127 lines
4.5 KiB
Python
127 lines
4.5 KiB
Python
import os
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from typing import List, Tuple
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import pandas as pd
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from mixedbread_ai.client import AsyncMixedbreadAI
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from autorag.nodes.passagereranker.base import BasePassageReranker
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from autorag.utils.util import (
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result_to_dataframe,
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get_event_loop,
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process_batch,
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pop_params,
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)
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class MixedbreadAIReranker(BasePassageReranker):
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def __init__(
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self,
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project_dir: str,
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*args,
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**kwargs,
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):
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"""
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Initialize mixedbread-ai rerank node.
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:param project_dir: The project directory path.
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:param api_key: The API key for MixedbreadAI rerank.
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You can set it in the environment variable MXBAI_API_KEY.
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Or, you can directly set it on the config YAML file using this parameter.
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Default is env variable "MXBAI_API_KEY".
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:param kwargs: Extra arguments that are not affected
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"""
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super().__init__(project_dir)
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api_key = kwargs.pop("api_key", None)
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api_key = os.getenv("MXBAI_API_KEY", None) if api_key is None else api_key
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if api_key is None:
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raise KeyError(
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"Please set the API key for Mixedbread AI rerank in the environment variable MXBAI_API_KEY "
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"or directly set it on the config YAML file."
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)
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self.client = AsyncMixedbreadAI(api_key=api_key)
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def __del__(self):
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del self.client
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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, scores, 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", 8)
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model = kwargs.pop("model", "mixedbread-ai/mxbai-rerank-large-v1")
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rerank_params = pop_params(self.client.reranking, kwargs)
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return self._pure(queries, contents, ids, top_k, model, batch, **rerank_params)
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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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model: str = "mixedbread-ai/mxbai-rerank-large-v1",
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batch: int = 8,
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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 with mixedbread-ai rerank models.
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You can get the API key from https://www.mixedbread.ai/api-reference#quick-start-guide and set it in the environment variable MXBAI_API_KEY.
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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 model: The model name for mixedbread-ai rerank.
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You can choose between "mixedbread-ai/mxbai-rerank-large-v1", "mixedbread-ai/mxbai-rerank-base-v1" and "mixedbread-ai/mxbai-rerank-xsmall-v1".
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Default is "mixedbread-ai/mxbai-rerank-large-v1".
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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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tasks = [
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mixedbreadai_rerank_pure(
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self.client, query, contents, ids, top_k=top_k, model=model
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)
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for query, contents, ids in zip(queries, contents_list, ids_list)
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]
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loop = get_event_loop()
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results = loop.run_until_complete(process_batch(tasks, batch))
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content_result, id_result, score_result = zip(*results)
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return list(content_result), list(id_result), list(score_result)
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async def mixedbreadai_rerank_pure(
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client: AsyncMixedbreadAI,
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query: str,
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documents: List[str],
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ids: List[str],
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top_k: int,
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model: str = "mixedbread-ai/mxbai-rerank-large-v1",
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) -> Tuple[List[str], List[str], List[float]]:
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"""
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Rerank a list of contents with mixedbread-ai rerank models.
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:param client: The mixedbread-ai client to use for reranking
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:param query: The query to use for reranking
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:param documents: The list of contents to rerank
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:param ids: The list of ids corresponding to the documents
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:param top_k: The number of passages to be retrieved
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:param model: The model name for mixedbread-ai rerank.
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You can choose between "mixedbread-ai/mxbai-rerank-large-v1" and "mixedbread-ai/mxbai-rerank-base-v1".
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Default is "mixedbread-ai/mxbai-rerank-large-v1".
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:return: Tuple of lists containing the reranked contents, ids, and scores
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"""
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results = await client.reranking(
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query=query,
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input=documents,
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top_k=top_k,
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model=model,
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)
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reranked_scores: List[float] = list(map(lambda x: x.score, results.data))
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reranked_scores_float = list(map(float, reranked_scores))
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indices = list(map(lambda x: x.index, results.data))
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reranked_contents = list(map(lambda x: documents[x], indices))
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reranked_ids: List[str] = list(map(lambda i: ids[i], indices))
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return reranked_contents, reranked_ids, reranked_scores_float
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