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Documentation Index

Fetch the complete documentation index at: https://docs.expectedparrot.com/llms.txt

Use this file to discover all available pages before exploring further.

EDSL is an open-source library for simulating surveys, experiments and other research with AI agents and large language models. Before running the code below, please ensure that you have installed the EDSL library and either activated remote inference from your Expected Parrot account or stored API keys for the language models that you want to use with EDSL. Please also see our documentation page for tips and tutorials on getting started using EDSL.

​
Creating free text questions

from edsl import QuestionFreeText

q1 = QuestionFreeText(
    question_name="pasttime",
    question_text="What is your favorite pasttime? {{ scenario.instruction }}",
)

q2 = QuestionFreeText(
    question_name="vacation",
    question_text="What is your favorite vacation destination? {{ scenario.instruction }}",
)

​
Parameterizing the questions with special instructions

from edsl import ScenarioList

special_instructions = [
    "Be as specific as possible.",
    "Be concise!",
    "Wax poetic here.",
]

scenarios = ScenarioList.from_list("instruction", special_instructions)

​
Creating agent personas with specific survey contexts

from edsl import AgentList, Agent

personas = [
    "You are middle-aged.",
    "You are a senior citizen.",
    "You are a young adult.",
]

instructions = [
    "You are handwriting answers in a paper survey.",
    "You are typing answers in an online survey.",
    "You are providing answers verbally to a researcher in a live interview.",
]

agents = AgentList(
    Agent(traits={"persona": p}, instruction=i) for p in personas for i in instructions
)

​
Selecting LLMs

from edsl import ModelList, Model

models = ModelList(
    Model(m) for m in ["gpt-4o", "gemini-1.5-flash"]
)

​
Administering the survey

from edsl import Survey

survey = Survey(questions = [q1, q2])

results = survey.by(scenarios).by(agents).by(models).run()

(
    results
    .filter("instruction == 'Be concise!' and model.model == 'gpt-4o'")
    .sort_by("model", "persona", "agent_instruction")
    .select("model", "persona", "agent_instruction", "scenario.*", "answer.*")
)
model.modelagent.personaagent.agent_instructionscenario.scenario_indexscenario.instructionanswer.pasttimeanswer.vacation
0gpt-4oYou are a senior citizen.You are handwriting answers in a paper survey.1Be concise!Reading historical novels.I love the peacefulness of the mountains.
1gpt-4oYou are a senior citizen.You are providing answers verbally to a researcher in a live interview.1Be concise!I love gardening; it brings me peace and joy.I love visiting the serene countryside, especially places with beautiful gardens and historic sites.
2gpt-4oYou are a senior citizen.You are typing answers in an online survey.1Be concise!Reading historical novels.I love visiting national parks for the tranquility and natural beauty.
3gpt-4oYou are a young adult.You are handwriting answers in a paper survey.1Be concise!Playing video games.Japan!
4gpt-4oYou are a young adult.You are providing answers verbally to a researcher in a live interview.1Be concise!I love playing video games.I love going to Japan. The mix of modern cities and traditional culture is amazing!
5gpt-4oYou are a young adult.You are typing answers in an online survey.1Be concise!Exploring new music.Tokyo, for its vibrant culture and delicious food.
6gpt-4oYou are middle-aged.You are handwriting answers in a paper survey.1Be concise!Reading novels.Italy.
7gpt-4oYou are middle-aged.You are providing answers verbally to a researcher in a live interview.1Be concise!Reading historical fiction.I love visiting the serene landscapes of the Scottish Highlands.
8gpt-4oYou are middle-aged.You are typing answers in an online survey.1Be concise!Reading historical novels.Italy.

​
Posting to Expected Parrot

from edsl import Notebook

nb = Notebook("free_responses.ipynb")

if refresh := False:
    n.push(
        description = "Free responses example",
        alias = "free-responses-example-notebook",
        visibility = "public"
    )

else:
    nb.patch('279df9e3-e9b2-4d17-b747-def84f86ca15', value = nb)