return langchain messages from predict

app.py

@@ -172,6 +172,8 @@ def predict(message, history, request: gr.Request):
         }
     )
 
+    messages.append(AIMessage(content=gpt_response.content))
+
     raw_source_lines = [
     f"{i+1} : {doc.metadata["source"].split("/")[-1]} (page {doc.metadata['page_label']}/{doc.metadata['total_pages']})\n---"
     for i, doc in enumerate(relevant_docs)]
@@ -184,10 +186,11 @@ def predict(message, history, request: gr.Request):
             unique_source_lines.append(line)
     source_context = "\nSources:\n" + "\n".join(unique_source_lines)
 
+    messages.append(AIMessage(content=source_context))
     print(gpt_response.content )
     print(source_context)
     
-    return WELCOME_TEXT + "\n\n" + gpt_response.content + "\n\n" + source_context
+    return messages
 
 
 # setup tracking