Update app.py

app.py

@@ -16,8 +16,7 @@ import pandas as pd
 
 
 # LLM Langchain Definition
-
-OPENAI_API_KEY = st.secrets["azure_api_key"]
+OPENAI_API_KEY = "86b631a9c0294e9698e327c59ff5ac2c"
 OPENAI_API_TYPE = "azure"
 OPENAI_API_BASE = "https://davidfearn-gpt4.openai.azure.com"
 # OPENAI_API_VERSION = "2024-02-01"
@@ -71,6 +70,8 @@ temp1 = st.sidebar.slider("Temperature", min_value=0.0, max_value=1.0, step=0.1,
 tokens1 = st.sidebar.slider("Tokens", min_value=0, max_value=4000, step=100, value=500, key='persona1_tokens')
 st.sidebar.subheader("Number of Search Results")
 k = st.sidebar.slider("Returned Docs", min_value=1, max_value=10, step=1, value=3, key='k')
+pagesReturned = st.sidebar.slider("Number of Returned Document", min_value=1, max_value=10, step=1, value=1, key='pagesReturned')
+
 
 st.sidebar.header("Engineered Prompt Config")
 persona2SystemMessage = st.sidebar.text_area("Answer Creation System Message", value=placeHolderPersona1, height=300)
@@ -106,29 +107,29 @@ if user_query is not None and user_query != "":
 
     with st.chat_message("AI"):
         qte = qt(persona1SystemMessage, st.session_state.chat_history, temp1, tokens1)
-        knowledge = search_and_reconstruct(qte, k)
+        knowledge = search_and_reconstruct(qte, k, pagesReturned)
         response = st.write_stream(get_response(st.session_state.chat_history, qte, knowledge, temp1, temp2, tokens1, tokens2, persona2SystemMessage, persona2UserMessage))
 
     st.session_state.chat_history.append(AIMessage(content=response))
     st.sidebar.header("QTE and Knowledge Results")
-    st.sidebar.subheader("Transformed Query")
+    st.sidebar.header("QTE")
     st.sidebar.text(qte)
-
+    
     if knowledge:
-            
+        
         # Prepare the data for the table
         table_data = {
             "Title": [entry['Title'] for entry in knowledge],
             "Score (%)": [f"{int(entry.get('Score', 0) * 100)}%" for entry in knowledge],  # Convert to percentage and remove decimals
             "Page": [entry['PageNumber'] for entry in knowledge],
             # "Grounding Text": [entry['ReconstructedText'] for entry in knowledge]
-            }
+        }
+
+        # Create a dataframe for displaying as a table
     
-            # Create a dataframe for displaying as a table
-        
         df = pd.DataFrame(table_data)
-    
-            # Display the table in the sidebar
+
+        # Display the table in the sidebar
         st.sidebar.write("### Knowledge Base Results")
         st.sidebar.dataframe(df)  # Adjust height as needed
     else: