3cc5e3f479
- Updated simulation preparation checks to exclude script files from the required files list, improving clarity on file management. - Implemented a robust retry mechanism for Zep API calls in the ZepEntityReader service, enhancing reliability. - Enhanced logging in simulation scripts to provide clearer insights into the simulation process and errors. - Updated simulation runner to manage stdout and stderr logs more effectively, ensuring better error tracking. - Improved profile generation to standardize gender fields and ensure all required fields are populated correctly.
628 lines
20 KiB
Python
628 lines
20 KiB
Python
"""
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OASIS 双平台并行模拟预设脚本
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同时运行Twitter和Reddit模拟，读取相同的配置文件
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使用方式:
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python run_parallel_simulation.py --config simulation_config.json [--action-log actions.jsonl]
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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 sys
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from datetime import datetime
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from typing import Dict, Any, List, Optional
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# 添加 backend 目录到路径
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# 脚本固定位于 backend/scripts/ 目录
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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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# 加载项目根目录的 .env 文件（包含 LLM_API_KEY 等配置）
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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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print(f"已加载环境配置: {_env_file}")
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else:
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# 尝试加载 backend/.env
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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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print(f"已加载环境配置: {_backend_env}")
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class UnicodeFormatter(logging.Formatter):
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"""
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自定义格式化器，将 Unicode 转义序列（如 \\uXXXX）转换为可读字符
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"""
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# 匹配 \uXXXX 形式的 Unicode 转义序列
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UNICODE_ESCAPE_PATTERN = None
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@classmethod
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def _get_pattern(cls):
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if cls.UNICODE_ESCAPE_PATTERN is None:
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import re
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cls.UNICODE_ESCAPE_PATTERN = re.compile(r'\\u([0-9a-fA-F]{4})')
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return cls.UNICODE_ESCAPE_PATTERN
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def format(self, record):
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# 先获取原始格式化结果
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result = super().format(record)
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# 使用正则表达式替换 Unicode 转义序列
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pattern = self._get_pattern()
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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 pattern.sub(replace_unicode, result)
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def setup_oasis_logging(log_dir: str):
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"""
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配置 OASIS 的日志，覆盖默认的带时间戳日志文件
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Args:
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log_dir: 日志目录路径
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"""
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os.makedirs(log_dir, exist_ok=True)
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# 清理旧的日志文件
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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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# 创建自定义格式化器（支持 Unicode 解码）
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formatter = UnicodeFormatter(
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"%(levelname)s - %(asctime)s - %(name)s - %(message)s"
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)
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# 重新配置 OASIS 使用的日志器，使用固定名称（不带时间戳）
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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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# 清除 OASIS 添加的现有处理器（带时间戳的日志文件）
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logger.handlers.clear()
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# 添加新的文件处理器（使用 UTF-8 编码，固定文件名）
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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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# 防止日志向上传播（避免重复）
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logger.propagate = False
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print(f"日志配置完成，日志目录: {log_dir}")
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def init_logging_for_simulation(simulation_dir: str):
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"""初始化模拟的日志配置"""
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log_dir = os.path.join(simulation_dir, "log")
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setup_oasis_logging(log_dir)
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from action_logger import ActionLogger
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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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generate_reddit_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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# Twitter可用动作
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TWITTER_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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# Reddit可用动作
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REDDIT_ACTIONS = [
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ActionType.LIKE_POST,
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ActionType.DISLIKE_POST,
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ActionType.CREATE_POST,
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ActionType.CREATE_COMMENT,
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ActionType.LIKE_COMMENT,
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ActionType.DISLIKE_COMMENT,
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ActionType.SEARCH_POSTS,
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ActionType.SEARCH_USER,
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ActionType.TREND,
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ActionType.REFRESH,
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ActionType.DO_NOTHING,
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ActionType.FOLLOW,
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ActionType.MUTE,
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]
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def load_config(config_path: str) -> Dict[str, Any]:
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"""加载配置文件"""
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with open(config_path, 'r', encoding='utf-8') as f:
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return json.load(f)
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def create_model(config: Dict[str, Any]):
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"""
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创建LLM模型
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统一使用项目根目录 .env 文件中的配置（优先级最高）：
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- LLM_API_KEY: API密钥
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- LLM_BASE_URL: API基础URL
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- LLM_MODEL_NAME: 模型名称
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OASIS使用camel-ai的ModelFactory，需要设置 OPENAI_API_KEY 和 OPENAI_API_BASE_URL 环境变量
