5f159f6d88
- 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.
503 lines
16 KiB
Python
503 lines
16 KiB
Python
"""
|
||
OASIS 双平台并行模拟预设脚本
|
||
同时运行Twitter和Reddit模拟，读取相同的配置文件
|
||
|
||
使用方式:
|
||
python run_parallel_simulation.py --config simulation_config.json [--action-log actions.jsonl]
|
||
"""
|
||
|
||
import argparse
|
||
import asyncio
|
||
import json
|
||
import os
|
||
import random
|
||
import sys
|
||
from datetime import datetime
|
||
from typing import Dict, Any, List, Optional
|
||
|
||
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
|
||
|
||
from action_logger import ActionLogger
|
||
|
||
try:
|
||
from camel.models import ModelFactory
|
||
from camel.types import ModelPlatformType
|
||
import oasis
|
||
from oasis import (
|
||
ActionType,
|
||
LLMAction,
|
||
ManualAction,
|
||
generate_twitter_agent_graph,
|
||
generate_reddit_agent_graph
|
||
)
|
||
except ImportError as e:
|
||
print(f"错误: 缺少依赖 {e}")
|
||
print("请先安装: pip install oasis-ai camel-ai")
|
||
sys.exit(1)
|
||
|
||
|
||
# Twitter可用动作
|
||
TWITTER_ACTIONS = [
|
||
ActionType.CREATE_POST,
|
||
ActionType.LIKE_POST,
|
||
ActionType.REPOST,
|
||
ActionType.FOLLOW,
|
||
ActionType.DO_NOTHING,
|
||
ActionType.QUOTE_POST,
|
||
]
|
||
|
||
# Reddit可用动作
|
||
REDDIT_ACTIONS = [
|
||
ActionType.LIKE_POST,
|
||
ActionType.DISLIKE_POST,
|
||
ActionType.CREATE_POST,
|
||
ActionType.CREATE_COMMENT,
|
||
ActionType.LIKE_COMMENT,
|
||
ActionType.DISLIKE_COMMENT,
|
||
ActionType.SEARCH_POSTS,
|
||
ActionType.SEARCH_USER,
|
||
ActionType.TREND,
|
||
ActionType.REFRESH,
|
||
ActionType.DO_NOTHING,
|
||
ActionType.FOLLOW,
|
||
ActionType.MUTE,
|
||
]
|
||
|
||
|
||
def load_config(config_path: str) -> Dict[str, Any]:
|
||
"""加载配置文件"""
|
||
with open(config_path, 'r', encoding='utf-8') as f:
|
||
return json.load(f)
|
||
|
||
|
||
def create_model(config: Dict[str, Any]):
|
||
"""
|
||
创建LLM模型
|
||
|
||
OASIS使用camel-ai的ModelFactory，配置方式：
|
||
- 标准OpenAI: 只需设置 OPENAI_API_KEY 环境变量
|
||
- 自定义API: 设置 OPENAI_API_KEY 和 OPENAI_API_BASE_URL 环境变量
|
||
"""
|
||
llm_model = config.get("llm_model", "gpt-4o-mini")
|
||
llm_base_url = config.get("llm_base_url", "")
|
||
|
||
# 如果配置了base_url，设置环境变量
|
||
if llm_base_url:
|
||
os.environ["OPENAI_API_BASE_URL"] = llm_base_url
|
||
|
||
return ModelFactory.create(
|
||
model_platform=ModelPlatformType.OPENAI,
|
||
model_type=llm_model,
|
||
)
|
||
|
||
|
||
def get_active_agents_for_round(
|
||
env,
|
||
config: Dict[str, Any],
|
||
current_hour: int,
|
||
round_num: int
|
||
) -> List:
|
||
"""根据时间和配置决定本轮激活哪些Agent"""
|
||
time_config = config.get("time_config", {})
|
||
agent_configs = config.get("agent_configs", [])
|
||
|
||
base_min = time_config.get("agents_per_hour_min", 5)
|
||
