from abc import ABC, abstractmethod class LeaderAgent(ABC): @abstractmethod def distribute_task(self, WAs, task): pass @abstractmethod def collect_results(self, WAs): pass @abstractmethod def process_results(self): pass class WorkerAgent(ABC): @abstractmethod def execute_task(self): pass class CollaborativeAgent(ABC): @abstractmethod def execute_task(self, task): pass @abstractmethod def collaborate(self): pass class CompetitiveAgent(ABC): @abstractmethod def execute_task(self, task): pass def evaluate_results(CompAs): pass # Example class MyWorkerAgent(WorkerAgent): def execute_task(self): # Insert your implementation here pass import json import logging from typing import Any, Dict, List from tiktoken import Tokenizer, TokenizerException # Helper function to count tokens def count_tokens(text: str, tokenizer: Tokenizer) -> int: try: tokens = tokenizer.tokenize(text) return len(tokens) except TokenizerException as e: logging.error(f"Error tokenizing text: {e}") return 0 def divide_and_conquer_v2(task: str, agents_memory: List[Dict[str, Any]], max_tokens: int = 8192) -> str: """ This function divides a complex task into smaller subtasks and assigns each subtask to a different agent. Then, it combines the results to form the final solution, considering the GPT-4 token limit. Args: task (str): The complex task to be solved. agents_memory (List[Dict[str, Any]]): A list of agent memory states. max_tokens (int, optional): The maximum number of tokens GPT-4 can handle. Defaults to 8192. Returns: str: The final solution to the complex task. Error handling: If the text exceeds the token limit, an error message is logged, and the function returns an empty string. """ logging.info(f"Divide and conquer started for task: {task}") subtasks = split_task_into_subtasks(task) results = [] tokenizer = Tokenizer() for subtask in subtasks: agent_memory = random.choice(agents_memory) chat_input = agent_memory + [{"role": "user", "content": subtask}] tokens = count_tokens(json.dumps(chat_input), tokenizer) if tokens >= max_tokens: logging.error("Token limit exceeded for divide_and_conquer_v2") return "" result, _ = chat(chat_input) results.append(result.strip()) final_solution = combine_subtask_results(results) logging.info(f"Divide and conquer completed. Final solution: {final_solution}") # Save the final solution to a database (e.g., a document-based database like MongoDB) save_solution_to_database(task, final_solution) return final_solution def collaborative_execution_v2(task: str, agents_memory: List[Dict[str, Any]], max_tokens: int = 8192) -> str: """ This function allows a group of agents to collaborate on solving a complex task, considering the GPT-4 token limit. Each agent proposes a solution, and a final solution is derived from the best aspects of each proposal. Args: task (str): The complex task to be solved. agents_memory (List[Dict[str, Any]]): A list of agent memory states. max_tokens (int, optional): The maximum number of tokens GPT-4 can handle. Defaults to 8192. Returns: str: The final solution to the complex task. Error handling: If the text exceeds the token limit, an error message is logged, and the function returns an empty string. """ logging.info(f"Collaborative execution started for task: {task}") solutions = [] tokenizer = Tokenizer() for agent_memory in agents_memory: chat_input = agent_memory + [{"role": "user", "content": f"Propose a solution for: {task}"}] tokens = count_tokens(json.dumps(chat_input), tokenizer) if tokens >= max_tokens: logging.error("Token limit exceeded for collaborative_execution_v2") return "" solution, _ = chat(chat_input) solutions.append({"role": "assistant", "content": solution.strip()}) chat_input = [{"role": "system", "content": "You are a collaborative AI agent. Work with other agents to solve the given task."}] + solutions + [{"role": "user", "content": "Combine the best aspects of each solution to create the final solution."