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438 | class RagEvaluatorPack(BaseLlamaPack):
"""A pack for performing evaluation with your own RAG pipeline.
Args:
query_engine: The RAG pipeline to evaluate.
rag_dataset: The BaseLlamaDataset to evaluate on.
judge_llm: The LLM to use as the evaluator.
"""
def __init__(
self,
query_engine: BaseQueryEngine,
rag_dataset: BaseLlamaDataset,
judge_llm: Optional[LLM] = None,
show_progress: bool = True,
):
self.query_engine = query_engine
self.rag_dataset = rag_dataset
self._num_examples = len(self.rag_dataset.examples)
if judge_llm is None:
self.judge_llm = OpenAI(temperature=0, model="gpt-4-1106-preview")
else:
assert isinstance(judge_llm, LLM)
self.judge_llm = judge_llm
self.show_progress = show_progress
self.evals = {
"correctness": [],
"relevancy": [],
"faithfulness": [],
"context_similarity": [],
}
self.eval_queue = deque(range(len(rag_dataset.examples)))
self.prediction_dataset = None
async def _amake_predictions(
self,
batch_size: int = 20,
sleep_time_in_seconds: int = 1,
):
"""Async make predictions with query engine."""
self.prediction_dataset: BaseLlamaPredictionDataset = (
await self.rag_dataset.amake_predictions_with(
self.query_engine,
show_progress=self.show_progress,
batch_size=batch_size,
sleep_time_in_seconds=sleep_time_in_seconds,
)
)
def _make_predictions(
self,
batch_size: int = 20,
sleep_time_in_seconds: int = 1,
):
"""Sync make predictions with query engine."""
self.prediction_dataset: BaseLlamaPredictionDataset = (
self.rag_dataset.make_predictions_with(
self.query_engine,
show_progress=self.show_progress,
batch_size=batch_size,
sleep_time_in_seconds=sleep_time_in_seconds,
)
)
def _prepare_judges(self):
"""Construct the evaluators."""
judges = {}
judges["correctness"] = CorrectnessEvaluator(
llm=self.judge_llm,
)
judges["relevancy"] = RelevancyEvaluator(
llm=self.judge_llm,
)
judges["faithfulness"] = FaithfulnessEvaluator(
llm=self.judge_llm,
)
judges["semantic_similarity"] = SemanticSimilarityEvaluator(
embed_model=OpenAIEmbedding()
)
return judges
async def _areturn_null_eval_result(self, query) -> EvaluationResult:
"""A dummy async method that returns None.
NOTE: this is used to handle case when creating async tasks for evaluating
predictions where contexts do not exist.
"""
return EvaluationResult(
query=query,
)
def _return_null_eval_result(self, query) -> EvaluationResult:
"""A dummy async method that returns None.
NOTE: this is used to handle case when creating async tasks for evaluating
predictions where contexts do not exist.
"""
return EvaluationResult(
query=query,
)
def _create_async_evaluate_example_prediction_tasks(
self, judges, example, prediction, sleep_time_in_seconds
):
"""Collect the co-routines."""
correctness_task = judges["correctness"].aevaluate(
query=example.query,
response=prediction.response,
reference=example.reference_answer,
sleep_time_in_seconds=sleep_time_in_seconds,
)
relevancy_task = judges["relevancy"].aevaluate(
query=example.query,
response=prediction.response,
contexts=prediction.contexts,
sleep_time_in_seconds=sleep_time_in_seconds,
)
faithfulness_task = judges["faithfulness"].aevaluate(
query=example.query,
response=prediction.response,
contexts=prediction.contexts,
sleep_time_in_seconds=sleep_time_in_seconds,
)
if example.reference_contexts and prediction.contexts:
semantic_similarity_task = judges["semantic_similarity"].aevaluate(
query=example.query,
response="\n".join(prediction.contexts),
reference="\n".join(example.reference_contexts),
)
else:
semantic_similarity_task = self._areturn_null_eval_result(
query=example.query
)
return (
correctness_task,
relevancy_task,
faithfulness_task,
semantic_similarity_task,
)
def _evaluate_example_prediction(self, judges, example, prediction):
"""Collect the co-routines."""
