CYarros10 opened a new pull request, #41940:
URL: https://github.com/apache/airflow/pull/41940

   The Gen AI Evaluation Service lets you evaluate your large language models 
(LLMs), both pointwise and pairwise, across several metrics, with your own 
criteria. You can provide inference-time inputs, LLM responses and additional 
parameters, and the Gen AI Evaluation Service returns metrics specific to the 
evaluation task.
   
   Metrics include model-based metrics, such as PointwiseMetric and 
PairwiseMetric, and in-memory computed metrics, such as rouge, bleu, and tool 
function-call metrics. PointwiseMetric and PairwiseMetric are generic 
model-based metrics that you can customize with your own criteria. Because the 
service takes the prediction results directly from models as input, the 
evaluation service can perform both inference and subsequent evaluation on all 
models supported by Vertex AI.
   
   For more information on evaluating a model, see [Generative AI evaluation 
service 
overview](https://cloud.google.com/vertex-ai/generative-ai/docs/models/evaluation-overview).
   
   Can be used as part of a larger LLMops DAG:
   
   Training Dataset Lands --> GCSObjectExistenceSensor --> 
[SupervisedFineTuningTrainOperator](https://github.com/apache/airflow/blob/0f5c25b416f23c6fdc2ab20cba6a147eccaabe2d/airflow/providers/google/cloud/operators/vertex_ai/generative_model.py#L577)
 --> **RunEvaluationOperator** --> 
[CountTokensOperator](https://github.com/apache/airflow/pull/41908) --> 
[GenerativeModelGenerateContentOperator](https://github.com/apache/airflow/blob/0f5c25b416f23c6fdc2ab20cba6a147eccaabe2d/airflow/providers/google/cloud/operators/vertex_ai/generative_model.py#L502)
   
   ---
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   Read the **[Pull Request 
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 for more information.
   In case of fundamental code changes, an Airflow Improvement Proposal 
([AIP](https://cwiki.apache.org/confluence/display/AIRFLOW/Airflow+Improvement+Proposals))
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