Update agent.py

agent.py

@@ -15,35 +15,10 @@ class ReActAgent:
     Initializes the agent with default tools, OpenAI LLM, and an empty history.
     """
     self.tools = [TavilySearchResults(max_results=1)]
-    self.prompt = '''
-    Answer the following questions as best you can, considering the conversation history:
-
-    {history}
-
-    You have access to the following tools:
-
-    {tools}
-
-    Use the following format:
-
-    Question: the input question you must answer
-    Thought: you should always think about what to do
-    Action: the action to take (use history first, then tools if needed)
-    Action Input: the input to the action
-    Observation: the result of the action
-    ... (this Thought/Action/Action Input/Observation can repeat N times)
-    Thought: I now know the final answer
-    Final Answer: the final answer to the original input question
-
-    Begin!
-
-    Question: {input}
-    Thought:{agent_scratchpad}
-
-    '''
+    self.prompt = hub.pull("hwchase17/react")
     self.llm = OpenAI()
   
-  def create_agent(self, history):
+  def create_agent(self):
     """
     Creates a ReAct agent based on the defined prompt, LLM, and history.
     """
@@ -54,8 +29,8 @@ class ReActAgent:
     """
     Executes the agent with the provided question, verbosity option, and updates history.
     """
-    agent = self.create_agent(history)
+    agent = self.create_agent()
     agent_executor = AgentExecutor(agent=agent, tools=self.tools, verbose=verbose)
-    answer = agent_executor.invoke({"input": question, "history": history})
+    answer = agent_executor.invoke({"input": question})
     return answer