Update agent.py

agent.py

@@ -2,17 +2,12 @@ import os
 from typing import TypedDict, List, Dict, Any, Optional
 from langgraph.prebuilt import create_react_agent
 from langgraph.graph import StateGraph, START, END
+from langchain.agents import create_tool_calling_agent, AgentExecutor
 from langchain_google_genai import ChatGoogleGenerativeAI
 from langchain_core.tools import tool
 from langchain_core.messages import HumanMessage
 from langchain_core.prompts import ChatPromptTemplate
 
-# %pip install -qU duckduckgo-search langchain-community
-# pip install requests
-# pip install pandas
-# pip install pypdf
-
-
 class AgentState(TypedDict):
     messages: List
     current_question: str
@@ -112,10 +107,13 @@ class BasicAgent:
                 There are few tools provided: web_search, visit_webpage, read_file and image_caption. 
                 Here are few examples demonstrating how to call and use the tools.
         """
-        self.app = self.__graph_compile__()
         tools = [web_search, visit_webpage, read_file, image_caption]
-        # self.model = model.bind_tools(tools) # LLM with tools
-        self.agent = create_react_agent(model, tools)
+        prompt = ChatPromptTemplate.from_messages([
+            ("system", self.sys_prompt),
+            ("human", "Question: {input}")
+        ])
+        self.agent = create_tool_calling_agent(model, tools, prompt)
+        self.agent_exe = AgentExecutor(agent=self.agent, tools=tools, verbose=True)
         print("BasicAgent initialized.")
     
     def __call__(self, question: str) -> str:
@@ -124,14 +122,9 @@ class BasicAgent:
             ("system", self.sys_prompt),
             ("human", f"Question: {question}")
         ]
-        response = self.model.invoke(prompt_msg)
+        # response = self.model.invoke(prompt_msg)
+        response = self.agent_exe.invoke({"input": question})
         fixed_answer = response['message'][-1].content
         # fixed_answer = "This is a default answer."
         print(f"Agent returning fixed answer: {fixed_answer}")
-        return fixed_answer
-    
-    # Maybe we no need this one
-    def __graph_compile__(self):
-        graph = StateGraph(AgentState)
-
-        pass
+        return fixed_answer