Update app.py

app.py

@@ -13,7 +13,7 @@ from langchain_community.document_loaders import PyPDFDirectoryLoader
 load_dotenv()
 groq_api_key = os.getenv("GROQ_API_KEY")
 
-llm = 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(
 """
@@ -30,7 +30,7 @@ st.title("Simple RAG Application")
 
 def create_vector_embedding():
     if "vectors" not in st.session_state:
-        st.session_state.embeddings = HuggingFaceBgeEmbeddings(model_name="all-MiniLM-L6-v2")
+        st.session_state.embeddings = HuggingFaceBgeEmbeddings(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)
@@ -47,7 +47,7 @@ if "vectors" not in st.session_state:
 if "vectors" in st.session_state:
     user_prompt = st.text_input("Enter your query here")
     if user_prompt:
-        document_chain = create_stuff_documents_chain(llm, prompt)
+        document_chain = create_stuff_documents_chain(model, prompt)
         retriever = st.session_state.vectors.as_retriever()
         retrieval_chain = create_retrieval_chain(retriever, document_chain)
         response = retrieval_chain.invoke({'input': user_prompt})