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Description here... Help the reviewer by:

  • linking to an issue that includes more details
  • if it's a new feature include samples of how to use the new feature
  • (optional if issue link is provided) if you fixed a bug include basic bug details

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Summary of Changes

Hello @MengqinShen, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request expands the genkit framework's capabilities by integrating the Mistral-Large model into its Model Garden, allowing developers to leverage this powerful model. It also refines the model resolution logic to ensure seamless compatibility with various OpenAI-compatible models and reactivates practical code samples for both Llama and Mistral models, providing immediate examples for users.

Highlights

  • Mistral-Large Model Integration: The Mistral-Large model has been added to the ModelGarden's supported OpenAI-compatible models, making it available for use within the genkit framework.
  • Flexible Model Lookup: The model lookup mechanism has been enhanced to correctly identify models from both standard and compatibility lists, ensuring broader model support across the system.
  • Llama and Mistral Sample Activation: Existing sample code demonstrating the usage of both Llama-3.2 and the newly integrated Mistral-Large models for generation and streaming has been re-enabled and updated.
  • Code Cleanup: The location assignment logic within the VertexAIModelGarden plugin has been streamlined for improved readability and conciseness.

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Code Review

This pull request adds support for the Mistral model in Model Garden and re-enables sample code for Llama models. The changes involve updating model information, adjusting model lookup logic, and uncommenting example flows. My review focuses on improving code readability and adhering to style conventions. I've suggested a more idiomatic way to look up models from multiple dictionaries and recommended reverting a formatting change that reduces code clarity.


model = SUPPORTED_OPENAI_MODELS[self._model]
if SupportedOutputFormat.JSON_MODE in model.supports.output:
model = SUPPORTED_OPENAI_MODELS.get(self._model, SUPPORTED_OPENAI_COMPAT_MODELS.get(self._model))
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medium

For improved readability and to make the logic clearer, you could simplify this line. Using or to chain the dictionary lookups is a more common and idiomatic Python pattern for this scenario. It also has the minor benefit of short-circuiting, so the second lookup is only performed if the first one fails.

Suggested change
model = SUPPORTED_OPENAI_MODELS.get(self._model, SUPPORTED_OPENAI_COMPAT_MODELS.get(self._model))
model = SUPPORTED_OPENAI_MODELS.get(self._model) or SUPPORTED_OPENAI_COMPAT_MODELS.get(self._model)

or os.getenv('GOOGLE_CLOUD_LOCATION')
or os.getenv('GOOGLE_CLOUD_REGION')
or const.DEFAULT_REGION
location or os.getenv('GOOGLE_CLOUD_LOCATION') or os.getenv('GOOGLE_CLOUD_REGION') or const.DEFAULT_REGION
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medium

This line is quite long (over 100 characters) and can be difficult to read. The previous multi-line formatting was more readable and aligned better with Python style guides like PEP 8, which recommend limiting line length. I'd suggest reverting to the multi-line format for better maintainability.

            location
            or os.getenv('GOOGLE_CLOUD_LOCATION')
            or os.getenv('GOOGLE_CLOUD_REGION')
            or const.DEFAULT_REGION

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