}] tokens = count_tokens(json.dumps(chat_input), tokenizer) if tokens >= max_tokens: logging.error("Token limit exceeded for collaborative_execution_v2") return "" final_solution, _ = chat(chat_input) logging.info(f"Collaborative execution completed. Final solution: {final_solution}") # Save the final solution to a database (e.g., a graph-based database like Neo4j for better analysis of connections) save_solution_to_database(task, final_solution) return final_solution.strip() def expert_agents_v2(task: str, domain_experts_memory: List[Dict[str, Any]], max_tokens: int = 8192) -> str: """ This function allows a group of domain expert agents to provide solutions to a given task. The function evaluates the quality of each solution and returns the best one. Args: task (str): The complex task to be solved. domain_experts_memory (List[Dict[str, Any]]): A list of domain expert agent memory states. max_tokens (int, optional): The maximum number of tokens GPT-4 can handle. Defaults to 8192. Returns: str: The best solution to the complex task. Error handling: If the text exceeds the token limit, an error message is logged, and the function returns an empty string. """ logging.info(f"Expert agents execution started for task: {task}") best_solution = None best_score = 0 tokenizer = Tokenizer() for expert_memory in domain_experts_memory: chat_input = expert_memory + [{"role": "user", "content": f"Provide a solution for: {task} based on your domain expertise."}] tokens = count_tokens(json.dumps(chat_input), tokenizer) if tokens >= max_tokens: logging.error("Token limit exceeded for expert_agents_v2") return "" expert_solution, _ = chat(chat_input) score = evaluate_solution_quality(task, expert_solution.strip()) if score > best_score: best_solution = expert_solution.strip() best_score = score logging.info(f"Expert agents execution completed. Best solution: {best_solution}") # Save the best solution to a database (e.g., a relational database like PostgreSQL for structured data) save_solution_to_database(task, best_solution) return best_solution def _v2(taskagent_delegation: str, manager_agents_memory: List[Dict[str, Any]], subordinate_agents_memory: List[Dict[str, Any]], max_tokens: int = 8192) -> str: """ This function allows a group of manager agents to delegate a complex task to a group of subordinate agents. Each manager agent selects the best subordinate agent for each subtask, and the results are combined. Args: task (str): The complex task to be solved. manager_agents_memory (List[Dict[str, Any]]): A list of manager agent memory states. subordinate_agents_memory (List[Dict[str, Any]]): A list of subordinate agent memory states. max_tokens (int, optional): The maximum number of tokens GPT-4 can handle. Defaults to 8192. Returns: str: The final combined result of the complex task. Error handling: If the text exceeds the token limit, an error message is logged, and the function returns an empty string. """ logging.info(f"Agent delegation execution started for task: {task}") subtasks = generate_tasks(task) results = [] for subtask in subtasks: manager_memory = random.choice(manager_agents_memory) selected_subordinate_memory = None while selected_subordinate_memory is None: chat_input = manager_memory + [{"role": "user", "content": f"Select the best subordinate to solve: {subtask}"}] tokens = count_tokens(json.dumps(chat_input), tokenizer) if tokens >= max_tokens: logging.error("Token limit exceeded for agent_delegation_v2") return "" suggested_subordinate, _ = chat(chat_input) subordinate_id = int(suggested_subordinate.strip()) if 0 <= subordinate_id < len(subordinate_agents_memory): selected_subordinate_memory = subordinate_agents_memory[subordinate_id] else: manager_memory.append({"role": "assistant", "content": "Invalid subordinate ID, please try again."}) result = continue_until_done(subtask, selected_subordinate_memory) results.append(result) final_result = combine_results(results) logging.info(f"Agent delegation execution completed. Final result: {final_result}") # Save the final result to a database (e.g., a graph database like Neo4j for mapping connections between entities) save_result_to_database(task, final_result) return final_result def parallel_execution_v2(task: str, num_agents: int, agents_memory: List[Dict[str, Any]], max_tokens: int = 8192) -> str: """ This function uses multiple agents to solve a complex task in parallel. Each agent works on a subtask, and the results are combined. Args: task (str): The complex task to be solved. num_agents (int): The number of agents working in parallel. agents_memory (List[Dict[str, Any]]): A list of agent memory states. max_tokens (int, optional): The maximum number of tokens GPT-4 can handle. Defaults to 8192. Returns: str: The final combined result of the complex task. Error handling: If the text exceeds the token limit, an error message is logged, and the function returns an empty string. """ logging.info(f"Parallel execution started for task: {task}") tasks = generate_tasks(task) results = [] threads = [] def threaded_execution(task: str, agent_memory: Dict[str, Any], results: List[str]) -> None: chat_input = agent_memory + [{"role": "user", "content": task}] tokens = count_tokens(json.dumps(chat_input), tokenizer) if tokens >= max_tokens: logging.error("Token limit exceeded for parallel_execution_v2") return result = continue_until_done(task, agent_memory) results.append(result) for task in tasks: agent_id = random.randint(0, num_agents - 1) t = threading.Thread(target=threaded_execution, args=(task, agents_memory[agent_id], results)) threads.append(t) t.start() for t in threads: t.join() final_result = combine_results(results) logging.info(f"Parallel execution completed. Final result: {final_result}") # Save the final result to a database (e.g., a relational database like PostgreSQL for structured data) save_result_to_database(task, final_result) return final_result def hierarchical_execution_v2(task: str, num_levels: int, agents_memory: List[Dict[str, Any]], max_tokens: int = 8192) -> str: """ This function solves a complex task by dividing it into smaller subtasks and assigning them to agents in a hierarchical manner. Args: task (str): The complex task to be solved. num_levels (int): The number of hierarchical levels in the agent hierarchy. agents_memory (List[Dict[str, Any]]): A list of agent memory states. max_tokens (int, optional): The maximum number of tokens GPT-4 can handle. Defaults to 8192. Returns: str: The final combined result of the complex task. Error handling: If the text exceeds the token limit, an error message is logged, and the function returns an empty string. """ logging.info(f"Hierarchical execution started for task: {task}") levels = divide_problem_into_modules(task) results = [] for level in levels: assigned_agent_memory = agents_memory[num_levels % len(agents_memory)] chat_input = assigned_agent_memory + [{"role": "user", "content": level}] tokens = count_tokens(json.dumps(chat_input), tokenizer) if tokens >= max_tokens: logging.error("Token limit exceeded for hierarchical_execution_v2") return "" result = continue_until_done(level, assigned_agent_memory) results.append(result) num_levels += 1 final_result = combine_results(results) logging.info(f"Hierarchical execution completed. Final result: {final_result}") # Save the final result to a database (e.g., a graph database like Neo4j for hierarchical relationships) save_result_to_database(task, final_result) return final_result def consensus_based_decision_v2(task_prompt: str, agents_memory: List[Dict[str, Any]], max_tokens: int = 8192) -> str: """ This function takes a task prompt and a list of agent memories, and it returns the consensus-based decision among the agents. Args: task_prompt (str): The task prompt to be solved. agents_memory (List[Dict[str, Any]]): A list of agent memory states. max_tokens (int, optional): The maximum number of tokens GPT-4 can handle. Defaults to 8192. Returns: str: The consensus-based decision among the agents. Error handling: If the text exceeds the token limit, an error message is logged, and the function returns an empty string. """ logging.info(f"Consensus-based decision started for task: {task_prompt}") options = collaborative_brainstorm(task_prompt, agents_memory[0], agents_memory[1]) votes = [] for option in options: vote_count = 0 for agent_memory in agents_memory: chat_input = agent_memory + [{"role": "user", "content": f"Which option do you prefer: {options[0]} or {option}?"