Update app.py

app.py

@@ -60,12 +60,16 @@ def get_text_chunks(text):
     return chunks
 
 def create_conversational_rag_chain(vectorstore):
-    retriever = retriever_from_chroma(docs, hf, "mmr", 6)
+    
+    script_dir = os.path.dirname(os.path.abspath(__file__))
+    model_path = os.path.join(script_dir, 'qwen2-0_5b-instruct-q4_0.gguf')
+    
+    retriever = vectorstore.as_retriever(search_type='mmr', search_kwargs={"k": 7})
 
     callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])
 
     llm = llamacpp.LlamaCpp(
-        model_path=os.path.join(script_dir, 'qwen2-0_5b-instruct-q4_0.gguf'),
+        model_path=os.path.join(model_path),
         n_gpu_layers=1,
         temperature=0.1,
         top_p=0.9,
@@ -110,8 +114,19 @@ def create_conversational_rag_chain(vectorstore):
 def main():
     """Main function for the Streamlit app."""
     # Initialize chat history if not already present in session state
+    script_dir = os.path.dirname(os.path.abspath(__file__))
+    data_path = os.path.join(script_dir, "data/")
+
+    
+    
+    documents = []
+    documents = load_txt_documents()
+    docs = split_docs(documents, 350, 40)
+
+    vectorstore = get_vectorstore(docs)
+    
     msgs = st.session_state.get("chat_history", StreamlitChatMessageHistory(key="special_app_key"))
-    chain_with_history = create_conversational_rag_chain()
+    chain_with_history = create_conversational_rag_chain(vectorstore)
 
     st.title("Conversational RAG Chatbot")