760 lines
27 KiB
Python
760 lines
27 KiB
Python
"""
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OASIS Twitter simulation preset script.
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This script reads parameters from a config file to run a fully automated simulation.
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Features:
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- Does not close the environment immediately when the simulation finishes; enters
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command-wait mode instead.
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- Receives Interview commands over IPC.
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- Supports both single-agent and batch interviews.
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- Supports a remote close-environment command.
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Usage:
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python run_twitter_simulation.py --config /path/to/simulation_config.json
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python run_twitter_simulation.py --config /path/to/simulation_config.json --no-wait # close immediately when done
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"""
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import argparse
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import asyncio
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import json
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import logging
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import os
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import random
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import signal
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import sys
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import sqlite3
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from datetime import datetime
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from typing import Dict, Any, List, Optional
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# Globals used by the signal handler.
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_shutdown_event = None
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_cleanup_done = False
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_scripts_dir = os.path.dirname(os.path.abspath(__file__))
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_backend_dir = os.path.abspath(os.path.join(_scripts_dir, '..'))
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_project_root = os.path.abspath(os.path.join(_backend_dir, '..'))
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sys.path.insert(0, _scripts_dir)
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sys.path.insert(0, _backend_dir)
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# Load the project-root .env (it carries LLM_API_KEY and friends).
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from dotenv import load_dotenv
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_env_file = os.path.join(_project_root, '.env')
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if os.path.exists(_env_file):
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load_dotenv(_env_file)
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else:
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_backend_env = os.path.join(_backend_dir, '.env')
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if os.path.exists(_backend_env):
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load_dotenv(_backend_env)
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import re
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class UnicodeFormatter(logging.Formatter):
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"""Custom formatter that turns Unicode escape sequences into readable characters."""
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UNICODE_ESCAPE_PATTERN = re.compile(r'\\u([0-9a-fA-F]{4})')
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def format(self, record):
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result = super().format(record)
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def replace_unicode(match):
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try:
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return chr(int(match.group(1), 16))
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except (ValueError, OverflowError):
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return match.group(0)
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return self.UNICODE_ESCAPE_PATTERN.sub(replace_unicode, result)
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class MaxTokensWarningFilter(logging.Filter):
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"""Suppress camel-ai's max_tokens warning — we intentionally leave it unset and let the model decide."""
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def filter(self, record):
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if "max_tokens" in record.getMessage() and "Invalid or missing" in record.getMessage():
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return False
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return True
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# Install the filter at import time so it is active before any camel code runs.
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logging.getLogger().addFilter(MaxTokensWarningFilter())
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def setup_oasis_logging(log_dir: str):
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"""Configure OASIS logging with fixed log filenames."""
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os.makedirs(log_dir, exist_ok=True)
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# Wipe stale log files from previous runs.
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for f in os.listdir(log_dir):
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old_log = os.path.join(log_dir, f)
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if os.path.isfile(old_log) and f.endswith('.log'):
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try:
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os.remove(old_log)
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except OSError:
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pass
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formatter = UnicodeFormatter("%(levelname)s - %(asctime)s - %(name)s - %(message)s")
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loggers_config = {
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"social.agent": os.path.join(log_dir, "social.agent.log"),
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"social.twitter": os.path.join(log_dir, "social.twitter.log"),
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"social.rec": os.path.join(log_dir, "social.rec.log"),
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"oasis.env": os.path.join(log_dir, "oasis.env.log"),
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"table": os.path.join(log_dir, "table.log"),
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}
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for logger_name, log_file in loggers_config.items():
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logger = logging.getLogger(logger_name)
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logger.setLevel(logging.DEBUG)
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logger.handlers.clear()
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file_handler = logging.FileHandler(log_file, encoding='utf-8', mode='w')
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file_handler.setLevel(logging.DEBUG)
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file_handler.setFormatter(formatter)
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logger.addHandler(file_handler)
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logger.propagate = False
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try:
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from camel.models import ModelFactory
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from camel.types import ModelPlatformType
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import oasis
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from oasis import (
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ActionType,
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LLMAction,
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ManualAction,
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generate_twitter_agent_graph
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)
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except ImportError as e:
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print(f"错误: 缺少依赖 {e}")
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print("请先安装: pip install oasis-ai camel-ai")
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sys.exit(1)
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# IPC-related constants.
