Enhance backend functionality with OASIS simulation features
- Updated README.md to include new simulation scripts and configuration details for OASIS, including API retry mechanisms and environment variable settings. - Added simulation management and configuration generation services to streamline the simulation process across Twitter and Reddit platforms. - Introduced new API routes for simulation-related operations, including entity retrieval and simulation status management. - Implemented a robust retry mechanism for external API calls to improve system stability. - Enhanced task management model to include detailed progress tracking. - Added logging capabilities for action tracking during simulations. - Included new scripts for running parallel simulations and testing profile formats.
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backend/scripts/run_twitter_simulation.py
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backend/scripts/run_twitter_simulation.py
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"""
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OASIS Twitter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_twitter_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 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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sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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try:
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from camel.models import ModelFactory
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from camel.types import ModelPlatformType
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import oasis
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from oasis import (
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ActionType,
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LLMAction,
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ManualAction,
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generate_twitter_agent_graph
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)
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except ImportError as e:
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print(f"错误: 缺少依赖 {e}")
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print("请先安装: pip install oasis-ai camel-ai")
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sys.exit(1)
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class TwitterSimulationRunner:
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"""Twitter模拟运行器"""
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# Twitter可用动作
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AVAILABLE_ACTIONS = [
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ActionType.CREATE_POST,
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ActionType.LIKE_POST,
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ActionType.REPOST,
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ActionType.FOLLOW,
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ActionType.DO_NOTHING,
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ActionType.QUOTE_POST,
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]
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def __init__(self, config_path: str):
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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（OASIS Twitter使用CSV格式）"""
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return os.path.join(self.simulation_dir, "twitter_profiles.csv")
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def _get_db_path(self) -> str:
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"""获取数据库路径"""
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return os.path.join(self.simulation_dir, "twitter_simulation.db")
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def _create_model(self):
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"""
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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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配置文件中的 llm_model 对应 model_type
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"""
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import os
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llm_model = self.config.get("llm_model", "gpt-4o-mini")
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llm_base_url = self.config.get("llm_base_url", "")
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# 如果配置了base_url，设置环境变量（OASIS通过环境变量读取）
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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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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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Args:
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env: OASIS环境
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current_hour: 当前模拟小时（0-23）
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round_num: 当前轮数
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Returns:
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激活的Agent列表
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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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# 基础激活数量
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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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# 根据时段调整
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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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# 根据每个Agent的配置计算激活概率
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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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# 检查是否在活跃时间
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if current_hour not in active_hours:
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continue
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# 根据活跃度计算概率
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if random.random() < activity_level:
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candidates.append(agent_id)
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# 随机选择
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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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# 转换为Agent对象
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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Twitter模拟"""
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print("=" * 60)
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print("OASIS Twitter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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# 加载时间配置
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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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# 计算总轮数
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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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# 创建模型
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print("\n初始化LLM模型...")
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model = self._create_model()
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# 加载Agent图
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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_twitter_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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# 数据库路径
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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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# 创建环境
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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.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("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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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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# 计算当前模拟时间
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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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# 获取本轮激活的Agent
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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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# 构建动作
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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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# 执行动作
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await env.step(actions)
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# 打印进度
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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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# 关闭环境
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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 Twitter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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runner = TwitterSimulationRunner(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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