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/conftest.py

@@ -0,0 +1,18 @@
+"""网关单测公用 fixtures。"""
+
+from __future__ import annotations
+
+import uuid
+
+import pytest
+from ww_llm_gateway.types import LlmRequest, Scope
+
+
+@pytest.fixture
+def scope() -> Scope:
+    return Scope(user_id=uuid.UUID(int=1), project_id=uuid.UUID(int=2))
+
+
+@pytest.fixture
+def req(scope: Scope) -> LlmRequest:
+    return LlmRequest(tier="writer", input="写第 1 章", scope=scope)

packages/llm_gateway/tests/fakes.py

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+"""网关单测替身（mock provider + 内存 ledger）——不碰真实 API / DB。
+
+放在独立模块（非 conftest）以便测试用绝对导入 `from fakes import ...`；
+本目录无 __init__.py（避免与顶层 tests 包同名冲突），故走 pytest 路径注入。
+"""
+
+from __future__ import annotations
+
+from collections.abc import AsyncIterator
+
+from ww_llm_gateway.adapters.base import (
+    Capabilities,
+    ProviderResult,
+    ProviderUsage,
+    StreamChunk,
+)
+from ww_llm_gateway.routing import Route
+from ww_llm_gateway.types import LlmRequest, Scope, Tier, Usage
+
+
+class FakeAdapter:
+    provider = "deepseek"
+
+    def __init__(self, text: str = "hello world", deltas: list[str] | None = None) -> None:
+        self.text = text
+        self.deltas = deltas if deltas is not None else ["hel", "lo ", "world"]
+        self.complete_calls = 0
+        self.stream_calls = 0
+
+    def capabilities(self) -> Capabilities:
+        return Capabilities(structured_output=True, prefix_cache=True)
+
+    async def complete(self, req: LlmRequest, model: str) -> ProviderResult:
+        self.complete_calls += 1
+        return ProviderResult(
+            text=self.text,
+            usage=ProviderUsage(input_tokens=100, output_tokens=50),
+        )
+
+    async def stream(self, req: LlmRequest, model: str) -> AsyncIterator[StreamChunk]:
+        self.stream_calls += 1
+        for d in self.deltas:
+            yield StreamChunk(text=d)
+        yield StreamChunk(usage=ProviderUsage(input_tokens=100, output_tokens=50))
+
+
+class FakeLedger:
+    def __init__(self) -> None:
+        self.records: list[Usage] = []
+
+    async def record(self, scope: Scope, usage: Usage) -> None:
+        self.records.append(usage)
+
+
+def fake_route(tier: Tier) -> Route:
+    return Route(provider="deepseek", model="deepseek-chat")

packages/llm_gateway/tests/test_gateway.py

@@ -0,0 +1,82 @@
+"""T1.1 网关核心单测：run / stream / 记账 / 路由 / 错误。"""
+
+from __future__ import annotations
+
+import uuid
+
+import pytest
+from fakes import FakeAdapter, FakeLedger, fake_route
+from ww_llm_gateway.gateway import Gateway
+from ww_llm_gateway.routing import resolve_route
+from ww_llm_gateway.types import LlmRequest, Scope
+from ww_shared import AppError, ErrorCode
+
+
+async def test_run_returns_text_usage_and_served_by(req: LlmRequest) -> None:
+    adapter = FakeAdapter(text="第一章正文")
+    ledger = FakeLedger()
+    gw = Gateway({"deepseek": adapter}, ledger, resolver=fake_route)
+
+    resp = await gw.run(req)
+
+    assert resp.text == "第一章正文"
+    assert resp.served_by.provider == "deepseek"
+    assert resp.served_by.fell_back is False
+    assert resp.usage.input_tokens == 100
+    assert resp.usage.output_tokens == 50
+    assert adapter.complete_calls == 1
+
+
+async def test_run_writes_exactly_one_ledger_record(req: LlmRequest) -> None:
+    ledger = FakeLedger()
+    gw = Gateway({"deepseek": FakeAdapter()}, ledger, resolver=fake_route)
+
+    await gw.run(req)
+
+    assert len(ledger.records) == 1
+    assert ledger.records[0].provider == "deepseek"
+    assert ledger.records[0].model == "deepseek-chat"
+
+
+async def test_run_computes_cost_from_pricing(req: LlmRequest) -> None:
+    ledger = FakeLedger()
+    gw = Gateway({"deepseek": FakeAdapter()}, ledger, resolver=fake_route)
+
+    resp = await gw.run(req)
+
+    # ceil(100/1e6*27 + 50/1e6*110) == 1 cent
+    assert resp.usage.cost_minor == 1
+    assert resp.usage.currency == "USD"
+
+
+async def test_stream_yields_deltas_and_records_once(req: LlmRequest) -> None:
+    ledger = FakeLedger()
+    gw = Gateway({"deepseek": FakeAdapter(deltas=["a", "b", "c"])}, ledger, resolver=fake_route)
+
+    collected = [d.text async for d in gw.stream(req)]
+
+    assert "".join(collected) == "abc"
+    assert len(ledger.records) == 1
+    assert ledger.records[0].output_tokens == 50
+
+
+async def test_unknown_provider_raises_llm_unavailable(req: LlmRequest) -> None:
+    gw = Gateway({}, FakeLedger(), resolver=fake_route)
+
+    with pytest.raises(AppError) as exc:
+        await gw.run(req)
+
+    assert exc.value.code == ErrorCode.LLM_UNAVAILABLE
+
+
+def test_resolve_route_parses_tier_defaults() -> None:
+    route = resolve_route("writer")
+    assert route.provider == "deepseek"
+    assert route.model == "deepseek-chat"
+
+
+def test_agent_passes_only_tier_never_model() -> None:
+    # 不变量 ②：LlmRequest 无 model 字段，只有 tier
+    req = LlmRequest(tier="analyst", input="x", scope=Scope(user_id=uuid.UUID(int=9)))
+    assert "model" not in LlmRequest.model_fields
+    assert req.tier == "analyst"

