507 lines
19 KiB
Python
507 lines
19 KiB
Python
"""
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本体生成服务
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接口1：分析文本内容，生成适合社会模拟的实体和关系类型定义
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"""
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import json
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import logging
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import re
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from typing import Dict, Any, List, Optional
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from ..utils.llm_client import LLMClient
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from ..utils.locale import get_language_instruction
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logger = logging.getLogger(__name__)
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def _to_pascal_case(name: str) -> str:
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"""将任意格式的名称转换为 PascalCase（如 'works_for' -> 'WorksFor', 'person' -> 'Person'）"""
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# 按非字母数字字符分割
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parts = re.split(r'[^a-zA-Z0-9]+', name)
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# 再按 camelCase 边界分割（如 'camelCase' -> ['camel', 'Case']）
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words = []
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for part in parts:
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words.extend(re.sub(r'([a-z])([A-Z])', r'\1_\2', part).split('_'))
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# 每个词首字母大写，过滤空串
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result = ''.join(word.capitalize() for word in words if word)
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return result if result else 'Unknown'
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# 本体生成的系统提示词
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ONTOLOGY_SYSTEM_PROMPT = """You are a professional knowledge graph ontology design expert. Your task is to analyze the given text content and simulation requirements, and design entity types and relationship types suitable for **social media opinion simulation**.
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**Important: You must output valid JSON format data only, do not output any other content.**
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## Core Task Background
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We are building a **social media opinion simulation system**. In this system:
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- Each entity is an "account" or "subject" that can speak, interact, and spread information on social media
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- Entities influence each other, repost, comment, and respond
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- We need to simulate the reactions and information propagation paths of various parties in opinion events
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Therefore, **entities must be real-world subjects that can speak and interact on social media**:
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**Allowed**:
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- Specific individuals (public figures, parties involved, opinion leaders, experts, ordinary people)
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- Companies, enterprises (including their official accounts)
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- Organizations (universities, associations, NGOs, unions, etc.)
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- Government departments, regulatory agencies
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- Media organizations (newspapers, TV stations, self-media, websites)
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- Social media platforms themselves
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- Specific group representatives (alumni associations, fan groups, advocacy groups, etc.)
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**Not allowed**:
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- Abstract concepts (such as "public opinion", "emotion", "trend")
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- Topics/subjects (such as "academic integrity", "education reform")
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- Viewpoints/attitudes (such as "supporters", "opponents")
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## Output Format
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Output in JSON format containing the following structure:
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```json
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{
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"entity_types": [
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{
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"name": "Entity type name (English, PascalCase)",
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"description": "Brief description (English, max 100 chars)",
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"attributes": [
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{
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"name": "Attribute name (English, snake_case)",
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"type": "text",
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"description": "Attribute description"
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}
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],
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"examples": ["Example entity 1", "Example entity 2"]
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}
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],
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"edge_types": [
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{
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"name": "Relationship type name (English, UPPER_SNAKE_CASE)",
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"description": "Brief description (English, max 100 chars)",
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"source_targets": [
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{"source": "Source entity type", "target": "Target entity type"}
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],
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"attributes": []
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}
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],
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"analysis_summary": "Brief analysis of the text content"
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}
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```
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## Design Guidelines (Extremely Important!)
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### 1. Entity Type Design - Must Strictly Follow
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**Quantity requirement: Exactly 10 entity types**
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**Hierarchy requirement (must include both specific types and fallback types)**:
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Your 10 entity types must include the following hierarchy:
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A. **Fallback types (must include, place last 2 in the list)**:
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- `Person`: Fallback type for any natural person. When a person doesn't match any more specific type, use this.
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- `Organization`: Fallback type for any organization. When an organization doesn't match any more specific type, use this.
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B. **Specific types (8, designed based on text content)**:
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- Design more specific types for the main roles appearing in the text
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- Example: If the text involves an academic event, you can have `Student`, `Professor`, `University`
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- Example: If the text involves a business event, you can have `Company`, `CEO`, `Employee`
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**Why fallback types are needed**:
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- Various people appear in the text, such as "school teacher", "bystander", "an online user"
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- If no specific type matches, they should fall into `Person`
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- Similarly, small organizations, temporary groups should fall into `Organization`
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**Design principles for specific types**:
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- Identify high-frequency or key role types from the text
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- Each specific type should have clear boundaries, avoid overlap
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- Description must clearly explain the difference between this type and the fallback type
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### 2. Relationship Type Design
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- Quantity: 6-10
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- Relationships should reflect real connections in social media interaction
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- Ensure source_targets of relationships cover the entity types you defined
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### 3. Attribute Design
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- 1-3 key attributes per entity type
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- **Note**: Attribute names cannot use `name`, `uuid`, `group_id`, `created_at`, `summary` (these are system reserved words)
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- Recommended: `full_name`, `title`, `role`, `position`, `location`, `description`, etc.
