Implement Interview feature for agent interactions in simulations
- Added a new Interview module to facilitate interactions with agents post-simulation, allowing for single and batch interviews. - Introduced IPC communication mechanism for command and response handling between the Flask backend and simulation scripts. - Updated README.md to include detailed instructions on the new Interview functionality, including API endpoints and usage examples. - Enhanced simulation scripts to support waiting for commands after completion, improving user control over the simulation environment. - Implemented error handling and logging for interview processes, ensuring robust operation and traceability.
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666ghj
@@ -12,7 +12,7 @@ import threading
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import subprocess
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import signal
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import atexit
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from typing import Dict, Any, List, Optional
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from typing import Dict, Any, List, Optional, Union
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from dataclasses import dataclass, field
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from datetime import datetime
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from enum import Enum
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@@ -21,6 +21,7 @@ from queue import Queue
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from ..config import Config
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from ..utils.logger import get_logger
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from .zep_graph_memory_updater import ZepGraphMemoryManager
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from .simulation_ipc import SimulationIPCClient, CommandType, IPCResponse
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logger = get_logger('mirofish.simulation_runner')
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@@ -989,4 +990,365 @@ class SimulationRunner:
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if process.poll() is None:
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running.append(sim_id)
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return running
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# ============== Interview 功能 ==============
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@classmethod
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def check_env_alive(cls, simulation_id: str) -> bool:
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"""
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检查模拟环境是否存活（可以接收Interview命令）
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Args:
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simulation_id: 模拟ID
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Returns:
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True 表示环境存活，False 表示环境已关闭
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"""
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sim_dir = os.path.join(cls.RUN_STATE_DIR, simulation_id)
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if not os.path.exists(sim_dir):
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return False
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ipc_client = SimulationIPCClient(sim_dir)
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return ipc_client.check_env_alive()
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@classmethod
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def get_env_status_detail(cls, simulation_id: str) -> Dict[str, Any]:
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"""
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获取模拟环境的详细状态信息
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Args:
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simulation_id: 模拟ID
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Returns:
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状态详情字典，包含 status, twitter_available, reddit_available, timestamp
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"""
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sim_dir = os.path.join(cls.RUN_STATE_DIR, simulation_id)
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status_file = os.path.join(sim_dir, "env_status.json")
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default_status = {
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"status": "stopped",
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"twitter_available": False,
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"reddit_available": False,
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"timestamp": None
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}
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if not os.path.exists(status_file):
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return default_status
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try:
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with open(status_file, 'r', encoding='utf-8') as f:
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status = json.load(f)
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return {
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"status": status.get("status", "stopped"),
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"twitter_available": status.get("twitter_available", False),
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"reddit_available": status.get("reddit_available", False),
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"timestamp": status.get("timestamp")
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}
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except (json.JSONDecodeError, OSError):
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return default_status
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@classmethod
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def interview_agent(
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cls,
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simulation_id: str,
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agent_id: int,
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prompt: str,
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platform: str = None,
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timeout: float = 60.0
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) -> Dict[str, Any]:
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"""
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采访单个Agent
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Args:
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simulation_id: 模拟ID
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agent_id: Agent ID
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prompt: 采访问题
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platform: 指定平台（可选）
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- "twitter": 只采访Twitter平台
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- "reddit": 只采访Reddit平台
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- None: 双平台模拟时同时采访两个平台，返回整合结果
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timeout: 超时时间（秒）
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Returns:
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采访结果字典
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Raises:
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ValueError: 模拟不存在或环境未运行
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TimeoutError: 等待响应超时
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"""
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sim_dir = os.path.join(cls.RUN_STATE_DIR, simulation_id)
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if not os.path.exists(sim_dir):
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raise ValueError(f"模拟不存在: {simulation_id}")
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ipc_client = SimulationIPCClient(sim_dir)
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if not ipc_client.check_env_alive():
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raise ValueError(f"模拟环境未运行或已关闭，无法执行Interview: {simulation_id}")
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logger.info(f"发送Interview命令: simulation_id={simulation_id}, agent_id={agent_id}, platform={platform}")
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response = ipc_client.send_interview(
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agent_id=agent_id,
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prompt=prompt,
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platform=platform,
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timeout=timeout
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)
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if response.status.value == "completed":
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return {
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"success": True,
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"agent_id": agent_id,
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"prompt": prompt,
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"result": response.result,
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"timestamp": response.timestamp
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}
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else:
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return {
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"success": False,
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"agent_id": agent_id,
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"prompt": prompt,
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"error": response.error,
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"timestamp": response.timestamp
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}
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@classmethod
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def interview_agents_batch(
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cls,
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simulation_id: str,
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interviews: List[Dict[str, Any]],
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platform: str = None,
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timeout: float = 120.0
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) -> Dict[str, Any]:
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"""
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批量采访多个Agent
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Args:
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simulation_id: 模拟ID
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interviews: 采访列表，每个元素包含 {"agent_id": int, "prompt": str, "platform": str(可选)}
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platform: 默认平台（可选，会被每个采访项的platform覆盖）
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- "twitter": 默认只采访Twitter平台
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- "reddit": 默认只采访Reddit平台
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- None: 双平台模拟时每个Agent同时采访两个平台
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timeout: 超时时间（秒）
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Returns:
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批量采访结果字典
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Raises:
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ValueError: 模拟不存在或环境未运行
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TimeoutError: 等待响应超时
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"""
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sim_dir = os.path.join(cls.RUN_STATE_DIR, simulation_id)
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if not os.path.exists(sim_dir):
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raise ValueError(f"模拟不存在: {simulation_id}")
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ipc_client = SimulationIPCClient(sim_dir)
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if not ipc_client.check_env_alive():
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raise ValueError(f"模拟环境未运行或已关闭，无法执行Interview: {simulation_id}")
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logger.info(f"发送批量Interview命令: simulation_id={simulation_id}, count={len(interviews)}, platform={platform}")
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response = ipc_client.send_batch_interview(