base_max = time_config.get("agents_per_hour_max", 20)
|
||
|
||
peak_hours = time_config.get("peak_hours", [9, 10, 11, 14, 15, 20, 21, 22])
|
||
off_peak_hours = time_config.get("off_peak_hours", [0, 1, 2, 3, 4, 5])
|
||
|
||
if current_hour in peak_hours:
|
||
multiplier = time_config.get("peak_activity_multiplier", 1.5)
|
||
elif current_hour in off_peak_hours:
|
||
multiplier = time_config.get("off_peak_activity_multiplier", 0.3)
|
||
else:
|
||
multiplier = 1.0
|
||
|
||
target_count = int(random.uniform(base_min, base_max) * multiplier)
|
||
|
||
candidates = []
|
||
for cfg in agent_configs:
|
||
agent_id = cfg.get("agent_id", 0)
|
||
active_hours = cfg.get("active_hours", list(range(8, 23)))
|
||
activity_level = cfg.get("activity_level", 0.5)
|
||
|
||
if current_hour not in active_hours:
|
||
continue
|
||
|
||
if random.random() < activity_level:
|
||
candidates.append(agent_id)
|
||
|
||
selected_ids = random.sample(
|
||
candidates,
|
||
min(target_count, len(candidates))
|
||
) if candidates else []
|
||
|
||
active_agents = []
|
||
for agent_id in selected_ids:
|
||
try:
|
||
agent = env.agent_graph.get_agent(agent_id)
|
||
active_agents.append((agent_id, agent))
|
||
except Exception:
|
||
pass
|
||
|
||
return active_agents
|
||
|
||
|
||
async def run_twitter_simulation(
|
||
config: Dict[str, Any],
|
||
simulation_dir: str,
|
||
action_logger: Optional[ActionLogger] = None
|
||
):
|
||
"""运行Twitter模拟"""
|
||
print("[Twitter] 初始化...")
|
||
|
||
model = create_model(config)
|
||
|
||
# OASIS Twitter使用CSV格式
|
||
profile_path = os.path.join(simulation_dir, "twitter_profiles.csv")
|
||
if not os.path.exists(profile_path):
|
||
print(f"[Twitter] 错误: Profile文件不存在: {profile_path}")
|
||
return
|
||
|
||
agent_graph = await generate_twitter_agent_graph(
|
||
profile_path=profile_path,
|
||
model=model,
|
||
available_actions=TWITTER_ACTIONS,
|
||
)
|
||
|
||
# 获取Agent名称映射
|
||
agent_names = {}
|
||
for agent_id, agent in agent_graph.get_agents():
|
||
agent_names[agent_id] = getattr(agent, 'name', f'Agent_{agent_id}')
|
||
|
||
db_path = os.path.join(simulation_dir, "twitter_simulation.db")
|
||
if os.path.exists(db_path):
|
||
os.remove(db_path)
|
||
|
||
env = oasis.make(
|
||
agent_graph=agent_graph,
|
||
platform=oasis.DefaultPlatformType.TWITTER,
|
||
database_path=db_path,
|
||
)
|
||
|
||
await env.reset()
|
||
print("[Twitter] 环境已启动")
|
||
|
||
if action_logger:
|
||
action_logger.log_simulation_start("twitter", config)
|
||
|
||
total_actions = 0
|
||
|
||
# 执行初始事件
|
||
event_config = config.get("event_config", {})
|
||
initial_posts = event_config.get("initial_posts", [])
|
||
|
||
if initial_posts:
|
||
initial_actions = {}
|
||
for post in initial_posts:
|
||
agent_id = post.get("poster_agent_id", 0)
