mirror of
https://github.com/666ghj/MiroFish.git
synced 2026-04-28 06:31:25 +00:00
991 lines
39 KiB
Python
991 lines
39 KiB
Python
"""
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模拟配置智能生成器
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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
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实现全程自动化，无需人工设置参数
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采用分步生成策略，避免一次性生成过长内容导致失败：
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1. 生成时间配置
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2. 生成事件配置
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3. 分批生成Agent配置
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4. 生成平台配置
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"""
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import json
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import math
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from typing import Dict, Any, List, Optional, Callable
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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 ..utils.locale import get_language_instruction, t
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from .zep_entity_reader import EntityNode, ZepEntityReader
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logger = get_logger('mirofish.simulation_config')
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# 中国作息时间配置（北京时间）
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CHINA_TIMEZONE_CONFIG = {
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# 深夜时段（几乎无人活动）
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"dead_hours": [0, 1, 2, 3, 4, 5],
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# 早间时段（逐渐醒来）
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"morning_hours": [6, 7, 8],
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# 工作时段
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"work_hours": [9, 10, 11, 12, 13, 14, 15, 16, 17, 18],
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# 晚间高峰（最活跃）
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"peak_hours": [19, 20, 21, 22],
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# 夜间时段（活跃度下降）
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"night_hours": [23],
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# 活跃度系数
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"activity_multipliers": {
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"dead": 0.05, # 凌晨几乎无人
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"morning": 0.4, # 早间逐渐活跃
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"work": 0.7, # 工作时段中等
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"peak": 1.5, # 晚间高峰
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"night": 0.5 # 深夜下降
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}
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}
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@dataclass
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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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# 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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# 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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# 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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# 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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# 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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# 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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# 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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@dataclass
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class TimeSimulationConfig:
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"""时间模拟配置（基于中国人作息习惯）"""
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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）- 默认60分钟（1小时），加快时间流速
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minutes_per_round: int = 60
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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（晚间19-22点，中国人最活跃的时间）
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peak_hours: List[int] = field(default_factory=lambda: [19, 20, 21, 22])
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peak_activity_multiplier: float = 1.5
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# 低谷时段（凌晨0-5点，几乎无人活动）
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off_peak_hours: List[int] = field(default_factory=lambda: [0, 1, 2, 3, 4, 5])
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off_peak_activity_multiplier: float = 0.05 # 凌晨活跃度极低
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# 早间时段
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morning_hours: List[int] = field(default_factory=lambda: [6, 7, 8])
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morning_activity_multiplier: float = 0.4
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# 工作时段
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work_hours: List[int] = field(default_factory=lambda: [9, 10, 11, 12, 13, 14, 15, 16, 17, 18])
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work_activity_multiplier: float = 0.7
