0302b8fd70
- Introduced MaxTokensWarningFilter to suppress specific warnings related to max_tokens in the logging output across simulation scripts. - Added a semaphore parameter to limit the maximum concurrent LLM requests in Twitter and Reddit simulation functions, preventing API overload. - Ensured the filter is applied immediately upon module loading for effective logging management.
400 lines
14 KiB
Python
400 lines
14 KiB
Python
"""
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OASIS Reddit模拟预设脚本
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此脚本读取配置文件中的参数来执行模拟，实现全程自动化
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使用方式:
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python run_reddit_simulation.py --config /path/to/simulation_config.json
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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
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# 添加项目路径
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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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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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"""自定义格式化器，将 Unicode 转义序列转换为可读字符"""
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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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"""过滤掉 camel-ai 关于 max_tokens 的警告（我们故意不设置 max_tokens，让模型自行决定）"""
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def filter(self, record):
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# 过滤掉包含 max_tokens 警告的日志
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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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# 在模块加载时立即添加过滤器，确保在 camel 代码执行前生效
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logging.getLogger().addFilter(MaxTokensWarningFilter())
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def setup_oasis_logging(log_dir: str):
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"""配置 OASIS 的日志，使用固定名称的日志文件"""
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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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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_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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class RedditSimulationRunner:
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"""Reddit模拟运行器"""
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# Reddit可用动作
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AVAILABLE_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 __init__(self, config_path: str):
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"""
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初始化模拟运行器
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Args:
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config_path: 配置文件路径 (simulation_config.json)
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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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def _load_config(self) -> Dict[str, Any]:
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"""加载配置文件"""
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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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"""获取Profile文件路径"""
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return os.path.join(self.simulation_dir, "reddit_profiles.json")
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def _get_db_path(self) -> str:
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"""获取数据库路径"""
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return os.path.join(self.simulation_dir, "reddit_simulation.db")
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def _create_model(self):
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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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"""
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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 = self.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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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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"""
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根据时间和配置决定本轮激活哪些Agent
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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_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(self):
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"""运行Reddit模拟"""
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print("=" * 60)
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print("OASIS Reddit模拟")
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print(f"配置文件: {self.config_path}")
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print(f"模拟ID: {self.config.get('simulation_id', 'unknown')}")
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print("=" * 60)
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time_config = self.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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print(f"\n模拟参数:")
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print(f" - 总模拟时长: {total_hours}小时")
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print(f" - 每轮时间: {minutes_per_round}分钟")
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print(f" - 总轮数: {total_rounds}")
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print(f" - Agent数量: {len(self.config.get('agent_configs', []))}")
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print("\n初始化LLM模型...")
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model = self._create_model()
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print("加载Agent Profile...")
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profile_path = self._get_profile_path()
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if not os.path.exists(profile_path):
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print(f"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=self.AVAILABLE_ACTIONS,
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)
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db_path = self._get_db_path()
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if os.path.exists(db_path):
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os.remove(db_path)
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print(f"已删除旧数据库: {db_path}")
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print("创建OASIS环境...")
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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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semaphore=30, # 限制最大并发 LLM 请求数，防止 API 过载
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)
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await env.reset()
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print("环境初始化完成\n")
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# 执行初始事件
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event_config = self.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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print(f"执行初始事件 ({len(initial_posts)}条初始帖子)...")
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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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except Exception as e:
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print(f" 警告: 无法为Agent {agent_id}创建初始帖子: {e}")
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if initial_actions:
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await env.step(initial_actions)
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print(f" 已发布 {len(initial_actions)} 条初始帖子")
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# 主模拟循环
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print("\n开始模拟循环...")
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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 = self._get_active_agents_for_round(
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env, 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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actions = {
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agent: LLMAction()
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for _, agent in active_agents
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}
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await env.step(actions)
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if (round_num + 1) % 10 == 0 or round_num == 0:
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elapsed = (datetime.now() - start_time).total_seconds()
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progress = (round_num + 1) / total_rounds * 100
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print(f" [Day {simulated_day}, {simulated_hour:02d}:00] "
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f"Round {round_num + 1}/{total_rounds} ({progress:.1f}%) "
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f"- {len(active_agents)} agents active "
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f"- elapsed: {elapsed:.1f}s")
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await env.close()
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total_elapsed = (datetime.now() - start_time).total_seconds()
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print(f"\n模拟完成!")
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print(f" - 总耗时: {total_elapsed:.1f}秒")
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print(f" - 数据库: {db_path}")
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print("=" * 60)
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async def main():
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parser = argparse.ArgumentParser(description='OASIS Reddit模拟')
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parser.add_argument(
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'--config',
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type=str,
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required=True,
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help='配置文件路径 (simulation_config.json)'
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)
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args = parser.parse_args()
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if not os.path.exists(args.config):
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print(f"错误: 配置文件不存在: {args.config}")
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sys.exit(1)
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# 初始化日志配置（使用固定文件名，清理旧日志）
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simulation_dir = os.path.dirname(args.config) or "."
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setup_oasis_logging(os.path.join(simulation_dir, "log"))
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runner = RedditSimulationRunner(args.config)
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await runner.run()
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if __name__ == "__main__":
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asyncio.run(main())
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