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.
561 lines
21 KiB
Python
561 lines
21 KiB
Python
"""
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OASIS Agent Profile生成器
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将Zep图谱中的实体转换为OASIS模拟平台所需的Agent Profile格式
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"""
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import json
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import random
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from typing import Dict, Any, List, Optional
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from dataclasses import dataclass, field
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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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logger = get_logger('mirofish.oasis_profile')
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@dataclass
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class OasisAgentProfile:
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"""OASIS Agent Profile数据结构"""
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# 通用字段
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user_id: int
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user_name: str
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name: str
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bio: str
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persona: str
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# 可选字段 - Reddit风格
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karma: int = 1000
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# 可选字段 - Twitter风格
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friend_count: int = 100
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follower_count: int = 150
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statuses_count: int = 500
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# 额外人设信息
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age: Optional[int] = None
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gender: Optional[str] = None
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mbti: Optional[str] = None
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country: Optional[str] = None
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profession: Optional[str] = None
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interested_topics: List[str] = field(default_factory=list)
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# 来源实体信息
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source_entity_uuid: Optional[str] = None
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source_entity_type: Optional[str] = None
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created_at: str = field(default_factory=lambda: datetime.now().strftime("%Y-%m-%d"))
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def to_reddit_format(self) -> Dict[str, Any]:
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"""转换为Reddit平台格式"""
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profile = {
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"user_id": self.user_id,
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"user_name": self.user_name,
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"name": self.name,
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"bio": self.bio,
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"persona": self.persona,
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"karma": self.karma,
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"created_at": self.created_at,
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}
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# 添加额外人设信息（如果有）
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if self.age:
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profile["age"] = self.age
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if self.gender:
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profile["gender"] = self.gender
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if self.mbti:
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profile["mbti"] = self.mbti
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if self.country:
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profile["country"] = self.country
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if self.profession:
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profile["profession"] = self.profession
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if self.interested_topics:
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profile["interested_topics"] = self.interested_topics
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return profile
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def to_twitter_format(self) -> Dict[str, Any]:
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"""转换为Twitter平台格式"""
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profile = {
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"user_id": self.user_id,
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"user_name": self.user_name,
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"name": self.name,
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"bio": self.bio,
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"persona": self.persona,
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"friend_count": self.friend_count,
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"follower_count": self.follower_count,
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"statuses_count": self.statuses_count,
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"created_at": self.created_at,
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}
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# 添加额外人设信息
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if self.age:
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profile["age"] = self.age
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if self.gender:
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profile["gender"] = self.gender
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if self.mbti:
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profile["mbti"] = self.mbti
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if self.country:
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profile["country"] = self.country
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if self.profession:
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profile["profession"] = self.profession
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if self.interested_topics:
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profile["interested_topics"] = self.interested_topics
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return profile
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def to_dict(self) -> Dict[str, Any]:
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"""转换为完整字典格式"""
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return {
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"user_id": self.user_id,
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"user_name": self.user_name,
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"name": self.name,
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"bio": self.bio,
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"persona": self.persona,
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"karma": self.karma,
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"friend_count": self.friend_count,
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"follower_count": self.follower_count,
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"statuses_count": self.statuses_count,
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"age": self.age,
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"gender": self.gender,
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"mbti": self.mbti,
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"country": self.country,
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"profession": self.profession,
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"interested_topics": self.interested_topics,
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"source_entity_uuid": self.source_entity_uuid,
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"source_entity_type": self.source_entity_type,
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"created_at": self.created_at,
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}
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class OasisProfileGenerator:
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"""
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OASIS Profile生成器
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将Zep图谱中的实体转换为OASIS模拟所需的Agent Profile
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"""
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# MBTI类型列表
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MBTI_TYPES = [
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"INTJ", "INTP", "ENTJ", "ENTP",
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"INFJ", "INFP", "ENFJ", "ENFP",
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"ISTJ", "ISFJ", "ESTJ", "ESFJ",
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"ISTP", "ISFP", "ESTP", "ESFP"
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]
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# 常见国家列表
