feat: M4 文风 + M5 生成/多provider/Skill + Kimi Code 订阅接入 + 本地联调修复
M4（文风）: style-auditor 双轨（提取指纹/漂移第四审）+ jobs 长任务框架(zombie reaper) + 回炉 refine + GET /style read-back。 M5（生成+扩展）: worldbuilder/character-gen（入库 continuity 409 gate + partition_writes 白名单 + schema→JSONB 形变）； 网关多 provider 回退链/熔断/能力降级（Anthropic/Gemini 适配器）；Skill registry + 表权限沙箱 + 规则； 前端 角色生成器/世界观/Codex/规则页/技能库/⌘K 命令面板。 K1（Kimi Code 订阅接入）: OAuth device-flow（kimi-code）+ 静态 Console key（kimi-code-key）两路径； coding 端点 KimiCLI 伪造头（实测 UA allow-list 门禁，缺则 403）+ JSON 模式结构化（thinking ⊥ tool_choice）。 本地联调修复: CORS 中间件；assemble 注入 premise+「写第N章」指令（修空 prompt 400）； GET /outline·/draft read-back + 大纲/工作台/审稿页重载；写页 client/server 常量边界 + notFound 健壮化； 字数 toLocaleString locale 水合；审稿页终稿从已存草稿 seed（修 accept 422）。 门禁: backend ruff/mypy(157)/alembic 无漂移/pytest 451 · frontend lint/tsc/vitest/build。 Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
This commit is contained in:
Yaojia Wang
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apps/api/ww_api/routers/style.py
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apps/api/ww_api/routers/style.py
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"""文风端点：学文风（异步长任务）+ 回炉 + 最新指纹读取
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（C3 扩 / ARCH §5.4 style-auditor 提取轨 / §7.2 / §7.4 jobs / UX §6.9 / §8.3）。
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独立 router（仿 outline.py），挂 `app.include_router`。三端点：
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- `POST /projects/{id}/style` → 202 `{job_id}`：学文风走 jobs（M4-c）。立即写一行 job
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返 202，提取经 BackgroundTask `run_job` 跑（**自建独立 session**，请求 session 已关闭）。
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无凭据 → 流前 `LLM_UNAVAILABLE`（503，在调度 job 之前拦下，避免凭空写注定失败的 job）。
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- `POST /projects/{id}/chapters/{no}/refine` → 200 `{original, refined}`：同步回炉，writer
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网关纯文本重写选中段；不写库（不变量 #3，作者采纳经既有 draft 自动保存合入）；末尾
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`commit()` 让 usage_ledger 落库（网关 ledger add-only，同 draft/review 纪律）。
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- `GET /projects/{id}/style` → 200 最新指纹（完整 16 维 + 证据 + 版本，UX §6.9）。
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不变量 #2：经 `get_*_gateway`（analyst/writer 档）注入，spec 只声明档位不传 model。
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不变量 #3：提取/回炉 agent 只读不写其它表；文风指纹落库经此作者发起的学文风路径。
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"""
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from __future__ import annotations
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import uuid
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from typing import Annotated, Any
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from fastapi import APIRouter, BackgroundTasks, Depends, Request, Response
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from sqlalchemy.ext.asyncio import AsyncSession
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from ww_agents import refiner_spec, style_extract_spec
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from ww_core.domain import JobRepo, ProjectRepo, StyleFingerprintWriteRepo
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from ww_core.domain.style_repo import SqlStyleFingerprintWriteRepo
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from ww_core.orchestrator import run_style_extraction
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from ww_db import get_session
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from ww_llm_gateway import Gateway
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from ww_llm_gateway.types import Block, LlmRequest, Scope
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from ww_shared import AppError, ErrorCode
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from ww_api.logging_config import get_logger
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from ww_api.schemas.style import (
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RefineRequest,
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RefineResponse,
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StyleFingerprintResponse,
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StyleLearnRequest,
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StyleLearnResponse,
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)
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from ww_api.services.credentials import STUB_OWNER_ID, SqlCredentialStore
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from ww_api.services.foreshadow_scan import SessionFactory
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from ww_api.services.job_runner import run_job
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from ww_api.services.project_deps import (
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build_gateway_for_tier,
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get_job_repo,
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get_project_repo,
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get_refine_gateway,
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get_session_factory,
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get_style_extract_gateway,
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get_style_write_repo,
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)
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log = get_logger("ww.api.style")
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router = APIRouter(prefix="/projects", tags=["style"])
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ProjectRepoDep = Annotated[ProjectRepo, Depends(get_project_repo)]
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JobRepoDep = Annotated[JobRepo, Depends(get_job_repo)]
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StyleWriteRepoDep = Annotated[StyleFingerprintWriteRepo, Depends(get_style_write_repo)]
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StyleExtractGatewayDep = Annotated[Gateway, Depends(get_style_extract_gateway)]
