graph.add_node("RL", agent) graph.add_edge("RAG", "RL", condition=lambda state: state.is_ready) logger.info("System initialized successfully.") ``` 2. **Implement State Management in Orchestrator.** ```python class StateManager: def __init__(self): self.state = {} def update_state(self, key, value): self.state[key] = value def get_state(self, key): return self.state.get(key, None) ``` 3. **Integrate Logging and Metrics.** - Use `loguru` for structured logs. - Track key metrics using a shared `MetricsTracker`. --- ### **Step 4: Testing and Validation** 1. **Unit Tests:** Test individual modules. 2. **Integration Tests:** Simulate workflows combining RAG, RL, Persona, and Orchestrator. 3. **Performance Tests:** Measure response time and scalability using mock datasets. --- ### **Step 5: Optimization Strategies** 1. **Performance Improvements:** - Cache RAG retrievals using `Redis`. - Optimize RL agent updates using batch processing. 2. **Scaling Considerations:** - Use containerization (Docker) for deployment. - Enable distributed processing for RAG queries. 3. **Resource Management:** - Set up memory usage monitors. - Limit token generation in RAG to prevent resource exhaustion. 4. **Caching Mechanisms:** - Cache embeddings for frequently queried documents. - Store intermediate RL states for reuse in similar scenarios. --- ### **Step 6: Documentation** 1. **API Reference:** - Document interfaces and modules. - Provide code snippets for common tasks. 2. **Usage Examples:** - Demonstrate RAG and RL workflows. - Include persona adaptation use cases. 3. **Configuration Guides:** - Explain YAML configurations for RAG, RL, and Persona modules. 4. **Troubleshooting:** - Address common errors (e.g., schema validation failures). - Provide debugging tips for performance issues. --- ### **Next Steps** - If any part of the integration or testing is unclear, prompts like these can be used: 1. "Refactor RL and Orchestrator state management for a unified implementation." 2. "Create caching logic for RAG document retrievals using `Redis`." 3. "Simulate workflows and log performance metrics." Would you like code for specific modules or testing frameworks implemented now?

help me write the attached program

To develop the integrated program as outlined, I'll guide you through the steps systematically: ### Step 1: Code Analysis and Selection 1. **Analyze Existing Code:** - Review functional modules (`RAG`, `RL`, `Persona`, `Orchestrator`) and identify: - Core functionalities and dependencies. - Overlapping responsibilities (e.g., state management). - Unused components. 2. **Exclude Redundant Modules:** - Remove overlapping or non-functional parts, such as incomplete stubs. - Keep only necessary elements for system-wide integration. 3. **Centralize Shared Interfaces:** - Consolidate interfaces like `IRetriever`, `IGenerator`, `IPersona`, and `IOrchestrator` into a `core/interfaces.py`. --- ### Step 2: Unified Architecture 1. **Directory Structure:** Organize the components logically: ``` unified_ai_system/ ├── src/ │ ├── rag/ │ ├── rl/ │ ├── persona/ │ ├── orchestrator/ │ ├── core/ │ └── utils/ ├── config/ ├── tests/ ├── examples/ └── requirements.txt ``` 2. **Unified State Management:** - Define `State` as a central entity in `core/interfaces.py`. - Ensure all components interact with this unified state structure. 3. **Orchestrator Logic:** - Use `networkx` for graph-based orchestration. - Define workflow transitions and conditional routing. --- ### Step 3: Core Integration 1. **Main Program:** - Develop a `main.py` script to initialize components and manage workflows. 2. **Integrations:** - `RAG` pipelines for document retrieval and generation. - `RL` for action selection and state transitions. - `Persona` for response adaptation. - `Orchestrator` for managing workflows and conditional logic. 3. **Error Handling:** - Use standardized logging (`loguru`). - Add exception handling for critical components. --- ### Step 4: Testing Framework 1. **Unit Tests:** - Validate individual modules like `retriever.py`, `agent.py`, etc. 2. **Integration Tests:** - Test interactions between `RAG`, `RL`, and `Persona`. 3. **Performance Tests:** - Stress test components and evaluate response times under heavy loads. --- ### Step 5: Deployment 1. **Docker Setup:** - Create a `Dockerfile` with dependencies for reproducible deployment. - Use `docker-compose` for multi-service orchestration (e.g., `Redis` for caching). 2. **Environment Configuration:** - Use `config/` for YAML-based configurations. --- ### Step 6: Documentation 1. **API Documentation:** - Include detailed explanations of each module. 2. **Usage Examples:** - Provide example workflows (`examples/basic_usage.py`). 3. **Troubleshooting Guide:** - Address common configuration and runtime issues. --- ### Follow-Up Prompts for Completing Each Phase - **Code Analysis:** - "Identify redundant code from the retrieved modules and summarize core components by their roles." - **Architecture Design:** - "Draft a consolidated state management schema and workflow orchestration logic using `networkx`." - **Integration Implementation:** - "Write a cohesive script to integrate RAG, RL, Persona, and Orchestrator with centralized state management." - **Testing:** - "Develop a test suite covering unit, integration, and performance testing for all components." - **Optimization and Deployment:** - "Optimize the integrated program for performance, add caching mechanisms, and provide Dockerized deployment scripts." Would you like me to expand on any specific phase or provide immediate implementation?

Identify redundant code from attached file from the retrieved modules and summarize core components by their roles.

