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Address PR review feedback: move constants to class level, add error handling, enhance logging
Co-Authored-By: Joe Moura <joao@crewai.com>
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@@ -246,6 +246,9 @@ class AccumulatedToolArgs(BaseModel):
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class LLM(BaseLLM):
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ANTHROPIC_PREFIXES = ("anthropic/", "claude-", "claude/")
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GEMINI_IDENTIFIERS = ("gemini", "gemma-")
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def __init__(
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self,
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model: str,
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@@ -319,8 +322,9 @@ class LLM(BaseLLM):
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Returns:
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bool: True if the model is from Anthropic, False otherwise.
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"""
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ANTHROPIC_PREFIXES = ("anthropic/", "claude-", "claude/")
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return any(prefix in model.lower() for prefix in ANTHROPIC_PREFIXES)
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if not isinstance(model, str):
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return False
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return any(prefix in model.lower() for prefix in self.ANTHROPIC_PREFIXES)
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def _is_gemini_model(self, model: str) -> bool:
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"""Determine if the model is from Google Gemini provider.
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@@ -331,8 +335,9 @@ class LLM(BaseLLM):
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Returns:
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bool: True if the model is from Gemini, False otherwise.
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"""
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GEMINI_IDENTIFIERS = ("gemini", "gemma-")
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return any(identifier in model.lower() for identifier in GEMINI_IDENTIFIERS)
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if not isinstance(model, str):
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return False
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return any(identifier in model.lower() for identifier in self.GEMINI_IDENTIFIERS)
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def _normalize_gemini_model(self, model: str) -> str:
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"""Normalize Gemini model name to the format expected by LiteLLM.
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@@ -345,7 +350,16 @@ class LLM(BaseLLM):
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Returns:
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str: Normalized model name.
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Raises:
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ValueError: If model is not a string or is empty.
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"""
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if not isinstance(model, str):
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raise ValueError(f"Model must be a string, got {type(model)}")
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if not model.strip():
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raise ValueError("Model name cannot be empty")
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if model.startswith("gemini/"):
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return model
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@@ -381,12 +395,17 @@ class LLM(BaseLLM):
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model = self.model
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if self._is_gemini_model(model):
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model = self._normalize_gemini_model(model)
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# --- 2.1) Map GOOGLE_API_KEY to GEMINI_API_KEY if needed
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if not os.environ.get("GEMINI_API_KEY") and os.environ.get("GOOGLE_API_KEY"):
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os.environ["GEMINI_API_KEY"] = os.environ["GOOGLE_API_KEY"]
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logging.info("Mapped GOOGLE_API_KEY to GEMINI_API_KEY for Gemini model")
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try:
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model = self._normalize_gemini_model(model)
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logging.info(f"Normalized Gemini model name from '{self.model}' to '{model}'")
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# --- 2.1) Map GOOGLE_API_KEY to GEMINI_API_KEY if needed
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if not os.environ.get("GEMINI_API_KEY") and os.environ.get("GOOGLE_API_KEY"):
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os.environ["GEMINI_API_KEY"] = os.environ["GOOGLE_API_KEY"]
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logging.info("Mapped GOOGLE_API_KEY to GEMINI_API_KEY for Gemini model")
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except ValueError as e:
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logging.error(f"Error normalizing Gemini model: {str(e)}")
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model = self.model
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# --- 3) Prepare the parameters for the completion call
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params = {
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