102 lines
2.9 KiB
Python
102 lines
2.9 KiB
Python
from typing import List, Tuple
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import pandas as pd
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from autorag.nodes.passagereranker.base import BasePassageReranker
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from autorag.nodes.passagereranker.flag_embedding import flag_embedding_run_model
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from autorag.utils.util import (
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flatten_apply,
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sort_by_scores,
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select_top_k,
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pop_params,
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result_to_dataframe,
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empty_cuda_cache,
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)
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class FlagEmbeddingLLMReranker(BasePassageReranker):
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def __init__(
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self,
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project_dir,
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model_name: str = "BAAI/bge-reranker-v2-gemma",
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*args,
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**kwargs,
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):
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"""
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Initialize the FlagEmbeddingReranker module.
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:param project_dir: The project directory.
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:param model_name: The name of the BAAI Reranker LLM-based-model name.
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Default is "BAAI/bge-reranker-v2-gemma"
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:param kwargs: Extra parameter for FlagEmbedding.FlagReranker
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"""
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super().__init__(project_dir)
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try:
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from FlagEmbedding import FlagLLMReranker
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except ImportError:
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raise ImportError(
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"FlagEmbeddingLLMReranker requires the 'FlagEmbedding' package to be installed."
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)
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model_params = pop_params(FlagLLMReranker.__init__, kwargs)
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model_params.pop("model_name_or_path", None)
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self.model = FlagLLMReranker(model_name_or_path=model_name, **model_params)
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def __del__(self):
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del self.model
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empty_cuda_cache()
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super().__del__()
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@result_to_dataframe(["retrieved_contents", "retrieved_ids", "retrieve_scores"])
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def pure(self, previous_result: pd.DataFrame, *args, **kwargs):
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queries, contents, _, ids = self.cast_to_run(previous_result)
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top_k = kwargs.pop("top_k")
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batch = kwargs.pop("batch", 64)
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return self._pure(queries, contents, ids, top_k, batch)
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def _pure(
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self,
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queries: List[str],
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contents_list: List[List[str]],
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ids_list: List[List[str]],
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top_k: int,
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batch: int = 64,
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) -> Tuple[List[List[str]], List[List[str]], List[List[float]]]:
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"""
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Rerank a list of contents based on their relevance to a query using BAAI LLM-based-Reranker model.
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:param queries: The list of queries to use for reranking
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:param contents_list: The list of lists of contents to rerank
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:param ids_list: The list of lists of ids retrieved from the initial ranking
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:param top_k: The number of passages to be retrieved
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:param batch: The number of queries to be processed in a batch
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Default is 64.
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:return: tuple of lists containing the reranked contents, ids, and scores
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"""
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nested_list = [
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list(map(lambda x: [query, x], content_list))
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for query, content_list in zip(queries, contents_list)
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]
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rerank_scores = flatten_apply(
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flag_embedding_run_model, nested_list, model=self.model, batch_size=batch
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)
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df = pd.DataFrame(
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{
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"contents": contents_list,
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"ids": ids_list,
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"scores": rerank_scores,
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}
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)
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df[["contents", "ids", "scores"]] = df.apply(
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sort_by_scores, axis=1, result_type="expand"
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)
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results = select_top_k(df, ["contents", "ids", "scores"], top_k)
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return (
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results["contents"].tolist(),
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results["ids"].tolist(),
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results["scores"].tolist(),
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)
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