|
||
content = post.get("content", "")
|
||
try:
|
||
agent = env.agent_graph.get_agent(agent_id)
|
||
initial_actions[agent] = ManualAction(
|
||
action_type=ActionType.CREATE_POST,
|
||
action_args={"content": content}
|
||
)
|
||
|
||
if action_logger:
|
||
action_logger.log_action(
|
||
round_num=0,
|
||
platform="twitter",
|
||
agent_id=agent_id,
|
||
agent_name=agent_names.get(agent_id, f"Agent_{agent_id}"),
|
||
action_type="CREATE_POST",
|
||
action_args={"content": content[:100] + "..." if len(content) > 100 else content}
|
||
)
|
||
total_actions += 1
|
||
except Exception:
|
||
pass
|
||
|
||
if initial_actions:
|
||
await env.step(initial_actions)
|
||
print(f"[Twitter] 已发布 {len(initial_actions)} 条初始帖子")
|
||
|
||
# 主模拟循环
|
||
time_config = config.get("time_config", {})
|
||
total_hours = time_config.get("total_simulation_hours", 72)
|
||
minutes_per_round = time_config.get("minutes_per_round", 30)
|
||
total_rounds = (total_hours * 60) // minutes_per_round
|
||
|
||
start_time = datetime.now()
|
||
|
||
for round_num in range(total_rounds):
|
||
simulated_minutes = round_num * minutes_per_round
|
||
simulated_hour = (simulated_minutes // 60) % 24
|
||
simulated_day = simulated_minutes // (60 * 24) + 1
|
||
|
||
active_agents = get_active_agents_for_round(
|
||
env, config, simulated_hour, round_num
|
||
)
|
||
|
||
if not active_agents:
|
||
continue
|
||
|
||
if action_logger:
|
||
action_logger.log_round_start(round_num + 1, simulated_hour, "twitter")
|
||
|
||
actions = {agent: LLMAction() for _, agent in active_agents}
|
||
await env.step(actions)
|
||
|
||
# 记录动作
|
||
for agent_id, agent in active_agents:
|
||
if action_logger:
|
||
action_logger.log_action(
|
||
round_num=round_num + 1,
|
||
platform="twitter",
|
||
agent_id=agent_id,
|
||
agent_name=agent_names.get(agent_id, f"Agent_{agent_id}"),
|
||
action_type="LLM_ACTION",
|
||
action_args={}
|
||
)
|
||
total_actions += 1
|
||
|
||
if action_logger:
|
||
action_logger.log_round_end(round_num + 1, len(active_agents), "twitter")
|
||
|
||
if (round_num + 1) % 20 == 0:
|
||
progress = (round_num + 1) / total_rounds * 100
|
||
print(f"[Twitter] Day {simulated_day}, {simulated_hour:02d}:00 "
|
||
f"- Round {round_num + 1}/{total_rounds} ({progress:.1f}%)")
|
||
|
||
await env.close()
|
||
|
||
if action_logger:
|
||
action_logger.log_simulation_end("twitter", total_rounds, total_actions)
|
||
|
||
elapsed = (datetime.now() - start_time).total_seconds()
|
||
print(f"[Twitter] 模拟完成! 耗时: {elapsed:.1f}秒, 总动作: {total_actions}")
|
||
|
||
|
||
async def run_reddit_simulation(
|
||
config: Dict[str, Any],
|
||
simulation_dir: str,
|
||
action_logger: Optional[ActionLogger] = None
|
||
):
|
||
"""运行Reddit模拟"""
|
||
print("[Reddit] 初始化...")