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IPC_COMMANDS_DIR = "ipc_commands"
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IPC_RESPONSES_DIR = "ipc_responses"
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ENV_STATUS_FILE = "env_status.json"
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class CommandType:
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"""Command type constants."""
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INTERVIEW = "interview"
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BATCH_INTERVIEW = "batch_interview"
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CLOSE_ENV = "close_env"
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class IPCHandler:
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"""Handles IPC commands directed at the running simulation."""
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def __init__(self, simulation_dir: str, env, agent_graph):
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self.simulation_dir = simulation_dir
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self.env = env
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self.agent_graph = agent_graph
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self.commands_dir = os.path.join(simulation_dir, IPC_COMMANDS_DIR)
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self.responses_dir = os.path.join(simulation_dir, IPC_RESPONSES_DIR)
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self.status_file = os.path.join(simulation_dir, ENV_STATUS_FILE)
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self._running = True
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os.makedirs(self.commands_dir, exist_ok=True)
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os.makedirs(self.responses_dir, exist_ok=True)
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def update_status(self, status: str):
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"""Write the current environment status to the status file."""
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with open(self.status_file, 'w', encoding='utf-8') as f:
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json.dump({
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"status": status,
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"timestamp": datetime.now().isoformat()
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}, f, ensure_ascii=False, indent=2)
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def poll_command(self) -> Optional[Dict[str, Any]]:
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"""Poll for the next pending command."""
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if not os.path.exists(self.commands_dir):
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return None
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# Collect command files ordered by mtime.
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command_files = []
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for filename in os.listdir(self.commands_dir):
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if filename.endswith('.json'):
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filepath = os.path.join(self.commands_dir, filename)
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command_files.append((filepath, os.path.getmtime(filepath)))
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command_files.sort(key=lambda x: x[1])
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for filepath, _ in command_files:
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try:
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with open(filepath, 'r', encoding='utf-8') as f:
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return json.load(f)
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except (json.JSONDecodeError, OSError):
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continue
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return None
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def send_response(self, command_id: str, status: str, result: Dict = None, error: str = None):
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"""Send a response for a processed command."""
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response = {
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"command_id": command_id,
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"status": status,
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"result": result,
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"error": error,
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"timestamp": datetime.now().isoformat()
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}
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response_file = os.path.join(self.responses_dir, f"{command_id}.json")
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with open(response_file, 'w', encoding='utf-8') as f:
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json.dump(response, f, ensure_ascii=False, indent=2)
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# Remove the command file once a response has been written.
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command_file = os.path.join(self.commands_dir, f"{command_id}.json")
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try:
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os.remove(command_file)
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except OSError:
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pass
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async def handle_interview(self, command_id: str, agent_id: int, prompt: str) -> bool:
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"""Handle a single-agent interview command.
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Returns:
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True on success, False on failure.
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"""
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try:
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agent = self.agent_graph.get_agent(agent_id)
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interview_action = ManualAction(
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action_type=ActionType.INTERVIEW,
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action_args={"prompt": prompt}
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)
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actions = {agent: interview_action}
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await self.env.step(actions)
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# Pull the resulting transcript from the simulation database.
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result = self._get_interview_result(agent_id)
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self.send_response(command_id, "completed", result=result)
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print(f" Interview完成: agent_id={agent_id}")
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return True
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except Exception as e:
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error_msg = str(e)
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print(f" Interview失败: agent_id={agent_id}, error={error_msg}")
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self.send_response(command_id, "failed", error=error_msg)
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return False
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async def handle_batch_interview(self, command_id: str, interviews: List[Dict]) -> bool:
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"""Handle a batch interview command.
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Args:
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interviews: [{"agent_id": int, "prompt": str}, ...]
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"""
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try:
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actions = {}
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agent_prompts = {} # Track the prompt issued to each agent for later result lookup.
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for interview in interviews:
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agent_id = interview.get("agent_id")
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prompt = interview.get("prompt", "")
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try:
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agent = self.agent_graph.get_agent(agent_id)
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actions[agent] = ManualAction(
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action_type=ActionType.INTERVIEW,
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action_args={"prompt": prompt}
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)
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agent_prompts[agent_id] = prompt
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except Exception as e:
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print(f" 警告: 无法获取Agent {agent_id}: {e}")
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if not actions:
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self.send_response(command_id, "failed", error="没有有效的Agent")
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return False
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await self.env.step(actions)
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# Collect the per-agent interview results.
