import os
from typing import List, Tuple
import cohere
import pandas as pd
from cohere import RerankResponseResultsItem
from autorag.nodes.passagereranker.base import BasePassageReranker
from autorag.utils.util import get_event_loop, process_batch, result_to_dataframe
[docs]
class CohereReranker(BasePassageReranker):
def __init__(self, project_dir: str, *args, **kwargs):
"""
Initialize Cohere rerank node.
:param project_dir: The project directory path.
:param api_key: The API key for Cohere rerank.
You can set it in the environment variable COHERE_API_KEY.
Or, you can directly set it on the config YAML file using this parameter.
Default is env variable "COHERE_API_KEY".
:param kwargs: Extra arguments that are not affected
"""
super().__init__(project_dir)
api_key = kwargs.pop("api_key", None)
api_key = os.getenv("COHERE_API_KEY", None) if api_key is None else api_key
if api_key is None:
raise KeyError(
"Please set the API key for Cohere rerank in the environment variable COHERE_API_KEY "
"or directly set it on the config YAML file."
)
self.cohere_client = cohere.AsyncClient(api_key)
def __del__(self):
del self.cohere_client
super().__del__()
[docs]
@result_to_dataframe(["retrieved_contents", "retrieved_ids", "retrieve_scores"])
def pure(self, previous_result: pd.DataFrame, *args, **kwargs):
queries, contents, scores, ids = self.cast_to_run(previous_result)
top_k = kwargs.pop("top_k")
batch = kwargs.pop("batch", 64)
model = kwargs.pop("model", "rerank-multilingual-v2.0")
return self._pure(queries, contents, scores, ids, top_k, batch, model)
def _pure(
self,
queries: List[str],
contents_list: List[List[str]],
scores_list: List[List[float]],
ids_list: List[List[str]],
top_k: int,
batch: int = 64,
model: str = "rerank-multilingual-v2.0",
) -> Tuple[List[List[str]], List[List[str]], List[List[float]]]:
"""
Rerank a list of contents with Cohere rerank models.
You can get the API key from https://cohere.com/rerank and set it in the environment variable COHERE_API_KEY.
:param queries: The list of queries to use for reranking
:param contents_list: The list of lists of contents to rerank
:param scores_list: The list of lists of scores retrieved from the initial ranking
: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 batch: The number of queries to be processed in a batch
:param model: The model name for Cohere rerank.
You can choose between "rerank-multilingual-v2.0" and "rerank-english-v2.0".
Default is "rerank-multilingual-v2.0".
:return: Tuple of lists containing the reranked contents, ids, and scores
"""
# Run async cohere_rerank_pure function
tasks = [
cohere_rerank_pure(self.cohere_client, model, query, document, ids, top_k)
for query, document, ids in zip(queries, contents_list, ids_list)
]
loop = get_event_loop()
results = loop.run_until_complete(process_batch(tasks, batch_size=batch))
content_result = list(map(lambda x: x[0], results))
id_result = list(map(lambda x: x[1], results))
score_result = list(map(lambda x: x[2], results))
return content_result, id_result, score_result
[docs]
async def cohere_rerank_pure(
cohere_client: cohere.AsyncClient,
model: str,
query: str,
documents: List[str],
ids: List[str],
top_k: int,
) -> Tuple[List[str], List[str], List[float]]:
"""
Rerank a list of contents with Cohere rerank models.
:param cohere_client: The Cohere AsyncClient to use for reranking
:param model: The model name for Cohere rerank
:param query: The query to use for reranking
:param documents: The list of contents to rerank
:param ids: The list of ids corresponding to the documents
:param top_k: The number of passages to be retrieved
:return: Tuple of lists containing the reranked contents, ids, and scores
"""
rerank_results = await cohere_client.rerank(
model=model,
query=query,
documents=documents,
top_n=top_k,
return_documents=False,
)
results: List[RerankResponseResultsItem] = rerank_results.results
reranked_scores: List[float] = list(map(lambda x: x.relevance_score, results))
indices = list(map(lambda x: x.index, results))
reranked_contents: List[str] = list(map(lambda i: documents[i], indices))
reranked_ids: List[str] = list(map(lambda i: ids[i], indices))
return reranked_contents, reranked_ids, reranked_scores