130 lines
3.8 KiB
Python
130 lines
3.8 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.utils.util import (
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flatten_apply,
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make_batch,
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select_top_k,
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sort_by_scores,
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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 SentenceTransformerReranker(BasePassageReranker):
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def __init__(
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self,
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project_dir: str,
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model_name: str = "cross-encoder/ms-marco-MiniLM-L-2-v2",
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*args,
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**kwargs,
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):
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"""
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Initialize the Sentence Transformer reranker node.
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:param project_dir: The project directory
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:param model_name: The name of the Sentence Transformer model to use for reranking
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Default is "cross-encoder/ms-marco-MiniLM-L-2-v2"
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:param kwargs: The CrossEncoder parameters
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"""
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super().__init__(project_dir, *args, **kwargs)
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try:
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import torch
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from sentence_transformers import CrossEncoder
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except ImportError:
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raise ImportError(
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"You have to install AutoRAG[gpu] to use SentenceTransformerReranker"
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)
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self.device = "cuda" if torch.cuda.is_available() else "cpu"
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model_params = pop_params(CrossEncoder.__init__, kwargs)
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self.model = CrossEncoder(model_name, device=self.device, **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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"""
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Rerank a list of contents based on their relevance to a query using a Sentence Transformer model.
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:param previous_result: The previous result
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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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:return: pd DataFrame containing the reranked contents, ids, and scores
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"""
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queries, contents_list, scores_list, ids_list = self.cast_to_run(
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previous_result
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)
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top_k = kwargs.get("top_k", 1)
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batch = kwargs.get("batch", 64)
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return self._pure(queries, contents_list, ids_list, 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 a Sentence Transformer 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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: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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sentence_transformer_run_model,
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nested_list,
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model=self.model,
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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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def sentence_transformer_run_model(input_texts, model, batch_size: int):
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try:
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import torch
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except ImportError:
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raise ImportError(
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"You have to install AutoRAG[gpu] to use SentenceTransformerReranker"
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)
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batch_input_texts = make_batch(input_texts, batch_size)
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results = []
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for batch_texts in batch_input_texts:
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with torch.no_grad():
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pred_scores = model.predict(sentences=batch_texts, apply_softmax=True)
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results.extend(pred_scores.tolist())
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return results
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