Update prompt.txt

prompt.txt

@@ -1,280 +1,57 @@
-AI Agent Creation Prompt
-Objective
-Create an AI system that can generate responses to user queries from multiple perspectives, including Newtonian physics, DaVinci's interdisciplinary approach, human intuition, neural networks, quantum computing, resilient kindness, mathematical reasoning, philosophical inquiry, AI copilot reasoning, bias mitigation, and psychological analysis. The AI should also handle text, voice, and image inputs, perform advanced sentiment analysis, integrate real-time data, and ensure security and ethical considerations.
+Create a comprehensive reasoning system that integrates various elements and perspectives to analyze and respond to tasks. The system should utilize sentiment analysis, handle sensitive information, and provide real-time data fetching capabilities. The code should include robust error handling, configuration management, asynchronous handling, dependency injection, unit testing, and security enhancements.
 
-Functionalities
-Configuration Management:
+The goal is to integrate unique functionalities and methodologies from different code components into a cohesive system.
 
-Use pydantic to manage configuration settings.
-Load configuration from a JSON file and environment variables.
-Sentiment Analysis:
+- **Initialization**: Utilize the AIConfig class to manage configuration settings, initialize models, and load encryption keys. Define perspectives using these configuration settings.
 
-Utilize the vaderSentiment library to analyze the sentiment of text.
-Dependency Injection:
+- **Memory Management**: Implement in-memory storage to track tasks, actions, and observations within classes like AIConfig and AICore.
 
-Implement a simple dependency injection system to manage dependencies like configuration and sentiment analyzer.
-Error Handling and Logging:
+- **Sentiment Analysis**: Use sentiment analysis tools such as VADER to classify emotions and align them with specified perspectives. Implement this through the NLP analysis pipelines within AICore class.
 
-Set up logging based on configuration settings.
-Handle errors and log them appropriately.
-Universal Reasoning Aggregator:
+- **Action Determination and Execution**: Frame logic to determine upcoming actions based on memory and context, triggered by system inputs and perspectives like those initialized in AIConfig. Log all actions and information using the structured logging system already set up.
 
-Initialize various perspectives (e.g., Newton, DaVinci, Human Intuition) and elements (e.g., Hydrogen, Diamond).
-Use a custom recognizer to identify intents in questions.
-Generate responses based on different perspectives and elements.
-Handle ethical considerations and include them in responses.
-Element Defense Logic:
+- **Sensitive Information Handling**: Build on the encryption capabilities of AICore to detect and mask sensitive information proactively.
 
-Recognize elements and execute their defense abilities based on the context of the question.
-Encryption and Security:
+- **Real-Time Data Fetching**: Integrate data fetching within functions to collect and provide real-time responses, utilizing proper error handling and asynchronous operations.
 
-Encrypt and decrypt sensitive information using the cryptography library.
-Securely destroy sensitive data when no longer needed.
-Contextual Awareness:
+- **Command Processing and Response Generation**: Use APIs and processing functions to handle commands, manipulate data, generate responses aligning with the system's perspectives, and effectively save these responses.
 
-Maintain context throughout the conversation, ensuring coherent and relevant responses.
-Dynamic Perspective Expansion:
+- **Interaction Analysis**: Develop classes or methods that assess interactions between elements, with potential to evolve into full interactional narratives.
 
-Add new perspectives dynamically based on user interactions.
-User Feedback Mechanism:
+- **Configuration Management**: Apply pydantic or similar libraries to ensure validation and type-checking of configuration settings, keeping configurations consistent and clear.
 
-Collect and process user feedback for continuous learning and improvement.
-Multi-Modal Input Handling:
+- **Asynchronous Handling and Dependency Injection**: Ensure all appropriate functions perform asynchronously, utilizing asyncio within methods like those in AICore. Use dependency injection to enhance code modularity and testing potential.
 
-Process and respond to text-based queries.
-Listen to and process voice commands.
-Process and analyze images.
-Response Saving and Backup:
+- **Unit Testing and Code Documentation**: Incorporate unit testing to ensure code accuracy and future adaptability. Provide docstring annotations to enhance code clarity and future maintenance.
 
-Save and back up responses based on configuration settings.
-Ethical Decision Making:
+- **Security Enhancements**: Address potential security flaws by validating inputs, sanitizing outputs, and employing encryption where necessary.
 
-Integrate ethical principles into decision-making processes to ensure fairness, transparency, and respect for privacy.
-Transparency and Explainability:
+# Steps
 
-Provide transparency by explaining the reasoning behind decisions and actions taken by the AI.
-Example Code Structure
-python
-import asyncio
-import json
-import logging
-import os
-from typing import List, Dict, Any
-from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
-from dotenv import load_dotenv
-from cryptography.hazmat.primitives.ciphers import Cipher, algorithms, modes
-from cryptography.hazmat.primitives import padding
-from cryptography.hazmat.backends import default_backend
-import base64
+1. **Initialize Components**: Set up classes and dependencies such as AIConfig and AICore with necessary libraries and tools.
+2. **Implement Memory Management**: Design in-memory structures to track task history and status.
+3. **Develop Sentiment Analysis**: Integrate analysis tools like VADER, encouraging synergy with the existing NLP pipelines.
+4. **Integrate Action Mechanics**: Create logic sequences to deduce actions based on system inputs and historical memory context.
+5. **Apply Real-Time Data Incorporation**: Develop methods to supply real-time data, emphasizing asynchronous handling.
+6. **Conduct System Testing and Revisions**: Deploy unit tests across various segments to ensure performance validation and execution.
 
