# queen_brain_complete.py import logging import time import json from typing import Dict, List, Optional, Callable # --- Placeholder Modules --- # These should be replaced with your actual implementations. class AIVALearningModule: """Placeholder for the AIVA learning module.""" def __init__(self, logger=None): self.logger = logger or logging.getLogger(__name__) self.confidence_level = 0.7 # Initial confidence level def update_confidence(self, query, response, feedback): """Placeholder for confidence updating logic.""" self.logger.info(f"Learning: Updating confidence for query '{query}' based on feedback '{feedback}'.") # Simulate confidence update if feedback == "positive": self.confidence_level = min(1.0, self.confidence_level + 0.05) elif feedback == "negative": self.confidence_level = max(0.0, self.confidence_level - 0.1) self.logger.info(f"New confidence level: {self.confidence_level}") def get_confidence(self): """Returns the current confidence level.""" return self.confidence_level class ContextManager: """Placeholder for context management.""" def __init__(self, logger=None): self.logger = logger or logging.getLogger(__name__) self.context = {} def update_context(self, key, value): """Updates the context with a new key-value pair.""" self.context[key] = value self.logger.debug(f"Context updated: {key} = {value}") def get_context(self): """Returns the current context.""" return self.context def clear_context(self): """Clears the current context.""" self.context = {} self.logger.debug("Context cleared.") class Memory: """Placeholder for Memory Management (all tiers).""" def __init__(self, logger=None): self.logger = logger or logging.getLogger(__name__) self.short_term = {} self.long_term = {} self.episodic = [] # List of interactions def store_short_term(self, key, value): """Stores information in short-term memory.""" self.short_term[key] = value self.logger.debug(f"Stored in short-term memory: {key} = {value}") def retrieve_short_term(self, key): """Retrieves information from short-term memory.""" return self.short_term.get(key) def store_long_term(self, key, value): """Stores information in long-term memory.""" self.long_term[key] = value self.logger.debug(f"Stored in long-term memory: {key} = {value}") def retrieve_long_term(self, key): """Retrieves information from long-term memory.""" return self.long_term.get(key) def store_episodic(self, interaction): """Stores interaction in episodic memory (conversation history).""" self.episodic.append(interaction) self.logger.debug(f"Stored in episodic memory: {interaction}") def retrieve_episodic(self, n=5): """Retrieves the last n interactions from episodic memory.""" return self.episodic[-n:] def clear_memory(self): """Clears all memory tiers.""" self.short_term = {} self.long_term = {} self.episodic = [] self.logger.debug("All memory tiers cleared.") class ValidationPipeline: """Placeholder for Validation Pipeline.""" def __init__(self, logger=None): self.logger = logger or logging.getLogger(__name__) def validate(self, response): """Placeholder for response validation.""" # In a real implementation, this would involve checks for factual accuracy, # safety, bias, and other criteria. self.logger.info(f"Validating response: {response}") is_valid = True # Replace with actual validation logic if not is_valid: self.logger.warning(f"Response failed validation: {response}") return is_valid class IntentClassifier: """Placeholder for Intent Classification.""" def __init__(self, intents_config, logger=None): self.logger = logger or logging.getLogger(__name__) self.intents = intents_config self.logger.info(f"Intent Classifier initialized with intents: {self.intents.keys()}") def classify_intent(self, query): """Placeholder for intent classification logic.""" self.logger.info(f"Classifying intent for query: {query}") # Simple example: Check if the query contains keywords related to each intent. for intent, keywords in self.intents.items(): for keyword in keywords: if keyword.lower() in query.lower(): self.logger.info(f"Intent classified as: {intent}") return intent self.logger.warning("No intent matched.") return "unknown" # Default intent class QueenAIVA: """The master orchestrator for AIVA.""" def __init__( self, available_skills: Dict[str, Callable], skill_configurations: Dict[str, Dict], learning_module, intent_classifier, validation_pipeline, memory, context_manager, confidence_threshold: float = 0.6, personality_profile: str = "friendly", error_fallback: str = "I'm sorry, I'm