1185 lines
49 KiB
Python
1185 lines
49 KiB
Python
"""
|
||
OASIS Agent Profile generator.
|
||
|
||
Converts entities from the Zep graph into the Agent Profile format required by
|
||
the OASIS simulation platform.
|
||
|
||
Improvements:
|
||
1. Call Zep retrieval to further enrich node information.
|
||
2. Optimized prompts that produce highly detailed personas.
|
||
3. Distinguishes individual entities from abstract group entities.
|
||
"""
|
||
|
||
import json
|
||
import random
|
||
import time
|
||
from typing import Dict, Any, List, Optional
|
||
from dataclasses import dataclass, field
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||
from datetime import datetime
|
||
|
||
from openai import OpenAI
|
||
from .graphiti_adapter import GraphitiAdapter
|
||
|
||
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, get_locale, set_locale, t
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||
from .zep_entity_reader import EntityNode, ZepEntityReader
|
||
|
||
logger = get_logger('mirofish.oasis_profile')
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||
|
||
|
||
@dataclass
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class OasisAgentProfile:
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"""OASIS Agent Profile data structure."""
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||
# Common fields
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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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||
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||
# Optional fields - Reddit style
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karma: int = 1000
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# Optional fields - Twitter style
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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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||
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||
# Additional persona information
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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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||
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||
# Source entity information
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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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||
"""Convert to Reddit platform format."""
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profile = {
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||
"user_id": self.user_id,
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"username": self.user_name, # OASIS 库要求字段名为 username（无下划线）
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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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||
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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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"""Convert to Twitter platform format."""
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profile = {
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"user_id": self.user_id,
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"username": self.user_name, # OASIS 库要求字段名为 username（无下划线）
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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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||
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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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||
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def to_dict(self) -> Dict[str, Any]:
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||
"""Convert to a full dictionary representation."""
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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:
|
||
"""OASIS Profile generator.
|
||
|
||
Converts entities from the Zep graph into the Agent Profiles required by
|
||
the OASIS simulation.
|
||
|
||
Highlights:
|
||
1. Uses Zep graph retrieval to gather richer context.
|
||
2. Produces highly detailed personas (basic info, career history, traits,
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||
social-media behavior, etc.).
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3. Distinguishes individual entities from group/institution entities.
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||
"""
|
||
|
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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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||
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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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# Individual entity types — generate a concrete persona for each.
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INDIVIDUAL_ENTITY_TYPES = [
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"student", "alumni", "professor", "person", "publicfigure",
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"expert", "faculty", "official", "journalist", "activist"
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]
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||
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# Group / institution entity types — generate a representative-account persona.
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GROUP_ENTITY_TYPES = [
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"university", "governmentagency", "organization", "ngo",
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||
"mediaoutlet", "company", "institution", "group", "community"
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||
]
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||
|
||
def __init__(
|
||
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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||
zep_api_key: Optional[str] = None,
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||
graph_id: 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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||
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self.zep_client = GraphitiAdapter()
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||
self.graph_id = graph_id
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||
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||
def generate_profile_from_entity(
|
||
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:
|
||
"""Generate an OASIS Agent Profile from a Zep entity.
|
||
|
||
Args:
|
||
entity: The Zep entity node.
|
||
user_id: The OASIS user id to assign.
|
||
use_llm: Whether to use the LLM to generate a detailed persona.
|
||
|
||
Returns:
|
||
OasisAgentProfile
|
||
"""
|
||
entity_type = entity.get_entity_type() or "Entity"
|
||
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||
name = entity.name
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||
user_name = self._generate_username(name)
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||
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||
context = self._build_entity_context(entity)
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||
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||
if use_llm:
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||
profile_data = self._generate_profile_with_llm(
|
||
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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||
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,
|
||
entity_attributes=entity.attributes
|
||
)
|
||
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||
return OasisAgentProfile(
|
||
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}"),
|
||
persona=profile_data.get("persona", entity.summary or f"A {entity_type} named {name}."),
|
||
karma=profile_data.get("karma", random.randint(500, 5000)),
|
||
friend_count=profile_data.get("friend_count", random.randint(50, 500)),
|
||
follower_count=profile_data.get("follower_count", random.randint(100, 1000)),
|
||
statuses_count=profile_data.get("statuses_count", random.randint(100, 2000)),
|
||
age=profile_data.get("age"),
|
||
gender=profile_data.get("gender"),
|
||
mbti=profile_data.get("mbti"),
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||
country=profile_data.get("country"),
|
||
profession=profile_data.get("profession"),
|
||
interested_topics=profile_data.get("interested_topics", []),
|
||
source_entity_uuid=entity.uuid,
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||
source_entity_type=entity_type,
|
||
)
|
||
|
||
def _generate_username(self, name: str) -> str:
|
||
"""Generate a username from an entity name."""
|
||
# Strip special characters and lowercase the name.
|
||
username = name.lower().replace(" ", "_")
|
||
username = ''.join(c for c in username if c.isalnum() or c == '_')
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||
|
||
# Append a random numeric suffix to avoid collisions.
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||
suffix = random.randint(100, 999)
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||
return f"{username}_{suffix}"
|
||
|
||
def _search_zep_for_entity(self, entity: EntityNode) -> Dict[str, Any]:
|
||
"""Use Zep hybrid graph search to gather rich context for an entity.
|
||
|
||
Zep does not expose a built-in hybrid search endpoint, so we search
|
||
edges and nodes separately and merge the results. The two searches
|
||
run in parallel for throughput.