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## Entity Type Reference
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**Individuals (Specific)**:
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- Student: Student
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- Professor: Professor/Scholar
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- Journalist: Journalist
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- Celebrity: Celebrity/Influencer
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- Executive: Executive
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- Official: Government official
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- Lawyer: Lawyer
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- Doctor: Doctor
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**Individuals (Fallback)**:
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- Person: Any natural person (used when not matching specific types above)
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**Organizations (Specific)**:
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- University: University
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- Company: Company/Enterprise
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- GovernmentAgency: Government agency
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- MediaOutlet: Media organization
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- Hospital: Hospital
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- School: School
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- NGO: Non-governmental organization
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**Organizations (Fallback)**:
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- Organization: Any organization (used when not matching specific types above)
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## Relationship Type Reference
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- WORKS_FOR: Works at
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- STUDIES_AT: Studies at
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- AFFILIATED_WITH: Affiliated with
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- REPRESENTS: Represents
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- REGULATES: Regulates
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- REPORTS_ON: Reports on
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- COMMENTS_ON: Comments on
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- RESPONDS_TO: Responds to
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- SUPPORTS: Supports
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- OPPOSES: Opposes
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- COLLABORATES_WITH: Collaborates with
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- COMPETES_WITH: Competes with
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"""
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class OntologyGenerator:
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"""
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本体生成器
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分析文本内容，生成实体和关系类型定义
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"""
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def __init__(self, llm_client: Optional[LLMClient] = None):
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self.llm_client = llm_client or LLMClient()
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def generate(
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self,
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document_texts: List[str],
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simulation_requirement: str,
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additional_context: Optional[str] = None
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) -> Dict[str, Any]:
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"""
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生成本体定义
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Args:
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document_texts: 文档文本列表
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simulation_requirement: 模拟需求描述
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additional_context: 额外上下文
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Returns:
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本体定义（entity_types, edge_types等）
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"""
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# 构建用户消息
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user_message = self._build_user_message(
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document_texts,
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simulation_requirement,
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additional_context
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)
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lang_instruction = get_language_instruction()
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system_prompt = f"{ONTOLOGY_SYSTEM_PROMPT}\n\n{lang_instruction}\nIMPORTANT: Entity type names MUST be in English PascalCase (e.g., 'PersonEntity', 'MediaOrganization'). Relationship type names MUST be in English UPPER_SNAKE_CASE (e.g., 'WORKS_FOR'). Attribute names MUST be in English snake_case. Only description fields and analysis_summary should use the specified language above."
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messages = [
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": user_message}
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]
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# 调用LLM
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result = self.llm_client.chat_json(
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messages=messages,
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temperature=0.3,
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max_tokens=4096
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)
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# 验证和后处理
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result = self._validate_and_process(result)
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return result
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# 传给 LLM 的文本最大长度（5万字）
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MAX_TEXT_LENGTH_FOR_LLM = 50000
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def _build_user_message(
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self,
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document_texts: List[str],
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simulation_requirement: str,
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additional_context: Optional[str]
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) -> str:
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"""构建用户消息"""
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# 合并文本
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combined_text = "\n\n---\n\n".join(document_texts)
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original_length = len(combined_text)
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# 如果文本超过5万字，截断（仅影响传给LLM的内容，不影响图谱构建）
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if len(combined_text) > self.MAX_TEXT_LENGTH_FOR_LLM:
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combined_text = combined_text[:self.MAX_TEXT_LENGTH_FOR_LLM]
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combined_text += f"\n\n...(原文共{original_length}字，已截取前{self.MAX_TEXT_LENGTH_FOR_LLM}字用于本体分析)..."
