Return control after function executed

[ebarrragn]

I'd like to stop execution after a function has been executed, mostly to save the time taken by another LLM iteration (I have an old P40). Until now, I was telling the agent things like say '(End of message)' or so, but the final response was returned incomplete.

Looking into the code, it turns out there is a commentary talking about a 'return_control' flag that seems not implemented (correct me if I am wrong).

I have implemented it in function_calling_agent.py, line 396, method generate_response, just before the line if agent_sent_message::

                if not isinstance(res, str):
                    if "params" in res:
                        params = res["params"]
                        if "return_control" in params:
                            if params["return_control"]:
                                break

I can try a pull request if that is fine.

[ebarrragn]

One example of use, I have left commented the original string I was using to return control:

class TaskReturnYesAndFinish(BaseModel):
    """
    Return 'Yes' then the task has been completed and there is nothing else to do.
    """
    return_control: bool

    def run(self, **kwargs):
        self.return_control = True
       # Using this string before
        # retval = "You answered 'Yes' and your task is completed, say: '<|end_of_turn|>' to finish."
        retval = "You answered 'Yes' and your task is completed."
        return retval

[Maximilian-Winter]

@ebarrragn You can use the normal LlamaCppAgent. It will just call the function and returns the result.

[ebarrragn]

Interesting, I may not understand it correctly. When I use it, depending on the model, they carry on calling functions until they decide to return control (1 to 4 iterations more), or they get into an infinite cycle, I've seen that using 'Phi-3-mini-4k-instruct-q4.gguf'. Am I doing something wrong when the agent doesn't return control after one single function call?

EDIT: The reason of the original modification (that I don't mind to keep in an independent patch) is that I am using a step to clean results from a search.
the steps are:

1 - Search (currently in a Qdrant database and on Wikipedia)
2 - Rerank,
3 - Eliminate L last results.
4 - Perform a pass with Mixtral to every result asking if the result is really relevant to the question, the ones that are not, are purged out. - This is slow, and here is where the reduction in iterations makes an impact, hence the patch.
5 - return the remaining results.

The creators of queries and receivers of the results are also llama agents.

Just to clarify my reasons, as I said, given that the use case might be no so general, I don't mind keeping it as a local patch here.

[Maximilian-Winter]

@ebarrragn Could you show me your code? I'm curious why this happens.

[ebarrragn]

Sure, I'll prepare a case example with that part only, so you don't have to grok all over the code.

[ebarrragn]

Finally, I found some time. After the changes done last weekend, my code is quite broken, will have a look at it this weekend. I adapted one of the examples to show the issue, I just made the llm local using phi3 as model (I hope it's not nonsense) .
function_calling_agent.py.txt

[ebarrragn]

The PHI3 problem seems related to llama.cpp.

[Maximilian-Winter]

You can simply use the LlamaCppAgent instead of the FunctionCallingAgent to return controll immeadeatly

[ebarrragn]

Yes, the phi3 bug confused me because even using the LlamaCppAgent, it wasn't stopping, but actually it seems to be a llama.cpp problem.
I am closing this issue.