include model_name

[bhancockio]

No description provided.

[joaomdmoura]

Disclaimer: This review was made by a crew of AI Agents.

Code Review Comment for PR #2310

Overview

The recent changes made to the model name resolution logic in llm_utils.py are structured to enhance the selection process for the Language Model (LLM). The modifications affect two main functions: create_llm() and _llm_via_environment_or_fallback().

Detailed Feedback

1. Function: create_llm()

• Change Summary:
  The attribute checking sequence was modified to prioritize the model attribute over the model_name, which improves clarity and maintains conventional ways of defining model attributes.

• Feedback:

  • ✅ This change is logical and adheres to a more intuitive prioritization of attributes.
  • ⚠️ Please consider adding a comment to clarify the rationale for the priority ordering for future maintainers.

• Suggested Improvement:
  It would be beneficial to document this behavior clearly in the function itself. For instance:

  def create_llm(
      llm_value: Optional[Union[str, LLM]] = None,
      model_attributes: Optional[dict] = None
  ) -> Optional[LLM]:
      try:
          # Extract model name with priority: model > model_name > deployment_name > string representation
          model = (
              getattr(llm_value, "model", None)
              or getattr(llm_value, "model_name", None)
              or getattr(llm_value, "deployment_name", None)
              or str(llm_value)
          )

2. Function: _llm_via_environment_or_fallback()

• Change Summary:
  The order of environment variable checks was adjusted to prioritize the MODEL environmental variable over the previous option OPENAI_MODEL_NAME.

• Feedback:

  • ✅ This improves flexibility by adding support for MODEL_NAME, and aligns the handling of environmental variables with the prioritization seen in create_llm().
  • ⚠️ However, the documentation for supported environment variables needs updating to reflect this change.

• Suggested Improvement:
  Enhancing the documentation within the function to clearly outline the priority of the environment variables would be invaluable, for example:

  def _llm_via_environment_or_fallback() -> Optional[LLM]:
      """
      Helper function: Loads LLM configuration from environment variables or uses fallback default model.
      
      Environment Variables (in order of priority):
      - MODEL: Primary model identifier
      - MODEL_NAME: Alternative model name
      - OPENAI_MODEL_NAME: Legacy OpenAI model name
      
      Returns:
          Optional[LLM]: Configured LLM instance or None
      """
      model_name = ...

General Recommendations

1. Documentation:

   • Update all relevant function docstrings to include details on new environment variable support and the model resolution hierarchy, ensuring clarity for upcoming developers.

2. Constants:

   • Defining environment variable names as constants at the module level can minimize the risk of errors due to typos and enhance readability:

   MODEL_ENV_VAR = "MODEL"
   MODEL_NAME_ENV_VAR = "MODEL_NAME"
   OPENAI_MODEL_NAME_ENV_VAR = "OPENAI_MODEL_NAME"

3. Testing:

   • Ensure comprehensive test cases that cover the new environment variable MODEL_NAME and validate the new retrieval priority for both functions.

Conclusion

The PR introduces commendable improvements that will enhance the model name resolution strategy. By addressing documentation and potential constant definitions, the maintainability of this code can be significantly augmented. Overall, these changes are acceptable with the suggested improvements aimed at clarity and maintainability.

[lorenzejay]

nice