final_assignment_agentic_course

Sleeping

final_assignment_agentic_course / misc.py

added new files for model, tools, misc, and agent

8b6df94 over 1 year ago

5.68 kB

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

	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