feat: M1 — 立项→写章草稿(SSE)→自动保存；连一家 provider

- 薄自建 LLM 网关：OpenAI 兼容适配器(DeepSeek) + instructor 结构化输出 + usage_ledger 记账 + 档位路由
- 记忆服务 assemble：确定性选择(显式+主角+近况) + 渲染卡 + 缓存断点(中性文本)
- LangGraph 写章节点 + Postgres checkpointer + SSE 归一(token/done/error)
- API：立项 + 写章 draft(SSE) + PUT 自动保存 + 提供商凭据(Fernet 加密/测试连接)
- 前端：AppShell + 作品库 + 5 步立项向导 + 写作工作台(流式打字机+自动保存) + 设置页
- M1 E2E：真实 DB + mock 网关零 token 走通闭环

packages/llm_gateway/tests/test_structured_output.py

@@ -0,0 +1,123 @@
+"""T2.1 结构化输出接线单测：output_schema → instructor → LlmResponse.parsed。
+
+注入 fake instructor client（返回 (parsed, raw_completion)），不联网。
+覆盖：① 带 schema → parsed 是实例且字段正确 + 仍记 1 条 ledger；
+② 无 schema → parsed is None 且纯文本路径不变。
+"""
+
+from __future__ import annotations
+
+import uuid
+from types import SimpleNamespace
+from typing import Any, cast
+
+from openai import AsyncOpenAI
+from pydantic import BaseModel
+from ww_llm_gateway.adapters.openai_compat import OpenAICompatAdapter
+from ww_llm_gateway.gateway import Gateway
+from ww_llm_gateway.routing import Route
+from ww_llm_gateway.types import LlmRequest, Scope, Tier
+
+
+class _Out(BaseModel):
+    label: str
+    score: int
+
+
+def _req(**kw: Any) -> LlmRequest:
+    kw.setdefault("tier", "analyst")
+    return LlmRequest(scope=Scope(user_id=uuid.UUID(int=1)), **kw)
+
+
+def _route(_tier: Tier) -> Route:
+    return Route(provider="deepseek", model="deepseek-chat")
+
+
+class _FakeStructured:
+    """模拟 instructor.AsyncInstructor：create_with_completion 返回 (parsed, raw)。"""
+
+    def __init__(self, parsed: BaseModel, raw: Any) -> None:
+        self._parsed = parsed
+        self._raw = raw
+        self.calls = 0
+        self.last_response_model: Any = None
+
+    async def create_with_completion(
+        self, *, messages: Any, response_model: Any, **kw: Any
+    ) -> tuple[BaseModel, Any]:
+        self.calls += 1
+        self.last_response_model = response_model
+        return self._parsed, self._raw
+
+
+def _raw_with_usage() -> Any:
+    return SimpleNamespace(
+        usage=SimpleNamespace(
+            prompt_tokens=42,
+            completion_tokens=7,
+            prompt_tokens_details=SimpleNamespace(cached_tokens=5),
+        )
+    )
+
+
+def _client() -> AsyncOpenAI:
+    return cast(AsyncOpenAI, SimpleNamespace(chat=SimpleNamespace(completions=None)))
+
+
+async def test_complete_with_schema_returns_parsed_instance() -> None:
+    structured = _FakeStructured(_Out(label="ok", score=9), _raw_with_usage())
+    adapter = OpenAICompatAdapter("deepseek", _client(), structured_client=structured)
+
+    result = await adapter.complete(_req(input="x", output_schema=_Out), "deepseek-chat")
+
+    assert isinstance(result.parsed, _Out)
+    assert result.parsed.label == "ok"
+    assert result.parsed.score == 9
+    assert structured.last_response_model is _Out
+    # usage 从 raw completion 提取
+    assert result.usage.input_tokens == 42
+    assert result.usage.output_tokens == 7
+    assert result.usage.cache_read_tokens == 5
+
+
+async def test_complete_without_schema_keeps_parsed_none() -> None:
+    response = SimpleNamespace(
+        choices=[SimpleNamespace(message=SimpleNamespace(content="纯文本"))],
+        usage=SimpleNamespace(prompt_tokens=3, completion_tokens=2, prompt_tokens_details=None),
+    )
+
+    class _Completions:
+        async def create(self, **kw: Any) -> Any:
+            return response
+
+    client = cast(
+        AsyncOpenAI,
+        SimpleNamespace(chat=SimpleNamespace(completions=_Completions())),
+    )
+    adapter = OpenAICompatAdapter("deepseek", client)
+
+    result = await adapter.complete(_req(input="x"), "deepseek-chat")
+
+    assert result.parsed is None
+    assert result.text == "纯文本"
+
+
+class _FakeLedger:
+    def __init__(self) -> None:
+        self.records: list[Any] = []
+
+    async def record(self, scope: Any, usage: Any) -> None:
+        self.records.append(usage)
+
+
+async def test_gateway_run_passes_parsed_through_and_records_once() -> None:
+    structured = _FakeStructured(_Out(label="hit", score=1), _raw_with_usage())
+    adapter = OpenAICompatAdapter("deepseek", _client(), structured_client=structured)
+    ledger = _FakeLedger()
+    gw = Gateway({"deepseek": adapter}, ledger, resolver=_route)
+
+    resp = await gw.run(_req(input="x", output_schema=_Out))
+
+    assert isinstance(resp.parsed, _Out)
+    assert resp.parsed.label == "hit"
+    assert len(ledger.records) == 1