.. _taskgen-function-guide:

TaskGen Function Usage Guide
============================

This guide details how to define and utilize functions within TaskGen agents, enabling them to execute tasks efficiently.

Define a LLM Function
---------------------
To enable your agent to interact with a large language model (LLM), define a function specifying how the agent should communicate with the LLM.

.. code-block:: python

    def llm(system_prompt: str, user_prompt: str) -> str:
        """
        Interacts with a specified LLM using system and user prompts to generate a response.

        :param system_prompt: A string that provides the context or instruction for the LLM.
        :param user_prompt: A string that represents the user's input or question.
        :return: A string response generated by the LLM.
        """
        from openai import ChatCompletion

        response = ChatCompletion.create(
            model="gpt-4o-mini",
            messages=[
                {"role": "system", "content": system_prompt},
                {"role": "user", "content": user_prompt}
            ]
        )
        return response.choices[0].message.content

Using Functions in Agents
-------------------------
Once functions are defined, they can be assigned to agents to carry out specific tasks.

1. **Creating an Agent**:

   Initialize an agent and assign it a function for interaction.

   .. code-block:: python

       from taskgen import Agent

       my_agent = Agent(name='HelperBot', description='A versatile helper agent.', llm=llm)

2. **Assigning Tasks**:

   Assign tasks to the agent using the defined functions, and specify the number of subtasks if necessary.

   .. code-block:: python

       task_output = my_agent.run(task="Schedule a meeting", num_subtasks=3)
       print(task_output)

3. **Interacting with Tasks**:

   Use the agent's capabilities to interact dynamically with ongoing tasks.

   .. code-block:: python

       response = my_agent.interact(user_query="Reschedule the meeting to a later date.")
       print(response)

Advanced Function Usage
-----------------------
Define advanced behaviors and interactions by leveraging the full capabilities of TaskGen functions.

.. code-block:: python

    def advanced_interaction(system_prompt: str, user_input: str) -> str:
        """
        An advanced function to handle more complex interactions and provide detailed responses based on the context.

        :param system_prompt: Contextual prompt for the LLM.
        :param user_input: User's specific query or command.
        :return: Detailed response from the LLM based on the interaction.
        """
        response = llm(system_prompt, user_input)
        return response  # Modify or process the response as needed for the task

Conclusion
----------
Utilizing these functions within your TaskGen agents allows for flexible, powerful task management and response generation tailored to specific user needs and contexts.