Log Evaluation Results

This guide shows how LLM evaluation results in dataframes can be sent to Phoenix.

An evaluation must have a name (e.g. "Q&A Correctness") and its DataFrame must contain identifiers for the subject of evaluation, e.g. a span or a document (more on that below), and values under either the score, label, or explanation columns. See Evaluations for more information.

Span Evaluations

A dataframe of span evaluations would look similar like the table below. It must contain span_id as an index or as a column. Once ingested, Phoenix uses the span_id to associate the evaluation with its target span.

span_id	label	score	explanation
5B8EF798A381	correct	1	"this is correct ..."
E19B7EC3GG02	incorrect	0	"this is incorrect ..."

The evaluations dataframe can be sent to Phoenix as follows. Note that the name of the evaluation must be supplied through the eval_name= parameter. In this case we name it "Q&A Correctness".

from phoenix.trace import SpanEvaluations

px.Client().log_evaluations(
    SpanEvaluations(
        dataframe=qa_correctness_eval_df,
        eval_name="Q&A Correctness",
    ),
)

Document Evaluations

A dataframe of document evaluations would look something like the table below. It must contain span_id and document_position as either indices or columns. document_position is the document's (zero-based) index in the span's list of retrieved documents. Once ingested, Phoenix uses the span_id and document_position to associate the evaluation with its target span and document.

span_id	document_position	label	score	explanation
5B8EF798A381	0	relevant	1	"this is ..."
5B8EF798A381	1	irrelevant	0	"this is ..."
E19B7EC3GG02	0	relevant	1	"this is ..."

The evaluations dataframe can be sent to Phoenix as follows. Note that the name of the evaluation must be supplied through the eval_name= parameter. In this case we name it "Relevance".

from phoenix.trace import DocumentEvaluations

px.Client().log_evaluations(
    DocumentEvaluations(
        dataframe=document_relevance_eval_df,
        eval_name="Relevance",
    ),
)

Logging Multiple Evaluation DataFrames

Multiple sets of Evaluations can be logged by the same px.Client().log_evaluations() function call.

px.Client().log_evaluations(
    SpanEvaluations(
        dataframe=qa_correctness_eval_df,
        eval_name="Q&A Correctness",
    ),
    DocumentEvaluations(
        dataframe=document_relevance_eval_df,
        eval_name="Relevance",
    ),
    SpanEvaluations(
        dataframe=hallucination_eval_df,
        eval_name="Hallucination",
    ),
    # ... as many as you like
)

Specifying A Project for the Evaluations

By default the client will push traces to the project specified in the PHOENIX_PROJECT_NAME environment variable or to the default project. If you want to specify the destination project explicitly, you can pass the project name as a parameter.

from phoenix.trace import SpanEvaluations

px.Client().log_evaluations(
    SpanEvaluations(
        dataframe=qa_correctness_eval_df,
        eval_name="Q&A Correctness",
    ),
    project_name="<my-project>"
)