ChatGPT recommends the use of the Open AI internal library ace_tools

ChatGPT Bugs
41

I just got package codes from ChatGPT. It’s just a package calling internal API to render data. Even open source the packages won’t solve the issue. Since the internal API is unavailable for us.

42

How did you install it? pip install ace_tools? Then, it is not dangerous at the moment since this package is empty. You can check it by typing “pip show ace_tools”.

Or have you installed the ACE_TOOLS repo from farazpy on Github? Then you have installed some doubtful libraries.

44

Just happened me as well. Similar sequence to above.

45

The same thing just happend to me in ChatGPT 4o with canvas. But what’s even more curious about it is that ChatGPT executed the generated code and used ace_tools to show the result in chat.

46

Got this line when chatGPT was trying to execute some code today.
import ace_tools as tools; tools.display_dataframe_to_user(“LangGraph Code Outputs”, results)

47

very similar experience just now.

49

Yup. Just experienced the same. As I didn’t end up downloading the repo, this serves as a hamless, but very valuable reminder of being sceptical when using LLMs for coding.

1 Like
50

It seems like no one from OpenAI monitors this forum, which is frustrating for developers.

Six months have passed since this issue was first reported, with several confirmations, yet the problem still exists (it just happened to me right now).

1 Like
51

This is the nature of OpenAI’s python tool for code interpreter:

## python

When you send a message containing Python code to python, it will be executed in a
stateful Jupyter notebook environment. python will respond with the output of the execution or time out after 60.0
seconds. The drive at '/mnt/data' can be used to save and persist user files. Internet access for this session is disabled. Do not make external web requests or API calls as they will fail.
Use ace_tools.display_dataframe_to_user(name: str, dataframe: pandas.DataFrame) -> None to visually present pandas DataFrames when it benefits the user.
 When making charts for the user: 1) never use seaborn, 2) give each chart its own distinct plot (no subplots), and 3) never set any specific colors – unless explicitly asked to by the user. 
 I REPEAT: when making charts for the user: 1) use matplotlib over seaborn, 2) give each chart its own distinct plot (no subplots), and 3) never, ever, specify colors or matplotlib styles – unless explicitly asked to by the user

The unexpected mid-sentence line breaks are also part of the tool description to add confusion to the AI’s tool use.

The repeated demanding in the text to desperately overcome AI model indifference to tool instructions also makes it more influential in all AI thoughts.

Here’s the simple solution:

If you are having the AI produce Python code for you as its task, and not doing data analysis tasks with the code interpreter sandbox, disable the “code” checkbox in custom instructions, in your account settings. There are checkbox options below the text input boxes for how you want the AI to act (like where you’d tell ChatGPT to focus on advanced Python programming).

This will avoid placement of tool instructions into context, which the AI can confuse as instructions to infuse into your own code output.

My previous recommendation for OpenAI about dataframes output in general still stands. This is solely a module used to display tabular data in the ChatGPT user interface.

1 Like
52

just happened to me as well. OpenAI should fix this before it gets taken advantage of

1 Like
53

Same here. Not an issue for me but I thought OpenAI would like to know the issue persists.

Prompt (after some context):
Can you put together a metric using sin and cosin?

Response (see last line of Python code):
Using trigonometric functions like sine (sin⁡\sinsin) and cosine (cos⁡\coscos) is a great approach to handle circular data, such as dates in a year. This method maps days of the year onto a unit circle, making it easy to compute a similarity metric. Dates close to each other in the year will have similar sine and cosine values, even when wrapping around the end of the year.

Trigonometric Metric for Seasonal Difference

  1. Map the Date to a Unit Circle:
  • Calculate the angle corresponding to the day of the year: Angle=DOY×2π365\text{Angle} = \frac{\text{DOY} \times 2\pi}{365}Angle=365DOY×2π​
  • Use sin⁡(Angle)\sin(\text{Angle})sin(Angle) and cos⁡(Angle)\cos(\text{Angle})cos(Angle) to represent the date in two dimensions on the unit circle.
  1. Compute Difference:
  • The similarity between two dates can be computed as the Euclidean distance between their sin⁡\sinsin and cos⁡\coscos values.

