Update app.py

app.py

@@ -64,13 +64,15 @@ def get_document_text(uploaded_files):
 
 # Split text into chunks
 def get_chunks(documents):
-    text_splitter = CharacterTextSplitter(separator="\n", chunk_size=1000, chunk_overlap=200, length_function=len)
-    return [chunk for doc in documents for chunk in text_splitter.split_text(doc.page_content)]
-
+    text_splitter = CharacterTextSplitter(separator="\n", chunk_size=600, chunk_overlap=200, length_function=len)
+    chunks = [chunk for doc in documents for chunk in text_splitter.split_text(doc.page_content)]
+    return chunks
 # Create vectorstore
+
 def get_vectorstore(chunks):
     embeddings = OpenAIEmbeddings()
-    return FAISS.from_texts(texts=chunks, embedding=embeddings)
+    vectorstore = FAISS.from_texts(texts=chunks, embedding=embeddings)
+    return vectorstore
 
 # Create a conversational chain
 def get_conversationchain(vectorstore):
@@ -78,7 +80,7 @@ def get_conversationchain(vectorstore):
     memory = ConversationBufferMemory(memory_key='chat_history', return_messages=True)
     conversation_chain = ConversationalRetrievalChain.from_llm(
         llm=llm,
-        retriever=vectorstore.as_retriever(search_type="similarity",search_kwargs={"k": 20}),
+        retriever=vectorstore.as_retriever(search_type="similarity",search_kwargs={"k": 10}),
         condense_question_prompt=CUSTOM_QUESTION_PROMPT,
         memory=memory,  
         combine_docs_chain_kwargs={'prompt': prompt}
@@ -100,6 +102,25 @@ def handle_question(question):
         else:
             st.markdown(f"**Bot:** {msg.content}")
 
+def handle_question(question):
+    if not st.session_state.conversation:
+        st.warning("Please process your documents first.")
+        return
+    
+    # Get the response from the conversation chain
+    response = st.session_state.conversation({'question': question})
+    
+    # Update chat history
+    st.session_state.chat_history = response['chat_history']
+    
+    # Display chat history
+    for i, msg in enumerate(st.session_state.chat_history):
+        if i % 2 == 0:
+            st.markdown(f"**You:** {msg.content}")
+        else:
+            st.markdown(f"**Bot:** {msg.content}")
+
+
 # Main Streamlit app
 def main():
     st.set_page_config(page_title="Chat with Documents", page_icon="📚")