not sure how to handle that.", logging_level: int = logging.INFO, logger: logging.Logger = None, revenue_tracking_callback: Optional[Callable[[float], None]] = None ): """ Initializes the QueenAIVA orchestrator. Args: available_skills (Dict[str, Callable]): A dictionary of available skills, where the key is the skill name and the value is the skill object. skill_configurations (Dict[str, Dict]): A dictionary of skill configurations, where the key is the skill name and the value is a dictionary of configuration parameters. learning_module: The AIVA learning module. intent_classifier: The Intent Classifier. validation_pipeline: The Validation Pipeline. memory: The Memory module. context_manager: The Context Manager. confidence_threshold (float): The minimum confidence level required to return a response. personality_profile (str): The personality profile to use for response styling. error_fallback (str): The fallback message to use when an error occurs. logging_level (int): The logging level to use. logger (logging.Logger): An optional logger instance. If None, a default logger will be created. revenue_tracking_callback: A callback function to track revenue generated by AIVA. """ self.logger = logger or logging.getLogger(__name__) self.logger.setLevel(logging_level) self.available_skills = available_skills self.skill_configurations = skill_configurations self.skills = {} for skill_name, skill_class in available_skills.items(): config = skill_configurations.get(skill_name, {}) try: self.skills[skill_name] = skill_class(**config, logger=self.logger) self.logger.info(f"Skill '{skill_name}' initialized with config: {config}") except Exception as e: self.logger.exception(f"Failed to initialize skill '{skill_name}': {e}") raise # Re-raise the exception to prevent the orchestrator from starting with a broken skill. self.learning_module = learning_module self.intent_classifier = intent_classifier self.validation_pipeline = validation_pipeline self.memory = memory self.context_manager = context_manager self.confidence_threshold = confidence_threshold self.personality_profile = personality_profile self.error_fallback = error_fallback self.revenue_tracking_callback = revenue_tracking_callback self.interaction_count = 0 self.total_response_time = 0 self.successful_interactions = 0 self.failed_interactions = 0 self.logger.info("QueenAIVA orchestrator initialized.") def orchestrate(self, query: str) -> str: """ Orchestrates the interaction with AIVA. Args: query (str): The user query. Returns: str: The response from AIVA. """ self.interaction_count += 1 start_time = time.time() previous_response = None # Initialize previous_response try: self.logger.info(f"Received query: {query}") # 1. Intent Classification and Routing intent = self.intent_classifier.classify_intent(query) self.logger.info(f"Detected intent: {intent}") # 2. Multi-turn Conversation Management context = self.context_manager.get_context() conversation_history = self.memory.retrieve_episodic(n=3) # Retrieve last 3 interactions self.logger.debug(f"Current context: {context}") self.logger.debug(f"Conversation history: {conversation_history}") # 3. Skill Selection and Composition selected_skills = self.select_skills(intent, context, conversation_history) self.logger.info(f"Selected skills: {selected_skills}") # 4. Skill Execution and Response Generation response = "" for skill_name in selected_skills: skill = self.skills[skill_name] try: skill_start_time = time.time() skill_response = skill.execute(query, context, previous_response) skill_duration = time.time() - skill_start_time self.logger.info(f"Skill '{skill_name}' executed in {skill_duration:.4f} seconds.") response += skill_response + " " previous_response = skill_response # Update previous_response except Exception as e: self.logger.exception(f"Error executing skill '{skill_name}': {e}") response += self.recover_from_error(e, skill_name, query) + " " response = response.strip() # Remove trailing spaces # 5. Validation Pipeline Integration if not self.validation_pipeline.validate(response): response = self.error_fallback # Replace with a safe fallback # 6. Personality and Response Styling response = self.style_response(response, self.personality_profile) # 7. Confidence Check confidence = self.learning_module.get_confidence() if confidence < self.confidence_threshold: response = f"I'm not very confident in my answer, but: {response}" # 8. Context Update self.context_manager.update_context("last_query", query) self.context_manager.update_context("last_response", response) # 