Create app.py

app.py

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+from langchain_openai import ChatOpenAI
+from langchain_community.tools import DuckDuckGoSearchRun
+
+class BasicAgent:
+    def __init__(self):
+        print("SmartAgent initialized.")
+        self.llm = ChatOpenAI(model="gpt-4", temperature=0)
+        self.search_tool = DuckDuckGoSearchRun()
+    
+    def __call__(self, question: str) -> str:
+        print(f"Agent received question: {question[:50]}...")
+
+        # Step 1: Ask LLM if it can confidently answer or needs to search
+        prompt = f"""
+You are a helpful assistant. Given the question below, decide if you know the answer with high confidence.
+If yes, answer concisely.
+If you need more information, respond with SEARCH: followed by the search query.
+Question: {question}
+Answer or SEARCH:
+"""
+        response = self.llm.invoke(prompt).content.strip()
+
+        if response.upper().startswith("SEARCH:"):
+            # Extract search query
+            query = response[len("SEARCH:"):].strip()
+            print(f"Performing search with query: {query}")
+            
+            # Use DuckDuckGo search to get info
+            search_result = self.search_tool.run(query)
+            
+            # Step 2: Now use LLM again with search results to answer
+            prompt2 = f"""
+You are a helpful assistant. Use the following search results to answer the question concisely.
+Question: {question}
+Search Results: {search_result}
+Answer:
+"""
+            final_answer = self.llm.invoke(prompt2).content.strip()
+            print(f"Agent final answer: {final_answer}")
+            return final_answer
+        
+        else:
+            # LLM confident answer directly
+            print(f"Agent direct answer: {response}")
+            return response