Update app.py

app.py

@@ -68,7 +68,7 @@ def get_vectorstore(text_chunks):
 
 
 
-def get_conversation_chain():
+def get_conversation_chain(vectorstore):
     
     callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])
 
@@ -90,33 +90,18 @@ def get_conversation_chain():
 
     
 
-
+    retriever = vectorstore.as_retriever(search_type='mmr', k=7)
 
     
     
     
     prompt = hub.pull("rlm/rag-prompt")
-    rag_chain = prompt  | llm | StrOutputParser()
+    rag_chain = retriever | prompt  | llm | StrOutputParser()
 
 
     return rag_chain
 
-def sidebar():
-    with st.sidebar:
-        st.subheader("Your documents")
-        pdf_docs = st.file_uploader("For Chatbot to get alive, upload your PDFs here and click on 'Process'", accept_multiple_files=True)
-
-        if st.button("Process"):
-            if pdf_docs:
-                with st.spinner("Processing"):
-                    # ... your processing code ...
-                    vectorstore = get_vectorstore(text_chunks)
-                    conversation = get_conversation_chain()
-                st.success("Files have been processed into a vector store.")
-        else:
-            st.write("Kazkas neto")
 
-        return vectorstore, conversation
 
          
     
@@ -141,7 +126,24 @@ def main():
         handle_userinput(user_question, vectorstore, conversation)
     
    
+    with st.sidebar:
+        st.subheader("Your documents")
+        pdf_docs = st.file_uploader(
+            "Upload your PDFs here and click on 'Process'", accept_multiple_files=True)
+        if st.button("Process"):
+            with st.spinner("Processing"):
+                # get pdf text
+                raw_text = get_pdf_text(pdf_docs)
+
+                # get the text chunks
+                text_chunks = get_text_chunks(raw_text)
+
+                # create vector store
+                vectorstore = get_vectorstore(text_chunks)
 
+                # create conversation chain
+                st.session_state.conversation = get_conversation_chain(
+                    vectorstore)
 
     
     
@@ -165,7 +167,7 @@ def handle_userinput(user_question,vectorstore,conversation ):
     st.session_state.chat_history.append({"role": "user", "content": user_question})
     
     
-    retriever = vectorstore.as_retriever(search_type = 'mmr', search_kwargs={"k": 7})
+    retriever = st.session_state.conversation.retriever()
     docs = retriever.invoke(user_question)
     
         
@@ -173,7 +175,7 @@ def handle_userinput(user_question,vectorstore,conversation ):
     doc_txt = [doc.page_content for doc in docs]
     
     # Invoke conversation chain
-    response = conversation.invoke({"context": docs, "question": user_question})
+    response = st.session_state.conversation.invoke({"context": docs, "question": user_question})
     st.session_state.chat_history.append({"role": "assistant", "content": response})
 
     for i, message in enumerate(st.session_state.chat_history):