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.
584 lines
21 KiB
Python
584 lines
21 KiB
Python
"""
|
||
模拟配置智能生成器
|
||
使用LLM根据模拟需求、文档内容、图谱信息自动生成细致的模拟参数
|
||
实现全程自动化，无需人工设置参数
|
||
"""
|
||
|
||
import json
|
||
from typing import Dict, Any, List, Optional
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||
from dataclasses import dataclass, field, asdict
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||
from datetime import datetime
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||
|
||
from openai import OpenAI
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||
|
||
from ..config import Config
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||
from ..utils.logger import get_logger
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from .zep_entity_reader import EntityNode, ZepEntityReader
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||
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||
logger = get_logger('mirofish.simulation_config')
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||
|
||
|
||
@dataclass
|
||
class AgentActivityConfig:
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||
"""单个Agent的活动配置"""
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||
agent_id: int
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||
entity_uuid: str
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||
entity_name: str
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||
entity_type: str
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||
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||
# 活跃度配置 (0.0-1.0)
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||
activity_level: float = 0.5 # 整体活跃度
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||
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# 发言频率（每小时预期发言次数）
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||
posts_per_hour: float = 1.0
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||
comments_per_hour: float = 2.0
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||
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||
# 活跃时间段（24小时制，0-23）
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||
active_hours: List[int] = field(default_factory=lambda: list(range(8, 23)))
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||
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||
# 响应速度（对热点事件的反应延迟，单位：模拟分钟）
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||
response_delay_min: int = 5
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response_delay_max: int = 60
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||
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||
# 情感倾向 (-1.0到1.0，负面到正面)
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||
sentiment_bias: float = 0.0
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||
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||
# 立场（对特定话题的态度）
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||
stance: str = "neutral" # supportive, opposing, neutral, observer
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||
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# 影响力权重（决定其发言被其他Agent看到的概率）
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influence_weight: float = 1.0
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||
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||
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@dataclass
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class TimeSimulationConfig:
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"""时间模拟配置"""
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# 模拟总时长（模拟小时数）
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total_simulation_hours: int = 72 # 默认模拟72小时（3天）
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# 每轮代表的时间（模拟分钟）
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minutes_per_round: int = 30
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||
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# 每小时激活的Agent数量范围
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agents_per_hour_min: int = 5
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agents_per_hour_max: int = 20
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# 高峰时段（活跃度提升）
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peak_hours: List[int] = field(default_factory=lambda: [9, 10, 11, 14, 15, 20, 21, 22])
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peak_activity_multiplier: float = 1.5
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# 低谷时段（活跃度降低）
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off_peak_hours: List[int] = field(default_factory=lambda: [0, 1, 2, 3, 4, 5, 6])
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off_peak_activity_multiplier: float = 0.3
