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from tinytroupe.agent import TinyPerson
from tinytroupe.environment import TinyWorld
from tinytroupe.openai_utils import LLMRequest
class Proposition:
def __init__(self, target, claim:str, first_n:int=None, last_n:int=None):
"""
Define a proposition as a (textual) claim about a target, which can be a TinyWorld, a TinyPerson or several of any.
Args:
target (TinyWorld, TinyPerson, list): the target or targets of the proposition
claim (str): the claim of the proposition
first_n (int): the number of first interactions to consider in the context
last_n (int): the number of last interactions (most recent) to consider in the context
"""
if isinstance(target, TinyWorld) or isinstance(target, TinyPerson):
self.targets = [target]
elif isinstance(target, list) and all(isinstance(t, TinyWorld) or isinstance(t, TinyPerson) for t in target):
self.targets = target
else:
raise ValueError("Target must be a TinyWorld, a TinyPerson or a list of them.")
self.claim = claim
self.first_n = first_n
self.last_n = last_n
self.value = None
self.justification = None
self.confidence = None
def __call__(self, additional_context=None):
return self.check(additional_context=additional_context)
def check(self, additional_context="No additional context available."):
context = ""
for target in self.targets:
target_trajectory = target.pretty_current_interactions(max_content_length=None, first_n=self.first_n, last_n=self.last_n)
if isinstance(target, TinyPerson):
context += f"## Agent '{target.name}' Simulation Trajectory\n\n"
elif isinstance(target, TinyWorld):
context += f"## Environment '{target.name}' Simulation Trajectory\n\n"
context += target_trajectory + "\n\n"
llm_request = LLMRequest(system_prompt="""
You are a system that evaluates whether a proposition is true or false with respect to a given context. This context
always refers to a multi-agent simulation. The proposition is a claim about the behavior of the agents or the state of their environment
in the simulation.
The context you receive can contain one or more of the following:
- the trajectory of a simulation of one or more agents. This means what agents said, did, thought, or perceived at different times.
- the state of the environment at a given time.
Your output **must**:
- necessarily start with the word "True" or "False";
- optionally be followed by a justification.
For example, the output could be of the form: "True, because <REASON HERE>." or merely "True" if no justification is needed.
""",
user_prompt=f"""
Evaluate the following proposition with respect to the context provided. Is it True or False?
# Proposition
This is the proposition you must evaluate:
{self.claim}
# Context
The context you must consider is the following.
{context}
# Additional Context (if any)
{additional_context}
""",
output_type=bool)
self.value = llm_request()
self.justification = llm_request.response_justification
self.confidence = llm_request.response_confidence
self.raw_llm_response = llm_request.response_raw
return self.value
def check_proposition(target, claim:str, additional_context="No additional context available.",
first_n:int=None, last_n:int=None):
"""
Check whether a propositional claim holds for the given target(s). This is meant as a
convenience method to avoid creating a Proposition object (which you might not need
if you are not interested in the justification or confidence of the claim, or will
not use it again).
Args:
target (TinyWorld, TinyPerson, list): the target or targets of the proposition
claim (str): the claim of the proposition
additional_context (str): additional context to provide to the LLM
first_n (int): the number of first interactions to consider in the context
last_n (int): the number of last interactions (most recent) to consider in the context
Returns:
bool: whether the proposition holds for the given target(s)
"""
proposition = Proposition(target, claim, first_n=first_n, last_n=last_n)
return proposition.check(additional_context=additional_context) |