To summarize the core components and identify redundant code based on the retrieved modules: ### Core Components by Role 1. **Retrieval-Augmented Generation (RAG):** - **Retriever (`retriever.py`)**: Handles document retrieval using FAISS or similar embeddings-based search. - **Document Store (`document_store.py`)**: Manages document storage for retrieval. - **Generator (`generator.py`)**: Generates responses by combining retrieval results and contextual information. 2. **Reinforcement Learning (RL):** - **Agent (`agent.py`)**: Implements the hierarchical RL agent and manages state-action policies. - **Environment (`environment.py`)**: Defines the RL training environment with observation and action spaces. - **World Model (`world_model.py`)**: Predicts state transitions and rewards based on actions. - **Policy (`policy.py`)**: Manages hierarchical and subgoal-level policies. - **Rewards (`rewards.py`)**: Provides reward functions for training the RL agent. - **Trainer (`training.py`)**: Runs the training loop and evaluates agent performance. - **Evaluation (`evaluation.py`)**: Evaluates the RL agent using performance metrics. 3. **Persona Management:** - **Schemas (`schemas.py`)**: Defines persona structures using Pydantic for validation. - **Manager (`manager.py`)**: Manages personas and applies them in various contexts. - **Adapter (`adapter.py`)**: Adapts generated responses to persona constraints and rules. 4. **Graph-Based Orchestrator:** - **Graph (`graph.py`)**: Implements graph structure for workflow orchestration. - **Nodes (`nodes.py`)**: Defines graph nodes as functional units. - **State Manager (`state_manager.py`)**: Manages system state transitions across workflows. 5. **Utilities:** - **Logging (`logging.py`)**: Handles logging for debugging and monitoring. - **Metrics (`metrics.py`)**: Tracks performance metrics. - **Miscellaneous (`utils.py`)**: Includes experience buffer, action management, and debugging utilities. --- ### Identified Redundancies 1. **Duplicate Logic Across Components:** - Both RAG and RL components include redundant implementations for state management. - Overlapping error handling logic in RAG (`retriever.py` and `generator.py`). 2. **Persona Validation:** - Validation logic in `schemas.py` and `adapter.py` overlaps in constraints handling. 3. **Logging and Metrics:** - Logging and metrics tracking functionality appear across `logging.py`, `metrics.py`, and within specific RL training/evaluation scripts. 4. **Action Management:** - Redundant checks for valid actions in `environment.py` and `action_manager` utilities. 5. **State Encoding:** - Similar encoding logic appears in the RL `StateEncoder` and the Orchestrator's `StateManager`. --- ### Steps to Optimize and Integrate 1. **Centralize State Management:** - Create a unified state management module to handle state updates, encoding, and transitions shared across RAG, RL, and Orchestrator. 2. **Combine Logging and Metrics:** - Merge `logging.py` and `metrics.py` functionalities into a single utility. 3. **Streamline Persona Validation:** - Consolidate validation logic in `adapter.py` and deprecate overlapping rules in `schemas.py`. 4. **Optimize Action Management:** - Use a single `ActionManager` utility for both RL environments and graph nodes. 5. **Shared Utility for Error Handling:** - Extract error handling into a shared utility used by RAG, RL, and Persona modules. --- ### Next Steps - Begin integrating these components by removing redundancies and implementing the optimizations. - Let me know if you’d like the code for these changes or help with the integration.

- Create a unified state management module to handle state updates, encoding, and transitions shared across RAG, RL, and Orchestrator.

To create a unified state management module that efficiently handles state updates, encoding, and transitions shared across RAG (Retrieval-Augmented Generation), RL (Reinforcement Learning), and Orchestrator components, follow the steps below: --- ### Key Objectives: 1. **Centralize state updates and retrieval** for all system components. 2. **Ensure compatibility** with RAG, RL, and Orchestrator interfaces. 3. **Implement encoding/decoding** mechanisms to maintain consistency. 4. **Provide an API** for safe state transitions with error handling and validation. --- ### Implementation Plan: #### 1. **Define the State Structure** The `State` class should be a shared resource for all components, encapsulating context, history, metadata, and transitions. ```python # state_manager.py from pydantic import BaseModel from typing import Dict, List, Any class State(BaseModel): """Unified system state representation.""" context: Dict[str, Any] = {} history: List[Dict[str, Any]] = [] metadata: Dict[str, Any] = {} def add_to_history(self, event: Dict[str, Any]) -> None: """Append an event to the history.""" self.history.append(event) ``` --- #### 2. **Create the State Manager** The `StateManager` class will handle state transitions, updates, and encoding/decoding for various components. ```python from typing import Dict, Any from .state_manager import State class StateManager: def __init__(self): self.current_state = State() def update_state(self, updates: Dict[str, Any]) -> State: """Update the state with new data.""" for key, value in updates.items(): if key in self.current_state.context: self.current_state.context[key].update(value) else: self.current_state.context[key] = value return self.current_state def encode_state(self) -> Dict[str, Any]: """Convert the state to a serialized format.""" return self.current_state.dict() def decode_state(self, serialized_state: Dict[str, Any]) -> State: """Reconstruct the state from a serialized format.""" self.current_state = State.parse_obj(serialized_state) return self.current_state def get_current_state(self) -> State: """Retrieve the current system state.""" return self.current_state ``` --- #### 3. **Integrate with Components** Modify each component to interact with `StateManager` for state handling. - **RAG Integration:** ```python class RAGComponent: def __init__(self, state_manager: StateManager): self.state_manager = state_manager def execute(self, query: str): state = self.state_manager.get_current_state() # Use state for retrieval and update results results = self.retrieve(query, state.context) self.state_manager.update_state({"last_query": query, "results": results}) ``` - **RL Integration:** ```python class RLComponent: def __init__(self, state_manager: StateManager): self.state_manager = state_manager def step(self, action: str): state = self.state_manager.get_current_state() # Perform RL step and update state reward, next_state = self.perform_action(action, state.context) self.state_manager.update_state({"reward": reward, "next_state": next_state}) ``` - **Orchestrator Integration:** ```python class Orchestrator: def __init__(self, state_manager: StateManager): self.state_manager = state_manager def run_workflow(self): state = self.state_manager.get_current_state() # Execute workflow steps based on the current state for step in self.workflow_steps: result = step.execute(state) self.state_manager.update_state(result) ``` --- #### 4. **Error Handling** Ensure robust error handling in the `StateManager`. ```python class StateManager: # Previous methods... def safe_update(self, updates: Dict[str, Any]) -> State: """Safely update the state, rolling back on errors.""" try: return self.update_state(updates) except Exception as e: print(f"Error updating state: {e}") return self.get_current_state() ``` --- #### 5. **Unit Testing** Verify that the `StateManager` correctly updates and retrieves state data. ```python import unittest from state_manager import StateManager class TestStateManager(unittest.TestCase): def test_update_state(self): manager = StateManager() updates = {"key1": {"subkey": "value"}} updated_state = manager.update_state(updates) self.assertIn("key1", updated_state.context) def test_encode_decode_state(self): manager = StateManager() updates = {"key1": {"subkey": "value"}} manager.update_state(updates) serialized = manager.encode_state() manager.decode_state(serialized) self.assertEqual(manager.get_current_state().context, updates) ``` --- ### Summary 1. **Centralized state management** ensures consistency across RAG, RL, and Orchestrator. 2. **StateManager API** handles updates, serialization, and validation. 3. **Component integration** uses `StateManager` as the single source of truth for state transitions. 4. **Unit tests** validate functionality and catch potential issues. This structure will provide a cohesive state management framework for the unified system. Let me know if you need further steps or enhancements!

