""" gen_content_digest.py - 批量生成 22 期节目内容摘要卡 用法: cd E:\tps-dashboard\ai-labeling python scripts/gen_content_digest.py 功能: - 读取 prompt4_content_digest.md 作为 system prompt - 遍历 doco/deliverables/ 下所有融合A稿 .docx 文件 - 用 python-docx 提取文稿文本，调用 MiMo API 生成结构化摘要卡 - 支持断点续跑（已存在的 digest 文件自动跳过） """ import sys sys.stdout.reconfigure(encoding='utf-8') sys.stderr.reconfigure(encoding='utf-8') import os import re import json import time from pathlib import Path from datetime import datetime from openai import OpenAI from dotenv import load_dotenv from docx import Document # 加载 .env（优先加载 ai-labeling 目录下的 .env） SCRIPT_DIR = Path(__file__).parent BASE_DIR = SCRIPT_DIR.parent # ai-labeling/ load_dotenv(BASE_DIR / ".env") load_dotenv() # 也尝试加载项目根目录的 .env # 目录配置 DELIVERABLES_DIR = BASE_DIR.parent / "doco" / "deliverables" PROMPTS_DIR = BASE_DIR / "prompts" EXPERIMENTS_DIR = BASE_DIR / "experiments" / "content_digests" # MiMo API 配置（与 run_labeling.py 一致） MIMO_CONFIG = { "base_url": "https://api.xiaomimimo.com/v1", "model_name": "mimo-v2.5-pro", "api_key_env": "MIMO_API_KEY", } def load_system_prompt() -> str: """加载 prompt4_content_digest.md 作为 system prompt。""" prompt_file = PROMPTS_DIR / "prompt4_content_digest.md" if not prompt_file.exists(): raise FileNotFoundError(f"找不到 prompt 文件: {prompt_file}") return prompt_file.read_text(encoding="utf-8") def parse_filename(filename: str) -> dict: """ 从文件名解析元信息。 文件名格式: 第02期_20260113_武器进化论：海战颠覆者_付天雨_融合A稿.docx 返回: {"ep": 2, "date": "2026-01-13", "title": "...", "editor": "..."} """ name = filename.replace(".docx", "") parts = name.split("_") if len(parts) < 4: return None # 解析期号 ep_match = re.search(r'第(\d+)期', parts[0]) if not ep_match: return None ep = int(ep_match.group(1)) # 解析日期（YYYYMMDD -> YYYY-MM-DD） raw_date = parts[1] if len(raw_date) == 8 and raw_date.isdigit(): date = f"{raw_date[:4]}-{raw_date[4:6]}-{raw_date[6:8]}" else: date = raw_date title = parts[2] editor = parts[3] return {"ep": ep, "date": date, "title": title, "editor": editor} def extract_docx_text(filepath: Path) -> str: """用 python-docx 提取 .docx 文件的全部文本，逐段拼接。""" doc = Document(str(filepath)) paragraphs = [] for para in doc.paragraphs: text = para.text.strip() if text: paragraphs.append(text) return "\n".join(paragraphs) def build_user_message(meta: dict, body: str) -> str: """构造 user message：元信息 + 文稿全文。""" return ( f"期号：第{meta['ep']:02d}期\n" f"播出日期：{meta['date']}\n" f"节目名：{meta['title']}\n" f"编导：{meta['editor']}\n" f"\n以下是节目文稿全文：\n\n{body}" ) def extract_json_from_response(raw: str) -> dict: """从模型响应中提取 JSON，兼容推理模型的...输出。""" # 先去掉...标签及其内容 text = re.sub(r'.*?', '', raw, flags=re.DOTALL) text = text.strip() # 去掉 markdown 代码块 text = re.sub(r'^```(?:json)?