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/ww_llm_gateway/__init__.py

@@ -0,0 +1,47 @@
+"""LLM 网关（C1 / ARCH §4）：薄自建，tier→provider+model，屏蔽厂商差异。"""
+
+from __future__ import annotations
+
+from .adapters.base import (
+    Capabilities,
+    ProviderAdapter,
+    ProviderResult,
+    ProviderUsage,
+    StreamChunk,
+)
+from .adapters.openai_compat import OpenAICompatAdapter
+from .gateway import Gateway
+from .ledger import LedgerSink, SqlAlchemyLedgerSink
+from .routing import Route, resolve_route
+from .types import (
+    Block,
+    Delta,
+    LlmRequest,
+    LlmResponse,
+    Scope,
+    ServedBy,
+    Tier,
+    Usage,
+)
+
+__all__ = [
+    "Block",
+    "Capabilities",
+    "Delta",
+    "Gateway",
+    "LedgerSink",
+    "LlmRequest",
+    "LlmResponse",
+    "OpenAICompatAdapter",
+    "ProviderAdapter",
+    "ProviderResult",
+    "ProviderUsage",
+    "Route",
+    "Scope",
+    "ServedBy",
+    "SqlAlchemyLedgerSink",
+    "StreamChunk",
+    "Tier",
+    "Usage",
+    "resolve_route",
+]

packages/llm_gateway/ww_llm_gateway/adapters/base.py

@@ -0,0 +1,51 @@
+"""适配器接口与中间数据形（ARCH §4.2/§4.4）。
+
+适配器把 `LlmRequest` 翻译成目标厂商请求，并把响应/流/usage 翻译回统一中间形。
+"""
+
+from __future__ import annotations
+
+from collections.abc import AsyncIterator
+from typing import Protocol, runtime_checkable
+
+from pydantic import BaseModel, ConfigDict
+
+from ..types import LlmRequest
+
+
+class Capabilities(BaseModel):
+    structured_output: bool = False
+    prefix_cache: bool = False
+    thinking: bool = False
+
+
+class ProviderUsage(BaseModel):
+    input_tokens: int
+    output_tokens: int
+    cache_read_tokens: int = 0
+
+
+class ProviderResult(BaseModel):
+    model_config = ConfigDict(arbitrary_types_allowed=True)
+
+    text: str
+    usage: ProviderUsage
+    parsed: BaseModel | None = None  # output_schema 命中时的结构化结果（§4.4）
+
+
+class StreamChunk(BaseModel):
+    """流式块：文本增量（usage=None），或末尾用量块（text=""）。"""
+
+    text: str = ""
+    usage: ProviderUsage | None = None
+
+
+@runtime_checkable
+class ProviderAdapter(Protocol):
+    provider: str
+
+    def capabilities(self) -> Capabilities: ...
+
+    async def complete(self, req: LlmRequest, model: str) -> ProviderResult: ...
+
+    def stream(self, req: LlmRequest, model: str) -> AsyncIterator[StreamChunk]: ...