Merge `logging.py` and `metrics.py` functionalities into a single utility.

To merge `logging.py` and `metrics.py` into a single utility module while ensuring proper functionality and eliminating redundancy, follow these steps: --- ### Combined Module: `log_metrics.py` This module will: 1. Handle logging of system events and errors. 2. Track metrics such as performance and usage statistics. 3. Support writing logs and metrics to persistent storage. 4. Allow integration with visualization tools like TensorBoard. ```python import logging import json from pathlib import Path from collections import defaultdict from typing import Dict, Any, List from torch.utils.tensorboard import SummaryWriter import numpy as np class LogMetricsUtility: def __init__(self, log_dir: str, log_level: str = "INFO"): # Setup directory self.log_dir = Path(log_dir) self.log_dir.mkdir(parents=True, exist_ok=True) # Setup logging self.logger = logging.getLogger("LogMetricsUtility") logging.basicConfig( filename=self.log_dir / "system.log", level=getattr(logging, log_level.upper(), logging.INFO), format="%(asctime)s - %(levelname)s - %(message)s" ) # Setup metrics tracking self.metrics = defaultdict(list) self.writer = SummaryWriter(str(self.log_dir / "tensorboard")) def log_event(self, event: str, details: Dict[str, Any] = None, level: str = "INFO"): """Logs a system event with optional details.""" message = f"{event} - {json.dumps(details, default=str)}" if details else event getattr(self.logger, level.lower(), self.logger.info)(message) def log_error(self, error: Exception, context: Dict[str, Any] = None): """Logs errors with context.""" self.logger.error(f"Error: {str(error)}") if context: self.logger.error(f"Context: {json.dumps(context, default=str)}") def track_metric(self, name: str, value: float, step: int): """Tracks a metric value for TensorBoard and logging.""" self.metrics[name].append(value) self.writer.add_scalar(name, value, step) def get_metric_average(self, name: str, window: int = 100) -> float: """Calculates a moving average for a metric.""" values = self.metrics[name] return np.mean(values[-window:]) if values else 0.0 def save_metrics(self): """Saves all metrics to a JSON file.""" metrics_path = self.log_dir / "metrics.json" with open(metrics_path, "w") as f: json.dump({k: v for k, v in self.metrics.items()}, f, indent=2) def close(self): """Closes the writer.""" self.writer.close() ``` --- ### Features and Methods 1. **Event Logging:** - `log_event(event, details, level)`: Log custom events with metadata. 2. **Error Logging:** - `log_error(error, context)`: Capture exceptions and their context. 3. **Metric Tracking:** - `track_metric(name, value, step)`: Record metrics for visualization. - `get_metric_average(name, window)`: Retrieve moving average of a metric. 4. **Persistence:** - Save metrics to disk (`save_metrics`). - Integrated with TensorBoard for real-time tracking. 5. **Unified Interface:** Combines logging and metrics tracking into a single module. --- ### Integration Steps 1. **Replace Imports:** Remove individual `logging` and `metrics` imports in your code: ```python from utils.log_metrics import LogMetricsUtility ``` 2. **Instantiate Utility:** Initialize `LogMetricsUtility` with the log directory: ```python log_metrics = LogMetricsUtility(log_dir="./logs", log_level="INFO") ``` 3. **Use for Logging and Metrics:** Example usage: ```python log_metrics.log_event("Model training started", {"epoch": 1}) log_metrics.track_metric("accuracy", 0.95, step=1) ``` 4. **Finalize on Completion:** Ensure resources are properly closed: ```python log_metrics.save_metrics() log_metrics.close() ``` --- This approach ensures both functionalities are consolidated efficiently while maintaining extensibility for future enhancements. Let me know if you’d like additional examples or configuration details!

- Consolidate validation logic in `adapter.py` and deprecate overlapping rules in `schemas.py`.

