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