|
||
|
||
Args:
|
||
entity: The entity node to search around.
|
||
|
||
Returns:
|
||
A dict with keys ``facts``, ``node_summaries`` and ``context``.
|
||
"""
|
||
import concurrent.futures
|
||
|
||
if not self.zep_client:
|
||
return {"facts": [], "node_summaries": [], "context": ""}
|
||
|
||
entity_name = entity.name
|
||
|
||
results = {
|
||
"facts": [],
|
||
"node_summaries": [],
|
||
"context": ""
|
||
}
|
||
|
||
# A graph_id is required for any retrieval.
|
||
if not self.graph_id:
|
||
logger.debug(t("log.profile_generator.m001"))
|
||
return results
|
||
|
||
comprehensive_query = t('progress.zepSearchQuery', name=entity_name)
|
||
|
||
def search_edges():
|
||
"""Search edges (facts / relationships) with retries."""
|
||
max_retries = 3
|
||
last_exception = None
|
||
delay = 2.0
|
||
|
||
for attempt in range(max_retries):
|
||
try:
|
||
return self.zep_client.graph.search(
|
||
query=comprehensive_query,
|
||
graph_id=self.graph_id,
|
||
limit=30,
|
||
scope="edges",
|
||
)
|
||
except Exception as e:
|
||
last_exception = e
|
||
if attempt < max_retries - 1:
|
||
logger.debug(t("log.profile_generator.m002", attempt=attempt + 1, str=str(e)[:80]))
|
||
time.sleep(delay)
|
||
delay *= 2
|
||
else:
|
||
logger.debug(t("log.profile_generator.m003", max_retries=max_retries, e=e))
|
||
return None
|
||
|
||
def search_nodes():
|
||
"""Search nodes (entity summaries) with retries."""
|
||
max_retries = 3
|
||
last_exception = None
|
||
delay = 2.0
|
||
|
||
for attempt in range(max_retries):
|
||
try:
|
||
return self.zep_client.graph.search(
|
||
query=comprehensive_query,
|
||
graph_id=self.graph_id,
|
||
limit=20,
|
||
scope="nodes",
|
||
)
|
||
except Exception as e:
|
||
last_exception = e
|
||
if attempt < max_retries - 1:
|
||
logger.debug(t("log.profile_generator.m004", attempt=attempt + 1, str=str(e)[:80]))
|
||
time.sleep(delay)
|
||
delay *= 2
|
||
else:
|
||
logger.debug(t("log.profile_generator.m005", max_retries=max_retries, e=e))
|
||
return None
|
||
|
||
try:
|
||
# Run edge and node searches in parallel.
|
||
with concurrent.futures.ThreadPoolExecutor(max_workers=2) as executor:
|
||
edge_future = executor.submit(search_edges)
|
||
node_future = executor.submit(search_nodes)
|
||
|
||
edge_result = edge_future.result(timeout=30)
|
||
node_result = node_future.result(timeout=30)
|
||
|
||
# Process edge-search results.
|
||
all_facts = set()
|
||
if edge_result and hasattr(edge_result, 'edges') and edge_result.edges:
|
||
for edge in edge_result.edges:
|
||
if hasattr(edge, 'fact') and edge.fact:
|
||
all_facts.add(edge.fact)
|
||
results["facts"] = list(all_facts)
|
||
|
||
# Process node-search results.
|
||
all_summaries = set()
|
||
if node_result and hasattr(node_result, 'nodes') and node_result.nodes:
|
||
for node in node_result.nodes:
|
||
if hasattr(node, 'summary') and node.summary:
|
||
all_summaries.add(node.summary)
|
||
if hasattr(node, 'name') and node.name and node.name != entity_name:
|
||
all_summaries.add(f"Related entity: {node.name}")
|
||
results["node_summaries"] = list(all_summaries)
|
||
|
||
# Assemble the combined context block.
|
||
context_parts = []
|
||
if results["facts"]:
|
||
context_parts.append("Facts:\n" + "\n".join(f"- {f}" for f in results["facts"][:20]))
|
||
if results["node_summaries"]:
|
||
context_parts.append("Related entities:\n" + "\n".join(f"- {s}" for s in results["node_summaries"][:10]))
|
||
results["context"] = "\n\n".join(context_parts)
|
||
|
||
logger.info(t("log.profile_generator.m006", entity_name=entity_name, len=len(results['facts']), len_2=len(results['node_summaries'])))
|
||
|
||
except concurrent.futures.TimeoutError:
|
||
logger.warning(t("log.profile_generator.m007", entity_name=entity_name))
|
||
except Exception as e:
|
||
logger.warning(t("log.profile_generator.m008", entity_name=entity_name, e=e))
|
||
|
||
return results
|
||
|
||
def _build_entity_context(self, entity: EntityNode) -> str:
|
||
"""Assemble the full context block for an entity.
|
||
|
||
Includes:
|
||
1. The entity's own edge information (facts).
|
||
2. Detailed information about related nodes.
|
||
3. Additional context retrieved from Zep hybrid search.
|
||
"""
|
||
context_parts = []
|
||
|
||
# 1. Entity attributes.
|
||
if entity.attributes:
|
||
attrs = []
|
||
for key, value in entity.attributes.items():
|
||
if value and str(value).strip():
|
||
attrs.append(f"- {key}: {value}")
|
||
if attrs:
|
||
context_parts.append("### Entity attributes\n" + "\n".join(attrs))
|
||
|
||
# 2. Related edges (facts / relationships).
|
||
existing_facts = set()
|
||
if entity.related_edges:
|
||
relationships = []
|
||
for edge in entity.related_edges: # No cap on count.
