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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,
        )
        # Load hydra configuration
        with hydra.initialize(version_base=None, config_path="../configs"):
            cfg = hydra.compose(config_name="config", overrides=["tools/ask_question=default"])
            cfg = cfg.tools.ask_question
        # Get the context of the question
        # and based on the context, get the data
        # and prompt content to ask the question
        if question_context == "steady_state":
            dic_context = state["dic_steady_state_data"]
            prompt_content = cfg.steady_state_prompt
        else:
            dic_context = state["dic_simulated_data"]
            prompt_content = cfg.simulation_prompt
        # Extract the
        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]]
        # Create a pandas dataframe of the data
        df_data = pd.DataFrame.from_dict(dic_data)
        # Extract the data for the experiment
        # matching the experiment name
        df = pd.DataFrame(df_data[df_data["name"] == experiment_name]["data"].iloc[0])
        logger.log(logging.INFO, "Shape of the dataframe: %s", df.shape)
        # # Extract the model units
        # model_units = basico.model_info.get_model_units()
        # Update the prompt content with the model units
        prompt_content += "Following are the model units:\n"
        prompt_content += f"{basico.model_info.get_model_units()}\n\n"
        # Create a pandas dataframe agent
        df_agent = create_pandas_dataframe_agent(
            state["llm_model"],
            allow_dangerous_code=True,
            agent_type="tool-calling",
            df=df,
            max_iterations=5,
            include_df_in_prompt=True,
            number_of_head_rows=df.shape[0],
            verbose=True,
            prefix=prompt_content,
        )
        # Invoke the agent with the question
        llm_result = df_agent.invoke(question, stream_mode=None)
        # print (llm_result)
        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,
    )
    # Load hydra configuration
    with hydra.initialize(version_base=None, config_path="../configs"):
        cfg = hydra.compose(config_name="config", overrides=["tools/ask_question=default"])
        cfg = cfg.tools.ask_question
    # Get the context of the question
    # and based on the context, get the data
    # and prompt content to ask the question
    if question_context == "steady_state":
        dic_context = state["dic_steady_state_data"]
        prompt_content = cfg.steady_state_prompt
    else:
        dic_context = state["dic_simulated_data"]
        prompt_content = cfg.simulation_prompt
    # Extract the
    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]]
    # Create a pandas dataframe of the data
    df_data = pd.DataFrame.from_dict(dic_data)
    # Extract the data for the experiment
    # matching the experiment name
    df = pd.DataFrame(df_data[df_data["name"] == experiment_name]["data"].iloc[0])
    logger.log(logging.INFO, "Shape of the dataframe: %s", df.shape)
    # # Extract the model units
    # model_units = basico.model_info.get_model_units()
    # Update the prompt content with the model units
    prompt_content += "Following are the model units:\n"
    prompt_content += f"{basico.model_info.get_model_units()}\n\n"
    # Create a pandas dataframe agent
    df_agent = create_pandas_dataframe_agent(
        state["llm_model"],
        allow_dangerous_code=True,
        agent_type="tool-calling",
        df=df,
        max_iterations=5,
        include_df_in_prompt=True,
        number_of_head_rows=df.shape[0],
        verbose=True,
        prefix=prompt_content,
    )
    # Invoke the agent with the question
    llm_result = df_agent.invoke(question, stream_mode=None)
    # print (llm_result)
    return llm_result["output"]