import os
from typing import List, Tuple
import pandas as pd
from mixedbread_ai.client import AsyncMixedbreadAI
from autorag.nodes.passagereranker.base import BasePassageReranker
from autorag.utils.util import (
result_to_dataframe,
get_event_loop,
process_batch,
pop_params,
)
[docs]
class MixedbreadAIReranker(BasePassageReranker):
def __init__(
self,
project_dir: str,
*args,
**kwargs,
):
"""
Initialize mixedbread-ai rerank node.
:param project_dir: The project directory path.
:param api_key: The API key for MixedbreadAI rerank.
You can set it in the environment variable MXBAI_API_KEY.
Or, you can directly set it on the config YAML file using this parameter.
Default is env variable "MXBAI_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("MXBAI_API_KEY", None) if api_key is None else api_key
if api_key is None:
raise KeyError(
"Please set the API key for Mixedbread AI rerank in the environment variable MXBAI_API_KEY "
"or directly set it on the config YAML file."
)
self.client = AsyncMixedbreadAI(api_key=api_key)
def __del__(self):
del self.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", 8)
model = kwargs.pop("model", "mixedbread-ai/mxbai-rerank-large-v1")
rerank_params = pop_params(self.client.reranking, kwargs)
return self._pure(queries, contents, ids, top_k, model, batch, **rerank_params)
def _pure(
self,
queries: List[str],
contents_list: List[List[str]],
ids_list: List[List[str]],
top_k: int,
model: str = "mixedbread-ai/mxbai-rerank-large-v1",
batch: int = 8,
) -> Tuple[List[List[str]], List[List[str]], List[List[float]]]:
"""
Rerank a list of contents with mixedbread-ai rerank models.
You can get the API key from https://www.mixedbread.ai/api-reference#quick-start-guide and set it in the environment variable MXBAI_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 mixedbread-ai rerank.
You can choose between "mixedbread-ai/mxbai-rerank-large-v1", "mixedbread-ai/mxbai-rerank-base-v1" and "mixedbread-ai/mxbai-rerank-xsmall-v1".
Default is "mixedbread-ai/mxbai-rerank-large-v1".
:param batch: The number of queries to be processed in a batch
:return: Tuple of lists containing the reranked contents, ids, and scores
"""
tasks = [
mixedbreadai_rerank_pure(
self.client, 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 mixedbreadai_rerank_pure(
client: AsyncMixedbreadAI,
query: str,
documents: List[str],
ids: List[str],
top_k: int,
model: str = "mixedbread-ai/mxbai-rerank-large-v1",
) -> Tuple[List[str], List[str], List[float]]:
"""
Rerank a list of contents with mixedbread-ai rerank models.
:param client: The mixedbread-ai client to use for reranking
: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
:param model: The model name for mixedbread-ai rerank.
You can choose between "mixedbread-ai/mxbai-rerank-large-v1" and "mixedbread-ai/mxbai-rerank-base-v1".
Default is "mixedbread-ai/mxbai-rerank-large-v1".
:return: Tuple of lists containing the reranked contents, ids, and scores
"""
results = await client.reranking(
query=query,
input=documents,
top_k=top_k,
model=model,
)
reranked_scores: List[float] = list(map(lambda x: x.score, results.data))
reranked_scores_float = list(map(float, reranked_scores))
indices = list(map(lambda x: x.index, results.data))
reranked_contents = list(map(lambda x: documents[x], indices))
reranked_ids: List[str] = list(map(lambda i: ids[i], indices))
return reranked_contents, reranked_ids, reranked_scores_float