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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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@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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# 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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@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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# 时间配置
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time_config: TimeSimulationConfig = field(default_factory=TimeSimulationConfig)
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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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# 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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def to_dict(self) -> Dict[str, Any]:
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"""转换为字典"""
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time_dict = asdict(self.time_config)
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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": time_dict,
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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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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
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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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采用分步生成策略：
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1. 生成时间配置和事件配置（轻量级）
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2. 分批生成Agent配置（每批10-20个）
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3. 生成平台配置
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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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# 每批生成的Agent数量
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AGENTS_PER_BATCH = 15
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# 各步骤的上下文截断长度（字符数）
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TIME_CONFIG_CONTEXT_LENGTH = 10000 # 时间配置
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EVENT_CONFIG_CONTEXT_LENGTH = 8000 # 事件配置
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ENTITY_SUMMARY_LENGTH = 300 # 实体摘要
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AGENT_SUMMARY_LENGTH = 300 # Agent配置中的实体摘要
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ENTITIES_PER_TYPE_DISPLAY = 20 # 每类实体显示数量
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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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if not self.api_key:
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raise ValueError("LLM_API_KEY 未配置")
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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(
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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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progress_callback: Optional[Callable[[int, int, str], None]] = None,
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) -> SimulationParameters:
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"""
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智能生成完整的模拟配置（分步生成）
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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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progress_callback: 进度回调函数(current_step, total_steps, message)
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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}, 实体数={len(entities)}")
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# 计算总步骤数
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num_batches = math.ceil(len(entities) / self.AGENTS_PER_BATCH)
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total_steps = 3 + num_batches # 时间配置 + 事件配置 + N批Agent + 平台配置
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current_step = 0
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def report_progress(step: int, message: str):
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nonlocal current_step
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current_step = step
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if progress_callback:
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progress_callback(step, total_steps, message)
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logger.info(f"[{step}/{total_steps}] {message}")
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# 1. 构建基础上下文信息
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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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reasoning_parts = []
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# ========== 步骤1: 生成时间配置 ==========
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report_progress(1, t('progress.generatingTimeConfig'))
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num_entities = len(entities)
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time_config_result = self._generate_time_config(context, num_entities)
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time_config = self._parse_time_config(time_config_result, num_entities)
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reasoning_parts.append(f"{t('progress.timeConfigLabel')}: {time_config_result.get('reasoning', t('common.success'))}")
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# ========== 步骤2: 生成事件配置 ==========
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report_progress(2, t('progress.generatingEventConfig'))
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event_config_result = self._generate_event_config(context, simulation_requirement, entities)
