""" OASIS 双平台并行模拟预设脚本 同时运行Twitter和Reddit模拟，读取相同的配置文件 使用方式: python run_parallel_simulation.py --config simulation_config.json [--action-log actions.jsonl] """ import argparse import asyncio import json import logging import os import random import sys from datetime import datetime from typing import Dict, Any, List, Optional # 添加 backend 目录到路径 # 脚本固定位于 backend/scripts/ 目录 _scripts_dir = os.path.dirname(os.path.abspath(__file__)) _backend_dir = os.path.abspath(os.path.join(_scripts_dir, '..')) _project_root = os.path.abspath(os.path.join(_backend_dir, '..')) sys.path.insert(0, _scripts_dir) sys.path.insert(0, _backend_dir) # 加载项目根目录的 .env 文件（包含 LLM_API_KEY 等配置） from dotenv import load_dotenv _env_file = os.path.join(_project_root, '.env') if os.path.exists(_env_file): load_dotenv(_env_file) print(f"已加载环境配置: {_env_file}") else: # 尝试加载 backend/.env _backend_env = os.path.join(_backend_dir, '.env') if os.path.exists(_backend_env): load_dotenv(_backend_env) print(f"已加载环境配置: {_backend_env}") class UnicodeFormatter(logging.Formatter): """ 自定义格式化器，将 Unicode 转义序列（如 \\uXXXX）转换为可读字符 """ # 匹配 \uXXXX 形式的 Unicode 转义序列 UNICODE_ESCAPE_PATTERN = None @classmethod def _get_pattern(cls): if cls.UNICODE_ESCAPE_PATTERN is None: import re cls.UNICODE_ESCAPE_PATTERN = re.compile(r'\\u([0-9a-fA-F]{4})') return cls.UNICODE_ESCAPE_PATTERN def format(self, record): # 先获取原始格式化结果 result = super().format(record) # 使用正则表达式替换 Unicode 转义序列 pattern = self._get_pattern() def replace_unicode(match): try: return chr(int(match.group(1), 16)) except (ValueError, OverflowError): return match.group(0) return pattern.sub(replace_unicode, result) def setup_oasis_logging(log_dir: str): """ 配置 OASIS 的日志，覆盖默认的带时间戳日志文件 Args: log_dir: 日志目录路径 """ os.makedirs(log_dir, exist_ok=True) # 清理旧的日志文件 for f in os.listdir(log_dir): old_log = os.path.join(log_dir, f) if os.path.isfile(old_log) and f.endswith('.log'): try: os.remove(old_log) except OSError: pass # 创建自定义格式化器（支持 Unicode 解码） formatter = UnicodeFormatter( "%(levelname)s - %(asctime)s - %(name)s - %(message)s" ) # 重新配置 OASIS 使用的日志器，使用固定名称（不带时间戳） loggers_config = { "social.agent": os.path.join(log_dir, "social.agent.log"), "social.twitter": os.path.join(log_dir, "social.twitter.log"), "social.rec": os.path.join(log_dir, "social.rec.log"), "oasis.env": os.path.join(log_dir, "oasis.env.log"), "table": os.path.join(log_dir, "table.log"), } for logger_name, log_file in loggers_config.items(): logger = logging.getLogger(logger_name) logger.setLevel(logging.DEBUG) # 清除 OASIS 添加的现有处理器（带时间戳的日志文件） logger.handlers.clear() # 添加新的文件处理器（使用 UTF-8 编码，固定文件名） file_handler = logging.FileHandler(log_file, encoding='utf-8', mode='w') file_handler.setLevel(logging.DEBUG) file_handler.setFormatter(formatter) logger.addHandler(file_handler) # 防止日志向上传播（避免重复） logger.propagate = False print(f"日志配置完成，日志目录: {log_dir}") def init_logging_for_simulation(simulation_dir: str): """初始化模拟的日志配置""" log_dir = os.path.join(simulation_dir, "log") setup_oasis_logging(log_dir) from action_logger import ActionLogger try: from camel.models import ModelFactory from camel.types import ModelPlatformType import oasis from oasis import ( ActionType, LLMAction, ManualAction, generate_twitter_agent_graph, generate_reddit_agent_graph ) except ImportError as e: print(f"错误: 缺少依赖 {e}") print("请先安装: pip install oasis-ai camel-ai") sys.exit(1) # Twitter可用动作 TWITTER_ACTIONS = [ ActionType.CREATE_POST, ActionType.LIKE_POST, ActionType.REPOST, ActionType.FOLLOW, ActionType.DO_NOTHING, ActionType.QUOTE_POST, ] # Reddit可用动作 REDDIT_ACTIONS = [ ActionType.LIKE_POST, ActionType.DISLIKE_POST, ActionType.CREATE_POST, ActionType.CREATE_COMMENT, ActionType.LIKE_COMMENT, ActionType.DISLIKE_COMMENT, ActionType.SEARCH_POSTS, ActionType.SEARCH_USER, ActionType.TREND, ActionType.REFRESH, ActionType.DO_NOTHING, ActionType.FOLLOW, ActionType.MUTE, ] def