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https://github.com/farcasclaudiu/TradingAgents.git
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javierdejesusda
047b38971c
Apply review suggestions: use concise `or` pattern for API key resolution, consolidate tests into parameterized subTest, move import to module level per PEP 8.
60 lines
2.3 KiB
Python
60 lines
2.3 KiB
Python
from typing import Any, Optional
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from langchain_google_genai import ChatGoogleGenerativeAI
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from .base_client import BaseLLMClient, normalize_content
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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.
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Gemini 3 models return content as list of typed blocks.
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This normalizes to string for consistent downstream handling.
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"""
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def invoke(self, input, config=None, **kwargs):
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return normalize_content(super().invoke(input, config, **kwargs))
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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", "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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# Unified api_key maps to provider-specific google_api_key
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google_api_key = self.kwargs.get("api_key") or self.kwargs.get("google_api_key")
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if google_api_key:
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llm_kwargs["google_api_key"] = google_api_key
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# Map thinking_level to appropriate API param based on model
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# Gemini 3 Pro: low, high
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# Gemini 3 Flash: minimal, low, medium, high
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# Gemini 2.5: thinking_budget (0=disable, -1=dynamic)
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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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# Gemini 3 Pro doesn't support "minimal", use "low" instead
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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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# Gemini 2.5: map to thinking_budget
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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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