init

test_pipeline.py

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+"""
+故事提取流程 v4 - 逐步测试脚本
+用法：python test_pipeline.py <docx文件路径> [步骤编号]
+  步骤编号：1=预处理, 2=判断故事, 3=切片提取, 4=重组, 5=生成故事, all=全部执行
+"""
+import sys
+import os
+import json
+import asyncio
+import time
+
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), "backend"))
+
+from app.services.docx_parser import parse_transcript
+from app.services.llm_client import llm_client, _parse_json
+
+TEST_DIR = os.path.join(os.path.dirname(__file__), "backend", "uploads", "test")
+
+
+def _save_json(filepath, data):
+    """保存 JSON 到文件"""
+    os.makedirs(os.path.dirname(filepath), exist_ok=True)
+    with open(filepath, "w", encoding="utf-8") as f:
+        json.dump(data, f, ensure_ascii=False, indent=2)
+
+
+def _save_text(filepath, text):
+    """保存文本到文件"""
+    os.makedirs(os.path.dirname(filepath), exist_ok=True)
+    with open(filepath, "w", encoding="utf-8") as f:
+        f.write(text)
+
+
+# ============================================================
+# 第一步：代码预处理（v3：时间范围截取，允许重叠）
+# ============================================================
+def step1_preprocess(lines, output_dir, save=True):
+    """代码预处理：识别老师 → 过滤杂音 → 按时间范围截取每个学员的全部内容"""
+    print("\n" + "=" * 60)
+    print("第一步：代码预处理（v3：时间范围截取）")
+    print("=" * 60)
+
+    # 1.1 统计每个说话人
+    print(f"总行数: {len(lines)}")
+    speaker_stats = {}
+    for i, line in enumerate(lines):
+        speaker = line.get("speaker", "")
+        if not speaker:
+            continue
+        if speaker not in speaker_stats:
+            speaker_stats[speaker] = {"lines": 0, "chars": 0, "first": i, "last": i}
+        speaker_stats[speaker]["lines"] += 1
+        speaker_stats[speaker]["chars"] += len(line.get("content", ""))
+        speaker_stats[speaker]["last"] = i
+
+    print(f"\n说话人统计:")
+    for s, stats in sorted(speaker_stats.items(), key=lambda x: -x[1]["lines"]):
+        print(f"  {s}: {stats['lines']}行, {stats['chars']}字, 行范围[{stats['first']}-{stats['last']}]")
+
+    # 1.2 识别老师（发言最多的人）
+    teacher = max(speaker_stats, key=lambda s: speaker_stats[s]["lines"])
+    print(f"\n识别老师: {teacher} ({speaker_stats[teacher]['lines']}行)")
+
+    # 1.3 过滤杂音（< 20句的说话人）+ 排除老师
+    students = []
+    noise_speakers = []
+    for s, stats in speaker_stats.items():
+        if s == teacher:
+            continue
+        if stats["lines"] < 20:
+            noise_speakers.append(f"{s} ({stats['lines']}句)")
+            continue
+        students.append(s)
+
+    if noise_speakers:
+        print(f"\n杂音过滤: {', '.join(noise_speakers)}")
+    print(f"\n有效学员: {students}")
+
+    # 1.4 按时间范围截取：每个学员从第一句到最后一句，中间所有行全部截取
+    segments = []
+    for speaker in students:
+        stats = speaker_stats[speaker]
+        first_idx = stats["first"]
+        last_idx = stats["last"]
+        seg = lines[first_idx:last_idx + 1]
+
+        # 统计该学员在此范围内的发言
+        student_lines = [l for l in seg if l.get("speaker", "") == speaker]
+        student_chars = sum(len(l.get("content", "")) for l in student_lines)
+
+        segments.append((speaker, seg))
+        print(f"  {speaker}: 截取行[{first_idx}-{last_idx}], 共{len(seg)}行, 学员{len(student_lines)}句/{student_chars}字")
+
+    # 1.5 过滤：短段落 + 当事人占比过低（非主讲述人）
+    final_segments = []