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@dataclass
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class EventConfig:
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"""事件配置"""
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# 初始事件（模拟开始时的触发事件）
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initial_posts: List[Dict[str, Any]] = field(default_factory=list)
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# 定时事件（在特定时间触发的事件）
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scheduled_events: List[Dict[str, Any]] = field(default_factory=list)
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# 热点话题关键词
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hot_topics: List[str] = field(default_factory=list)
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# 舆论引导方向
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narrative_direction: str = ""
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||
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||
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@dataclass
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class PlatformConfig:
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"""平台特定配置"""
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platform: str # twitter or reddit
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# 推荐算法权重
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recency_weight: float = 0.4 # 时间新鲜度
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popularity_weight: float = 0.3 # 热度
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||
relevance_weight: float = 0.3 # 相关性
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||
|
||
# 病毒传播阈值（达到多少互动后触发扩散）
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viral_threshold: int = 10
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||
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# 回声室效应强度（相似观点聚集程度）
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echo_chamber_strength: float = 0.5
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||
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||
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@dataclass
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class SimulationParameters:
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"""完整的模拟参数配置"""
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# 基础信息
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simulation_id: str
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||
project_id: str
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graph_id: str
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||
simulation_requirement: str
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||
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# 时间配置
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time_config: TimeSimulationConfig = field(default_factory=TimeSimulationConfig)
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||
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# Agent配置列表
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agent_configs: List[AgentActivityConfig] = field(default_factory=list)
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# 事件配置
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event_config: EventConfig = field(default_factory=EventConfig)
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# 平台配置
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twitter_config: Optional[PlatformConfig] = None
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reddit_config: Optional[PlatformConfig] = None
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# LLM配置
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llm_model: str = ""
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llm_base_url: str = ""
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||
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# 生成元数据
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generated_at: str = field(default_factory=lambda: datetime.now().isoformat())
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generation_reasoning: str = "" # LLM的推理说明
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||
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def to_dict(self) -> Dict[str, Any]:
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||
"""转换为字典"""
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||
return {
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"simulation_id": self.simulation_id,
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"project_id": self.project_id,
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"graph_id": self.graph_id,
