similarity.py

from langchain_huggingface import HuggingFaceEmbeddings
from langchain_community.vectorstores import FAISS ,Chroma

model_name = "intfloat/multilingual-e5-large"
load_from_dir = "Hadith_Chroma_db"

embedding_llm = HuggingFaceEmbeddings(model_name=model_name)

loaded_vector_db = Chroma(
    persist_directory=load_from_dir,
    embedding_function=embedding_llm
)

def get_similar_docs(query):
    similar_docs = loaded_vector_db.similarity_search(query ,k =2)
    return similar_docs