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event_config = self._parse_event_config(event_config_result)
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reasoning_parts.append(f"{t('progress.eventConfigLabel')}: {event_config_result.get('reasoning', t('common.success'))}")
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# ========== 步骤3-N: 分批生成Agent配置 ==========
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all_agent_configs = []
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for batch_idx in range(num_batches):
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start_idx = batch_idx * self.AGENTS_PER_BATCH
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end_idx = min(start_idx + self.AGENTS_PER_BATCH, len(entities))
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batch_entities = entities[start_idx:end_idx]
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report_progress(
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3 + batch_idx,
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t('progress.generatingAgentConfig', start=start_idx + 1, end=end_idx, total=len(entities))
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)
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batch_configs = self._generate_agent_configs_batch(
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context=context,
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entities=batch_entities,
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start_idx=start_idx,
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simulation_requirement=simulation_requirement
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)
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all_agent_configs.extend(batch_configs)
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reasoning_parts.append(t('progress.agentConfigResult', count=len(all_agent_configs)))
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# ========== 为初始帖子分配发布者 Agent ==========
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logger.info("为初始帖子分配合适的发布者 Agent...")
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event_config = self._assign_initial_post_agents(event_config, all_agent_configs)
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assigned_count = len([p for p in event_config.initial_posts if p.get("poster_agent_id") is not None])
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reasoning_parts.append(t('progress.postAssignResult', count=assigned_count))
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# ========== 最后一步: 生成平台配置 ==========
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report_progress(total_steps, t('progress.generatingPlatformConfig'))
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twitter_config = None
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reddit_config = None
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if enable_twitter:
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twitter_config = PlatformConfig(
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platform="twitter",
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recency_weight=0.4,
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popularity_weight=0.3,
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relevance_weight=0.3,
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viral_threshold=10,
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echo_chamber_strength=0.5
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)
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if enable_reddit:
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reddit_config = PlatformConfig(
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platform="reddit",
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recency_weight=0.3,
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popularity_weight=0.4,
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relevance_weight=0.3,
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viral_threshold=15,
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echo_chamber_strength=0.6
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)
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# 构建最终参数
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params = SimulationParameters(
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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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time_config=time_config,
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agent_configs=all_agent_configs,
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event_config=event_config,
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twitter_config=twitter_config,
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reddit_config=reddit_config,
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llm_model=self.model_name,
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llm_base_url=self.base_url,
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generation_reasoning=" | ".join(reasoning_parts)
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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(
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self,
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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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) -> str:
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"""构建LLM上下文，截断到最大长度"""