\s*', '', text) text = re.sub(r'\s*```$', '', text) text = text.strip() # 从第一个 { 开始，到最后一个 } 结束 first_brace = text.find('{') last_brace = text.rfind('}') if first_brace != -1 and last_brace != -1 and last_brace >= first_brace: json_str = text[first_brace:last_brace + 1] return json.loads(json_str) # 兜底：直接尝试解析 return json.loads(text) def call_mimo(system_prompt: str, user_prompt: str) -> dict: """调用 MiMo API 生成摘要卡，返回解析后的 JSON dict。""" api_key = os.environ.get(MIMO_CONFIG["api_key_env"]) if not api_key: raise EnvironmentError( f"环境变量 {MIMO_CONFIG['api_key_env']} 未设置，请检查 ai-labeling/.env 或根目录 .env 文件" ) client = OpenAI( api_key=api_key, base_url=MIMO_CONFIG["base_url"], ) response = client.chat.completions.create( model=MIMO_CONFIG["model_name"], messages=[ {"role": "system", "content": system_prompt}, {"role": "user", "content": user_prompt}, ], temperature=0.0, # 关闭 thinking（与 run_labeling.py 一致） extra_body={"thinking": {"type": "disabled"}}, ) raw = response.choices[0].message.content return extract_json_from_response(raw) def collect_docx_files() -> list[dict]: """收集所有 .docx 文件并按期号排序。""" files = [] for f in sorted(DELIVERABLES_DIR.iterdir()): if not f.name.endswith(".docx"): continue meta = parse_filename(f.name) if meta is None: print(f"⚠️ 跳过无法解析的文件: {f.name}") continue meta["filepath"] = f files.append(meta) # 按期号排序 files.sort(key=lambda x: x["ep"]) return files def main(): # 确保输出目录存在 EXPERIMENTS_DIR.mkdir(parents=True, exist_ok=True) # 加载 system prompt system_prompt = load_system_prompt() print(f"✅ 已加载 system prompt: prompt4_content_digest.md") # 收集 docx 文件 docx_files = collect_docx_files() print(f"✅ 找到 {len(docx_files)} 个融合A稿 docx 文件\n") all_digests = [] skipped = 0 success = 0 failed = 0 for i, meta in enumerate(docx_files): ep = meta["ep"] out_file = EXPERIMENTS_DIR / f"ep{ep:02d}_digest.json" # 断点续跳：已存在则跳过 if out_file.exists(): print(f"[{i+1}/{len(docx_files)}] ep{ep:02d} 已存在，跳过") try: existing = json.loads(out_file.read_text(encoding="utf-8")) all_digests.append(existing) except Exception: pass skipped += 1 continue print(f"[{i+1}/{len(docx_files)}] 正在处理 ep{ep:02d} - {meta['title']} ...") try: # 提取 docx 文本 body = extract_docx_text(meta["filepath"]) if not body.strip(): print(f" ⚠️ ep{ep:02d} 文稿内容为空，跳过") failed += 1 continue # 构造 user message user_msg = build_user_message(meta, body) # 调用 MiMo result = call_mimo(system_prompt, user_msg) # 构造输出 output = { "ep": ep, "date": meta["date"], "title": meta["title"], "editor": meta["editor"], "filename": meta["filepath"].name, "digest": result, "generated_at": datetime.now().isoformat(), } # 写入单期文件 out_file.write_text( json.dumps(output, ensure_ascii=False, indent=2), encoding="utf-8", ) all_digests.append(output) print(f" ✅ ep{ep:02d} 摘要卡已生成 -> {out_file.name}") success += 1 except json.JSONDecodeError as e: print(f" ⚠️ ep{ep:02d} LLM 返回的不是合法 JSON: {e}") failed += 1 except Exception as e: print(f" ❌ ep{ep:02d} 处理失败: {e}") failed += 1 # 限流保护：每期之间 sleep 1 秒 if i < len(docx_files) - 1: time.sleep(1) # 写入汇总文件 summary_file = EXPERIMENTS_DIR / "_all_digests.json" summary_file.write_text( json.dumps(all_digests, ensure_ascii=False, indent=2), encoding="utf-8", ) print("\n" + "=" * 60) print(f"📊 处理完成:") print(f" 成功: {success}") print(f" 跳过(已存在): {skipped}") print(f" 失败: {failed}") print(f" 汇总文件: {summary_file}") print("=" * 60) if __name__ == "__main__": main()