retriever.py

from langchain_core.runnables import RunnablePassthrough
from langchain_core.output_parsers import StrOutputParser
from langchain_community.chat_models import ChatOllama
from langchain_core.prompts import ChatPromptTemplate
from langchain_pinecone import PineconeVectorStore
from langchain_community.embeddings import SentenceTransformerEmbeddings

import os
from dotenv import load_dotenv
from langchain_community.retrievers import BM25Retriever
from langchain.retrievers import EnsembleRetriever
from kiwipiepy import Kiwi
load_dotenv()

kiwi = Kiwi()

def kiwi_tokenize(text):
    return [token.form for token in kiwi.tokenize(text)]
# embedding_model = SentenceTransformerEmbeddings(model_name='BM-K/KoSimCSE-roberta-multitask', model_kwargs={"trust_remote_code":True}) 

def retriever(pc, bm25):
    pcretriever = pc.as_retriever(search_kwargs={'k':4})
    kiwi_bm25 = BM25Retriever.from_documents(bm25,preprocess_func=kiwi_tokenize)
    kiwi_bm25.k=4
    
    kiwibm25_pc_37 = EnsembleRetriever(
        retrievers=[kiwi_bm25, pcretriever],  # 사용할 검색 모델의 리스트
        weights=[0.3, 0.7],  # 각 검색 모델의 결과에 적용할 가중치
        search_type="mmr",  # 검색 결과의 다양성을 증진시키는 MMR 방식을 사용
    ) 
        # Pinecone vector store 초기화  
    # vectorstore = PineconeVectorStore(  
    # index_name=os.getenv("INDEX_NAME"), embedding=embedding_model
    # )  

    # retriever = vectorstore.as_retriever(search_kwargs={'k': 2})

    return kiwibm25_pc_37