Introduce Gemini Model Handler#35036
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| """ A sample pipeline using the RunInference API to classify text using an LLM. | ||
| This pipeline creates a set of prompts and sends it to a Gemini service then | ||
| returns the predictions from the classifier model. This example uses the | ||
| gemini-2.0-flash-001 model. |
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probably need to update sdks/python/apache_beam/examples/inference/README.md with a new remote handler section.
| """ | ||
| if not isinstance(exception, errors.APIError): | ||
| return False | ||
| return exception.code == 429 or exception.code >= 500 |
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Shall we log these to make this more explicit to users?
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exceptions get logged by the RemoteModelHandler, no need to log them here (
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| Args: | ||
| batch: a sequence of any values to be passed to the Gemini service. | ||
| Should be a list of strings. |
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Sequence[Any]: I assume we plan to support other types soon. So we use Any here, right?
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Ah yeah that was a missed line, I originally wrote this as string-only but decided it was better in the long-term to go ahead and make it more general. Let me update that docstring
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Introduces a basic Gemini Model Handler along with an example and tests. Currently provides a function for text prompts and responses, but is written to be extensible by allowing for custom request functions. The Gemini SDK contains a large amount of options, so this allows for the most general model handler possible (although post-processing to pull the responses out of the nested return type will still be necessary.)
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