import os
from typing import List, Tuple
import pandas as pd
import voyageai
from autorag.nodes.passagereranker.base import BasePassageReranker
from autorag.utils.util import result_to_dataframe, get_event_loop, process_batch
[docs]
class VoyageAIReranker(BasePassageReranker):
def __init__(self, project_dir: str, *args, **kwargs):
super().__init__(project_dir)
api_key = kwargs.pop("api_key", None)
api_key = os.getenv("VOYAGE_API_KEY", None) if api_key is None else api_key
if api_key is None:
raise KeyError(
"Please set the API key for VoyageAI rerank in the environment variable VOYAGE_API_KEY "
"or directly set it on the config YAML file."
)
self.voyage_client = voyageai.AsyncClient(api_key=api_key)
def __del__(self):
del self.voyage_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", "rerank-2")
truncation = kwargs.pop("truncation", True)
return self._pure(queries, contents, ids, top_k, model, batch, truncation)
def _pure(
self,
queries: List[str],
contents_list: List[List[str]],
ids_list: List[List[str]],
top_k: int,
model: str = "rerank-2",
batch: int = 8,
truncation: bool = True,
) -> Tuple[List[List[str]], List[List[str]], List[List[float]]]:
"""
Rerank a list of contents with VoyageAI rerank models.
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.
: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 VoyageAI rerank.
You can choose between "rerank-2" and "rerank-2-lite".
Default is "rerank-2".
:param batch: The number of queries to be processed in a batch
:param truncation: Whether to truncate the input to satisfy the 'context length limit' on the query and the documents.
:return: Tuple of lists containing the reranked contents, ids, and scores
"""
tasks = [
voyageai_rerank_pure(
self.voyage_client, model, query, contents, ids, top_k, truncation
)
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 voyageai_rerank_pure(
voyage_client: voyageai.AsyncClient,
model: str,
query: str,
documents: List[str],
ids: List[str],
top_k: int,
truncation: bool = True,
) -> Tuple[List[str], List[str], List[float]]:
"""
Rerank a list of contents with VoyageAI rerank models.
:param voyage_client: The Voyage Client to use for reranking
:param model: The model name for VoyageAI 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
:param truncation: Whether to truncate the input to satisfy the 'context length limit' on the query and the documents.
:return: Tuple of lists containing the reranked contents, ids, and scores
"""
rerank_results = await voyage_client.rerank(
model=model,
query=query,
documents=documents,
top_k=top_k,
truncation=truncation,
)
reranked_scores: List[float] = list(
map(lambda x: x.relevance_score, rerank_results.results)
)
indices = list(map(lambda x: x.index, rerank_results.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