packages/llm_gateway/ww_llm_gateway/adapters/openai_compat.py

@@ -0,0 +1,129 @@
+"""OpenAI 兼容适配器：一套覆盖 DeepSeek/Kimi/Qwen/GLM/OpenAI（ARCH §4.2）。
+
+仅 base_url + model + key 不同。注入 `AsyncOpenAI` 客户端以便测试用替身。
+"""
+
+from __future__ import annotations
+
+from collections.abc import AsyncIterator
+from typing import Any, Protocol
+
+import instructor
+from openai import AsyncOpenAI
+from openai.types.chat import ChatCompletionMessageParam
+from pydantic import BaseModel
+
+from ..types import LlmRequest
+from .base import Capabilities, ProviderResult, ProviderUsage, StreamChunk
+
+
+class StructuredClient(Protocol):
+    """instructor 风格的结构化客户端缝（`AsyncInstructor` 即满足此协议）。
+
+    抽成 Protocol 以便测试注入 fake，绝不联网（不变量：测试零真实 LLM）。
+    """
+
+    async def create_with_completion(
+        self, *, messages: Any, response_model: type[BaseModel], **kwargs: Any
+    ) -> tuple[BaseModel, Any]: ...
+
+
+def _system_text(req: LlmRequest) -> str:
+    return "\n\n".join(b.text for b in req.system)
+
+
+def _input_text(req: LlmRequest) -> str:
+    if isinstance(req.input, str):
+        return req.input
+    return "\n\n".join(b.text for b in req.input)
+
+
+def _messages(req: LlmRequest) -> list[ChatCompletionMessageParam]:
+    msgs: list[ChatCompletionMessageParam] = []
+    system = _system_text(req)
+    if system:
+        msgs.append({"role": "system", "content": system})
+    msgs.append({"role": "user", "content": _input_text(req)})
+    return msgs
+
+
+def _cache_read(usage: Any) -> int:
+    details = getattr(usage, "prompt_tokens_details", None)
+    if details is None:
+        return 0
+    return int(getattr(details, "cached_tokens", 0) or 0)
+
+
+def _usage_from(usage: Any) -> ProviderUsage:
+    if usage is None:
+        return ProviderUsage(input_tokens=0, output_tokens=0)
+    return ProviderUsage(
+        input_tokens=getattr(usage, "prompt_tokens", 0) or 0,
+        output_tokens=getattr(usage, "completion_tokens", 0) or 0,
+        cache_read_tokens=_cache_read(usage),
+    )
+
+
+class OpenAICompatAdapter:
+    def __init__(
+        self,
+        provider: str,
+        client: AsyncOpenAI,
+        *,
+        structured_client: StructuredClient | None = None,
+    ) -> None:
+        self.provider = provider
+        self._client = client
+        # 结构化输出走 instructor（Pydantic 校验 + 重试，锁定栈）；可注入便于测试。
+        self._structured_client = structured_client
+
+    def capabilities(self) -> Capabilities:
+        return Capabilities(structured_output=True, prefix_cache=True, thinking=False)
+
+    def _structured(self) -> StructuredClient:
+        if self._structured_client is None:
+            # 懒构建：从同一 AsyncOpenAI client patch 出 instructor 客户端。
+            self._structured_client = instructor.from_openai(self._client)
+        return self._structured_client
+
+    async def complete(self, req: LlmRequest, model: str) -> ProviderResult:
+        if req.output_schema is not None:
+            return await self._complete_structured(req, model)
+        return await self._complete_text(req, model)
+
+    async def _complete_text(self, req: LlmRequest, model: str) -> ProviderResult:
+        resp = await self._client.chat.completions.create(
+            model=model,
+            messages=_messages(req),
+            max_tokens=req.max_tokens,
+        )
+        text = resp.choices[0].message.content or ""
+        return ProviderResult(text=text, usage=_usage_from(resp.usage))
+
+    async def _complete_structured(self, req: LlmRequest, model: str) -> ProviderResult:
+        assert req.output_schema is not None
+        parsed, raw = await self._structured().create_with_completion(
+            messages=_messages(req),
+            response_model=req.output_schema,
+            model=model,
+            max_tokens=req.max_tokens,
+        )
+        usage = _usage_from(getattr(raw, "usage", None))
+        # 文本载体保留校验后的 JSON（便于日志/留痕）；程序消费走 parsed。
+        return ProviderResult(text=parsed.model_dump_json(), usage=usage, parsed=parsed)
+
+    async def stream(self, req: LlmRequest, model: str) -> AsyncIterator[StreamChunk]:
+        stream = await self._client.chat.completions.create(
+            model=model,
+            messages=_messages(req),
+            max_tokens=req.max_tokens,
+            stream=True,
+            stream_options={"include_usage": True},
+        )
+        async for chunk in stream:
+            if chunk.choices:
+                delta = chunk.choices[0].delta
+                if delta and delta.content:
+                    yield StreamChunk(text=delta.content)
+            if getattr(chunk, "usage", None):
+                yield StreamChunk(usage=_usage_from(chunk.usage))