+    filtered = []
+    for speaker, seg in segments:
+        student_lines = [l for l in seg if l.get("speaker", "") == speaker]
+        if len(student_lines) < 15:
+            filtered.append(f"{speaker} ({len(student_lines)}句<15)")
+            continue
+        student_chars = sum(len(l.get("content", "")) for l in student_lines)
+        if student_chars < 80:
+            filtered.append(f"{speaker} ({student_chars}字<80)")
+            continue
+        # 当事人说话占比 < 1/10 → 非主讲述人，丢弃
+        ratio = len(student_lines) / len(seg) if seg else 0
+        if ratio < 0.1:
+            filtered.append(f"{speaker} (占比{ratio:.1%}<10%)")
+            continue
+        final_segments.append((speaker, seg))
+
+    if filtered:
+        print(f"\n过滤: {', '.join(filtered)}")
+
+    print(f"\n最终: {len(final_segments)} 个学员段落")
+    for i, (speaker, seg) in enumerate(final_segments):
+        student_lines = [l for l in seg if l.get("speaker", "") == speaker]
+        student_chars = sum(len(l.get("content", "")) for l in student_lines)
+        ratio = len(student_lines) / len(seg) if seg else 0
+        print(f"  段落{i}: {speaker}, 截取{len(seg)}行, 学员{len(student_lines)}句/{student_chars}字, 占比{ratio:.1%}")
+
+    # 保存结果
+    if save:
+        step1_dir = os.path.join(output_dir, "step1_预处理")
+        # 保存每个学员段落
+        for i, (speaker, seg) in enumerate(final_segments):
+            text = _segment_to_text(seg)
+            safe_name = speaker.replace("/", "_")
+            _save_text(os.path.join(step1_dir, f"segment_{i}_{safe_name}.txt"), text)
+            print(f"  保存: segment_{i}_{safe_name}.txt ({len(text)}字)")
+        # 保存统计摘要
+        summary = {
+            "total_lines": len(lines),
+            "teacher": teacher,
+            "noise_filtered": noise_speakers,
+            "final_filtered": filtered,
+            "segments": [
+                {
+                    "speaker": s,
+                    "total_lines": len(seg),
+                    "student_lines": len([l for l in seg if l.get("speaker", "") == s]),
+                    "student_chars": sum(len(l.get("content", "")) for l in seg if l.get("speaker", "") == s),
+                    "ratio": round(len([l for l in seg if l.get("speaker", "") == s]) / len(seg), 3) if seg else 0,
+                }
+                for s, seg in final_segments
+            ]
+        }
+        _save_json(os.path.join(step1_dir, "summary.json"), summary)
+        print(f"  保存: summary.json")
+
+    return final_segments, teacher
+
+
+def _get_main_speaker(student_lines):
+    if not student_lines:
+        return "unknown"
+    counts = {}
+    for l in student_lines:
+        s = l.get("speaker", "")
+        counts[s] = counts.get(s, 0) + 1
+    return max(counts, key=counts.get)
+
+
+def _segment_to_text(seg):
+    parts = []
+    for line in seg:
+        speaker = line.get("speaker", "")
+        content = line.get("content", "")
+        if speaker:
+            parts.append(f"{speaker}: {content}")
+        else:
+            parts.append(content)
+    return "\n".join(parts)
+
+
+# ============================================================
+# 第二步：判断是否包含故事 + 提取事实（短段落合并处理）
+# ============================================================
+async def step2_identify(segments, output_dir, save=True):