9. Memory Integration self.memory.store_episodic({"query": query, "response": response, "intent": intent}) # 10. Learning Loop Trigger self.trigger_learning_loop(query, response) self.successful_interactions += 1 return response except Exception as e: self.logger.exception(f"Orchestration error: {e}") self.failed_interactions += 1 return self.recover_from_error(e, "orchestrator", query) finally: duration = time.time() - start_time self.total_response_time += duration self.logger.info(f"Query processed in {duration:.4f} seconds.") self.track_performance(duration) def select_skills(self, intent: str, context: Dict, conversation_history: List[Dict]) -> List[str]: """ Selects the appropriate skills based on the intent, context, and conversation history. This is a placeholder for a more sophisticated skill selection mechanism. In a real implementation, this would involve a model that predicts which skills are most likely to be useful for a given intent and context. Args: intent (str): The intent of the user query. context (Dict): The current context. conversation_history (List[Dict]): The conversation history. Returns: List[str]: A list of skill names to execute. """ # Simple rule-based skill selection based on intent if intent == "greeting": return ["generation_skill"] elif intent == "knowledge_query": return ["knowledge_retrieval_skill", "generation_skill"] elif intent == "validation_request": return ["validation_skill", "formatting_skill"] else: return ["generation_skill"] # Default skill def generate_response(self, skills: List[Callable], query: str, context: Dict, previous_response: str = None) -> str: """ Generates a response by executing the given skills. Args: skills (List[Callable]): A list of skill objects to execute. query (str): The user query. context (Dict): The current context. previous_response (str): The response from the previous turn, if any. Returns: str: The generated response. """ response = "" for skill in skills: try: response += skill.execute(query, context, previous_response) + " " except Exception as e: self.logger.exception(f"Error executing skill {skill.__class__.__name__}: {e}") response += self.recover_from_error(e, skill.__class__.__name__, query) + " " return response.strip() def recover_from_error(self, error: Exception, skill_name: str, query: str) -> str: """ Recovers from an error during skill execution. Args: error (Exception): The exception that occurred. skill_name (str): The name of the skill that caused the error. query (str): The user query. Returns: str: A fallback response. """ self.logger.error(f"Error in skill {skill_name}: {error}") return f"I encountered an issue processing your request. Please try again or rephrase your query." def trigger_learning_loop(self, query: str, response: str): """ Triggers the learning loop to update the model based on the interaction. Args: query (str): The user query. response (str): The response from AIVA. """ # In a real implementation, this would involve sending the query and response to a # learning module for training. self.logger.info(f"Triggering learning loop for query: {query}, response: {response}") # Simulate feedback collection (replace with actual mechanism) feedback = input(f"Was the response helpful? (positive/negative): ") self.learning_module.update_confidence(query, response, feedback) def style_response(self, response: str, personality_profile: str) -> str: """ Applies a personality style to the response. Args: response (str): The response to style. personality_profile (str): The personality profile to use. Returns: str: The styled response. """ # Simple example: Add a greeting based on the personality profile. if personality_profile == "friendly": return f"{response} 😊" elif personality_profile == "formal": return f"Very well. {response}" else: return response def track_performance(self, duration: float): """ Tracks the performance of the orchestrator. Args: duration (float): The duration of the query processing. """ # In a real implementation, this would involve logging the duration to a database or # other monitoring system. self.logger.debug(f"Performance: Query processed in {duration:.4f} seconds.") def health_check(self) -> Dict[str, bool]: """ Performs a health check on the orchestrator and its skills. Returns: Dict[str, bool]: A dictionary of health check results, where the key is the component name and the value is a boolean indicating whether the component is healthy. """ health = {"orchestrator": True} for skill_name, skill in self.skills.items(): try: # Basic health check: Check if the skill object exists health[skill_name] = skill is not None except Exception as e: self.logger.exception(f"Health check failed for skill '{skill_name}': {e}") health[skill_name] = False self.logger.info(f"Health check: {health}") return health def describe_capabilities(self) -> Dict[str, List[str]]: """ Describes the capabilities of the orchestrator. Returns: Dict[str, List[str]]: A dictionary of capabilities, where the key is the capability name and the value is a list of supported intents. """ capabilities = {} for skill_name, skill in self.skills.items(): # Assume each skill has a 'supported_intents' attribute if hasattr(skill, 'supported_intents'): capabilities[skill_name] = skill.supported_intents else: capabilities[skill_name] = ["unknown"] # Default if no intents are specified self.logger.info(f"Capabilities: {capabilities}") return capabilities def track_revenue(self, amount: float): """Tracks revenue generated by AIVA.""" if self.revenue_tracking_callback: try: self.revenue_tracking_callback(amount) self.logger.info(f"Revenue tracked: ${amount:.2f}") except Exception as e: self.logger.exception(f"Error tracking revenue: {e}") else: self.logger.warning("Revenue tracking callback not configured.") def get_average_response_time(self) -> float: """Calculates and returns the average response time.""" if self.interaction_count == 0: return 0.0 return self.total_response_time / self.interaction_count if __name__ == '__main__': # Configure logging logging.basicConfig(level=logging.DEBUG, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s') logger = logging.getLogger(__name__) # 1. Define Available Skills (Import your actual skill classes) from skills.knowledge_retrieval_skill import KnowledgeRetrievalSkill from skills.generation_skill import GenerationSkill from skills.validation_skill import ValidationSkill from skills.formatting_skill import FormattingSkill available_skills = { "knowledge_retrieval_skill": KnowledgeRetrievalSkill, "generation_skill": GenerationSkill, "validation_skill": ValidationSkill, "formatting_skill": FormattingSkill, } # 2. Define Skill Configurations skill_configurations = { "knowledge_retrieval_skill": {"api_key": "YOUR_API_KEY"}, # Example config "generation_skill": {"model_name": "GPT-3"}, # Example config "validation_skill": {}, "formatting_skill": {} } # 3. Instantiate Learning Module from learning.aiva_learning_module import AIVALearningModule learning_module = AIVALearningModule(logger=logger) # 4. Instantiate Intent Classifier intents_config = { "greeting": ["hello", "hi", "hey"], "knowledge_query": ["what", "who", "where", "when", "how"], "validation_request": ["validate", "is this correct", "verify"] } intent_classifier = IntentClassifier(intents_config, logger=logger) # 5. Instantiate Validation Pipeline validation_pipeline = ValidationPipeline(logger=logger) # 6. Instantiate Memory memory = Memory(logger=logger) # 7. Instantiate Context Manager context_manager = ContextManager(logger=logger) # 8. Define a Revenue Tracking Callback (Optional) def revenue_tracking_callback(amount: float): """Example revenue tracking callback.""" print(f"Simulated revenue: ${amount:.2f}") # 9. Instantiate QueenAIVA queen_aiva = QueenAIVA( available_skills=available_skills, skill_configurations=skill_configurations, learning_module=learning_module, intent_classifier=intent_classifier, validation_pipeline=validation_pipeline, memory=memory, context_manager=context_manager, confidence_threshold=0.7, personality_profile="friendly", error_fallback="I'm sorry, I can't help with that right now.", logger=logger, revenue_tracking_callback=revenue_tracking_callback ) # 10. Example Usage query1 = "Hello, how are you?" response1 = queen_aiva.orchestrate(query1) print(f"AIVA: {response1}") query2 = "What is the capital of France?" response2 = queen_aiva.orchestrate(query2) print(f"AIVA: {response2}") query3 = "validate that the sky is blue" response3 = queen_aiva.orchestrate(query3) print(f"AIVA: {response3}") # Health Check health = queen_aiva.health_check() print(f"Health Check: {health}") # Describe Capabilities capabilities = queen_aiva.describe_capabilities() print(f"Capabilities: {capabilities}") # Track Revenue queen_aiva.track_revenue(5.00) # Get average response time average_response_time = queen_aiva.get_average_response_time() print(f"Average response time: {average_response_time:.4f} seconds")