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"""
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# 优先从 .env 读取配置
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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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# 如果 .env 中没有，则使用 config 作为备用
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if not llm_model:
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llm_model = config.get("llm_model", "gpt-4o-mini")
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# 设置 camel-ai 所需的环境变量
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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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env,
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config: Dict[str, Any],
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current_hour: int,
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round_num: int
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) -> List:
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"""根据时间和配置决定本轮激活哪些Agent"""
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time_config = config.get("time_config", {})
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agent_configs = config.get("agent_configs", [])
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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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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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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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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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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)
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active_agents.append((agent_id, agent))
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except Exception:
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pass
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return active_agents
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async def run_twitter_simulation(
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config: Dict[str, Any],
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simulation_dir: str,
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action_logger: Optional[ActionLogger] = None
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):
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"""运行Twitter模拟"""
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print("[Twitter] 初始化...")
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model = create_model(config)
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# OASIS Twitter使用CSV格式
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profile_path = os.path.join(simulation_dir, "twitter_profiles.csv")
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if not os.path.exists(profile_path):
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print(f"[Twitter] 错误: Profile文件不存在: {profile_path}")
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return
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agent_graph = await generate_twitter_agent_graph(
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profile_path=profile_path,
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model=model,
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available_actions=TWITTER_ACTIONS,
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)
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# 获取Agent名称映射
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agent_names = {}
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for agent_id, agent in agent_graph.get_agents():
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agent_names[agent_id] = getattr(agent, 'name', f'Agent_{agent_id}')
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db_path = os.path.join(simulation_dir, "twitter_simulation.db")
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if os.path.exists(db_path):
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os.remove(db_path)
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env = oasis.make(
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agent_graph=agent_graph,
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platform=oasis.DefaultPlatformType.TWITTER,
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database_path=db_path,
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)
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await env.reset()
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print("[Twitter] 环境已启动")
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if action_logger:
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action_logger.log_simulation_start("twitter", config)
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total_actions = 0
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# 执行初始事件
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event_config = config.get("event_config", {})
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initial_posts = event_config.get("initial_posts", [])
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if initial_posts:
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initial_actions = {}
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for post in initial_posts:
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agent_id = post.get("poster_agent_id", 0)
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content = post.get("content", "")
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try:
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agent = env.agent_graph.get_agent(agent_id)
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initial_actions[agent] = ManualAction(
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action_type=ActionType.CREATE_POST,
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action_args={"content": content}
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)
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if action_logger:
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action_logger.log_action(
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round_num=0,
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platform="twitter",
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agent_id=agent_id,
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agent_name=agent_names.get(agent_id, f"Agent_{agent_id}"),
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action_type="CREATE_POST",
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action_args={"content": content[:100] + "..." if len(content) > 100 else content}
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)
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total_actions += 1
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except Exception:
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pass
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if initial_actions:
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await env.step(initial_actions)
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print(f"[Twitter] 已发布 {len(initial_actions)} 条初始帖子")
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# 主模拟循环
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time_config = config.get("time_config", {})
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total_hours = time_config.get("total_simulation_hours", 72)
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minutes_per_round = time_config.get("minutes_per_round", 30)
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total_rounds = (total_hours * 60) // minutes_per_round
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start_time = datetime.now()
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for round_num in range(total_rounds):
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simulated_minutes = round_num * minutes_per_round
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simulated_hour = (simulated_minutes // 60) % 24
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simulated_day = simulated_minutes // (60 * 24) + 1