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RefineGatewayDep = Annotated[Gateway, Depends(get_refine_gateway)]
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SessionDep = Annotated[AsyncSession, Depends(get_session)]
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SessionFactoryDep = Annotated[SessionFactory, Depends(get_session_factory)]
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_JOB_KIND_STYLE_LEARN = "style_learn"
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def _split_fingerprint(result: Any) -> tuple[dict[str, str], dict[str, list[str]]]:
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"""把 `StyleFingerprintResult` 拆成 DB 两列：`{name:value}` + `{name:[evidence]}`。"""
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dimensions = {dim.name: dim.value for dim in result.dimensions}
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evidence = {dim.name: list(dim.evidence) for dim in result.dimensions}
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return dimensions, evidence
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def _make_style_learn_work(project_id: uuid.UUID, samples_text: str) -> Any:
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"""构造学文风后台工作闭包：在 `run_job` 自建的独立 session 上跑提取 + 落库。
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`work(session)` 自建 analyst 网关（从凭据）+ 写侧 repo（用 `run_job` 传入的独立
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session）→ `run_style_extraction` → 拆指纹 → `append`（版本化）→ 返回 result 摘要
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`{version, dims_count}`。提交归 `run_job`（业务写 + job done 同一事务一次 commit）。
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"""
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async def work(session: AsyncSession) -> dict[str, Any]:
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store = SqlCredentialStore(session)
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gateway = await build_gateway_for_tier(session, store, "analyst")
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result = await run_style_extraction(
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style_extract_spec,
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samples_text=samples_text,
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gateway=gateway,
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user_id=STUB_OWNER_ID,
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project_id=project_id,
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)
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dimensions, evidence = _split_fingerprint(result)
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repo = SqlStyleFingerprintWriteRepo(session)
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version = await repo.append(project_id, dimensions_json=dimensions, evidence_json=evidence)
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return {"version": version, "dims_count": len(dimensions)}
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return work
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@router.post("/{project_id}/style", status_code=202)
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async def learn_style(
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project_id: uuid.UUID,
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body: StyleLearnRequest,
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request: Request,
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response: Response,
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background_tasks: BackgroundTasks,
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project_repo: ProjectRepoDep,
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job_repo: JobRepoDep,
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gateway: StyleExtractGatewayDep, # 凭据探测（无凭据 → dep 解析阶段 503）
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session: SessionDep,
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session_factory: SessionFactoryDep,
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) -> StyleLearnResponse:
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"""学文风：写一行 job 返 202，提取经 BackgroundTask 异步跑。
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项目不存在 → 404；无凭据 → 503（在调度 job 前拦下）。`mode` 不影响落库逻辑
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（写侧始终 append 新版本，version+1 自动），仅供前端区分首学/更新语义。
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"""
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request_id = getattr(request.state, "request_id", None)
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project = await project_repo.get(STUB_OWNER_ID, project_id)
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if project is None:
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raise AppError(ErrorCode.NOT_FOUND, f"project not found: {project_id}")
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# `gateway` 已在 dep 解析阶段验过凭据（无凭据 → 503）；提取本体在 BackgroundTask
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# 里用独立 session 重新构网关跑，此处不复用它（请求 session 即将关闭）。
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samples_text = "\n\n".join(body.samples)
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job = await job_repo.create(project_id, _JOB_KIND_STYLE_LEARN)
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await session.commit() # job 行需在 202 返回前持久化（供前端立即轮询）。
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background_tasks.add_task(
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run_job,
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session_factory,
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job.id,
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_make_style_learn_work(project_id, samples_text),
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request_id=request_id,
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)
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log.info(
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"style_learn_scheduled",
