110 lines
4.0 KiB
Python
110 lines
4.0 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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import voyageai
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from autorag.nodes.passagereranker.base import BasePassageReranker
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from autorag.utils.util import result_to_dataframe, get_event_loop, process_batch
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class VoyageAIReranker(BasePassageReranker):
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def __init__(self, project_dir: str, *args, **kwargs):
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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("VOYAGE_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 VoyageAI rerank in the environment variable VOYAGE_API_KEY "
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"or directly set it on the config YAML file."
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)
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self.voyage_client = voyageai.AsyncClient(api_key=api_key)
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def __del__(self):
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del self.voyage_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", "rerank-2")
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truncation = kwargs.pop("truncation", True)
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return self._pure(queries, contents, ids, top_k, model, batch, truncation)
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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 = "rerank-2",
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batch: int = 8,
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truncation: bool = True,
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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 VoyageAI rerank models.
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You can get the API key from https://docs.voyageai.com/docs/api-key-and-installation and set it in the environment variable VOYAGE_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 VoyageAI rerank.
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You can choose between "rerank-2" and "rerank-2-lite".
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Default is "rerank-2".
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:param batch: The number of queries to be processed in a batch
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:param truncation: Whether to truncate the input to satisfy the 'context length limit' on the query and the documents.
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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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voyageai_rerank_pure(
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self.voyage_client, model, query, contents, ids, top_k, truncation
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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 voyageai_rerank_pure(
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voyage_client: voyageai.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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truncation: bool = True,
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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 VoyageAI rerank models.
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:param voyage_client: The Voyage Client to use for reranking
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:param model: The model name for VoyageAI 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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:param truncation: Whether to truncate the input to satisfy the 'context length limit' on the query and the documents.
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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 voyage_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_k=top_k,
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truncation=truncation,
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)
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reranked_scores: List[float] = list(
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map(lambda x: x.relevance_score, rerank_results.results)
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)
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indices = list(map(lambda x: x.index, rerank_results.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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