retriever.py

import os
from langchain_community.vectorstores import FAISS
from langchain_community.embeddings import HuggingFaceEmbeddings

# Hàm khởi tạo retriever
def load_retriever():
    embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
    retriever = FAISS.load_local("vectorstore", embeddings, allow_dangerous_deserialization=True).as_retriever(search_kwargs={"k": 5})
    return retriever

# Lần đầu load retriever
retriever = load_retriever()

# Hàm reload retriever khi thêm tài liệu
def reload_retriever():
    global retriever
    retriever = load_retriever()

# Hàm retrieve_docs để lấy tài liệu
def retrieve_docs(query):
    return retriever.get_relevant_documents(query)