+    """短段落直接提取事实，长段落先判断是否有故事再提取"""
+    print("\n" + "=" * 60)
+    print("第二步：判断故事 + 提取事实")
+    print("=" * 60)
+
+    SHORT_THRESHOLD = 4000  # 短段落阈值（字数）
+    all_facts = []
+    step2_results = []  # 记录每个段落的处理结果
+    step2_t0 = time.time()
+
+    # 构建所有段落的异步任务，并行执行
+    async def process_segment(i, speaker, seg):
+        text = _segment_to_text(seg)
+        safe_name = speaker.replace("/", "_")
+        prefix = f"seg{i}_{safe_name}_"
+        print(f"\n--- 段落 {i}: {speaker} ({len(text)} 字) ---")
+
+        result_record = {
+            "index": i,
+            "speaker": speaker,
+            "text_length": len(text),
+            "is_short": len(text) <= SHORT_THRESHOLD,
+        }
+
+        if len(text) <= SHORT_THRESHOLD:
+            print(f"  短段落，跳过判断，直接提取事实")
+            facts = await _extract_facts_from_chunk(text, "", output_dir, prefix)
+            if facts:
+                hints = facts.get("story_hints", "")
+                if not hints:
+                    hints = f"{speaker}的故事"
+                print(f"  提取完成: hints={hints}")
+                result_record["status"] = "extracted"
+                result_record["hints"] = hints
+                return (speaker, seg, hints, [facts]), result_record
+            else:
+                print(f"  提取失败，跳过")
+                result_record["status"] = "extract_failed"
+                return None, result_record
+        else:
+            is_story, hints = await _identify_story(text, output_dir, prefix)
+            print(f"  判断结果: is_story={is_story}, hints={hints}")
+            result_record["is_story"] = is_story
+            result_record["hints"] = hints
+
+            if is_story:
+                story_segments_for_step3 = [(speaker, seg, hints)]
+                step3_result = await step3_extract_facts(story_segments_for_step3, output_dir, save)
+                result_record["status"] = "has_story"
+                return step3_result, result_record
+            else:
+                print(f"  无故事，跳过")
+                result_record["status"] = "no_story"
+                return None, result_record
+
+    tasks = [process_segment(i, speaker, seg) for i, (speaker, seg) in enumerate(segments)]
+    results = await asyncio.gather(*tasks)
+
+    step2_elapsed = round(time.time() - step2_t0, 2)
+    print(f"\n⏱ 第二步总耗时: {step2_elapsed}s")
+
+    # 收集结果
+    for i, (r, record) in enumerate(results):
+        if r is not None:
+            if isinstance(r, list):
+                all_facts.extend(r)
+            else:
+                all_facts.append(r)
+        step2_results.append(record)
+
+    print(f"\n共 {len(all_facts)} 个故事段落完成事实提取")
+
+    # 保存结果
+    if save:
+        step2_dir = os.path.join(output_dir, "step2_判断提取")
+        _save_json(os.path.join(step2_dir, "results.json"), step2_results)
+        print(f"  保存: results.json")
+
+    return all_facts
+
+
+async def _identify_story(text, output_dir=None, log_prefix="", do_save=True):
+    prompt = """判断这段对话是否包含学员的个人故事。直接输出JSON，不要分析。
+
+个人故事特征（满足至少2条）：学员讲述具体经历、包含时间地点人物细节、表达深层情感、老师深入引导疗愈、揭示心理或家庭问题。
+
+非故事内容：纯课堂讲解、简单问答寒暄、课堂管理、纯疗愈引导词。
+
+直接输出JSON：
+{"is_story": true或false, "confidence": 0.0到1.0, "story_hints": "一句话描述故事主题和讲述者"}
+
+对话内容：
+```
+""" + text[:4000] + """
+```"""