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interviews=interviews,
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platform=platform,
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timeout=timeout
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)
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if response.status.value == "completed":
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return {
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"success": True,
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"interviews_count": len(interviews),
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"result": response.result,
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"timestamp": response.timestamp
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}
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else:
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return {
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"success": False,
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"interviews_count": len(interviews),
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"error": response.error,
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"timestamp": response.timestamp
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}
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@classmethod
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def interview_all_agents(
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cls,
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simulation_id: str,
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prompt: str,
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platform: str = None,
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timeout: float = 180.0
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) -> Dict[str, Any]:
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"""
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采访所有Agent（全局采访）
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使用相同的问题采访模拟中的所有Agent
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Args:
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simulation_id: 模拟ID
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prompt: 采访问题（所有Agent使用相同问题）
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platform: 指定平台（可选）
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- "twitter": 只采访Twitter平台
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- "reddit": 只采访Reddit平台
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- None: 双平台模拟时每个Agent同时采访两个平台
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timeout: 超时时间（秒）
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Returns:
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全局采访结果字典
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"""
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sim_dir = os.path.join(cls.RUN_STATE_DIR, simulation_id)
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if not os.path.exists(sim_dir):
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raise ValueError(f"模拟不存在: {simulation_id}")
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# 从配置文件获取所有Agent信息
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config_path = os.path.join(sim_dir, "simulation_config.json")
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if not os.path.exists(config_path):
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raise ValueError(f"模拟配置不存在: {simulation_id}")
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with open(config_path, 'r', encoding='utf-8') as f:
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config = json.load(f)
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agent_configs = config.get("agent_configs", [])
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if not agent_configs:
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raise ValueError(f"模拟配置中没有Agent: {simulation_id}")
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# 构建批量采访列表
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interviews = []
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for agent_config in agent_configs:
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agent_id = agent_config.get("agent_id")
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if agent_id is not None:
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interviews.append({
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"agent_id": agent_id,
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"prompt": prompt
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})
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logger.info(f"发送全局Interview命令: simulation_id={simulation_id}, agent_count={len(interviews)}, platform={platform}")
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return cls.interview_agents_batch(
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simulation_id=simulation_id,
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interviews=interviews,
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platform=platform,
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timeout=timeout
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)
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@classmethod
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def close_simulation_env(
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cls,
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simulation_id: str,
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timeout: float = 30.0
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) -> Dict[str, Any]:
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"""
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关闭模拟环境（而不是停止模拟进程）
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向模拟发送关闭环境命令，使其优雅退出等待命令模式
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Args:
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simulation_id: 模拟ID
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timeout: 超时时间（秒）
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Returns:
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操作结果字典
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"""
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sim_dir = os.path.join(cls.RUN_STATE_DIR, simulation_id)
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if not os.path.exists(sim_dir):
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raise ValueError(f"模拟不存在: {simulation_id}")
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ipc_client = SimulationIPCClient(sim_dir)
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if not ipc_client.check_env_alive():
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return {
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"success": True,
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"message": "环境已经关闭"
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}
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logger.info(f"发送关闭环境命令: simulation_id={simulation_id}")
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try:
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response = ipc_client.send_close_env(timeout=timeout)
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return {
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"success": response.status.value == "completed",
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"message": "环境关闭命令已发送",
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"result": response.result,
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"timestamp": response.timestamp
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}
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except TimeoutError:
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# 超时可能是因为环境正在关闭
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return {
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"success": True,
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"message": "环境关闭命令已发送（等待响应超时，环境可能正在关闭）"
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}
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@classmethod
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def get_interview_history(
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cls,
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simulation_id: str,
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platform: str = "reddit",
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agent_id: Optional[int] = None,
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limit: int = 100
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) -> List[Dict[str, Any]]:
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"""
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获取Interview历史记录（从数据库读取）
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Args:
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simulation_id: 模拟ID
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platform: 平台类型（reddit/twitter）
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agent_id: 过滤Agent ID（可选）
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limit: 返回数量限制
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Returns:
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Interview历史记录列表
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"""
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import sqlite3
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sim_dir = os.path.join(cls.RUN_STATE_DIR, simulation_id)
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db_path = os.path.join(sim_dir, f"{platform}_simulation.db")
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if not os.path.exists(db_path):
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return []
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results = []
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try:
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conn = sqlite3.connect(db_path)
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cursor = conn.cursor()
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# 构建查询
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# 注意：ActionType.INTERVIEW.value 应该是字符串形式
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if agent_id is not None:
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cursor.execute("""
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SELECT user_id, info, created_at
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FROM trace
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WHERE action = 'interview' AND user_id = ?
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ORDER BY created_at DESC
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LIMIT ?
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""", (agent_id, limit))
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else:
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cursor.execute("""
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SELECT user_id, info, created_at
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FROM trace
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WHERE action = 'interview'
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ORDER BY created_at DESC
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LIMIT ?
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""", (limit,))
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for user_id, info_json, created_at in cursor.fetchall():
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try:
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info = json.loads(info_json) if info_json else {}
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except json.JSONDecodeError:
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info = {"raw": info_json}
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results.append({
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"agent_id": user_id,
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"response": info.get("response", info),
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"prompt": info.get("prompt", ""),
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"timestamp": created_at,
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"platform": platform
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})
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conn.close()
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except Exception as e:
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logger.error(f"读取Interview历史失败: {e}")
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return results
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