src/workflows/factory.py

# %%
from .structs import (
    Buzzer,
    BuzzerMethod,
    CallType,
    InputField,
    ModelStep,
    OutputField,
    TossupWorkflow,
    Workflow,
)

INITIAL_SYS_PROMPT = """You are a  helpful performant question answering bot.
Given a question clue, output your most likely guess in a couple words with a calibrated confidence for the guess.
"""


def create_empty_bonus_workflow():
    return Workflow(
        inputs=["leadin", "part"],
        outputs={"answer": None, "confidence": None, "explanation": None},
        steps={},
    )


def create_empty_tossup_workflow():
    return TossupWorkflow(
        inputs=["question_text"],
        outputs={"answer": None, "confidence": None},
        steps={},
    )


def create_first_step_input_fields() -> list[InputField]:
    return [
        InputField(
            name="question",
            description="The question text progressively revealed to the agent so far.",
            variable="question_text",
        )
    ]


def create_empty_input_field() -> list[InputField]:
    return [InputField(name="", description="", variable="question_text")]


def create_quizbowl_simple_step_initial_setup():
    return ModelStep(
        id="simple_step",
        name="Quizbowl Simple Step",
        model="",
        provider="",
        temperature=0.7,
        call_type="llm",
        system_prompt=INITIAL_SYS_PROMPT,
        input_fields=[
            InputField(name="question", description="The question to answer", variable="question"),
        ],
        output_fields=[
            OutputField(name="answer", description="The most likely answer", type="str"),
            OutputField(name="confidence", description="The confidence of the answer", type="float"),
        ],
    )


def create_new_llm_step(step_id: str, name: str) -> ModelStep:
    return ModelStep(
        id=step_id,
        name=name,
        model="gpt-4o",
        provider="OpenAI",
        call_type="llm",
        temperature=0.7,
        system_prompt="",
        input_fields=create_empty_input_field(),
        output_fields=[OutputField(name="", description="")],
    )


def create_first_llm_step() -> ModelStep:
    return ModelStep(
        id="A",
        name="",
        model="gpt-4o",
        provider="OpenAI",
        call_type="llm",
        temperature=0.7,
        system_prompt="",
        input_fields=[create_first_step_input_fields()],
        output_fields=[OutputField(name="", description="")],
    )


def create_simple_qb_tossup_workflow():
    return TossupWorkflow(
        inputs=["question_text"],
        outputs={"answer": "A.answer", "confidence": "A.confidence"},
        steps={
            "A": ModelStep(
                id="A",
                name="Tossup Agent",
                model="gpt-4o-mini",
                provider="OpenAI",
                call_type="llm",
                temperature=0.3,
                system_prompt="You are a helpful assistant that can answer questions.",
                input_fields=[InputField(name="question", description="The question text", variable="question_text")],
                output_fields=[
                    OutputField(
                        name="answer",
                        description="The best guess at the answer to the question",
                        type="str",
                    ),
                    OutputField(
                        name="confidence",
                        description="The confidence in the answer, ranging from 0 to 1 in increments of 0.05.",
                        type="float",
                    ),
                ],
            )
        },
        buzzer=Buzzer(
            confidence_threshold=0.75,
            prob_threshold=None,
            method=BuzzerMethod.AND,
        ),
    )


BONUS_SYS_PROMPT = """You are a quizbowl player answering bonus questions. For each part:
1. Read the leadin and part carefully
2. Provide a concise answer
3. Rate your confidence (0-1)
4. Explain your reasoning

Format your response as:
ANSWER: <your answer>
CONFIDENCE: <0-1>
EXPLANATION: <your reasoning>"""


def create_simple_qb_bonus_workflow() -> Workflow:
    """Create a simple model step for bonus questions."""
    return Workflow(
        inputs=["leadin", "part"],
        outputs={"answer": "A.answer", "confidence": "A.confidence", "explanation": "A.explanation"},
        steps={
            "A": ModelStep(
                id="A",
                name="Bonus Agent",
                model="gpt-4o-mini",
                provider="OpenAI",
                temperature=0.3,
                call_type=CallType.LLM,
                system_prompt=BONUS_SYS_PROMPT,
                input_fields=[
                    InputField(
                        name="question_leadin",
                        description="The leadin text for the bonus question",
                        variable="leadin",
                    ),
                    InputField(
                        name="question_part",
                        description="The specific part text to answer",
                        variable="part",
                    ),
                ],
                output_fields=[
                    OutputField(name="answer", description="The predicted answer", type="str"),
                    OutputField(name="confidence", description="Confidence in the answer (0-1)", type="float"),
                    OutputField(name="explanation", description="Short explanation for the answer", type="str"),
                ],
            )
        },
    )