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+#!/usr/bin/env python3
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+"""
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+结构化索引系统(Structured Index System)
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+
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+目标:取代向量化检索,使用 SQLite 提供精确、快速的结构化查询
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+
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+核心功能:
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+1. 章节元数据索引(location, characters, word_count)
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+2. 伏笔追踪索引(status, urgency calculation)
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+3. 文件 Hash 自愈机制(auto-rebuild on change)
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+
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+性能目标:
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+- 查询速度:2-5ms(vs 文件遍历 500ms,提升 250x)
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+- 索引构建:10ms/章(增量更新)
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+- 存储开销:200 章 ≈ 100 KB
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+
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+使用方式:
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+ # 更新单章索引
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+ python structured_index.py --update-chapter 7 --metadata "正文/第0007章.md"
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+
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+ # 批量重建索引(历史章节)
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+ python structured_index.py --rebuild-index
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+
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+ # 查询地点相关章节
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+ python structured_index.py --query-location "血煞秘境"
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+
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+ # 查询紧急伏笔
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+ python structured_index.py --query-urgent-foreshadowing
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+
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+ # 模糊查询角色
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+ python structured_index.py --fuzzy-search "姓李" "女弟子"
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+
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+ # 导出关系图
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+ python structured_index.py --export-graph > relationships.md
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+"""
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+
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+import json
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+import os
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+import sys
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+import argparse
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+import sqlite3
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+import hashlib
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+import re
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+from datetime import datetime
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+from pathlib import Path
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+from typing import Optional, List, Dict, Tuple
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+
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+
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+class StructuredIndex:
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+ """结构化索引管理器(取代向量化检索)"""
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+
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+ def __init__(self, project_root=None):
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+ if project_root is None:
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+ project_root = Path.cwd()
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+ else:
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+ project_root = Path(project_root)
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+
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+ self.project_root = project_root
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+ self.state_file = project_root / ".webnovel" / "state.json"
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+ self.chapters_dir = project_root / "正文"
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+ self.index_db = project_root / ".webnovel" / "index.db"
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+
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+ # 确保数据库目录存在
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+ self.index_db.parent.mkdir(parents=True, exist_ok=True)
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+
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+ # 连接数据库
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+ self.conn = sqlite3.connect(str(self.index_db))
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+ self.conn.row_factory = sqlite3.Row # 返回字典式行
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+
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+ # 创建表结构
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+ self._create_tables()
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+
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+ def _create_tables(self):
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+ """创建索引表结构"""
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+
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+ # 1. 章节元数据表
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+ self.conn.execute("""
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+ CREATE TABLE IF NOT EXISTS chapters (
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+ chapter_num INTEGER PRIMARY KEY,
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+ title TEXT,
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+ location TEXT,
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+ characters TEXT, -- JSON: ["李雪", "主角"]
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+ word_count INTEGER,
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+ content_hash TEXT,
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+ created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
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+ updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
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+ )
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+ """)
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+
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+ # 地点索引(加速查询)
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+ self.conn.execute("""
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+ CREATE INDEX IF NOT EXISTS idx_location
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+ ON chapters(location)
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+ """)
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+
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+ # 2. 伏笔追踪表
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+ self.conn.execute("""
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+ CREATE TABLE IF NOT EXISTS foreshadowing_index (
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+ id INTEGER PRIMARY KEY,
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+ content TEXT,
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+ location TEXT,
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+ characters TEXT, -- JSON: ["李雪", "主角"]
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+ introduced_chapter INTEGER,
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+ resolved_chapter INTEGER,
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+ status TEXT, -- '未回收' / '已回收'
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+ urgency INTEGER DEFAULT 0, -- 0-100,自动计算
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+ created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
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+ updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
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+ )
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+ """)
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+
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+ # 状态索引
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+ self.conn.execute("""
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+ CREATE INDEX IF NOT EXISTS idx_status
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+ ON foreshadowing_index(status)
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+ """)
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+
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+ # 紧急度索引
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+ self.conn.execute("""
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+ CREATE INDEX IF NOT EXISTS idx_urgency
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+ ON foreshadowing_index(urgency)
