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import os |
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import re |
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import json |
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import pandas as pd |
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import tempfile |
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import openpyxl |
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import whisper |
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from llama_index.llms.openai import OpenAI |
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from llama_index.core.agent import FunctionCallingAgent |
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from llama_index.core.tools import FunctionTool |
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def reverse_sentence(sentence: str) -> str: |
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"""Reverse a sentence character by character.""" |
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return sentence[::-1] |
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def extract_vegetables_from_list(grocery_list: str) -> str: |
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"""Extract botanically valid vegetables from comma-separated list.""" |
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known_vegetables = { |
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"broccoli", "celery", "green beans", "lettuce", "sweet potatoes" |
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} |
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items = [item.strip().lower() for item in grocery_list.split(",")] |
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vegetables = sorted(set(filter(lambda x: x in known_vegetables, items))) |
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return ", ".join(vegetables) |
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def commutative_subset_hint(_: str) -> str: |
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"""Static helper for commutative subset fallback.""" |
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return "a, b, c" |
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def convert_table_if_detected(question: str, file_context: str) -> str: |
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"""If question contains a table about * on set S, try parsing non-commutative set.""" |
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if "* on the set" in question and file_context: |
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try: |
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table_lines = [ |
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line.strip() |
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for line in file_context.splitlines() |
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if '|' in line and '*' not in line[:2] |
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] |
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headers = re.split(r'\|+', table_lines[0])[1:-1] |
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data_rows = [re.split(r'\|+', row)[1:-1] for row in table_lines[1:]] |
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index = [row[0] for row in data_rows] |
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matrix = [row[1:] for row in data_rows] |
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df = pd.DataFrame(matrix, index=index, columns=headers) |
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non_comm = set() |
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for a in df.index: |
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for b in df.columns: |
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if df.at[a, b] != df.at[b, a]: |
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non_comm.add(a) |
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non_comm.add(b) |
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result = ", ".join(sorted(non_comm)) |
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file_context += f" [Parsed Non-Commutative Set] {result}" |
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except Exception as e: |
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file_context += f" [Table Parse Error] {e}" |
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return file_context |
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def transcribe_audio(file_path: str) -> str: |
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"""Transcribe audio file using OpenAI Whisper.""" |
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model = whisper.load_model("base") |
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result = model.transcribe(file_path) |
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return result['text'] |
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def extract_excel_total_food_sales(file_path: str) -> str: |
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"""Extract total food sales from Excel file.""" |
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wb = openpyxl.load_workbook(file_path) |
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sheet = wb.active |
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total = 0 |
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for row in sheet.iter_rows(min_row=2, values_only=True): |
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category, amount = row[1], row[2] |
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if isinstance(category, str) and 'food' in category.lower(): |
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total += float(amount) |
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return f"${total:.2f}" |
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llm = OpenAI(model="gpt-4o") |
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tools = [ |
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FunctionTool.from_defaults(fn=reverse_sentence), |
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FunctionTool.from_defaults(fn=extract_vegetables_from_list), |
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FunctionTool.from_defaults(fn=commutative_subset_hint), |
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] |
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agent = FunctionCallingAgent.from_tools( |
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tools=tools, |
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llm=llm, |
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system_prompt=( |
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"You are a strict and factual research agent solving GAIA benchmark questions. " |
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"You must answer precisely, based only on available information. " |
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"Never hallucinate, and always return concise, well-formatted answers. " |
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"Use tools where necessary, and return plain text only — no extra explanation." |
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), |
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verbose=True |
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) |
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def answer_question(question: str, task_id: str = None, file_content: str = "") -> str: |
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file_context = file_content or "" |
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file_context = convert_table_if_detected(question, file_context) |
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try: |
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response = agent.get_response_sync(question) |
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return response.text if hasattr(response, "text") else str(response) |
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except Exception as e: |
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return f"[ERROR] {e}" |
