Update app.py

app.py

@@ -42,12 +42,10 @@ def query_index(index, similarity_top_k=3, streaming=True):
                 Given this context, please respond to the following user query:
                 {query_str}
                 Your response should:
-
                 Directly address the query using information from the context
                 Include relevant code examples or direct quotes if applicable
                 Mention specific sections or pages of the documentation
                 Highlight any best practices or potential pitfalls related to the query
-
                 After your response, suggest 3 follow-up questions based on the context that the user might find helpful for deeper understanding.
                 Your response:"""
         ),
@@ -66,6 +64,16 @@ def ingest_documents(url):
 # Streamlit app
 st.title("Talk to Software Documentation")
 
+st.markdown("""
+This tool allows you to chat with software documentation. Here's how to use it:
+
+1. Enter the URL of the documentation you want to chat about.
+2. Click the "Ingest and Setup" button to crawl the documentation and set up the query engine.
+3. Once setup is complete, enter your query in the text box.
+4. Click "Search" to get a response based on the documentation.
+5. View your chat history in the sidebar.
+""")
+
 # Initialize session state
 if 'chat_engine' not in st.session_state:
     st.session_state['chat_engine'] = None
@@ -76,32 +84,23 @@ if 'chat_history' not in st.session_state:
 if 'last_response' not in st.session_state:
     st.session_state['last_response'] = None
 
-
 # URL input for document ingestion
 url = st.text_input("Enter URL to crawl and ingest documents:")
 
-# Ingest documents button
-if st.button("Ingest Documents"):
+# Combined Ingest and Setup button
+if st.button("Ingest and Setup"):
     if url:
-        with st.spinner("Crawling and ingesting documents..."):
+        with st.spinner("Crawling, ingesting documents, and setting up query engine..."):
             st.session_state['documents'] = ingest_documents(url)
-        st.success(f"Documents ingested from {url}")
-    else:
-        st.error("Please enter a URL")
-
-# Setup button
-if st.button("Setup Query Engine"):
-    if st.session_state['documents'] is None:
-        st.error("Please ingest documents first")
-    else:
-        with st.spinner("Setting up query engine..."):
             embed_setup()
             client = qdrant_setup()
             llm = llm_setup()
             vector_store = QdrantVectorStore(client=client, collection_name=os.getenv("COLLECTION_NAME"))
             index = VectorStoreIndex.from_documents(st.session_state['documents'], vector_store=vector_store)
             st.session_state['chat_engine'] = query_index(index)
-        st.success("Query engine setup completed successfully!")
+        st.success(f"Documents ingested from {url} and query engine setup completed successfully!")
+    else:
+        st.error("Please enter a URL")
 
 # Query input
 query = st.text_input("Enter your query:")
@@ -124,12 +123,12 @@ if st.button("Search"):
     else:
         st.error("Please enter a query")
 
-if st.session_state['chat_history']:
-    st.subheader("Chat History")
-    for role, message in st.session_state['chat_history']:
-        st.text(f"{role}: {message}")
+# Sidebar for chat history
+st.sidebar.title("Chat History")
+for role, message in st.session_state['chat_history']:
+    st.sidebar.text(f"{role}: {message}")
 
-# Clear chat history button
-if st.button("Clear Chat History"):
+# Clear chat history button in sidebar
+if st.sidebar.button("Clear Chat History"):
     st.session_state['chat_history'] = []
-    st.success("Chat history cleared!")
+    st.sidebar.success("Chat history cleared!")