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chore: change variable names in run_relevance_eval for clarity #1570

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chore: change variable names in run_relevance_eval for clarity #1570

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26 changes: 14 additions & 12 deletions src/phoenix/experimental/evals/functions/binary.py
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Original file line number Diff line number Diff line change
Expand Up @@ -67,8 +67,8 @@ def run_relevance_eval(
template: Union[PromptTemplate, str] = RAG_RELEVANCY_PROMPT_TEMPLATE_STR,
rails: List[str] = list(RAG_RELEVANCY_PROMPT_RAILS_MAP.values()),
system_instruction: Optional[str] = None,
query_column_name: str = "query",
document_column_name: str = "reference",
query_variable_name: str = "query",
document_variable_name: str = "reference",
) -> List[List[str]]:
"""
Given a pandas dataframe containing queries and retrieved documents, classifies the relevance of
Expand Down Expand Up @@ -103,11 +103,13 @@ def run_relevance_eval(
rails (List[str], optional): A list of strings representing the possible output classes of
the model's predictions.

query_column_name (str, optional): The name of the query column in the dataframe, which
should also be a template variable.
query_variable_name (str, optional): The name of the query variable in the evaluation prompt
template. This must also be a column in the dataframe, unless the dataframe is in
OpenInference trace format.

reference_column_name (str, optional): The name of the document column in the dataframe,
which should also be a template variable.
reference_variable_name (str, optional): The name of the reference variable in the
evaluation prompt template. This must also be a column in the dataframe, unless the
dataframe is in OpenInference trace format.

system_instruction (Optional[str], optional): An optional system message.

Expand All @@ -120,15 +122,15 @@ def run_relevance_eval(
be parsed.
"""

query_column = dataframe.get(query_column_name)
document_column = dataframe.get(document_column_name)
query_column = dataframe.get(query_variable_name)
document_column = dataframe.get(document_variable_name)
if query_column is None or document_column is None:
openinference_query_column = dataframe.get(OPENINFERENCE_QUERY_COLUMN_NAME)
openinference_document_column = dataframe.get(OPENINFERENCE_DOCUMENT_COLUMN_NAME)
if openinference_query_column is None or openinference_document_column is None:
raise ValueError(
f'Dataframe columns must include either "{query_column_name}" and '
f'"{document_column_name}", or "{OPENINFERENCE_QUERY_COLUMN_NAME}" and '
f'Dataframe columns must include either "{query_variable_name}" and '
f'"{document_variable_name}", or "{OPENINFERENCE_QUERY_COLUMN_NAME}" and '
f'"{OPENINFERENCE_DOCUMENT_COLUMN_NAME}".'
)
query_column = openinference_query_column
Expand All @@ -153,8 +155,8 @@ def run_relevance_eval(
predictions = llm_eval_binary(
dataframe=pd.DataFrame(
{
query_column_name: expanded_queries,
document_column_name: expanded_documents,
query_variable_name: expanded_queries,
document_variable_name: expanded_documents,
}
),
model=model,
Expand Down
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