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| import aiohttp | |
| import json | |
| import logging | |
| import faiss | |
| import numpy as np | |
| import ollama # Using Ollama for local Llama 3 inference | |
| from typing import List, Dict, Any | |
| from cryptography.fernet import Fernet | |
| from jwt import encode, decode, ExpiredSignatureError | |
| from datetime import datetime, timedelta | |
| import pyttsx3 | |
| from components.adaptive_learning import AdaptiveLearningEnvironment | |
| from components.real_time_data import RealTimeDataIntegrator | |
| from components.sentiment_analysis import EnhancedSentimentAnalyzer | |
| from components.self_improving_ai import SelfImprovingAI | |
| from components.multi_agent import MultiAgentSystem | |
| from utils.database import Database | |
| from utils.logger import logger | |
| class AICore: | |
| def __init__(self, config_path: str = "config.json"): | |
| self.config = self._load_config(config_path) | |
| self.http_session = aiohttp.ClientSession() | |
| self.database = Database() | |
| self.sentiment_analyzer = EnhancedSentimentAnalyzer() | |
| self.data_fetcher = RealTimeDataIntegrator() | |
| self.self_improving_ai = SelfImprovingAI() | |
| self.multi_agent_system = MultiAgentSystem() | |
| self._encryption_key = Fernet.generate_key() | |
| self.jwt_secret = "your_jwt_secret_key" | |
| self.speech_engine = pyttsx3.init() | |
| # FAISS Vector Memory | |
| self.memory_index = faiss.IndexFlatL2(768) | |
| self.memory_vectors = [] | |
| self.memory_texts = [] | |
| def _load_config(self, config_path: str) -> dict: | |
| with open(config_path, 'r') as file: | |
| return json.load(file) | |
| async def generate_response(self, query: str, user_id: int) -> Dict[str, Any]: | |
| try: | |
| self.store_interaction(query) | |
| previous_context = self.retrieve_past_context(query) | |
| refined_query = f"{previous_context}\nUser: {query}" if previous_context else query | |
| model_response = await self._recursive_refinement(refined_query, 3) | |
| agent_response = self.multi_agent_system.delegate_task(query) | |
| sentiment = self.sentiment_analyzer.detailed_analysis(query) | |
| final_response = self._apply_security_filters(model_response + agent_response) | |
| self.database.log_interaction(user_id, query, final_response) | |
| self._speak_response(final_response) | |
| return { | |
| "response": final_response, | |
| "sentiment": sentiment, | |
| "security_level": self._evaluate_risk(final_response), | |
| "real_time_data": self.data_fetcher.fetch_latest_data(), | |
| "token_optimized": True | |
| } | |
| except Exception as e: | |
| logger.error(f"Response generation failed: {e}") | |
| return {"error": "Processing failed - safety protocols engaged"} | |
| async def _recursive_refinement(self, query: str, depth: int = 3) -> str: | |
| best_response = await self._generate_local_model_response(query) | |
| for _ in range(depth): | |
| new_response = await self._generate_local_model_response(f"Refine this answer: {best_response}") | |
| if len(new_response) > len(best_response): | |
| best_response = new_response | |
| return best_response | |
| async def _generate_local_model_response(self, query: str) -> str: | |
| response = ollama.chat(model="llama3", messages=[{"role": "user", "content": query}]) | |
| return response["message"]["content"] | |
| def store_interaction(self, query: str): | |
| query_vector = self._vectorize_text(query) | |
| self.memory_index.add(np.array([query_vector])) | |
| self.memory_vectors.append(query_vector) | |
| self.memory_texts.append(query) | |
| def retrieve_past_context(self, query: str): | |
| query_vector = self._vectorize_text(query) | |
| _, indices = self.memory_index.search(np.array([query_vector]), k=3) | |
| return "\n".join([self.memory_texts[i] for i in indices[0] if i < len(self.memory_texts)]) | |
| def _vectorize_text(self, text: str): | |
| return np.random.rand(768) # Replace with an actual embedding model | |
| def _apply_security_filters(self, response: str): | |
| return response.replace("malicious", "[filtered]") | |
| def _speak_response(self, response: str): | |
| self.speech_engine.say(response) | |
| self.speech_engine.runAndWait() | |
| def _evaluate_risk(self, response: str) -> str: | |
| if "dangerous" in response or "malicious" in response: | |
| return "High Risk" | |
| return "Low Risk" | |