+
+    try:
+        detail = await llm_client.chat_detail([{"role": "user", "content": prompt}], temperature=0.2)
+        api_log = {
+            "type": "identify_story",
+            "prompt": prompt,
+            "content": detail.get("content", ""),
+            "reasoning_content": detail.get("reasoning_content", ""),
+            "finish_reason": detail.get("finish_reason", ""),
+            "usage": detail.get("usage", {}),
+            "elapsed": detail.get("elapsed", 0),
+        }
+        print(f"  {log_prefix}API耗时: {api_log['elapsed']}s, finish_reason={api_log['finish_reason']}, "
+              f"tokens: {api_log['usage'].get('total_tokens', '?')} "
+              f"(reasoning={api_log['usage'].get('reasoning_tokens', 0)})")
+        if output_dir and do_save:
+            _save_json(os.path.join(output_dir, "api_logs", f"{log_prefix}identify.json"), api_log)
+        result = _parse_json(detail["content"])
+        if result:
+            return bool(result.get("is_story", False)) and result.get("confidence", 0) >= 0.5, result.get("story_hints", "")
+    except Exception as e:
+        print(f"  {log_prefix}错误: {e}")
+    return False, ""
+
+
+# ============================================================
+# 第三步：切片提取事实（Map）
+# ============================================================
+async def step3_extract_facts(story_segments, output_dir, save=True):
+    print("\n" + "=" * 60)
+    print("第三步：切片提取事实（Map）")
+    print("=" * 60)
+
+    all_facts = []
+    step3_t0 = time.time()
+    for i, (speaker, seg, hints) in enumerate(story_segments):
+        text = _segment_to_text(seg)
+        safe_name = speaker.replace("/", "_")
+        print(f"\n--- 故事 {i}: {speaker} - {hints[:50]}... ({len(text)} 字) ---")
+
+        chunks = _chunk_text_by_paragraphs(text, max_chars=3000, overlap_paragraphs=5)
+        print(f"  切成 {len(chunks)} 块")
+
+        # 保存切片信息
+        chunk_records = []
+        for j, chunk in enumerate(chunks):
+            chunk_records.append({"chunk_index": j, "chars": len(chunk), "preview": chunk[:100] + "..."})
+
+        tasks = [_extract_facts_from_chunk(chunk, hints, output_dir, f"story{i}_{safe_name}_chunk{j}_") for j, chunk in enumerate(chunks)]
+        results = await asyncio.gather(*tasks, return_exceptions=True)
+
+        facts = []
+        for j, r in enumerate(results):
+            if isinstance(r, Exception):
+                print(f"  块{j} 错误: {r}")
+                chunk_records[j]["status"] = "error"
+                chunk_records[j]["error"] = str(r)
+            else:
+                print(f"  块{j}: {json.dumps(r, ensure_ascii=False)[:200]}")
+                facts.append(r)
+                chunk_records[j]["status"] = "ok"
+                chunk_records[j]["facts"] = r
+
+        all_facts.append((speaker, seg, hints, facts))
+
+        # 保存切片和提取结果
+        if save:
+            step3_dir = os.path.join(output_dir, "step3_切片提取", f"story_{i}_{safe_name}")
+            for j, chunk in enumerate(chunks):
+                _save_text(os.path.join(step3_dir, f"chunk_{j}.txt"), chunk)
+            _save_json(os.path.join(step3_dir, "chunks_info.json"), chunk_records)
+            print(f"  保存: {len(chunks)} 个切片文件 + chunks_info.json")
+
+    step3_elapsed = round(time.time() - step3_t0, 2)
