Flexible model client#163
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Deploying kura with
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| self.model = model | ||
| # Handle both string model names and instructor client instances | ||
| if isinstance(model, str): | ||
| self.client = instructor.from_provider(model, async_client=True) |
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Runtime error: 'instructor' is referenced in init but only imported inside a TYPE_CHECKING block. Import it at runtime (e.g. add a local import) to avoid NameError.
| # Handle both string model names and instructor client instances | ||
| if isinstance(model, str): | ||
| self.client = instructor.from_provider(model, async_client=True) | ||
| if isinstance(model, instructor.AsyncInstructor): |
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Conditional logic bug: Use 'elif' instead of a second 'if' when checking for instructor.AsyncInstructor, so valid string models don’t trigger the else block.
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| if isinstance(model, instructor.AsyncInstructor): | |
| elif isinstance(model, instructor.AsyncInstructor): |
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If we pass the Gemini model, it is failing because the temperature is passing directly to |
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This makes it possible to either use a model string or just override and pass in an instructor client directly. This helps deal with cases where people might need to manually override the client
Important
Enhance model client flexibility by allowing
ClusterDescriptionModel,MetaClusterModel, andSummaryModelto accept either a model string or aninstructor.AsyncInstructorclient.ClusterDescriptionModel,MetaClusterModel, andSummaryModelnow accept either a model string or aninstructor.AsyncInstructorclient.ValueErrorif the model type is neither a string nor anAsyncInstructor.cluster.py,meta_cluster.py, andsummarisation.py, the__init__methods are updated to handle both string model names and instructor client instances.instructorwithin the__init__methods to ensure availability when needed.clientinstead ofmodelin initialization logs across the three files.This description was created by
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