|
||
|
||
model = create_model(config)
|
||
|
||
profile_path = os.path.join(simulation_dir, "reddit_profiles.json")
|
||
if not os.path.exists(profile_path):
|
||
print(f"[Reddit] 错误: Profile文件不存在: {profile_path}")
|
||
return
|
||
|
||
agent_graph = await generate_reddit_agent_graph(
|
||
profile_path=profile_path,
|
||
model=model,
|
||
available_actions=REDDIT_ACTIONS,
|
||
)
|
||
|
||
# 获取Agent名称映射
|
||
agent_names = {}
|
||
for agent_id, agent in agent_graph.get_agents():
|
||
agent_names[agent_id] = getattr(agent, 'name', f'Agent_{agent_id}')
|
||
|
||
db_path = os.path.join(simulation_dir, "reddit_simulation.db")
|
||
if os.path.exists(db_path):
|
||
os.remove(db_path)
|
||
|
||
env = oasis.make(
|
||
agent_graph=agent_graph,
|
||
platform=oasis.DefaultPlatformType.REDDIT,
|
||
database_path=db_path,
|
||
)
|
||
|
||
await env.reset()
|
||
print("[Reddit] 环境已启动")
|
||
|
||
if action_logger:
|
||
action_logger.log_simulation_start("reddit", config)
|
||
|
||
total_actions = 0
|
||
|
||
# 执行初始事件
|
||
event_config = config.get("event_config", {})
|
||
initial_posts = event_config.get("initial_posts", [])
|
||
|
||
if initial_posts:
|
||
initial_actions = {}
|
||
for post in initial_posts:
|
||
agent_id = post.get("poster_agent_id", 0)
|
||
content = post.get("content", "")
|
||
try:
|
||
agent = env.agent_graph.get_agent(agent_id)
|
||
if agent in initial_actions:
|
||
if not isinstance(initial_actions[agent], list):
|
||
initial_actions[agent] = [initial_actions[agent]]
|
||
initial_actions[agent].append(ManualAction(
|
||
action_type=ActionType.CREATE_POST,
|
||
action_args={"content": content}
|
||
))
|
||
else:
|
||
initial_actions[agent] = ManualAction(
|
||
action_type=ActionType.CREATE_POST,
|
||
action_args={"content": content}
|
||
)
|
||
|
||
if action_logger:
|
||
action_logger.log_action(
|
||
round_num=0,
|
||
platform="reddit",
|
||
agent_id=agent_id,
|
||
agent_name=agent_names.get(agent_id, f"Agent_{agent_id}"),
|
||
action_type="CREATE_POST",
|
||
action_args={"content": content[:100] + "..." if len(content) > 100 else content}
|
||
)
|
||
total_actions += 1
|
||
except Exception:
|
||
pass
|
||
|
||
if initial_actions:
|
||
await env.step(initial_actions)
|
||
print(f"[Reddit] 已发布 {len(initial_actions)} 条初始帖子")
|
||
|
||
# 主模拟循环
|
||
time_config = config.get("time_config", {})
|
||
total_hours = time_config.get("total_simulation_hours", 72)
|
||
minutes_per_round = time_config.get("minutes_per_round", 30)
|
||
total_rounds = (total_hours * 60) // minutes_per_round
|
||
|
||
start_time = datetime.now()
|
||
|
||
for round_num in range(total_rounds):
|
||
simulated_minutes = round_num * minutes_per_round
|
||
simulated_hour = (simulated_minutes // 60) % 24
|
||
simulated_day = simulated_minutes // (60 * 24) + 1
|
||
|
||
active_agents = get_active_agents_for_round(
|
||
env, config, simulated_hour, round_num
|
||
)
|
||
|
||
if not active_agents:
|
||
continue
|
||
|
||
if action_logger:
|
||
action_logger.log_round_start(round_num + 1, simulated_hour, "reddit")
|
||
|
||
actions = {agent: LLMAction() for _, agent in active_agents}
|
||
await env.step(actions)
|
||
|
||
# 记录动作
|
||
for agent_id, agent in active_agents:
|
||
if action_logger:
|
||
action_logger.log_action(
|
||
