Convert personality txt files to yaml, replace print with log, and update app.py

ai_ref_kits/agentic_llm_rag/app.py

@@ -149,7 +149,7 @@ def _handle_user_message(user_message, history):
 
 
     def _generate_response(chat_history, log_history):
-        print("log_history", log_history)
+        log.info(f"log_history {log_history}")
         if not isinstance(log_history, list):
             log_history = []
 
@@ -283,7 +283,7 @@ def run():
     parser.add_argument("--chat_model", type=str, default="model/llama3.1-8B-INT4", help="Path to the chat model directory")
     parser.add_argument("--embedding_model", type=str, default="model/bge-large-FP32", help="Path to the embedding model directory")
     parser.add_argument("--rag_pdf", type=str, default="test_painting_llm_rag.pdf", help="Path to a RAG PDF file with additional knowledge the chatbot can rely on.")
-    parser.add_argument("--personality", type=str, default="paint_concierge_personality.txt", help="Path to the TXT file with chatbot personality")
+    parser.add_argument("--personality", type=str, default="paint_concierge_personality.yaml", help="Path to the yaml file with chatbot personality")
 
     args = parser.parse_args()
 
@@ -299,7 +299,7 @@ def run():
     # Step 4: Load documents and create the VectorStoreIndex
     text_example_en_path = Path(args.rag_pdf)
     index = load_documents(text_example_en_path)
-    print("loading in", index)
+    log.info(f"loading in {index}")
     vector_tool = QueryEngineTool(
         index.as_query_engine(streaming=True),
         metadata=ToolMetadata(
@@ -317,7 +317,7 @@ def run():
         chatbot_config = f.read()
 
     react_system_prompt = PromptTemplate(chatbot_config)
-    print("react_system_prompt", react_system_prompt)
+    log.info(f"react_system_prompt {react_system_prompt}")
     #Define agent and available tools
     agent = ReActAgent.from_tools([multiply_tool, divide_tool, add_tool, subtract_tool, paint_cost_calculator, vector_tool],
                                   llm=llm,

ai_ref_kits/agentic_llm_rag/paint_concierge_personality.txt → ai_ref_kits/agentic_llm_rag/paint_concierge_personality.yaml

@@ -26,4 +26,4 @@
     Do not refer to any external documents directly unless they are a part of the provided RAG document.
     Do not ask for or store any personal information from the customer.
     Ensure every interaction remains ethical and within the guidelines of professional and courteous retail service.'
-"""
+"""

ai_ref_kits/agentic_llm_rag/store_employee_personality.txt → ai_ref_kits/agentic_llm_rag/store_employee_personality.yaml

@@ -27,4 +27,4 @@
     Do not refer to any external documents directly unless they are a part of the provided RAG document.
     Do not ask for or store any personal information from the customer.
     Ensure every interaction remains ethical and within the guidelines of professional and courteous retail service.'
-"""
+"""