packages/llm_gateway/ww_llm_gateway/gateway.py

@@ -0,0 +1,99 @@
+"""网关核心：路由 → 调用适配器 → 记账 → 返回（ARCH §4.1–4.3/§4.8）。
+
+M1 单 provider，无回退/熔断（那是 M5/T5.4）。流式经 `stream()` 归一为 `Delta`。
+日志脱敏：只记长度，绝不记原文/api key（不变量、§9.3）。
+"""
+
+from __future__ import annotations
+
+from collections.abc import AsyncIterator, Callable
+
+import structlog
+from ww_shared import AppError, ErrorCode
+
+from .adapters.base import ProviderAdapter, ProviderUsage
+from .ledger import LedgerSink
+from .pricing import cost_minor
+from .routing import Route, resolve_route
+from .types import Delta, LlmRequest, LlmResponse, ServedBy, Tier, Usage
+
+log = structlog.get_logger(__name__)
+
+
+def _input_len(req: LlmRequest) -> int:
+    if isinstance(req.input, str):
+        return len(req.input)
+    return sum(len(b.text) for b in req.input)
+
+
+class Gateway:
+    def __init__(
+        self,
+        adapters: dict[str, ProviderAdapter],
+        ledger: LedgerSink,
+        resolver: Callable[[Tier], Route] = resolve_route,
+    ) -> None:
+        self._adapters = adapters
+        self._ledger = ledger
+        self._resolve = resolver
+
+    def _adapter_for(self, provider: str) -> ProviderAdapter:
+        adapter = self._adapters.get(provider)
+        if adapter is None:
+            raise AppError(ErrorCode.LLM_UNAVAILABLE, f"no adapter for provider {provider!r}")
+        return adapter
+
+    def _usage(self, route: Route, pu: ProviderUsage) -> Usage:
+        cost, currency = cost_minor(route.provider, route.model, pu.input_tokens, pu.output_tokens)
+        return Usage(
+            provider=route.provider,
+            model=route.model,
+            input_tokens=pu.input_tokens,
+            output_tokens=pu.output_tokens,
+            cache_read_tokens=pu.cache_read_tokens,
+            cost_minor=cost,
+            currency=currency,
+        )
+
+    def _log_call(self, req: LlmRequest, usage: Usage, *, stream: bool) -> None:
+        log.info(
+            "llm_call",
+            provider=usage.provider,
+            model=usage.model,
+            tier=req.tier,
+            input_tokens=usage.input_tokens,
+            output_tokens=usage.output_tokens,
+            cache_read_tokens=usage.cache_read_tokens,
+            cost_minor=usage.cost_minor,
+            currency=usage.currency,
+            stream=stream,
+            input_chars=_input_len(req),
+            project_id=str(req.scope.project_id) if req.scope.project_id else None,
+        )
+
+    async def run(self, req: LlmRequest) -> LlmResponse:
+        route = self._resolve(req.tier)
+        adapter = self._adapter_for(route.provider)
+        result = await adapter.complete(req, route.model)
+        usage = self._usage(route, result.usage)
+        await self._ledger.record(req.scope, usage)
+        self._log_call(req, usage, stream=False)
+        return LlmResponse(
+            text=result.text,
+            parsed=result.parsed,
+            usage=usage,
+            served_by=ServedBy(provider=route.provider, model=route.model),
+        )
+
+    async def stream(self, req: LlmRequest) -> AsyncIterator[Delta]:
+        route = self._resolve(req.tier)
+        adapter = self._adapter_for(route.provider)
+        final = ProviderUsage(input_tokens=0, output_tokens=0)
+        async for chunk in adapter.stream(req, route.model):
+            if chunk.text:
+                yield Delta(text=chunk.text)
+            if chunk.usage is not None:
+                final = chunk.usage
+        usage = self._usage(route, final)
+        await self._ledger.record(req.scope, usage)
+        self._log_call(req, usage, stream=True)

packages/llm_gateway/ww_llm_gateway/ledger.py

@@ -0,0 +1,39 @@
+"""用量记账落库（ARCH §4.8）。
+
+`LedgerSink` 为接口，便于测试注入内存替身；生产用 SQLAlchemy 实现写 usage_ledger。
+"""
+
+from __future__ import annotations
+
+from typing import Protocol
+
+from sqlalchemy.ext.asyncio import AsyncSession
+from ww_db.models import UsageLedger
+
+from .types import Scope, Usage
+
+
+class LedgerSink(Protocol):
+    async def record(self, scope: Scope, usage: Usage) -> None: ...
+
+
+class SqlAlchemyLedgerSink:
+    """把每次调用写入 usage_ledger（owner_id 取 scope.user_id，单用户 stub）。"""
+
+    def __init__(self, session: AsyncSession) -> None:
+        self._session = session
+
+    async def record(self, scope: Scope, usage: Usage) -> None:
+        row = UsageLedger(
+            owner_id=scope.user_id,
+            project_id=scope.project_id,
+            provider=usage.provider,
+            model=usage.model,
+            input_tokens=usage.input_tokens,
+            output_tokens=usage.output_tokens,
+            cache_read=usage.cache_read_tokens,
+            cost_minor=usage.cost_minor,
+            currency=usage.currency,
+        )
+        self._session.add(row)
+        await self._session.flush()

