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Ask questions

Tool for asking a question about the simulation results.

AskQuestionInput

Bases: BaseModel

Input schema for the AskQuestion tool.

Source code in aiagents4pharma/talk2biomodels/tools/ask_question.py 
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class AskQuestionInput(BaseModel):
    """
    Input schema for the AskQuestion tool.
    """
    question: str = Field(description="question about the simulation and steady state results")
    experiment_name: str = Field(description="""Name assigned to the simulation
                                            or steady state analysis when the tool 
                                            simulate_model or steady_state is invoked.""")
    question_context: Literal["simulation", "steady_state"] = Field(
        description="Context of the question")
    state: Annotated[dict, InjectedState]

AskQuestionTool

Bases: BaseTool

Tool for asking a question about the simulation or steady state results.

Source code in aiagents4pharma/talk2biomodels/tools/ask_question.py 
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class AskQuestionTool(BaseTool):
    """
    Tool for asking a question about the simulation or steady state results.
    """
    name: str = "ask_question"
    description: str = """A tool to ask question about the
                        simulation or steady state results."""
    args_schema: Type[BaseModel] = AskQuestionInput
    return_direct: bool = False

    def _run(self,
             question: str,
             experiment_name: str,
             question_context: Literal["simulation", "steady_state"],
             state: Annotated[dict, InjectedState]) -> str:
        """
        Run the tool.

        Args:
            question (str): The question to ask about the simulation or steady state results.
            state (dict): The state of the graph.
            experiment_name (str): The name assigned to the simulation or steady state analysis.

        Returns:
            str: The answer to the question.
        """
        logger.log(logging.INFO,
                   "Calling ask_question tool %s, %s, %s",
                   question,
                   question_context,
                   experiment_name)
        # print (f'Calling ask_question tool {question}, {question_context}, {experiment_name}')
        if question_context == "steady_state":
            dic_context = state["dic_steady_state_data"]
        else:
            dic_context = state["dic_simulated_data"]
        dic_data = {}
        for data in dic_context:
            for key in data:
                if key not in dic_data:
                    dic_data[key] = []
                dic_data[key] += [data[key]]
        # print (dic_data)
        df_data = pd.DataFrame.from_dict(dic_data)
        df = pd.DataFrame(
            df_data[df_data['name'] == experiment_name]['data'].iloc[0]
        )
        prompt_content = None
        # if run_manager and 'prompt' in run_manager.metadata:
        #     prompt_content = run_manager.metadata['prompt']
        # Create a pandas dataframe agent with OpenAI
        df_agent = create_pandas_dataframe_agent(
                        ChatOpenAI(model=state['llm_model']),
                        allow_dangerous_code=True,
                        agent_type=AgentType.OPENAI_FUNCTIONS,
                        df=df,
                        prefix=prompt_content)
        llm_result = df_agent.invoke(question)
        return llm_result["output"]

_run(question, experiment_name, question_context, state)

Run the tool.

Parameters:

Name Type Description Default
question str

The question to ask about the simulation or steady state results.

 required
state dict

The state of the graph.

 required
experiment_name str

The name assigned to the simulation or steady state analysis.

 required

Returns:

Name Type Description
str str

The answer to the question.
Source code in aiagents4pharma/talk2biomodels/tools/ask_question.py 
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def _run(self,
         question: str,
         experiment_name: str,
         question_context: Literal["simulation", "steady_state"],
         state: Annotated[dict, InjectedState]) -> str:
    """
    Run the tool.

    Args:
        question (str): The question to ask about the simulation or steady state results.
        state (dict): The state of the graph.
        experiment_name (str): The name assigned to the simulation or steady state analysis.

    Returns:
        str: The answer to the question.
    """
    logger.log(logging.INFO,
               "Calling ask_question tool %s, %s, %s",
               question,
               question_context,
               experiment_name)
    # print (f'Calling ask_question tool {question}, {question_context}, {experiment_name}')
    if question_context == "steady_state":
        dic_context = state["dic_steady_state_data"]
    else:
        dic_context = state["dic_simulated_data"]
    dic_data = {}
    for data in dic_context:
        for key in data:
            if key not in dic_data:
                dic_data[key] = []
            dic_data[key] += [data[key]]
    # print (dic_data)
    df_data = pd.DataFrame.from_dict(dic_data)
    df = pd.DataFrame(
        df_data[df_data['name'] == experiment_name]['data'].iloc[0]
    )
    prompt_content = None
    # if run_manager and 'prompt' in run_manager.metadata:
    #     prompt_content = run_manager.metadata['prompt']
    # Create a pandas dataframe agent with OpenAI
    df_agent = create_pandas_dataframe_agent(
                    ChatOpenAI(model=state['llm_model']),
                    allow_dangerous_code=True,
                    agent_type=AgentType.OPENAI_FUNCTIONS,
                    df=df,
                    prefix=prompt_content)
    llm_result = df_agent.invoke(question)
    return llm_result["output"]