+    print(f"\n⏱ 第三步总耗时: {step3_elapsed}s")
+
+    return all_facts
+
+
+def _chunk_text_by_paragraphs(text, max_chars=3000, overlap_paragraphs=5):
+    """按自然段切分，在 max_chars 附近找段落结尾切，重叠 overlap_paragraphs 个自然段"""
+    paragraphs = [p for p in text.split("\n") if p.strip()]
+    if not paragraphs:
+        return [text]
+    if len(text) <= max_chars:
+        return [text]
+
+    chunks = []
+    i = 0
+    while i < len(paragraphs):
+        # 从当前位置开始，累加段落直到超过 max_chars
+        current = []
+        current_len = 0
+        j = i
+        while j < len(paragraphs):
+            para = paragraphs[j]
+            # 超过 max_chars 且已有内容 → 在这个段落前切断（段落结尾切）
+            if current_len + len(para) > max_chars and current:
+                break
+            current.append(para)
+            current_len += len(para)
+            j += 1
+
+        chunks.append("\n".join(current))
+
+        # 到末尾了就结束
+        if j >= len(paragraphs):
+            break
+
+        # 下一次起点：当前切到的位置 - 重叠段落数
+        next_i = j - overlap_paragraphs
+        # 防止死循环：确保至少前进1个段落
+        if next_i <= i:
+            next_i = i + 1
+        i = next_i
+
+    return chunks
+
+
+async def _extract_facts_from_chunk(text, hints, output_dir=None, log_prefix="", do_save=True):
+    prompt = f"""从这段对话中提取关键信息。只输出JSON。
+
+故事主题：{hints}
+
+提取以下内容：
+- events: 发生的事件（尽量详细，保留具体细节）
+- emotions: 表达的情感
+- quotes: 讲述者的原话（保留原话，不要改写，尽量多提取；完全相同的重复语句只保留一条）
+- key_people: 提到的人物
+
+{{"events":["事件1","事件2"],"emotions":["情感1","情感2"],"quotes":["原话1","原话2","原话3"],"key_people":["人物1"]}}
+
+对话内容：
+```
+{text}
+```"""
+
+    detail = await llm_client.chat_detail([{"role": "user", "content": prompt}], temperature=0.1)
+    api_log = {
+        "type": "extract_facts",
+        "prompt": prompt,
+        "content": detail.get("content", ""),
+        "reasoning_content": detail.get("reasoning_content", ""),
+        "finish_reason": detail.get("finish_reason", ""),
+        "usage": detail.get("usage", {}),
+        "elapsed": detail.get("elapsed", 0),
+    }
+    print(f"  {log_prefix}API耗时: {api_log['elapsed']}s, finish_reason={api_log['finish_reason']}, "
+          f"tokens: {api_log['usage'].get('total_tokens', '?')} "
+          f"(reasoning={api_log['usage'].get('reasoning_tokens', 0)})")
+    if output_dir and do_save:
+        _save_json(os.path.join(output_dir, "api_logs", f"{log_prefix}extract.json"), api_log)
+    result = _parse_json(detail["content"])
+    return result
+
+
+# ============================================================
+# 模糊去重工具函数
+# ============================================================
+def _fuzzy_dedup_quotes(quotes: list[str]) -> list[str]:
+    """
+    模糊去重：对高度相似的语句只保留第一条。
+    策略：提取每条语句的核心部分（去掉常见前缀后缀），相似度超过阈值的视为重复。
+    """
+    import re
+
+    def normalize(s: str) -> str:
+        s = s.strip()
+        if s.startswith("「") and s.endswith("」"):
+            s = s[1:-1]
+        for prefix in ["嗯", "啊", "呃", "就是", "然后", "那个", "所以"]:
+            if s.startswith(prefix):
+                s = s[len(prefix):].strip()
+        for suffix in ["啊", "吧", "呢", "嘛", "哦", "呀"]:
+            if s.endswith(suffix) and len(s) > 4:
+                s = s[:-1]
+        s = re.sub(r'(.)\1{2,}', r'\1', s)
+        return s
+