correctness_result = judges["correctness"].evaluate(
query=example.query,
response=prediction.response,
reference=example.reference_answer,
)
relevancy_result = judges["relevancy"].evaluate(
query=example.query,
response=prediction.response,
contexts=prediction.contexts,
)
faithfulness_result = judges["faithfulness"].evaluate(
query=example.query,
response=prediction.response,
contexts=prediction.contexts,
)
if example.reference_contexts and prediction.contexts:
semantic_similarity_result = judges["semantic_similarity"].evaluate(
query=example.query,
response="\n".join(prediction.contexts),
reference="\n".join(example.reference_contexts),
)
else:
semantic_similarity_result = self._return_null_eval_result(
query=example.query
)
return (
correctness_result,
relevancy_result,
faithfulness_result,
semantic_similarity_result,
)
def _save_evaluations(self):
"""Save evaluation json object."""
# saving evaluations
evaluations_objects = {
"context_similarity": [e.dict() for e in self.evals["context_similarity"]],
"correctness": [e.dict() for e in self.evals["correctness"]],
"faithfulness": [e.dict() for e in self.evals["faithfulness"]],
"relevancy": [e.dict() for e in self.evals["relevancy"]],
}
with open("_evaluations.json", "w") as json_file:
json.dump(evaluations_objects, json_file)
def _prepare_and_save_benchmark_results(self):
"""Get mean score across all of the evaluated examples-predictions."""
_, mean_correctness_df = get_eval_results_df(
["base_rag"] * len(self.evals["correctness"]),
self.evals["correctness"],
metric="correctness",
)
_, mean_relevancy_df = get_eval_results_df(
["base_rag"] * len(self.evals["relevancy"]),
self.evals["relevancy"],
metric="relevancy",
)
_, mean_faithfulness_df = get_eval_results_df(
["base_rag"] * len(self.evals["faithfulness"]),
self.evals["faithfulness"],
metric="faithfulness",
)
_, mean_context_similarity_df = get_eval_results_df(
["base_rag"] * len(self.evals["context_similarity"]),
self.evals["context_similarity"],
metric="context_similarity",
)
mean_scores_df = pd.concat(
[
mean_correctness_df.reset_index(),
mean_relevancy_df.reset_index(),
mean_faithfulness_df.reset_index(),
mean_context_similarity_df.reset_index(),
],
axis=0,
ignore_index=True,
)
mean_scores_df = mean_scores_df.set_index("index")
mean_scores_df.index = mean_scores_df.index.set_names(["metrics"])
# save mean_scores_df
mean_scores_df.to_csv("benchmark.csv")
return mean_scores_df
def _make_evaluations(
self,
batch_size,
sleep_time_in_seconds,
):
"""Sync make evaluations."""
judges = self._prepare_judges()
start_ix = self.eval_queue[0]
for batch in self._batch_examples_and_preds(
self.rag_dataset.examples,
self.prediction_dataset.predictions,
batch_size=batch_size,
start_position=start_ix,
):
examples, predictions = batch
for example, prediction in tqdm.tqdm(zip(examples, predictions)):
(
correctness_result,
relevancy_result,
faithfulness_result,
semantic_similarity_result,
) = self._evaluate_example_prediction(
judges=judges, example=example, prediction=prediction
)
self.evals["correctness"].append(correctness_result)
self.evals["relevancy"].append(relevancy_result)
self.evals["faithfulness"].append(faithfulness_result)
self.evals["context_similarity"].append(semantic_similarity_result)
time.sleep(sleep_time_in_seconds)
self._save_evaluations()
return self._prepare_and_save_benchmark_results()
def _batch_examples_and_preds(
self,
examples: List[Any],
predictions: List[Any],
batch_size: int = 10,
start_position: int = 0,
):
"""Batches examples and predictions with a given batch_size."""
assert self._num_examples == len(predictions)
for ndx in range(start_position, self._num_examples, batch_size):
yield examples[
ndx : min(ndx + batch_size, self._num_examples)
], predictions[ndx : min(ndx + batch_size, self._num_examples)]
async def _amake_evaluations(self, batch_size, sleep_time_in_seconds):
"""Async make evaluations."""
