116 lines
3.8 KiB
Python
116 lines
3.8 KiB
Python
import os
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from typing import List, Tuple
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import aiohttp
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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 get_event_loop, process_batch, result_to_dataframe
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JINA_API_URL = "https://api.jina.ai/v1/rerank"
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class JinaReranker(BasePassageReranker):
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def __init__(self, project_dir: str, api_key: str = None, *args, **kwargs):
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"""
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Initialize Jina rerank node.
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:param project_dir: The project directory path.
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:param api_key: The API key for Jina rerank.
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You can set it in the environment variable JINAAI_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 "JINAAI_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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if api_key is None:
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api_key = os.getenv("JINAAI_API_KEY", None)
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if api_key is None:
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raise ValueError(
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"API key is not provided."
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"You can set it as an argument or as an environment variable 'JINAAI_API_KEY'"
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)
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self.session = aiohttp.ClientSession(loop=get_event_loop())
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self.session.headers.update(
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{"Authorization": f"Bearer {api_key}", "Accept-Encoding": "identity"}
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)
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def __del__(self):
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self.session.close()
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del self.session
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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", 8)
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model = kwargs.pop("model", "jina-reranker-v1-base-en")
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return self._pure(queries, contents, ids, top_k, model, 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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model: str = "jina-reranker-v1-base-en",
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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 Jina rerank models.
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You can get the API key from https://jina.ai/reranker and set it in the environment variable JINAAI_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 Cohere rerank.
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You can choose between "jina-reranker-v1-base-en" and "jina-colbert-v1-en".
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Default is "jina-reranker-v1-base-en".
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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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jina_reranker_pure(
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self.session, 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 jina_reranker_pure(
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session,
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query: str,
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contents: List[str],
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ids: List[str],
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top_k: int,
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model: str = "jina-reranker-v1-base-en",
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) -> Tuple[List[str], List[str], List[float]]:
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async with session.post(
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JINA_API_URL,
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json={
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"query": query,
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"documents": contents,
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"model": model,
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"top_n": top_k,
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},
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) as resp:
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resp_json = await resp.json()
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if "results" not in resp_json:
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raise RuntimeError(f"Invalid response from Jina API: {resp_json['detail']}")
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results = resp_json["results"]
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indices = list(map(lambda x: x["index"], results))
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score_result = list(map(lambda x: x["relevance_score"], results))
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id_result = list(map(lambda x: ids[x], indices))
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content_result = list(map(lambda x: contents[x], indices))
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return content_result, id_result, score_result
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