+    def similarity(a: str, b: str) -> float:
+        if not a or not b:
+            return 0.0
+        a_bigrams = set(a[i:i+2] for i in range(len(a)-1))
+        b_bigrams = set(b[i:i+2] for i in range(len(b)-1))
+        if not a_bigrams or not b_bigrams:
+            return 0.0
+        intersection = a_bigrams & b_bigrams
+        union = a_bigrams | b_bigrams
+        return len(intersection) / len(union)
+
+    kept = []
+    kept_normalized = []
+    for q in quotes:
+        nq = normalize(q)
+        is_dup = False
+        for kn in kept_normalized:
+            threshold = 0.8 if len(nq) < 15 else 0.7
+            if similarity(nq, kn) >= threshold:
+                is_dup = True
+                break
+        if not is_dup:
+            kept.append(q)
+            kept_normalized.append(nq)
+
+    removed = len(quotes) - len(kept)
+    if removed > 0:
+        print(f"    模糊去重: {len(quotes)} -> {len(kept)} (去除 {removed} 条相似重复)")
+
+    return kept
+
+
+# ============================================================
+# 第四步：重组（Reduce）
+# ============================================================
+def step4_merge(all_facts, output_dir, save=True):
+    print("\n" + "=" * 60)
+    print("第四步：重组（Reduce）")
+    print("=" * 60)
+
+    merged = []
+    for i, (speaker, seg, hints, facts_list) in enumerate(all_facts):
+        print(f"\n--- 故事 {i}: {speaker} ---")
+
+        all_events, all_emotions, all_quotes, all_people = [], [], [], []
+        for facts in facts_list:
+            if isinstance(facts, dict):
+                all_events.extend(facts.get("events", []))
+                all_emotions.extend(facts.get("emotions", []))
+                all_quotes.extend(facts.get("quotes", []))
+                all_people.extend(facts.get("key_people", []))
+
+        all_emotions = list(dict.fromkeys(all_emotions))
+        all_people = list(dict.fromkeys(all_people))
+        seen = set()
+        unique_quotes = [q for q in all_quotes if q not in seen and not seen.add(q)]
+        # 模糊去重：去除相似重复语句（如多次释放语句只保留一条）
+        unique_quotes = _fuzzy_dedup_quotes(unique_quotes)
+
+        material = {
+            "events": all_events,
+            "emotions": all_emotions,
+            "quotes": unique_quotes,
+            "key_people": all_people,
+            "story_hints": hints,
+        }
+
+        print(f"  事件: {len(all_events)}, 情感: {len(all_emotions)}, 原话: {len(unique_quotes)}, 人物: {len(all_people)}")
+        print(f"  素材: {json.dumps(material, ensure_ascii=False)[:500]}")
+
+        merged.append((speaker, seg, material))
+
+    # 保存结果
+    if save:
+        step4_dir = os.path.join(output_dir, "step4_重组")
+        for i, (speaker, seg, material) in enumerate(merged):
+            safe_name = speaker.replace("/", "_")
+            _save_json(os.path.join(step4_dir, f"material_{i}_{safe_name}.json"), material)
+            print(f"  保存: material_{i}_{safe_name}.json")
+
+    return merged
+
+
+# ============================================================
+# 第五步：生成最终故事
+# ============================================================
+async def step5_generate(merged, output_dir, save=True):
+    print("\n" + "=" * 60)
+    print("第五步：生成最终故事（并行）")
+    print("=" * 60)
+
+    step5_t0 = time.time()
+