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"simulation_requirement": self.simulation_requirement,
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"time_config": asdict(self.time_config),
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"agent_configs": [asdict(a) for a in self.agent_configs],
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"event_config": asdict(self.event_config),
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"twitter_config": asdict(self.twitter_config) if self.twitter_config else None,
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"reddit_config": asdict(self.reddit_config) if self.reddit_config else None,
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"llm_model": self.llm_model,
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"llm_base_url": self.llm_base_url,
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"generated_at": self.generated_at,
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"generation_reasoning": self.generation_reasoning,
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}
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||
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def to_json(self, indent: int = 2) -> str:
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||
"""转换为JSON字符串"""
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||
return json.dumps(self.to_dict(), ensure_ascii=False, indent=indent)
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||
|
||
|
||
class SimulationConfigGenerator:
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"""
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模拟配置智能生成器
|
||
|
||
使用LLM分析模拟需求、文档内容、图谱实体信息，
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自动生成最佳的模拟参数配置
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"""
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||
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# 上下文最大字符数
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MAX_CONTEXT_LENGTH = 50000
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def __init__(
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||
self,
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api_key: Optional[str] = None,
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||
base_url: Optional[str] = None,
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model_name: Optional[str] = None
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):
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self.api_key = api_key or Config.LLM_API_KEY
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self.base_url = base_url or Config.LLM_BASE_URL
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||
self.model_name = model_name or Config.LLM_MODEL_NAME
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||
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if not self.api_key:
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raise ValueError("LLM_API_KEY 未配置")
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||
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self.client = OpenAI(
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api_key=self.api_key,
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base_url=self.base_url
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)
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def generate_config(
|
||
self,
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simulation_id: str,
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project_id: str,
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graph_id: str,
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simulation_requirement: str,
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document_text: str,
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||
entities: List[EntityNode],
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enable_twitter: bool = True,
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enable_reddit: bool = True,
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) -> SimulationParameters:
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"""
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智能生成完整的模拟配置
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||
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Args:
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simulation_id: 模拟ID
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project_id: 项目ID
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||
graph_id: 图谱ID
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||
simulation_requirement: 模拟需求描述
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||
document_text: 原始文档内容
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||