To consolidate the validation logic from `adapter.py` into a centralized module and deprecate overlapping rules in `schemas.py`, we will follow these steps: --- ### Steps to Consolidate 1. **Analyze Overlap**: Identify duplicated validation checks between `adapter.py` and `schemas.py`. 2. **Centralize Logic**: Create a new module, `validation.py`, to house shared validation logic. 3. **Deprecate Redundancies**: Modify `schemas.py` and `adapter.py` to delegate their validation needs to `validation.py`. 4. **Integrate and Test**: Ensure seamless integration with existing systems by updating imports and testing. --- ### Example Consolidated `validation.py` ```python # src/persona/validation.py from typing import List, Dict import re class PersonaValidation: @staticmethod def validate_prohibited_phrases(response: str, prohibited_phrases: List[str]) -> bool: for phrase in prohibited_phrases: if phrase.lower() in response.lower(): return False return True @staticmethod def validate_length(response: str, min_length: int = None, max_length: int = None) -> bool: if min_length and len(response) < min_length: return False if max_length and len(response) > max_length: return False return True @staticmethod def validate_required_keywords(response: str, required_keywords: List[str]) -> bool: for keyword in required_keywords: if keyword.lower() not in response.lower(): return False return True @staticmethod def validate_regex_checks(response: str, regex_checks: Dict[str, str]) -> bool: for _, pattern in regex_checks.items(): if not re.search(pattern, response): return False return True ``` --- ### Updated `schemas.py` Remove validation checks and replace them with references to `validation.py`: ```python # src/persona/schemas.py from pydantic import BaseModel, Field class PersonaStyle(BaseModel): tone: str = Field(..., description="Tone of the persona, e.g., 'friendly'") formality: str = Field(..., description="Level of formality: 'formal', 'informal', etc.") class PersonaConstraints(BaseModel): prohibited_phrases: List[str] = Field(default_factory=list) max_length: int = None required_keywords: List[str] = Field(default_factory=list) class PersonaValidationRules(BaseModel): regex_checks: Dict[str, str] = Field(default_factory=dict) min_length: int = None max_length: int = None class PersonaSchema(BaseModel): name: str = Field(..., description="Unique identifier for the persona") description: str style: PersonaStyle constraints: PersonaConstraints validation_rules: PersonaValidationRules ``` --- ### Updated `adapter.py` Delegate validation responsibilities to `validation.py`: ```python # src/persona/adapter.py from src.persona.validation import PersonaValidation from src.persona.schemas import PersonaSchema class PersonaAdapter: def __init__(self, persona: PersonaSchema): self.persona = persona def validate_response(self, response: str) -> bool: constraints = self.persona.constraints rules = self.persona.validation_rules return ( PersonaValidation.validate_prohibited_phrases(response, constraints.prohibited_phrases) and PersonaValidation.validate_length(response, rules.min_length, rules.max_length) and PersonaValidation.validate_required_keywords(response, constraints.required_keywords) and PersonaValidation.validate_regex_checks(response, rules.regex_checks) ) ``` --- ### Benefits of Consolidation - **Reusability**: Shared logic can be reused across multiple modules. - **Maintenance**: Easier to maintain and update validation rules. - **Testability**: Centralized logic simplifies unit testing and debugging. --- This approach will streamline validation processes and reduce redundancy while maintaining functionality. Let me know if you'd like specific unit tests or additional modifications!

- Use a single `ActionManager` utility for both RL environments and graph nodes

To consolidate the `ActionManager` utility for both RL environments and graph nodes while leveraging the provided data: ### Steps to Create a Unified `ActionManager` 1. **Analyze Overlapping Functionalities**: - Both the RL environment and graph nodes involve action validation, availability checks, and contextual behavior based on state transitions. - The `ActionManager` needs to unify action handling logic, removing redundancy. 2. **Define Unified ActionManager**: - Standardize action validation and state update methods. - Ensure compatibility with both the RL workflow and graph-based orchestration. 3. **Implementation**: Here's an example: ```python class UnifiedActionManager: def __init__(self, action_space: int, environment_config: Dict[str, Any]): """ Initialize with action space and environment configuration. """ self.action_space = action_space self.environment_config = environment_config def is_valid_action(self, action: int, state: Dict[str, Any]) -> bool: """ Check if an action is valid in the current state. """ if action not in range(self.action_space): return False action_type = ActionType(action) # Example conditions for RL environment if action_type == ActionType.FINALIZE and not state.get('subgoals', []): return True elif action_type in [ActionType.RETRIEVE, ActionType.REQUEST_MORE_INFO]: return True # Add graph-specific validation logic here return state.get('graph_validations', {}).get(action_type.value, False) def get_valid_actions(self, state: Dict[str, Any]) -> List[int]: """ List all valid actions for the current state. """ return [action for action in range(self.action_space) if self.is_valid_action(action, state)] def update_state(self, state: Dict[str, Any], action: int) -> Dict[str, Any]: """ Apply an action and return the updated state. """ action_type = ActionType(action) if action_type == ActionType.RETRIEVE: state['retrieved_docs'] = True elif action_type == ActionType.FINALIZE: state['completed'] = True # Custom logic for RL and graph-based state transitions state['last_action'] = action return state ``` 4. **Integration Across Modules**: - Replace existing `ActionManager` calls in the RL and Orchestrator modules. - Standardize state attributes like `subgoals`, `graph_validations`, and `last_action`. 5. **Testing**: - Unit test for validation logic. - Integration tests with RL training and graph workflows. Let me know if you’d like further assistance in implementing this, or in creating documentation, examples, or testing frameworks!

- Extract error handling into a shared utility used by RAG, RL, and Persona modules.

