cb9b16e5a8
包含： - 核心配置文件（AGENTS.md, SOUL.md, USER.md等） - 记忆系统（memory/文件夹） - 技能库（skills/文件夹） - 小说内容（novel/文件夹） - .gitignore配置
261 lines
9.4 KiB
Python
261 lines
9.4 KiB
Python
#!/usr/bin/env python3
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"""
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Novel Character Relationship Graph Generator (Corrected Names)
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Extracts entities and relationships from novel character notes
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and generates visualizable relationship graph.
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"""
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import os
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import re
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import json
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from pathlib import Path
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from typing import Dict, List, Any
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class NovelCharacterGraph:
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def __init__(self, characters_path: str):
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self.characters_path = Path(characters_path)
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self.characters = {}
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self.relationships = []
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def load_characters(self):
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"""Load all character markdown files"""
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md_files = list(self.characters_path.rglob('*.md'))
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for md_file in md_files:
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self.load_character_file(md_file)
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def load_character_file(self, file_path: Path):
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"""Load and parse a character file"""
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with open(file_path, 'r', encoding='utf-8') as f:
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content = f.read()
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# Extract name (first H1)
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title_match = re.search(r'^#\s+(.+)$', content, re.MULTILINE)
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name = title_match.group(1) if title_match else file_path.stem
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# Skip relationship graph file
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if '关系图谱' in name:
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return
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# Generate ID
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char_id = self.generate_id(name)
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# Extract properties
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properties = {
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'name': name,
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'file': str(file_path.relative_to(self.characters_path.parent))
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}
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# Extract age
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age_match = re.search(r'\*\*年龄\*\*:\s*(\d+)', content)
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if age_match:
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properties['age'] = age_match.group(1)
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# Extract gender
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gender_match = re.search(r'\*\*性别\*\*:\s*([男女])', content)
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if gender_match:
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properties['gender'] = gender_match.group(1)
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# Extract occupation
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occupation_match = re.search(r'\*\*职业\*\*:\s*([^\n]+)', content)
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if occupation_match:
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properties['occupation'] = occupation_match.group(1).strip()
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# Extract status
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status_match = re.search(r'\*\*状态\*\*:\s*([^\n]+)', content)
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if status_match:
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properties['status'] = status_match.group(1).strip()
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# Extract relationships from [[link]] format
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link_matches = re.findall(r'\[\[([^\]]+)\]\]', content)
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for link in link_matches:
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link_id = self.generate_id(link)
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self.relationships.append({
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'from': char_id,
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'to': link_id,
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'type': 'mentioned'
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})
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# Extract specific relationships from content
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self.extract_relationships_from_content(content, char_id)
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# Add character
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self.characters[char_id] = {
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'id': char_id,
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'type': 'Character',
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'properties': properties
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}
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def extract_relationships_from_content(self, content: str, char_id: str):
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"""Extract relationships from content sections"""
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# Family relationships
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if '## 家庭关系' in content:
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family_section = re.search(r'## 家庭关系\s*\n(.*?)(?=##|$)', content, re.DOTALL)
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if family_section:
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# Extract mother
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mother_match = re.search(r'- \*\*母亲\*\*: \[\[([^\]]+)\]\]', family_section.group(1))
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if mother_match:
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mother_id = self.generate_id(mother_match.group(1))
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self.relationships.append({
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'from': char_id,
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'to': mother_id,
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'type': 'mother_of'
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})
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# Extract father
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father_match = re.search(r'- \*\*父亲\*\*: \[\[([^\]]+)\]\]', family_section.group(1))
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if father_match:
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father_id = self.generate_id(father_match.group(1))
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self.relationships.append({
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'from': char_id,
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'to': father_id,
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'type': 'father_of'
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})
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# Extract son
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son_match = re.search(r'- \*\*儿子\*\*: \[\[([^\]]+)\]\]', family_section.group(1))
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if son_match:
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son_id = self.generate_id(son_match.group(1))
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self.relationships.append({
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'from': char_id,
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'to': son_id,
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'type': 'son_of'
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})
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# Extract wife
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wife_match = re.search(r'- \*\*妻子\*\*: \[\[([^\]]+)\]\]', family_section.group(1))