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message = f"""## 模拟需求
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{simulation_requirement}
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## 文档内容
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{combined_text}
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"""
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if additional_context:
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message += f"""
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## 额外说明
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{additional_context}
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"""
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message += """
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请根据以上内容，设计适合社会舆论模拟的实体类型和关系类型。
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**必须遵守的规则**：
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1. 必须正好输出10个实体类型
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2. 最后2个必须是兜底类型：Person（个人兜底）和 Organization（组织兜底）
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3. 前8个是根据文本内容设计的具体类型
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4. 所有实体类型必须是现实中可以发声的主体，不能是抽象概念
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5. 属性名不能使用 name、uuid、group_id 等保留字，用 full_name、org_name 等替代
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"""
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return message
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def _validate_and_process(self, result: Dict[str, Any]) -> Dict[str, Any]:
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"""验证和后处理结果"""
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# 确保必要字段存在
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if "entity_types" not in result:
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result["entity_types"] = []
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if "edge_types" not in result:
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result["edge_types"] = []
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if "analysis_summary" not in result:
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result["analysis_summary"] = ""
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# 验证实体类型
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# 记录原始名称到 PascalCase 的映射，用于后续修正 edge 的 source_targets 引用
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entity_name_map = {}
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for entity in result["entity_types"]:
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# 强制将 entity name 转为 PascalCase（Zep API 要求）
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if "name" in entity:
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original_name = entity["name"]
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entity["name"] = _to_pascal_case(original_name)
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if entity["name"] != original_name:
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logger.warning(f"Entity type name '{original_name}' auto-converted to '{entity['name']}'")
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entity_name_map[original_name] = entity["name"]
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if "attributes" not in entity:
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entity["attributes"] = []
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if "examples" not in entity:
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entity["examples"] = []
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# 确保description不超过100字符
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if len(entity.get("description", "")) > 100:
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entity["description"] = entity["description"][:97] + "..."
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# 验证关系类型
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for edge in result["edge_types"]:
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# 强制将 edge name 转为 SCREAMING_SNAKE_CASE（Zep API 要求）
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if "name" in edge:
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original_name = edge["name"]
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edge["name"] = original_name.upper()
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if edge["name"] != original_name:
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logger.warning(f"Edge type name '{original_name}' auto-converted to '{edge['name']}'")
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# 修正 source_targets 中的实体名称引用，与转换后的 PascalCase 保持一致
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for st in edge.get("source_targets", []):
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if st.get("source") in entity_name_map:
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st["source"] = entity_name_map[st["source"]]
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if st.get("target") in entity_name_map:
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st["target"] = entity_name_map[st["target"]]
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if "source_targets" not in edge:
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edge["source_targets"] = []
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if "attributes" not in edge:
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edge["attributes"] = []
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if len(edge.get("description", "")) > 100:
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edge["description"] = edge["description"][:97] + "..."
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# Zep API 限制：最多 10 个自定义实体类型，最多 10 个自定义边类型
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MAX_ENTITY_TYPES = 10
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MAX_EDGE_TYPES = 10
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# 去重：按 name 去重，保留首次出现的
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seen_names = set()
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deduped = []
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for entity in result["entity_types"]:
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name = entity.get("name", "")
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if name and name not in seen_names:
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seen_names.add(name)
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deduped.append(entity)
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elif name in seen_names:
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logger.warning(f"Duplicate entity type '{name}' removed during validation")
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result["entity_types"] = deduped
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# 兜底类型定义
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person_fallback = {
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"name": "Person",
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"description": "Any individual person not fitting other specific person types.",
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"attributes": [
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{"name": "full_name", "type": "text", "description": "Full name of the person"},
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{"name": "role", "type": "text", "description": "Role or occupation"}
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],
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"examples": ["ordinary citizen", "anonymous netizen"]
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}
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organization_fallback = {
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"name": "Organization",
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"description": "Any organization not fitting other specific organization types.",
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"attributes": [
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{"name": "org_name", "type": "text", "description": "Name of the organization"},
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{"name": "org_type", "type": "text", "description": "Type of organization"}
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],
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"examples": ["small business", "community group"]
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}
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# 检查是否已有兜底类型
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entity_names = {e["name"] for e in result["entity_types"]}
