Skip to main content

CLI tool for debugging clingo using a combination of MUS and LLMs

Project description

ExplaidLLM

ExplaidLLM is a command line tool aimed for debugging clingo programs. This is achieved by combining the Minimal Unsatisfiable Subset (MUS) functionalities of clingexplaid together with the natural language capabilities of an LLM.

Installation

From Source

pip install .

From PyPi

pip install explaidllm

Configuration

[!NOTE] Currently ExplaidLLM only supports the OpenAI LLM API. The implementation of other API's can be requested through Issues.

LLM API

Before using ExplaidLLM you need to store your API Key to prompt your LLM of choice. You can do this in two different ways:

Using a .env file

  • Recommended for installation from source
OPENAI_API_KEY=<YOUR-KEY-GOES-HERE>

Using the -k API-KEY option

  • Recommended for installation from PYPI
explaidllm examples/test.lp -k=$YOUR_OPENAI_KEY_ENV_VAR

Usage

ExplaidLLM allows you to quickly debug your unsatisfiable ASP Programs. For the default behaviour just use this command:

explaidllm example/test.lp

The default behaviour converts all you program atoms to assumptions, computes an MUS (Minimal Unsatisfiable Subset) from them, finds a matching unsatisfiable constraint and then prompts the configured LLM for an explanation.

Assumption Signatures

If you want to filter only certain assumption signatures use the option -a to specify the signatures to be converted.

explaidllm example/test.lp -a x/1

LLM Models

You can also specify which LLM Model should be used for the explanation using the -m model option. A list of available models can be found on the --help page.

explaidllm example/test.lp -m='gpt-4o'

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

explaidllm-0.1.4.tar.gz (126.7 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

explaidllm-0.1.4-py3-none-any.whl (16.9 kB view details)

Uploaded Python 3

File details

Details for the file explaidllm-0.1.4.tar.gz.

File metadata

  • Download URL: explaidllm-0.1.4.tar.gz
  • Upload date:
  • Size: 126.7 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.7

File hashes

Hashes for explaidllm-0.1.4.tar.gz
Algorithm Hash digest
SHA256 32e6e25cd30e3b25b5550d35724f16c6ec43663e954db72dda4ba04bcd80ff58
MD5 638336c341fe68c1fa7fac263c389831
BLAKE2b-256 66e10f57338f4ca9379d7bab105a5b7065ff9d9cb9e93151074c61b5b154ad76

See more details on using hashes here.

Provenance

The following attestation bundles were made for explaidllm-0.1.4.tar.gz:

Publisher: deploy.yml on hweichelt/explaidllm

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file explaidllm-0.1.4-py3-none-any.whl.

File metadata

  • Download URL: explaidllm-0.1.4-py3-none-any.whl
  • Upload date:
  • Size: 16.9 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.7

File hashes

Hashes for explaidllm-0.1.4-py3-none-any.whl
Algorithm Hash digest
SHA256 9a44a6583a6d258425865e75262c127561633a5047cc482f3dd84fe1ed54640a
MD5 833e706423ae6eb4012363cca40147d3
BLAKE2b-256 e4913c4d8a1a2b75605846982a9ef68fe35ce3dc7ba2e540bdbcf42722bf52da

See more details on using hashes here.

Provenance

The following attestation bundles were made for explaidllm-0.1.4-py3-none-any.whl:

Publisher: deploy.yml on hweichelt/explaidllm

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page