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| from src.langgraphagenticai.state.state import State | |
| #State is like a memory that stores chat messages | |
| #creates a chatbot that can Take user messages as input, Use an AI model (llm) to generate responses and Integrate tools to improve its responses | |
| #This class manages a chatbot that can use tools | |
| class ChatbotWithToolNode: | |
| """ | |
| Chatbot logic enhanced with tool integration. | |
| """ | |
| #constructor method that takes self (the instance of the class) and model (a parameter) | |
| def __init__(self,model): | |
| self.llm = model # Attribute (self.llm) stores the parameter (model) | |
| #This method processes user input and generates responses. Methods always have self as the first parameter | |
| def process(self, state: State) -> dict: #-> dict is a return type hint and state:State is also a type hint, expects state to be of type State | |
| """ | |
| Processes the input state and generates a response with tool integration. | |
| """ | |
| user_input = state["messages"][-1] if state["messages"] else "" #gets the latest user message from state else nothing | |
| llm_response = self.llm.invoke([{"role": "user", "content": user_input}]) #AI model (self.llm) generates a response based on user input | |
| # Simulate tool-specific logic | |
| tools_response = f"Tool integration for: '{user_input}'" | |
| return {"messages": [llm_response, tools_response]} | |
| #This function adds tools to the chatbot. | |
| def create_chatbot(self, tools): | |
| """ | |
| Returns a chatbot node function. | |
| """ | |
| llm_with_tools = self.llm.bind_tools(tools) #connects the chatbot to external tools | |
| #function that processes messages | |
| def chatbot_node(state: State): | |
| """ | |
| Chatbot logic for processing the input state and returning a response. | |
| """ | |
| return {"messages": [llm_with_tools.invoke(state["messages"])]} | |
| return chatbot_node | |