print prompt template

app.py

@@ -11,6 +11,7 @@ from langchain.vectorstores import Pinecone
 from langchain.agents import initialize_agent
 from langchain.agents import AgentType
 from langchain.agents import Tool
+from langchain.agents import load_tools
 from langchain.tools import BaseTool
 from langchain.tools import DuckDuckGoSearchRun
 from langchain.utilities import WikipediaAPIWrapper
@@ -74,15 +75,17 @@ chat = AzureChatOpenAI(
 )
 llm = chat
 
-llm_math = LLMMathChain(llm = llm)
+# llm_math = LLMMathChain(llm = llm)
 
-math_tool = Tool(
-    name ='Calculator',
-    func = llm_math.run,
-    description ='Useful for when you need to answer questions about math.'
-)
+# math_tool = Tool(
+#     name ='Calculator',
+#     func = llm_math.run,
+#     description ='Useful for when you need to answer questions about math.'
+# )
 
-tools = [DB_Search(), duckduckgo_tool, python_tool, math_tool]
+tools = [DB_Search(), duckduckgo_tool, python_tool]
+
+tools = load_tools(["llm-math"], llm=llm)
 
 embeddings = OpenAIEmbeddings(deployment="model_embedding", chunk_size=15)
 
@@ -104,6 +107,8 @@ agent = initialize_agent(tools, llm,
                          verbose = True,
                          handle_parsing_errors = True)
 
+print(agent.agent.llm_chain.prompt.template)
+
 global vectordb
 vectordb = Chroma(persist_directory='db', embedding_function=embeddings)
 global vectordb_p