Refactor simulation management and enhance logging capabilities
- 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.
This commit is contained in:
666ghj
@@ -9,13 +9,118 @@ OASIS 双平台并行模拟预设脚本
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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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sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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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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@@ -74,17 +179,34 @@ def create_model(config: Dict[str, Any]):
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"""
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创建LLM模型
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OASIS使用camel-ai的ModelFactory，配置方式：
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- 标准OpenAI: 只需设置 OPENAI_API_KEY 环境变量
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- 自定义API: 设置 OPENAI_API_KEY 和 OPENAI_API_BASE_URL 环境变量
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"""
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llm_model = config.get("llm_model", "gpt-4o-mini")
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llm_base_url = config.get("llm_base_url", "")
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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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# 如果配置了base_url，设置环境变量
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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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@@ -453,6 +575,9 @@ async def main():
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config = load_config(args.config)
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simulation_dir = os.path.dirname(args.config) or "."
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# 初始化日志配置（清理旧日志文件，使用固定名称）
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init_logging_for_simulation(simulation_dir)
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# 创建动作日志记录器
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action_log_path = os.path.join(simulation_dir, args.action_log)
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action_logger = ActionLogger(action_log_path)
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