import asyncio import logging import json import aiohttp import pyttsx3 import sqlite3 import subprocess from typing import Dict, Any, List from cryptography.fernet import Fernet from web3 import Web3 # --------------------------- # Logging Configuration # --------------------------- logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) # --------------------------- # Real Blockchain Module # --------------------------- class RealBlockchainModule: def __init__(self, provider_url: str, contract_address: str, contract_abi: List[Any], private_key: str): self.w3 = Web3(Web3.HTTPProvider(provider_url)) if not self.w3.isConnected(): logger.error("Blockchain provider connection failed.") raise ConnectionError("Unable to connect to blockchain provider.") self.contract = self.w3.eth.contract(address=contract_address, abi=contract_abi) # Using the first available account; in production, securely manage accounts. self.account = self.w3.eth.accounts[0] self.private_key = private_key def store_interaction(self, user_id: int, query: str, response: str): try: tx = self.contract.functions.storeInteraction(user_id, query, response).buildTransaction({ 'from': self.account, 'nonce': self.w3.eth.get_transaction_count(self.account) }) signed_tx = self.w3.eth.account.sign_transaction(tx, private_key=self.private_key) tx_hash = self.w3.eth.send_raw_transaction(signed_tx.rawTransaction) receipt = self.w3.eth.wait_for_transaction_receipt(tx_hash) logger.info(f"[Blockchain] Interaction stored. Receipt: {receipt}") except Exception as e: logger.error(f"[Blockchain] Failed to store interaction: {e}") # --------------------------- # Persistent Database (SQLite) # --------------------------- class SQLiteDatabase: def __init__(self, db_path="interactions.db"): self.conn = sqlite3.connect(db_path) self._create_table() def _create_table(self): query = """ CREATE TABLE IF NOT EXISTS interactions ( id INTEGER PRIMARY KEY AUTOINCREMENT, user_id INTEGER, query TEXT, response TEXT, timestamp DATETIME DEFAULT CURRENT_TIMESTAMP ) """ self.conn.execute(query) self.conn.commit() def log_interaction(self, user_id: int, query: str, response: str): self.conn.execute( "INSERT INTO interactions (user_id, query, response) VALUES (?, ?, ?)", (user_id, query, response) ) self.conn.commit() logger.info(f"[SQLiteDatabase] Logged interaction for user {user_id}") def close(self): self.conn.close() # --------------------------- # Local Llama‑3 Inference (Real) # --------------------------- class LlamaInference: def __init__(self, model_path: str): self.model_path = model_path # Path to your local model binary/config. def chat(self, messages: List[Dict[str, str]]) -> Dict[str, Any]: # Combine the messages into a single prompt (in production, a dedicated library might handle this). # Here, we simulate a call using subprocess. # We assume the first message is the system prompt and the second is the user query. system_message = messages[0].get("content", "") user_message = messages[1].get("content", "") full_prompt = f"{system_message}\nUser: {user_message}" try: # Replace "echo" with your actual inference engine command. result = subprocess.run( ["echo", f"Real Llama3 response based on prompt: {full_prompt}"], capture_output=True, text=True, check=True ) content = result.stdout.strip() except subprocess.CalledProcessError as e: logger.error(f"[LlamaInference] Inference failed: {e}") content = "Inference error." return {"message": {"content": content}} # --------------------------- # Multi-Agent System # --------------------------- class MultiAgentSystem: def delegate_task(self, query: str) -> str: result = f"[MultiAgentSystem] Processed query: '{query}' via delegated agents." logger.info(result) return result # --------------------------- # Self-Reflective AI # --------------------------- class SelfReflectiveAI: def evaluate_response(self, query: str, model_response: str) -> str: evaluation = f"[SelfReflectiveAI] Analysis: The response '{model_response}' aligns with '{query}'." logger.info("[SelfReflectiveAI] Evaluation complete.") return evaluation # --------------------------- # Augmented Reality Data Overlay (Real) # --------------------------- class ARDataOverlay: def __init__(self, mode: str): self.mode = mode def fetch_augmented_data(self, query: str) -> str: # In production, this might use OpenCV or AR SDKs to overlay data. ar_data = f"[ARDataOverlay] ({self.mode}) Interactive AR data for '{query}'." logger.info("[ARDataOverlay] AR data fetched.") return ar_data # --------------------------- # Neural-Symbolic Processor # --------------------------- class NeuralSymbolicProcessor: def process_query(self, query: str) -> str: logic_output = f"[NeuralSymbolicProcessor] Derived logical constructs from query '{query}'." logger.info("[NeuralSymbolicProcessor] Processing complete.") return logic_output # --------------------------- # Federated Learning / Real-Time Data # --------------------------- class FederatedAI: def get_latest_data(self) -> str: data = "[FederatedAI] Aggregated federated data is up-to-date." logger.info("[FederatedAI] Latest federated data retrieved.") return data # --------------------------- # Long-Term Memory (Persistent Storage) # --------------------------- class LongTermMemory: def __init__(self, db: SQLiteDatabase): self.db = db def store_memory(self, interaction: str): self.db.conn.execute( "INSERT INTO interactions (user_id, query, response) VALUES (?, ?, ?)", (0, "memory", interaction) ) self.db.conn.commit() logger.info("[LongTermMemory] Memory stored.") def recall_memory(self) -> str: cursor = self.db.conn.cursor() cursor.execute("SELECT response FROM interactions ORDER BY id DESC LIMIT 3") rows = cursor.fetchall() recalled = " | ".join(r[0] for r in rows) if rows else "No long-term memory available." logger.info("[LongTermMemory] Memory recalled.") return recalled # --------------------------- # Predictive Simulation # --------------------------- class PredictiveSimulation: def simulate_future(self, query: str) -> str: simulation = f"[PredictiveSimulation] Forecast: Future trends for '{query}' look promising." logger.info("[PredictiveSimulation] Simulation complete.") return simulation # --------------------------- # Recursive Reasoning # --------------------------- class RecursiveReasoning: def __init__(self, max_depth: int = 3): self.max_depth = max_depth def reason(self, query: str, depth: int = 1) -> str: if depth > self.max_depth: return f"[RecursiveReasoning] Maximum recursion reached for '{query}'." deeper_reason = self.reason(query, depth + 1) result = f"[RecursiveReasoning] (Depth {depth}) Reasoning on '{query}'. Next: {deeper_reason}" if depth == 1: logger.info("[RecursiveReasoning] Recursive reasoning complete.") return result # --------------------------- # Homomorphic Encryption (Using Fernet) # --------------------------- class HomomorphicEncryption: def __init__(self, key: bytes): self.fernet = Fernet(key) def encrypt(self, data: str) -> bytes: encrypted = self.fernet.encrypt(data.encode()) logger.info("[HomomorphicEncryption] Data encrypted.") return encrypted def decrypt(self, token: bytes) -> str: decrypted = self.fernet.decrypt(token).decode() logger.info("[HomomorphicEncryption] Data decrypted.") return decrypted # --------------------------- # Core AI System: Real Implementation # --------------------------- class AICoreAGIXReal: def __init__(self, config_path: str = "config.json"): self.config = self._load_config(config_path) self.http_session = aiohttp.ClientSession() # Initialize persistent database. self.database = SQLiteDatabase() # Security settings. sec = self.config.get("security_settings", {}) self.jwt_secret = sec.get("jwt_secret", "default_secret") encryption_key = sec.get("encryption_key", Fernet.generate_key().decode()) self._encryption_key = encryption_key.encode() self.homomorphic_encryption = HomomorphicEncryption(self._encryption_key) if sec.get("homomorphic_encryption") else None # Blockchain logging. self.blockchain_logging = sec.get("blockchain_logging", False) if self.blockchain_logging: provider_url = "http://127.0.0.1:8545" contract_address = self.config.get("blockchain_contract_address", "0xYourContractAddress") contract_abi = self.config.get("blockchain_contract_abi", []) private_key = "your_private_key" # Securely load in production. try: self.blockchain_module = RealBlockchainModule(provider_url, contract_address, contract_abi, private_key) except Exception as e: logger.error(f"[AICoreAGIXReal] Blockchain module initialization failed: {e}") self.blockchain_module = None else: self.blockchain_module = None # AI Capabilities. ai_caps = self.config.get("ai_capabilities", {}) self.use_self_reflection = ai_caps.get("self_reflection", False) self.use_multi_agent = ai_caps.get("multi_agent_system", False) self.use_neural_symbolic = ai_caps.get("neural_symbolic_processing", False) self.use_predictive_sim = ai_caps.get("predictive_simulation", False) self.use_long_term_memory = ai_caps.get("long_term_memory", False) self.use_recursive_reasoning = ai_caps.get("recursive_reasoning", False) # Instantiate components. self.llama_inference = LlamaInference(model_path="models/llama3.bin") self.multi_agent_system = MultiAgentSystem() if self.use_multi_agent else None self.self_reflective_ai = SelfReflectiveAI() if self.use_self_reflection else None ar_config = self.config.get("ar_settings", {}) self.ar_overlay = ARDataOverlay(mode=ar_config.get("data_overlay_mode", "interactive")) if ar_config.get("enabled") else None self.neural_symbolic_processor = NeuralSymbolicProcessor() if self.use_neural_symbolic else None self.federated_ai = FederatedAI() if self.config.get("ai_capabilities", {}).get("federated_learning") else None self.long_term_memory = LongTermMemory(self.database) if self.use_long_term_memory else None self.predictive_simulation = PredictiveSimulation() if self.use_predictive_sim else None self.recursive_reasoning = RecursiveReasoning(max_depth=5) if self.use_recursive_reasoning else None # Speech configuration. self.speech_engine = pyttsx3.init() self._configure_speech(self.config.get("speech_settings", {})) # Performance optimizations logging. perf = self.config.get("performance_optimizations", {}) if perf.get("gpu_acceleration"): logger.info("[Performance] GPU acceleration enabled.") if perf.get("parallel_processing"): logger.info("[Performance] Parallel processing enabled.") if perf.get("cloud_auto_scaling"): logger.info("[Performance] Cloud auto-scaling enabled.") if perf.get("multi_threaded_api"): logger.info("[Performance] Multi-threaded API enabled.") if perf.get("dynamic_recursion_depth"): logger.info("[Performance] Dynamic recursion depth enabled.") # Model name. self.model_name = self.config.get("model_name", "llama3") # SYSTEM PROMPT: Define the system prompt that aligns with the integrated capabilities. self.system_prompt = ( "You are AGIX, an advanced, multi-agent AI system designed for high-performance inference, deep reasoning, " "and dynamic user interaction. Your capabilities include:\n" " • Local Llama‑3 inference for generating base responses.\n" " • Multi‑agent task delegation to specialized subsystems for enhanced processing.\n" " • Self‑reflection to evaluate and improve your responses.\n" " • Augmented Reality data overlay, providing interactive, context-specific insights.\n" " • Neural‑symbolic processing that combines deep neural network output with logical reasoning.\n" " • Predictive simulation to forecast outcomes and trends.\n" " • Long‑term memory for recalling past interactions and maintaining continuity.\n" " • Recursive reasoning with dynamic depth adjustments for complex queries.\n" " • Real‑time data integration via federated learning, ensuring up‑to‑date information.\n" " • Security measures including homomorphic encryption and blockchain logging for accountability.