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Download src/langgraphagenticai/node/chatbot_with_tool_node.py from genaitiwari/langgraphAgenticAI: direct link, hf CLI and curl.
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https://huggingface.co/spaces/genaitiwari/langgraphAgenticAI/resolve/main/src/langgraphagenticai/node/chatbot_with_tool_node.py
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hf download hf://spaces/genaitiwari/langgraphAgenticAI/src/langgraphagenticai/node/chatbot_with_tool_node.py
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curl -L -o chatbot_with_tool_node.py https://huggingface.co/spaces/genaitiwari/langgraphAgenticAI/resolve/main/src/langgraphagenticai/node/chatbot_with_tool_node.py
1.09 kB
| from src.langgraphagenticai.state.state import State | |
| class ChatbotWithToolNode: | |
| """ | |
| Chatbot logic enhanced with tool integration. | |
| """ | |
| def __init__(self,model): | |
| self.llm = model | |
| def process(self, state: State) -> dict: | |
| """ | |
| Processes the input state and generates a response with tool integration. | |
| """ | |
| user_input = state["messages"][-1] if state["messages"] else "" | |
| llm_response = self.llm.invoke([{"role": "user", "content": user_input}]) | |
| # Simulate tool-specific logic | |
| tools_response = f"Tool integration for: '{user_input}'" | |
| return {"messages": [llm_response, tools_response]} | |
| def create_chatbot(self, tools): | |
| """ | |
| Returns a chatbot node function. | |
| """ | |
| llm_with_tools = self.llm.bind_tools(tools) | |
| 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 | |