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# 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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if remaining_length > 0 and document_text:
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doc_text = document_text[:remaining_length]
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if len(document_text) > remaining_length:
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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}")
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return "\n".join(context_parts)
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def _summarize_entities(self, entities: List[EntityNode]) -> str:
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"""生成实体摘要"""
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lines = []
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# 按类型分组
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by_type: Dict[str, List[EntityNode]] = {}
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for e in entities:
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t = e.get_entity_type() or "Unknown"
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if t not in by_type:
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by_type[t] = []
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by_type[t].append(e)
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for entity_type, type_entities in by_type.items():
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lines.append(f"\n### {entity_type} ({len(type_entities)}个)")
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# 使用配置的显示数量和摘要长度
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display_count = self.ENTITIES_PER_TYPE_DISPLAY
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summary_len = self.ENTITY_SUMMARY_LENGTH
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for e in type_entities[:display_count]:
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summary_preview = (e.summary[:summary_len] + "...") if len(e.summary) > summary_len else e.summary
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lines.append(f"- {e.name}: {summary_preview}")
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if len(type_entities) > display_count:
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lines.append(f" ... 还有 {len(type_entities) - display_count} 个")
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return "\n".join(lines)
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def _call_llm_with_retry(self, prompt: str, system_prompt: str) -> Dict[str, Any]:
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"""带重试的LLM调用，包含JSON修复逻辑"""
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import re
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max_attempts = 3
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last_error = None
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for attempt in range(max_attempts):
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try:
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response = self.client.chat.completions.create(
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model=self.model_name,
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messages=[
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": prompt}
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],
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response_format={"type": "json_object"},
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temperature=0.7 - (attempt * 0.1) # 每次重试降低温度
|
||
# 不设置max_tokens，让LLM自由发挥
|
||
)
|
||
|
||
content = response.choices[0].message.content
|
||
finish_reason = response.choices[0].finish_reason
|
||
|
||
# 检查是否被截断
|
||
if finish_reason == 'length':
|
||
logger.warning(f"LLM输出被截断 (attempt {attempt+1})")
|
||
content = self._fix_truncated_json(content)
|
||
|
||
# 尝试解析JSON
|
||
try:
|
||
return json.loads(content)
|
||
except json.JSONDecodeError as e:
|
||
logger.warning(f"JSON解析失败 (attempt {attempt+1}): {str(e)[:80]}")
|
||
|
||
# 尝试修复JSON
|
||
fixed = self._try_fix_config_json(content)
|
||
if fixed:
|
||
return fixed
|
||
|
||
last_error = e
|
||
|
||
except Exception as e:
|
||
logger.warning(f"LLM调用失败 (attempt {attempt+1}): {str(e)[:80]}")
|
||
last_error = e
|
||
import time
|
||
time.sleep(2 * (attempt + 1))
|
||
|
||
raise last_error or Exception("LLM调用失败")
|
||
|
||
def _fix_truncated_json(self, content: str) -> str:
|
||
"""修复被截断的JSON"""
|
||
content = content.strip()
|
||
|
||
# 计算未闭合的括号
|
||
open_braces = content.count('{') - content.count('}')
|
||
open_brackets = content.count('[') - content.count(']')
|
||
|
||
# 检查是否有未闭合的字符串
|
||
if content and content[-1] not in '",}]':
|
||
content += '"'
|
||
|
||
# 闭合括号
|
||
content += ']' * open_brackets
|
||
content += '}' * open_braces
|
||
|
||
return content
|
||
|
||
def _try_fix_config_json(self, content: str) -> Optional[Dict[str, Any]]:
|
||
"""尝试修复配置JSON"""
|
||
import re
|
||
|
||
# 修复被截断的情况
|
||
content = self._fix_truncated_json(content)
|
||
|
||
# 提取JSON部分
|
||
json_match = re.search(r'\{[\s\S]*\}', content)
|
||
if json_match:
|
||
json_str = json_match.group()
|
||
|
||
# 移除字符串中的换行符
|
||
def fix_string(match):
|
||
s = match.group(0)
|
||
s = s.replace('\n', ' ').replace('\r', ' ')
|
||
s = re.sub(r'\s+', ' ', s)
|
||
return s
|
||
|
||
json_str = re.sub(r'"[^"\\]*(?:\\.[^"\\]*)*"', fix_string, json_str)
|
||
|
||
try:
|
||
return json.loads(json_str)
|
||
except:
|
||
# 尝试移除所有控制字符
|
||
json_str = re.sub(r'[\x00-\x1f\x7f-\x9f]', ' ', json_str)
|
||
json_str = re.sub(r'\s+', ' ', json_str)
|
||
try:
|
||
return json.loads(json_str)
|
||
except:
|
||
pass
|
||
|
||
return None
|
||
|
||