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active_agents = get_active_agents_for_round(
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env, config, simulated_hour, round_num
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)
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if not active_agents:
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continue
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if action_logger:
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action_logger.log_round_start(round_num + 1, simulated_hour, "twitter")
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actions = {agent: LLMAction() for _, agent in active_agents}
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await env.step(actions)
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# 记录动作
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for agent_id, agent in active_agents:
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if action_logger:
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action_logger.log_action(
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round_num=round_num + 1,
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platform="twitter",
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agent_id=agent_id,
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agent_name=agent_names.get(agent_id, f"Agent_{agent_id}"),
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action_type="LLM_ACTION",
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action_args={}
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)
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total_actions += 1
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if action_logger:
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action_logger.log_round_end(round_num + 1, len(active_agents), "twitter")
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if (round_num + 1) % 20 == 0:
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progress = (round_num + 1) / total_rounds * 100
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print(f"[Twitter] Day {simulated_day}, {simulated_hour:02d}:00 "
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f"- Round {round_num + 1}/{total_rounds} ({progress:.1f}%)")
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await env.close()
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if action_logger:
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action_logger.log_simulation_end("twitter", total_rounds, total_actions)
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elapsed = (datetime.now() - start_time).total_seconds()
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print(f"[Twitter] 模拟完成! 耗时: {elapsed:.1f}秒, 总动作: {total_actions}")
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async def run_reddit_simulation(
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config: Dict[str, Any],
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simulation_dir: str,
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action_logger: Optional[ActionLogger] = None
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):
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"""运行Reddit模拟"""
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print("[Reddit] 初始化...")
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model = create_model(config)
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profile_path = os.path.join(simulation_dir, "reddit_profiles.json")
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if not os.path.exists(profile_path):
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print(f"[Reddit] 错误: Profile文件不存在: {profile_path}")
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return
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agent_graph = await generate_reddit_agent_graph(
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profile_path=profile_path,
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model=model,
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available_actions=REDDIT_ACTIONS,
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)
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# 获取Agent名称映射
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agent_names = {}
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for agent_id, agent in agent_graph.get_agents():
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agent_names[agent_id] = getattr(agent, 'name', f'Agent_{agent_id}')
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db_path = os.path.join(simulation_dir, "reddit_simulation.db")
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if os.path.exists(db_path):
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os.remove(db_path)
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env = oasis.make(
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agent_graph=agent_graph,
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platform=oasis.DefaultPlatformType.REDDIT,
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database_path=db_path,
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)
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await env.reset()
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print("[Reddit] 环境已启动")
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if action_logger:
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action_logger.log_simulation_start("reddit", config)
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total_actions = 0
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# 执行初始事件
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event_config = config.get("event_config", {})
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initial_posts = event_config.get("initial_posts", [])
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if initial_posts:
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initial_actions = {}
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for post in initial_posts:
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agent_id = post.get("poster_agent_id", 0)
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content = post.get("content", "")
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try:
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agent = env.agent_graph.get_agent(agent_id)
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if agent in initial_actions:
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if not isinstance(initial_actions[agent], list):
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initial_actions[agent] = [initial_actions[agent]]
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initial_actions[agent].append(ManualAction(
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action_type=ActionType.CREATE_POST,
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action_args={"content": content}
|
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))
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else:
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initial_actions[agent] = ManualAction(
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action_type=ActionType.CREATE_POST,
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action_args={"content": content}
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)
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|
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if action_logger:
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action_logger.log_action(
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round_num=0,
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platform="reddit",
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agent_id=agent_id,
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agent_name=agent_names.get(agent_id, f"Agent_{agent_id}"),
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action_type="CREATE_POST",
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action_args={"content": content[:100] + "..." if len(content) > 100 else content}
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)
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total_actions += 1
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except Exception:
|
||
pass
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|
||
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())
|
||
|