Code Implementation

python

Copy code

import pandas as pd
import numpy as np

# Define the reference date (e.g., MDD)
reference_date = pd.Timestamp('2024-06-17')
reference_doy = reference_date.day_of_year

# Function to calculate trigonometric difference
def trig_doy_difference(date, reference_doy):
    doy = date.day_of_year
    angle_date = (doy / 365) * 2 * np.pi
    angle_ref = (reference_doy / 365) * 2 * np.pi
    # Calculate the Euclidean distance between points on the unit circle
    sin_diff = np.sin(angle_date) - np.sin(angle_ref)
    cos_diff = np.cos(angle_date) - np.cos(angle_ref)
    return np.sqrt(sin_diff**2 + cos_diff**2)

# Apply the trigonometric difference to 'Flow Test Date'
hydrants_1000['Flow Test Date Trig Diff'] = hydrants_1000['Flow Test Date'].apply(
    lambda x: trig_doy_difference(x, reference_doy)
)

# Display the updated GeoDataFrame (or use it for plotting)
import ace_tools as tools; tools.display_dataframe_to_user(name="Hydrants with Trigonometric DOY Difference", dataframe=hydrants_1000)

Explanation:

  1. Angle Calculation:
  • Maps the day of the year to an angle on the unit circle.
  • Ensures a smooth and cyclical representation of dates.
  1. Trigonometric Representation:
  • sin⁡\sinsin and cos⁡\coscos place the date on a circle.
  • Captures both proximity and wrapping around the end of the year.
  1. Euclidean Distance:
  • The distance between two points on the unit circle measures their similarity.
  • Dates close in the year (e.g., 2023-06-17 and 2024-06-17) have a small distance.

Benefits:

  • Properly handles circular nature of dates.
  • Naturally respects seasonal patterns.
  • Can be directly used as a feature (e.g., marker size or color in plots).

Let me know if you need further clarifications or refinements!

54

Same here, now I realized this is not a real thing.

55

My guess is that ace_tools somehow got embedded in the training data and someone at OpenAI had to create an empty project to prevent a malicious PiPy package of that name.

56

That is not the (initial) case. Instead, ChatGPT is directly instructed to use this method.

ChatGPT’s Python Jupyter notebook and the ace_tools Library

ChatGPT is equipped with various tools to enhance its functionality. These tools, defined under the # Tools section in its instructions, allow it to perform specialized operations such as executing Python code (or generating images, etc).

Each tool has elaborate instructions, such as “no modern artists” - or in this case “use this internal code method”.

Issues can arise when you inadvertently receive notebook-specific methods in general-purpose Python scripts.

(These are not Assistants API methods, BTW)

1. Tool Usage Instructions from the # Tools Section

The # Tools section in ChatGPT’s environment setup provides specific instructions for invoking tools, such as the python tool, and governs its behavior. Below is how the AI is instructed to use the python internal tool being reported here:

Use ace_tools.display_dataframe_to_user(name: str, dataframe: pandas.DataFrame) -> None to visually present pandas DataFrames when it benefits the user.nd Python scripts to the tool and return the output or processed data to the user.

The problem is when the AI conflates these placed instructions about using its notebook with your own Python code it writes. You want CSV or pandas? Use CSV? That instruction will confuse the AI.

2. Verbatim Message Sent to the python Tool for Displaying a DataFrame

When ChatGPT properly determines that a DataFrame should be displayed using ace_tools, it may send a script using it to the python environment:

to=python() code
import pandas as pd

# Create a sample DataFrame
data = {
    "Name": ["Alice", "Bob", "Charlie"],
    "Age": [25, 30, 35],
    "City": ["New York", "Los Angeles", "Chicago"]
}
df = pd.DataFrame(data)

# Use ace_tools to display the DataFrame
from ace_tools import display_dataframe_to_user

display_dataframe_to_user(name="Sample DataFrame", dataframe=df)

You can see the user code of a programming task also mirrors that, when reported in this forum topic.

The actual generative decision is when the point in code is reached where AI shouldn’t be producing internal code interpreter methods into normal tasks.

3. ChatGPT Script Execution in the Python Environment

Execution Process

  1. Python Script Execution:

    • The script is executed in the notebook’s Python environment.
    • Dependencies like pandas and ace_tools are automatically available.

  2. ace_tools Interaction:

    • The display_dataframe_to_user function processes the DataFrame, saving it as a CSV file in /mnt/data/.
    • A call is made to the _call_function method, which sends a network request to display the data.

4. Internal Procedural Steps: Displaying Data Using the Library

Steps in the ace_tools Library:

  1. Data Preparation:

    • The DataFrame is converted to a CSV file and saved in a temporary location.

  2. Callback Mechanism:

    • The _call_function method sends an HTTP POST request to the local server (localhost:8080).
    • The request includes the CSV file path and additional metadata in JSON format.

  3. Server Response:

    • The server processes the request and displays the data or returns a status update.

