Update app.py

app.py

@@ -1,4 +1,4 @@
-from langchain.agents import AgentType, Tool, initialize_agent
+from langchain.agents import AgentType, Tool, initialize_agent,AgentExecutor, create_react_agent
 from langchain.callbacks import StreamlitCallbackHandler
 from langchain.chains import RetrievalQA
 from langchain.chains.conversation.memory import ConversationBufferMemory
@@ -20,6 +20,8 @@ serp_token = os.environ['SERP_TOKEN']
 repo_id = "sentence-transformers/all-mpnet-base-v2"
 from datetime import datetime
 from langchain.tools import Tool
+from langchain import hub
+prompt = hub.pull("hwchase17/react")
 
 
 
@@ -135,22 +137,25 @@ def main():
         mode = "Agent with AskHuman tool"
 
         # initialize agent
-        agent = initialize_agent(
-            agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
-                    tools=[db_search_tool,search_tool,datetime_tool],
-            llm=llm,
-            verbose=True,
-            max_iterations=5,
-            early_stopping_method="generate",
-            memory=conversational_memory,
-            agent_kwargs={
-                "prefix": prefix,
-                "format_instructions": format_instructions,
-                "suffix": suffix,
-            },
-            handle_parsing_errors=True,
+        # agent = initialize_agent(
+        #     agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
+        #             tools=[db_search_tool,search_tool,datetime_tool],
+        #     llm=llm,
+        #     verbose=True,
+        #     max_iterations=5,
+        #     early_stopping_method="generate",
+        #     memory=conversational_memory,
+        #     agent_kwargs={
+        #         "prefix": prefix,
+        #         "format_instructions": format_instructions,
+        #         "suffix": suffix,
+        #     },
+        #     handle_parsing_errors=True,
 
         )
+        agent = create_react_agent(model = llm, tools=[db_search_tool,search_tool,datetime_tool], prompt = prompt)
+        tools = [db_search_tool,search_tool,datetime_tool]
+        agent_executor = AgentExecutor(agent=agent, tools=tools)
 
         # question form
         with st.form(key="form"):
@@ -162,7 +167,8 @@ def main():
         if submit_clicked:
             # st_callback = StreamlitCallbackHandler(st.container())
             # response = agent.run(user_input,callbacks = [st_callback])
-            response = agent.run(user_input)
+            response = agent_executor.invoke({"input": user_input})
+            # response = agent.run(user_input)
             st.write(response)
             # output_container = output_container.container()
             # output_container.chat_message("user").write(user_input)