retriever.py

import pickle
from langchain.retrievers import EnsembleRetriever
from langchain.vectorstores import FAISS
from langchain.embeddings import GooglePalmEmbeddings
from langchain.memory import ConversationBufferMemory




def retriever():

    embeddings = GooglePalmEmbeddings(google_api_key= google_api_key)
    

    #to read bm25 object
    with open('./bm25', 'rb') as file:
        bm25_retriever = pickle.load(file)

    bm25_retriever.k = 2

    # Load FAISS 
    faiss_vectorstore = FAISS.load_local("./vector_db/", embeddings)
    faiss_retriever = faiss_vectorstore.as_retriever(search_kwargs={"k": 1})

    # initialize the ensemble retriever
    return  EnsembleRetriever( retrievers=[bm25_retriever, faiss_retriever], weights=[0.5, 0.5] )