load_config(config_path: str) -> Dict[str, Any]: """加载配置文件""" with open(config_path, 'r', encoding='utf-8') as f: return json.load(f) def create_model(config: Dict[str, Any]): """ 创建LLM模型 统一使用项目根目录 .env 文件中的配置（优先级最高）： - LLM_API_KEY: API密钥 - LLM_BASE_URL: API基础URL - LLM_MODEL_NAME: 模型名称 OASIS使用camel-ai的ModelFactory，需要设置 OPENAI_API_KEY 和 OPENAI_API_BASE_URL 环境变量 """ # 优先从 .env 读取配置 llm_api_key = os.environ.get("LLM_API_KEY", "") llm_base_url = os.environ.get("LLM_BASE_URL", "") llm_model = os.environ.get("LLM_MODEL_NAME", "") # 如果 .env 中没有，则使用 config 作为备用 if not llm_model: llm_model = config.get("llm_model", "gpt-4o-mini") # 设置 camel-ai 所需的环境变量 if llm_api_key: os.environ["OPENAI_API_KEY"] = llm_api_key if not os.environ.get("OPENAI_API_KEY"): raise ValueError("缺少 API Key 配置，请在项目根目录 .env 文件中设置 LLM_API_KEY") if llm_base_url: os.environ["OPENAI_API_BASE_URL"] = llm_base_url print(f"LLM配置: model={llm_model}, base_url={llm_base_url[:40] if llm_base_url else '默认'}...") return ModelFactory.create( model_platform=ModelPlatformType.OPENAI, model_type=llm_model, ) def get_active_agents_for_round( env, config: Dict[str, Any], current_hour: int, round_num: int ) -> List: """根据时间和配置决定本轮激活哪些Agent""" time_config = config.get("time_config", {}) agent_configs = config.get("agent_configs", []) base_min = time_config.get("agents_per_hour_min", 5) base_max = time_config.get("agents_per_hour_max", 20) peak_hours = time_config.get("peak_hours", [9, 10, 11, 14, 15, 20, 21, 22]) off_peak_hours = time_config.get("off_peak_hours", [0, 1, 2, 3, 4, 5]) if current_hour in peak_hours: multiplier = time_config.get("peak_activity_multiplier", 1.5) elif current_hour in off_peak_hours: multiplier = time_config.get("off_peak_activity_multiplier", 0.3) else: multiplier = 1.0 target_count = int(random.uniform(base_min, base_max) * multiplier) candidates = [] for cfg in agent_configs: agent_id = cfg.get("agent_id", 0) active_hours = cfg.get("active_hours", list(range(8, 23))) activity_level = cfg.get("activity_level", 0.5) if current_hour not in active_hours: continue if random.random() < activity_level: candidates.append(agent_id) selected_ids = random.sample( candidates, min(target_count, len(candidates)) ) if candidates else [] active_agents = [] for agent_id in selected_ids: try: agent = env.agent_graph.get_agent(agent_id) active_agents.append((agent_id, agent)) except Exception: pass return active_agents async def run_twitter_simulation( config: Dict[str, Any], simulation_dir: str, action_logger: Optional[ActionLogger] = None ): """运行Twitter模拟""" print("[Twitter] 初始化...") model = create_model(config) # OASIS Twitter使用CSV格式 profile_path = os.path.join(simulation_dir, "twitter_profiles.csv") if not os.path.exists(profile_path): print(f"[Twitter] 错误: Profile文件不存在: {profile_path}") return agent_graph = await generate_twitter_agent_graph( profile_path=profile_path, model=model, available_actions=TWITTER_ACTIONS, ) # 获取Agent名称映射 agent_names = {} for agent_id, agent in agent_graph.get_agents(): agent_names[agent_id] = getattr(agent, 'name', f'Agent_{agent_id}') db_path = os.path.join(simulation_dir, "twitter_simulation.db") if os.path.exists(db_path): os.remove(db_path) env = oasis.make( agent_graph=agent_graph, platform=oasis.DefaultPlatformType.TWITTER, database_path=db_path, ) await env.reset() print("[Twitter] 环境已启动") if action_logger: action_logger.log_simulation_start("twitter", config) total_actions = 0 # 执行初始事件 event_config = config.get("event_config", {}) initial_posts = event_config.get("initial_posts", []) if initial_posts: initial_actions = {} for post in initial_posts: agent_id = post.get("poster_agent_id", 0) content = post.get("content", "") try: agent = env.agent_graph.get_agent(agent_id) initial_actions[agent] = ManualAction( action_type=ActionType.CREATE_POST, action_args={"content": content} ) if action_logger: action_logger.log_action( round_num=0, platform="twitter", agent_id=agent_id, agent_name=agent_names.get(agent_id, f"Agent_{agent_id}"), action_type="CREATE_POST", action_args={"content": content[:100] + "..." if len(content) > 100 else content} ) total_actions += 1 except Exception: pass if initial_actions: await env.step(initial_actions) print(f"[Twitter] 已发布 {len(initial_actions)} 条初始帖子") # 主模拟循环 time_config = 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 start_time = datetime.now() for round_num in range(total_rounds): simulated_minutes = round_num * minutes_per_round simulated_hour = (simulated_minutes // 60) % 24 simulated_day = simulated_minutes // (60 * 24) + 1 active_agents = get_active_agents_for_round( env, config, simulated_hour, round_num ) if not active_agents: continue if action_logger: action_logger.log_round_start(round_num + 1, simulated_hour, "twitter") actions = {agent: LLMAction() for _, agent in active_agents} await env.step(actions) # 记录动作 for agent_id, agent in active_agents: if action_logger: action_logger.log_action( round_num=round_num + 1, platform="twitter", agent_id=agent_id, agent_name=agent_names.get(agent_id, f"Agent_{agent_id}"), action_type="LLM_ACTION", action_args={} ) total_actions += 1 if action_logger: action_logger.log_round_end(round_num + 1, len(active_agents), "twitter") if (round_num + 1) % 20 == 0: progress = (round_num + 1) / total_rounds * 100 print(f"[Twitter] Day {simulated_day}, {simulated_hour:02d}:00 " f"- Round {round_num + 1}/{total_rounds} ({progress:.1f}%)") await env.close() if action_logger: action_logger.log_simulation_end("twitter", total_rounds, total_actions) elapsed = (datetime.now() - start_time).total_seconds() print(f"[Twitter] 模拟完成! 耗时: {elapsed:.1f}秒, 总动作: {total_actions}") async def run_reddit_simulation( config: Dict[str, Any], simulation_dir: str, action_logger: Optional[ActionLogger] = None ): """运行Reddit模拟""" print("[Reddit] 初始化...") model = create_model(config) profile_path = os.path.join(simulation_dir, "reddit_profiles.json") if not os.path.exists(profile_path): print(f"[Reddit] 错误: Profile文件不存在: {profile_path}") return agent_graph = await generate_reddit_agent_graph( profile_path=profile_path, model=model, available_actions=REDDIT_ACTIONS, ) # 获取Agent名称映射 agent_names = {} for agent_id, agent in agent_graph.get_agents(): agent_names[agent_id] = getattr(agent, 'name', f'Agent_{agent_id}') db_path = os.path.join(simulation_dir, "reddit_simulation.db") if os.path.exists(db_path): os.remove(db_path) env = oasis.make( agent_graph=agent_graph, platform=oasis.DefaultPlatformType.REDDIT, database_path=db_path, ) await env.reset() print("[Reddit] 环境已启动") if action_logger: action_logger.log_simulation_start("reddit", config) total_actions = 0 # 执行初始事件 event_config = config.get("event_config", {}) initial_posts = event_config.get("initial_posts", []) if initial_posts: initial_actions = {} for post in initial_posts: agent_id = post.get("poster_agent_id", 0) content = post.get("content", "") try: agent = env.agent_graph.get_agent(agent_id) if agent in initial_actions: if not isinstance(initial_actions[agent], list): initial_actions[agent] = [initial_actions[agent]] initial_actions[agent].append(ManualAction( action_type=ActionType.CREATE_POST, action_args={"content": content} )) else: initial_actions[agent] = ManualAction( action_type=ActionType.CREATE_POST, action_args={"content": content} ) if action_logger: action_logger.log_action( round_num=0, platform="reddit", agent_id=agent_id, agent_name=agent_names.get(agent_id, f"Agent_{agent_id}"), action_type="CREATE_POST", action_args={"content": content[:100] + "..." if