Note:This method can only be used locally. It is not available with remote inference.
How it works
Agents are created by passing a dictionary of traits to an Agent object. For example:
from edsl import Agent
my_agent = Agent(
name="Robin",
traits={"persona": "You are a middle-aged mom.", "current_vehicle": "minivan"}
)
from edsl import QuestionFreeText, Survey
q1 = QuestionFreeText(question_name="age", question_text="How old are you?")
q2 = QuestionFreeText(question_name="car", question_text="What are you driving?")
survey = Survey([q1, q2])
results = survey.by(my_agent).run() # this survey can be run remotely or locally
results.select("agent_name", "answer.*")
| agent.agent_name | answer.age | answer.car |
|---|
| 0 | Robin | Oh, I’m in my mid-40s. Just old enough to have seen my fair share of life, but still young enough to keep up with the kids! | Oh, I drive a minivan. It’s perfect for hauling the kids around and fitting all their stuff, plus it’s super comfy for road trips! |
Designing question-based traits
For efficiency or other reasons, we may want to minimize the set of traits that we pass to the agent when we create it, and instead only generate certain traits to use with specific questions. To do this, we can create a method for the desired conditional logic and pass it to an agent as the dynamic_traits_function parameter:
# Create a method for the desired logic
def politics_response(question):
if question.question_name == "politics":
return {
"your_politics": "No comment."
} # trait to be passed to the agent
# Pass it to the agent
a = Agent(dynamic_traits_function=politics_response)
# Test it
q = QuestionFreeText(question_name="politics", question_text="What do you think of politics?")
results = q.by(a).run(disable_remote_inference = True) # this question must be run locally
# Inspect the response
results.select("answer.*")
| answer.politics |
|---|
| 0 | No comment. |
