Update app.py

app.py

@@ -2,7 +2,7 @@ import streamlit as st
 import os
 from dotenv import load_dotenv
 from langchain_groq import ChatGroq
-from langchain_community.embeddings import HuggingFaceBgeEmbeddings
+from langchain_community.embeddings import HuggingFaceEmbeddings
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain.chains.combine_documents import create_stuff_documents_chain
 from langchain_core.prompts import ChatPromptTemplate
@@ -30,19 +30,19 @@ st.title("Simple RAG Application")
 
 def create_vector_embedding():
     if "vectors" not in st.session_state:
-        st.session_state.embeddings = HuggingFaceBgeEmbeddings(model_name="mxbai-embed-large-v1")
+        st.session_state.embeddings = HuggingFaceEmbeddings(model_name="mxbai-embed-large-v1")
         st.session_state.loader = PyPDFDirectoryLoader("documents")
         st.session_state.docs = st.session_state.loader.load()
         st.session_state.text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
         st.session_state.final_documents = st.session_state.text_splitter.split_documents(st.session_state.docs[:50])
         st.session_state.vectors = FAISS.from_documents(st.session_state.final_documents, st.session_state.embeddings)
+        st.experimental_rerun()
 
 if "vectors" not in st.session_state:
     st.write("The vector store database is not yet ready")
     if st.button("Create"):
         with st.spinner("Working..."):
             create_vector_embedding()
-        st.write("Done")
 
 if "vectors" in st.session_state:
     user_prompt = st.text_input("Enter your query here")