To create a shared utility for error handling across RAG, RL, and Persona modules, follow these steps: ### Steps for Implementation: 1. **Design the Utility Module:** - Define the types of errors and structured logging requirements. - Include methods for handling exceptions, logging errors, and generating actionable feedback for debugging. 2. **Implement a Centralized Error Handler:** - Create a `ErrorHandler` class in a new file (`error_utils.py`) within the `utils` folder. - Incorporate logging mechanisms and exception handling strategies. 3. **Integrate into Components:** - Replace existing individual error handling in RAG, RL, and Persona modules with the centralized utility. 4. **Testing:** - Add unit tests for all error scenarios. --- ### Implementation: #### `utils/error_utils.py` ```python import logging from typing import Optional, Dict class ErrorHandler: def __init__(self, log_file: Optional[str] = None): self.logger = logging.getLogger("ErrorHandler") self.logger.setLevel(logging.ERROR) handler = logging.FileHandler(log_file or "system_errors.log") handler.setFormatter(logging.Formatter("%(asctime)s - %(levelname)s - %(message)s")) self.logger.addHandler(handler) def log_error(self, error: Exception, context: Optional[Dict] = None): """ Log error details and context. :param error: Exception instance. :param context: Additional context or metadata about the error. """ error_message = f"Error: {str(error)}" if context: error_message += f" | Context: {context}" self.logger.error(error_message) def handle_exception(self, error: Exception, context: Optional[Dict] = None) -> str: """ Handle the exception and return a user-friendly error message. :param error: Exception instance. :param context: Additional context or metadata about the error. :return: A user-friendly error message. """ self.log_error(error, context) return "An unexpected error occurred. Please contact support." def raise_custom_exception(self, error_message: str, exception_class: Exception): """ Raise a custom exception. :param error_message: Error message to be included. :param exception_class: Custom exception class to raise. """ self.logger.error(f"Custom Exception: {error_message}") raise exception_class(error_message) ``` --- ### Integrating into Components #### Example Integration in `rag/retriever.py`: Replace existing error handling with the centralized utility: ```python from utils.error_utils import ErrorHandler class FaissRetriever(IRetriever): def __init__(self, index, embeddings, top_k=3): self.index = index self.embeddings = embeddings self.top_k = top_k self.error_handler = ErrorHandler() def retrieve(self, query: str, k: int = None) -> List[Document]: k = k or self.top_k try: query_vec = self.embeddings.embed_text(query) D, I = self.index.search(query_vec, k) docs = self._get_documents_from_ids(I) return self._rank_documents(query, docs) except Exception as e: self.error_handler.handle_exception(e, {"query": query}) raise RAGException("Failed to retrieve documents.") ``` #### Example Integration in `persona/adapter.py`: ```python from utils.error_utils import ErrorHandler class PersonaAdapter: def __init__(self, persona: PersonaSchema): self.persona = persona self.error_handler = ErrorHandler() def adapt_response(self, raw_response: str, context: Dict[str, Any]) -> str: try: # Adapt response logic... return adapted_response except Exception as e: self.error_handler.handle_exception(e, {"response": raw_response, "context": context}) raise ValueError("Response adaptation failed.") ``` --- ### Unit Tests #### `tests/utils/test_error_utils.py`: ```python import pytest from utils.error_utils import ErrorHandler def test_log_error(): error_handler = ErrorHandler() try: raise ValueError("Test error") except Exception as e: error_handler.log_error(e, {"test_case": "log_error"}) def test_handle_exception(): error_handler = ErrorHandler() try: raise KeyError("Key not found") except Exception as e: message = error_handler.handle_exception(e, {"test_case": "handle_exception"}) assert message == "An unexpected error occurred. Please contact support." def test_raise_custom_exception(): error_handler = ErrorHandler() with pytest.raises(RuntimeError): error_handler.raise_custom_exception("Custom error", RuntimeError) ``` --- This centralized error handling approach improves maintainability and ensures a consistent error management strategy across the system.

- "Draft a consolidated state management schema and workflow orchestration logic using `networkx`."

To draft a **consolidated state management schema and workflow orchestration logic using `networkx`**, here's how you can proceed: --- ### **State Management Schema** 1. **State Representation:** - Define a `State` class that holds context, history, and metadata for any given workflow. - Include `update_state` and `get_state` methods for state transitions. 2. **Code Implementation:** ```python from pydantic import BaseModel from typing import Dict, List, Any class State(BaseModel): """System state representation.""" context: Dict[str, Any] # Holds current context history: List[Dict[str, Any]] # Tracks state transitions metadata: Dict[str, Any] # Additional metadata (e.g., timestamps) class StateManager: """Manages state updates and retrievals.""" def __init__(self): self.current_state = State(context={}, history=[], metadata={}) def update_state(self, updates: Dict[str, Any]): """Update the state with new data.""" self.current_state.history.append(self.current_state.dict()) self.current_state.context.update(updates) self.current_state.metadata['last_updated'] = updates.get('timestamp') def get_current_state(self) -> State: """Retrieve the current state.""" return self.current_state ``` --- ### **Workflow Orchestration Logic** 1. **Setup `networkx` Graph:** - Nodes represent workflow steps (e.g., RAG retrieval, RL agent decision). - Edges represent transitions with optional conditions. 2. **Workflow Management Class:** ```python import networkx as nx from typing import Callable, Dict, Any class WorkflowOrchestrator: """Manages graph-based workflow orchestration.""" def __init__(self): self.graph = nx.DiGraph() # Directed graph for workflows self.current_node = None def add_node(self, node_id: str, func: Callable[[State], State]): """Add a node representing a workflow step.""" self.graph.add_node(node_id, func=func) def add_edge(self, from_node: str, to_node: str, condition: Callable[[State], bool] = None): """Add a directed edge with a condition.""" self.graph.add_edge(from_node, to_node, condition=condition) def execute_workflow(self, initial_node: str, state: State): """Execute the workflow starting from a specific node.""" self.current_node = initial_node while self.current_node: node_func = self.graph.nodes[self.current_node]['func'] state = node_func(state) # Execute the node's function next_nodes = list(self.graph.successors(self.current_node)) self.current_node = None for next_node in next_nodes: condition = self.graph.edges[self.current_node, next_node].get('condition') if condition is None or condition(state): self.current_node = next_node break # Move to the next valid node ``` --- ### **Integration Example** - **Define Workflow Steps:** ```python def rag_retrieval(state: State) -> State: print("Executing RAG Retrieval") # Add retrieval logic state.context['retrieval_done'] = True return state def rl_decision(state: State) -> State: print("Executing RL Decision") # Add decision-making logic state.context['decision'] = "Action Selected" return state ``` - **Setup and Execute Workflow:** ```python # Initialize StateManager and Orchestrator state_manager = StateManager() orchestrator = WorkflowOrchestrator() # Add Nodes orchestrator.add_node('RAG', rag_retrieval) orchestrator.add_node('RL', rl_decision) # Add Conditional Edge orchestrator.add_edge('RAG', 'RL', condition=lambda state: state.context.get('retrieval_done')) # Start Workflow initial_state = state_manager.get_current_state() orchestrator.execute_workflow('RAG', initial_state) ``` --- ### **Benefits** - **Scalability:** Adding new steps to the workflow only requires defining a function and adding nodes/edges. - **Reusability:** Both state and graph logic can be reused across different workflows. - **Maintainability:** Clear separation of state, workflow logic, and conditions. --- Let me know if you need further assistance, or we can implement advanced features like error handling and logging!

"Write a cohesive script to integrate RAG, RL, Persona, and Orchestrator with centralized state management."

