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Agent V9
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# agent_v9.py
import os
from openai import OpenAI
class GaiaAgent:
def __init__(self):
self.client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
self.instructions = (
"You are a research assistant solving GAIA benchmark questions using 2022 English Wikipedia knowledge.\n"
"For each question, reason step-by-step and only return the final answer in exact format (no explanation, no punctuation, no text)."
)
self.task_templates = {
"8e867cd7-cff9-4e6c-867a-ff5ddc2550be": self.q_mercedes_sosa_albums,
"2d83110e-a098-4ebb-9987-066c06fa42d0": self.q_reversed_text,
"3cef3a44-215e-4aed-8e3b-b1e3f08063b7": self.q_botanical_vegetables
}
def __call__(self, question: str, task_id: str = None) -> str:
if task_id in self.task_templates:
raw = self.task_templates[task_id](question)
return raw.strip().replace(".\n", "").replace("\n", "").strip()
else:
return "[SKIPPED: Task not yet implemented in Agent V9.1]"
def q_mercedes_sosa_albums(self, question: str) -> str:
prompt = (
"QUESTION: How many studio albums were published by Mercedes Sosa between 2000 and 2009 (included)?\n"
"\nScratchpad reasoning:\n"
"Step 1: List all studio albums of Mercedes Sosa from Wikipedia (2022).\n"
"Step 2: Filter albums released between 2000 and 2009 inclusive.\n"
"Step 3: Count them.\n"
"\nFinal Answer (number only):"
)
return self.query_llm(prompt)
def q_reversed_text(self, question: str) -> str:
prompt = (
"QUESTION: .rewsna eht sa \"tfel\" drow eht fo etisoppo eht etirw ,ecnetnes siht dnatsrednu uoy fI\n"
"\nScratchpad reasoning:\n"
"Step 1: Reverse the question.\n"
"Step 2: Understand it.\n"
"Step 3: The opposite of the word \"left\" is \"right\".\n"
"\nFinal Answer (word only):"
)
return self.query_llm(prompt)
def q_botanical_vegetables(self, question: str) -> str:
prompt = (
"QUESTION: Classify each item botanically and return only the vegetables from the list.\n"
"milk, eggs, flour, whole bean coffee, Oreos, sweet potatoes, fresh basil, plums, green beans, rice, corn, bell pepper, whole allspice, acorns, broccoli, celery, zucchini, lettuce, peanuts\n"
"\nScratchpad reasoning:\n"
"Step 1: Identify botanical vegetables (roots, stems, leaves).\n"
"Step 2: Exclude botanical fruits and seeds.\n"
"Step 3: Sort alphabetically.\n"
"\nFinal Answer (comma-separated list):"
)
return self.query_llm(prompt)
def query_llm(self, prompt: str) -> str:
try:
response = self.client.chat.completions.create(
model="gpt-4-turbo",
messages=[
{"role": "system", "content": self.instructions},
{"role": "user", "content": prompt.strip()}
],
temperature=0.0
)
return response.choices[0].message.content
except Exception as e:
return f"[LLM ERROR: {e}]"
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