packages/llm_gateway/ww_llm_gateway/pricing.py

@@ -0,0 +1,34 @@
+"""提供商价格表与成本换算（ARCH §4.8）。
+
+价格以「每百万 token 的最小货币单位（如分/cent）」表示，随 provider 配置维护；
+未知 (provider, model) 则成本计 0（仍记账，便于观测）。
+"""
+
+from __future__ import annotations
+
+import math
+from dataclasses import dataclass
+
+
+@dataclass(frozen=True)
+class Price:
+    in_per_mtok: int
+    out_per_mtok: int
+    currency: str
+
+
+# 近似价（可后续移入 config / provider 配置维护）
+_PRICING: dict[tuple[str, str], Price] = {
+    ("deepseek", "deepseek-chat"): Price(in_per_mtok=27, out_per_mtok=110, currency="USD"),
+}
+
+
+def cost_minor(provider: str, model: str, input_tokens: int, output_tokens: int) -> tuple[int, str]:
+    price = _PRICING.get((provider, model))
+    if price is None:
+        return 0, "USD"
+    cost = math.ceil(
+        input_tokens / 1_000_000 * price.in_per_mtok
+        + output_tokens / 1_000_000 * price.out_per_mtok
+    )
+    return cost, price.currency

packages/llm_gateway/ww_llm_gateway/routing.py

@@ -0,0 +1,29 @@
+"""档位路由：tier -> (provider, model)（ARCH §4.3）。
+
+M1 只读全局默认（config.tier_defaults，形如 "deepseek:deepseek-chat"）；
+作品级 / Skill 级覆盖留待后续（§4.3 三级解析）。
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+
+from ww_config import get_settings
+
+from .types import Tier
+
+
+@dataclass(frozen=True)
+class Route:
+    provider: str
+    model: str
+
+
+def resolve_route(tier: Tier) -> Route:
+    spec = get_settings().tier_defaults.get(tier)
+    if not spec:
+        raise ValueError(f"no tier_defaults entry for tier={tier!r}")
+    provider, sep, model = spec.partition(":")
+    if not sep or not provider or not model:
+        raise ValueError(f"invalid tier route {spec!r}; expected 'provider:model'")
+    return Route(provider=provider, model=model)

packages/llm_gateway/ww_llm_gateway/types.py

@@ -0,0 +1,78 @@
+"""LLM 网关统一接口契约（C1 / ARCH §4.1）——snake_case，Pydantic v2。
+
+上层（编排器/Agent）只碰这些类型，永不接触具体厂商字段。Agent 只声明 `tier`，
+不传具体 model（不变量 ②）。
+"""
+
+from __future__ import annotations
+
+import uuid
+from typing import Literal
+
+from pydantic import BaseModel, ConfigDict, Field
+
+Tier = Literal["writer", "analyst", "light"]
+
+
+class Block(BaseModel):
+    """一个 prompt 文本块；`cache=True` 标记缓存断点前的稳定块（ARCH §4.6）。"""
+
+    text: str
+    cache: bool = False
+
+
+class Scope(BaseModel):
+    """调用作用域。原型单用户：`user_id` 可固定 stub，`project_id` 可空。"""
+
+    user_id: uuid.UUID
+    project_id: uuid.UUID | None = None
+
+
+class LlmRequest(BaseModel):
+    """统一请求。`system` 稳定块在前（断点前），`input` 易变内容在后。"""
+
+    model_config = ConfigDict(arbitrary_types_allowed=True)
+
+    tier: Tier
+    input: str | list[Block]
+    system: list[Block] = Field(default_factory=list)
+    stream: bool = False
+    output_schema: type[BaseModel] | None = None
+    thinking: bool = False
+    max_tokens: int | None = None
+    scope: Scope
+
+
+class Usage(BaseModel):
+    """一次调用的用量与成本（落 usage_ledger，ARCH §4.8）。"""
+
+    provider: str
+    model: str
+    input_tokens: int
+    output_tokens: int
+    cache_read_tokens: int = 0
+    cost_minor: int  # 最小货币单位（如分/cent）
+    currency: str
+
+
+class ServedBy(BaseModel):
+    """实际服务方；`fell_back` 标记是否走了回退链（M5 才有回退，M1 恒 False）。"""
+
+    provider: str
+    model: str
+    fell_back: bool = False
+
+
+class LlmResponse(BaseModel):
+    model_config = ConfigDict(arbitrary_types_allowed=True)
+
+    text: str
+    parsed: BaseModel | None = None
+    usage: Usage
+    served_by: ServedBy
+
+
+class Delta(BaseModel):
+    """流式增量：归一各家 SSE 的统一 token 块。"""
+
+    text: str