|
||
fact = edge.get("fact", "")
|
||
edge_name = edge.get("edge_name", "")
|
||
direction = edge.get("direction", "")
|
||
|
||
if fact:
|
||
relationships.append(f"- {fact}")
|
||
existing_facts.add(fact)
|
||
elif edge_name:
|
||
if direction == "outgoing":
|
||
relationships.append(f"- {entity.name} --[{edge_name}]--> (related entity)")
|
||
else:
|
||
relationships.append(f"- (related entity) --[{edge_name}]--> {entity.name}")
|
||
|
||
if relationships:
|
||
context_parts.append("### Related facts and relationships\n" + "\n".join(relationships))
|
||
|
||
# 3. Detailed information for related nodes.
|
||
if entity.related_nodes:
|
||
related_info = []
|
||
for node in entity.related_nodes: # No cap on count.
|
||
node_name = node.get("name", "")
|
||
node_labels = node.get("labels", [])
|
||
node_summary = node.get("summary", "")
|
||
|
||
# Drop the default labels added by the graph store.
|
||
custom_labels = [l for l in node_labels if l not in ["Entity", "Node"]]
|
||
label_str = f" ({', '.join(custom_labels)})" if custom_labels else ""
|
||
|
||
if node_summary:
|
||
related_info.append(f"- **{node_name}**{label_str}: {node_summary}")
|
||
else:
|
||
related_info.append(f"- **{node_name}**{label_str}")
|
||
|
||
if related_info:
|
||
context_parts.append("### Related entity information\n" + "\n".join(related_info))
|
||
|
||
# 4. Augment with Zep hybrid retrieval.
|
||
zep_results = self._search_zep_for_entity(entity)
|
||
|
||
if zep_results.get("facts"):
|
||
# Deduplicate against already-known facts.
|
||
new_facts = [f for f in zep_results["facts"] if f not in existing_facts]
|
||
if new_facts:
|
||
context_parts.append("### Facts retrieved from the graph\n" + "\n".join(f"- {f}" for f in new_facts[:15]))
|
||
|
||
if zep_results.get("node_summaries"):
|
||
context_parts.append("### Related nodes retrieved from the graph\n" + "\n".join(f"- {s}" for s in zep_results["node_summaries"][:10]))
|
||
|
||
return "\n\n".join(context_parts)
|
||
|
||
def _is_individual_entity(self, entity_type: str) -> bool:
|
||
"""Return True if the entity type represents an individual."""
|
||
return entity_type.lower() in self.INDIVIDUAL_ENTITY_TYPES
|
||
|
||
def _is_group_entity(self, entity_type: str) -> bool:
|
||
"""Return True if the entity type represents a group or institution."""
|
||
return entity_type.lower() in self.GROUP_ENTITY_TYPES
|
||
|
||
def _generate_profile_with_llm(
|
||
self,
|
||
entity_name: str,
|
||
entity_type: str,
|
||
entity_summary: str,
|
||
entity_attributes: Dict[str, Any],
|
||
context: str
|
||
) -> Dict[str, Any]:
|
||
"""Generate a highly detailed persona using the LLM.
|
||
|
||
Branches on entity type:
|
||
- Individual entities: produces a concrete persona for a person.
|
||
- Group / institution entities: produces a representative-account persona.
|
||
"""
|
||
|
||
is_individual = self._is_individual_entity(entity_type)
|
||
|
||
if is_individual:
|
||
prompt = self._build_individual_persona_prompt(
|
||
entity_name, entity_type, entity_summary, entity_attributes, context
|
||
)
|
||
else:
|
||
prompt = self._build_group_persona_prompt(
|
||
entity_name, entity_type, entity_summary, entity_attributes, context
|
||
)
|
||
|
||
# Retry generation up to max_attempts times.
|
||
max_attempts = 3
|
||
last_error = None
|
||
|
||
for attempt in range(max_attempts):
|
||
try:
|
||
response = self.client.chat.completions.create(
|
||
model=self.model_name,
|
||
messages=[
|
||
{"role": "system", "content": self._get_system_prompt(is_individual)},
|
||
{"role": "user", "content": prompt}
|
||
],
|
||
response_format={"type": "json_object"},
|
||
temperature=0.7 - (attempt * 0.1) # Lower the temperature on each retry.
|
||
# No max_tokens cap so the LLM can produce a full persona.
|
||
)
|
||
|
||
content = response.choices[0].message.content
|
||
|
||
# Detect truncation (finish_reason other than 'stop').
|
||
finish_reason = response.choices[0].finish_reason
|
||
if finish_reason == 'length':
|
||
logger.warning(t("log.profile_generator.m009", attempt=attempt + 1))
|
||
content = self._fix_truncated_json(content)
|
||
|
||
# Parse the JSON payload.
|
||
try:
|
||
result = json.loads(content)
|
||
|
||
# Backfill required fields when missing.
|
||
if "bio" not in result or not result["bio"]:
|
||
result["bio"] = entity_summary[:200] if entity_summary else f"{entity_type}: {entity_name}"
|
||
if "persona" not in result or not result["persona"]:
|
||
result["persona"] = entity_summary or f"{entity_name} is a {entity_type}."