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+ """)
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+
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+ # 3. 角色关系表
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+ self.conn.execute("""
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+ CREATE TABLE IF NOT EXISTS relationships (
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+ id INTEGER PRIMARY KEY AUTOINCREMENT,
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+ char1 TEXT,
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+ char2 TEXT,
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+ relation_type TEXT, -- 'ally', 'enemy', 'romance', 'mentor', 'debtor'
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+ intensity INTEGER, -- 关系强度 0-100
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+ description TEXT,
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+ last_update_chapter INTEGER,
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+ created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
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+ updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
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+ UNIQUE(char1, char2, relation_type) -- 防止重复
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+ )
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+ """)
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+
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+ # 关系索引
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+ self.conn.execute("""
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+ CREATE INDEX IF NOT EXISTS idx_char1_char2
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+ ON relationships(char1, char2)
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+ """)
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+
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+ # 4. 角色索引表(优化模糊搜索性能)
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+ self.conn.execute("""
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+ CREATE TABLE IF NOT EXISTS characters (
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+ name TEXT PRIMARY KEY,
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+ description TEXT,
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+ personality TEXT,
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+ importance TEXT, -- 'major' / 'minor'
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+ power_level TEXT,
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+ first_appearance INTEGER,
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+ last_appearance INTEGER,
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+ status TEXT DEFAULT 'active', -- 'active' / 'archived'
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+ archived_at TEXT, -- ISO timestamp
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+ created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
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+ updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
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+ )
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+ """)
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+
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+ # 角色名索引(加速模糊搜索)
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+ self.conn.execute("""
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+ CREATE INDEX IF NOT EXISTS idx_character_name
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+ ON characters(name)
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+ """)
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+
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+ # 状态索引
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+ self.conn.execute("""
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+ CREATE INDEX IF NOT EXISTS idx_character_status
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+ ON characters(status)
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+ """)
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+
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+ self.conn.commit()
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+
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+ # ================== 核心功能 1:章节元数据索引 ==================
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+
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+ def index_chapter(self, chapter_num: int, metadata: Dict):
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+ """为新章节建立索引(在 webnovel-write Step 4.6 调用)
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+
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+ Args:
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+ chapter_num: 章节编号
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+ metadata: {
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+ 'title': '章节标题',
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+ 'location': '地点',
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+ 'characters': ['李雪', '主角'],
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+ 'word_count': 3500,
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+ 'hash': 'md5_hash'
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+ }
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+ """
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+ self.conn.execute("""
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+ INSERT OR REPLACE INTO chapters
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+ (chapter_num, title, location, characters, word_count, content_hash, updated_at)
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+ VALUES (?, ?, ?, ?, ?, ?, CURRENT_TIMESTAMP)
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+ """, (
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+ chapter_num,
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+ metadata['title'],
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+ metadata['location'],
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+ json.dumps(metadata['characters'], ensure_ascii=False),
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+ metadata['word_count'],
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+ metadata['hash']
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+ ))
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+
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+ self.conn.commit()
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+ print(f"✅ 章节索引已更新:Ch{chapter_num} - {metadata['title']}")
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+
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+ def query_chapters_by_location(self, location: str, limit: int = 10) -> List[Tuple]:
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+ """O(log n) 查询:返回该地点的最近 N 章
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+
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+ Args:
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+ location: 地点名称
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+ limit: 返回数量
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+
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+ Returns:
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+ [(chapter_num, title, characters), ...]
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+ """
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+ cursor = self.conn.execute("""
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+ SELECT chapter_num, title, characters
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+ FROM chapters
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+ WHERE location = ?
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+ ORDER BY chapter_num DESC
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+ LIMIT ?
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+ """, (location, limit))
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+
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+ return cursor.fetchall()
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+
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+ def calculate_chapter_hash(self, chapter_file: Path) -> str:
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+ """计算章节文件 MD5 Hash(用于自愈机制)"""
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+ if not chapter_file.exists():
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+ return ""
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+
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+ with open(chapter_file, 'rb') as f:
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+ return hashlib.md5(f.read()).hexdigest()
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+
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+ def get_stored_hash(self, chapter_num: int) -> Optional[str]:
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+ """从索引中读取存储的 Hash"""
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+ cursor = self.conn.execute("""
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+ SELECT content_hash FROM chapters WHERE chapter_num = ?
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+ """, (chapter_num,))
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+
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+ row = cursor.fetchone()
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+ return row['content_hash'] if row else None
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+
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+ def validate_and_rebuild_if_needed(self, chapter_num: int):
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+ """校验章节 Hash,不一致则自动重建索引(Self-Healing Index)
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+
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+ 触发时机:
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+ - context_manager.py 查询章节前调用
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+ - 增加耗时:~5ms(Hash 计算 + 对比)
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+ - 仅当检测到变更时才重建(增量成本)
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+ """
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+ chapter_file = self.chapters_dir / f"第{chapter_num:04d}章.md"
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+
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+ if not chapter_file.exists():
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+ return # 文件不存在,跳过
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+
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+ # 计算当前文件 Hash
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+ current_hash = self.calculate_chapter_hash(chapter_file)
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+
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+ # 从索引中读取存储的 Hash
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+ stored_hash = self.get_stored_hash(chapter_num)
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+
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+ if current_hash != stored_hash:
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+ print(f"⚠️ 检测到 Ch{chapter_num} 已修改,自动重建索引...")
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+ self._rebuild_chapter_index(chapter_num, chapter_file)
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+ print(f"✅ Ch{chapter_num} 索引已更新")
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+
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+ def _rebuild_chapter_index(self, chapter_num: int, chapter_file: Path):
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+ """重建单章索引(自动提取元数据)"""
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+
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+ # 读取章节内容
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+ with open(chapter_file, 'r', encoding='utf-8') as f:
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+ content = f.read()
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+
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+ # 提取元数据
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+ metadata = self._extract_metadata_from_content(content, chapter_num)
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+
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+ # 重建索引
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+ self.index_chapter(chapter_num, metadata)
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+
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+ def _extract_metadata_from_content(self, content: str, chapter_num: int) -> Dict:
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+ """从章节内容中提取元数据"""
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+
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+ # 提取标题(第一行)
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+ lines = content.split('\n')
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+ title = lines[0].strip('# ').strip() if lines else f"第{chapter_num}章"
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+
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+ # 提取地点(在章节开头查找,通常格式为 **地点:XXX**)
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+ location_match = re.search(r'\*\*地点[::]\s*(.+?)\*\*', content)
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+ location = location_match.group(1).strip() if location_match else "未知"
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+
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+ # 提取角色(查找所有对话和描述中的角色名)
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+ # 简化实现:从 state.json 读取已知角色,匹配出现频率
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+ characters = self._extract_characters_from_content(content)
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+
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+ # 计算字数
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+ word_count = len(content)
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+
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+ # 计算 Hash
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+ content_hash = hashlib.md5(content.encode('utf-8')).hexdigest()
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+
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+ return {
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+ 'title': title,
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+ 'location': location,
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+ 'characters': characters[:5], # 最多 5 个主要角色
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+ 'word_count': word_count,
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+ 'hash': content_hash
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+ }
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+
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+ def _extract_characters_from_content(self, content: str) -> List[str]:
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+ """从内容中提取角色(简化实现:读取 state.json 已知角色)"""
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+
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+ if not self.state_file.exists():
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+ return []
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+
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+ # 读取 state.json
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+ with open(self.state_file, 'r', encoding='utf-8') as f:
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+ state = json.load(f)
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+
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+ # 获取已知角色列表
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+ known_characters = [
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+ char['name']
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+ for char in state.get('entities', {}).get('characters', [])
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+ ]
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+
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+ # 统计每个角色在内容中的出现次数
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+ char_counts = {}
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+ for char_name in known_characters:
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+ count = content.count(char_name)
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+ if count > 0:
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+ char_counts[char_name] = count
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+
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+ # 按出现次数排序,返回前 5 个
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+ sorted_chars = sorted(char_counts.items(), key=lambda x: x[1], reverse=True)
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+ return [char for char, _ in sorted_chars[:5]]
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+
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+ # ================== 核心功能 2:伏笔追踪索引 ==================
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+
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+ def sync_foreshadowing_from_state(self):
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+ """从 state.json 同步伏笔数据到索引
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+
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+ 触发时机:
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+ - update_state.py 更新伏笔后调用
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+ - --rebuild-index 批量重建时调用
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+ """
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+ if not self.state_file.exists():
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+ print("❌ state.json 不存在,跳过伏笔同步")
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+ return
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+
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+ # 读取 state.json
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+ with open(self.state_file, 'r', encoding='utf-8') as f:
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+ state = json.load(f)
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+
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+ current_chapter = state.get('progress', {}).get('current_chapter', 0)
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+
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+ # 同步活跃伏笔(未回收)
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+ active_plots = state.get('plot_threads', {}).get('active', [])
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+ for plot in active_plots:
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+ self._index_foreshadowing(plot, current_chapter, status="未回收")
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+
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+ # 同步已回收伏笔
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+ resolved_plots = state.get('plot_threads', {}).get('resolved', [])
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+ for plot in resolved_plots:
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+ self._index_foreshadowing(plot, current_chapter, status="已回收")
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+
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+ self.conn.commit()
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+ print(f"✅ 伏笔索引已同步:{len(active_plots)} 条活跃 + {len(resolved_plots)} 条已回收")
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+
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+ def _index_foreshadowing(self, plot: Dict, current_chapter: int, status: str):
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+ """为单个伏笔建立索引"""
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+
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+ # 计算紧急度
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+ urgency = self._calculate_urgency(plot, current_chapter)
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+
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+ # 提取地点和角色(如果有)
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+ location = plot.get('location', '')
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+ characters = plot.get('characters', [])
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+
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+ self.conn.execute("""
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+ INSERT OR REPLACE INTO foreshadowing_index
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+ (id, content, location, characters, introduced_chapter, resolved_chapter, status, urgency, updated_at)
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+ VALUES ((SELECT id FROM foreshadowing_index WHERE content = ?), ?, ?, ?, ?, ?, ?, ?, CURRENT_TIMESTAMP)
|
|
|
+ """, (
|
|
|
+ plot['description'], # 用于查重
|
|
|
+ plot['description'],
|
|
|
+ location,
|
|
|
+ json.dumps(characters, ensure_ascii=False),
|
|
|
+ plot.get('introduced_chapter', 0),
|
|
|
+ plot.get('resolved_chapter', None),
|
|
|
+ status,
|
|
|
+ urgency
|
|
|
+ ))
|
|
|
+
|
|
|
+ def _calculate_urgency(self, plot: Dict, current_chapter: int) -> int:
|
|
|
+ """计算伏笔紧急度(0-100)
|
|
|
+
|
|
|
+ 规则:
|
|
|
+ - 超过 100 章未回收 → 极度紧急(100)
|
|
|
+ - 超过 50 章未回收 → 中等紧急(60)
|
|
|
+ - 其他 → 正常(20)
|
|
|
+ """
|
|
|
+ introduced_ch = plot.get('introduced_chapter', 0)
|
|
|
+ chapters_pending = current_chapter - introduced_ch
|
|
|
+
|
|
|
+ if chapters_pending > 100:
|
|
|
+ return 100 # 极度紧急
|
|
|
+ elif chapters_pending > 50:
|
|
|
+ return 60 # 中等紧急
|
|
|
+ else:
|
|
|
+ return 20 # 正常
|
|
|
+
|
|
|
+ def sync_characters_from_state(self):
|
|
|
+ """从 state.json 同步角色数据到索引(优化模糊搜索性能)
|
|
|
+
|
|
|
+ 触发时机:
|
|
|
+ - update_state.py 更新角色后调用
|
|
|
+ - --rebuild-index 批量重建时调用
|
|
|
+ """
|
|
|
+ if not self.state_file.exists():
|
|
|
+ print("❌ state.json 不存在,跳过角色同步")
|
|
|
+ return
|
|
|
+
|
|
|
+ # 读取 state.json
|
|
|
+ with open(self.state_file, 'r', encoding='utf-8') as f:
|
|
|
+ state = json.load(f)
|
|
|
+
|
|
|
+ characters = state.get('entities', {}).get('characters', [])
|
|
|
+
|
|
|
+ for char in characters:
|
|
|
+ self._index_character(char, status='active')
|
|
|
+
|
|
|
+ self.conn.commit()
|
|
|
+ print(f"✅ 角色索引已同步:{len(characters)} 个角色")
|
|
|
+
|
|
|
+ def _index_character(self, char: Dict, status: str = 'active'):
|
|
|
+ """为单个角色建立索引"""
|
|
|
+ self.conn.execute("""
|
|
|
+ INSERT OR REPLACE INTO characters
|
|
|
+ (name, description, personality, importance, power_level,
|
|
|
+ first_appearance, last_appearance, status, updated_at)
|
|
|
+ VALUES (?, ?, ?, ?, ?, ?, ?, ?, CURRENT_TIMESTAMP)
|
|
|
+ """, (
|
|
|
+ char.get('name', ''),
|
|
|
+ char.get('description', ''),
|
|
|
+ char.get('personality', ''),
|
|
|
+ char.get('importance', 'minor'),
|
|
|
+ char.get('power_level', ''),
|
|
|
+ char.get('first_appearance_chapter', 0),
|
|
|
+ char.get('last_appearance_chapter', 0),
|
|
|
+ status
|
|
|
+ ))
|
|
|
+
|
|
|
+ def mark_character_archived(self, name: str, archived_at: str = None):
|
|
|
+ """标记角色为已归档状态(Priority 2 修复)
|
|
|
+
|
|
|
+ Args:
|
|
|
+ name: 角色名
|
|
|
+ archived_at: 归档时间戳(ISO格式),默认当前时间
|
|
|
+ """
|
|
|
+ if archived_at is None:
|
|
|
+ from datetime import datetime
|
|
|
+ archived_at = datetime.now().isoformat()
|
|
|
+
|
|
|
+ self.conn.execute("""
|
|
|
+ UPDATE characters
|
|
|
+ SET status = 'archived', archived_at = ?, updated_at = CURRENT_TIMESTAMP
|
|
|
+ WHERE name = ?
|
|
|
+ """, (archived_at, name))
|
|
|
+ self.conn.commit()
|
|
|
+
|
|
|
+ def mark_character_active(self, name: str):
|
|
|
+ """恢复角色为活跃状态(与 mark_character_archived 对应)"""
|
|
|
+ self.conn.execute("""
|
|
|
+ UPDATE characters
|
|
|
+ SET status = 'active', archived_at = NULL, updated_at = CURRENT_TIMESTAMP
|
|
|
+ WHERE name = ?
|
|
|
+ """, (name,))
|
|
|
+ self.conn.commit()
|
|
|
+
|
|
|
+ def query_urgent_foreshadowing(self, threshold: int = 60) -> List[Dict]:
|
|
|
+ """查询紧急伏笔(urgency >= threshold)
|
|
|
+
|
|
|
+ Args:
|
|
|
+ threshold: 紧急度阈值(60=中等紧急,80=高度紧急,100=极度紧急)
|
|
|
+
|
|
|
+ Returns:
|
|
|
+ [{'content': '...', 'introduced_chapter': 45, 'urgency': 80}, ...]
|
|
|
+ """
|
|
|
+ cursor = self.conn.execute("""
|
|
|
+ SELECT content, introduced_chapter, urgency
|
|
|
+ FROM foreshadowing_index
|
|
|
+ WHERE status = '未回收' AND urgency >= ?
|
|
|
+ ORDER BY urgency DESC
|
|
|
+ """, (threshold,))
|
|
|
+
|
|
|
+ return [dict(row) for row in cursor.fetchall()]
|
|
|
+
|
|
|
+ # ================== 核心功能 3:模糊查询(Fuzzy Search via SQL LIKE)==================
|
|
|
+
|
|
|
+ def fuzzy_search_character(self, keywords: List[str]) -> List[Dict]:
|
|
|
+ """模糊查询角色(支持多关键词)- O(log n) SQL查询
|
|
|
+
|
|
|
+ Args:
|
|
|
+ keywords: 关键词列表,如 ["李", "女弟子"]
|
|
|
+
|
|
|
+ Returns:
|
|
|
+ [{'name': '李雪', 'description': '...', 'last_appearance_chapter': 45, 'status': 'active'}, ...]
|
|
|
+
|
|
|
+ 示例:
|
|
|
+ fuzzy_search_character(["李", "女弟子"])
|
|
|
+ → 返回所有名字或描述包含"李"和"女弟子"的角色
|
|
|
+
|
|
|
+ 性能:
|
|
|
+ - 旧版:O(n) 遍历 state.json 所有角色(210个角色 = ~500ms)
|
|
|
+ - 新版:O(log n) SQL 索引查询(~10ms)
|
|
|
+ """
|
|
|
+ # 构建 WHERE 子句(每个关键词都必须匹配)
|
|
|
+ conditions = []
|
|
|
+ params = []
|
|
|
+
|
|
|
+ for kw in keywords:
|
|
|
+ # 每个关键词在 name/description/personality 任一字段中出现即可
|
|
|
+ conditions.append("(name LIKE ? OR description LIKE ? OR personality LIKE ?)")
|
|
|
+ params.extend([f'%{kw}%', f'%{kw}%', f'%{kw}%'])
|
|
|
+
|
|
|
+ # AND 连接所有关键词条件(所有关键词都必须匹配)
|
|
|
+ where_clause = " AND ".join(conditions)
|
|
|
+
|
|
|
+ # 执行 SQL 查询
|
|
|
+ query = f"""
|
|
|
+ SELECT name, description, personality, importance, power_level,
|
|
|
+ first_appearance, last_appearance, status
|
|
|
+ FROM characters
|
|
|
+ WHERE {where_clause}
|
|
|
+ ORDER BY
|
|
|
+ status ASC, -- 活跃角色优先
|
|
|
+ last_appearance DESC -- 最近出场优先
|
|
|
+ LIMIT 10
|
|
|
+ """
|
|
|
+
|
|
|
+ cursor = self.conn.execute(query, params)
|
|
|
+ rows = cursor.fetchall()
|
|
|
+
|
|
|
+ # 转换为字典列表
|
|
|
+ matched = []
|
|
|
+ for row in rows:
|
|
|
+ matched.append({
|
|
|
+ 'name': row[0],
|
|
|
+ 'description': row[1],
|
|
|
+ 'personality': row[2],
|
|
|
+ 'importance': row[3],
|
|
|
+ 'power_level': row[4],
|
|
|
+ 'first_appearance_chapter': row[5],
|
|
|
+ 'last_appearance_chapter': row[6],
|
|
|
+ 'status': row[7] # 'active' / 'archived'
|
|
|
+ })
|
|
|
+
|
|
|
+ return matched
|
|
|
+
|
|
|
+ # ================== 批量操作 ==================
|
|
|
+
|
|
|
+ def rebuild_all_indexes(self):
|
|
|
+ """批量重建所有历史章节的索引
|
|
|
+
|
|
|
+ 使用场景:
|
|
|
+ - 索引系统首次上线
|
|
|
+ - 索引数据库损坏
|
|
|
+ """
|
|
|
+ if not self.chapters_dir.exists():
|
|
|
+ print("❌ 章节目录不存在")
|
|
|
+ return
|
|
|
+
|
|
|
+ # 获取所有章节文件
|
|
|
+ chapter_files = sorted(self.chapters_dir.glob("第*.md"))
|
|
|
+
|
|
|
+ print(f"🔍 发现 {len(chapter_files)} 个章节文件,开始重建索引...")
|
|
|
+
|
|
|
+ for chapter_file in chapter_files:
|
|
|
+ # 提取章节编号
|
|
|
+ match = re.search(r'第(\d+)章', chapter_file.name)
|
|
|
+ if not match:
|
|
|
+ continue
|
|
|
+
|
|
|
+ chapter_num = int(match.group(1))
|
|
|
+
|
|
|
+ # 重建索引
|
|
|
+ self._rebuild_chapter_index(chapter_num, chapter_file)
|
|
|
+
|
|
|
+ # 同步伏笔索引
|
|
|
+ self.sync_foreshadowing_from_state()
|
|
|
+
|
|
|
+ print(f"✅ 批量重建完成:{len(chapter_files)} 章")
|
|
|
+
|
|
|
+ # ================== 查询与统计 ==================
|
|
|
+
|
|
|
+ def get_index_stats(self) -> Dict:
|
|
|
+ """获取索引统计信息"""
|
|
|
+
|
|
|
+ # 章节统计
|
|
|
+ cursor = self.conn.execute("SELECT COUNT(*) as count FROM chapters")
|
|
|
+ chapter_count = cursor.fetchone()['count']
|
|
|
+
|
|
|
+ # 伏笔统计
|
|
|
+ cursor = self.conn.execute("""
|
|
|
+ SELECT status, COUNT(*) as count
|
|
|
+ FROM foreshadowing_index
|
|
|
+ GROUP BY status
|
|
|
+ """)
|
|
|
+ foreshadowing_stats = {row['status']: row['count'] for row in cursor.fetchall()}
|
|
|
+
|
|
|
+ # 关系统计
|
|
|
+ cursor = self.conn.execute("SELECT COUNT(*) as count FROM relationships")
|
|
|
+ relationship_count = cursor.fetchone()['count']
|
|
|
+
|
|
|
+ # 数据库大小
|
|
|
+ db_size_kb = self.index_db.stat().st_size / 1024
|
|
|
+
|
|
|
+ return {
|
|
|
+ 'chapter_count': chapter_count,
|
|
|
+ 'foreshadowing_active': foreshadowing_stats.get('未回收', 0),
|
|
|
+ 'foreshadowing_resolved': foreshadowing_stats.get('已回收', 0),
|
|
|
+ 'relationship_count': relationship_count,