def _generate_time_config(self, context: str, num_entities: int) -> Dict[str, Any]:
|
||
"""生成时间配置"""
|
||
# 使用配置的上下文截断长度
|
||
context_truncated = context[:self.TIME_CONFIG_CONTEXT_LENGTH]
|
||
|
||
# 计算最大允许值（80%的agent数）
|
||
max_agents_allowed = max(1, int(num_entities * 0.9))
|
||
|
||
prompt = f"""基于以下模拟需求，生成时间模拟配置。
|
||
|
||
{context_truncated}
|
||
|
||
## 任务
|
||
请生成时间配置JSON。
|
||
|
||
### 基本原则（仅供参考，需根据具体事件和参与群体灵活调整）：
|
||
- 请根据模拟场景推断目标用户群体所在时区和作息习惯，以下为东八区(UTC+8)的参考示例
|
||
- 凌晨0-5点几乎无人活动（活跃度系数0.05）
|
||
- 早上6-8点逐渐活跃（活跃度系数0.4）
|
||
- 工作时间9-18点中等活跃（活跃度系数0.7）
|
||
- 晚间19-22点是高峰期（活跃度系数1.5）
|
||
- 23点后活跃度下降（活跃度系数0.5）
|
||
- 一般规律：凌晨低活跃、早间渐增、工作时段中等、晚间高峰
|
||
- **重要**：以下示例值仅供参考，你需要根据事件性质、参与群体特点来调整具体时段
|
||
- 例如：学生群体高峰可能是21-23点；媒体全天活跃；官方机构只在工作时间
|
||
- 例如：突发热点可能导致深夜也有讨论，off_peak_hours 可适当缩短
|
||
|
||
### 返回JSON格式（不要markdown）
|
||
|
||
示例：
|
||
{{
|
||
"total_simulation_hours": 72,
|
||
"minutes_per_round": 60,
|
||
"agents_per_hour_min": 5,
|
||
"agents_per_hour_max": 50,
|
||
"peak_hours": [19, 20, 21, 22],
|
||
"off_peak_hours": [0, 1, 2, 3, 4, 5],
|
||
"morning_hours": [6, 7, 8],
|
||
"work_hours": [9, 10, 11, 12, 13, 14, 15, 16, 17, 18],
|
||
"reasoning": "针对该事件的时间配置说明"
|
||
}}
|
||
|
||
字段说明：
|
||
- total_simulation_hours (int): 模拟总时长，24-168小时，突发事件短、持续话题长
|
||
- minutes_per_round (int): 每轮时长，30-120分钟，建议60分钟
|
||
- agents_per_hour_min (int): 每小时最少激活Agent数（取值范围: 1-{max_agents_allowed}）
|
||
- agents_per_hour_max (int): 每小时最多激活Agent数（取值范围: 1-{max_agents_allowed}）
|
||
- peak_hours (int数组): 高峰时段，根据事件参与群体调整
|
||
- off_peak_hours (int数组): 低谷时段，通常深夜凌晨
|
||
- morning_hours (int数组): 早间时段
|
||
- work_hours (int数组): 工作时段
|
||
- reasoning (string): 简要说明为什么这样配置"""
|
||
|
||
system_prompt = "你是社交媒体模拟专家。返回纯JSON格式，时间配置需符合模拟场景中目标用户群体的作息习惯。"
|
||
system_prompt = f"{system_prompt}\n\n{get_language_instruction()}"
|
||
|
||
try:
|
||
return self._call_llm_with_retry(prompt, system_prompt)
|
||
except Exception as e:
|
||
logger.warning(f"时间配置LLM生成失败: {e}, 使用默认配置")
|
||
return self._get_default_time_config(num_entities)
|
||
|
||
def _get_default_time_config(self, num_entities: int) -> Dict[str, Any]:
|
||
"""获取默认时间配置（中国人作息）"""
|
||
return {
|
||
"total_simulation_hours": 72,
|
||
"minutes_per_round": 60, # 每轮1小时，加快时间流速
|
||
"agents_per_hour_min": max(1, num_entities // 15),
|
||
"agents_per_hour_max": max(5, num_entities // 5),
|
||
"peak_hours": [19, 20, 21, 22],
|
||
"off_peak_hours": [0, 1, 2, 3, 4, 5],
|
||
"morning_hours": [6, 7, 8],
|
||
"work_hours": [9, 10, 11, 12, 13, 14, 15, 16, 17, 18],
|
||
"reasoning": "使用默认中国人作息配置（每轮1小时）"
|
||
}
|
||
|
||
def _parse_time_config(self, result: Dict[str, Any], num_entities: int) -> TimeSimulationConfig:
|
||
"""解析时间配置结果，并验证agents_per_hour值不超过总agent数"""
|
||
# 获取原始值
|
||
agents_per_hour_min = result.get("agents_per_hour_min", max(1, num_entities // 15))
|
||
agents_per_hour_max = result.get("agents_per_hour_max", max(5, num_entities // 5))
|
||
|
||
# 验证并修正：确保不超过总agent数
|
||
if agents_per_hour_min > num_entities:
|
||
logger.warning(f"agents_per_hour_min ({agents_per_hour_min}) 超过总Agent数 ({num_entities})，已修正")
|
||
agents_per_hour_min = max(1, num_entities // 10)
|
||
|
||
if agents_per_hour_max > num_entities:
|
||
logger.warning(f"agents_per_hour_max ({agents_per_hour_max}) 超过总Agent数 ({num_entities})，已修正")
|
||
agents_per_hour_max = max(agents_per_hour_min + 1, num_entities // 2)
|
||
|
||
# 确保 min < max
|
||
if agents_per_hour_min >= agents_per_hour_max:
|
||
agents_per_hour_min = max(1, agents_per_hour_max // 2)
|
||
logger.warning(f"agents_per_hour_min >= max，已修正为 {agents_per_hour_min}")
|
||
|
||
return TimeSimulationConfig(
|
||
total_simulation_hours=result.get("total_simulation_hours", 72),
|
||
minutes_per_round=result.get("minutes_per_round", 60), # 默认每轮1小时
|
||
agents_per_hour_min=agents_per_hour_min,
|
||
agents_per_hour_max=agents_per_hour_max,
|
||
peak_hours=result.get("peak_hours", [19, 20, 21, 22]),
|
||
off_peak_hours=result.get("off_peak_hours", [0, 1, 2, 3, 4, 5]),
|
||
off_peak_activity_multiplier=0.05, # 凌晨几乎无人
|
||
morning_hours=result.get("morning_hours", [6, 7, 8]),
|
||
morning_activity_multiplier=0.4,
|
||
work_hours=result.get("work_hours", list(range(9, 19))),
|
||
work_activity_multiplier=0.7,
|
||
peak_activity_multiplier=1.5
|
||
)
|
||
|
||
def _generate_event_config(
|
||
self,
|
||
context: str,
|
||
simulation_requirement: str,
|
||
entities: List[EntityNode]
|
||
) -> Dict[str, Any]:
|
||
"""生成事件配置"""
|
||
|
||
# 获取可用的实体类型列表，供 LLM 参考
|
||
entity_types_available = list(set(
|
||
e.get_entity_type() or "Unknown" for e in entities
|
||
))
|
||
|
||
# 为每种类型列出代表性实体名称
|
||
type_examples = {}
|
||
for e in entities:
|
||
etype = e.get_entity_type() or "Unknown"
|
||
if etype not in type_examples:
|
||
type_examples[etype] = []
|
||
if len(type_examples[etype]) < 3:
|
||
type_examples[etype].append(e.name)
|
||
|
||
type_info = "\n".join([
|
||
f"- {t}: {', '.join(examples)}"
|
||
for t, examples in type_examples.items()
|
||
])
|
||
|
||
# 使用配置的上下文截断长度
|
||
context_truncated = context[:self.EVENT_CONFIG_CONTEXT_LENGTH]
|
||
|
||
prompt = f"""基于以下模拟需求，生成事件配置。
|
||
|
||
模拟需求: {simulation_requirement}
|
||
|
||
{context_truncated}
|
||
|
||
## 可用实体类型及示例
|
||
{type_info}
|
||
|
||
## 任务
|
||
请生成事件配置JSON：
|
||
- 提取热点话题关键词
|
||
- 描述舆论发展方向
|
||
- 设计初始帖子内容，**每个帖子必须指定 poster_type（发布者类型）**
|
||
|
||
**重要**: poster_type 必须从上面的"可用实体类型"中选择，这样初始帖子才能分配给合适的 Agent 发布。
|
||
例如：官方声明应由 Official/University 类型发布，新闻由 MediaOutlet 发布，学生观点由 Student 发布。
|
||
|
||
返回JSON格式（不要markdown）：
|
||
{{
|
||
"hot_topics": ["关键词1", "关键词2", ...],
|
||
"narrative_direction": "<舆论发展方向描述>",
|
||
"initial_posts": [
|
||
{{"content": "帖子内容", "poster_type": "实体类型（必须从可用类型中选择）"}},
|
||
...