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COUNTRIES = [
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"China", "US", "UK", "Japan", "Germany", "France",
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"Canada", "Australia", "Brazil", "India", "South Korea"
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]
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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_profile_from_entity(
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self,
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entity: EntityNode,
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user_id: int,
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use_llm: bool = True
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) -> OasisAgentProfile:
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"""
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从Zep实体生成OASIS Agent Profile
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Args:
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entity: Zep实体节点
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user_id: 用户ID（用于OASIS）
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use_llm: 是否使用LLM生成详细人设
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Returns:
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OasisAgentProfile
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"""
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entity_type = entity.get_entity_type() or "Entity"
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# 基础信息
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name = entity.name
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user_name = self._generate_username(name)
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# 构建上下文信息
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context = self._build_entity_context(entity)
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if use_llm:
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# 使用LLM生成详细人设
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profile_data = self._generate_profile_with_llm(
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entity_name=name,
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entity_type=entity_type,
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entity_summary=entity.summary,
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entity_attributes=entity.attributes,
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context=context
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)
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else:
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# 使用规则生成基础人设
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profile_data = self._generate_profile_rule_based(
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entity_name=name,
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entity_type=entity_type,
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entity_summary=entity.summary,
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entity_attributes=entity.attributes
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)
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return OasisAgentProfile(
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user_id=user_id,
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user_name=user_name,
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name=name,
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bio=profile_data.get("bio", f"{entity_type}: {name}"),
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persona=profile_data.get("persona", entity.summary or f"A {entity_type} named {name}."),
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karma=profile_data.get("karma", random.randint(500, 5000)),
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friend_count=profile_data.get("friend_count", random.randint(50, 500)),
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follower_count=profile_data.get("follower_count", random.randint(100, 1000)),
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statuses_count=profile_data.get("statuses_count", random.randint(100, 2000)),
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age=profile_data.get("age"),
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gender=profile_data.get("gender"),
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mbti=profile_data.get("mbti"),
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country=profile_data.get("country"),
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profession=profile_data.get("profession"),
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interested_topics=profile_data.get("interested_topics", []),
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source_entity_uuid=entity.uuid,
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source_entity_type=entity_type,
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)
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def _generate_username(self, name: str) -> str:
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"""生成用户名"""
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# 移除特殊字符，转换为小写
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username = name.lower().replace(" ", "_")
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username = ''.join(c for c in username if c.isalnum() or c == '_')
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# 添加随机后缀避免重复
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suffix = random.randint(100, 999)
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return f"{username}_{suffix}"
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def _build_entity_context(self, entity: EntityNode) -> str:
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"""构建实体的上下文信息"""
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context_parts = []
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# 添加相关边信息
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if entity.related_edges:
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relationships = []
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for edge in entity.related_edges[:10]: # 最多取10条
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if edge.get("fact"):
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relationships.append(edge["fact"])
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if relationships:
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context_parts.append("Related facts:\n" + "\n".join(f"- {r}" for r in relationships))
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# 添加关联节点信息
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if entity.related_nodes:
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related_names = [n["name"] for n in entity.related_nodes[:5]]
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if related_names:
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context_parts.append(f"Related to: {', '.join(related_names)}")
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return "\n\n".join(context_parts)
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def _generate_profile_with_llm(
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self,
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entity_name: str,
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entity_type: str,
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entity_summary: str,
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entity_attributes: Dict[str, Any],
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context: str
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) -> Dict[str, Any]:
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"""使用LLM生成详细人设"""
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prompt = f"""Based on the following entity information, generate a detailed social media user profile for simulation purposes.
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Entity Information:
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- Name: {entity_name}
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- Type: {entity_type}
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- Summary: {entity_summary}
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- Attributes: {json.dumps(entity_attributes, ensure_ascii=False)}
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Context:
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{context}
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Generate a JSON object with the following fields:
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{{
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"bio": "A short bio (max 150 chars) suitable for social media",
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"persona": "A detailed persona description (2-3 sentences) describing personality, interests, and behavior patterns",
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"age": <integer between 18-65, or null if not applicable>,
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"gender": "<male/female/other, or null if not applicable>",
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"mbti": "<MBTI type like INTJ, ENFP, etc., or null>",
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"country": "<country name, or null>",
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"profession": "<profession/occupation, or null>",
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"interested_topics": ["topic1", "topic2", ...]
