b523b4fd21
- 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
57 lines
1.8 KiB
Python
57 lines
1.8 KiB
Python
"""网关单测替身（mock provider + 内存 ledger）——不碰真实 API / DB。
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放在独立模块（非 conftest）以便测试用绝对导入 `from fakes import ...`；
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本目录无 __init__.py（避免与顶层 tests 包同名冲突），故走 pytest 路径注入。
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"""
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from __future__ import annotations
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from collections.abc import AsyncIterator
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from ww_llm_gateway.adapters.base import (
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Capabilities,
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ProviderResult,
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ProviderUsage,
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StreamChunk,
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)
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from ww_llm_gateway.routing import Route
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from ww_llm_gateway.types import LlmRequest, Scope, Tier, Usage
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class FakeAdapter:
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provider = "deepseek"
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def __init__(self, text: str = "hello world", deltas: list[str] | None = None) -> None:
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self.text = text
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self.deltas = deltas if deltas is not None else ["hel", "lo ", "world"]
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self.complete_calls = 0
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self.stream_calls = 0
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def capabilities(self) -> Capabilities:
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return Capabilities(structured_output=True, prefix_cache=True)
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async def complete(self, req: LlmRequest, model: str) -> ProviderResult:
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self.complete_calls += 1
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return ProviderResult(
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text=self.text,
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usage=ProviderUsage(input_tokens=100, output_tokens=50),
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)
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async def stream(self, req: LlmRequest, model: str) -> AsyncIterator[StreamChunk]:
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self.stream_calls += 1
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for d in self.deltas:
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yield StreamChunk(text=d)
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yield StreamChunk(usage=ProviderUsage(input_tokens=100, output_tokens=50))
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class FakeLedger:
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def __init__(self) -> None:
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self.records: list[Usage] = []
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async def record(self, scope: Scope, usage: Usage) -> None:
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self.records.append(usage)
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def fake_route(tier: Tier) -> Route:
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return Route(provider="deepseek", model="deepseek-chat")
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