File size: 5,677 Bytes
8b6df94 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 | import os
import tempfile
from typing import Optional
def save_test_file(task_id: str, content: str) -> str:
"""Save a test file to a temporary location."""
temp_dir = tempfile.gettempdir()
file_path = os.path.join(temp_dir, f"test_file_{task_id}.csv")
with open(file_path, "w") as f:
f.write(content)
return file_path
def answer_question(
agent, verbose, question: str, task_file_path: Optional[str] = None
) -> str:
"""
Process a GAIA benchmark question and return the answer
Args:
question: The question to answer
task_file_path: Optional path to a file associated with the question
Returns:
The answer to the question
"""
try:
if verbose:
print(f"Processing question: {question}")
if task_file_path:
print(f"With associated file: {task_file_path}")
# Create a context with file information if available
context = question
file_content = None
# If there's a file, read it and include its content in the context
if task_file_path:
try:
with open(task_file_path, "r") as f:
file_content = f.read()
# Determine file type from extension
file_ext = os.path.splitext(task_file_path)[1].lower()
context = f""" Question: {question} This question has an associated file. Here is the file content: ```{file_ext} {file_content}```Analyze the file content above to answer the question."""
except Exception as file_e:
context = f""" Question: {question} This question has an associated file at path: {task_file_path}. However, there was an error reading the file: {file_e}. You can still try to answer the question based on the information provided."""
# Check for special cases that need specific formatting
# Reversed text questions
if question.startswith(".") or ".rewsna eht sa" in question:
context = f""" This question appears to be in reversed text. Here's the reversed version: {question[::-1]} Now answer the question above. Remember to format your answer exactly as requested. """
# Add a prompt to ensure precise answers
rules = "When answering, your answer should be a number OR as few words as possible OR a comma separated list of numbers and/or strings. If you are asked for a number, don't use comma to write your number neither use units such as $ or percent sign unless specified otherwise. If you are asked for a string, don't use articles, neither abbreviations (e.g. for cities), and write the digits in plain text unless specified otherwise. If you are asked for a comma separated list, do not include brackets and apply the above rules depending of whether the element to be put in the list is a number or a string."
# full_prompt = f"""{context}. When answering, provide ONLY the precise answer requested. Do not include explanations, steps, reasoning, or additional text. Be direct and specific. GAIA benchmark requires exact matching answers. For example, if asked "What is the capital of France?", respond simply with "Paris"."""
full_prompt = f"""{context}. {rules}"""
# Run the agent with the question
answer = agent.run(full_prompt)
# Clean up the answer to ensure it's in the expected format
# Remove common prefixes that models often add
answer = _clean_answer(answer)
if verbose:
print(f"Generated answer: {answer}")
return answer
except Exception as e:
error_msg = f"Error answering question: {e}"
if verbose:
print(error_msg)
return error_msg
def _clean_answer(answer: any) -> str:
"""
Clean up the answer to remove common prefixes and formatting
that models often add but that can cause exact match failures.
Args:
answer: The raw answer from the model
Returns:
The cleaned answer as a string
"""
# Convert non-string types to strings
if not isinstance(answer, str):
# Handle numeric types (float, int)
if isinstance(answer, float):
# Format floating point numbers properly
# Check if it's an integer value in float form (e.g., 12.0)
if answer.is_integer():
formatted_answer = str(int(answer))
else:
# For currency values that might need formatting
if abs(answer) >= 1000:
formatted_answer = f"${answer:,.2f}"
else:
formatted_answer = str(answer)
return formatted_answer
elif isinstance(answer, int):
return str(answer)
else:
# For any other type
return str(answer)
# Now we know answer is a string, so we can safely use string methods
# Normalize whitespace
answer = answer.strip()
# Remove common prefixes and formatting that models add
prefixes_to_remove = [
"The answer is ",
"Answer: ",
"Final answer: ",
"The result is ",
"To answer this question: ",
"Based on the information provided, ",
"According to the information: ",
]
for prefix in prefixes_to_remove:
if answer.startswith(prefix):
answer = answer[len(prefix) :].strip()
# Remove quotes if they wrap the entire answer
if (answer.startswith('"') and answer.endswith('"')) or (
answer.startswith("'") and answer.endswith("'")
):
answer = answer[1:-1].strip()
return answer
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