len(content) > 100 else content} ) total_actions += 1 except Exception: pass if initial_actions: await env.step(initial_actions) print(f"[Reddit] 已发布 {len(initial_actions)} 条初始帖子") # 主模拟循环 time_config = 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 start_time = datetime.now() for round_num in range(total_rounds): simulated_minutes = round_num * minutes_per_round simulated_hour = (simulated_minutes // 60) % 24 simulated_day = simulated_minutes // (60 * 24) + 1 active_agents = get_active_agents_for_round( env, config, simulated_hour, round_num ) if not active_agents: continue if action_logger: action_logger.log_round_start(round_num + 1, simulated_hour, "reddit") actions = {agent: LLMAction() for _, agent in active_agents} await env.step(actions) # 记录动作 for agent_id, agent in active_agents: if action_logger: action_logger.log_action( round_num=round_num + 1, platform="reddit", agent_id=agent_id, agent_name=agent_names.get(agent_id, f"Agent_{agent_id}"), action_type="LLM_ACTION", action_args={} ) total_actions += 1 if action_logger: action_logger.log_round_end(round_num + 1, len(active_agents), "reddit") if (round_num + 1) % 20 == 0: progress = (round_num + 1) / total_rounds * 100 print(f"[Reddit] Day {simulated_day}, {simulated_hour:02d}:00 " f"- Round {round_num + 1}/{total_rounds} ({progress:.1f}%)") await env.close() if action_logger: action_logger.log_simulation_end("reddit", total_rounds, total_actions) elapsed = (datetime.now() - start_time).total_seconds() print(f"[Reddit] 模拟完成! 耗时: {elapsed:.1f}秒, 总动作: {total_actions}") async def main(): parser = argparse.ArgumentParser(description='OASIS双平台并行模拟') parser.add_argument( '--config', type=str, required=True, help='配置文件路径 (simulation_config.json)' ) parser.add_argument( '--twitter-only', action='store_true', help='只运行Twitter模拟' ) parser.add_argument( '--reddit-only', action='store_true', help='只运行Reddit模拟' ) parser.add_argument( '--action-log', type=str, default='actions.jsonl', help='动作日志文件路径 (默认: actions.jsonl)' ) args = parser.parse_args() if not os.path.exists(args.config): print(f"错误: 配置文件不存在: {args.config}") sys.exit(1) config = load_config(args.config) simulation_dir = os.path.dirname(args.config) or "." # 初始化日志配置（清理旧日志文件，使用固定名称） init_logging_for_simulation(simulation_dir) # 创建动作日志记录器 action_log_path = os.path.join(simulation_dir, args.action_log) action_logger = ActionLogger(action_log_path) print("=" * 60) print("OASIS 双平台并行模拟") print(f"配置文件: {args.config}") print(f"模拟ID: {config.get('simulation_id', 'unknown')}") print(f"动作日志: {action_log_path}") print("=" * 60) time_config = config.get("time_config", {}) print(f"\n模拟参数:") print(f" - 总模拟时长: {time_config.get('total_simulation_hours', 72)}小时") print(f" - 每轮时间: {time_config.get('minutes_per_round', 30)}分钟") print(f" - Agent数量: {len(config.get('agent_configs', []))}") # LLM推理说明 reasoning = config.get("generation_reasoning", "") if reasoning: print(f"\nLLM配置推理:") print(f" {reasoning[:500]}..." if len(reasoning) > 500 else f" {reasoning}") print("\n" + "=" * 60) start_time = datetime.now() if args.twitter_only: await run_twitter_simulation(config, simulation_dir, action_logger) elif args.reddit_only: await run_reddit_simulation(config, simulation_dir, action_logger) else: # 并行运行（共享同一个action_logger） await asyncio.gather( run_twitter_simulation(config, simulation_dir, action_logger), run_reddit_simulation(config, simulation_dir, action_logger), ) total_elapsed = (datetime.now() - start_time).total_seconds() print("\n" + "=" * 60) print(f"全部模拟完成! 总耗时: {total_elapsed:.1f}秒") print(f"动作日志已保存到: {action_log_path}") print("=" * 60) if __name__ == "__main__": asyncio.run(main())