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project_id=str(project_id),
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request_id=request_id,
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job_id=str(job.id),
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sample_count=len(body.samples),
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mode=body.mode,
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)
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response.status_code = 202
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return StyleLearnResponse(job_id=job.id)
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@router.get("/{project_id}/style")
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async def get_style(
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project_id: uuid.UUID,
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project_repo: ProjectRepoDep,
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style_repo: StyleWriteRepoDep,
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) -> StyleFingerprintResponse:
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"""取最新文风指纹（完整 16 维 + 证据 + 版本）。项目不存在 → 404；无指纹 → 404。"""
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project = await project_repo.get(STUB_OWNER_ID, project_id)
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if project is None:
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raise AppError(ErrorCode.NOT_FOUND, f"project not found: {project_id}")
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latest = await style_repo.latest(project_id)
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if latest is None:
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raise AppError(ErrorCode.NOT_FOUND, f"no style fingerprint for project: {project_id}")
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return StyleFingerprintResponse(
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dimensions=latest.dimensions,
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evidence=latest.evidence,
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version=latest.version,
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)
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def _build_refine_input(segment: str, instruction: str | None) -> str:
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"""组回炉输入：待重写段 + 可选指令（保留上下文语气，只重写该段）。"""
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parts = [f"【待重写段落】\n{segment}"]
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if instruction:
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parts.append(f"【改写指令】\n{instruction}")
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return "\n\n".join(parts)
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@router.post("/{project_id}/chapters/{chapter_no}/refine")
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async def refine_segment(
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project_id: uuid.UUID,
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chapter_no: int,
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body: RefineRequest,
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request: Request,
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project_repo: ProjectRepoDep,
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gateway: RefineGatewayDep,
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session: SessionDep,
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) -> RefineResponse:
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"""同步回炉：writer 网关纯文本重写选中段，返回 {original, refined}。
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项目不存在 → 404；无凭据 → 503。不写库（不变量 #3）；末尾 `commit()` 让网关
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usage_ledger 落库（add-only，否则记账静默丢失，同 draft/review 纪律）。
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"""
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request_id = getattr(request.state, "request_id", None)
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project = await project_repo.get(STUB_OWNER_ID, project_id)
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if project is None:
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raise AppError(ErrorCode.NOT_FOUND, f"project not found: {project_id}")
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req = LlmRequest(
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tier=refiner_spec.tier, # 不变量 #2：writer 档，不传 model
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system=[Block(text=refiner_spec.system_prompt, cache=True)],
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input=_build_refine_input(body.segment, body.instruction),
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output_schema=None, # refiner 纯文本（无结构化 schema）
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scope=Scope(user_id=STUB_OWNER_ID, project_id=project_id),
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)
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resp = await gateway.run(req)
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# 提交边界：网关 ledger add-only → 端点末尾 commit（否则记账静默丢失，见 gotcha）。
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await session.commit()
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log.info(
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"refine_done",
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project_id=str(project_id),
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chapter_no=chapter_no,
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request_id=request_id,
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segment_len=len(body.segment),
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refined_len=len(resp.text),
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has_instruction=body.instruction is not None,
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)
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return RefineResponse(original=body.segment, refined=resp.text)
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