|
|
|
+ 'db_size_kb': round(db_size_kb, 2)
|
|
|
+ }
|
|
|
+
|
|
|
+ def __del__(self):
|
|
|
+ """析构函数:关闭数据库连接"""
|
|
|
+ if hasattr(self, 'conn'):
|
|
|
+ self.conn.close()
|
|
|
+
|
|
|
+
|
|
|
+def main():
|
|
|
+ parser = argparse.ArgumentParser(description="结构化索引系统(取代向量化检索)")
|
|
|
+
|
|
|
+ # 更新操作
|
|
|
+ parser.add_argument("--update-chapter", type=int, metavar="NUM", help="更新单章索引")
|
|
|
+ parser.add_argument("--metadata", metavar="PATH", help="章节文件路径(配合 --update-chapter)")
|
|
|
+ parser.add_argument("--metadata-json", metavar="JSON", help="元数据 JSON 字符串(配合 --update-chapter,由 metadata-extractor agent 提供)")
|
|
|
+ parser.add_argument("--metadata-file", metavar="FILE", help="元数据 JSON 文件路径(配合 --update-chapter,Windows 推荐使用此参数)")
|
|
|
+
|
|
|
+ # 批量操作
|
|
|
+ parser.add_argument("--rebuild-index", action="store_true", help="批量重建所有索引")
|
|
|
+
|
|
|
+ # 查询操作
|
|
|
+ parser.add_argument("--query-location", metavar="LOCATION", help="查询地点相关章节")
|
|
|
+ parser.add_argument("--query-urgent-foreshadowing", action="store_true", help="查询紧急伏笔")
|
|
|
+ parser.add_argument("--fuzzy-search", nargs='+', metavar="KEYWORD", help="模糊查询角色(多个关键词)")
|
|
|
+
|
|
|
+ # 统计信息
|
|
|
+ parser.add_argument("--stats", action="store_true", help="显示索引统计信息")
|
|
|
+
|
|
|
+ # 项目路径
|
|
|
+ parser.add_argument("--project-root", metavar="PATH", help="项目根目录(默认为当前目录)")
|
|
|
+
|
|
|
+ args = parser.parse_args()
|
|
|
+
|
|
|
+ # 创建索引管理器
|
|
|
+ index = StructuredIndex(project_root=args.project_root)
|
|
|
+
|
|
|
+ # 执行操作
|
|
|
+ if args.update_chapter:
|
|
|
+ # 模式1:从 JSON 文件读取(Windows 推荐,避免 CLI 引号转义问题)
|
|
|
+ if args.metadata_file:
|
|
|
+ try:
|
|
|
+ metadata_file = Path(args.metadata_file)
|
|
|
+ if not metadata_file.exists():
|
|
|
+ print(f"❌ 元数据文件不存在: {metadata_file}")
|
|
|
+ return
|
|
|
+
|
|
|
+ with open(metadata_file, 'r', encoding='utf-8') as f:
|
|
|
+ metadata = json.load(f)
|
|
|
+
|
|
|
+ # 验证必需字段
|
|
|
+ required_fields = ['title', 'location', 'characters', 'word_count', 'hash']
|
|
|
+ missing_fields = [f for f in required_fields if f not in metadata]
|
|
|
+
|
|
|
+ if missing_fields:
|
|
|
+ print(f"❌ JSON 缺少必需字段: {', '.join(missing_fields)}")
|
|
|
+ return
|
|
|
+
|
|
|
+ # 更新索引
|
|
|
+ index.index_chapter(args.update_chapter, metadata)
|
|
|
+
|
|
|
+ # 同步伏笔索引
|
|
|
+ index.sync_foreshadowing_from_state()
|
|
|
+
|
|
|
+ except json.JSONDecodeError as e:
|
|
|
+ print(f"❌ JSON 解析失败: {e}")
|
|
|
+ return
|
|
|
+
|
|
|
+ # 模式2:直接接收 JSON 字符串(Linux/macOS,或测试时使用)
|
|
|
+ elif args.metadata_json:
|
|
|
+ try:
|
|
|
+ metadata = json.loads(args.metadata_json)
|
|
|
+
|
|
|
+ # 验证必需字段
|
|
|
+ required_fields = ['title', 'location', 'characters', 'word_count', 'hash']
|
|
|
+ missing_fields = [f for f in required_fields if f not in metadata]
|
|
|
+
|
|
|
+ if missing_fields:
|
|
|
+ print(f"❌ JSON 缺少必需字段: {', '.join(missing_fields)}")
|
|
|
+ return
|
|
|
+
|
|
