from typing import List, Tuple
import numpy as np
import pandas as pd
from autorag.nodes.passagereranker.base import BasePassageReranker
from autorag.utils.util import (
make_batch,
sort_by_scores,
flatten_apply,
select_top_k,
result_to_dataframe,
empty_cuda_cache,
)
[docs]
class KoReranker(BasePassageReranker):
def __init__(self, project_dir: str, *args, **kwargs):
super().__init__(project_dir)
try:
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer
except ImportError:
raise ImportError("For using KoReranker, please install torch first.")
model_path = "Dongjin-kr/ko-reranker"
self.tokenizer = AutoTokenizer.from_pretrained(model_path)
self.model = AutoModelForSequenceClassification.from_pretrained(model_path)
self.model.eval()
# Determine the device to run the model on (GPU if available, otherwise CPU)
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
self.model.to(self.device)
def __del__(self):
del self.model
empty_cuda_cache()
super().__del__()
[docs]
@result_to_dataframe(["retrieved_contents", "retrieved_ids", "retrieve_scores"])
def pure(self, previous_result: pd.DataFrame, *args, **kwargs):
queries, contents, _, ids = self.cast_to_run(previous_result)
top_k = kwargs.pop("top_k")
batch = kwargs.pop("batch", 64)
return self._pure(queries, contents, ids, top_k, batch)
def _pure(
self,
queries: List[str],
contents_list: List[List[str]],
ids_list: List[List[str]],
top_k: int,
batch: int = 64,
) -> Tuple[List[List[str]], List[List[str]], List[List[float]]]:
"""
Rerank a list of contents based on their relevance to a query using ko-reranker.
ko-reranker is a reranker based on korean (https://huggingface.co/Dongjin-kr/ko-reranker).
:param queries: The list of queries to use for reranking
:param contents_list: The list of lists of contents to rerank
:param ids_list: The list of lists of ids retrieved from the initial ranking
:param top_k: The number of passages to be retrieved
:param batch: The number of queries to be processed in a batch
Default is 64.
:return: Tuple of lists containing the reranked contents, ids, and scores
"""
nested_list = [
list(map(lambda x: [query, x], content_list))
for query, content_list in zip(queries, contents_list)
]
scores_nps = flatten_apply(
koreranker_run_model,
nested_list,
model=self.model,
batch_size=batch,
tokenizer=self.tokenizer,
device=self.device,
)
rerank_scores = list(
map(
lambda scores: exp_normalize(np.array(scores)).astype(float), scores_nps
)
)
df = pd.DataFrame(
{
"contents": contents_list,
"ids": ids_list,
"scores": rerank_scores,
}
)
df[["contents", "ids", "scores"]] = df.apply(
sort_by_scores, axis=1, result_type="expand"
)
results = select_top_k(df, ["contents", "ids", "scores"], top_k)
return (
results["contents"].tolist(),
results["ids"].tolist(),
results["scores"].tolist(),
)
[docs]
def koreranker_run_model(input_texts, model, tokenizer, device, batch_size: int):
try:
import torch
except ImportError:
raise ImportError("For using KoReranker, please install torch first.")
batch_input_texts = make_batch(input_texts, batch_size)
results = []
for batch_texts in batch_input_texts:
inputs = tokenizer(
batch_texts,
padding=True,
truncation=True,
return_tensors="pt",
max_length=512,
)
inputs = inputs.to(device)
with torch.no_grad():
scores = (
model(**inputs, return_dict=True)
.logits.view(
-1,
)
.float()
)
scores_np = scores.cpu().numpy()
results.extend(scores_np)
return results
[docs]
def exp_normalize(x):
b = x.max()
y = np.exp(x - b)
return y / y.sum()