Create agent.py

agent.py

@@ -0,0 +1,49 @@
+from langchain_core.messages import HumanMessage, AIMessage
+from langchain_community.tools import DuckDuckGoSearchRun
+from langchain.prompts import PromptTemplate
+from langchain_community.chat_models import ChatOpenAI
+from langchain.chains import LLMChain
+import os
+
+class AdvancedAgent:
+    def __init__(self):
+        print("AdvancedAgent initialized.")
+        # Initialize the language model (assumes OPENAI_API_KEY is set)
+        try:
+            self.llm = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0.7)
+        except Exception as e:
+            print(f"Error initializing LLM: {e}")
+            raise e
+
+        # Initialize web search tool
+        self.search_tool = DuckDuckGoSearchRun()
+
+        # Define prompt template for answering questions
+        self.prompt_template = PromptTemplate(
+            input_variables=["question", "context"],
+            template="""
+            You are an intelligent agent designed to answer test questions accurately.
+            Use the provided context (if any) to inform your answer.
+            If the context is insufficient, rely on your knowledge or indicate if further information is needed.
+            Question: {question}
+            Context: {context}
+            Answer: 
+            """
+        )
+        self.chain = LLMChain(llm=self.llm, prompt=self.prompt_template)
+
+    def __call__(self, question: str) -> str:
+        print(f"Agent processing question (first 50 chars): {question[:50]}...")
+        try:
+            # Perform a web search to gather context
+            search_query = f"{question}"
+            search_results = self.search_tool.run(search_query)
+            context = search_results[:1000]  # Limit context length
+
+            # Generate answer using LLM and context
+            response = self.chain.run(question=question, context=context)
+            print(f"Agent generated answer: {response[:50]}...")
+            return response.strip()
+        except Exception as e:
+            print(f"Error processing question: {e}")
+            return f"Error: Unable to process question - {e}"