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results = {}
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for agent_id in agent_prompts.keys():
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result = self._get_interview_result(agent_id)
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results[agent_id] = result
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self.send_response(command_id, "completed", result={
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"interviews_count": len(results),
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"results": results
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})
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print(f" 批量Interview完成: {len(results)} 个Agent")
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return True
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except Exception as e:
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error_msg = str(e)
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print(f" 批量Interview失败: {error_msg}")
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self.send_response(command_id, "failed", error=error_msg)
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return False
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def _get_interview_result(self, agent_id: int) -> Dict[str, Any]:
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"""Fetch the most recent interview result for an agent from the database."""
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db_path = os.path.join(self.simulation_dir, "twitter_simulation.db")
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result = {
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"agent_id": agent_id,
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"response": None,
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"timestamp": None
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}
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if not os.path.exists(db_path):
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return result
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try:
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conn = sqlite3.connect(db_path)
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cursor = conn.cursor()
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# Pull the most recent INTERVIEW trace row for this agent.
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cursor.execute("""
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SELECT user_id, info, created_at
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FROM trace
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WHERE action = ? AND user_id = ?
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ORDER BY created_at DESC
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LIMIT 1
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""", (ActionType.INTERVIEW.value, agent_id))
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row = cursor.fetchone()
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if row:
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user_id, info_json, created_at = row
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try:
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info = json.loads(info_json) if info_json else {}
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result["response"] = info.get("response", info)
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result["timestamp"] = created_at
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except json.JSONDecodeError:
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result["response"] = info_json
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conn.close()
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except Exception as e:
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print(f" 读取Interview结果失败: {e}")
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return result
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async def process_commands(self) -> bool:
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"""Process pending commands.
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Returns:
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True if the run loop should continue, False if it should exit.
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"""
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command = self.poll_command()
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if not command:
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return True
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command_id = command.get("command_id")
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command_type = command.get("command_type")
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args = command.get("args", {})
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print(f"\n收到IPC命令: {command_type}, id={command_id}")
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if command_type == CommandType.INTERVIEW:
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await self.handle_interview(
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command_id,
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args.get("agent_id", 0),
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args.get("prompt", "")
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)
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return True
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elif command_type == CommandType.BATCH_INTERVIEW:
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await self.handle_batch_interview(
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command_id,
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args.get("interviews", [])
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)
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return True
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elif command_type == CommandType.CLOSE_ENV:
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print("收到关闭环境命令")
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self.send_response(command_id, "completed", result={"message": "环境即将关闭"})
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return False
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else:
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self.send_response(command_id, "failed", error=f"未知命令类型: {command_type}")
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return True
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class TwitterSimulationRunner:
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"""Drives a single Twitter simulation run."""
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# Available Twitter actions. INTERVIEW is intentionally excluded — it can only be triggered via ManualAction.
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AVAILABLE_ACTIONS = [
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ActionType.CREATE_POST,
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ActionType.LIKE_POST,
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ActionType.REPOST,
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ActionType.FOLLOW,
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ActionType.DO_NOTHING,
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ActionType.QUOTE_POST,
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]
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def __init__(self, config_path: str, wait_for_commands: bool = True):
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"""Initialize the simulation runner.
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Args:
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config_path: Path to the config file (simulation_config.json).
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wait_for_commands: Whether to wait for IPC commands after the simulation completes (default True).
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"""
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self.config_path = config_path
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self.config = self._load_config()
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self.simulation_dir = os.path.dirname(config_path)
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self.wait_for_commands = wait_for_commands
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self.env = None
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self.agent_graph = None
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self.ipc_handler = None
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def _load_config(self) -> Dict[str, Any]:
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"""Load the simulation config file."""
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with open(self.config_path, 'r', encoding='utf-8') as f:
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return json.load(f)
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def _get_profile_path(self) -> str:
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"""Return the agent profile path (OASIS Twitter expects CSV)."""
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return os.path.join(self.simulation_dir, "twitter_profiles.csv")
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def _get_db_path(self) -> str:
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"""Return the simulation SQLite database path."""
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return os.path.join(self.simulation_dir, "twitter_simulation.db")
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def _create_model(self):
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"""Create the LLM model.
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Uses the project-root .env file (highest precedence):
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- LLM_API_KEY: API key
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- LLM_BASE_URL: API base URL
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- LLM_MODEL_NAME: model name
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"""
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# Prefer values from .env.
