616 lines
24 KiB
Python
616 lines
24 KiB
Python
#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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"""
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Poem Classification System with LLM API Integration
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基于大模型的中国古代诗词多维度分类系统
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分类维度包括:
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- 季节时序（四季、节气、时辰）
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- 题材类型（山水、边塞、咏史、咏物等）
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- 情感心境（喜怒哀乐、忧思愁绪等）
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- 景物意象（自然、植物、动物、建筑等）
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- 哲理思想（儒释道、人生感悟等）
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- 艺术手法（比兴、用典、对仗等）
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- 人物关系（送别、怀人、思乡等）
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- 生活场景（宴饮、耕作、读书等）
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Configuration via environment variables:
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- LLM_BASE_URL: API base URL (e.g., http://localhost:11434/v1)
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- LLM_API_KEY: API key (optional for local models)
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- LLM_MODEL: Model name (e.g., qwen:7b, gpt-4)
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"""
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import json
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import os
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import re
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import hashlib
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from typing import Dict, List, Set, Optional
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from datetime import datetime
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from concurrent.futures import ThreadPoolExecutor, as_completed
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import urllib.request
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import urllib.error
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import threading
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def generate_poem_signature(poem_data: Dict) -> str:
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"""
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基于标题、作者、内容生成诗词的唯一签名（MD5）
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用于检测重复诗词
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"""
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title = poem_data.get('title', '').strip()
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author = poem_data.get('author', '').strip()
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content_parts = []
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if 'paragraphs' in poem_data:
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for para in poem_data['paragraphs']:
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if isinstance(para, str):
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content_parts.append(para.strip())
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elif isinstance(para, list):
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content_parts.extend([p.strip() for p in para])
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if 'content' in poem_data:
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if isinstance(poem_data['content'], str):
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content_parts.append(poem_data['content'].strip())
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elif isinstance(poem_data['content'], list):
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content_parts.extend([p.strip() for p in poem_data['content']])
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content = '\n'.join(content_parts)
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signature_str = f"title:{title}|author:{author}|content:{content}"
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return hashlib.md5(signature_str.encode('utf-8')).hexdigest()
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def load_existing_poems(output_file: str) -> Set[str]:
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"""
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从输出文件加载已存在的诗词签名
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"""
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existing_signatures = set()
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if not os.path.exists(output_file):
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return existing_signatures
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try:
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with open(output_file, 'r', encoding='utf-8') as f:
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for line_num, line in enumerate(f, 1):
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line = line.strip()
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if not line:
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continue
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try:
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data = json.loads(line)
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if 'title' in data and 'author' in data:
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sig = generate_poem_signature({
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'title': data.get('title', ''),
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'author': data.get('author', ''),
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'paragraphs': data.get('llm_classification', {}).get('analysis', '').split('\n')
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if data.get('llm_classification') else []
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})
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existing_signatures.add(sig)
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except json.JSONDecodeError:
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print(f" Warning: Skipping invalid JSON on line {line_num}")
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except IOError as e:
