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.
This commit is contained in:
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backend/scripts/run_parallel_simulation.py
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backend/scripts/run_parallel_simulation.py
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"""
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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 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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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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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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# 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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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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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:
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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"[Reddit] 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, "reddit")
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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="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="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), "reddit")
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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"[Reddit] 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("reddit", total_rounds, total_actions)
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elapsed = (datetime.now() - start_time).total_seconds()
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print(f"[Reddit] 模拟完成! 耗时: {elapsed:.1f}秒, 总动作: {total_actions}")
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async def main():
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parser = argparse.ArgumentParser(description='OASIS双平台并行模拟')
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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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parser.add_argument(
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'--twitter-only',
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action='store_true',
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help='只运行Twitter模拟'
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)
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parser.add_argument(
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'--reddit-only',
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action='store_true',
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help='只运行Reddit模拟'
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)
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parser.add_argument(
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'--action-log',
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type=str,
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default='actions.jsonl',
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help='动作日志文件路径 (默认: actions.jsonl)'
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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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config = load_config(args.config)
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simulation_dir = os.path.dirname(args.config) or "."
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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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print("=" * 60)
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print("OASIS 双平台并行模拟")
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print(f"配置文件: {args.config}")
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print(f"模拟ID: {config.get('simulation_id', 'unknown')}")
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print(f"动作日志: {action_log_path}")
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print("=" * 60)
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time_config = config.get("time_config", {})
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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())
|
||||
|
||||
Reference in New Issue
Block a user