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llm_api_key = os.environ.get("LLM_API_KEY", "")
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llm_base_url = os.environ.get("LLM_BASE_URL", "")
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llm_model = os.environ.get("LLM_MODEL_NAME", "")
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# Fall back to the simulation config if .env did not provide a model name.
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if not llm_model:
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llm_model = self.config.get("llm_model", "gpt-4o-mini")
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# camel-ai reads OPENAI_API_KEY from the environment.
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if llm_api_key:
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os.environ["OPENAI_API_KEY"] = llm_api_key
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if not os.environ.get("OPENAI_API_KEY"):
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raise ValueError("缺少 API Key 配置，请在项目根目录 .env 文件中设置 LLM_API_KEY")
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if llm_base_url:
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os.environ["OPENAI_API_BASE_URL"] = llm_base_url
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print(f"LLM配置: model={llm_model}, base_url={llm_base_url[:40] if llm_base_url else '默认'}...")
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return ModelFactory.create(
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model_platform=ModelPlatformType.OPENAI,
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model_type=llm_model,
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)
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def _get_active_agents_for_round(
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self,
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env,
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current_hour: int,
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round_num: int
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) -> List:
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"""Decide which agents activate this round, based on time and config.
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Args:
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env: The OASIS environment.
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current_hour: Current simulated hour (0-23).
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round_num: Current round number.
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Returns:
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The list of agents activated this round.
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"""
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time_config = self.config.get("time_config", {})
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agent_configs = self.config.get("agent_configs", [])
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# Base activation count per round.
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base_min = time_config.get("agents_per_hour_min", 5)
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base_max = time_config.get("agents_per_hour_max", 20)
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# Adjust by time-of-day (peak vs. off-peak hours).
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peak_hours = time_config.get("peak_hours", [9, 10, 11, 14, 15, 20, 21, 22])
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off_peak_hours = time_config.get("off_peak_hours", [0, 1, 2, 3, 4, 5])
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if current_hour in peak_hours:
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multiplier = time_config.get("peak_activity_multiplier", 1.5)
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elif current_hour in off_peak_hours:
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multiplier = time_config.get("off_peak_activity_multiplier", 0.3)
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else:
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multiplier = 1.0
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target_count = int(random.uniform(base_min, base_max) * multiplier)
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# Compute activation probability for each configured agent.
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candidates = []
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for cfg in agent_configs:
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agent_id = cfg.get("agent_id", 0)
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active_hours = cfg.get("active_hours", list(range(8, 23)))
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activity_level = cfg.get("activity_level", 0.5)
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if current_hour not in active_hours:
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continue
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if random.random() < activity_level:
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candidates.append(agent_id)
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# Pick a random subset of the eligible candidates.
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selected_ids = random.sample(
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candidates,
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min(target_count, len(candidates))
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) if candidates else []
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# Resolve IDs to Agent objects.
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active_agents = []
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for agent_id in selected_ids:
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try:
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agent = env.agent_graph.get_agent(agent_id)
|
|
active_agents.append((agent_id, agent))
|
|
except Exception:
|
|
pass
|
|
|
|
return active_agents
|
|
|
|
async def run(self, max_rounds: int = None):
|
|
"""Run the Twitter simulation.
|
|
|
|
Args:
|
|
max_rounds: Optional cap on the number of rounds, used to truncate overly long simulations.
|
|
"""
|
|
print("=" * 60)
|
|
print("OASIS Twitter模拟")
|
|
print(f"配置文件: {self.config_path}")
|
|
print(f"模拟ID: {self.config.get('simulation_id', 'unknown')}")
|
|
print(f"等待命令模式: {'启用' if self.wait_for_commands else '禁用'}")
|
|
print("=" * 60)
|
|
|
|
time_config = self.config.get("time_config", {})
|
|
total_hours = time_config.get("total_simulation_hours", 72)
|
|
minutes_per_round = time_config.get("minutes_per_round", 30)
|
|
|
|
total_rounds = (total_hours * 60) // minutes_per_round
|
|
|
|
# Truncate to max_rounds when one was supplied.
|
|
if max_rounds is not None and max_rounds > 0:
|
|
original_rounds = total_rounds
|
|
total_rounds = min(total_rounds, max_rounds)
|
|
if total_rounds < original_rounds:
|
|
print(f"\n轮数已截断: {original_rounds} -> {total_rounds} (max_rounds={max_rounds})")
|
|
|
|
print(f"\n模拟参数:")
|
|
print(f" - 总模拟时长: {total_hours}小时")
|
|
print(f" - 每轮时间: {minutes_per_round}分钟")
|
|
print(f" - 总轮数: {total_rounds}")
|
|
if max_rounds:
|
|
print(f" - 最大轮数限制: {max_rounds}")
|
|
print(f" - Agent数量: {len(self.config.get('agent_configs', []))}")
|
|
|
|
print("\n初始化LLM模型...")
