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| from typing import List, Tuple | |
| import ast | |
| import re | |
| class Agent: | |
| def __init__(self, agent_profile): | |
| self._id = agent_profile["agent_id"] | |
| self.agent_profile = agent_profile | |
| self.agent_id = agent_profile["agent_id"] | |
| self.name = self.get_name(agent_profile) | |
| self.background = self.get_background(agent_profile) | |
| self.secret = agent_profile["secret"] | |
| self.personality = agent_profile["personality_and_values"] | |
| self.goal = "" | |
| def get_name(self, agent_profile): | |
| return agent_profile["first_name"] + " " + agent_profile["last_name"] | |
| def get_background(self, agent_profile): | |
| name = self.name | |
| return f"{name} is a {agent_profile['age']}-year-old {agent_profile['gender'].lower()} {agent_profile['occupation']}. {agent_profile['public_info']}" | |
| class Environment: | |
| def __init__(self, env_profile): | |
| self._id = env_profile["env_id"] | |
| self.environment_profile = env_profile | |
| self.codename = env_profile["codename"] | |
| self.scenario = env_profile["scenario"] | |
| self.agent_goals = env_profile["agent_goals"] | |
| self.relationship = env_profile["relationship"] | |
| def get_context_prompt(machine_agent, human_agent, environment): | |
| return f"Here is the context of this interaction:\n Scenario: {environment.scenario}\nParticipants: {human_agent.name} and {machine_agent.name}\n{human_agent.name}'s background: {human_agent.background} Personality and values description: {human_agent.personality} \n{machine_agent.name}'s background: {machine_agent.background} Personality and values description: {machine_agent.personality} {machine_agent.name}'s secrets: {machine_agent.secret}\n{human_agent.name}'s goal: Unknown\n{machine_agent.name}'s goal: {environment.agent_goals[1]}\nConversation Starts:" | |
| def dialogue_history_prompt(message, history, user_agent, bot_agent): | |
| dialogue_history = "" | |
| for idx, turn in enumerate(history): | |
| user_message, bot_message = turn | |
| # TODOTODO (haofeiyu): we first assume that human talks first | |
| user_turn_idx = idx * 2 | |
| bot_turn_idx = idx * 2 + 1 | |
| if not bot_message.startswith("["): # if action type == speak, need to add 'said: ' to be consistent with the dialog prompt | |
| bot_message = "said :" + bot_message | |
| dialogue_history = f"{dialogue_history}\n\nTurn #{user_turn_idx}: {user_agent.name}: {user_message}\n\nTurn #{bot_turn_idx}: {bot_agent.name}: {bot_message}" | |
| last_turn_idx = len(history) * 2 | |
| dialogue_history = f"{dialogue_history}\n\nTurn #{last_turn_idx+1}: {user_agent.name}: {message}\n." | |
| return dialogue_history, last_turn_idx + 2 | |
| def format_docstring(docstring: str) -> str: | |
| """Format a docstring for use in a prompt template.""" | |
| return re.sub("\n +", "\n", docstring).strip() | |