|
||
],
|
||
"reasoning": "<简要说明>"
|
||
}}"""
|
||
|
||
system_prompt = "你是舆论分析专家。返回纯JSON格式。注意 poster_type 必须精确匹配可用实体类型。"
|
||
system_prompt = f"{system_prompt}\n\n{get_language_instruction()}\nIMPORTANT: The 'poster_type' field value MUST be in English PascalCase exactly matching the available entity types. Only 'content', 'narrative_direction', 'hot_topics' and 'reasoning' fields should use the specified language."
|
||
|
||
try:
|
||
return self._call_llm_with_retry(prompt, system_prompt)
|
||
except Exception as e:
|
||
logger.warning(f"事件配置LLM生成失败: {e}, 使用默认配置")
|
||
return {
|
||
"hot_topics": [],
|
||
"narrative_direction": "",
|
||
"initial_posts": [],
|
||
"reasoning": "使用默认配置"
|
||
}
|
||
|
||
def _parse_event_config(self, result: Dict[str, Any]) -> EventConfig:
|
||
"""解析事件配置结果"""
|
||
return EventConfig(
|
||
initial_posts=result.get("initial_posts", []),
|
||
scheduled_events=[],
|
||
hot_topics=result.get("hot_topics", []),
|
||
narrative_direction=result.get("narrative_direction", "")
|
||
)
|
||
|
||
def _assign_initial_post_agents(
|
||
self,
|
||
event_config: EventConfig,
|
||
agent_configs: List[AgentActivityConfig]
|
||
) -> EventConfig:
|
||
"""
|
||
为初始帖子分配合适的发布者 Agent
|
||
|
||
根据每个帖子的 poster_type 匹配最合适的 agent_id
|
||
"""
|
||
if not event_config.initial_posts:
|
||
return event_config
|
||
|
||
# 按实体类型建立 agent 索引
|
||
agents_by_type: Dict[str, List[AgentActivityConfig]] = {}
|
||
for agent in agent_configs:
|
||
etype = agent.entity_type.lower()
|
||
if etype not in agents_by_type:
|
||
agents_by_type[etype] = []
|
||
agents_by_type[etype].append(agent)
|
||
|
||
# 类型映射表（处理 LLM 可能输出的不同格式）
|
||
type_aliases = {
|
||
"official": ["official", "university", "governmentagency", "government"],
|
||
"university": ["university", "official"],
|
||
"mediaoutlet": ["mediaoutlet", "media"],
|
||
"student": ["student", "person"],
|
||
"professor": ["professor", "expert", "teacher"],
|
||
"alumni": ["alumni", "person"],
|
||
"organization": ["organization", "ngo", "company", "group"],
|
||
"person": ["person", "student", "alumni"],
|
||
}
|
||
|
||
# 记录每种类型已使用的 agent 索引，避免重复使用同一个 agent
|
||
used_indices: Dict[str, int] = {}
|
||
|
||
updated_posts = []
|
||
for post in event_config.initial_posts:
|
||
poster_type = post.get("poster_type", "").lower()
|
||
content = post.get("content", "")
|
||
|
||
# 尝试找到匹配的 agent
|
||
matched_agent_id = None
|
||
|
||
# 1. 直接匹配
|
||
if poster_type in agents_by_type:
|
||
agents = agents_by_type[poster_type]
|
||
idx = used_indices.get(poster_type, 0) % len(agents)
|
||
matched_agent_id = agents[idx].agent_id
|
||
used_indices[poster_type] = idx + 1
|
||
else:
|
||
# 2. 使用别名匹配
|
||
for alias_key, aliases in type_aliases.items():
|
||
if poster_type in aliases or alias_key == poster_type:
|
||
for alias in aliases:
|
||
if alias in agents_by_type:
|
||
agents = agents_by_type[alias]
|
||