|
|
model = self._create_model()
|
|
|
|
# Load the agent graph from the profile CSV.
|
|
print("加载Agent Profile...")
|
|
profile_path = self._get_profile_path()
|
|
if not os.path.exists(profile_path):
|
|
print(f"错误: Profile文件不存在: {profile_path}")
|
|
return
|
|
|
|
self.agent_graph = await generate_twitter_agent_graph(
|
|
profile_path=profile_path,
|
|
model=model,
|
|
available_actions=self.AVAILABLE_ACTIONS,
|
|
)
|
|
|
|
# Reset the simulation database for a clean run.
|
|
db_path = self._get_db_path()
|
|
if os.path.exists(db_path):
|
|
os.remove(db_path)
|
|
print(f"已删除旧数据库: {db_path}")
|
|
|
|
print("创建OASIS环境...")
|
|
self.env = oasis.make(
|
|
agent_graph=self.agent_graph,
|
|
platform=oasis.DefaultPlatformType.TWITTER,
|
|
database_path=db_path,
|
|
semaphore=30, # Cap concurrent LLM requests to avoid API overload.
|
|
)
|
|
|
|
await self.env.reset()
|
|
print("环境初始化完成\n")
|
|
|
|
self.ipc_handler = IPCHandler(self.simulation_dir, self.env, self.agent_graph)
|
|
self.ipc_handler.update_status("running")
|
|
|
|
# Run the initial seeded events (kickoff posts).
|
|
event_config = self.config.get("event_config", {})
|
|
initial_posts = event_config.get("initial_posts", [])
|
|
|
|
if initial_posts:
|
|
print(f"执行初始事件 ({len(initial_posts)}条初始帖子)...")
|
|
initial_actions = {}
|
|
for post in initial_posts:
|
|
agent_id = post.get("poster_agent_id", 0)
|
|
content = post.get("content", "")
|
|
try:
|
|
agent = self.env.agent_graph.get_agent(agent_id)
|
|
initial_actions[agent] = ManualAction(
|
|
action_type=ActionType.CREATE_POST,
|
|
action_args={"content": content}
|
|
)
|
|
except Exception as e:
|
|
print(f" 警告: 无法为Agent {agent_id}创建初始帖子: {e}")
|
|
|
|
if initial_actions:
|
|
await self.env.step(initial_actions)
|
|
print(f" 已发布 {len(initial_actions)} 条初始帖子")
|
|
|
|
# Main simulation loop.
|
|
print("\n开始模拟循环...")
|
|
start_time = datetime.now()
|
|
|
|
for round_num in range(total_rounds):
|
|
# Map round number to simulated wall-clock time.
|
|
simulated_minutes = round_num * minutes_per_round
|
|
simulated_hour = (simulated_minutes // 60) % 24
|
|
simulated_day = simulated_minutes // (60 * 24) + 1
|
|
|
|
active_agents = self._get_active_agents_for_round(
|
|
self.env, simulated_hour, round_num
|
|
)
|
|
|
|
if not active_agents:
|
|
continue
|
|
|
|
actions = {
|
|
agent: LLMAction()
|
|
for _, agent in active_agents
|
|
}
|
|
|
|
await self.env.step(actions)
|
|
|
|
# Periodic progress log.
|
|
if (round_num + 1) % 10 == 0 or round_num == 0:
|
|
elapsed = (datetime.now() - start_time).total_seconds()
|
|
progress = (round_num + 1) / total_rounds * 100
|
|
print(f" [Day {simulated_day}, {simulated_hour:02d}:00] "
|
|
f"Round {round_num + 1}/{total_rounds} ({progress:.1f}%) "
|
|
f"- {len(active_agents)} agents active "
|
|
f"- elapsed: {elapsed:.1f}s")
|
|
|
|
total_elapsed = (datetime.now() - start_time).total_seconds()
|
|
print(f"\n模拟循环完成!")