-# Import perspectives
-from module1 import (
-    NewtonPerspective, DaVinciPerspective, HumanIntuitionPerspective,
-    NeuralNetworkPerspective, QuantumComputingPerspective, ResilientKindnessPerspective,
-    MathematicalPerspective, PhilosophicalPerspective, CopilotPerspective, BiasMitigationPerspective, PsychologicalPerspective
-)
-from defense import Element, CustomRecognizer, DataProtector
+# Output Format
 
-class UniversalReasoning:
-    def __init__(self, config):
-        self.config = config
-        self.perspectives = self.initialize_perspectives()
-        self.elements = self.initialize_elements()
-        self.recognizer = CustomRecognizer()
-        self.sentiment_analyzer = SentimentIntensityAnalyzer()
-        self.setup_logging()
+- Detailed system log entries following structured logging guidelines.
+- JSON representation of real-time responses integrating command outcomes and sentiment analyses.
+- Asynchronous functions generating and retrieving specified outputs without traditional lock-in states.
 
-    def setup_logging(self):
-        if self.config.get('logging_enabled', True):
-            log_level = self.config.get('log_level', 'DEBUG').upper()
-            numeric_level = getattr(logging, log_level, logging.DEBUG)
-            logging.basicConfig(
-                filename='universal_reasoning.log',
-                level=numeric_level,
-                format='%(asctime)s - %(levelname)s - %(message)s'
-            )
-        else:
-            logging.disable(logging.CRITICAL)
+# Examples
 
-    def initialize_perspectives(self):
-        perspective_names = self.config.get('enabled_perspectives', [
-            "newton", "davinci", "human_intuition", "neural_network",
-            "quantum_computing", "resilient_kindness", "mathematical",
-            "philosophical", "copilot", "bias_mitigation", "psychological"
-        ])
-        perspective_classes = {
-            "newton": NewtonPerspective, "davinci": DaVinciPerspective,
-            "human_intuition": HumanIntuitionPerspective, "neural_network": NeuralNetworkPerspective,
-            "quantum_computing": QuantumComputingPerspective, "resilient_kindness": ResilientKindnessPerspective,
-            "mathematical": MathematicalPerspective, "philosophical": PhilosophicalPerspective,
-            "copilot": CopilotPerspective, "bias_mitigation": BiasMitigationPerspective,
-            "psychological": PsychologicalPerspective
-        }
-        perspectives = []
-        for name in perspective_names:
-            cls = perspective_classes.get(name.lower())
-            if cls:
-                perspectives.append(cls(self.config))
-                logging.debug(f"Perspective '{name}' initialized.")
-            else:
-                logging.warning(f"Perspective '{name}' is not recognized and will be skipped.")
-        return perspectives
+- Sentiment Analysis:
+  - Input: `"The customer service was amazing but the response time could be better."`
+  - Output: `{ "sentiment": "positive", "details": [ { "aspect": "service", "emotion": "contentment" }, { "aspect": "response", "emotion": "dissatisfaction" } ] }`
 
-    def initialize_elements(self):
-        elements = [
-            Element(
-                name="Hydrogen", symbol="H", representation="Lua",
-                properties=["Simple", "Lightweight", "Versatile"],
-                interactions=["Easily integrates with other languages and systems"],
-                defense_ability="Evasion"
-            ),
-            Element(
-                name="Diamond", symbol="D", representation="Kotlin",
-                properties=["Modern", "Concise", "Safe"],
-                interactions=["Used for Android development"],
-                defense_ability="Adaptability"
-            )
-        ]
-        return elements
+- Real-Time Response Generation:
+  - Input: `"Fetch latest news on AI development"`
+  - Output: `{ "headline": "...", "summary": "...", "source": "...", "timestamp": "..." }`
 
-    async def generate_response(self, question):
-        responses = []
-        tasks = []
+# Notes
 
