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import os |
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import asyncio |
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import re |
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from openai import OpenAI as OpenAIClient |
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from llama_index.llms.openai import OpenAI |
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from llama_index.core.agent.react import ReActAgent |
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from llama_index.core.tools import FunctionTool |
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from duckduckgo_search import DDGS |
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def duckduckgo_search(query: str) -> str: |
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try: |
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with DDGS() as ddg: |
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results = ddg.text(query=query, region="wt-wt", max_results=3) |
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return "\n".join(r.get('body', '') for r in results if r.get('body')) |
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except Exception as e: |
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return f"ERROR: {e}" |
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def eval_python_code(code: str) -> str: |
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try: |
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return str(eval(code, {"__builtins__": {}})) |
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except Exception as e: |
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return f"ERROR: {e}" |
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def format_gaia_answer(answer: str, question: str = "") -> str: |
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if not answer: |
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return "" |
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ans = re.sub(r'(?i)final answer:?\s*', '', answer).strip() |
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ans = re.sub(r'(?i)i(\'?m| cannot| can\'t| unable| apologize| not available).*', '', ans).strip() |
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if ans.startswith('"') and ans.endswith('"'): |
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ans = ans[1:-1] |
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if ans.startswith('[') and ans.endswith(']'): |
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ans = ans[1:-1] |
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if not re.match(r'^[A-Za-z]+\.$', ans): |
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ans = re.sub(r'\.$', '', ans) |
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if question: |
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if re.search(r'how many|number of|at bats|total sales|albums|output.*python|highest number', question, re.I): |
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m = re.search(r'(\$?\d[\d,\.]*)', ans) |
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if m: return m.group(1).replace(',', '') |
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if 'first name' in question: |
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return ans.split()[0] |
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if 'surname' in question: |
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return ans.split()[-1] |
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if 'city' in question: |
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return ans.split()[0] |
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if re.search(r'IOC country code|award number|NASA', question, re.I): |
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c = re.search(r'[A-Z0-9]{3,}', ans) |
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if c: return c.group(0) |
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if re.search(r'list|comma.*separated|page numbers', question, re.I): |
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items = [x.strip('",.').lower() for x in re.split(r'[,\n]', ans) if x.strip()] |
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if 'page numbers' in question: |
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nums = sorted(int(x) for x in re.findall(r'\d+', ans)) |
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return ', '.join(str(n) for n in nums) |
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if 'ingredient' in question or 'vegetable' in question or 'grocery' in question: |
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merged, skip = [], False |
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for i, x in enumerate(items): |
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if skip: |
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skip = False |
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continue |
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if i+1 < len(items) and x in ['sweet','green','lemon','ripe','whole','fresh']: |
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merged.append(f"{x} {items[i+1]}") |
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skip = True |
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else: |
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merged.append(x) |
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return ', '.join(sorted(set(merged))) |
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return ', '.join(items) |
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return ans.strip().rstrip('.') |
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llm = OpenAI(api_key=os.getenv("OPENAI_API_KEY")) |
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tools = [ |
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FunctionTool.from_defaults(duckduckgo_search, name="duckduckgo_search", description="Web search tool"), |
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FunctionTool.from_defaults(eval_python_code, name="python_eval", description="Evaluate Python code"), |
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FunctionTool.from_defaults(format_gaia_answer, name="format_gaia_answer", description="GAIA output formatting") |
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] |
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agent = ReActAgent.from_tools( |
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tools=tools, |
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llm=llm, |
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system_prompt="You're a GAIA benchmark agent. Always use tools and strictly output only the final answer in GAIA format—no apologies or explanations.", |
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verbose=False |
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) |
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async def answer_question(question: str, task_id: str = None, file_path: str = None) -> str: |
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resp = await agent.achat(question) |
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return resp.response |
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def answer_question_sync(question: str, task_id: str = None, file_path: str = None) -> str: |
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return asyncio.run(answer_question(question, task_id, file_path)) |
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class GaiaAgent: |
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def __call__(self, question: str, task_id: str = None) -> str: |
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return answer_question_sync(question, task_id) |
