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/app/services/fission_pipeline.py
+++ b/backend/app/services/fission_pipeline.py
@@ -0,0 +1,179 @@
+"""
+app/services/fission_pipeline.py — 第11环裂变 编排层（从 fission_service 拆出）
+
+Celery 内调用：一次 LLM 出 N 套完整笔记包 → 解析/兜底 → 评分@80 → 落 FissionNote
+→ 逐套生图存 images_json。拆分原因：fission_service.py 超 200 行红线上限。
+🔴 生图引擎零改动：复用单篇 generate_storyboard_images（codeproxy edits 带产品参考图）。
+🔴 评分沿用 llm_score_copy，合格线 QUALITY_PASS_SCORE=80（禁改）。
+🔴 FISSION_SYSTEM 作 messages[0].role=system 传（chat_complete 是 OpenAI 兼容）。
+"""
+import logging
+
+from sqlalchemy.orm import Session
+
+logger = logging.getLogger(__name__)
+
+MAX_FANOUT = 5  # 每用户并发上限5(红线)；裂变 N 套直出受同等约束
+
+
+def _parse_fission_response(
+    raw: str, source_note: dict, product: dict, note_count: int, image_count: int,
+) -> list[dict]:
+    """解析 LLM 返回的 N 套笔记；不可解析则品类兜底（不卡用户）。
+
+    解析链：parse_json_array(容错markdown) → notes_array_from_parsed(拎数组) →
+    空则 build_fallback_notes 品类草稿。每套补 imagePlan/tags 归一化。
+    """
+    import json as _json
+    from app.services.ai_engine._text_prompt import parse_json_array
+    from app.services.ai_engine.fission_prompt import (
+        normalize_tags, notes_array_from_parsed,
+    )
+    from app.services.ai_engine.fission_fallback import (
+        build_fallback_image_plan, build_fallback_notes,
+    )
+
+    notes = notes_array_from_parsed(parse_json_array(raw))
+    if not notes:
+        # parse_json_array 只认数组；再试整段当对象解析（容错单对象返回）
+        try:
+            notes = notes_array_from_parsed(_json.loads(raw))
+        except (ValueError, TypeError):
+            notes = []
+    if not notes:
+        logger.warning("裂变 LLM 返回不可解析，启用品类兜底。raw[:120]=%s", str(raw)[:120])
+        return build_fallback_notes(source_note, product, note_count, image_count)
+
+    out: list[dict] = []
+    for n in notes:
+        if not isinstance(n, dict):
+            continue
+        n["tags"] = normalize_tags(n.get("tags", []), n.get("keywords", []))
+        plan = n.get("imagePlan")
+        if not isinstance(plan, list) or len(plan) != image_count:
+            n["imagePlan"] = build_fallback_image_plan(n, image_count)
+        out.append(n)
+    return out or build_fallback_notes(source_note, product, note_count, image_count)
+
+
+def _score_notes(clients, notes: list[dict], source_note: dict, banned: list[str]) -> None:
+    """对每套笔记 LLM 评分（限2并发），结果就地写回 note['_score']/_passed/_block。
+
+    Celery 同步环境：用 asyncio.run 跑一个内部 gather（信号量限2并发，
+    对齐生图限流，避免中转站 429）。
+    """
+    import asyncio
+    from app.services.ai_engine.llm_scorer import llm_score_copy
+    from app.services.ai_engine.constants import QUALITY_PASS_SCORE
+
+    async def _run() -> None:
+        sem = asyncio.Semaphore(2)
+
+        async def _one(note: dict) -> None:
+            copy = {
+                "title": note.get("title", ""),
+                "content": note.get("content", ""),
+                "tags": note.get("tags", []),
+            }
+            async with sem:
+                try:
+                    r = await llm_score_copy(
+                        clients, copy, source_note, banned, pass_score=QUALITY_PASS_SCORE,
+                    )
+                except Exception as exc:  # noqa: BLE001
+                    logger.warning("裂变评分异常，记0分不达标: %s", exc)
+                    r = {"score": 0, "passed": False, "banned_words_found": []}
+            note["_score"] = int(r.get("score", 0))
+            note["_block"] = bool(r.get("banned_words_found"))
+            # 过线 = score≥80 且无硬拦违禁词
+            note["_passed"] = bool(r.get("passed")) and not note["_block"]
+
+        await asyncio.gather(*(_one(n) for n in notes))
