Which evaluations to use for RAG applications?

import os
from athina import evals
from athina.loaders import Loader
from athina.keys import OpenAiApiKey
from athina.runner.run import EvalRunner
from athina.datasets import yc_query_mini
import pandas as pd

from dotenv import load_dotenv
load_dotenv()

OpenAiApiKey.set_key(os.getenv('OPENAI_API_KEY'))

# Load a dataset from list of dicts
raw_data = yc_query_mini.data
dataset = Loader().load_dict(raw_data)

# View dataset in a dataframe
pd.DataFrame(dataset)

# Define evaluation suite
model = "gpt-4-turbo-preview"
eval_suite = [
    evals.RagasAnswerCorrectness(model=model),
    evals.RagasContextPrecision(model=model),
    evals.RagasContextRelevancy(model=model),
    evals.RagasContextRecall(model=model),
    evals.ContextContainsEnoughInformation(model=model),
    evals.RagasFaithfulness(model=model),
    evals.Faithfulness(model=model),
    evals.Groundedness(model=model),
    evals.DoesResponseAnswerQuery(model=model)
]

# Run the evaluation suite
batch_eval_result = EvalRunner.run_suite(
    evals=eval_suite,
    data=dataset,
    max_parallel_evals=8
)
batch_eval_result