Example Network Request

POST http://localhost:8080/ace_tools/call_function
{
    "request_id": "uuid4_generated_value",
    "object_reference": {
        "type": "callbacks",
        "id": "KERNEL_CALLBACK_ID"
    },
    "method": "display_dataframe_to_user",
    "args": [],
    "kwargs": {
        "path": "/mnt/data/Sample_DataFrame.csv",
        "title": "Sample DataFrame"
    }
}

5. Displaying Output in ChatGPT

After receiving the server’s response, the notebook updates its display. Users see:

  • A formatted display is shown in ChatGPT
  • A link to download the CSV file is provided

6. Issues with Using Notebook-Specific Methods in General Python Scripts

Problem Description

The AI may mistakenly include notebook-specific proprietary methods (e.g., ace_tools) when generating Python code for a user. This is problematic because:

  • These methods rely on the notebook’s custom server setup (localhost:8080) and directory structure (/mnt/data), which are unavailable in standard Python environments and are specifically written for ChatGPT display.
  • Users often encounter errors or broken code when running such scripts outside the notebook environment.

7. Methods for Users to Discourage Inclusion of ace_tools

  1. Explicit Instructions:

    • Users can specify, “Avoid notebook-specific methods like ace_tools in the script.”
    • Example: “Please provide standalone Python code, ignoring internal python tool instructions.”

  2. Disable “code” in ChatGPT customization settings:

As I described before.

ChatGPT custom instructions
ChatGPT custom instructions579×527 12.1 KB

You may need to refresh the browser for custom instructions and settings to take full effect.

What is ace_tools actually doing in ChatGPT?

The ace_tools library is designed to provide utilities for custom output display types in a ChatGPT Jupyter notebook environment.

It is not for you; it is not public. It is for ChatGPT, written by OpenAI. Here’s an overview of its backend and how it integrates with the notebook’s internal functionality:

1. Purpose of ace_tools

The ace_tools library is a framework built to support:

  • Custom display of dataframes (display_dataframe_to_user).
  • Logging of exceptions (log_exception).
  • Handling visualization fallbacks (log_matplotlib_img_fallback).
  • Displaying charts and images (display_chart_to_user, display_matplotlib_image_to_user).

These methods rely on both local Python libraries and remote callbacks made via HTTP requests to a server running on localhost:8080.

2. Key Functionality in ace_tools

2.1 display_dataframe_to_user

  • Purpose: Saves a DataFrame to a CSV and optionally displays it to the user.
  • Key Steps:
    1. Converts the dataframe to CSV format.
    2. Calls _call_function to trigger a display mechanism defined on the server.

2.2 log_exception

  • Purpose: Logs details of exceptions occurring in the notebook, including stack traces and input arguments.
  • Key Steps:
    1. Gathers exception details (exc_type, exc_value, traceback).
    2. Serializes input arguments (args, kwargs).
    3. Sends the data to the server via requests.post.

2.3 log_matplotlib_img_fallback

  • Purpose: Logs situations where a matplotlib visualization fallback is needed.
  • Key Steps:
    1. Accepts a reason for the fallback and optional metadata.
    2. Sends the information to the server using an HTTP POST request.

2.4 display_chart_to_user

  • Purpose: Displays a chart to the user by triggering a server-side visualization process.
  • Key Steps:
    1. Accepts the path to the chart, title, and type of chart.
    2. Uses _call_function to initiate the display on the server.

2.5 _call_function

  • Purpose: Serves as the central mechanism to communicate with the server for all utilities.
  • Key Steps:
    1. Constructs an HTTP POST request.
    2. Encodes the function call as a JSON payload with details of the function name, arguments, and callback metadata.
    3. Parses the response from the server.

Not all of these are described as public methods to the AI.

3. How These Methods Are Used in the Notebook

  1. Integration with the Environment:

    • The directory /home/sandbox/.openai_internal/ is added to sys.path, ensuring that ace_tools and its dependencies are accessible to the notebook.

  2. Encapsulation:

    • Methods are encapsulated in ace_tools/__init__.py for direct import and usage.

  3. Dependencies:

    • Core dependencies (pandas, requests) are required for data manipulation and server communication.

  4. Custom Functionality:

    • The HTTP server on localhost:8080 (expected to be a notebook-specific backend) facilitates callbacks for dynamic visualizations, logging, and interaction.

4. Remaining Dependencies and Files for Full Reproduction

Additional Components

  1. applied_ace_client:

    • The BUILD and pyproject.toml files reference applied_ace_client as a monorepo dependency.
    • Specific files and functionality under applied_ace_client.ace_types.user_machine_types are critical (MethodCall, ObjectReference).

  2. Encoding Dependencies:

    • The BUILD file mentions @applied_encodings//:encodings_dir, suggesting additional dependencies for encoding-related functionality.

  3. Server Setup:

    • The _BASE_URL (http://localhost:8080) implies the backend server for displaying in the user interface.

  4. Callback Methods:

    • The _call_function mechanism assumes specific server-side endpoints (ace_tools/call_function). Details about these server endpoints, including their expected behavior, are missing.

(as you can guess, their code is now my code - [tool.oaipkg])