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}}
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Important:
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- The profile should be consistent with the entity type and context
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- Make the persona feel realistic and suitable for social media simulation
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- If the entity is an organization, institution, or non-person, adapt the profile accordingly (e.g., as an official account)
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- Return ONLY the JSON object, no additional text"""
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try:
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# 使用重试机制调用LLM API
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from ..utils.retry import RetryableAPIClient
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retry_client = RetryableAPIClient(max_retries=3, initial_delay=1.0)
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def call_llm():
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return 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": "You are a profile generator for social media simulation. Generate realistic user profiles based on entity information."},
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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
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)
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response = retry_client.call_with_retry(call_llm)
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result = json.loads(response.choices[0].message.content)
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return result
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except Exception as e:
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logger.warning(f"LLM生成人设失败（已重试）: {str(e)}, 使用规则生成")
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return self._generate_profile_rule_based(
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entity_name, entity_type, entity_summary, entity_attributes
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)
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def _generate_profile_rule_based(
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self,
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entity_name: str,
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entity_type: str,
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entity_summary: str,
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entity_attributes: Dict[str, Any]
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) -> Dict[str, Any]:
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"""使用规则生成基础人设"""
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# 根据实体类型生成不同的人设
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entity_type_lower = entity_type.lower()
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if entity_type_lower in ["student", "alumni"]:
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return {
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"bio": f"{entity_type} with interests in academics and social issues.",
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"persona": f"{entity_name} is a {entity_type.lower()} who is actively engaged in academic and social discussions. They enjoy sharing perspectives and connecting with peers.",
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"age": random.randint(18, 30),
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"gender": random.choice(["male", "female"]),
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"mbti": random.choice(self.MBTI_TYPES),
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"country": random.choice(self.COUNTRIES),
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"profession": "Student",
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"interested_topics": ["Education", "Social Issues", "Technology"],
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}
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elif entity_type_lower in ["publicfigure", "expert", "faculty"]:
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return {
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"bio": f"Expert and thought leader in their field.",
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"persona": f"{entity_name} is a recognized {entity_type.lower()} who shares insights and opinions on important matters. They are known for their expertise and influence in public discourse.",
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"age": random.randint(35, 60),
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"gender": random.choice(["male", "female"]),
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"mbti": random.choice(["ENTJ", "INTJ", "ENTP", "INTP"]),
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"country": random.choice(self.COUNTRIES),
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"profession": entity_attributes.get("occupation", "Expert"),
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"interested_topics": ["Politics", "Economics", "Culture & Society"],
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}
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elif entity_type_lower in ["mediaoutlet", "socialmediaplatform"]:
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return {
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"bio": f"Official account for {entity_name}. News and updates.",
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"persona": f"{entity_name} is a media entity that reports news and facilitates public discourse. The account shares timely updates and engages with the audience on current events.",
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"profession": "Media",
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"interested_topics": ["General News", "Current Events", "Public Affairs"],
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}
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elif entity_type_lower in ["university", "governmentagency", "ngo", "organization"]:
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return {
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"bio": f"Official account of {entity_name}.",
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"persona": f"{entity_name} is an institutional entity that communicates official positions, announcements, and engages with stakeholders on relevant matters.",
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"profession": entity_type,
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"interested_topics": ["Public Policy", "Community", "Official Announcements"],
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}
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else:
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# 默认人设
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return {
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"bio": entity_summary[:150] if entity_summary else f"{entity_type}: {entity_name}",
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"persona": entity_summary or f"{entity_name} is a {entity_type.lower()} participating in social discussions.",
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"age": random.randint(25, 50),
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"gender": random.choice(["male", "female"]),
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"mbti": random.choice(self.MBTI_TYPES),
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"country": random.choice(self.COUNTRIES),
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"profession": entity_type,
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"interested_topics": ["General", "Social Issues"],
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}
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def generate_profiles_from_entities(
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self,
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entities: List[EntityNode],
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use_llm: bool = True,
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progress_callback: Optional[callable] = None
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) -> List[OasisAgentProfile]:
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"""
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批量从实体生成Agent Profile
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Args:
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entities: 实体列表
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use_llm: 是否使用LLM生成详细人设
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progress_callback: 进度回调函数 (current, total, message)
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Returns:
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Agent Profile列表
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"""
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profiles = []
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total = len(entities)
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for idx, entity in enumerate(entities):
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if progress_callback:
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progress_callback(idx + 1, total, f"生成 {entity.name} 的人设...")