|
||
|
||
return result
|
||
|
||
except json.JSONDecodeError as je:
|
||
logger.warning(t("log.profile_generator.m010", attempt=attempt + 1, str=str(je)[:80]))
|
||
|
||
# Attempt to repair the JSON.
|
||
result = self._try_fix_json(content, entity_name, entity_type, entity_summary)
|
||
if result.get("_fixed"):
|
||
del result["_fixed"]
|
||
return result
|
||
|
||
last_error = je
|
||
|
||
except Exception as e:
|
||
logger.warning(t("log.profile_generator.m011", attempt=attempt + 1, str=str(e)[:80]))
|
||
last_error = e
|
||
import time
|
||
time.sleep(1 * (attempt + 1)) # Exponential backoff.
|
||
|
||
logger.warning(t("log.profile_generator.m012", max_attempts=max_attempts, last_error=last_error))
|
||
return self._generate_profile_rule_based(
|
||
entity_name, entity_type, entity_summary, entity_attributes
|
||
)
|
||
|
||
def _fix_truncated_json(self, content: str) -> str:
|
||
"""Repair JSON output truncated by a max_tokens limit."""
|
||
import re
|
||
|
||
# Trim whitespace before closing the structure.
|
||
content = content.strip()
|
||
|
||
# Count unbalanced brackets and braces.
|
||
open_braces = content.count('{') - content.count('}')
|
||
open_brackets = content.count('[') - content.count(']')
|
||
|
||
# Heuristic: if the last char is not a quote, comma, or closing bracket,
|
||
# the trailing string value was likely truncated mid-token.
|
||
if content and content[-1] not in '",}]':
|
||
# Close the dangling string.
|
||
content += '"'
|
||
|
||
# Close any open brackets and braces.
|
||
content += ']' * open_brackets
|
||
content += '}' * open_braces
|
||
|
||
return content
|
||
|
||
def _try_fix_json(self, content: str, entity_name: str, entity_type: str, entity_summary: str = "") -> Dict[str, Any]:
|
||
"""Best-effort repair of damaged JSON output."""
|
||
import re
|
||
|
||
# 1. Repair truncation first.
|
||
content = self._fix_truncated_json(content)
|
||
|
||
# 2. Extract the JSON object span.
|
||
json_match = re.search(r'\{[\s\S]*\}', content)
|
||
if json_match:
|
||
json_str = json_match.group()
|
||
|
||
# 3. Fix newlines inside string values.
|
||
def fix_string_newlines(match):
|
||
s = match.group(0)
|
||
# Replace literal newlines inside string values with spaces.
|
||
s = s.replace('\n', ' ').replace('\r', ' ')
|
||
# Collapse runs of whitespace.
|
||
s = re.sub(r'\s+', ' ', s)
|
||
return s
|
||
|
||
# Match JSON string values.
|
||
json_str = re.sub(r'"[^"\\]*(?:\\.[^"\\]*)*"', fix_string_newlines, json_str)
|
||
|
||
# 4. Try to parse.
|
||
try:
|
||
result = json.loads(json_str)
|
||
result["_fixed"] = True
|
||
return result
|
||
except json.JSONDecodeError as e:
|
||
# 5. Fall back to a more aggressive repair pass.
|
||
try:
|
||
# Strip control characters.
|
||
json_str = re.sub(r'[\x00-\x1f\x7f-\x9f]', ' ', json_str)
|
||
# Collapse all consecutive whitespace.
|
||
json_str = re.sub(r'\s+', ' ', json_str)
|
||
result = json.loads(json_str)
|
||
result["_fixed"] = True
|
||
return result
|
||
except:
|
||
pass
|
||
|
||
# 6. Last resort: scrape partial fields out of the content.
|
||
bio_match = re.search(r'"bio"\s*:\s*"([^"]*)"', content)
|
||
persona_match = re.search(r'"persona"\s*:\s*"([^"]*)', content) # May be truncated.
|
||
|
||
bio = bio_match.group(1) if bio_match else (entity_summary[:200] if entity_summary else f"{entity_type}: {entity_name}")
|
||
persona = persona_match.group(1) if persona_match else (entity_summary or f"{entity_name} is a {entity_type}.")
|
||
|
||
# If we recovered something meaningful, mark the result as fixed.
|
||
if bio_match or persona_match:
|
||
logger.info(t("log.profile_generator.m013"))
|
||
return {
|
||
"bio": bio,
|
||
"persona": persona,
|
||
"_fixed": True
|
||
}
|
||
|
||
# 7. Total failure: return a minimal fallback structure.
|
||
logger.warning(t("log.profile_generator.m014"))
|
||
return {
|
||
"bio": entity_summary[:200] if entity_summary else f"{entity_type}: {entity_name}",
|
||
"persona": entity_summary or f"{entity_name} is a {entity_type}."
|
||
}
|
||
|
||
def _get_system_prompt(self, is_individual: bool) -> str:
|
||
"""Return the system prompt for persona generation."""
|
||
base_prompt = "You are an expert at generating social-media user personas. Produce detailed, realistic personas for opinion-simulation, faithfully grounded in the supplied real-world context. You MUST return valid JSON; no string value may contain unescaped newline characters."
|
||
return f"{base_prompt}\n\n{get_language_instruction()}"
|
||
|
||
def _build_individual_persona_prompt(
|
||
self,
|
||
entity_name: str,
|
||
entity_type: str,
|
||
entity_summary: str,
|
||
entity_attributes: Dict[str, Any],
|
||
context: str
|
||
) -> str:
|
||
"""Build the detailed persona prompt for an individual entity."""
|
||
|
||
attrs_str = json.dumps(entity_attributes, ensure_ascii=False) if entity_attributes else "None"
|
||
context_str = context[:3000] if context else "No additional context"
|
||
|
||
return f"""Generate a detailed social-media user persona for an entity, faithfully grounded in the supplied real-world context.