To write a cohesive script that integrates RAG, RL, Persona, and Orchestrator components with centralized state management, follow these steps: --- ### 1. **Centralized State Management** First, design a centralized `StateManager` module that supports state sharing across RAG, RL, Persona, and Orchestrator components. ```python # src/core/state_manager.py from typing import Dict, Any from threading import Lock class StateManager: def __init__(self): self.state = {} self.lock = Lock() def update_state(self, updates: Dict[str, Any]): with self.lock: self.state.update(updates) def get_state(self) -> Dict[str, Any]: with self.lock: return self.state.copy() def reset_state(self): with self.lock: self.state = {} ``` --- ### 2. **RAG Integration** Connect the `Retriever` and `Generator` into the workflow, updating the state with retrieved documents and generated text. ```python # src/rag/rag_pipeline.py from core.state_manager import StateManager from rag.retriever import FaissRetriever from rag.generator import LMGenerator class RAGPipeline: def __init__(self, state_manager: StateManager, retriever: FaissRetriever, generator: LMGenerator): self.state_manager = state_manager self.retriever = retriever self.generator = generator def process_query(self, query: str): documents = self.retriever.retrieve(query) self.state_manager.update_state({'retrieved_docs': documents}) generated_response = self.generator.generate(query, documents) self.state_manager.update_state({'generated_response': generated_response}) return generated_response ``` --- ### 3. **RL Agent Integration** Integrate RL for decision-making, feeding the centralized state into the agent and updating it with actions and outcomes. ```python # src/rl/rl_integration.py from core.state_manager import StateManager from rl.agent import HierarchicalRLAgent class RLIntegration: def __init__(self, state_manager: StateManager, rl_agent: HierarchicalRLAgent): self.state_manager = state_manager self.rl_agent = rl_agent def process_state(self): current_state = self.state_manager.get_state() action = self.rl_agent.select_action(current_state) self.state_manager.update_state({'selected_action': action}) return action ``` --- ### 4. **Persona Module Integration** Use the Persona Adapter to ensure that responses align with specific persona constraints. ```python # src/persona/persona_integration.py from core.state_manager import StateManager from persona.adapter import PersonaAdapter from persona.schemas import PersonaSchema class PersonaIntegration: def __init__(self, state_manager: StateManager, persona: PersonaSchema): self.state_manager = state_manager self.adapter = PersonaAdapter(persona) def adapt_response(self): raw_response = self.state_manager.get_state().get('generated_response', '') adapted_response = self.adapter.adapt_response(raw_response, {}) self.state_manager.update_state({'adapted_response': adapted_response}) return adapted_response ``` --- ### 5. **Orchestrator Integration** The orchestrator manages the workflow using a graph-based approach. ```python # src/orchestrator/orchestrator.py import networkx as nx from core.state_manager import StateManager class Orchestrator: def __init__(self, state_manager: StateManager): self.state_manager = state_manager self.graph = nx.DiGraph() def add_node(self, node_id, func): self.graph.add_node(node_id, func=func) def add_edge(self, from_node, to_node, condition=None): self.graph.add_edge(from_node, to_node, condition=condition) def execute(self): current_node = 'start' while current_node: func = self.graph.nodes[current_node]['func'] func() next_nodes = list(self.graph.successors(current_node)) current_node = next_nodes[0] if next_nodes else None ``` --- ### 6. **Unified Script** Combine all the components into a single cohesive script. ```python # src/main.py from core.state_manager import StateManager from rag.rag_pipeline import RAGPipeline from rl.rl_integration import RLIntegration from persona.persona_integration import PersonaIntegration from orchestrator.orchestrator import Orchestrator def main(): # Initialize State Manager state_manager = StateManager() # Initialize Components rag_pipeline = RAGPipeline(state_manager, FaissRetriever(...), LMGenerator(...)) rl_integration = RLIntegration(state_manager, HierarchicalRLAgent(...)) persona_integration = PersonaIntegration(state_manager, PersonaSchema(...)) # Orchestrator Setup orchestrator = Orchestrator(state_manager) orchestrator.add_node('start', lambda: rag_pipeline.process_query("Example Query")) orchestrator.add_node('decision', lambda: rl_integration.process_state()) orchestrator.add_node('response', lambda: persona_integration.adapt_response()) orchestrator.add_edge('start', 'decision') orchestrator.add_edge('decision', 'response') # Execute Workflow orchestrator.execute() if __name__ == "__main__": main() ``` --- ### Follow-Up Steps - Add configuration handling for dependencies and models. - Write unit tests for each module and integration tests for the workflow. - Document the system architecture and API endpoints.

"Develop a test suite covering unit, integration, and performance testing for all components."

The uploaded file is extensive and cannot be fully processed in one go. To create a cohesive testing framework for your AI system, I recommend breaking the task into manageable parts. ### Steps to Create the Test Suite: 1. **Unit Testing**: - Develop individual tests for: - RAG components (`retriever.py`, `generator.py`). - RL components (`agent.py`, `policy.py`, `world_model.py`). - Persona components (`schemas.py`, `adapter.py`). - Orchestrator components (`state_manager.py`, `graph.py`). 2. **Integration Testing**: - Test the interactions between components: - Ensure RAG integrates with the orchestrator. - Validate RL action decisions based on orchestrator inputs. - Check persona adjustments to outputs from RAG. 3. **Performance Testing**: - Simulate high-load scenarios to test system scalability. - Measure latency of the RAG pipeline and RL decision-making. 4. **Mock Implementations**: - Use mock environments for RL. - Simulate RAG document retrieval with dummy datasets. - Mock personas for response validation. ### Next Steps: 1. **Focus Area**: Let me know which part of the test suite you’d like to tackle first (e.g., unit tests for RAG, integration with RL). 2. **Relevant Components**: Specify which components or code files are essential for the immediate task. Would you like a detailed outline or examples of test cases to start with? If so, I can draft these based on the provided structure. Alternatively, if you need assistance with a specific module, let me know!

"Optimize the integrated program for performance, add caching mechanisms, and provide Dockerized deployment scripts."

