update

agent_utils.py

@@ -112,3 +112,29 @@ def handle_file_tool(input: dict) -> str:
     else:
         return f"Unsupported file type: .{file_ext}"
 
+@tool
+def add(a: float, b: float):
+  """calculate summation of two numbers"""
+  return a + b
+
+@tool
+def subtract(a: float, b: float):
+  """calculate subtraction of two numbers"""
+  return a - b
+
+@tool
+def multiplication(a: float, b: float):
+  """calculate multiplication of two numbers"""
+  return a * b
+
+@tool
+def division(a: float, b: float):
+  """calculate division of two numbers"""
+
+  return a / b
+
+@tool
+def mode(a: float, b: float):
+  """calculate remainder of two numbers"""
+
+  return a % b

app.py

@@ -14,23 +14,30 @@ from typing import  Annotated
 from langchain_groq import ChatGroq
 from langchain_core.messages import HumanMessage
 
-from langgraph.graph import START, END, StateGraph
+from langgraph.graph import START, END, StateGraph, MessagesState
 from langgraph.prebuilt import ToolNode, tools_condition
 
-from agent_utils import wiki_ret, arxiv_ret, tavily_ret, handle_file_tool
+from agent_utils import wiki_ret, arxiv_ret, tavily_ret, handle_file_tool, add, subtract, division, multiplication, mode
 
 # (Keep Constants as is)
 # --- Constants ---
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
 groq_api_key = os.getenv("GROQ_API_KEY")
-tools = [wiki_ret, arxiv_ret, tavily_ret, handle_file_tool]
-llm = ChatGroq(api_key=groq_api_key, model="gemma2-9b-it")
+tools = [wiki_ret, arxiv_ret, tavily_ret, handle_file_tool, add, subtract, division, multiplication, mode]
+llm = ChatGroq(api_key=groq_api_key, model="qwen-qwq-32b")
 llm_with_tools = llm.bind_tools(tools)
 class GraphState(TypedDict):
     messages: str #Annotated[Dict, operator.add]
     context: str
 
+# --- Basic Agent Definition ---
+# ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
+llm_with_tools = llm.bind_tools(tools)
+class GraphState(MessagesState):
+    # messages: Annotated[BaseMessage, operator.add]
+    context: str
+
 # --- Basic Agent Definition ---
 # ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
 class BasicAgent:
@@ -40,48 +47,39 @@ class BasicAgent:
 
     def __call__(self, question: str) -> str:
         print("question: ", question)
-        response = (self.graph).invoke({"messages": question})
+
+        prompt = f"""
+        You are an AI assistant designed to answer user questions using available tools.
+        Provide your final answer in one of the following formats:
+
+        A plain number (without commas, currency symbols, or percent signs unless explicitly requested).
+
+        A concise phrase (no articles or abbreviations).
+
+        With as few words as possible.
+
+        A comma-separated list of numbers and/or strings, following the above rules.
+
+        If you are asked for a number, don't use comma to write your number neither use units such as $ or percent sign unless specified otherwise.
+
+        If you are asked for a string, don't use articles, neither abbreviations (e.g. for cities), and write the digits in plain text unless specified otherwise.
+
+        If you are asked for a comma separated list, apply the above rules depending of whether the element to be put in the list is a number or a string.
+
+        Use the tools at your disposal to find the correct answer. Question: {question}
+        """
+  
+        messages = [HumanMessage(content=prompt)]
+        response = (self.graph).invoke({"messages": messages})
         return response["messages"]
 
     def agent(self, state: GraphState):
 
-        # print(f"Agent received question (first 50 chars): {question[:50]}...")
-        # fixed_answer = "This is a default answer. --- 1"
-        # print(f"Agent returning fixed answer: {fixed_answer}")
-        # context = self.wiki_ret(question)
-        context = state.get("context", "")
-        question = state.get("messages", "")
-        print("question agent: ", question)
-        print("context: ", context)
-        prompt = f"""
-        You are a general AI assistant. I will ask you a question. 
-        YOUR FINAL ANSWER should be a number OR 
-        as few words as possible OR a comma separated list of numbers and/or strings. 
-        If you are asked for a number, don't use comma to write your number neither use 
-        units such as $ or percent sign unless specified otherwise. If you are asked for 
-        a string, don't use articles, neither abbreviations (e.g. for cities), and write 
-        the digits in plain text unless specified otherwise. If you are asked for a comma 
-        separated list, apply the above rules depending of whether the element to be put 
-        in the list is a number or a string. Use the tools available to you to answer the question. Question: {question}
-        For answering the question use this context: {context}, if no context is provided
-        use your knowledge to answer the question."""
-        # Your answer must be in the following format:
-
-        # {{"task_id": "task_id_1", "model_answer": "Answer 1 from your model", "reasoning_trace": "The different steps by which your model reached answer 1"}}
-        # {{"task_id": "task_id_2", "model_answer": "Answer 2 from your model", "reasoning_trace": "The different steps by which your model reached answer 2"}}
-
-        # Just make up a task_id.
-        # Call the LLM
-        messages = [HumanMessage(content=prompt)]
-        print("messages: ", messages)
+
+        messages = state["messages"]
+
         response = llm_with_tools.invoke(messages)
         print("response: ", response)
-        # cleaned_text = re.sub(r"<think>.*?</think>", "", response.content, flags=re.DOTALL)
-
-        # json_start = response.find('{')
-        # json_end = response.rfind('}') + 1
-        # json_str = response[json_start:json_end]
-        # res = json.loads(json_str)
         return {"messages": response}
 
     def create_graph(self):
@@ -90,12 +88,13 @@ class BasicAgent:
         builder.add_node("tools", ToolNode(tools = tools))
 
         builder.add_edge(START, "agent")
-        builder.add_conditional_edges("agent", tools_condition, ["agent", END])
+        builder.add_conditional_edges("agent", tools_condition, ["tools", END])
+        builder.add_edge("tools", "agent")
         builder.add_edge("agent", END)
         graph = builder.compile()
-        image = graph.get_graph().draw_mermaid_png()
-        with open("output_graph.png", "wb") as file:
-            file.write(image)
+        # image = graph.get_graph().draw_mermaid_png()
+        # with open("output_graph.png", "wb") as file:
+        #     file.write(image)
         return graph