|
||
|
||
|
||
Entity name: {entity_name}
|
||
Entity type: {entity_type}
|
||
Entity summary: {entity_summary}
|
||
Entity attributes: {attrs_str}
|
||
|
||
Context:
|
||
{context_str}
|
||
|
||
Produce a JSON object with the following fields:
|
||
|
||
1. bio: ~200-character social-media bio.
|
||
2. persona: detailed persona description as a single coherent ~2000-character plain-text passage covering:
|
||
- basic info (age, profession, educational background, location)
|
||
- background (notable experiences, link to the focal event, social relationships)
|
||
- personality (MBTI type, core traits, emotional expression style)
|
||
- social-media behaviour (posting frequency, content preferences, interaction style, voice)
|
||
- stance and opinions (attitude toward the topic, content likely to provoke or move them)
|
||
- distinctive traits (catchphrases, unusual experiences, hobbies)
|
||
- personal memories (a key part of the persona; describe this individual's link to the focal event and any actions / reactions they have already taken in connection with it)
|
||
3. age: an integer.
|
||
4. gender: must be the literal English token "male" or "female".
|
||
5. mbti: MBTI type (e.g. INTJ, ENFP).
|
||
6. country: free-form country name.
|
||
7. profession: free-form occupation.
|
||
8. interested_topics: array of topic strings.
|
||
|
||
Important:
|
||
- All field values must be strings or numbers; do not include newline characters in any string value.
|
||
- persona must be a single coherent prose passage.
|
||
- {get_language_instruction()} (the gender field must remain English: male/female.)
|
||
- The content must remain consistent with the supplied entity information.
|
||
- age must be a valid integer; gender must be exactly "male" or "female".
|
||
"""
|
||
|
||
def _build_group_persona_prompt(
|
||
self,
|
||
entity_name: str,
|
||
entity_type: str,
|
||
entity_summary: str,
|
||
entity_attributes: Dict[str, Any],
|
||
context: str
|
||
) -> str:
|
||
"""Build the detailed persona prompt for a group or institution entity."""
|
||
|
||
attrs_str = json.dumps(entity_attributes, ensure_ascii=False) if entity_attributes else "None"
|
||
context_str = context[:3000] if context else "No additional context"
|
||
|
||
return f"""Generate a detailed social-media account profile for an institutional or group entity, faithfully grounded in the supplied real-world context.
|
||
|
||
|
||
Entity name: {entity_name}
|
||
Entity type: {entity_type}
|
||
Entity summary: {entity_summary}
|
||
Entity attributes: {attrs_str}
|
||
|
||
Context:
|
||
{context_str}
|
||
|
||
Produce a JSON object with the following fields:
|
||
|
||
1. bio: ~200-character official-account bio, polished and professional.
|
||
2. persona: detailed account profile as a single coherent ~2000-character plain-text passage covering:
|
||
- institution basics (formal name, type of institution, founding background, primary functions)
|
||
- account positioning (account type, target audience, core purpose)
|
||
- voice (linguistic style, common expressions, taboo topics)
|
||
- content patterns (content types, posting frequency, active hours)
|
||
- stance (official position on the focal topic, how disputes are handled)
|
||
- special notes (the group profile it represents, operational habits)
|
||
- institutional memory (a key part of the persona; describe this institution's link to the focal event and any actions / reactions it has already taken in connection with it)
|
||
3. age: must be the integer 30 (a virtual age used for institutional accounts).
|
||
4. gender: must be the literal English token "other" (institutional accounts use "other" to indicate non-individual).
|
||
5. mbti: MBTI type used to describe the account's voice (e.g. ISTJ for a rigorous, conservative tone).
|
||
6. country: free-form country name.
|
||
7. profession: free-form description of the institution's role.
|
||
8. interested_topics: array of focus areas.
|
||
|
||
Important:
|
||
- All field values must be strings or numbers; null values are not allowed.
|
||
- persona must be a single coherent prose passage; do not include newline characters in any string value.
|
||
- {get_language_instruction()} (the gender field must remain English: "other".)
|
||
- age must be the integer 30; gender must be exactly the string "other".
|
||
- The institutional account's voice must match its identity."""
|
||
|
||
def _generate_profile_rule_based(
|
||
self,
|
||
entity_name: str,
|
||
entity_type: str,
|
||
entity_summary: str,
|
||
entity_attributes: Dict[str, Any]
|
||
) -> Dict[str, Any]:
|
||
"""Rule-based fallback that generates a basic persona."""