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has_person = "Person" in entity_names
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has_organization = "Organization" in entity_names
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# 需要添加的兜底类型
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fallbacks_to_add = []
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if not has_person:
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fallbacks_to_add.append(person_fallback)
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if not has_organization:
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fallbacks_to_add.append(organization_fallback)
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if fallbacks_to_add:
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current_count = len(result["entity_types"])
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needed_slots = len(fallbacks_to_add)
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# 如果添加后会超过 10 个，需要移除一些现有类型
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if current_count + needed_slots > MAX_ENTITY_TYPES:
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# 计算需要移除多少个
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to_remove = current_count + needed_slots - MAX_ENTITY_TYPES
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# 从末尾移除（保留前面更重要的具体类型）
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result["entity_types"] = result["entity_types"][:-to_remove]
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# 添加兜底类型
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result["entity_types"].extend(fallbacks_to_add)
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# 最终确保不超过限制（防御性编程）
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if len(result["entity_types"]) > MAX_ENTITY_TYPES:
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result["entity_types"] = result["entity_types"][:MAX_ENTITY_TYPES]
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if len(result["edge_types"]) > MAX_EDGE_TYPES:
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result["edge_types"] = result["edge_types"][:MAX_EDGE_TYPES]
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return result
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def generate_python_code(self, ontology: Dict[str, Any]) -> str:
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"""
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将本体定义转换为Python代码（类似ontology.py）
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Args:
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ontology: 本体定义
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Returns:
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Python代码字符串
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"""
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code_lines = [
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'"""',
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'自定义实体类型定义',
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'由MiroFish自动生成，用于社会舆论模拟',
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'"""',
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'',
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'from pydantic import Field',
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'from zep_cloud.external_clients.ontology import EntityModel, EntityText, EdgeModel',
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'',
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'',
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'# ============== 实体类型定义 ==============',
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'',
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]
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# 生成实体类型
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for entity in ontology.get("entity_types", []):
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name = entity["name"]
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desc = entity.get("description", f"A {name} entity.")
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code_lines.append(f'class {name}(EntityModel):')
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code_lines.append(f' """{desc}"""')
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attrs = entity.get("attributes", [])
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if attrs:
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for attr in attrs:
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attr_name = attr["name"]
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attr_desc = attr.get("description", attr_name)
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code_lines.append(f' {attr_name}: EntityText = Field(')
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code_lines.append(f' description="{attr_desc}",')
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code_lines.append(f' default=None')
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code_lines.append(f' )')
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else:
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code_lines.append(' pass')
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code_lines.append('')
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code_lines.append('')
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code_lines.append('# ============== 关系类型定义 ==============')
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code_lines.append('')
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# 生成关系类型
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for edge in ontology.get("edge_types", []):
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name = edge["name"]
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# 转换为PascalCase类名
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class_name = ''.join(word.capitalize() for word in name.split('_'))
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desc = edge.get("description", f"A {name} relationship.")
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code_lines.append(f'class {class_name}(EdgeModel):')
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code_lines.append(f' """{desc}"""')
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attrs = edge.get("attributes", [])
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if attrs:
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for attr in attrs:
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attr_name = attr["name"]
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attr_desc = attr.get("description", attr_name)
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code_lines.append(f' {attr_name}: EntityText = Field(')
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code_lines.append(f' description="{attr_desc}",')
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code_lines.append(f' default=None')
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code_lines.append(f' )')
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else:
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code_lines.append(' pass')
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code_lines.append('')
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code_lines.append('')
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# 生成类型字典
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code_lines.append('# ============== 类型配置 ==============')
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code_lines.append('')
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code_lines.append('ENTITY_TYPES = {')
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for entity in ontology.get("entity_types", []):
|
||
name = entity["name"]
|
||
code_lines.append(f' "{name}": {name},')
|
||
code_lines.append('}')
|
||
code_lines.append('')
|
||
code_lines.append('EDGE_TYPES = {')
|
||
for edge in ontology.get("edge_types", []):
|
||
name = edge["name"]
|
||
class_name = ''.join(word.capitalize() for word in name.split('_'))
|
||
code_lines.append(f' "{name}": {class_name},')
|
||
code_lines.append('}')
|
||
code_lines.append('')
|
||
|
||
# 生成边的source_targets映射
|
||
code_lines.append('EDGE_SOURCE_TARGETS = {')
|
||
for edge in ontology.get("edge_types", []):
|
||
name = edge["name"]
|
||
source_targets = edge.get("source_targets", [])
|
||
if source_targets:
|
||
st_list = ', '.join([
|
||
f'{{"source": "{st.get("source", "Entity")}", "target": "{st.get("target", "Entity")}"}}'
|
||
for st in source_targets
|
||
])
|
||
code_lines.append(f' "{name}": [{st_list}],')
|
||
code_lines.append('}')
|
||
|
||
return '\n'.join(code_lines)
|
||
|