The attached files are extensive and exceed what can be processed all at once. Here's a structured way to proceed: ### **Plan for Consolidating and Enhancing the Program** 1. **Extract Core Components**: - Identify reusable parts (RAG, RL, Persona, Orchestrator, State Management, etc.). - Remove overlapping functionality or redundant logic. 2. **Create Unified Modules**: - Build a single `StateManager` for state transitions and updates across components. - Merge logging and metrics tracking into a unified utility. - Develop a shared `ActionManager` for managing RL and graph-based orchestration. 3. **Write the Final Integrated Program**: - Combine the modules into a cohesive script. - Implement a single workflow orchestrating RAG, RL, and Persona-based adaptations. 4. **Testing**: - Write unit and integration tests for each module and the full system. 5. **Optimization**: - Add caching where necessary (e.g., for document retrieval or policy evaluations). - Optimize resource usage (e.g., batching computations, limiting redundant calculations). 6. **Deployment**: - Create Docker and `docker-compose` configurations for reproducibility. 7. **Documentation**: - Provide usage examples, configuration guides, and troubleshooting instructions. --- ### **Immediate Next Steps** - **Extract Core Components**: If you're looking to begin step-by-step: 1. **Confirm which modules should be consolidated (State Management, Logging, Metrics, etc.).** 2. **Detail which areas should be simplified or removed for optimization.** Please let me know if you'd like me to proceed with the analysis, draft a specific module, or provide a detailed implementation plan. Alternatively, you can specify which section or module you'd like to prioritize.

Help me build this application using the refinements in Next Gen PersonaGen 02 to improve Next Gen PersonaGen, take me step by step and provide the working code, if you can not complete everything in one prompt supply a follow up prompt that will recursively complete the job

To assist you in building a cohesive application by integrating the refinements mentioned in **Next Gen PersonaGen 02** to enhance **Next Gen PersonaGen**, I will provide a step-by-step guide to complete the task. Here’s an outline: --- ### Step-by-Step Plan #### **Step 1: Setup and Architecture Design** 1. **Create the unified project directory structure.** 2. **Identify core components** (RAG, RL, Persona, Orchestrator, and Utilities). 3. **Define central interfaces and state management.** #### **Step 2: Implementation of Core Components** 1. **Develop a unified `StateManager`** for handling shared state. 2. **Combine logging and metrics utilities into a unified module.** 3. **Refactor persona validation logic into a single validation utility.** 4. **Develop the graph-based orchestrator using `networkx`.** 5. **Implement action management utility for shared RL and graph logic.** #### **Step 3: Integration** 1. **Connect RAG, RL, Persona, and Orchestrator components** using the unified state and action manager. 2. **Centralize error handling and ensure resilience.** 3. **Establish APIs for component communication.** #### **Step 4: Testing** 1. **Write unit tests** for each component. 2. **Perform integration testing** to ensure seamless interaction. 3. **Run performance tests and refine bottlenecks.** #### **Step 5: Deployment** 1. **Prepare Docker containers and deployment scripts.** 2. **Document configuration and runtime setup.** --- ### Detailed Execution I will now create the code to **set up the unified `StateManager`** and integrate the core components for RAG, RL, Persona, and the Orchestrator. Each step will be provided in separate prompts if necessary. --- ### Step 1: Unified `StateManager` Here’s the implementation of the `StateManager` that will serve as the backbone for state management across all components. ```python # src/core/state_manager.py from typing import Dict, Any from pydantic import BaseModel class State(BaseModel): """Unified state representation.""" context: Dict[str, Any] = {} history: list[Dict[str, Any]] = [] metadata: Dict[str, Any] = {} def add_event(self, event: Dict[str, Any]) -> None: """Add an event to the state history.""" self.history.append(event) class StateManager: """Centralized state manager for the unified system.""" def __init__(self): self.state = State() def update_state(self, updates: Dict[str, Any]) -> None: """Update the state with new information.""" for key, value in updates.items(): if key in self.state.context: self.state.context[key].update(value) else: self.state.context[key] = value def get_state(self) -> State: """Retrieve the current state.""" return self.state def encode_state(self) -> Dict[str, Any]: """Encode the state for serialization.""" return self.state.dict() def decode_state(self, serialized_state: Dict[str, Any]) -> None: """Restore the state from a serialized format.""" self.state = State.parse_obj(serialized_state) ``` --- ### Step 2: Integration Example (RAG + `StateManager`) The `RAG` component interacts with the `StateManager` for context updates and retrieval logging. ```python # src/rag/rag_component.py from src.core.state_manager import StateManager class RAGComponent: def __init__(self, state_manager: StateManager): self.state_manager = state_manager def execute(self, query: str): """Perform retrieval and update state.""" state = self.state_manager.get_state() results = self.retrieve_documents(query, state.context) self.state_manager.update_state({"last_query": query, "results": results}) return results def retrieve_documents(self, query: str, context: Dict[str, Any]): """Simulate document retrieval logic.""" # Dummy implementation for demonstration return [{"id": 1, "content": f"Relevant content for {query}"}] ``` --- ### Follow-Up Prompts 1. **Develop the unified `ActionManager`** to coordinate actions for RL and Orchestrator workflows. 2. **Implement persona adaptation logic** integrated with `StateManager`. 3. **Write unit tests for `StateManager` and RAG interaction.** 4. **Develop Docker deployment scripts.** Would you like to proceed with the next component, or should I focus on the integration or deployment first?

Help me build this application using the refinements in Next Gen PersonaGen 02 to improve Next Gen PersonaGen, take me step by step and provide the working code, if you can not complete everything in one prompt supply a follow up prompt that will recursively complete the job: Develop the unified ActionManager to coordinate actions for RL and Orchestrator workflows.