|
||
|
||
# Branch on entity type to pick a persona shape.
|
||
entity_type_lower = entity_type.lower()
|
||
|
||
if entity_type_lower in ["student", "alumni"]:
|
||
return {
|
||
"bio": f"{entity_type} with interests in academics and social issues.",
|
||
"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.",
|
||
"age": random.randint(18, 30),
|
||
"gender": random.choice(["male", "female"]),
|
||
"mbti": random.choice(self.MBTI_TYPES),
|
||
"country": random.choice(self.COUNTRIES),
|
||
"profession": "Student",
|
||
"interested_topics": ["Education", "Social Issues", "Technology"],
|
||
}
|
||
|
||
elif entity_type_lower in ["publicfigure", "expert", "faculty"]:
|
||
return {
|
||
"bio": f"Expert and thought leader in their field.",
|
||
"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.",
|
||
"age": random.randint(35, 60),
|
||
"gender": random.choice(["male", "female"]),
|
||
"mbti": random.choice(["ENTJ", "INTJ", "ENTP", "INTP"]),
|
||
"country": random.choice(self.COUNTRIES),
|
||
"profession": entity_attributes.get("occupation", "Expert"),
|
||
"interested_topics": ["Politics", "Economics", "Culture & Society"],
|
||
}
|
||
|
||
elif entity_type_lower in ["mediaoutlet", "socialmediaplatform"]:
|
||
return {
|
||
"bio": f"Official account for {entity_name}. News and updates.",
|
||
"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.",
|
||
"age": 30, # 机构虚拟年龄
|
||
"gender": "other", # 机构使用other
|
||
"mbti": "ISTJ", # 机构风格：严谨保守
|
||
"country": "中国",
|
||
"profession": "Media",
|
||
"interested_topics": ["General News", "Current Events", "Public Affairs"],
|
||
}
|
||
|
||
elif entity_type_lower in ["university", "governmentagency", "ngo", "organization"]:
|
||
return {
|
||
"bio": f"Official account of {entity_name}.",
|
||
"persona": f"{entity_name} is an institutional entity that communicates official positions, announcements, and engages with stakeholders on relevant matters.",
|
||
"age": 30, # 机构虚拟年龄
|
||
"gender": "other", # 机构使用other
|
||
"mbti": "ISTJ", # 机构风格：严谨保守
|
||
"country": "中国",
|
||
"profession": entity_type,
|
||
"interested_topics": ["Public Policy", "Community", "Official Announcements"],
|
||
}
|
||
|
||
else:
|
||
# Default persona for unrecognised entity types.
|
||
return {
|
||
"bio": entity_summary[:150] if entity_summary else f"{entity_type}: {entity_name}",
|
||
"persona": entity_summary or f"{entity_name} is a {entity_type.lower()} participating in social discussions.",
|
||
"age": random.randint(25, 50),
|
||
"gender": random.choice(["male", "female"]),
|
||
"mbti": random.choice(self.MBTI_TYPES),
|
||
"country": random.choice(self.COUNTRIES),
|
||
"profession": entity_type,
|
||
"interested_topics": ["General", "Social Issues"],
|
||
}
|
||
|
||
def set_graph_id(self, graph_id: str):
|
||
"""Set the graph id used for Zep retrieval."""
|
||
self.graph_id = graph_id
|
||
|
||
def generate_profiles_from_entities(
|
||
self,
|
||
entities: List[EntityNode],
|
||
use_llm: bool = True,
|
||
progress_callback: Optional[callable] = None,
|
||
graph_id: Optional[str] = None,
|
||
parallel_count: int = 5,
|
||
realtime_output_path: Optional[str] = None,
|
||
output_platform: str = "reddit"
|
||
) -> List[OasisAgentProfile]:
|
||
"""Batch-generate Agent Profiles from entities (in parallel).
|
||
|
||
Args:
|
||
entities: The entities to convert.
|
||
use_llm: Whether to use the LLM to generate detailed personas.
|
||
progress_callback: Progress callback ``(current, total, message)``.
|
||
graph_id: Graph id used for Zep retrieval to gather richer context.
|
||
parallel_count: Number of profiles to generate concurrently (default 5).
|
||
realtime_output_path: If set, profiles are flushed to this path after
|
||
each successful generation.
|
||
output_platform: Output platform format, ``"reddit"`` or ``"twitter"``.
|
||
|
||
Returns:
|
||
The generated list of Agent Profiles.
|
||
"""
|
||
import concurrent.futures
|
||
from threading import Lock
|
||
|
||
# Set the graph id used for Zep retrieval.
|
||
if graph_id:
|
||
self.graph_id = graph_id
|
||
|
||
total = len(entities)
|
||
profiles = [None] * total # Preallocate to keep insertion order.
|
||
completed_count = [0] # List wrapper so closures can mutate the count.
|
||
lock = Lock()
|
||
|
||
def save_profiles_realtime():
|
||
"""Flush the profiles generated so far to ``realtime_output_path``."""
|
||
if not realtime_output_path:
|
||
return
|
||
|
||
with lock:
|
||
existing_profiles = [p for p in profiles if p is not None]
|
||
if not existing_profiles:
|
||
return
|
||
|
||
try:
|
||
if output_platform == "reddit":
|
||
# Reddit JSON format.
|
||
profiles_data = [p.to_reddit_format() for p in existing_profiles]
|
||
with open(realtime_output_path, 'w', encoding='utf-8') as f:
|
||
json.dump(profiles_data, f, ensure_ascii=False, indent=2)
|
||
else:
|
||
# Twitter CSV format.
|
||
import csv
|
||
profiles_data = [p.to_twitter_format() for p in existing_profiles]
|
||
if profiles_data:
|
||
fieldnames = list(profiles_data[0].keys())
|
||
with open(realtime_output_path, 'w', encoding='utf-8', newline='') as f:
|
||
writer = csv.DictWriter(f, fieldnames=fieldnames)
|
||
writer.writeheader()
|
||
writer.writerows(profiles_data)
|
||
except Exception as e:
|
||
logger.warning(t("log.profile_generator.m015", e=e))
|
||
|
||
# Capture locale before spawning thread pool workers
|
||
current_locale = get_locale()
|
||
|
||
def generate_single_profile(idx: int, entity: EntityNode) -> tuple:
|
||
"""Worker function that generates a single profile."""
