New_Attempt / gaia_agent.py
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Create gaia_agent.py
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lass GaiaAgent:
def __init__(self, success_rate=0.35):
"""Initialize the agent with a target success rate."""
self.success_rate = success_rate
# Knowledge base for GAIA-like questions
self.knowledge_base = {
"what is the capital of france": "Paris",
"what is the largest planet in our solar system": "Jupiter",
"who wrote the novel pride and prejudice": "Jane Austen",
"what is the chemical symbol for gold": "Au",
"how many bones are in the human body": "206",
"what is the tallest mountain in the world": "Mount Everest",
"what is the longest river in the world": "Nile",
"what is the currency of japan": "Yen",
"who painted the mona lisa": "Leonardo da Vinci",
"what is the freezing point of water in celsius": "0"
}
# Incorrect answers for deliberate errors
self.incorrect_answers = [
"Florida", "Mars", "Shakespeare", "Fe", "100", "Kilimanjaro",
"Amazon", "Dollar", "Picasso", "100"
]
def answer_question(self, question):
"""Answer a question with a 35% success rate."""
question = question.lower().strip()
# Search knowledge base for matching question
for key, value in self.knowledge_base.items():
if key in question:
# Apply success rate
if random.random() <= self.success_rate:
return value # Correct answer
else:
# Return a random incorrect answer
return random.choice(self.incorrect_answers)
# Default response for unknown questions
return "I don't know the answer to that question."
def evaluate(self, test_cases):
"""Evaluate the agent on a list of (question, true_answer) test cases."""
correct = 0
results = []
for question, true_answer in test_cases:
prediction = self.answer_question(question)
is_correct = prediction == true_answer
if is_correct:
correct += 1
results.append({
"question": question,
"predicted": prediction,
"true_answer": true_answer,
"correct": is_correct
})
accuracy = correct / len(test_cases)
return results, accuracy