from typing import List, Tuple
import pandas as pd
from autorag.nodes.passagereranker.base import BasePassageReranker
from autorag.nodes.passagereranker.flag_embedding import flag_embedding_run_model
from autorag.utils.util import (
flatten_apply,
sort_by_scores,
select_top_k,
pop_params,
result_to_dataframe,
empty_cuda_cache,
)
[docs]
class FlagEmbeddingLLMReranker(BasePassageReranker):
def __init__(
self,
project_dir,
model_name: str = "BAAI/bge-reranker-v2-gemma",
*args,
**kwargs,
):
"""
Initialize the FlagEmbeddingReranker module.
:param project_dir: The project directory.
:param model_name: The name of the BAAI Reranker LLM-based-model name.
Default is "BAAI/bge-reranker-v2-gemma"
:param kwargs: Extra parameter for FlagEmbedding.FlagReranker
"""
super().__init__(project_dir)
try:
from FlagEmbedding import FlagLLMReranker
except ImportError:
raise ImportError(
"FlagEmbeddingLLMReranker requires the 'FlagEmbedding' package to be installed."
)
model_params = pop_params(FlagLLMReranker.__init__, kwargs)
model_params.pop("model_name_or_path", None)
self.model = FlagLLMReranker(model_name_or_path=model_name, **model_params)
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 BAAI LLM-based-Reranker model.
: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)
]
rerank_scores = flatten_apply(
flag_embedding_run_model, nested_list, model=self.model, batch_size=batch
)
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(),
)