75 lines
2.9 KiB
Python
75 lines
2.9 KiB
Python
import time
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import random
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from typing import Any, Optional
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from langchain_google_genai import ChatGoogleGenerativeAI
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from .base_client import BaseLLMClient
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from .validators import validate_model
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class NormalizedChatGoogleGenerativeAI(ChatGoogleGenerativeAI):
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"""ChatGoogleGenerativeAI with normalized content output and auto-retry on SSL/connection errors."""
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def _normalize_content(self, response):
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content = response.content
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if isinstance(content, list):
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texts = [
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item.get("text", "") if isinstance(item, dict) and item.get("type") == "text"
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else item if isinstance(item, str) else ""
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for item in content
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]
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response.content = "\n".join(t for t in texts if t)
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return response
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def invoke(self, input, config=None, **kwargs):
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max_retries = 5
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for attempt in range(1, max_retries + 1):
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try:
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return self._normalize_content(super().invoke(input, config, **kwargs))
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except Exception as e:
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err = str(e)
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# 判断是否为可重试的网络错误
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retryable = any(kw in err for kw in [
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"SSL", "EOF", "RemoteProtocolError", "ConnectError",
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"Server disconnected", "ConnectionError", "timeout",
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"503", "502", "500",
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])
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if retryable and attempt < max_retries:
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wait = 2 ** attempt + random.uniform(0, 2)
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print(f"\n⚠️ Gemini 连接失败（第 {attempt}/{max_retries} 次），{wait:.1f}s 后重试... [{type(e).__name__}]")
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time.sleep(wait)
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else:
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raise
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class GoogleClient(BaseLLMClient):
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"""Client for Google Gemini models."""
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def __init__(self, model: str, base_url: Optional[str] = None, **kwargs):
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super().__init__(model, base_url, **kwargs)
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def get_llm(self) -> Any:
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"""Return configured ChatGoogleGenerativeAI instance."""
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llm_kwargs = {"model": self.model}
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for key in ("timeout", "max_retries", "google_api_key", "callbacks", "http_client", "http_async_client"):
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if key in self.kwargs:
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llm_kwargs[key] = self.kwargs[key]
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thinking_level = self.kwargs.get("thinking_level")
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if thinking_level:
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model_lower = self.model.lower()
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if "gemini-3" in model_lower:
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if "pro" in model_lower and thinking_level == "minimal":
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thinking_level = "low"
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llm_kwargs["thinking_level"] = thinking_level
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else:
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llm_kwargs["thinking_budget"] = -1 if thinking_level == "high" else 0
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return NormalizedChatGoogleGenerativeAI(**llm_kwargs)
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def validate_model(self) -> bool:
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"""Validate model for Google."""
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return validate_model("google", self.model)
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