Can someone please explain the

from llama_index.core.evaluation import (
    CorrectnessEvaluator,
    SemanticSimilarityEvaluator,
    RelevancyEvaluator,
    FaithfulnessEvaluator,
    PairwiseComparisonEvaluator,
)

from llama_index.core.evaluation.eval_utils import (
    get_responses,
    get_results_df,
)
from llama_index.core.evaluation import BatchEvalRunner

import numpy as np

evaluator_c = CorrectnessEvaluator(llm=eval_llm)
evaluator_s = SemanticSimilarityEvaluator()
evaluator_r = RelevancyEvaluator(llm=eval_llm)
evaluator_f = FaithfulnessEvaluator(llm=eval_llm)

pairwise_evaluator = PairwiseComparisonEvaluator(llm=eval_llm)


max_samples = 5

eval_qs = eval_dataset.questions
qr_pairs = eval_dataset.qr_pairs
ref_response_strs = [r for (_, r) in qr_pairs]

base_query_engine = vector_indices[-1].as_query_engine(similarity_top_k=2)

query_engine = RetrieverQueryEngine(retriever, node_postprocessors=[reranker])

base_pred_responses = get_responses(
    eval_qs[:max_samples], base_query_engine, show_progress=True
)

pred_responses = get_responses(
    eval_qs[:max_samples], query_engine, show_progress=True
)

sponse_strs = [str(p) for p in pred_responses]
base_pred_response_strs = [str(p) for p in base_pred_responses]

evaluator_dict = {
    "correctness": evaluator_c,
    "faithfulness": evaluator_f,
    "semantic_similarity": evaluator_s,
}
batch_runner = BatchEvalRunner(evaluator_dict, workers=1, show_progress=True)

eval_results = await batch_runner.aevaluate_responses(
    queries=eval_qs[:max_samples],
    responses=pred_responses[:max_samples],
    reference=ref_response_strs[:max_samples],
)

base_eval_results = await batch_runner.aevaluate_responses(
    queries=eval_qs[:max_samples],
    responses=base_pred_responses[:max_samples],
    reference=ref_response_strs[:max_samples],
)

results_df = get_results_df(
    [eval_results, base_eval_results],
    ["Ensemble Retriever", "Base Retriever"],
    ["correctness", "faithfulness", "semantic_similarity"],
)
display(results_df)