120 lines
4.4 KiB
Python
120 lines
4.4 KiB
Python
import os
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from typing import List, Tuple
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import cohere
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import pandas as pd
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from cohere import RerankResponseResultsItem
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from autorag.nodes.passagereranker.base import BasePassageReranker
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from autorag.utils.util import get_event_loop, process_batch, result_to_dataframe
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class CohereReranker(BasePassageReranker):
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def __init__(self, project_dir: str, *args, **kwargs):
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"""
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Initialize Cohere rerank node.
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:param project_dir: The project directory path.
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:param api_key: The API key for Cohere rerank.
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You can set it in the environment variable COHERE_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 "COHERE_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("COHERE_API_KEY", None) if api_key is None else api_key
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if api_key is None:
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api_key = os.getenv("CO_API_KEY", None)
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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 Cohere rerank in the environment variable COHERE_API_KEY "
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"or directly set it on the config YAML file."
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)
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self.cohere_client = cohere.AsyncClientV2(api_key=api_key)
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def __del__(self):
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del self.cohere_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", 64)
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model = kwargs.pop("model", "rerank-v3.5")
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return self._pure(queries, contents, scores, ids, top_k, batch, model)
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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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scores_list: List[List[float]],
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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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model: str = "rerank-v3.5",
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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 Cohere rerank models.
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You can get the API key from https://cohere.com/rerank and set it in the environment variable COHERE_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 scores_list: The list of lists of scores retrieved from the initial ranking
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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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:param model: The model name for Cohere rerank.
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You can choose between "rerank-v3.5", "rerank-english-v3.0", and "rerank-multilingual-v3.0".
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Default is "rerank-v3.5".
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:return: Tuple of lists containing the reranked contents, ids, and scores
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"""
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# Run async cohere_rerank_pure function
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tasks = [
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cohere_rerank_pure(self.cohere_client, model, query, document, ids, top_k)
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for query, document, 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_size=batch))
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content_result = list(map(lambda x: x[0], results))
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id_result = list(map(lambda x: x[1], results))
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score_result = list(map(lambda x: x[2], results))
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return content_result, id_result, score_result
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async def cohere_rerank_pure(
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cohere_client: cohere.AsyncClient,
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model: str,
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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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) -> Tuple[List[str], List[str], List[float]]:
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"""
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Rerank a list of contents with Cohere rerank models.
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:param cohere_client: The Cohere AsyncClient to use for reranking
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:param model: The model name for Cohere rerank
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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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:return: Tuple of lists containing the reranked contents, ids, and scores
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"""
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rerank_results = await cohere_client.rerank(
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model=model,
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query=query,
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documents=documents,
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top_n=top_k,
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return_documents=False,
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)
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results: List[RerankResponseResultsItem] = rerank_results.results
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reranked_scores: List[float] = list(map(lambda x: x.relevance_score, results))
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indices = list(map(lambda x: x.index, results))
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reranked_contents: List[str] = list(map(lambda i: documents[i], 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
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