}] tokens = count_tokens(json.dumps(chat_input), tokenizer) if tokens >= max_tokens: logging.error("Token limit exceeded for consensus_based_decision_v2") return "" vote, _ = chat(chat_input) if vote.strip() == option: vote_count += 1 votes.append(vote_count) consensus_option = options[votes.index(max(votes))] logging.info(f"Consensus-based decision completed. Final result: {consensus_option}") # Save the final result to a database (e.g., a relational database like PostgreSQL for structured data) save_result_to_database(task_prompt, consensus_option) return consensus_option def ask_for_help_v2(chatbot1_memory: List[Dict[str, Any]], chatbot2_memory: List[Dict[str, Any]], max_tokens: int = 8192) -> str: """ This function facilitates the interaction between two chatbots. Chatbot1 asks Chatbot2 for help on a task. Args: chatbot1_memory (List[Dict[str, Any]]): Memory state of Chatbot1. chatbot2_memory (List[Dict[str, Any]]): Memory state of Chatbot2. max_tokens (int, optional): The maximum number of tokens GPT-4 can handle. Defaults to 8192. Returns: str: The suggestion provided by Chatbot2 to help Chatbot1 with the task. Error handling: If the text exceeds the token limit, an error message is logged, and the function returns an empty string. """ logging.info("Ask for help started") chat_input1 = chatbot1_memory + [{"role": "user", "content": "Chatbot1, I need help with this task."}] tokens1 = count_tokens(json.dumps(chat_input1), tokenizer) if tokens1 >= max_tokens: logging.error("Token limit exceeded for ask_for_help_v2") return "" chatbot1_help_request, chatbot1_tokens = chat(chat_input1) chatbot1_memory.append({"role": "assistant", "content": chatbot1_help_request}) chat_input2 = chatbot2_memory + [{"role": "user", "content": f"Chatbot2, please help me with this: {chatbot1_help_request}"}] tokens2 = count_tokens(json.dumps(chat_input2), tokenizer) if tokens2 >= max_tokens: logging.error("Token limit exceeded for ask_for_help_v2") return "" chatbot2_suggestion, chatbot2_tokens = chat(chat_input2) chatbot2_memory.append({"role": "assistant", "content": chatbot2_suggestion}) logging.info(f"Ask for help completed. Chatbot2's suggestion: {chatbot2_suggestion}") # Save the chat history to a database (e.g., a graph database like Neo4j for interconnected data) save_chat_history_to_database(chatbot1_memory, chatbot2_memory) return chatbot2_suggestion def collaborative_brainstorm_v2(topic: str, chatbot1_memory: List[Dict[str, Any]], chatbot2_memory: List[Dict[str, Any]], max_tokens: int = 8192) -> List[str]: """ This function enables two chatbots to collaboratively brainstorm ideas on a given topic. Args: topic (str): The topic for brainstorming. chatbot1_memory (List[Dict[str, Any]]): Memory state of Chatbot1. chatbot2_memory (List[Dict[str, Any]]): Memory state of Chatbot2. max_tokens (int, optional): The maximum number of tokens GPT-4 can handle. Defaults to 8192. Returns: List[str]: A list of brainstormed ideas from both chatbots. Error handling: If the text exceeds the token limit, an error message is logged, and the function returns an empty list. """ logging.info(f"Collaborative brainstorming started for topic: {topic}") ideas = [] for i in range(3): chat_input1 = chatbot1_memory + [{"role": "user", "content": f"Chatbot1, brainstorm an idea for {topic}"}] tokens1 = count_tokens(json.dumps(chat_input1), tokenizer) if tokens1 >= max_tokens: logging.error("Token limit exceeded for collaborative_brainstorm_v2") return [] chatbot1_idea, chatbot1_tokens = chat(chat_input1) chatbot1_memory.append({"role": "assistant", "content": chatbot1_idea}) ideas.append(chatbot1_idea) chat_input2 = chatbot2_memory + [{"role": "user", "content": f"Chatbot2, brainstorm an idea for {topic}"}] tokens2 = count_tokens(json.dumps(chat_input2), tokenizer) if tokens2 >= max_tokens: logging.error("Token limit exceeded for collaborative_brainstorm_v2") return [] chatbot2_idea, chatbot2_tokens = chat(chat_input2) chatbot2_memory.append({"role": "assistant", "content": chatbot2_idea}) ideas.append(chatbot2_idea) logging.info(f"Collaborative brainstorming completed. Ideas: {ideas}") # Save the brainstorming session to a database (e.g., a document database like MongoDB for storing complex data structures) save_brainstorming_session_to_database(topic, ideas, chatbot1_memory, chatbot2_memory) return ideas def graph_based_chat_v2(chatbot_memory: List[Dict[str, Any]], user_id: str, user_message: str, graph_database: GraphDatabase, max_tokens: int = 8192) -> str: """ This function allows a chatbot to engage in a conversation with a user, utilizing a graph database to provide insights and connections between users, keywords, and topics. Args: chatbot_memory (List[Dict[str, Any]]): Memory state of the chatbot. user_id (str): The unique identifier for the user. user_message (str): The message from the user. graph_database (GraphDatabase): The graph database containing connections between