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print(f" Warning: Could not read existing output file: {e}")
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return existing_signatures
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def append_to_output_file(output_file: str, data: Dict, lock: threading.Lock):
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"""
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线程安全地追加写入输出文件
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"""
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with lock:
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with open(output_file, 'a', encoding='utf-8') as f:
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f.write(json.dumps(data, ensure_ascii=False) + '\n')
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class LLMClassifier:
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"""
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基于大模型的诗词分类器
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支持通过 OpenAI 兼容 API 调用本地或远程模型
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提供 20+ 维度的诗词分类标签
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"""
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CLASSIFICATION_PROMPT = """你是一位中国古代诗词分类专家。请深入分析以下诗词，并从多个维度进行分类标注。
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【诗词信息】
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- 标题：{title}
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- 作者：{author}
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- 内容：{content}
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【重要说明】
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- 如果诗词内容是繁体中文，请在输出中包含简体中文版本
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- 分类标签一律使用简体中文
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【分类维度与标签选项】
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1. 季节（season）：["春", "夏", "秋", "冬", "四季", "无明确季节"]
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2. 节气（solar_terms）：24 节气中的具体节气，如 "立春"、"清明"、"冬至" 等，无则空数组
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3. 时辰（time_of_day）：["清晨", "上午", "正午", "下午", "黄昏", "夜晚", "深夜", "黎明", "不明确"]
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4. 题材类型（genre）：["山水田园", "边塞征战", "咏史怀古", "咏物言志", "送别怀人", "思乡怀远",
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"爱情闺怨", "友情赠答", "羁旅漂泊", "隐逸闲适", "讽喻时事", "节日习俗",
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"宴饮酬唱", "读书治学", "农耕劳作", "宗教禅理", "其他"]
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5. 情感基调（emotion_tone）：["喜悦欢快", "悲伤哀愁", "愤怒激愤", "忧郁伤感", "孤独寂寞",
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"宁静淡泊", "豪迈激昂", "思念眷恋", "惆怅失落", "平和超脱", "复杂混合"]
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6. 具体情感（emotions）：从以下选择 3-5 个最贴切的：
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["喜", "怒", "哀", "乐", "忧", "思", "悲", "恐", "惊", "愁", "恨", "爱",
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"恋", "盼", "悔", "愧", "傲", "谦", "静", "躁"]
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7. 景物 - 自然（nature_scenery）：["山", "水", "云", "雨", "雪", "风", "雷", "电", "日", "月", "星", "霜", "露", "霞"]
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8. 景物 - 植物（plants）：["松", "竹", "梅", "兰", "菊", "荷", "柳", "桃", "李", "杏", "梨", "枫", "梧桐", "芭蕉", "其他"]
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9. 景物 - 动物（animals）：["鸟", "雁", "燕", "鹊", "蝉", "蛙", "鱼", "龙", "凤", "马", "牛", "羊", "犬", "其他"]
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10. 景物 - 建筑（buildings）：["楼", "阁", "亭", "台", "轩", "榭", "桥", "寺", "塔", "城", "关", "宫", "殿", "院", "其他"]
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11. 意象关键词（imagery）：提取 5-10 个诗中最具代表性的意象词汇（简体中文）
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12. 哲理思想（philosophy）：["儒家思想", "道家思想", "佛家禅理", "人生感悟", "历史兴叹", "自然之道", "无明显哲理"]
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13. 人生阶段（life_stage）：["少年", "青年", "中年", "老年", "不明确"]
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14. 社会身份（social_role）：["士人", "官员", "隐士", "游子", "征人", "商贾", "农夫", "僧道", "闺中", "其他"]
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15. 写作手法（technique）：["比兴", "赋", "对仗", "用典", "借景抒情", "托物言志", "虚实结合", "动静结合", "其他"]
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16. 修辞手法（rhetoric）：["比喻", "拟人", "夸张", "对偶", "排比", "反复", "设问", "反问", "其他"]
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17. 色彩意象（colors）：提取诗中的色彩词，如 ["青", "绿", "红", "白", "黄", "紫", "碧", "翠", "苍", "金"]
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18. 声音意象（sounds）：["钟声", "鼓声", "笛声", "琴声", "风声", "雨声", "鸟鸣", "蝉鸣", "其他"]
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19. 地理方位（location）：["江南", "塞北", "中原", "巴蜀", "关中", "岭南", "吴越", "荆楚", "其他"]
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20. 节日习俗（festival）：["春节", "元宵", "清明", "端午", "七夕", "中秋", "重阳", "除夕", "无"]
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【返回格式】
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请返回严格的 JSON 格式，结构如下：
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{{
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"original_text": ["空山新雨後，天氣晚來秋。", ...], // 原始文本（如果输入是繁体）
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"simplified_text": ["空山新雨后，天气晚来秋。", ...], // 简体中文版本
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"season": ["秋"],
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"solar_terms": ["白露"],
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"time_of_day": "黄昏",
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"genre": ["山水田园", "隐逸闲适"],
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"emotion_tone": "宁静淡泊",
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"emotions": ["静", "喜", "乐"],
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"nature_scenery": ["山", "水", "月"],
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"plants": ["松", "竹"],
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"animals": ["鸟"],
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"buildings": [],
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"imagery": ["空山", "新雨", "明月", "青松"],
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"philosophy": ["道家思想", "自然之道"],
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"life_stage": "中年",
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"social_role": "隐士",
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"technique": ["借景抒情", "动静结合"],
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"rhetoric": ["拟人"],
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"colors": ["青", "白"],
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"sounds": [],
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"location": "终南山",
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"festival": "无",
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"analysis": "简要分析这首诗的主题思想、艺术特色和情感内涵（100-200 字，使用简体中文）"
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}}
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【注意事项】
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- 只返回 JSON，不要有任何其他文字说明
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- 每个维度根据诗意选择最贴切的标签，可以是 1 个或多个
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- 如果某个维度没有明确对应，选择"无"、"不明确"或空数组
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- 意象关键词应从原诗中提取或合理归纳（使用简体中文）
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- 分析要准确、深入、简洁（使用简体中文）
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- 如果输入是繁体中文，original_text 和 simplified_text 都要填写
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- 如果输入已经是简体中文，original_text 和 simplified_text 填写相同内容"""
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def __init__(self, base_url: Optional[str] = None, api_key: Optional[str] = None,
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model: Optional[str] = None, output_file: Optional[str] = None):
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"""
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初始化 LLM 分类器
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Args:
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base_url: API 基础 URL
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api_key: API 密钥（本地模型可选）
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model: 模型名称
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output_file: 输出文件路径（用于实时写入）
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"""
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self.base_url = (base_url or os.getenv('LLM_BASE_URL') or 'https://api.siliconflow.cn/v1').rstrip('/')
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self.api_key = api_key or os.getenv('LLM_API_KEY') or ''
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self.model = model or os.getenv('LLM_MODEL') or 'qwen:7b'
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self.output_file = output_file
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self.file_lock = threading.Lock() if output_file else None
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def _call_api(self, messages: List[Dict], temperature: float = 0.3, max_retries: int = 3) -> str:
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"""
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调用 LLM API，带重试机制
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Args:
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messages: 消息列表
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temperature: 温度参数
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max_retries: 最大重试次数
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Returns:
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API 返回的文本内容
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"""
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url = f"{self.base_url}/chat/completions"
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headers = {
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'Content-Type': 'application/json',
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}
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if self.api_key:
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headers['Authorization'] = f'Bearer {self.api_key}'
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payload = {
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'model': self.model,
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'messages': messages,
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'temperature': temperature,
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'stream': False
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}
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for attempt in range(max_retries):
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try:
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data = json.dumps(payload).encode('utf-8')
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req = urllib.request.Request(url, data=data, headers=headers, method='POST')
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with urllib.request.urlopen(req, timeout=180) as response:
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result = json.loads(response.read().decode('utf-8'))
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return result['choices'][0]['message']['content']
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except urllib.error.URLError as e:
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print(f" API request failed (attempt {attempt + 1}/{max_retries}): {e}")
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if attempt == max_retries - 1:
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return ""
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except json.JSONDecodeError as e:
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print(f" Failed to parse API response: {e}")
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return ""
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except Exception as e:
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print(f" Unexpected error (attempt {attempt + 1}/{max_retries}): {e}")
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if attempt == max_retries - 1:
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return ""
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return ""
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def classify_poem(self, poem_data: Dict, skip_if_exists: bool = False,
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existing_signatures: Optional[Set[str]] = None) -> Optional[Dict]:
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"""
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使用 LLM 对单首诗词进行分类
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Args:
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poem_data: 诗词数据字典
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skip_if_exists: 是否跳过已存在的诗词
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existing_signatures: 已存在的诗词签名集合
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Returns:
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分类结果字典，失败返回 None
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"""
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title = poem_data.get('title', '')
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author = poem_data.get('author', '')
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content_parts = []
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if 'paragraphs' in poem_data:
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for para in poem_data['paragraphs']:
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if isinstance(para, str):
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content_parts.append(para)
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elif isinstance(para, list):
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content_parts.extend(para)
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if 'content' in poem_data:
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if isinstance(poem_data['content'], str):
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content_parts.append(poem_data['content'])
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elif isinstance(poem_data['content'], list):
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content_parts.extend(poem_data['content'])
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content = '\n'.join(content_parts)
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if not title and not content:
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return None
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# 检查是否已存在
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if skip_if_exists and existing_signatures is not None:
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poem_sig = generate_poem_signature(poem_data)
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if poem_sig in existing_signatures:
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return None
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prompt = self.CLASSIFICATION_PROMPT.format(
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title=title or '无题',
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author=author or '佚名',
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content=content
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)
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messages = [
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{'role': 'system', 'content': '你是一位中国古代诗词分类专家，精通诗词鉴赏和分类，能够准确识别诗词的题材、情感、意象和艺术特色。'},
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{'role': 'user', 'content': prompt}
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]
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response = self._call_api(messages, temperature=0.3)
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if not response:
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return None
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try:
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# 提取 JSON 部分
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json_match = re.search(r'\{[\s\S]*\}', response)
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if json_match:
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result = json.loads(json_match.group())
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else:
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result = json.loads(response)
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# 处理简繁转换结果
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paragraphs = poem_data.get('paragraphs', [])
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simplified_paragraphs = []
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# 如果 LLM 返回了简体版本，使用它
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if 'simplified_text' in result:
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simplified_paragraphs = result['simplified_text']
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elif 'original_text' in result:
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# 如果只有 original_text，说明输入已经是简体
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simplified_paragraphs = paragraphs
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else:
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# 没有文本转换信息，使用原始内容
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simplified_paragraphs = paragraphs
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classification_result = {
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'id': poem_data.get('id', 'unknown'),
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'title': title,
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'author': author,
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'paragraphs': simplified_paragraphs, # 使用简体版本
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'original_paragraphs': paragraphs if paragraphs != simplified_paragraphs else None, # 保留原始（如果不同）
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'llm_classification': result,
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'signature': generate_poem_signature({
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'title': title,
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'author': author,
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'paragraphs': simplified_paragraphs
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}), # 基于简体内容生成签名
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'timestamp': datetime.now().isoformat(),
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'read_mark': False
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}
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# 实时写入文件
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if self.output_file and self.file_lock:
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append_to_output_file(self.output_file, classification_result, self.file_lock)
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return classification_result
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except json.JSONDecodeError as e:
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print(f" Failed to parse LLM response as JSON: {e}")
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print(f" Raw response: {response[:300]}...")
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return None
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def is_valid_poem_data(poem_data: Dict) -> bool:
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"""
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验证诗词数据格式是否有效
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"""
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required_fields = {'author', 'paragraphs', 'title'}
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if not isinstance(poem_data, dict):
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return False
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if not required_fields.issubset(poem_data.keys()):
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return False
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paragraphs = poem_data.get('paragraphs')
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if paragraphs is None or not isinstance(paragraphs, list):
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return False
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for para in paragraphs:
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if not isinstance(para, str):
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return False
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if 'id' not in poem_data or not isinstance(poem_data['id'], str):
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return False
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if not isinstance(poem_data['author'], str) or len(poem_data['author'].strip()) == 0:
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return False
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if not isinstance(poem_data['title'], str) or len(poem_data['title'].strip()) == 0:
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return False
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return True
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def load_poems_from_file(file_path: str) -> List[Dict]:
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"""
|
||
从文件加载诗词数据
|
||
支持 JSON 数组、单个 JSON 对象、JSONL 三种格式
|
||
"""
|
||
poems = []
|
||
|
||
try:
|
||
with open(file_path, 'r', encoding='utf-8') as f:
|
||
content = f.read().strip()
|
||
|
||
if not content:
|
||
return poems
|
||
|
||
# 尝试解析为 JSON
|
||
try:
|
||
data = json.loads(content)
|
||
if isinstance(data, list):
|
||
poems = data
|
||
elif isinstance(data, dict):
|
||
poems = [data]
|
||
except json.JSONDecodeError:
|
||
# 按 JSONL 处理
|
||
for line in content.splitlines():
|
||
line = line.strip()
|
||
if line:
|
||
try:
|
||
obj = json.loads(line)
|
||
if isinstance(obj, dict):
|
||
poems.append(obj)
|
||
except json.JSONDecodeError:
|
||
continue
|
||
except IOError as e:
|
||
print(f"Error reading file {file_path}: {e}")
|
||
|
||
return poems
|
||
|
||
|
||
def main():
|
||
"""主入口函数"""
|
||
import argparse
|
||
|
||
parser = argparse.ArgumentParser(
|
||
description='基于大模型的中国古代诗词多维度分类系统',
|
||
formatter_class=argparse.RawDescriptionHelpFormatter,
|
||
epilog="""
|
||
示例:
|
||
# 使用本地模型分类（递归搜索子目录）
|
||
python classify_pois.py ./poems ./output.jsonl 10 --llm \\
|
||
--base-url http://localhost:11434/v1 --model qwen:7b
|
||
|
||
# 使用远程模型分类
|
||
python classify_pois.py ./poems ./output.jsonl 10 --llm \\
|
||
--base-url https://api.openai.com/v1 --model gpt-4 --api-key sk-xxx
|
||
|
||
# 断点续跑（自动跳过已处理的诗词）
|
||
python classify_pois.py ./poems ./output.jsonl 10 --llm \\
|
||
--base-url http://localhost:11434/v1 --model qwen:7b
|
||
|
||
# 只扫描当前目录，不递归子目录
|
||
python classify_pois.py ./poems ./output.jsonl 10 --llm --no-recursive \\
|
||
--base-url http://localhost:11434/v1 --model qwen:7b
|
||
|
||
环境变量（备选）:
|
||
LLM_BASE_URL, LLM_API_KEY, LLM_MODEL
|
||
"""
|
||
)
|
||
|
||
parser.add_argument('input_folder', help='输入文件夹路径（包含诗词 JSON/JSONL 文件）')
|
||
parser.add_argument('output_file', help='输出 JSONL 文件路径')
|
||
parser.add_argument('max_workers', nargs='?', type=int, default=10, help='并发数（默认：10）')
|
||
|
||
parser.add_argument('--llm', action='store_true', help='使用 LLM 分类（默认）')
|
||
parser.add_argument('--base-url', type=str, help='LLM API 基础 URL')
|
||
parser.add_argument('--api-key', type=str, help='LLM API 密钥')
|
||
parser.add_argument('--model', type=str, help='LLM 模型名称')
|
||
parser.add_argument('--no-recursive', action='store_true', help='不递归搜索子目录')
|
||
|
||
args = parser.parse_args()
|
||
|
||
# 获取配置
|
||
base_url = args.base_url or os.getenv('LLM_BASE_URL', 'http://localhost:11434/v1')
|
||
api_key = args.api_key or os.getenv('LLM_API_KEY', '')
|
||
model = args.model or os.getenv('LLM_MODEL', 'qwen:7b')
|
||
|
||
print(f"\n{'='*70}")
|
||
print(f"基于大模型的诗词多维度分类系统")
|
||
print(f"{'='*70}")
|
||
print(f"输入目录：{args.input_folder}")
|
||
print(f"输出文件：{args.output_file}")
|
||
print(f"API 地址：{base_url}")
|
||
print(f"模型名称：{model}")
|
||
print(f"{'='*70}\n")
|
||
|
||
# 加载已存在的诗词签名
|
||
existing_signatures = load_existing_poems(args.output_file)
|
||
if existing_signatures:
|
||
print(f"检测到输出文件中已有 {len(existing_signatures)} 首诗词，将自动跳过重复项\n")
|
||
|
||
# 创建分类器
|
||
llm_classifier = LLMClassifier(
|
||
base_url=base_url,
|
||
api_key=api_key,
|
||
model=model,
|
||
output_file=args.output_file
|
||
)
|
||
|
||
# 扫描输入文件（支持递归搜索子目录）
|
||
valid_extensions = {'.json', '.jsonl'}
|
||
json_files = []
|
||
|
||
if args.no_recursive:
|
||
# 不递归，只扫描当前目录
|
||
for f in os.listdir(args.input_folder):
|
||
if not any(f.lower().endswith(ext) for ext in valid_extensions):
|
||
continue
|
||
file_path = os.path.join(args.input_folder, f)
|
||
if not os.path.isfile(file_path):
|
||
continue
|
||
json_files.append(file_path)
|
||
else:
|
||
# 递归扫描所有子目录
|
||
for root, dirs, files in os.walk(args.input_folder):
|
||
# 跳过隐藏目录
|
||
dirs[:] = [d for d in dirs if not d.startswith('.')]