round_num=round_num + 1,
|
||
platform="reddit",
|
||
agent_id=agent_id,
|
||
agent_name=agent_names.get(agent_id, f"Agent_{agent_id}"),
|
||
action_type="LLM_ACTION",
|
||
action_args={}
|
||
)
|
||
total_actions += 1
|
||
|
||
if action_logger:
|
||
action_logger.log_round_end(round_num + 1, len(active_agents), "reddit")
|
||
|
||
if (round_num + 1) % 20 == 0:
|
||
progress = (round_num + 1) / total_rounds * 100
|
||
print(f"[Reddit] Day {simulated_day}, {simulated_hour:02d}:00 "
|
||
f"- Round {round_num + 1}/{total_rounds} ({progress:.1f}%)")
|
||
|
||
await env.close()
|
||
|
||
if action_logger:
|
||
action_logger.log_simulation_end("reddit", total_rounds, total_actions)
|
||
|
||
elapsed = (datetime.now() - start_time).total_seconds()
|
||
print(f"[Reddit] 模拟完成! 耗时: {elapsed:.1f}秒, 总动作: {total_actions}")
|
||
|
||
|
||
async def main():
|
||
parser = argparse.ArgumentParser(description='OASIS双平台并行模拟')
|
||
parser.add_argument(
|
||
'--config',
|
||
type=str,
|
||
required=True,
|
||
help='配置文件路径 (simulation_config.json)'
|
||
)
|
||
parser.add_argument(
|
||
'--twitter-only',
|
||
action='store_true',
|
||
help='只运行Twitter模拟'
|
||
)
|
||
parser.add_argument(
|
||
'--reddit-only',
|
||
action='store_true',
|
||
help='只运行Reddit模拟'
|
||
)
|
||
parser.add_argument(
|
||
'--action-log',
|
||
type=str,
|
||
default='actions.jsonl',
|
||
help='动作日志文件路径 (默认: actions.jsonl)'
|
||
)
|
||
|
||
args = parser.parse_args()
|
||
|
||
if not os.path.exists(args.config):
|
||
print(f"错误: 配置文件不存在: {args.config}")
|
||
sys.exit(1)
|
||
|
||
config = load_config(args.config)
|
||
simulation_dir = os.path.dirname(args.config) or "."
|
||
|
||
# 创建动作日志记录器
|
||
action_log_path = os.path.join(simulation_dir, args.action_log)
|
||
action_logger = ActionLogger(action_log_path)
|
||
|
||
print("=" * 60)
|
||
print("OASIS 双平台并行模拟")
|
||
print(f"配置文件: {args.config}")
|
||
print(f"模拟ID: {config.get('simulation_id', 'unknown')}")
|
||
print(f"动作日志: {action_log_path}")
|
||
print("=" * 60)
|
||
|
||
time_config = config.get("time_config", {})
|
||
print(f"\n模拟参数:")
|
||
print(f" - 总模拟时长: {time_config.get('total_simulation_hours', 72)}小时")
|
||
print(f" - 每轮时间: {time_config.get('minutes_per_round', 30)}分钟")
|
||
print(f" - Agent数量: {len(config.get('agent_configs', []))}")
|
||
|
||
# LLM推理说明
|
||
reasoning = config.get("generation_reasoning", "")
|
||
if reasoning:
|
||
print(f"\nLLM配置推理:")
|
||
print(f" {reasoning[:500]}..." if len(reasoning) > 500 else f" {reasoning}")
|
||
|
||
print("\n" + "=" * 60)
|
||
|
||
start_time = datetime.now()
|
||
|
||
if args.twitter_only:
|
||
await run_twitter_simulation(config, simulation_dir, action_logger)
|
||
elif args.reddit_only:
|
||
await run_reddit_simulation(config, simulation_dir, action_logger)
|
||
else:
|
||
# 并行运行（共享同一个action_logger）
|
||
await asyncio.gather(
|
||
run_twitter_simulation(config, simulation_dir, action_logger),
|
||
run_reddit_simulation(config, simulation_dir, action_logger),
|
||
)
|
||
|
||
total_elapsed = (datetime.now() - start_time).total_seconds()
|
||
print("\n" + "=" * 60)
|
||
print(f"全部模拟完成! 总耗时: {total_elapsed:.1f}秒")
|
||
print(f"动作日志已保存到: {action_log_path}")
|
||
print("=" * 60)
|
||
|
||
|
||
if __name__ == "__main__":
|
||
asyncio.run(main())
|
||
|