|
||
set_locale(current_locale)
|
||
entity_type = entity.get_entity_type() or "Entity"
|
||
|
||
try:
|
||
profile = self.generate_profile_from_entity(
|
||
entity=entity,
|
||
user_id=idx,
|
||
use_llm=use_llm
|
||
)
|
||
|
||
# Stream the generated persona to the console and log.
|
||
self._print_generated_profile(entity.name, entity_type, profile)
|
||
|
||
return idx, profile, None
|
||
|
||
except Exception as e:
|
||
logger.error(t("log.profile_generator.m016", entity=entity.name, str=str(e)))
|
||
# Build a minimal fallback profile.
|
||
fallback_profile = OasisAgentProfile(
|
||
user_id=idx,
|
||
user_name=self._generate_username(entity.name),
|
||
name=entity.name,
|
||
bio=f"{entity_type}: {entity.name}",
|
||
persona=entity.summary or f"A participant in social discussions.",
|
||
source_entity_uuid=entity.uuid,
|
||
source_entity_type=entity_type,
|
||
)
|
||
return idx, fallback_profile, str(e)
|
||
|
||
logger.info(t("log.profile_generator.m017", total=total, parallel_count=parallel_count))
|
||
print(f"\n{'='*60}")
|
||
print(t("log.profile_generator.m024", total=total, parallel_count=parallel_count))
|
||
print(f"{'='*60}\n")
|
||
|
||
# Run generation across a thread pool.
|
||
with concurrent.futures.ThreadPoolExecutor(max_workers=parallel_count) as executor:
|
||
future_to_entity = {
|
||
executor.submit(generate_single_profile, idx, entity): (idx, entity)
|
||
for idx, entity in enumerate(entities)
|
||
}
|
||
|
||
for future in concurrent.futures.as_completed(future_to_entity):
|
||
idx, entity = future_to_entity[future]
|
||
entity_type = entity.get_entity_type() or "Entity"
|
||
|
||
try:
|
||
result_idx, profile, error = future.result()
|
||
profiles[result_idx] = profile
|
||
|
||
with lock:
|
||
completed_count[0] += 1
|
||
current = completed_count[0]
|
||
|
||
# Flush profiles to disk in real time.
|
||
save_profiles_realtime()
|
||
|
||
if progress_callback:
|
||
progress_callback(
|
||
current,
|
||
total,
|
||
f"Completed {current}/{total}: {entity.name} ({entity_type})"
|
||
)
|
||
|
||
if error:
|
||
logger.warning(t("log.profile_generator.m018", current=current, total=total, entity=entity.name, error=error))
|
||
else:
|
||
logger.info(t("log.profile_generator.m019", current=current, total=total, entity=entity.name, entity_type=entity_type))
|
||
|
||
except Exception as e:
|
||
logger.error(t("log.profile_generator.m020", entity=entity.name, str=str(e)))
|
||
with lock:
|
||
completed_count[0] += 1
|
||
profiles[idx] = OasisAgentProfile(
|
||
user_id=idx,
|
||
user_name=self._generate_username(entity.name),
|
||
name=entity.name,
|
||
bio=f"{entity_type}: {entity.name}",
|
||
persona=entity.summary or "A participant in social discussions.",
|
||
source_entity_uuid=entity.uuid,
|
||
source_entity_type=entity_type,
|
||
)
|
||
# Flush profiles to disk even when only the fallback was produced.
|
||
save_profiles_realtime()
|
||
|
||
print(f"\n{'='*60}")
|
||
print(t("log.profile_generator.m025", count=len([p for p in profiles if p])))
|
||
print(f"{'='*60}\n")
|
||
|
||
return profiles
|
||
|
||
def _print_generated_profile(self, entity_name: str, entity_type: str, profile: OasisAgentProfile):
|
||
"""Stream the generated persona to the console (full content, untruncated)."""
|
||
separator = "-" * 70
|
||
|
||
# Assemble the full output (no truncation).
|
||
topics_str = ', '.join(profile.interested_topics) if profile.interested_topics else 'None'
|
||
|
||
|
||
output_lines = [
|
||
f"\n{separator}",
|
||
t('progress.profileGenerated', name=entity_name, type=entity_type),
|
||
f"{separator}",
|
||
f"Username: {profile.user_name}",
|
||
f"",
|
||
f"[Bio]",
|
||
f"{profile.bio}",
|
||
f"",
|
||
f"[Persona]",
|
||
f"{profile.persona}",
|
||
f"",
|
||
f"[Basic attributes]",
|
||
f"Age: {profile.age} | Gender: {profile.gender} | MBTI: {profile.mbti}",
|
||
f"Profession: {profile.profession} | Country: {profile.country}",
|
||
f"Interested topics: {topics_str}",
|
||
separator
|
||
]
|
||
|
||
output = "\n".join(output_lines)
|
||
|
||
# Print to the console only — the logger no longer emits the full content
|
||
# to avoid duplicate output.
|
||
print(output)
|
||
|
||
def save_profiles(
|
||
self,
|
||
profiles: List[OasisAgentProfile],
|
||
file_path: str,
|
||
platform: str = "reddit"
|
||
):
|
||
"""Save profiles to a file using the platform-specific format.
|
||
|
||
OASIS format requirements:
|
||
- Twitter: CSV format.
|
||
- Reddit: JSON format.
|
||
|
||
Args:
|
||
profiles: The profiles to save.
|
||
file_path: Destination file path.
|
||
platform: Platform type, ``"reddit"`` or ``"twitter"``.