|
|
print(f" - 总耗时: {total_elapsed:.1f}秒")
|
|
print(f" - 数据库: {db_path}")
|
|
|
|
# Optionally enter command-wait mode.
|
|
if self.wait_for_commands:
|
|
print("\n" + "=" * 60)
|
|
print("进入等待命令模式 - 环境保持运行")
|
|
print("支持的命令: interview, batch_interview, close_env")
|
|
print("=" * 60)
|
|
|
|
self.ipc_handler.update_status("alive")
|
|
|
|
# Command-wait loop, driven by the global _shutdown_event.
|
|
try:
|
|
while not _shutdown_event.is_set():
|
|
should_continue = await self.ipc_handler.process_commands()
|
|
if not should_continue:
|
|
break
|
|
try:
|
|
await asyncio.wait_for(_shutdown_event.wait(), timeout=0.5)
|
|
break # Shutdown signal received.
|
|
except asyncio.TimeoutError:
|
|
pass
|
|
except KeyboardInterrupt:
|
|
print("\n收到中断信号")
|
|
except asyncio.CancelledError:
|
|
print("\n任务被取消")
|
|
except Exception as e:
|
|
print(f"\n命令处理出错: {e}")
|
|
|
|
print("\n关闭环境...")
|
|
|
|
self.ipc_handler.update_status("stopped")
|
|
await self.env.close()
|
|
|
|
print("环境已关闭")
|
|
print("=" * 60)
|
|
|
|
|
|
async def main():
|
|
parser = argparse.ArgumentParser(description='OASIS Twitter模拟')
|
|
parser.add_argument(
|
|
'--config',
|
|
type=str,
|
|
required=True,
|
|
help='配置文件路径 (simulation_config.json)'
|
|
)
|
|
parser.add_argument(
|
|
'--max-rounds',
|
|
type=int,
|
|
default=None,
|
|
help='最大模拟轮数（可选，用于截断过长的模拟）'
|
|
)
|
|
parser.add_argument(
|
|
'--no-wait',
|
|
action='store_true',
|
|
default=False,
|
|
help='模拟完成后立即关闭环境，不进入等待命令模式'
|
|
)
|
|
|
|
args = parser.parse_args()
|
|
|
|
# Create the shutdown event inside the running event loop.
|
|
global _shutdown_event
|
|
_shutdown_event = asyncio.Event()
|
|
|
|
if not os.path.exists(args.config):
|
|
print(f"错误: 配置文件不存在: {args.config}")
|
|
sys.exit(1)
|
|
|
|
# Initialize logging with fixed filenames; old logs are wiped.
|
|
simulation_dir = os.path.dirname(args.config) or "."
|
|
setup_oasis_logging(os.path.join(simulation_dir, "log"))
|
|
|
|
runner = TwitterSimulationRunner(
|
|
config_path=args.config,
|
|
wait_for_commands=not args.no_wait
|
|
)
|
|
await runner.run(max_rounds=args.max_rounds)
|
|
|
|
|
|
def setup_signal_handlers():
|
|
"""Install signal handlers so SIGTERM/SIGINT trigger an orderly shutdown.
|
|
|
|
The handler gives the program a chance to clean up resources properly
|
|
(closing the database, the OASIS environment, etc.) on the first signal,
|
|
and only force-exits on a repeated signal.
|
|
"""
|
|
def signal_handler(signum, frame):
|
|
global _cleanup_done
|
|
sig_name = "SIGTERM" if signum == signal.SIGTERM else "SIGINT"
|
|
print(f"\n收到 {sig_name} 信号，正在退出...")
|
|
if not _cleanup_done:
|
|
_cleanup_done = True
|
|
if _shutdown_event:
|
|
_shutdown_event.set()
|
|
else:
|
|
# Force exit only on a repeat signal.
|
|
print("强制退出...")
|
|
sys.exit(1)
|
|
|
|
signal.signal(signal.SIGTERM, signal_handler)
|
|
signal.signal(signal.SIGINT, signal_handler)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
setup_signal_handlers()
|
|
try:
|
|
asyncio.run(main())
|
|
except KeyboardInterrupt:
|
|
print("\n程序被中断")
|
|
except SystemExit:
|
|
pass
|
|
finally:
|
|
print("模拟进程已退出")
|