entities: 过滤后的实体列表
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||
enable_twitter: 是否启用Twitter
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||
enable_reddit: 是否启用Reddit
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||
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||
Returns:
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||
SimulationParameters: 完整的模拟参数
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"""
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||
logger.info(f"开始智能生成模拟配置: simulation_id={simulation_id}")
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||
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# 1. 构建上下文信息（截断到50000字符）
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context = self._build_context(
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simulation_requirement=simulation_requirement,
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document_text=document_text,
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||
entities=entities
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)
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||
# 2. 调用LLM生成配置
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llm_result = self._generate_config_with_llm(
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context=context,
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||
entities=entities,
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||
enable_twitter=enable_twitter,
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||
enable_reddit=enable_reddit
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)
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||
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||
# 3. 构建SimulationParameters对象
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||
params = self._build_parameters(
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simulation_id=simulation_id,
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project_id=project_id,
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||
graph_id=graph_id,
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||
simulation_requirement=simulation_requirement,
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||
entities=entities,
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||
llm_result=llm_result,
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||
enable_twitter=enable_twitter,
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enable_reddit=enable_reddit
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||
)
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||
|
||
logger.info(f"模拟配置生成完成: {len(params.agent_configs)} 个Agent配置")
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||
|
||
return params
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||
|
||
def _build_context(
|
||
self,
|
||
simulation_requirement: str,
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||
document_text: str,
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||
entities: List[EntityNode]
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||
) -> str:
|
||
"""构建LLM上下文，截断到最大长度"""
|
||
|
||
# 实体摘要
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||
entity_summary = self._summarize_entities(entities)
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||
|
||
# 构建上下文
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||
context_parts = [
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||
f"## 模拟需求\n{simulation_requirement}",
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||
f"\n## 实体信息 ({len(entities)}个)\n{entity_summary}",
|
||
]
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||
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||
current_length = sum(len(p) for p in context_parts)
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||
remaining_length = self.MAX_CONTEXT_LENGTH - current_length - 500 # 留500字符余量
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||
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||
if remaining_length > 0 and document_text:
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||
doc_text = document_text[:remaining_length]
|
||
if len(document_text) > remaining_length:
|
||
doc_text += "\n...(文档已截断)"
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||
context_parts.append(f"\n## 原始文档内容\n{doc_text}")
|
||
|
||
return "\n".join(context_parts)
|
||
|
||
def _summarize_entities(self, entities: List[EntityNode]) -> str:
|
||
"""生成实体摘要"""
|
||
lines = []
|
||
|
||
# 按类型分组
|
||
by_type: Dict[str, List[EntityNode]] = {}
|
||
for e in entities:
|
||
t = e.get_entity_type() or "Unknown"
|
||
if t not in by_type:
|
||
by_type[t] = []
|
||
by_type[t].append(e)