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try:
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profile = self.generate_profile_from_entity(
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entity=entity,
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user_id=idx,
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use_llm=use_llm
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)
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profiles.append(profile)
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except Exception as e:
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logger.error(f"生成实体 {entity.name} 的人设失败: {str(e)}")
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# 创建一个基础profile
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profiles.append(OasisAgentProfile(
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user_id=idx,
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user_name=self._generate_username(entity.name),
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name=entity.name,
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bio=f"{entity.get_entity_type() or 'Entity'}: {entity.name}",
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persona=entity.summary or f"A participant in social discussions.",
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source_entity_uuid=entity.uuid,
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source_entity_type=entity.get_entity_type(),
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))
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return profiles
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def save_profiles(
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self,
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profiles: List[OasisAgentProfile],
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file_path: str,
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platform: str = "reddit"
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):
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"""
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保存Profile到文件（根据平台选择正确格式）
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OASIS平台格式要求：
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- Twitter: CSV格式
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- Reddit: JSON格式
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Args:
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profiles: Profile列表
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file_path: 文件路径
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platform: 平台类型 ("reddit" 或 "twitter")
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"""
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if platform == "twitter":
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self._save_twitter_csv(profiles, file_path)
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else:
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self._save_reddit_json(profiles, file_path)
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def _save_twitter_csv(self, profiles: List[OasisAgentProfile], file_path: str):
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"""
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保存Twitter Profile为CSV格式
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OASIS Twitter要求的CSV字段：
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user_id, user_name, name, bio, friend_count, follower_count, statuses_count, created_at
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"""
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import csv
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# 确保文件扩展名是.csv
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if not file_path.endswith('.csv'):
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file_path = file_path.replace('.json', '.csv')
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with open(file_path, 'w', newline='', encoding='utf-8') as f:
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writer = csv.writer(f)
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# 写入表头
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headers = ['user_id', 'user_name', 'name', 'bio', 'friend_count',
|
||
'follower_count', 'statuses_count', 'created_at']
|
||
writer.writerow(headers)
|
||
|
||
# 写入数据行
|
||
for profile in profiles:
|
||
# bio需要处理换行符和逗号
|
||
bio = profile.bio.replace('\n', ' ').replace('\r', ' ')
|
||
row = [
|
||
profile.user_id,
|
||
profile.user_name,
|
||
profile.name,
|
||
bio,
|
||
profile.friend_count,
|
||
profile.follower_count,
|
||
profile.statuses_count,
|
||
profile.created_at
|
||
]
|
||
writer.writerow(row)
|
||
|
||
logger.info(f"已保存 {len(profiles)} 个Twitter Profile到 {file_path} (CSV格式)")
|
||
|
||
def _save_reddit_json(self, profiles: List[OasisAgentProfile], file_path: str):
|
||
"""
|
||
保存Reddit Profile为JSON格式
|
||
|
||
OASIS Reddit支持两种JSON格式：
|
||
1. 基础格式: user_id, user_name, name, bio, karma, created_at
|
||
2. 详细格式: realname, username, bio, persona, age, gender, mbti, country, profession, interested_topics
|
||
|
||
我们使用详细格式，与用户示例数据(36个简单人设.json)保持一致
|
||
"""
|
||
data = []
|
||
for profile in profiles:
|
||
# 使用详细格式（与用户示例兼容）
|
||
item = {
|
||
"realname": profile.name,
|
||
"username": profile.user_name,
|
||
"bio": profile.bio[:150] if profile.bio else "", # OASIS bio限制150字符
|
||
"persona": profile.persona or f"{profile.name} is a participant in social discussions.",
|
||
}
|
||
|
||
# 添加人设详情字段
|
||
if profile.age:
|
||
item["age"] = profile.age
|
||
if profile.gender:
|
||
item["gender"] = profile.gender
|
||
if profile.mbti:
|
||
item["mbti"] = profile.mbti
|
||
if profile.country:
|
||
item["country"] = profile.country
|
||
if profile.profession:
|
||
item["profession"] = profile.profession
|
||
if profile.interested_topics:
|
||
item["interested_topics"] = profile.interested_topics
|
||
|
||
data.append(item)
|
||
|
||
with open(file_path, 'w', encoding='utf-8') as f:
|
||
json.dump(data, f, ensure_ascii=False, indent=2)
|
||
|
||
logger.info(f"已保存 {len(profiles)} 个Reddit Profile到 {file_path} (JSON详细格式)")
|
||
|
||
# 保留旧方法名作为别名，保持向后兼容
|
||
def save_profiles_to_json(
|
||
self,
|
||
profiles: List[OasisAgentProfile],
|
||
file_path: str,
|
||
platform: str = "reddit"
|
||
):
|
||
"""[已废弃] 请使用 save_profiles() 方法"""
|
||
logger.warning("save_profiles_to_json已废弃，请使用save_profiles方法")
|
||
self.save_profiles(profiles, file_path, platform)
|
||
|