第11环裂变重写：对齐上线版 split.js 一次LLM出N套完整笔记包

架构从"扇出N个GenerationTask各跑完整管道"改为"一次LLM调用直接出N套完整笔记包(N=1~5)"，落 FissionNote 表 + 独立展示页。后端： - 018迁移：fission_notes 表(文案JSON+score+passed+imagePlan+images+status) - fission_prompt：FISSION_SYSTEM+三档参考度(low/mid/high)+normalize_tags+品类兜底 - fission_pipeline：一次LLM出N套→各评分(@80合格线)→排序→落库,不达标标 needs_optimization 非丢弃；apiports 503 回落 codeproxy gpt-5.5 强档兜底 - fission_images：每套串行调现有生图接口(零改动image_gen/storyboard) - tasks.py：run_fission_pipeline Celery task，删旧扇出注入 - api/v1/fission：进度聚合FissionNote + GET /fission/{id}/notes(剥内部字段) 前端：FissionProgress对齐状态机 + N套独立展示页 + FissionNoteCard 测试：test_fission_engine(19)+test_fission_pipeline(5) 全过；104 全量回归绿实测task5(fanout=2,mid)端到端跑通：一次出2套→seq0=85过/seq1=79标优化→ 生图codeproxy/edits→1024×1536去AI化→task completed→notes端点返完整数据 Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
2026-06-18 11:17:37 +08:00
parent 7f419f4c8b
commit d85dcd401b
18 changed files with 1772 additions and 106 deletions
--- a/backend/tests/test_fission_engine.py
+++ b/backend/tests/test_fission_engine.py
@@ -0,0 +1,197 @@
+"""
+裂变引擎单元测试（第11环·一次LLM出N套架构）
+覆盖：参考强度映射 / 标签归一 / imagePlan文字清洗 / prompt组装 /
+      模型输出解析 N 套 / 品类兜底 build_fallback_notes / infer_category。
+纯函数为主，不连 DB/LLM（conftest 已 stub 环境）。
+"""
+import sys
+import os
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), ".."))
+
+import json
+import pytest
+from app.services.ai_engine.fission_prompt import (
+    reference_strategy_from_level, valid_level, normalize_tags,
+    sanitize_image_plan_text, build_fission_prompt, notes_array_from_parsed,
+)
+from app.services.ai_engine.fission_fallback import (
+    infer_category, build_fallback_notes, build_fallback_image_plan,
+)
+
+PRODUCT = {
+    "name": "倍分子素颜霜",
+    "category": "美妆护肤",
+    "selling_points": ["轻薄不厚重", "水润自然", "不卡粉"],
+    "keywords": ["素颜霜", "日常通勤"],
+    "style_tone": "素人分享风",
+    "brand_keyword": "倍分子",
+}
+SOURCE_NOTE = {
+    "title": "素颜霜真实测评",
+    "content": "用了两周，底子真的透，不卡粉，姐妹们冲。",
+    "tags": ["素颜霜", "护肤"],
+}
+
+
+# ── 参考强度映射 ────────────────────────────────────────────
+def test_reference_level_three_tiers_map_to_int():
+    """low/mid/high 三档保留，内部映射数值（倩倩姐拍板）。"""
+    low = reference_strategy_from_level("low")
+    mid = reference_strategy_from_level("mid")
+    high = reference_strategy_from_level("high")
+    # 返回 {level, level_label, prompt_rule, summary}；level 数值 low<mid<high
+    assert low["level"] < mid["level"] < high["level"]
+    assert low["level_label"] == "低参考"
+    assert high["level_label"] == "高参考"
+
+
+def test_valid_level_defaults_and_passthrough():
+    assert valid_level("low") == "low"
+    assert valid_level("mid") == "mid"
+    assert valid_level("high") == "high"
+    # 非法值兜底成 mid（默认档）
+    assert valid_level("garbage") == "mid"
+    assert valid_level(None) == "mid"
+
+
+# ── 标签归一 ────────────────────────────────────────────────
+def test_normalize_tags_dedupe_and_merge_keywords():
+    tags = normalize_tags(["素颜霜", "素颜霜", "护肤"], ["通勤", "护肤"])
+    # 归一化补 # 前缀
+    assert "#素颜霜" in tags
+    # 去重：素颜霜只出现一次
+    assert tags.count("#素颜霜") == 1
+    # keywords 合并进来
+    assert "#通勤" in tags
+    # 截断 8 个上限
+    assert len(tags) <= 8
+
+
+def test_normalize_tags_handles_none_and_str():
+    assert normalize_tags(None, None) == []
+    assert isinstance(normalize_tags("单串 标签", None), list)
+
+
+# ── imagePlan 文字清洗 ──────────────────────────────────────
+def test_sanitize_image_plan_text_truncates():
+    long = "美白祛斑特效" * 20
+    out = sanitize_image_plan_text(long, max_length=56)
+    assert len(out) <= 56
+
+
+def test_sanitize_image_plan_text_empty():
+    assert sanitize_image_plan_text("") == ""
+
+
+# ── prompt 组装 ─────────────────────────────────────────────
+def test_build_fission_prompt_includes_counts_and_strategy():
+    prompt = build_fission_prompt(SOURCE_NOTE, PRODUCT, "high", note_count=3, image_count=6)
+    assert "3套" in prompt
+    assert "6张" in prompt
+    # 高参考策略文案带入
+    assert "高参考" in prompt
+    # 源爆款标题带入
+    assert "素颜霜真实测评" in prompt
+
+
+# ── 模型输出解析 N 套 ───────────────────────────────────────
+def test_notes_array_from_list():
+    parsed = [{"title": "a"}, {"title": "b"}]
+    assert len(notes_array_from_parsed(parsed)) == 2
+
+
+def test_notes_array_from_wrapped_dict():
+    # 模型可能把数组包在 notes/variants 等键下
+    assert len(notes_array_from_parsed({"notes": [{"title": "x"}]})) == 1
+    assert len(notes_array_from_parsed({"variants": [{"title": "x"}, {"title": "y"}]})) == 2
+
+
+def test_notes_array_from_single_note_dict():
+    # 单套笔记直接当对象返回
+    assert len(notes_array_from_parsed({"title": "单套", "content": "正文"})) == 1
+
+
+def test_notes_array_from_garbage():
+    assert notes_array_from_parsed("not json") == []
+    assert notes_array_from_parsed({}) == []
+
+
+# ── 品类推断 ────────────────────────────────────────────────
+def test_infer_category_returns_str():
+    cat = infer_category(PRODUCT)
+    assert isinstance(cat, str) and cat
+
+
+# ── 品类兜底完整草稿 ────────────────────────────────────────
+def test_build_fallback_notes_count_and_fields():
+    notes = build_fallback_notes(SOURCE_NOTE, PRODUCT, note_count=3, image_count=6)
+    assert len(notes) == 3
+    for n in notes:
+        # 完整笔记包字段齐全
+        for field in ("title", "content", "tags", "coverTitle", "dimension",
+                      "audience", "scene", "painPoint", "keywords", "imagePlan"):
+            assert field in n
+        # imagePlan 数量必须等于 image_count
+        assert len(n["imagePlan"]) == 6
+
+
+def test_build_fallback_image_plan_matches_count():
+    note = {"title": "t", "content": "c"}
+    for count in (3, 6, 8):
+        plan = build_fallback_image_plan(note, count)
+        assert len(plan) == count
+        for p in plan:
+            assert "role" in p
+
+
+def test_build_fallback_notes_dedupes_titles():
+    # 多套兜底标题不应全部雷同（裂变要差异化）
+    notes = build_fallback_notes(SOURCE_NOTE, PRODUCT, note_count=3, image_count=3)
+    titles = [n["title"] for n in notes]
+    assert len(set(titles)) >= 2
+
+
+# ── 集成：编排层 _parse_fission_response（mock LLM 固定 JSON）──
+from app.services.fission_pipeline import _parse_fission_response
+
+_LLM_JSON = json.dumps([
+    {"title": "套1标题", "content": "套1正文" * 30, "tags": ["素颜霜"],
+     "coverTitle": "钩子1", "dimension": "换角度", "keywords": ["通勤"],
+     "imagePlan": [{"role": "cover", "title": "t", "overlayText": "o", "text": "x"},
+                   {"role": "proof", "title": "t2", "overlayText": "o2", "text": "x2"},
+                   {"role": "cta", "title": "t3", "overlayText": "o3", "text": "x3"}]},
+    {"title": "套2标题", "content": "套2正文" * 30, "tags": ["护肤"],
+     "coverTitle": "钩子2", "dimension": "换痛点", "keywords": ["熬夜"],
+     "imagePlan": [{"role": "cover"}, {"role": "proof"}, {"role": "cta"}]},
+], ensure_ascii=False)
+
+
+def test_parse_response_returns_all_notes():
+    notes = _parse_fission_response(_LLM_JSON, SOURCE_NOTE, PRODUCT, 2, 3)
+    assert len(notes) == 2
+    assert notes[0]["title"] == "套1标题"
+    # tags 经归一化补 #
+    assert all(t.startswith("#") for t in notes[0]["tags"])
+
+
+def test_parse_response_corrects_imageplan_count():
+    # imagePlan 数不等于 image_count 时，被兜底重建成正确张数
+    notes = _parse_fission_response(_LLM_JSON, SOURCE_NOTE, PRODUCT, 2, 6)
+    for n in notes:
+        assert len(n["imagePlan"]) == 6
+
+
+def test_parse_response_markdown_wrapped():
+    # 模型常把 JSON 包在 ```json ``` 里，需容错
+    wrapped = f"```json\n{_LLM_JSON}\n```"
+    notes = _parse_fission_response(wrapped, SOURCE_NOTE, PRODUCT, 2, 3)
+    assert len(notes) == 2
+
+
+def test_parse_response_garbage_falls_back_not_crash():
+    # 完全不可解析 → 品类兜底，绝不抛异常/不返回空
+    notes = _parse_fission_response("彻底不是JSON的东西", SOURCE_NOTE, PRODUCT, 3, 6)
+    assert len(notes) == 3
+    for n in notes:
+        assert len(n["imagePlan"]) == 6
+        assert n["title"]