Update app.py

app.py

@@ -9,6 +9,59 @@ from langchain.memory import ConversationBufferMemory as MEM,RedisChatMessageHis
 from langchain.schema import SystemMessage as SM,HumanMessage as HM, AIMessage as AM
 from langchain import hub
 import os
+from langchain_core.prompts.chat import ChatPromptTemplate, MessagesPlaceholder
+system = '''Respond to the human as helpfully and accurately as possible. You have access to the following tools:
+
+{tools}
+
+Use a json blob to specify a tool by providing an action key (tool name) and an action_input key (tool input).
+
+Valid "action" values: "Final Answer" or {tool_names}
+
+Provide only ONE action per $JSON_BLOB, as shown:
+
+```
+{{
+  "action": $TOOL_NAME,
+  "action_input": $INPUT
+}}
+```
+
+Follow this format:
+
+Question: input question to answer
+Thought: consider previous and subsequent steps
+Action:
+```
+$JSON_BLOB
+```
+Observation: action result
+... (repeat Thought/Action/Observation N times)
+Thought: I know what to respond
+Action:
+```
+{{
+  "action": "Final Answer",
+  "action_input": "Final response to human"
+}}
+
+Begin! Reminder to ALWAYS respond with a valid json blob of a single action. Use tools if necessary. Respond directly if appropriate. Format is Action:```$JSON_BLOB```then Observation'''
+
+human = '''
+
+{input}
+
+{agent_scratchpad}
+
+(reminder to respond in a JSON blob no matter what)'''
+
+prompt = ChatPromptTemplate.from_messages(
+    [
+        ("system", system),
+        MessagesPlaceholder("chat_history", optional=True),
+        ("human", human),
+    ]
+)
 from langchain.retrievers import WikipediaRetriever as Wiki
 import gradio as gr
 chatbot = gr.Chatbot(
@@ -41,7 +94,7 @@ def chat(message,
 
     messages.append(HM(content=message))
     memory=MEM(memory_key="history")
-    agent=Ex(agent=Agent(llm=llm,tools=tools),tools=tools,memory=memory,verbose=True,handle_parsing_errors=True)
+    agent=Ex(agent=Agent(llm=llm,tools=tools,prompt=prompt),tools=tools,memory=memory,verbose=True,handle_parsing_errors=True)
     yield agent.invoke(messages)
 ai1=gr.ChatInterface(
     chat,