Update app.py

app.py

@@ -10,6 +10,7 @@ from langchain_community.retrievers import PineconeHybridSearchRetriever
 from langchain.tools.retriever import create_retriever_tool
 from langgraph.prebuilt import create_react_agent
 
+LANGCHAIN_API_KEY = os.environ['LANGCHAIN_API_KEY']
 
 os.environ['LANGCHAIN_TRACING_V2'] = "true"
 
@@ -197,7 +198,7 @@ def init_agent(namespace1: str, top_k1: int, namespace2: str, top_k2: int):
 st.sidebar.header("Retriever Tool Settings")
 
 # Dropdown and slider for Retriever Tool 1 (empty option available)
-namespace_options = ["langgraph-main", "autogen", ""]
+namespace_options = ["langgraph-main", "autogen", "llm-cli", ""]
 namespace1 = st.sidebar.selectbox("Select namespace for Retriever Tool 1:", namespace_options, index=0)
 top_k1 = st.sidebar.slider("Select top_k for Retriever Tool 1:", min_value=1, max_value=4, value=1, step=1)
 
@@ -242,40 +243,52 @@ with st.form("chat_form", clear_on_submit=True):
 if st.session_state.chat_history and st.session_state.chat_history[-1][0] == "user":
     inputs = {"messages": st.session_state.chat_history}
 
-    # Create separate placeholders for tool output and final answer.
+    # Create separate placeholders for each section.
+    tool_calls_placeholder = st.empty()
     tool_output_placeholder = st.empty()
     final_answer_placeholder = st.empty()
 
     # Accumulators for each section.
+    tool_calls_text = ""
     tool_output_text = ""
     final_answer_text = ""
 
     # Stream the agent's response chunk-by-chunk.
     for s in graph.stream(inputs, stream_mode="values"):
-        # Get the last message in the chunk.
+        # Extract the last message from the current chunk.
         message = s["messages"][-1]
         
         if isinstance(message, tuple):
-            # This is a tool output message (e.g., retrieved docs or tool usage).
+            # This is a tool-related message.
+            # We use a simple heuristic: if the text contains "call_" (case-insensitive), we treat it as a tool call.
             role, text = message
-            tool_output_text += text
-            # Update the tool output area with a heading and the accumulated text.
-            tool_output_placeholder.markdown(
-                f"### Tool Output\n\n{tool_output_text}",
-                unsafe_allow_html=True
-            )
+            if "call_" in text.lower():
+                tool_calls_text += text + "\n\n"
+                tool_calls_placeholder.markdown(
+                    f"### Tool Calls\n\n{tool_calls_text}",
+                    unsafe_allow_html=True
+                )
+            else:
+                tool_output_text += text + "\n\n"
+                tool_output_placeholder.markdown(
+                    f"### Tool Output\n\n{tool_output_text}",
+                    unsafe_allow_html=True
+                )
         else:
             # This is the final answer generated by the AI.
             text = message.content
-            final_answer_text += text
-            # Update the final answer area with a heading and the accumulated text.
+            final_answer_text += text + "\n\n"
             final_answer_placeholder.markdown(
                 f"### Final Answer\n\n{final_answer_text}",
                 unsafe_allow_html=True
             )
 
-    # Once complete, combine both sections into the chat history.
-    combined_response = f"**Tool Output:**\n\n{tool_output_text}\n\n**Final Answer:**\n\n{final_answer_text}"
+    # Once complete, combine all sections into one record for persistence.
+    combined_response = (
+        f"**Tool Calls:**\n\n{tool_calls_text}\n\n"
+        f"**Tool Output:**\n\n{tool_output_text}\n\n"
+        f"**Final Answer:**\n\n{final_answer_text}"
+    )
     st.session_state.chat_history.append(("assistant", combined_response))