import os
from typing import List, Tuple
import aiohttp
import pandas as pd
from autorag.nodes.passagereranker.base import BasePassageReranker
from autorag.utils.util import get_event_loop, process_batch, result_to_dataframe
JINA_API_URL = "https://api.jina.ai/v1/rerank"
[docs]
class JinaReranker(BasePassageReranker):
def __init__(self, project_dir: str, api_key: str = None, *args, **kwargs):
"""
Initialize Jina rerank node.
:param project_dir: The project directory path.
:param api_key: The API key for Jina rerank.
You can set it in the environment variable JINAAI_API_KEY.
Or, you can directly set it on the config YAML file using this parameter.
Default is env variable "JINAAI_API_KEY".
:param kwargs: Extra arguments that are not affected
"""
super().__init__(project_dir)
if api_key is None:
api_key = os.getenv("JINAAI_API_KEY", None)
if api_key is None:
raise ValueError(
"API key is not provided."
"You can set it as an argument or as an environment variable 'JINAAI_API_KEY'"
)
self.session = aiohttp.ClientSession(loop=get_event_loop())
self.session.headers.update(
{"Authorization": f"Bearer {api_key}", "Accept-Encoding": "identity"}
)
def __del__(self):
self.session.close()
del self.session
super().__del__()
[docs]
@result_to_dataframe(["retrieved_contents", "retrieved_ids", "retrieve_scores"])
def pure(self, previous_result: pd.DataFrame, *args, **kwargs):
queries, contents, _, ids = self.cast_to_run(previous_result)
top_k = kwargs.pop("top_k")
batch = kwargs.pop("batch", 8)
model = kwargs.pop("model", "jina-reranker-v1-base-en")
return self._pure(queries, contents, ids, top_k, model, batch)
def _pure(
self,
queries: List[str],
contents_list: List[List[str]],
ids_list: List[List[str]],
top_k: int,
model: str = "jina-reranker-v1-base-en",
batch: int = 8,
) -> Tuple[List[List[str]], List[List[str]], List[List[float]]]:
"""
Rerank a list of contents with Jina rerank models.
You can get the API key from https://jina.ai/reranker and set it in the environment variable JINAAI_API_KEY.
:param queries: The list of queries to use for reranking
:param contents_list: The list of lists of contents to rerank
:param ids_list: The list of lists of ids retrieved from the initial ranking
:param top_k: The number of passages to be retrieved
:param model: The model name for Cohere rerank.
You can choose between "jina-reranker-v1-base-en" and "jina-colbert-v1-en".
Default is "jina-reranker-v1-base-en".
:param batch: The number of queries to be processed in a batch
:return: Tuple of lists containing the reranked contents, ids, and scores
"""
tasks = [
jina_reranker_pure(
self.session, query, contents, ids, top_k=top_k, model=model
)
for query, contents, ids in zip(queries, contents_list, ids_list)
]
loop = get_event_loop()
results = loop.run_until_complete(process_batch(tasks, batch))
content_result, id_result, score_result = zip(*results)
return list(content_result), list(id_result), list(score_result)
[docs]
async def jina_reranker_pure(
session,
query: str,
contents: List[str],
ids: List[str],
top_k: int,
model: str = "jina-reranker-v1-base-en",
) -> Tuple[List[str], List[str], List[float]]:
async with session.post(
JINA_API_URL,
json={
"query": query,
"documents": contents,
"model": model,
"top_n": top_k,
},
) as resp:
resp_json = await resp.json()
if "results" not in resp_json:
raise RuntimeError(f"Invalid response from Jina API: {resp_json['detail']}")
results = resp_json["results"]
indices = list(map(lambda x: x["index"], results))
score_result = list(map(lambda x: x["relevance_score"], results))
id_result = list(map(lambda x: ids[x], indices))
content_result = list(map(lambda x: contents[x], indices))
return content_result, id_result, score_result