+
+    asyncio.run(_run())
+
+
+def execute_fission_pipeline(db: Session, clients, fission_id: int, source_task_id: int) -> dict:
+    """裂变主编排（Celery 内调用，clients 已构建好）。
+
+    流程：查 FissionTask+源产品 → 一次 chat_complete 出 N 套 → 解析/兜底
+    → 每套评分@80 → 按分排序取 N 套（不够用兜底补，不达标标 needs_optimization）
+    → 落 FissionNote → 逐套生图存 images_json → 回写 FissionTask.status。
+    """
+    import json as _json
+
+    from app.models.fission import FissionTask, FissionNote
+    from app.models.task import GenerationTask
+    from app.models.product import Product
+    from app.workers.pipeline_steps import build_product_dict
+    from app.services.ai_engine.fission_prompt import build_fission_prompt
+
+    ft = db.query(FissionTask).filter(FissionTask.id == fission_id).first()
+    if not ft:
+        return {"fission_id": fission_id, "status": "not_found"}
+
+    n = max(1, min(ft.fanout_count or 3, MAX_FANOUT))
+    try:
+        source_note = _json.loads(ft.source_note) if ft.source_note else {}
+    except (ValueError, TypeError):
+        source_note = {}
+
+    src = db.query(GenerationTask).filter(GenerationTask.id == source_task_id).first()
+    product_row = db.query(Product).filter(Product.id == src.product_id).first() if src else None
+    if not src or not product_row:
+        ft.status = "failed"; db.commit()
+        return {"fission_id": fission_id, "status": "failed", "reason": "源任务或产品缺失"}
+
+    product = build_product_dict(product_row)
+    image_count = max(1, src.image_count or 3)
+    banned = []  # 违禁词分级表后续接入；评分器内置 BANNED_WORDS_DEFAULT 已兜底
+
+    # 一次 LLM 出 N 套（FISSION_SYSTEM 作 messages[0].role=system）
+    from app.services.ai_engine.fission_prompt import FISSION_SYSTEM
+    user_prompt = build_fission_prompt(
+        source_note, product, ft.reference_level, n, image_count,
+    )
+    # max_tokens 按套数缩放：每套完整笔记包约 1200 token，留足余量
+    max_tokens = min(8192, 1800 + n * 1400)
+    raw = ""
+    try:
+        import asyncio
+        raw = asyncio.run(clients.chat_complete(
+            messages=[{"role": "system", "content": FISSION_SYSTEM},
+                      {"role": "user", "content": user_prompt}],
+            model=clients._model, max_tokens=max_tokens, temperature=0.8,
+        ))
+    except Exception as exc:  # noqa: BLE001
+        logger.warning("裂变 LLM 调用失败，启用品类兜底: %s", exc)
+
+    notes = _parse_fission_response(raw, source_note, product, n, image_count)
+    _score_notes(clients, notes, source_note, banned)
+
+    # 排序：过线优先，再按分降序；取前 N 套（不足用兜底草稿补齐）
+    notes.sort(key=lambda x: (x.get("_passed", False), x.get("_score", 0)), reverse=True)
+    chosen = notes[:n]
+
+    saved_ids: list[int] = []
+    for seq, note in enumerate(chosen):
+        passed = bool(note.get("_passed"))
+        fn = FissionNote(
+            fission_id=fission_id, workspace_id=ft.workspace_id, seq=seq,
+            note_json=_json.dumps(note, ensure_ascii=False),
+            score=int(note.get("_score", 0)),
+            passed=1 if passed else 0,
+            needs_optimization=0 if passed else 1,  # 不达标不丢弃，标降级草稿
+            dimension=str(note.get("dimension", ""))[:64],
+            status="scored",
+        )
+        db.add(fn); db.flush()
+        saved_ids.append(fn.id)
+    db.commit()
+
+    logger.info("裂变出 %s 套已落库 fission=%s ids=%s", len(saved_ids), fission_id, saved_ids)
+
+    # 逐套生图（复用单篇引擎），存 images_json
+    from app.services.fission_images import generate_fission_images
+    generate_fission_images(db, clients, ft, product, image_count, saved_ids)
+
+    ft.status = "completed"; db.commit()
+    return {"fission_id": fission_id, "status": "completed", "note_ids": saved_ids}