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writer-work-flow/packages/llm_gateway/ww_llm_gateway/adapters/gemini.py

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"""Google Gemini 适配器ARCH §4.2/§4.4)。
- 能力:原生结构化输出(`response_mime_type=application/json` + `response_schema`)、思考。
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`。
- 客户端注入(`GeminiClient` Protocol`google.genai.Client().aio` 满足)——测试注入替身,
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。
- 瞬时故障翻译为 `TransientProviderError`,交网关退避/回退。
"""
from __future__ import annotations
from collections.abc import AsyncIterator
from typing import Any, Protocol
from ..errors import TransientProviderError
from ..types import LlmRequest
from .base import (
Capabilities,
ProviderResult,
ProviderUsage,
StreamChunk,
is_transient_by_name,
provider_error_summary,
)
_TRANSIENT_NAMES = frozenset(
{
"ResourceExhausted",
"ServiceUnavailable",
"DeadlineExceeded",
"InternalServerError",
"ServerError",
"APIError",
}
)
class GeminiModels(Protocol):
async def generate_content(self, **kwargs: Any) -> Any: ...
async def generate_content_stream(self, **kwargs: Any) -> Any: ...
class GeminiAio(Protocol):
models: GeminiModels
class GeminiClient(Protocol):
"""`google.genai.Client()` 满足此 Protocol用其 `.aio.models`)。"""
aio: GeminiAio
def _is_transient(exc: Exception) -> bool:
# Gemini SDK 部分错误用 `exc.code` 携带状态码 → extra_codes=True 一并检查。
return is_transient_by_name(exc, _TRANSIENT_NAMES, extra_codes=True)
def _contents(req: LlmRequest) -> str:
if isinstance(req.input, str):
return req.input
return "\n\n".join(b.text for b in req.input)
def _system_text(req: LlmRequest) -> str:
return "\n\n".join(b.text for b in req.system)
def _usage_from(meta: Any) -> ProviderUsage:
if meta is None:
return ProviderUsage(input_tokens=0, output_tokens=0)
return ProviderUsage(
input_tokens=int(getattr(meta, "prompt_token_count", 0) or 0),
output_tokens=int(getattr(meta, "candidates_token_count", 0) or 0),
cache_read_tokens=int(getattr(meta, "cached_content_token_count", 0) or 0),
)
def _config(req: LlmRequest) -> dict[str, Any]:
cfg: dict[str, Any] = {}
system = _system_text(req)
if system:
cfg["system_instruction"] = system
if req.max_tokens is not None:
cfg["max_output_tokens"] = req.max_tokens
if req.output_schema is not None:
cfg["response_mime_type"] = "application/json"
cfg["response_schema"] = req.output_schema
return cfg
class GeminiAdapter:
def __init__(self, provider: str, client: GeminiClient) -> None:
self.provider = provider
self._client = client
def capabilities(self) -> Capabilities:
return Capabilities(structured_output=True, prefix_cache=False, thinking=True)
async def complete(self, req: LlmRequest, model: str) -> ProviderResult:
try:
resp = await self._client.aio.models.generate_content(
model=model, contents=_contents(req), config=_config(req)
)
except Exception as exc:
if _is_transient(exc):
raise TransientProviderError(
provider_error_summary(exc), provider=self.provider
) from exc
raise
text = getattr(resp, "text", "") or ""
usage = _usage_from(getattr(resp, "usage_metadata", None))
if req.output_schema is not None:
parsed = req.output_schema.model_validate_json(text)
return ProviderResult(text=text, usage=usage, parsed=parsed)
return ProviderResult(text=text, usage=usage)
async def stream(self, req: LlmRequest, model: str) -> AsyncIterator[StreamChunk]:
try:
stream = await self._client.aio.models.generate_content_stream(
model=model, contents=_contents(req), config=_config(req)
)
async for chunk in stream:
text = getattr(chunk, "text", "") or ""
if text:
yield StreamChunk(text=text)
meta = getattr(chunk, "usage_metadata", None)
if meta is not None:
yield StreamChunk(usage=_usage_from(meta))
except Exception as exc:
if _is_transient(exc):
raise TransientProviderError(
provider_error_summary(exc), provider=self.provider
) from exc
raise