feat: L4 AI 诊断报告优化 + Prompt/摘要卡/迁移/文档完善

- 修正 DeepSeek 模型名 deepseek-chat → deepseek-v4-pro - 摘要块：修复 **粗体** markdown 渲染、左右块可滚动、左块固定粉红色系 - 新增 prompt4（内容摘要卡）+ prompt5（诊断报告）+ 三处 prompt5 优化 - 新增 004 迁移（episodes +content_digest JSONB）、导入脚本、摘要卡生成脚本 - 更新 CLAUDE.md 状态栏/进度/交接备注/关键决策 Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-07-03 21:25:38 +08:00
parent 3dd718f53d
commit 9df4868191
15 changed files with 1993 additions and 42 deletions
@@ -0,0 +1,260 @@
+"""
+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<think>...输出。"""
+    # 先去掉<think>...标签及其内容
+    text = re.sub(r'<think>.*?</think>', '', 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()