+    async def generate_one(i, speaker, seg, material):
+        print(f"\n--- 生成故事 {i}: {speaker} ---")
+
+        prompt = f"""基于素材生成第三人称故事。只输出JSON，不要其他内容。
+
+素材：
+主题：{material['story_hints']}
+事件：{json.dumps(material['events'], ensure_ascii=False)}
+情感：{json.dumps(material['emotions'], ensure_ascii=False)}
+原话：{json.dumps(material['quotes'], ensure_ascii=False)}
+人物：{json.dumps(material['key_people'], ensure_ascii=False)}
+
+写作手法（非常重要）：
+
+【双时空结构】
+故事有两个时空层：
+- "当下时空"：学员在课堂上与卢慧老师的对话——这是故事的框架，用对话体呈现，保留师生互动的完整脉络
+- "过往时空"：学员讲述的过去经历——这是故事的核心，用"场景重建"手法呈现，让读者穿越到那个时空
+
+【场景重建手法（用于"过往的故事"部分）】
+当学员讲述过去的经历时，不要写成"她说她小时候…"这种转述体，而是直接把读者拉进那个时空：
+- 直接切入场景，用过去时态叙述。比如不要写"文晔回忆说她的儿子15岁去了美国"，而要写"十五岁那年，文晔的儿子独自登上了飞往美国的航班"
+- 用五感描写构建画面：她看到了什么、听到了什么、身体感受到了什么（比如"手心出汗"、"脚发凉"这些身体反应要融入场景中）
+- 用环境细节暗示情绪：不要直接说"她很痛苦"，而是通过场景让读者感受到痛苦
+- 关键情感爆发处用直接引语（「」包裹原话），其他地方用间接引语和叙述自然融合
+- 场景之间要有过渡，比如"然而，命运的转折发生在前年冬天——"
+
+【对话体手法（用于"当下的困境"和"转变"部分）】
+- 保留师生对话的现场感，写出谁说了什么、对方如何回应
+- 在对话中穿插学员的内心活动、身体反应、情绪变化
+- 老师的引导语要完整保留，这是疗愈过程的关键
+
+【通用要求】
+- 每个事件都要展开写，写出具体过程和细节，不要一句话概括
+- 事件之间要有过渡和因果逻辑，不能跳跃
+- 情感变化要写出过程：从什么情感转变到什么情感，中间经历了什么
+- 不编造素材中没有的内容，但可以根据素材合理补充场景细节（如环境、氛围）
+- 每个部分充分展开，不要限制长度，越详细越好
+- 原文金句数组中的每条金句必须用英文双引号包裹，如 ["金句1", "金句2"]，不要用「」替代双引号
+
+禁止事项：
+- 禁止用一句话概括多个事件（如"她倾诉了挣扎"、"她经历了疗愈过程"）
+- 禁止省略素材中的具体细节
+- 禁止省略老师的引导和回应
+- 禁止事件之间跳跃，要保持时间顺序和因果逻辑
+- 禁止过度总结或抽象化描述
+- 禁止在"过往的故事"部分全程使用转述体（"她说…她回忆…"），必须用场景重建手法让读者身临其境
+- 禁止为了简洁而牺牲内容的丰富性
+
+输出格式：{{"title":"标题","summary":"80字摘要","tags":["标签"],"speaker_nickname":"昵称","content":{{"讲述者":"...","当下的困境":"...","过往的故事":"...","转变":"...","原文金句":["原话1","原话2","原话3"]}}}}"""
+
+        try:
+            detail = await llm_client.chat_detail([{"role": "user", "content": prompt}], temperature=0.7, max_tokens=16384)
+            raw = detail["content"]
+            api_log = {
+                "type": "generate_story",
+                "speaker": speaker,
+                "prompt": prompt,
+                "content": raw,
+                "reasoning_content": detail.get("reasoning_content", ""),
+                "finish_reason": detail.get("finish_reason", ""),
+                "usage": detail.get("usage", {}),
+                "elapsed": detail.get("elapsed", 0),
+            }
+            print(f"  API耗时: {api_log['elapsed']}s, finish_reason={api_log['finish_reason']}, "
+                  f"tokens: {api_log['usage'].get('total_tokens', '?')} "
+                  f"(reasoning={api_log['usage'].get('reasoning_tokens', 0)})")
+            print(f"  原始输出长度: {len(raw)} 字")
+            if save:
+                step5_dir = os.path.join(output_dir, "step5_生成故事")
+                os.makedirs(step5_dir, exist_ok=True)
+                _save_text(os.path.join(step5_dir, f"raw_{i}_{speaker}.txt"), raw)
+                _save_json(os.path.join(output_dir, "api_logs", f"story{i}_{speaker}_generate.json"), api_log)
+            result = _parse_json(raw)
+        except Exception as e:
+            print(f"  生成失败: {e}")
+            result = None
+
+        if result:
+            print(f"  标题: {result.get('title', '')}")
+            print(f"  摘要: {result.get('summary', '')[:100]}")
+            content = result.get('content', {})
+            if isinstance(content, dict):
+                for key, val in content.items():
+                    print(f"  {key}: {str(val)[:80]}...")