packages/llm_gateway/tests/test_openai_adapter.py

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+"""OpenAI 兼容适配器单测：用替身 client 验证消息翻译 + usage 映射（不联网）。"""
+
+from __future__ import annotations
+
+import uuid
+from collections.abc import AsyncIterator
+from types import SimpleNamespace
+from typing import Any, cast
+
+from openai import AsyncOpenAI
+from ww_llm_gateway.adapters.openai_compat import OpenAICompatAdapter, _messages
+from ww_llm_gateway.types import Block, LlmRequest, Scope
+
+
+def _req(**kw: Any) -> LlmRequest:
+    kw.setdefault("tier", "writer")
+    return LlmRequest(scope=Scope(user_id=uuid.UUID(int=1)), **kw)
+
+
+class _FakeStream:
+    def __init__(self, chunks: list[Any]) -> None:
+        self._chunks = chunks
+
+    async def __aiter__(self) -> AsyncIterator[Any]:
+        for c in self._chunks:
+            yield c
+
+
+class _FakeCompletions:
+    def __init__(self, response: Any, stream_chunks: list[Any]) -> None:
+        self._response = response
+        self._stream_chunks = stream_chunks
+        self.last_messages: Any = None
+
+    async def create(self, **kw: Any) -> Any:
+        self.last_messages = kw.get("messages")
+        if kw.get("stream"):
+            return _FakeStream(self._stream_chunks)
+        return self._response
+
+
+class _FakeClient:
+    def __init__(self, completions: _FakeCompletions) -> None:
+        self.chat = SimpleNamespace(completions=completions)
+
+
+def test_messages_put_system_first_then_user() -> None:
+    req = _req(input="正文", system=[Block(text="世界观硬规则", cache=True)])
+    msgs = _messages(req)
+    assert msgs[0]["role"] == "system"
+    assert msgs[0]["content"] == "世界观硬规则"
+    assert msgs[1]["role"] == "user"
+    assert msgs[1]["content"] == "正文"
+
+
+async def test_complete_maps_text_and_usage() -> None:
+    response = SimpleNamespace(
+        choices=[SimpleNamespace(message=SimpleNamespace(content="草稿"))],
+        usage=SimpleNamespace(
+            prompt_tokens=120,
+            completion_tokens=80,
+            prompt_tokens_details=SimpleNamespace(cached_tokens=30),
+        ),
+    )
+    completions = _FakeCompletions(response, [])
+    adapter = OpenAICompatAdapter("deepseek", cast(AsyncOpenAI, _FakeClient(completions)))
+
+    result = await adapter.complete(_req(input="x"), "deepseek-chat")
+
+    assert result.text == "草稿"
+    assert result.usage.input_tokens == 120
+    assert result.usage.output_tokens == 80
+    assert result.usage.cache_read_tokens == 30
+
+
+async def test_stream_yields_text_then_final_usage() -> None:
+    chunks = [
+        SimpleNamespace(choices=[SimpleNamespace(delta=SimpleNamespace(content="他"))], usage=None),
+        SimpleNamespace(choices=[SimpleNamespace(delta=SimpleNamespace(content="说"))], usage=None),
+        SimpleNamespace(
+            choices=[],
+            usage=SimpleNamespace(
+                prompt_tokens=10,
+                completion_tokens=2,
+                prompt_tokens_details=None,
+            ),
+        ),
+    ]
+    completions = _FakeCompletions(None, chunks)
+    adapter = OpenAICompatAdapter("deepseek", cast(AsyncOpenAI, _FakeClient(completions)))
+
+    texts: list[str] = []
+    usage_seen = None
+    async for ch in adapter.stream(_req(input="x"), "deepseek-chat"):
+        if ch.text:
+            texts.append(ch.text)
+        if ch.usage is not None:
+            usage_seen = ch.usage
+
+    assert "".join(texts) == "他说"
+    assert usage_seen is not None
+    assert usage_seen.output_tokens == 2

packages/llm_gateway/tests/test_structured_output.py

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+"""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