users, keywords, topics, etc. max_tokens (int, optional): The maximum number of tokens GPT-4 can handle. Defaults to 8192. Returns: str: The chatbot's response to the user's message. Error handling: If the text exceeds the token limit, an error message is logged, and the function returns an empty string. """ logging.info(f"Received message from user {user_id}: {user_message}") # Update the graph database with user's message update_graph_database(user_id, user_message, graph_database) # Retrieve insights from the graph database insights = get_insights(graph_database) chat_input = chatbot_memory + [{"role": "user", "content": f"{user_message}\nInsights: {insights}"}] tokens = count_tokens(json.dumps(chat_input), tokenizer) if tokens >= max_tokens: logging.error("Token limit exceeded for graph_based_chat_v2") return "" chatbot_response, chatbot_tokens = chat(chat_input) chatbot_memory.append({"role": "assistant", "content": chatbot_response}) logging.info(f"Chatbot response to user {user_id}: {chatbot_response}") # Save the chat conversation to a database (e.g., a relational database like MySQL for structured data) save_chat_conversation_to_database(user_id, user_message, chatbot_response) return chatbot_response def multi_platform_chat_v2(platform: str, chatbot_memory: List[Dict[str, Any]], user_id: str, user_message: str, max_tokens: int = 8192) -> str: """ This function allows a chatbot to engage in a conversation with a user on various platforms such as WhatsApp, Snapchat, Facebook, Twitter, etc. Args: platform (str): The platform on which the chat is taking place (e.g., "WhatsApp", "Facebook"). chatbot_memory (List[Dict[str, Any]]): Memory state of the chatbot. user_id (str): The unique identifier for the user. user_message (str): The message from the user. max_tokens (int, optional): The maximum number of tokens GPT-4 can handle. Defaults to 8192. Returns: str: The chatbot's response to the user's message. Error handling: If the text exceeds the token limit, an error message is logged, and the function returns an empty string. """ logging.info(f"Received message from user {user_id} on {platform}: {user_message}") chat_input = chatbot_memory + [{"role": "user", "content": f"Platform: {platform}\nUser message: {user_message}"}] tokens = count_tokens(json.dumps(chat_input), tokenizer) if tokens >= max_tokens: logging.error("Token limit exceeded for multi_platform_chat_v2") return "" chatbot_response, chatbot_tokens = chat(chat_input) chatbot_memory.append({"role": "assistant", "content": chatbot_response}) logging.info(f"Chatbot response to user {user_id} on {platform}: {chatbot_response}") # Save the chat conversation to a database (e.g., a document-based database like MongoDB for unstructured data) save_chat_conversation_to_database(user_id, platform, user_message, chatbot_response) return chatbot_response def agent_swapping_v2(task_prompt: str, agents_memory: List[Dict[str, Any]], max_tokens: int = 8192) -> str: """ This function allows multiple agents to collaboratively solve a task by swapping in and out when their individual knowledge is insufficient. Args: task_prompt (str): The task to be solved. agents_memory (List[Dict[str, Any]]): List of memory states for each agent. max_tokens (int, optional): The maximum number of tokens GPT-4 can handle. Defaults to 8192. Returns: str: The final solution to the task. Error handling: If the text exceeds the token limit, an error message is logged, and the function returns an empty string. """ current_agent_index = 0 current_agent_memory = agents_memory[current_agent_index] input_messages = current_agent_memory + [{"role": "user", "content": f"Task: {task_prompt}"}] tokens = count_tokens(json.dumps(input_messages), tokenizer) if tokens >= max_tokens: logging.error("Token limit exceeded for agent_swapping_v2") return "" partial_solution, remaining_task = chat(input_messages) while remaining_task: current_agent_index = (current_agent_index + 1) % len(agents_memory) current_agent_memory = agents_memory[current_agent_index] input_messages = current_agent_memory + [{"role": "user", "content": f"Remaining task: {remaining_task}"}] tokens = count_tokens(json.dumps(input_messages), tokenizer) if tokens >= max_tokens: logging.error("Token limit exceeded for agent_swapping_v2") return "" next_partial_solution, remaining_task = chat(input_messages) partial_solution += next_partial_solution return partial_solution def multi_agent_voting_v2(task_prompt: str, agents_memory: List[Dict[str, Any]], max_tokens: int = 8192) -> str: """ This function allows multiple agents to collaboratively solve a task by proposing solutions and voting on the best one. Args: task_prompt (str): The task to be solved. agents_memory (List[Dict[str, Any]]): List of memory states for each agent. max_tokens (int, optional): The maximum number of tokens GPT-4 can handle. Defaults to 8192. Returns: str: The final solution to the task. Error handling: If the text exceeds the token limit, an error message is logged, and the function returns an empty string. """ proposed_solutions = [] for agent_memory in agents_memory: input_messages = agent_memory + [{"role": "user", "content": f"Propose a solution for: {task_prompt}"}] tokens = count_tokens(json.dumps(input_messages), tokenizer) if tokens >= max_tokens: logging.error("Token limit exceeded for multi_agent_voting_v2") return "" proposed_solution, _ = chat(input_messages) proposed_solutions.append(proposed_solution.strip()) input_messages = [{"role": "system", "content": "You are an AI agent. Vote on the best solution from the following options:"}] + [{"role": "assistant", "content": option} for option in proposed_solutions] tokens = count_tokens(json.dumps(input_messages), tokenizer) if tokens >= max_tokens: logging.error("Token limit exceeded for multi_agent_voting_v2") return "" winning_solution, _ = chat(input_messages + [{"role": "user", "content": "Which solution is the best?"}]) return winning_solution.strip() def multi_agent_brainstorming_v2(topic: str, agents_memory: List[Dict[str, Any]], max_tokens: int = 8192) -> List[str]: """ This function allows multiple agents to collaboratively brainstorm ideas on a given topic. Args: topic (str): The topic for brainstorming. agents_memory (List[Dict[str, Any]]): List of memory states for each agent. max_tokens (int, optional): The maximum number of tokens GPT-4 can handle. Defaults to 8192. Returns: List[str]: List of brainstormed ideas. Error handling: If the text exceeds the token limit, an error message is logged, and the function returns an empty list. """ ideas = [] for agent_memory in agents_memory: input_messages = agent_memory + [{"role": "user", "content": f"Brainstorm an idea for: {topic}"}] tokens = count_tokens(json.dumps(input_messages), tokenizer) if tokens >= max_tokens: logging.error("Token limit exceeded for multi_agent_brainstorming_v2") return [] idea, _ = chat(input_messages) ideas.append(idea.strip()) return ideas def multi_agent_emotion_analysis_v2(text: str, agents_memory: List[Dict[str, Any]], max_tokens: int = 8192) -> Dict[str, float]: """ This function allows multiple agents to perform emotion analysis on a given text. Args: text (str): The text to perform emotion analysis on. agents_memory (List[Dict[str, Any]]): List of memory states for each agent. max_tokens (int, optional): The maximum number of tokens GPT-4 can handle. Defaults to 8192. Returns: Dict[str, float]: A dictionary containing emotion scores for the text. Error handling: If the text exceeds the token limit, an error message is logged, and the function returns an empty dictionary. """ emotion_scores = defaultdict(float) for agent_memory in agents_memory: input_messages = agent_memory + [{"role": "user", "content": f"Analyze the emotions in this text: {text}"}] tokens = count_tokens(json.dumps(input_messages), tokenizer) if tokens >= max_tokens: logging.error("Token limit exceeded for multi_agent_emotion_analysis_v2") return {} emotion_analysis, _ = chat(input_messages) parsed_scores = json.loads(emotion_analysis.strip()) for emotion, score in parsed_scores.items(): emotion_scores[emotion] += score for emotion in emotion_scores: emotion_scores[emotion] /= len(agents_memory) return emotion_scores def swarm_intelligence(task_prompt, agents_memory): subtasks = generate_tasks(task_prompt) results = [] for subtask in subtasks: agent_votes = [] for agent_memory in agents_memory: agent_vote, _ = chat(agent_memory + [{"role": "user", "content": f"Propose a solution for: {subtask}"}]) agent_votes.append(agent_vote.strip()) most_common_solution = max(set(agent_votes), key=agent_votes.count) results.append(most_common_solution) return results