idx = used_indices.get(alias, 0) % len(agents)
|
||
matched_agent_id = agents[idx].agent_id
|
||
used_indices[alias] = idx + 1
|
||
break
|
||
if matched_agent_id is not None:
|
||
break
|
||
|
||
# 3. 如果仍未找到，使用影响力最高的 agent
|
||
if matched_agent_id is None:
|
||
logger.warning(f"未找到类型 '{poster_type}' 的匹配 Agent，使用影响力最高的 Agent")
|
||
if agent_configs:
|
||
# 按影响力排序，选择影响力最高的
|
||
sorted_agents = sorted(agent_configs, key=lambda a: a.influence_weight, reverse=True)
|
||
matched_agent_id = sorted_agents[0].agent_id
|
||
else:
|
||
matched_agent_id = 0
|
||
|
||
updated_posts.append({
|
||
"content": content,
|
||
"poster_type": post.get("poster_type", "Unknown"),
|
||
"poster_agent_id": matched_agent_id
|
||
})
|
||
|
||
logger.info(f"初始帖子分配: poster_type='{poster_type}' -> agent_id={matched_agent_id}")
|
||
|
||
event_config.initial_posts = updated_posts
|
||
return event_config
|
||
|
||
def _generate_agent_configs_batch(
|
||
self,
|
||
context: str,
|
||
entities: List[EntityNode],
|
||
start_idx: int,
|
||
simulation_requirement: str
|
||
) -> List[AgentActivityConfig]:
|
||
"""分批生成Agent配置"""
|
||
|
||
# 构建实体信息（使用配置的摘要长度）
|
||
entity_list = []
|
||
summary_len = self.AGENT_SUMMARY_LENGTH
|
||
for i, e in enumerate(entities):
|
||
entity_list.append({
|
||
"agent_id": start_idx + i,
|
||
"entity_name": e.name,
|
||
"entity_type": e.get_entity_type() or "Unknown",
|
||
"summary": e.summary[:summary_len] if e.summary else ""
|
||
})
|
||
|
||
prompt = f"""基于以下信息，为每个实体生成社交媒体活动配置。
|
||
|
||
模拟需求: {simulation_requirement}
|
||
|
||
## 实体列表
|
||
```json
|
||
{json.dumps(entity_list, ensure_ascii=False, indent=2)}
|
||
```
|
||
|
||
## 任务
|
||
为每个实体生成活动配置，注意：
|
||
- **时间符合目标用户群体作息**：以下为参考（东八区），请根据模拟场景调整
|
||
- **官方机构**（University/GovernmentAgency）：活跃度低(0.1-0.3)，工作时间(9-17)活动，响应慢(60-240分钟)，影响力高(2.5-3.0)
|
||
- **媒体**（MediaOutlet）：活跃度中(0.4-0.6)，全天活动(8-23)，响应快(5-30分钟)，影响力高(2.0-2.5)
|
||
- **个人**（Student/Person/Alumni）：活跃度高(0.6-0.9)，主要晚间活动(18-23)，响应快(1-15分钟)，影响力低(0.8-1.2)
|
||
- **公众人物/专家**：活跃度中(0.4-0.6)，影响力中高(1.5-2.0)
|
||
|
||
返回JSON格式（不要markdown）：
|
||
{{
|
||
"agent_configs": [
|
||
{{
|
||
"agent_id": <必须与输入一致>,
|
||
"activity_level": <0.0-1.0>,
|
||
"posts_per_hour": <发帖频率>,
|
||
"comments_per_hour": <评论频率>,
|
||
"active_hours": [<活跃小时列表，考虑中国人作息>],
|
||
"response_delay_min": <最小响应延迟分钟>,
|
||
"response_delay_max": <最大响应延迟分钟>,
|
||
"sentiment_bias": <-1.0到1.0>,
|
||
"stance": "<supportive/opposing/neutral/observer>",
|
||
"influence_weight": <影响力权重>
|
||
}},
|
||
...
|
||
]
|
||
}}"""
|
||
|
||
system_prompt = "你是社交媒体行为分析专家。返回纯JSON，配置需符合模拟场景中目标用户群体的作息习惯。"
|
||
system_prompt = f"{system_prompt}\n\n{get_language_instruction()}\nIMPORTANT: The 'stance' field value MUST be one of the English strings: 'supportive', 'opposing', 'neutral', 'observer'. All JSON field names and numeric values must remain unchanged. Only natural language text fields should use the specified language."