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if wife_match:
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wife_id = self.generate_id(wife_match.group(1))
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self.relationships.append({
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'from': char_id,
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'to': wife_id,
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'type': 'wife_of'
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})
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# Extract husband
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husband_match = re.search(r'- \*\*丈夫\*\*: \[\[([^\]]+)\]\]', family_section.group(1))
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if husband_match:
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husband_id = self.generate_id(husband_match.group(1))
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self.relationships.append({
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'from': char_id,
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'to': husband_id,
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'type': 'husband_of'
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})
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def generate_id(self, name: str) -> str:
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"""Generate consistent ID from name"""
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normalized = re.sub(r'[^a-z0-9\u4e00-\u9fff]+', '_', name.lower()).strip('_')
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return f"char_{normalized}"
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def visualize_text(self):
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"""Generate ASCII text visualization"""
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print("=" * 60)
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print("《杀了婆婆的我却无人追责？》人物关系图谱")
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print("=" * 60)
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# Print characters
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print("\n【人物列表】")
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for char_id, char in self.characters.items():
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props = char['properties']
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print(f"\n {props.get('name', 'Unknown')}")
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if 'age' in props:
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print(f" 年龄: {props['age']}")
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if 'gender' in props:
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print(f" 性别: {props['gender']}")
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if 'occupation' in props:
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print(f" 职业: {props['occupation']}")
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if 'status' in props:
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print(f" 状态: {props['status']}")
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# Print relationships
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print("\n【人物关系】")
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for rel in self.relationships:
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from_char = self.characters.get(rel['from'])
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to_char = self.characters.get(rel['to'])
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if from_char and to_char:
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from_name = from_char['properties']['name']
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to_name = to_char['properties']['name']
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rel_type = rel['type']
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print(f"\n {from_name} --[{rel_type}]--> {to_name}")
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# Print ASCII graph
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print("\n【ASCII 关系图】")
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self.print_ascii_graph()
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def print_ascii_graph(self):
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"""Print simple ASCII relationship graph"""
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print("""
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┌─────────────┐
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│ 顾国强 │
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│ (已故) │
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└──────┬──────┘
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│
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│ 夫妻
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│
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┌──────▼──────┐
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│ 刘婉清 │
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│ (68岁) │
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│ 婆婆 │
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└──────┬──────┘
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│
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┌───────────┼───────────┐
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│ │ │
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母子 控制压迫 婆媳
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│ │ │
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┌────▼────┐ │ ┌────▼────┐
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│ 顾长风 │ │ │ 叶知秋 │
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│ (35岁) │ │ │ (28岁) │
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│ 丈夫 │ │ │ 女主 │
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└────┬────┘ │ └────┬────┘
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│ │ │
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夫妻 想要 记忆
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│ 遗产 混乱
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│ │ │
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└───────────┼───────────┘
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│
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调查案件
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│
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┌──────▼──────┐
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│ 张明远 │
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│ (所长) │
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└─────────────┘
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""")
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def export_json(self, output_path: str):
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"""Export to JSON for further processing"""
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output = {
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'characters': list(self.characters.values()),
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'relationships': self.relationships
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}
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with open(output_path, 'w', encoding='utf-8') as f:
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json.dump(output, f, ensure_ascii=False, indent=2)
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print(f"\n✅ 导出到: {output_path}")
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if __name__ == '__main__':
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import argparse
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parser = argparse.ArgumentParser(description='Novel Character Relationship Graph (Corrected Names)')
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parser.add_argument('--path', type=str,
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default='/root/.openclaw/workspace/novel/characters',
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help='Path to character notes')
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parser.add_argument('--export', type=str,
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help='Export to JSON file')
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args = parser.parse_args()
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# Create graph
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graph = NovelCharacterGraph(args.path)
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# Load characters
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graph.load_characters()
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# Visualize
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graph.visualize_text()
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# Export if requested
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if args.export:
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graph.export_json(args.export)
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