mlflow--mlflow
wehub-resource-sync
48 行
1.2 KiB
Python
48 行
1.2 KiB
Python
class AgentEvaluationReserverKey:
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"""
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Expectation column names that are used by Agent Evaluation.
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Ref: https://docs.databricks.com/aws/en/generative-ai/agent-evaluation/evaluation-schema
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"""
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EXPECTED_RESPONSE = "expected_response"
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EXPECTED_RETRIEVED_CONTEXT = "expected_retrieved_context"
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EXPECTED_FACTS = "expected_facts"
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GUIDELINES = "guidelines"
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@classmethod
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def get_all(cls) -> set[str]:
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return {
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cls.EXPECTED_RESPONSE,
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cls.EXPECTED_RETRIEVED_CONTEXT,
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cls.EXPECTED_FACTS,
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cls.GUIDELINES,
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}
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# A column name for storing custom expectations dictionary in Agent Evaluation.
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AGENT_EVAL_CUSTOM_EXPECTATION_KEY = "custom_expected"
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# Input dataset column names
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class InputDatasetColumn:
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REQUEST_ID = "request_id"
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INPUTS = "inputs"
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REQUEST = "request"
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RESPONSE = "response"
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OUTPUTS = "outputs"
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EXPECTATIONS = "expectations"
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TAGS = "tags"
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TRACE = "trace"
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SOURCE = "source"
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# Result Dataframe column names
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class ResultDataFrameColumn:
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REQUEST_ID = "request_id"
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INPUTS = "inputs"
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OUTPUTS = "outputs"
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EXPECTATIONS = "expectations"
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TAGS = "tags"
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TRACE = "trace"
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ERROR_MESSAGE = "error_message"
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