|
||
|
||
try:
|
||
result = self._call_llm_with_retry(prompt, system_prompt)
|
||
llm_configs = {cfg["agent_id"]: cfg for cfg in result.get("agent_configs", [])}
|
||
except Exception as e:
|
||
logger.warning(f"Agent配置批次LLM生成失败: {e}, 使用规则生成")
|
||
llm_configs = {}
|
||
|
||
# 构建AgentActivityConfig对象
|
||
configs = []
|
||
for i, entity in enumerate(entities):
|
||
agent_id = start_idx + i
|
||
cfg = llm_configs.get(agent_id, {})
|
||
|
||
# 如果LLM没有生成，使用规则生成
|
||
if not cfg:
|
||
cfg = self._generate_agent_config_by_rule(entity)
|
||
|
||
config = AgentActivityConfig(
|
||
agent_id=agent_id,
|
||
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(9, 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)
|
||
)
|
||
configs.append(config)
|
||
|
||
return configs
|
||
|
||
def _generate_agent_config_by_rule(self, entity: EntityNode) -> Dict[str, Any]:
|
||
"""基于规则生成单个Agent配置（中国人作息）"""
|
||
entity_type = (entity.get_entity_type() or "Unknown").lower()
|
||
|
||
if entity_type in ["university", "governmentagency", "ngo"]:
|
||
# 官方机构：工作时间活动，低频率，高影响力
|
||
return {
|
||
"activity_level": 0.2,
|
||
"posts_per_hour": 0.1,
|
||
"comments_per_hour": 0.05,
|
||
"active_hours": list(range(9, 18)), # 9:00-17:59
|
||
"response_delay_min": 60,
|
||
"response_delay_max": 240,
|
||
"sentiment_bias": 0.0,
|
||
"stance": "neutral",
|
||
"influence_weight": 3.0
|
||
}
|
||
elif entity_type in ["mediaoutlet"]:
|
||
# 媒体：全天活动，中等频率，高影响力
|
||
return {
|
||
"activity_level": 0.5,
|
||
"posts_per_hour": 0.8,
|
||
"comments_per_hour": 0.3,
|
||
"active_hours": list(range(7, 24)), # 7:00-23:59
|
||
"response_delay_min": 5,
|
||
"response_delay_max": 30,
|
||
"sentiment_bias": 0.0,
|
||
"stance": "observer",
|
||
"influence_weight": 2.5
|
||
}
|
||
elif entity_type in ["professor", "expert", "official"]:
|
||
# 专家/教授：工作+晚间活动，中等频率
|
||
return {
|
||
"activity_level": 0.4,
|
||
"posts_per_hour": 0.3,
|
||
"comments_per_hour": 0.5,
|
||
"active_hours": list(range(8, 22)), # 8:00-21:59
|
||
"response_delay_min": 15,
|
||
"response_delay_max": 90,
|
||
"sentiment_bias": 0.0,
|
||
"stance": "neutral",
|
||
"influence_weight": 2.0
|
||
}
|
||
elif entity_type in ["student"]:
|
||
# 学生：晚间为主，高频率
|
||
return {
|
||
"activity_level": 0.8,
|
||
"posts_per_hour": 0.6,
|
||
"comments_per_hour": 1.5,
|
||
"active_hours": [8, 9, 10, 11, 12, 13, 18, 19, 20, 21, 22, 23], # 上午+晚间
|
||
"response_delay_min": 1,
|
||
"response_delay_max": 15,
|
||
"sentiment_bias": 0.0,
|
||
"stance": "neutral",
|
||
"influence_weight": 0.8
|
||
}
|
||
elif entity_type in ["alumni"]:
|
||
# 校友：晚间为主
|
||
return {
|
||
"activity_level": 0.6,
|
||
"posts_per_hour": 0.4,
|
||
"comments_per_hour": 0.8,
|
||
"active_hours": [12, 13, 19, 20, 21, 22, 23], # 午休+晚间
|
||
"response_delay_min": 5,
|
||
"response_delay_max": 30,
|
||
"sentiment_bias": 0.0,
|
||
"stance": "neutral",
|
||
"influence_weight": 1.0
|
||
}
|
||
else:
|
||
# 普通人：晚间高峰
|
||
return {
|
||
"activity_level": 0.7,
|
||
"posts_per_hour": 0.5,
|
||
"comments_per_hour": 1.2,
|
||
"active_hours": [9, 10, 11, 12, 13, 18, 19, 20, 21, 22, 23], # 白天+晚间
|
||
"response_delay_min": 2,
|
||
"response_delay_max": 20,
|
||
"sentiment_bias": 0.0,
|
||
"stance": "neutral",
|
||
"influence_weight": 1.0
|
||
}
|
||
|
||
|