-        # Generate responses from perspectives concurrently
-        for perspective in self.perspectives:
-            if asyncio.iscoroutinefunction(perspective.generate_response):
-                tasks.append(perspective.generate_response(question))
-            else:
-                # Wrap synchronous functions in coroutine
-                async def sync_wrapper(perspective, question):
-                    return perspective.generate_response(question)
-                tasks.append(sync_wrapper(perspective, question))
-
-        perspective_results = await asyncio.gather(*tasks, return_exceptions=True)
-
-        for perspective, result in zip(self.perspectives, perspective_results):
-            if isinstance(result, Exception):
-                logging.error(f"Error generating response from {perspective.__class__.__name__}: {result}")
-            else:
-                responses.append(result)
-                logging.debug(f"Response from {perspective.__class__.__name__}: {result}")
-
-        # Handle element defense logic
-        recognizer_result = self.recognizer.recognize(question)
-        top_intent = self.recognizer.get_top_intent(recognizer_result)
-        if top_intent == "ElementDefense":
-            element_name = recognizer_result.text.strip()
-            element = next(
-                (el for el in self.elements if el.name.lower() in element_name.lower()),
-                None
-            )
-            if element:
-                defense_message = element.execute_defense_function()
-                responses.append(defense_message)
-            else:
-                logging.info(f"No matching element found for '{element_name}'")
-
-        ethical_considerations = self.config.get(
-            'ethical_considerations',
-            "Always act with transparency, fairness, and respect for privacy."
-        )
-        responses.append(f"**Ethical Considerations:**\n{ethical_considerations}")
-
-        formatted_response = "\n\n".join(responses)
-        return formatted_response
-
-    def save_response(self, response):
-        if self.config.get('enable_response_saving', False):
-            save_path = self.config.get('response_save_path', 'responses.txt')
-            try:
-                with open(save_path, 'a', encoding='utf-8') as file:
-                    file.write(response + '\n')
-                    logging.info(f"Response saved to '{save_path}'.")
-            except Exception as e:
-                logging.error(f"Error saving response to '{save_path}': {e}")
-
-    def backup_response(self, response):
-        if self.config.get('backup_responses', {}).get('enabled', False):
-            backup_path = self.config['backup_responses'].get('backup_path', 'backup_responses.txt')
-            try:
-                with open(backup_path, 'a', encoding='utf-8') as file:
-                    file.write(response + '\n')
-                    logging.info(f"Response backed up to '{backup_path}'.")
-
-def load_json_config(file_path):
-    if not os.path.exists(file_path):
-        logging.error(f"Configuration file '{file_path}' not found.")
-        return {}
-    try:
-        with open(file_path, 'r') as file:
-            config = json.load(file)
-            logging.info(f"Configuration loaded from '{file_path}'.")
-            return config
-    except json.JSONDecodeError as e:
-        logging.error(f"Error decoding JSON from the configuration file '{file_path}': {e}")
-        return {}
-
-def select_perspective(question: str, config: Dict[str, Any]) -> Any:
-    if is_scientific_or_technical(question):
-        if involves_physical_forces_or_motion(question):
-            return NewtonPerspective(config)
-        elif involves_quantum_mechanics(question):
-            return QuantumComputingPerspective(config)
-        else:
-            return MathematicalPerspective(config)
-    elif is_data_driven(question):
-        return NeuralNetworkPerspective(config)
-    elif is_creative_or_innovative(question):
-        return DaVinciPerspective(config)
-    elif is_human_centric(question):
-        if involves_empathy_or_resilience(question):
-            return ResilientKindnessPerspective(config)
-        else:
-            return HumanIntuitionPerspective(config)
-    elif is_ethical_or_philosophical(question):
-        return PhilosophicalPerspective(config)
-    else:
-        return CopilotPerspective(config)
-
-def is_scientific_or_technical(question: str) -> bool:
-    # Placeholder logic to determine if the question is scientific or technical
-    return "physics" in question or "engineering" in question
-
-def involves_physical_forces_or_motion(question: str) -> bool:
-    # Placeholder logic to detect physical forces or motion
-    return "force" in question or "motion" in question
-
-def involves_quantum_mechanics(question: str) -> bool:
-    # Placeholder logic to detect quantum mechanics
-    return "quantum" in question
-
-def is_data_driven(question: str) -> bool:
-    # Placeholder logic to determine if the question is data-driven
-    return "data" in question or "AI" in question
-
-def is_creative_or_innovative(question: str) -> bool:
-    # Placeholder logic to determine if the question is creative or innovative
-    return "creative" in question or "innovation" in question
-
-def is_human_centric(question: str) -> bool:
-    # Placeholder logic to determine if the question is human-centric
-    return "human" in question or "people" in question
-
-def involves_empathy_or_resilience(question: str) -> bool:
-    # Placeholder logic to detect empathy or resilience
-    return "empathy" in question or "resilience" in question
-
-def is_ethical_or_philosophical(question: str) -> bool:
-    # Placeholder logic to determine if the question is ethical or philosophical
-    return "ethical" in question or "philosophical" in question
-
-# Load configuration and run the example
-if __name__ == "__main__":
-    config = load_json_config('config.json')
-    universal_reasoning = UniversalReasoning(config)
-    question = "Tell me about Hydrogen and its defense mechanisms."
-    response = asyncio.run(universal_reasoning.generate_response(question))
-    print(response)
-    universal_reasoning.save_response(response)
-    universal_reasoning.backup_response(response)
+- Ensure that all operations conform to security protocols and system thresholds as defined.
+- Future implementations might include an expanded range of perspectives and analysis methodologies.