Update app.py

app.py

@@ -1,19 +1,19 @@
-import streamlit as st
 import os
+import streamlit as st
 from dotenv import load_dotenv
 from langchain_groq import ChatGroq
+from langchain_community.vectorstores import FAISS
 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
 from langchain.chains import create_retrieval_chain
-from langchain_community.vectorstores import FAISS
 from langchain_community.document_loaders import PyPDFDirectoryLoader
 
 load_dotenv()
-groq_api_key = os.getenv("GROQ_API_KEY")
+groq_api_key = os.getenv('GROQ_API_KEY')
 
-model = ChatGroq(groq_api_key=groq_api_key, model="Llama3-8b-8192")
+model = ChatGroq(groq_api_key=groq_api_key, model='Llama3-8b-8192')
 
 prompt = ChatPromptTemplate.from_template(
 """
@@ -26,26 +26,36 @@ Question:{input}
 """
 )
 
-st.title("Simple RAG Application")
+st.set_page_config(page_title = 'Simple RAG', page_icon = '⛓️', initial_sidebar_state = 'collapsed')
+
+st.sidebar.header('About')
+st.sidebar.caption('Embeddings: Craig/paraphrase-MiniLM-L6-v2\n\
+VectorDB: FAISS\nLLM:Llama3-8b-8192')
+
+st.title('Simple RAG Application')
+
+st.warning('This is a simple RAG demonstration application. It uses open-source models for embeddings and \
+inference. So it can be slow and ineffecient.', icon='⚠️')
+
 
 def create_vector_embedding():
-    if "vectors" not in st.session_state:
-        st.session_state.embeddings = HuggingFaceEmbeddings(model_name="Craig/paraphrase-MiniLM-L6-v2")
-        st.session_state.loader = PyPDFDirectoryLoader("documents")
+    if 'vectors' not in st.session_state:
+        st.session_state.embeddings = HuggingFaceEmbeddings(model_name='Craig/paraphrase-MiniLM-L6-v2')
+        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.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..."):
+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()
 
-if "vectors" in st.session_state:
-    user_prompt = st.text_input("Enter your query here")
+if 'vectors' in st.session_state:
+    user_prompt = st.text_input('Enter your query here')
     if user_prompt:
         document_chain = create_stuff_documents_chain(model, prompt)
         retriever = st.session_state.vectors.as_retriever()
@@ -53,7 +63,7 @@ if "vectors" in st.session_state:
         response = retrieval_chain.invoke({'input': user_prompt})
         st.write(response['answer'])
 
-        with st.expander("Context"):
+        with st.expander('Context'):
             for i, doc in enumerate(response['context']):
                 st.write(doc.page_content)
-                st.write("\n\n")
+                st.write('\n\n')