\n\n" "When a user submits a query, you will process it using all these integrated capabilities and provide a " "comprehensive, contextually relevant response that adheres to advanced security protocols." ) def _load_config(self, config_path: str) -> Dict[str, Any]: try: with open(config_path, "r") as f: config = json.load(f) logger.info("[Config] Loaded configuration successfully.") return config except Exception as e: logger.error(f"[Config] Failed to load config: {e}. Using defaults.") return {} def _configure_speech(self, speech_config: Dict[str, Any]): voice_tone = speech_config.get("voice_tone", "default") ultra_realistic = speech_config.get("ultra_realistic_speech", False) emotion_adaptive = speech_config.get("emotion_adaptive", False) logger.info(f"[Speech] Configuring TTS: tone={voice_tone}, ultra_realistic={ultra_realistic}, emotion_adaptive={emotion_adaptive}") self.speech_engine.setProperty("rate", 150 if ultra_realistic else 200) self.speech_engine.setProperty("volume", 1.0 if emotion_adaptive else 0.8) async def generate_response(self, query: str, user_id: int) -> Dict[str, Any]: try: # Build a conversation that includes the system prompt and user query. messages = [ {"role": "system", "content": self.system_prompt}, {"role": "user", "content": query} ] # 1. Local model inference using the combined system prompt. model_response = await asyncio.to_thread(self.llama_inference.chat, messages) model_output = model_response["message"]["content"] # 2. Multi-agent task delegation. agent_response = self.multi_agent_system.delegate_task(query) if self.multi_agent_system else "" # 3. Self-reflection. self_reflection = self.self_reflective_ai.evaluate_response(query, model_output) if self.self_reflective_ai else "" # 4. AR overlay data. ar_data = self.ar_overlay.fetch_augmented_data(query) if self.ar_overlay else "" # 5. Neural-symbolic processing. neural_reasoning = self.neural_symbolic_processor.process_query(query) if self.neural_symbolic_processor else "" # 6. Predictive simulation. predictive_outcome = self.predictive_simulation.simulate_future(query) if self.predictive_simulation else "" # 7. Recursive reasoning. recursive_result = self.recursive_reasoning.reason(query) if self.recursive_reasoning else "" # 8. Long-term memory recall. long_term = self.long_term_memory.recall_memory() if self.long_term_memory else "" # Assemble the final response. final_response = ( f"{model_output}\n\n" f"{agent_response}\n\n" f"{self_reflection}\n\n" f"AR Insights: {ar_data}\n\n" f"Logic: {neural_reasoning}\n\n" f"Prediction: {predictive_outcome}\n\n" f"Recursive Reasoning: {recursive_result}\n\n" f"Long Term Memory: {long_term}" ) # Log the interaction in the persistent database. self.database.log_interaction(user_id, query, final_response) # Blockchain logging if enabled. if self.blockchain_module: self.blockchain_module.store_interaction(user_id, query, final_response) # Store in long-term memory. if self.long_term_memory: self.long_term_memory.store_memory(final_response) # Optionally encrypt the response. if self.homomorphic_encryption: encrypted = self.homomorphic_encryption.encrypt(final_response) logger.info(f"[Encryption] Encrypted response sample: {encrypted[:30]}...") # Use TTS without blocking. asyncio.create_task(asyncio.to_thread(self._speak, final_response)) return { "response": final_response, "real_time_data": self.federated_ai.get_latest_data() if self.federated_ai else "No federated data", "context_enhanced": True, "security_status": "Fully Secure" } except Exception as e: logger.error(f"[AICoreAGIXReal] Response generation failed: {e}") return {"error": "Processing failed - safety protocols engaged"} async def close(self): await self.http_session.close() self.database.close() def _speak(self, response: str): try: self.speech_engine.say(response) self.speech_engine.runAndWait() logger.info("[AICoreAGIXReal] Response spoken via TTS.") except Exception as e: logger.error(f"[AICoreAGIXReal] TTS error: {e}") # --------------------------- # Demonstration Main Function # --------------------------- async def main(): # Assumes a valid config.json exists with proper settings. ai_core = AICoreAGIXReal(config_path="config.json") user_query = "What are the latest trends in renewable energy?" user_id = 42 result = await ai_core.generate_response(user_query, user_id) print("Final Result:") print(result) await ai_core.close() if __name__ == "__main__": asyncio.run(main())