|
||
|
||
for entity_type, type_entities in by_type.items():
|
||
lines.append(f"\n### {entity_type} ({len(type_entities)}个)")
|
||
for e in type_entities[:10]: # 每类最多显示10个
|
||
summary_preview = (e.summary[:100] + "...") if len(e.summary) > 100 else e.summary
|
||
lines.append(f"- {e.name}: {summary_preview}")
|
||
if len(type_entities) > 10:
|
||
lines.append(f" ... 还有 {len(type_entities) - 10} 个")
|
||
|
||
return "\n".join(lines)
|
||
|
||
def _generate_config_with_llm(
|
||
self,
|
||
context: str,
|
||
entities: List[EntityNode],
|
||
enable_twitter: bool,
|
||
enable_reddit: bool
|
||
) -> Dict[str, Any]:
|
||
"""调用LLM生成配置"""
|
||
|
||
# 构建实体列表用于Agent配置
|
||
entity_list = []
|
||
for i, e in enumerate(entities):
|
||
entity_list.append({
|
||
"agent_id": i,
|
||
"entity_uuid": e.uuid,
|
||
"entity_name": e.name,
|
||
"entity_type": e.get_entity_type() or "Unknown",
|
||
"summary": e.summary[:200] if e.summary else ""
|
||
})
|
||
|
||
prompt = f"""你是一个社交媒体舆论模拟专家。请根据以下信息，生成详细的模拟参数配置。
|
||
|
||
{context}
|
||
|
||
## 实体列表（需要为每个实体生成活动配置）
|
||
```json
|
||
{json.dumps(entity_list, ensure_ascii=False, indent=2)}
|
||
```
|
||
|
||
## 任务
|
||
请生成一个JSON配置，包含以下部分：
|
||
|
||
1. **time_config** - 时间模拟配置
|
||
- total_simulation_hours: 模拟总时长（小时），根据事件性质决定（短期热点24-72小时，长期舆论168-336小时）
|
||
- minutes_per_round: 每轮代表的时间（分钟），建议15-60
|
||
- agents_per_hour_min/max: 每小时激活的Agent数量范围
|
||
- peak_hours: 高峰时段列表（0-23）
|
||
- off_peak_hours: 低谷时段列表
|
||
|
||
2. **agent_configs** - 每个Agent的活动配置（必须为每个实体生成）
|
||
对于每个agent_id，设置：
|
||
- activity_level: 活跃度(0.0-1.0)，官方机构通常0.1-0.3，媒体0.3-0.5，个人0.5-0.9
|
||
- posts_per_hour: 每小时发帖频率，官方机构0.05-0.2，媒体0.5-2，个人0.1-1
|
||
- comments_per_hour: 每小时评论频率
|
||
- active_hours: 活跃时间段列表，官方通常工作时间，个人更分散
|
||
- response_delay_min/max: 响应延迟（模拟分钟），官方较慢(30-180)，个人较快(1-30)
|
||
- sentiment_bias: 情感倾向(-1到1)，根据实体立场设置
|
||
- stance: 立场(supportive/opposing/neutral/observer)
|
||
- influence_weight: 影响力权重，知名人物和媒体较高
|
||
|
||
3. **event_config** - 事件配置
|
||
- initial_posts: 初始帖子列表，包含content和poster_agent_id
|
||
- hot_topics: 热点话题关键词列表
|
||
- narrative_direction: 舆论发展方向描述
|
||
|
||
4. **platform_configs** - 平台配置（如果启用）
|
||
- viral_threshold: 病毒传播阈值
|
||
- echo_chamber_strength: 回声室效应强度(0-1)
|
||
|
||
5. **reasoning** - 你的推理说明，解释为什么这样设置参数
|
||
|
||
## 重要原则
|
||
- 官方机构（University、GovernmentAgency）发言频率低但影响力大
|
||
- 媒体（MediaOutlet）发言频率中等，传播速度快
|
||
- 个人（Student、PublicFigure）发言频率高但影响力分散
|
||
- 根据模拟需求判断各实体的立场和情感倾向
|
||
- 时间配置要符合真实社交媒体的使用规律
|
||
|
||
请返回JSON格式，不要包含markdown代码块标记。"""
|
||
|
||
try:
|
||
# 使用重试机制调用LLM API
|
||
from ..utils.retry import RetryableAPIClient
|
||
|
||
retry_client = RetryableAPIClient(max_retries=3, initial_delay=2.0, max_delay=60.0)
|
||
|
||
def call_llm():
|
||
return self.client.chat.completions.create(
|
||
model=self.model_name,
|
||
messages=[
|
||
{
|
||
"role": "system",
|
||
"content": "你是社交媒体舆论模拟专家，擅长设计真实的模拟参数。返回纯JSON格式，不要markdown。"
|
||
},
|
||
{"role": "user", "content": prompt}
|
||
],
|
||
response_format={"type": "json_object"},
|
||
temperature=0.7,
|
||
max_tokens=8000
|
||
)
|
||
|
||
response = retry_client.call_with_retry(call_llm)
|
||
result = json.loads(response.choices[0].message.content)
|
||
logger.info(f"LLM配置生成成功")
|
||
return result
|
||
|
||
except Exception as e:
|
||
logger.error(f"LLM配置生成失败（已重试）: {str(e)}")
|
||
# 返回默认配置
|
||
return self._generate_default_config(entities)
|
||
|
||
def _generate_default_config(self, entities: List[EntityNode]) -> Dict[str, Any]:
|
||
"""生成默认配置（LLM失败时的fallback）"""
|
||
agent_configs = []
|
||
|
||
for i, e in enumerate(entities):
|
||
entity_type = (e.get_entity_type() or "Unknown").lower()
|
||
|
||
# 根据实体类型设置默认参数
|
||
if entity_type in ["university", "governmentagency", "ngo"]:
|
||
config = {
|
||
"agent_id": i,
|
||
"activity_level": 0.2,
|
||
"posts_per_hour": 0.1,
|
||
"comments_per_hour": 0.05,
|
||
"active_hours": list(range(9, 18)),
|
||
"response_delay_min": 60,
|
||
"response_delay_max": 240,
|
||
"sentiment_bias": 0.0,
|
||
"stance": "neutral",
|
||
"influence_weight": 3.0
|
||
}
|
||
elif entity_type in ["mediaoutlet"]:
|
||
config = {
|
||
"agent_id": i,
|
||
"activity_level": 0.6,
|
||
"posts_per_hour": 1.0,
|
||
"comments_per_hour": 0.5,
|
||
"active_hours": list(range(6, 24)),
|
||
"response_delay_min": 5,
|
||
"response_delay_max": 30,
|
||
"sentiment_bias": 0.0,
|
||
"stance": "observer",
|
||
"influence_weight": 2.5
|
||
}
|
||
elif entity_type in ["publicfigure", "expert"]:
|
||
config = {
|
||
"agent_id": i,
|
||
"activity_level": 0.5,
|
||
"posts_per_hour": 0.3,
|
||
"comments_per_hour": 0.5,
|
||
"active_hours": list(range(8, 23)),
|
||
"response_delay_min": 10,
|
||
"response_delay_max": 60,
|
||
"sentiment_bias": 0.0,
|
||
"stance": "neutral",
|
||