To develop the unified `ActionManager` utility for coordinating actions across RL and Orchestrator workflows, follow these steps: ### Step 1: Define the Unified `ActionManager` The `ActionManager` will handle action validation, state updates, and compatibility with both RL environments and Orchestrator graph nodes. #### Code Implementation ```python from typing import Dict, Any, List from enum import Enum # Define action types class ActionType(Enum): RETRIEVE = "retrieve" GENERATE = "generate" REFINE = "refine" FINALIZE = "finalize" REQUEST_MORE_INFO = "request_more_info" class UnifiedActionManager: def __init__(self, action_space: int): """ Initialize with action space. """ self.action_space = action_space def is_valid_action(self, action: int, state: Dict[str, Any]) -> bool: """ Check if an action is valid in the current state. """ if action not in range(self.action_space): return False action_type = ActionType(action) # Example conditions if action_type == ActionType.FINALIZE: return len(state.get('subgoals', [])) == 0 elif action_type == ActionType.REFINE: return len(state.get('context', {})) > 0 elif action_type == ActionType.GENERATE: return 'query' in state elif action_type in [ActionType.RETRIEVE, ActionType.REQUEST_MORE_INFO]: return True return False def get_valid_actions(self, state: Dict[str, Any]) -> List[int]: """ List all valid actions for the current state. """ return [action for action in range(self.action_space) if self.is_valid_action(action, state)] def update_state(self, state: Dict[str, Any], action: int) -> Dict[str, Any]: """ Apply an action and return the updated state. """ action_type = ActionType(action) # Simulate updates to the state if action_type == ActionType.RETRIEVE: state['retrieved_docs'] = True elif action_type == ActionType.FINALIZE: state['completed'] = True # Common state updates state['last_action'] = action_type.value return state ``` --- ### Step 2: Integrate the `ActionManager` with RL and Orchestrator Modules #### RL Integration ```python class RLComponent: def __init__(self, action_manager: UnifiedActionManager): self.action_manager = action_manager def step(self, action: int, state: Dict[str, Any]): if not self.action_manager.is_valid_action(action, state): raise ValueError(f"Invalid action: {action}") # Update state based on the action return self.action_manager.update_state(state, action) ``` #### Orchestrator Integration ```python class OrchestratorNode: def __init__(self, action_manager: UnifiedActionManager): self.action_manager = action_manager def process(self, state: Dict[str, Any]): valid_actions = self.action_manager.get_valid_actions(state) for action in valid_actions: state = self.action_manager.update_state(state, action) return state ``` --- ### Step 3: Testing the `ActionManager` Create a test suite to validate the `ActionManager` functionality. ```python import unittest class TestUnifiedActionManager(unittest.TestCase): def setUp(self): self.action_manager = UnifiedActionManager(action_space=5) def test_valid_actions(self): state = {'subgoals': [], 'context': {'query': 'test'}} valid_actions = self.action_manager.get_valid_actions(state) self.assertIn(ActionType.RETRIEVE.value, valid_actions) self.assertNotIn(ActionType.FINALIZE.value, valid_actions) def test_update_state(self): state = {'subgoals': ['subgoal1'], 'context': {'query': 'test'}} updated_state = self.action_manager.update_state(state, ActionType.RETRIEVE.value) self.assertTrue(updated_state['retrieved_docs']) ``` --- ### Next Steps 1. **Logging Integration:** Use the centralized logging module to record state transitions and actions. 2. **Error Handling:** Integrate the error utility for robust exception management. 3. **Graph Integration:** Extend the `ActionManager` to interface with graph-based orchestration workflows. Let me know if you'd like detailed steps for any specific integration!

**Implement persona adaptation logic** integrated with `StateManager`.

To implement **persona adaptation logic** integrated with a centralized `StateManager`, here’s a step-by-step breakdown with code examples: --- ### Step 1: Centralize State Management Ensure `StateManager` is set up to provide a unified state representation. #### `state_manager.py` ```python from pydantic import BaseModel from typing import Dict, Any, List class State(BaseModel): """Unified system state representation.""" context: Dict[str, Any] = {} history: List[Dict[str, Any]] = [] metadata: Dict[str, Any] = {} class StateManager: def __init__(self): self.current_state = State() def update_state(self, updates: Dict[str, Any]) -> State: """Update the state with new data.""" for key, value in updates.items(): if key in self.current_state.context: self.current_state.context[key].update(value) else: self.current_state.context[key] = value return self.current_state def get_current_state(self) -> State: """Retrieve the current system state.""" return self.current_state ``` --- ### Step 2: Persona Adaptation Logic Integrate persona rules into response adaptation based on the state. #### `persona_adapter.py` ```python from typing import Dict, Any from state_manager import StateManager class PersonaAdapter: def __init__(self, persona: Dict[str, Any], state_manager: StateManager): self.persona = persona self.state_manager = state_manager def adapt_response(self, raw_response: str) -> str: """Adapt the response according to persona rules.""" state = self.state_manager.get_current_state() # Apply tone adaptation tone = self.persona.get("tone", "neutral") adapted_response = f"{tone.capitalize()} response: {raw_response}" # Apply keyword enrichment based on persona required_keywords = self.persona.get("required_keywords", []) for keyword in required_keywords: if keyword not in adapted_response: adapted_response += f" {keyword}" # Log adaptation in state self.state_manager.update_state({"last_response": adapted_response}) return adapted_response ``` --- ### Step 3: Integration Example Demonstrate integration of `StateManager` and `PersonaAdapter`. #### `main.py` ```python from state_manager import StateManager from persona_adapter import PersonaAdapter # Initialize StateManager state_manager = StateManager() # Define persona persona = { "tone": "friendly", "required_keywords": ["thank you", "please"] } # Create PersonaAdapter persona_adapter = PersonaAdapter(persona, state_manager) # Process response raw_response = "Here is the information you requested." adapted_response = persona_adapter.adapt_response(raw_response) # Print results print("Adapted Response:", adapted_response) print("Updated State:", state_manager.get_current_state().dict()) ``` --- ### Expected Output For a raw response like `"Here is the information you requested."` and a persona requiring keywords `"thank you"` and `"please"`, the output should be: ``` Adapted Response: Friendly response: Here is the information you requested. thank you please Updated State: { "context": { "last_response": "Friendly response: Here is the information you requested. thank you please" }, "history": [], "metadata": {} } ``` --- ### Follow-Up Prompts for Full Integration 1. **Testing**: - "Write unit tests for PersonaAdapter, focusing on keyword adaptation and tone handling." 2. **Error Handling**: - "Add error handling for invalid personas or missing state attributes." 3. **Logging**: - "Integrate logging for persona rules applied and state updates." 4. **Scaling**: - "Optimize persona adaptation for dynamic persona switching in real-time workflows." This step-by-step guide integrates persona logic with state management while setting a foundation for further enhancements. Let me know if you'd like to dive deeper into any specific part!