mirror of
https://github.com/TauricResearch/TradingAgents.git
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0ef56e6a33
- some model/gateway combinations emit unbounded reasoning/output and hang or trip an idle timeout (e.g. some deepseek-v4-flash deployments) - add an opt-in max_tokens config knob + TRADINGAGENTS_MAX_TOKENS, forwarded to every provider when set (Gemini takes it as max_output_tokens); int-coerced, rejects non-positive/boolean values #1204
59 lines
2.2 KiB
Python
59 lines
2.2 KiB
Python
from typing import Any
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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: str | None = 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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self.warn_if_unknown_model()
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llm_kwargs = {"model": self.model}
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if self.base_url:
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llm_kwargs["base_url"] = self.base_url
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for key in ("timeout", "max_retries", "temperature", "max_output_tokens",
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"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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# Gemini 3.x takes the string ``thinking_level`` (the integer
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# ``thinking_budget`` was for the now-retired 2.5 line). Pro accepts
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# low/high; Flash also accepts minimal/medium — so map an unsupported
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# "minimal" on Pro to the nearest level it does accept.
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thinking_level = self.kwargs.get("thinking_level")
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if thinking_level:
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if "pro" in self.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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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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