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import os
from dotenv import load_dotenv
from scipy.special import expit, logit
from rerankers import Reranker
from sentence_transformers import CrossEncoder
load_dotenv()
def get_reranker(model = "nano", cohere_api_key = None):
assert model in ["nano","tiny","small","large", "jina"]
if model == "nano":
reranker = Reranker('ms-marco-TinyBERT-L-2-v2', model_type='flashrank')
elif model == "tiny":
reranker = Reranker('ms-marco-MiniLM-L-12-v2', model_type='flashrank')
elif model == "small":
reranker = Reranker("mixedbread-ai/mxbai-rerank-xsmall-v1", model_type='cross-encoder')
elif model == "large":
if cohere_api_key is None:
cohere_api_key = os.environ["COHERE_API_KEY"]
reranker = Reranker("cohere", lang='en', api_key = cohere_api_key)
elif model == "jina":
# Reached token quota so does not work
reranker = Reranker("jina-reranker-v2-base-multilingual", api_key = os.getenv("JINA_RERANKER_API_KEY"))
# marche pas sans gpu ? et anyways returns with another structure donc faudrait changer le code du retriever node
# reranker = CrossEncoder("jinaai/jina-reranker-v2-base-multilingual", automodel_args={"torch_dtype": "auto"}, trust_remote_code=True,)
return reranker
def rerank_docs(reranker,docs,query):
# Get a list of texts from langchain docs
input_docs = [x.page_content for x in docs]
# Rerank using rerankers library
results = reranker.rank(query=query, docs=input_docs)
# Prepare langchain list of docs
docs_reranked = []
for result in results.results:
doc_id = result.document.doc_id
doc = docs[doc_id]
doc.metadata["reranking_score"] = result.score
doc.metadata["query_used_for_retrieval"] = query
docs_reranked.append(doc)
return docs_reranked |