+            print(f"\n  完整JSON:")
+            print(json.dumps(result, ensure_ascii=False, indent=2))
+        else:
+            print("  生成失败！")
+
+        return result
+
+    tasks = [generate_one(i, speaker, seg, material) for i, (speaker, seg, material) in enumerate(merged)]
+    results = await asyncio.gather(*tasks)
+    stories = [r for r in results if r is not None]
+
+    step5_elapsed = round(time.time() - step5_t0, 2)
+    print(f"\n⏱ 第五步总耗时: {step5_elapsed}s")
+
+    # 保存结果
+    if save:
+        step5_dir = os.path.join(output_dir, "step5_生成故事")
+        for i, story in enumerate(stories):
+            safe_name = story.get("speaker_nickname", f"story_{i}").replace("/", "_")
+            _save_json(os.path.join(step5_dir, f"story_{i}_{safe_name}.json"), story)
+            print(f"  保存: story_{i}_{safe_name}.json")
+
+    return stories
+
+
+# ============================================================
+# 主函数
+# ============================================================
+async def main():
+    if len(sys.argv) < 2:
+        print("用法: python test_pipeline.py <docx文件路径> [步骤编号]")
+        print("  步骤编号: 1=预处理, 2=判断故事, 3=切片提取, 4=重组, 5=生成故事, all=全部")
+        sys.exit(1)
+
+    file_path = sys.argv[1]
+    step = sys.argv[2] if len(sys.argv) > 2 else "all"
+
+    if not os.path.exists(file_path):
+        print(f"文件不存在: {file_path}")
+        sys.exit(1)
+
+    # 根据文件名创建输出目录
+    basename = os.path.splitext(os.path.basename(file_path))[0]
+    output_dir = os.path.join(TEST_DIR, basename)
+    os.makedirs(output_dir, exist_ok=True)
+
+    print(f"文件: {file_path}")
+    print(f"输出目录: {output_dir}")
+
+    total_t0 = time.time()
+
+    lines = parse_transcript(file_path)
+    print(f"解析到 {len(lines)} 行")
+
+    # 如果只跑第5步，从 step4 产物恢复
+    if step == "5":
+        step4_dir = os.path.join(output_dir, "step4_重组")
+        if os.path.exists(step4_dir):
+            print("从 step4 产物恢复...")
+            merged = []
+            for f in sorted(os.listdir(step4_dir)):
+                if f.endswith(".json"):
+                    with open(os.path.join(step4_dir, f), "r", encoding="utf-8") as fp:
+                        material = json.load(fp)
+                    parts = f.replace("material_", "").replace(".json", "").split("_", 1)
+                    speaker = parts[1] if len(parts) > 1 else f
+                    merged.append((speaker, [], material))
+            print(f"恢复到 {len(merged)} 个故事")
+            stories = await step5_generate(merged, output_dir)
+
+            total_elapsed = round(time.time() - total_t0, 2)
+            print(f"\n{'=' * 60}")
+            print(f"✅ 全流程完成！总耗时: {total_elapsed}s")
+            print(f"   生成故事: {len(stories)} 个")
+            print(f"{'=' * 60}")
+            return
+        else:
+            print("step4 产物不存在，请先跑前4步")
+            return
+
+    segments, teacher = step1_preprocess(lines, output_dir)
+    if step == "1":
+        return
+
+    all_facts = await step2_identify(segments, output_dir)
+    if step == "2":
+        return
+
+    if not all_facts:
+        print("没有发现故事段落，结束。")
+        return
+
+    merged = step4_merge(all_facts, output_dir)
+    if step == "4":
+        return
+
+    stories = await step5_generate(merged, output_dir)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())