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import os
from abc import abstractmethod
from typing import Type
import cohere
from loguru import logger as log
from sentence_transformers import CrossEncoder
from rag.generator.prompt import Prompt
class AbstractReranker(type):
_instances = {}
def __call__(cls, *args, **kwargs):
if cls not in cls._instances:
instance = super().__call__(*args, **kwargs)
cls._instances[cls] = instance
return cls._instances[cls]
@abstractmethod
def rank(self, prompt: Prompt) -> Prompt:
return prompt
class Reranker(metaclass=AbstractReranker):
def __init__(self) -> None:
self.model = CrossEncoder(os.environ["RERANK_MODEL"])
self.top_k = int(os.environ["RERANK_TOP_K"])
def rank(self, prompt: Prompt) -> Prompt:
if prompt.documents:
results = self.model.rank(
query=prompt.query,
documents=[d.text for d in prompt.documents],
return_documents=False,
top_k=self.top_k,
)
ranking = list(filter(lambda x: x.get("score", 0.0) > 0.5, results))
log.debug(
f"Reranking gave {len(ranking)} relevant documents of {len(prompt.documents)}"
)
prompt.documents = [
prompt.documents[r.get("corpus_id", 0)] for r in ranking
]
return prompt
class CohereReranker(metaclass=AbstractReranker):
def __init__(self) -> None:
self.client = cohere.Client(os.environ["COHERE_API_KEY"])
self.top_k = int(os.environ["RERANK_TOP_K"])
def rank(self, prompt: Prompt) -> Prompt:
if prompt.documents:
response = self.client.rerank(
model="rerank-english-v3.0",
query=prompt.query,
documents=[d.text for d in prompt.documents],
top_n=self.top_k,
)
ranking = list(filter(lambda x: x.relevance_score > 0.5, response.results))
log.debug(
f"Reranking gave {len(ranking)} relevant documents of {len(prompt.documents)}"
)
prompt.documents = [prompt.documents[r.index] for r in ranking]
return prompt
def get_reranker(model: str) -> Type[AbstractReranker]:
match model:
case "local":
return Reranker()
case "cohere":
return CohereReranker()
case _:
exit(1)
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