judges = self._prepare_judges()
ix = self.eval_queue[0]
batch_iterator = self._batch_examples_and_preds(
self.rag_dataset.examples,
self.prediction_dataset.predictions,
batch_size=batch_size,
start_position=ix,
)
total_batches = (self._num_examples - ix + 1) / batch_size + (
(self._num_examples - ix + 1) % batch_size != 0
)
if self.show_progress:
batch_iterator = tqdm_asyncio(
batch_iterator,
desc="Batch processing of evaluations",
total=total_batches,
)
for batch in batch_iterator:
examples, predictions = batch
tasks = []
for example, prediction in zip(examples, predictions):
(
correctness_task,
relevancy_task,
faithfulness_task,
semantic_similarity_task,
) = self._create_async_evaluate_example_prediction_tasks(
judges=judges,
example=example,
prediction=prediction,
sleep_time_in_seconds=sleep_time_in_seconds,
)
tasks += [
correctness_task,
relevancy_task,
faithfulness_task,
semantic_similarity_task,
]
# do this in batches to avoid RateLimitError
try:
eval_results: List[EvaluationResult] = await asyncio.gather(*tasks)
except RateLimitError as err:
if self.show_progress:
batch_iterator.close()
raise ValueError(
"You've hit rate limits on your OpenAI subscription. This"
" `RagEvaluatorPack` maintains state of evaluations. Simply"
" re-invoke .arun() in order to continue from where you left"
" off."
) from err
# store in memory
# since final result of eval_results respects order of inputs
# just take appropriate slices
self.evals["correctness"] += eval_results[::4]
self.evals["relevancy"] += eval_results[1::4]
self.evals["faithfulness"] += eval_results[2::4]
self.evals["context_similarity"] += eval_results[3::4]
# update queue
for _ in range(batch_size):
if self.eval_queue:
self.eval_queue.popleft()
ix += 1
if self.show_progress:
batch_iterator.update()
batch_iterator.refresh()
self._save_evaluations()
return self._prepare_and_save_benchmark_results()
def run(self, batch_size: int = 10, sleep_time_in_seconds: int = 1):
if batch_size > 10:
warnings.warn(
"You've set a large batch_size (>10). If using OpenAI GPT-4 as "
" `judge_llm` (which is the default judge_llm),"
" you may experience a RateLimitError. Previous successful eval "
" responses are cached per batch. So hitting a RateLimitError"
" would mean you'd lose all of the current batches successful "
" GPT-4 calls."
)
if self.prediction_dataset is None:
self._make_predictions(batch_size, sleep_time_in_seconds)
# evaluate predictions
eval_sleep_time_in_seconds = (
sleep_time_in_seconds * 2
) # since we make 3 evaluator llm calls
eval_batch_size = int(max(batch_size / 4, 1))
return self._make_evaluations(
batch_size=eval_batch_size, sleep_time_in_seconds=eval_sleep_time_in_seconds
)
async def arun(
self,
batch_size: int = 10,
sleep_time_in_seconds: int = 1,
):
if batch_size > 10:
warnings.warn(
"You've set a large batch_size (>10). If using OpenAI GPT-4 as "
" `judge_llm` (which is the default judge_llm),"
" you may experience a RateLimitError. Previous successful eval "
" responses are cached per batch. So hitting a RateLimitError"
" would mean you'd lose all of the current batches successful "
" GPT-4 calls."
)
# make predictions
if self.prediction_dataset is None:
await self._amake_predictions(batch_size, sleep_time_in_seconds)
# evaluate predictions
eval_sleep_time_in_seconds = (
sleep_time_in_seconds * 2
) # since we make 3 evaluator llm calls and default is gpt-4
# which is heavily rate-limited
eval_batch_size = int(max(batch_size / 4, 1))
return await self._amake_evaluations(
batch_size=eval_batch_size, sleep_time_in_seconds=eval_sleep_time_in_seconds
)
|