"influence_weight": 2.0
|
||
}
|
||
else: # Student, Person, etc.
|
||
config = {
|
||
"agent_id": i,
|
||
"activity_level": 0.7,
|
||
"posts_per_hour": 0.5,
|
||
"comments_per_hour": 1.0,
|
||
"active_hours": list(range(7, 24)),
|
||
"response_delay_min": 1,
|
||
"response_delay_max": 20,
|
||
"sentiment_bias": 0.0,
|
||
"stance": "neutral",
|
||
"influence_weight": 1.0
|
||
}
|
||
|
||
agent_configs.append(config)
|
||
|
||
return {
|
||
"time_config": {
|
||
"total_simulation_hours": 72,
|
||
"minutes_per_round": 30,
|
||
"agents_per_hour_min": max(1, len(entities) // 10),
|
||
"agents_per_hour_max": max(5, len(entities) // 3),
|
||
"peak_hours": [9, 10, 11, 14, 15, 20, 21, 22],
|
||
"off_peak_hours": [0, 1, 2, 3, 4, 5]
|
||
},
|
||
"agent_configs": agent_configs,
|
||
"event_config": {
|
||
"initial_posts": [],
|
||
"hot_topics": [],
|
||
"narrative_direction": ""
|
||
},
|
||
"reasoning": "使用默认配置（LLM生成失败）"
|
||
}
|
||
|
||
def _build_parameters(
|
||
self,
|
||
simulation_id: str,
|
||
project_id: str,
|
||
graph_id: str,
|
||
simulation_requirement: str,
|
||
entities: List[EntityNode],
|
||
llm_result: Dict[str, Any],
|
||
enable_twitter: bool,
|
||
enable_reddit: bool
|
||
) -> SimulationParameters:
|
||
"""根据LLM结果构建SimulationParameters对象"""
|
||
|
||
# 时间配置
|
||
time_cfg = llm_result.get("time_config", {})
|
||
time_config = TimeSimulationConfig(
|
||
total_simulation_hours=time_cfg.get("total_simulation_hours", 72),
|
||
minutes_per_round=time_cfg.get("minutes_per_round", 30),
|
||
agents_per_hour_min=time_cfg.get("agents_per_hour_min", 5),
|
||
agents_per_hour_max=time_cfg.get("agents_per_hour_max", 20),
|
||
peak_hours=time_cfg.get("peak_hours", [9, 10, 11, 14, 15, 20, 21, 22]),
|
||
off_peak_hours=time_cfg.get("off_peak_hours", [0, 1, 2, 3, 4, 5]),
|
||
peak_activity_multiplier=time_cfg.get("peak_activity_multiplier", 1.5),
|
||
off_peak_activity_multiplier=time_cfg.get("off_peak_activity_multiplier", 0.3)
|
||
)
|
||
|
||
# Agent配置
|
||
agent_configs = []
|
||
llm_agent_configs = {cfg["agent_id"]: cfg for cfg in llm_result.get("agent_configs", [])}
|
||
|
||
for i, entity in enumerate(entities):
|
||
cfg = llm_agent_configs.get(i, {})
|
||
|
||
agent_config = AgentActivityConfig(
|
||
agent_id=i,
|
||
entity_uuid=entity.uuid,
|
||
entity_name=entity.name,
|
||
entity_type=entity.get_entity_type() or "Unknown",
|
||
activity_level=cfg.get("activity_level", 0.5),
|
||
posts_per_hour=cfg.get("posts_per_hour", 0.5),
|
||
comments_per_hour=cfg.get("comments_per_hour", 1.0),
|
||
active_hours=cfg.get("active_hours", list(range(8, 23))),
|
||
response_delay_min=cfg.get("response_delay_min", 5),
|
||
response_delay_max=cfg.get("response_delay_max", 60),
|
||
sentiment_bias=cfg.get("sentiment_bias", 0.0),
|
||
stance=cfg.get("stance", "neutral"),
|
||
influence_weight=cfg.get("influence_weight", 1.0)
|
||
)
|
||
agent_configs.append(agent_config)
|
||
|
||
# 事件配置
|
||
event_cfg = llm_result.get("event_config", {})
|
||
event_config = EventConfig(
|
||
initial_posts=event_cfg.get("initial_posts", []),
|
||
scheduled_events=event_cfg.get("scheduled_events", []),
|
||
hot_topics=event_cfg.get("hot_topics", []),
|
||
narrative_direction=event_cfg.get("narrative_direction", "")
|
||
)
|
||
|
||
# 平台配置
|
||
twitter_config = None
|
||
reddit_config = None
|
||
|
||
platform_cfgs = llm_result.get("platform_configs", {})
|
||
|
||
if enable_twitter:
|
||
tw_cfg = platform_cfgs.get("twitter", {})
|
||
twitter_config = PlatformConfig(
|
||
platform="twitter",
|
||
recency_weight=tw_cfg.get("recency_weight", 0.4),
|
||
popularity_weight=tw_cfg.get("popularity_weight", 0.3),
|
||
relevance_weight=tw_cfg.get("relevance_weight", 0.3),
|
||
viral_threshold=tw_cfg.get("viral_threshold", 10),
|
||
echo_chamber_strength=tw_cfg.get("echo_chamber_strength", 0.5)
|
||
)
|
||
|
||
if enable_reddit:
|
||
rd_cfg = platform_cfgs.get("reddit", {})
|
||
reddit_config = PlatformConfig(
|
||
platform="reddit",
|
||
recency_weight=rd_cfg.get("recency_weight", 0.3),
|
||
popularity_weight=rd_cfg.get("popularity_weight", 0.4),
|
||
relevance_weight=rd_cfg.get("relevance_weight", 0.3),
|
||
viral_threshold=rd_cfg.get("viral_threshold", 15),
|
||
echo_chamber_strength=rd_cfg.get("echo_chamber_strength", 0.6)
|
||
)
|
||
|
||
return SimulationParameters(
|
||
simulation_id=simulation_id,
|
||
project_id=project_id,
|
||
graph_id=graph_id,
|
||
simulation_requirement=simulation_requirement,
|
||
time_config=time_config,
|
||
agent_configs=agent_configs,
|
||
event_config=event_config,
|
||
twitter_config=twitter_config,
|
||
reddit_config=reddit_config,
|
||
llm_model=self.model_name,
|
||
llm_base_url=self.base_url,
|
||
generation_reasoning=llm_result.get("reasoning", "")
|
||
)
|
||
|
||
|