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import os
from typing import List
from loguru import logger as log
from sentence_transformers import CrossEncoder
from rag.generator.prompt import Prompt
from rag.retriever.memory import Log
from rag.retriever.rerank.abstract import AbstractReranker
class Reranker(metaclass=AbstractReranker):
def __init__(self) -> None:
self.model = CrossEncoder(os.environ["RERANK_MODEL"])
self.top_k = int(os.environ["RERANK_TOP_K"])
self.relevance_threshold = float(os.environ["RETRIEVER_RELEVANCE_THRESHOLD"])
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) > self.relevance_threshold, 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
def rank_memory(self, prompt: Prompt, history: List[Log]) -> List[Log]:
if history:
results = self.model.rank(
query=prompt.query,
documents=[m.bot.message for m in history],
return_documents=False,
top_k=self.top_k,
)
ranking = list(
filter(
lambda x: x.get("score", 0.0) > self.relevance_threshold, results
)
)
log.debug(
f"Reranking gave {len(ranking)} relevant messages of {len(history)}"
)
history = [history[r.get("corpus_id", 0)] for r in ranking]
return history
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