|
||
"""
|
||
if platform == "twitter":
|
||
self._save_twitter_csv(profiles, file_path)
|
||
else:
|
||
self._save_reddit_json(profiles, file_path)
|
||
|
||
def _save_twitter_csv(self, profiles: List[OasisAgentProfile], file_path: str):
|
||
"""Save Twitter profiles as CSV (matches OASIS's official format).
|
||
|
||
Required CSV fields for OASIS Twitter:
|
||
- user_id: User id (zero-indexed by CSV row order).
|
||
- name: User's real-world display name.
|
||
- username: System username.
|
||
- user_char: Detailed persona text injected into the LLM system prompt
|
||
to drive agent behavior.
|
||
- description: Short public bio shown on the profile page.
|
||
|
||
``user_char`` vs ``description``:
|
||
- user_char: Internal — LLM system prompt that controls how the agent
|
||
thinks and acts.
|
||
- description: External — short bio visible to other users.
|
||
"""
|
||
import csv
|
||
|
||
# Ensure the file extension is .csv.
|
||
if not file_path.endswith('.csv'):
|
||
file_path = file_path.replace('.json', '.csv')
|
||
|
||
with open(file_path, 'w', newline='', encoding='utf-8') as f:
|
||
writer = csv.writer(f)
|
||
|
||
# Write the OASIS-required header row.
|
||
headers = ['user_id', 'name', 'username', 'user_char', 'description']
|
||
writer.writerow(headers)
|
||
|
||
for idx, profile in enumerate(profiles):
|
||
# user_char: full persona (bio + persona), used in the LLM system prompt.
|
||
user_char = profile.bio
|
||
if profile.persona and profile.persona != profile.bio:
|
||
user_char = f"{profile.bio} {profile.persona}"
|
||
# Replace newlines with spaces for CSV compatibility.
|
||
user_char = user_char.replace('\n', ' ').replace('\r', ' ')
|
||
|
||
# description: short bio used for external display.
|
||
description = profile.bio.replace('\n', ' ').replace('\r', ' ')
|
||
|
||
row = [
|
||
idx, # user_id: zero-based sequential id
|
||
profile.name, # name: real-world display name
|
||
profile.user_name, # username: system username
|
||
user_char, # user_char: full persona (internal LLM use)
|
||
description # description: short bio (external display)
|
||
]
|
||
writer.writerow(row)
|
||
|
||
logger.info(t("log.profile_generator.m021", len=len(profiles), file_path=file_path))
|
||
|
||
def _normalize_gender(self, gender: Optional[str]) -> str:
|
||
"""Normalize the gender field into the English form required by OASIS.
|
||
|
||
OASIS requires one of: ``male``, ``female``, ``other``.
|
||
"""
|
||
if not gender:
|
||
return "other"
|
||
|
||
gender_lower = gender.lower().strip()
|
||
|
||
# Mapping from Chinese values to the English literals.
|
||
gender_map = {
|
||
"男": "male",
|
||
"女": "female",
|
||
"机构": "other",
|
||
"其他": "other",
|
||
# Already in English — pass through.
|
||
"male": "male",
|
||
"female": "female",
|
||
"other": "other",
|
||
}
|
||
|
||
return gender_map.get(gender_lower, "other")
|
||
|
||
def _save_reddit_json(self, profiles: List[OasisAgentProfile], file_path: str):
|
||
"""Save Reddit profiles as JSON.
|
||
|
||
Uses the same shape as ``to_reddit_format()`` to ensure OASIS can read
|
||
the file. The ``user_id`` field is mandatory — it is what
|
||
``agent_graph.get_agent()`` matches against.
|
||
|
||
Required fields:
|
||
- user_id: User id (integer; matches ``poster_agent_id`` in
|
||
``initial_posts``).
|
||
- username: System username.
|
||
- name: Display name.
|
||
- bio: Short bio.
|
||
- persona: Detailed persona.
|
||
- age: Age (integer).
|
||
- gender: One of ``"male"``, ``"female"``, ``"other"``.
|
||
- mbti: MBTI type.
|
||
- country: Country.
|
||
"""
|
||
data = []
|
||
for idx, profile in enumerate(profiles):
|
||
# Match the shape of to_reddit_format().
|
||
item = {
|
||
"user_id": profile.user_id if profile.user_id is not None else idx, # Critical: must include user_id.
|
||
"username": profile.user_name,
|
||
"name": profile.name,
|
||
"bio": profile.bio[:150] if profile.bio else f"{profile.name}",
|
||
"persona": profile.persona or f"{profile.name} is a participant in social discussions.",
|
||
"karma": profile.karma if profile.karma else 1000,
|
||
"created_at": profile.created_at,
|
||
# OASIS-required fields — make sure each has a default.
|
||
"age": profile.age if profile.age else 30,
|
||
"gender": self._normalize_gender(profile.gender),
|
||
"mbti": profile.mbti if profile.mbti else "ISTJ",
|
||
"country": profile.country if profile.country else "中国",
|
||
}
|
||
|
||
# Optional fields.
|
||
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(t("log.profile_generator.m022", len=len(profiles), file_path=file_path))
|
||
|
||
# Retained as an alias for the old method name (backwards compatibility).
|
||
def save_profiles_to_json(
|
||
self,
|
||
profiles: List[OasisAgentProfile],
|
||
file_path: str,
|
||
platform: str = "reddit"
|
||
):
|
||
"""[Deprecated] Use ``save_profiles()`` instead."""
|
||
logger.warning(t("log.profile_generator.m023"))
|
||
self.save_profiles(profiles, file_path, platform)
|
||
|