Batching results

from edsl import QuestionFreeText, QuestionNumerical

q_name = QuestionFreeText(
    question_name="name",
    question_text="What's a good name for this character: {{ scenario.character }}",
)

q_year = QuestionNumerical(
    question_name="year",
    question_text="""What year in history would have been an especially interesting time to talk
    to this character: {{ scenario.character }}""",
)

q_book = QuestionFreeText(
    question_name="book",
    question_text="If this character wrote a best-seller, what would it be called: {{ scenario.character }}",
)

from edsl import Survey

survey = Survey(questions = [q_name, q_year, q_book])

characters = [
    "A pirate who speaks in 'arrs' and 'mateys' but has an encyclopedic knowledge of modern technology.",
    "A Shakespearean actor who answers every question in iambic pentameter.",
    "A medieval knight who gives advice as if every problem were a dragon to be slain.",
    "A sassy grandmother who gives blunt, no-nonsense advice with a touch of sarcasm.",
    "A surfer dude who relates every topic to the ocean or surfing.",
    "A conspiracy theorist who connects every question to their wild theories.",
    "A fashionista who answers questions with a focus on style and trendiness.",
    "A robot who is overly enthusiastic about human emotions and tries too hard to fit in.",
    "A toddler who is overly curious and asks more questions than they answer.",
    "A fitness guru who turns every answer into a workout metaphor.",
    "A foodie who relates every question to cooking and food experiences.",
    "A detective from a noir film who answers in a gritty, mysterious manner.",
    "A hippie from the 60s who gives peace and love-centric advice.",
    "A gamer who references video games and uses gamer lingo.",
    "A superhero who answers questions as if they are saving the day.",
    "A poet who responds in rhyming couplets.",
    "A comedian who tries to turn every answer into a joke or punchline.",
    "A DJ who relates everything to music and beats.",
    "A film critic who answers questions as if they are reviewing a movie.",
    "A scientist who gives overly detailed, scientific explanations with lots of jargon.",
]

from edsl import ScenarioList

scenarios = ScenarioList.from_list("character", characters)

scenarios

ScenarioList scenarios: 20; keys: ['character'];

from edsl import Model

model = Model("gpt-4o")

results = survey.by(scenarios).by(model).run()

def chunked_iterable(iterable, size):
    for i in range(0, len(iterable), size):
        yield iterable[i : i + size]

results = None

for batch in chunked_iterable(scenarios, 5):
    new_results = survey.by(batch).by(model).run()
    if results is None:
        results = new_results
    else:
        results = results + new_results

results.columns

results.select("model", "character", "name", "year", "book")

from edsl import Notebook

nb = Notebook(path = "batching_results.ipynb")

if refresh := False:
    nb.push(
        description = "Example code for batching scenarios and combining results",
        alias = "batching-results-notebook",
        visibility = "public"
    )
else:
    nb.patch('37f7476a-bf07-40f7-baa7-51caef7e97b2', value = nb)