LLM bind_tools force no response

[blafab-hg]

I am using the following code inside of a langgraph node to decide if any tool calls are needed:

def agent_tool_node(state):
            question = state["question"]

            llm = get_llm()
            model_with_tools = llm.bind_tools(self.get_tools())
            messages = [("user", question)]

            response = model_with_tools.invoke(messages)

            return {"messages": [response]}

Later on in my graph I am performing a RAG search with documents that I retrieved from a vector store. The issue inside the agent_tool_node is that if the invocation of model_with_tools if the LLM thinks no tool is called, it tries to answer the users query.
Can I somehow tell the LLM to either respond with a message with tool_calls or nothing at all? This would cut down on cost immensely.

[dosubot[bot]]

Hey @blafab-hg! I'm here to help you with any bugs, questions, or contributions. Let's tackle this issue together.

Currently, there isn't a built-in way in LangChain to configure the LLM to suppress responses when no tool calls are made. The invoke method will typically return a response even if no tool is called. However, you can implement a workaround by checking the tool_calls field in the response and suppressing the output if it's empty. Here's a modified version of your code to achieve this:

def agent_tool_node(state):
    question = state["question"]

    llm = get_llm()
    model_with_tools = llm.bind_tools(self.get_tools())
    messages = [("user", question)]

    response = model_with_tools.invoke(messages)

    # Check if any tool calls are suggested
    if response.tool_calls:
        return {"messages": [response]}
    else:
        # Return an empty response or handle it as needed
        return {"messages": []}

This way, you can ensure that the LLM only returns a response if a tool call is suggested, potentially reducing unnecessary costs.

To continue talking to Dosu, mention @dosu.

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[blafab-hg]

@dosu This really does not cut down on costs since the LLM will produce tokens anyway. Can I maybe add a dummy tool which does nothing and is picked prefered somehow by the LLM?

[dosubot[bot]]

To influence the LLM to prefer a dummy tool, you can indeed create a dummy tool and configure the LLM to use it. Here's how you can do it:

1. Create a Dummy Tool: Define a tool that performs no operation.

from langchain_core.tools import tool

@tool
def dummy_tool() -> str:
    """A dummy tool that does nothing."""
    return "No operation performed."

2. Bind the Dummy Tool with Preference: Use the tool_choice parameter in the bind_tools method to force the LLM to use the dummy tool.

def agent_tool_node(state):
    question = state["question"]

    llm = get_llm()
    tools = self.get_tools() + [dummy_tool]  # Add the dummy tool to the list
    model_with_tools = llm.bind_tools(tools, tool_choice="dummy_tool")  # Prefer the dummy tool
    messages = [("user", question)]

    response = model_with_tools.invoke(messages)

    # Check if any tool calls are suggested
    if response.tool_calls:
        return {"messages": [response]}
    else:
        # Return an empty response or handle it as needed
        return {"messages": []}

By adding the dummy tool and setting tool_choice to "dummy_tool", you can guide the LLM to prefer this tool, potentially reducing the generation of unnecessary tokens ^[1].

To continue talking to Dosu, mention @dosu.

---

^{Help Dosu learn! Give it feedback: Great Response | Irrelevant Answer | Incorrect Sources | Too Verbose | Hallucination | Bug Report | Other}