765dbdfbd4
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>
128 lines
4.4 KiB
Python
128 lines
4.4 KiB
Python
"""Google Gemini 适配器（ARCH §4.2/§4.4）。
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- 能力：原生结构化输出（`response_mime_type=application/json` + `response_schema`）、思考。
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前缀缓存走 Gemini 隐式/显式缓存，机制差异大，原型保守标 `prefix_cache=False`。
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- 客户端注入（`GeminiClient` Protocol，`google.genai.Client().aio` 满足）——测试注入替身，
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绝不联网；真实 SDK（`google-genai`）仅在 apps/api 构造客户端时需要，本模块不硬 import。
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- 瞬时故障翻译为 `TransientProviderError`，交网关退避/回退。
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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 typing import Any, Protocol
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from ..errors import TransientProviderError
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from ..types import LlmRequest
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from .base import Capabilities, ProviderResult, ProviderUsage, StreamChunk
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_TRANSIENT_NAMES = frozenset(
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{
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"ResourceExhausted",
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"ServiceUnavailable",
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"DeadlineExceeded",
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"InternalServerError",
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"ServerError",
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"APIError",
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}
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)
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class GeminiModels(Protocol):
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async def generate_content(self, **kwargs: Any) -> Any: ...
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def generate_content_stream(self, **kwargs: Any) -> Any: ...
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class GeminiAio(Protocol):
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models: GeminiModels
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class GeminiClient(Protocol):
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"""`google.genai.Client()` 满足此 Protocol（用其 `.aio.models`）。"""
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aio: GeminiAio
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def _is_transient(exc: Exception) -> bool:
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name = type(exc).__name__
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if name in _TRANSIENT_NAMES:
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return True
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status = getattr(exc, "code", None) or getattr(exc, "status_code", None)
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return isinstance(status, int) and (status == 429 or status >= 500)
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def _contents(req: LlmRequest) -> str:
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if isinstance(req.input, str):
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return req.input
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return "\n\n".join(b.text for b in req.input)
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def _system_text(req: LlmRequest) -> str:
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return "\n\n".join(b.text for b in req.system)
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def _usage_from(meta: Any) -> ProviderUsage:
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if meta is None:
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return ProviderUsage(input_tokens=0, output_tokens=0)
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return ProviderUsage(
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input_tokens=int(getattr(meta, "prompt_token_count", 0) or 0),
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output_tokens=int(getattr(meta, "candidates_token_count", 0) or 0),
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cache_read_tokens=int(getattr(meta, "cached_content_token_count", 0) or 0),
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)
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def _config(req: LlmRequest) -> dict[str, Any]:
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cfg: dict[str, Any] = {}
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system = _system_text(req)
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if system:
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cfg["system_instruction"] = system
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if req.max_tokens is not None:
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cfg["max_output_tokens"] = req.max_tokens
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if req.output_schema is not None:
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cfg["response_mime_type"] = "application/json"
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cfg["response_schema"] = req.output_schema
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return cfg
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class GeminiAdapter:
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def __init__(self, provider: str, client: GeminiClient) -> None:
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self.provider = provider
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self._client = client
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def capabilities(self) -> Capabilities:
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return Capabilities(structured_output=True, prefix_cache=False, thinking=True)
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async def complete(self, req: LlmRequest, model: str) -> ProviderResult:
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try:
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resp = await self._client.aio.models.generate_content(
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model=model, contents=_contents(req), config=_config(req)
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)
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except Exception as exc:
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if _is_transient(exc):
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raise TransientProviderError(str(exc), provider=self.provider) from exc
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raise
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text = getattr(resp, "text", "") or ""
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usage = _usage_from(getattr(resp, "usage_metadata", None))
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if req.output_schema is not None:
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parsed = req.output_schema.model_validate_json(text)
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return ProviderResult(text=text, usage=usage, parsed=parsed)
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return ProviderResult(text=text, usage=usage)
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async def stream(self, req: LlmRequest, model: str) -> AsyncIterator[StreamChunk]:
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try:
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stream = await self._client.aio.models.generate_content_stream(
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model=model, contents=_contents(req), config=_config(req)
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)
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async for chunk in stream:
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text = getattr(chunk, "text", "") or ""
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if text:
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yield StreamChunk(text=text)
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meta = getattr(chunk, "usage_metadata", None)
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if meta is not None:
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yield StreamChunk(usage=_usage_from(meta))
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except Exception as exc:
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if _is_transient(exc):
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raise TransientProviderError(str(exc), provider=self.provider) from exc
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raise
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