Python use

Python use (aipython) is a Python command-line interpreter integrated with LLM.

What

Python use provides the entire Python execution environment to LLM. Imagine LLM sitting in front of a computer, typing various commands into the Python command-line interpreter, pressing Enter to execute, observing the results, and then typing and executing more code.

Unlike Agents, Python use does not define any tools interface. LLM can freely use all the features provided by the Python runtime environment.

Why

If you are a data engineer, you are likely familiar with the following scenarios:

• Handling various data file formats: csv/excel, json, html, sqlite, parquet, etc.
• Performing operations like data cleaning, transformation, computation, aggregation, sorting, grouping, filtering, analysis, and visualization.

This process often requires:

• Starting Python, importing pandas as pd, and typing a bunch of commands to process data.
• Generating a bunch of intermediate temporary files.
• Describing your needs to ChatGPT/Claude, copying the generated data processing code, and running it manually.

So, why not start the Python command-line interpreter, directly describe your data processing needs, and let it be done automatically? The benefits are:

• No need to manually input a bunch of Python commands temporarily.
• No need to describe your needs to GPT, copy the program, and run it manually.

This is the problem Python use aims to solve!

How

Python use (aipython) is a Python command-line interpreter integrated with LLM. You can:

• Enter and execute Python commands as usual.
• Describe your needs in natural language, and aipython will automatically generate Python commands and execute them.

Moreover, the two modes can access data interchangeably. For example, after aipython processes your natural language commands, you can use standard Python commands to view various data.

Interfaces

ai Object

• __call__(instruction): Execute the automatic processing loop until LLM no longer returns code messages
• save(path): Save the interaction process to an svg or html file
• llm Property: LLM object
• runner Property: Runner object

LLM Object

• history Property: Message history of the interaction process between the user and LLM

Runner Object

• globals: Global variables of the Python environment executing the code returned by LLM
• locals: Local variables of the Python environment executing the code returned by LLM

runtime Object

For the code generated by LLM to call, providing the following interface:

• install_packages(packages): Request to install third-party packages
• getenv(name, desc=None): Get environment variables
• display(path=None, url=None): Display images in the terminal

Usage

AIPython has two running modes:

• Task mode: Very simple and easy to use, just input your task, suitable for users unfamiliar with Python.
• Python mode: Suitable for users familiar with Python, allowing both task input and Python commands, ideal for advanced users.

The default running mode is task mode, which can be switched to Python mode using the --python parameter.

Task Mode

uv run aipython

>>> Get the latest posts from Reddit r/LocalLLaMA
......
......
>>> /done

Python Mode

Basic Usage

Automatic task processing:

>>> ai("Get the title of Google's homepage")

Automatically Request to Install Third-Party Libraries

Python use - AIPython (Quit with 'exit()')
>>> ai("Use psutil to list all processes on MacOS")

📦 LLM requests to install third-party packages: ['psutil']
If you agree and have installed, please enter 'y [y/n] (n): y

TODO

• Use AST to automatically detect and fix Python code returned by LLM

Thanks

• Hei Ge: Product manager/senior user/chief tester
• Sonnet 3.7: Generated the first version of the code, which was almost ready to use without modification.
• ChatGPT: Provided many suggestions and code snippets, especially for the command-line interface.
• Codeium: Intelligent code completion
• Copilot: Code improvement suggestions and README translation