|
||
|
||
for f in files:
|
||
if not any(f.lower().endswith(ext) for ext in valid_extensions):
|
||
continue
|
||
# 跳过隐藏文件
|
||
if f.startswith('.'):
|
||
continue
|
||
file_path = os.path.join(root, f)
|
||
if os.path.isfile(file_path):
|
||
json_files.append(file_path)
|
||
|
||
# 按文件路径排序，保证处理顺序一致
|
||
json_files.sort()
|
||
|
||
print(f"发现 {len(json_files)} 个有效的 JSON/JSONL 文件\n")
|
||
|
||
# 显示目录结构（如果有子目录）
|
||
subdirs = set(os.path.dirname(f).replace(args.input_folder, '').lstrip('\\').lstrip('/') for f in json_files)
|
||
if subdirs:
|
||
print(f"包含子目录：{', '.join(sorted(subdirs))}")
|
||
|
||
# 统计信息
|
||
stats = {
|
||
'processed': 0,
|
||
'skipped': 0,
|
||
'failed': 0,
|
||
'invalid': 0,
|
||
'total': 0
|
||
}
|
||
stats_lock = threading.Lock()
|
||
|
||
def process_poem_batch(file_path: str) -> dict:
|
||
"""处理单个文件的诗词"""
|
||
file_stats = {'processed': 0, 'skipped': 0, 'failed': 0, 'invalid': 0}
|
||
poems = load_poems_from_file(file_path)
|
||
|
||
for idx, poem in enumerate(poems):
|
||
if not is_valid_poem_data(poem):
|
||
file_stats['invalid'] += 1
|
||
print(f" [{idx+1}/{len(poems)}] 格式无效：{poem.get('title', 'Unknown')}")
|
||
continue
|
||
|
||
poem_sig = generate_poem_signature(poem)
|
||
if poem_sig in existing_signatures:
|
||
file_stats['skipped'] += 1
|
||
print(f" [{idx+1}/{len(poems)}] 跳过（已存在）: {poem.get('title', 'Unknown')}")
|
||
continue
|
||
|
||
print(f" [{idx+1}/{len(poems)}] 分类中：{poem.get('title', 'Unknown')}")
|
||
result = llm_classifier.classify_poem(poem, skip_if_exists=True, existing_signatures=existing_signatures)
|
||
|
||
if result:
|
||
file_stats['processed'] += 1
|
||
existing_signatures.add(poem_sig)
|
||
print(f" ✓ 成功")
|
||
else:
|
||
file_stats['failed'] += 1
|
||
print(f" ✗ 失败")
|
||
|
||
return file_stats
|
||
|
||
# 使用线程池并发处理
|
||
print(f"使用 {args.max_workers} 个并发线程处理\n")
|
||
|
||
with ThreadPoolExecutor(max_workers=args.max_workers) as executor:
|
||
# 提交所有文件处理任务
|
||
future_to_file = {executor.submit(process_poem_batch, fp): fp for fp in json_files}
|
||
|
||
# 收集结果
|
||
for future in as_completed(future_to_file):
|
||
file_path = future_to_file[future]
|
||
try:
|
||
file_stats = future.result()
|
||
with stats_lock:
|
||
stats['processed'] += file_stats['processed']
|
||
stats['skipped'] += file_stats['skipped']
|
||
stats['failed'] += file_stats['failed']
|
||
stats['invalid'] += file_stats['invalid']
|
||
stats['total'] += file_stats['processed'] + file_stats['skipped'] + file_stats['failed'] + file_stats['invalid']
|
||
except Exception as e:
|
||
print(f"处理文件 {file_path} 时出错：{e}")
|
||
|
||
print()
|
||
|
||
# 输出统计
|
||
print(f"\n{'='*70}")
|
||
print(f"分类完成!")
|
||
print(f"{'='*70}")
|
||
print(f"诗词总数：{stats['total']}")
|
||
print(f"成功分类：{stats['processed']}")
|
||
print(f"跳过重复：{stats['skipped']}")
|
||
print(f"分类失败：{stats['failed']}")
|
||
print(f"格式无效：{stats['invalid']}")
|
||
print(f"输出文件：{args.output_file}")
|
||
print(f"{'='*70}\n")
|
||
|
||
|
||
if __name__ == "__main__":
|
||
main() |