TGS (Terminal Generative Shell)

TGS is a conversational shell that combines Rust's power with the intelligence of GPT-based natural language processing. Designed as a spinoff of the TerminalGPT project, it aims to redefine the way users interact with their terminals.

Features

• Conversational Interface: Interact with your terminal in natural language. Instead of remembering complex commands, tell TGS what you want to do.
• Rust Integration: It is built with the robustness and efficiency of Rust, ensuring a fast and reliable terminal experience.
• Intelligent Suggestions: TGS provides smart suggestions and corrections, making your terminal experience smoother and more intuitive.
• Safety Measures: TGS has built-in safety checks to prevent accidental execution of potentially harmful commands.

Installation

You can install TGS using the following command. This script will automatically determine your OS, download the latest version of TGS, and install it:

curl -sSL https://raw.githubusercontent.com/warpy-ai/tgs/main/install.sh | bash

To install a specific version of TGS, append the version number to the command:

curl -sSL https://raw.githubusercontent.com/warpy-ai/tgs/main/install.sh | bash -s -- <version_number>

For example, to install version v0.2.8:

curl -sSL https://raw.githubusercontent.com/warpy-ai/tgs/main/install.sh | bash -s -- v0.2.8

Usage

Start TGS with the following command:

tgs

Once inside TGS, you can start conversing with your terminal. Here are a few examples:

tgs> Create a new directory called "projects"
Directory "projects" created.

tgs> List all files in the current directory
...output...

Testing

To run the unit tests for TGS:

cargo test

Contributing

We welcome contributions! Please see our CONTRIBUTING.md for guidelines.

Working with the project

This library relies on the tch crate for bindings to the C++ Libtorch API. The libtorch library must be downloaded automatically or manually. The following references how to set up your environment to use these bindings. Please refer to tch for detailed information and support.

Furthermore, this library relies on a cache folder for downloading pre-trained models. The default cache folder is ~/.cache/.rustbert but can be changed by setting the RUSTBERT_CACHE environment variable. Note that the language models used by this library are in the order of 100s of MBs to GBs.

Manual installation (recommended)

1. Download libtorch from https://pytorch.org/get-started/locally/. This package requires v2.1: if this version is no longer available on the "get started" page, the file should be accessible by modifying the target link, for example https://download.pytorch.org/libtorch/cu118/libtorch-cxx11-abi-shared-with-deps-2.1.1%2Bcu118.zip for a Linux version with CUDA11. NOTE: When using rust-bert as a dependency from crates.io, please check the required LIBTORCH on the published package readme as it may differ from the version documented here (applying to the current repository version).
2. Extract the library to a location of your choice
3. Set the following environment variables

Convert weights to .ot

python ./utils/convert_model.py model/pytorch_model.bin

Linux:

export LIBTORCH=/path/to/libtorch
export LD_LIBRARY_PATH=${LIBTORCH}/lib:$LD_LIBRARY_PATH

Windows

$Env:LIBTORCH = "X:\path\to\libtorch"
$Env:Path += ";X:\path\to\libtorch\lib"

macOS + Homebrew

brew install pytorch jq
export LIBTORCH=$(brew --cellar pytorch)/$(brew info --json pytorch | jq -r '.[0].installed[0].version')
export LD_LIBRARY_PATH=${LIBTORCH}/lib:$LD_LIBRARY_PATH

Automatic installation

Alternatively, you can let the build script automatically download the libtorch library. The download-libtorch feature flag needs to be enabled. The CPU version of libtorch will be downloaded by default. To download a CUDA version, please set the environment variable TORCH_CUDA_VERSION to cu118. Note that the libtorch library is large (several GBs for the CUDA-enabled version), and the first build may, therefore, take several minutes to complete.

Verifying installation

Verify your installation (and linking with libtorch) by adding the rust-bert dependency to your Cargo.toml or by cloning the rust-bert source and running an example:

git clone git@github.com:guillaume-be/rust-bert.git
cd rust-bert
cargo run --example sentence_embeddings

License

TGS is licensed under the Apache License.

Research references

• Translating Natural Language to Bash Commands using Deep Neural Networks
• NL2CMD: An Updated Workflow for Natural Language to Bash Commands Translation
• Dataset
• End-to-end NLP Pipelines in Rust

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Citations

@inproceedings{becquin-2020-end,
    title = "End-to-end {NLP} Pipelines in Rust",
    author = "Becquin, Guillaume",
    booktitle = "Proceedings of Second Workshop for NLP Open Source Software (NLP-OSS)",
    year = "2020",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlposs-1.4",
    pages = "20--25",
}

Troubleshooting

For Mac.

In case of running into torch-sys error, refer to this solution

Contributors

Acknowledgements

This project is a fork of the shrs repository. The core functionality of the CLI architecture is derived from it. We want to express our gratitude to the original authors for their work. The original project is licensed under Apache-2.0/MIT.