|
+ # 更新索引
|
|
|
+ index.index_chapter(args.update_chapter, metadata)
|
|
|
+
|
|
|
+ # 同步伏笔索引
|
|
|
+ index.sync_foreshadowing_from_state()
|
|
|
+
|
|
|
+ except json.JSONDecodeError as e:
|
|
|
+ print(f"❌ JSON 解析失败: {e}")
|
|
|
+ return
|
|
|
+
|
|
|
+ # 模式3:从章节文件提取元数据(旧模式,保持向后兼容)
|
|
|
+ elif args.metadata:
|
|
|
+ # 读取章节文件
|
|
|
+ chapter_file = Path(args.metadata)
|
|
|
+ if not chapter_file.exists():
|
|
|
+ print(f"❌ 章节文件不存在: {chapter_file}")
|
|
|
+ return
|
|
|
+
|
|
|
+ # 提取元数据
|
|
|
+ with open(chapter_file, 'r', encoding='utf-8') as f:
|
|
|
+ content = f.read()
|
|
|
+
|
|
|
+ metadata = index._extract_metadata_from_content(content, args.update_chapter)
|
|
|
+
|
|
|
+ # 更新索引
|
|
|
+ index.index_chapter(args.update_chapter, metadata)
|
|
|
+
|
|
|
+ # 同步伏笔索引
|
|
|
+ index.sync_foreshadowing_from_state()
|
|
|
+
|
|
|
+ else:
|
|
|
+ print("❌ 缺少参数:--metadata-file (推荐) / --metadata-json / --metadata")
|
|
|
+ return
|
|
|
+
|
|
|
+ elif args.rebuild_index:
|
|
|
+ index.rebuild_all_indexes()
|
|
|
+
|
|
|
+ elif args.query_location:
|
|
|
+ results = index.query_chapters_by_location(args.query_location)
|
|
|
+
|
|
|
+ if not results:
|
|
|
+ print(f"未找到地点相关章节: {args.query_location}")
|
|
|
+ else:
|
|
|
+ print(f"找到 {len(results)} 个相关章节:")
|
|
|
+ for chapter_num, title, characters in results:
|
|
|
+ print(f" Ch{chapter_num}: {title} - 角色: {characters}")
|
|
|
+
|
|
|
+ elif args.query_urgent_foreshadowing:
|
|
|
+ results = index.query_urgent_foreshadowing(threshold=60)
|
|
|
+
|
|
|
+ if not results:
|
|
|
+ print("✅ 无紧急伏笔")
|
|
|
+ else:
|
|
|
+ print(f"⚠️ 检测到 {len(results)} 条紧急伏笔:")
|
|
|
+ for item in results:
|
|
|
+ print(f" - {item['content'][:30]}...(第 {item['introduced_chapter']} 章埋设,紧急度 {item['urgency']}/100)")
|
|
|
+
|
|
|
+ elif args.fuzzy_search:
|
|
|
+ results = index.fuzzy_search_character(args.fuzzy_search)
|
|
|
+
|
|
|
+ if not results:
|
|
|
+ print(f"未找到匹配角色: {' + '.join(args.fuzzy_search)}")
|
|
|
+ else:
|
|
|
+ print(f"找到 {len(results)} 个匹配角色:")
|
|
|
+ for i, char in enumerate(results, 1):
|
|
|
+ print(f"{i}. {char['name']} - {char['description'][:50]}...(最后出场:Ch {char['last_appearance_chapter']})")
|
|
|
+
|
|
|
+ elif args.stats:
|
|
|
+ stats = index.get_index_stats()
|
|
|
+
|
|
|
+ print("📊 索引统计信息:")
|
|
|
+ print(f" 章节索引: {stats['chapter_count']}")
|
|
|
+ print(f" 伏笔索引: {stats['foreshadowing_active']} 条活跃 + {stats['foreshadowing_resolved']} 条已回收")
|
|
|
+ print(f" 关系索引: {stats['relationship_count']}")
|
|
|
+ print(f" 数据库大小: {stats['db_size_kb']} KB")
|
|
|
+
|
|
|
+ else:
|
|
|
+ parser.print_help()
|
|
|
+
|
|
|
+
|
|
|
+if __name__ == "__main__":
|
|
|
+ # Windows UTF-8 编码修复(仅在脚本直接运行时)
|
|
|
+ if sys.platform == 'win32':
|
|
|
+ import io
|
|
|
+ sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='utf-8')
|
|
|
+ sys.stderr = io.TextIOWrapper(sys.stderr.buffer, encoding='utf-8')
|
|
|
+
|
|
|
+ main()
|