lastmile-auto-eval 0.0.2

An API for using metric models (either provided by default or fine-tuned yourself) to evaluate LLMs.

A library for using models (either default ones provided by LastMile or your own that are fine-tuned) to evaluate LLMs.

Evaluations are run on dataframes that include any combination of input, ground_truth, and output columns. At least one of these columns must be defined and all values must be strings.

Example usage:

from lastmile_auto_eval import (
    EvaluationMetric,
    EvaluationResult,
    evaluate,
    stream_evaluate,
)
import pandas as pd
import json
from typing import Any, Generator

queries = ["what color is the sky?", "what color is the sky?"]
statement_1 = "the sky is red"
statement_2 = "the sky is blue"
ground_truth_values = [statement_1, statement_1]
responses = [statement_1, statement_2]

df = pd.DataFrame(
    {
        "input": queries,
        "ground_truth": ground_truth_values,
        "output": responses,
    }
)

# Non-streaming
result: EvaluationResult = evaluate(
    dataframe=df,
    metrics=[
        EvaluationMetric.P_FAITHFUL,
        EvaluationMetric.SUMMARIZATION,
    ],
)
print(json.dumps(result, indent=2))

# Response will look something like this:
"""
{
  "p_faithful": [
    0.999255359172821,
    0.00011296303273411468
  ],
  "summarization": [
    0.9995583891868591,
    6.86283819959499e-05
  ]
}
"""

# Response-streaming
result_iterator: Generator[EvaluationResult, Any, Any] = (
    stream_evaluate(
        dataframe=df,
        metrics=[
            EvaluationMetric.P_FAITHFUL,
            EvaluationMetric.SUMMARIZATION,
        ],
    )
)
for result_chunk in result_iterator:
    print(json.dumps(result_chunk, indent=2))

# Bidirectional-streaming
df_iterator = gen_df_stream(
    input=queries, gt=ground_truth_values, output=responses
)
result_iterator: Generator[EvaluationResult, Any, Any] = (
    stream_evaluate(
        dataframe=df_iterator,
        metrics=[
            EvaluationMetric.P_FAITHFUL,
            EvaluationMetric.SUMMARIZATION,
        ],
    )
)
for result_chunk in result_iterator:
    print(json.dumps(result_chunk, indent=2))