Skip to main content

A library containing core components for Gen AI applications.

Project description

GLLM Core

Description

A core library providing foundational components and utilities for Generative AI applications.

Installation

Prerequisites

1. Installation from Artifact Registry

Choose one of the following methods to install the package:

Using pip

pip install gllm-core-binary

Using Poetry

poetry add gllm-core-binary

2. Development Installation (Git)

For development purposes, you can install directly from the Git repository:

poetry add "git+ssh://git@github.com/GDP-ADMIN/gen-ai-internal.git#subdirectory=libs/gllm-core"

Managing Dependencies

  1. Go to root folder of gllm-core module, e.g. cd libs/gllm-core.
  2. Run poetry shell to create a virtual environment.
  3. Run poetry lock to create a lock file if you haven't done it yet.
  4. Run poetry install to install the gllm-core requirements for the first time.
  5. Run poetry update if you update any dependency module version at pyproject.toml.

Contributing

Please refer to this Python Style Guide to get information about code style, documentation standard, and SCA that you need to use when contributing to this project

  1. Activate pre-commit hooks using pre-commit install
  2. Run poetry shell to create a virtual environment.
  3. Run poetry lock to create a lock file if you haven't done it yet.
  4. Run poetry install to install the gllm-core requirements for the first time.
  5. Run which python to get the path to be referenced at Visual Studio Code interpreter path (Ctrl+Shift+P or Cmd+Shift+P)
  6. Try running the unit test to see if it's working:
poetry run pytest -s tests/unit_tests/

Project details


Release history Release notifications | RSS feed

Download files

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

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distributions

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

gllm_core_binary-0.0.0-cp313-cp313-win_amd64.whl (350.0 kB view details)

Uploaded CPython 3.13Windows x86-64

gllm_core_binary-0.0.0-cp312-cp312-win_amd64.whl (352.7 kB view details)

Uploaded CPython 3.12Windows x86-64

File details

Details for the file gllm_core_binary-0.0.0-cp313-cp313-win_amd64.whl.

File metadata

File hashes

Hashes for gllm_core_binary-0.0.0-cp313-cp313-win_amd64.whl
Algorithm Hash digest
SHA256 0a602a3845ab6bda064f72b9e983e803957d44b3f20d8f463dc0ac920e0e46b2
MD5 41d758fc1a2ed37fac78fa63b398b7b2
BLAKE2b-256 b08d0aa17f23e93684f76979064b59fcc3b375815733b6c8406dbf24c9df336a

See more details on using hashes here.

Provenance

The following attestation bundles were made for gllm_core_binary-0.0.0-cp313-cp313-win_amd64.whl:

Publisher: build-binary.yml on GDP-ADMIN/gen-ai-internal

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

File details

Details for the file gllm_core_binary-0.0.0-cp312-cp312-win_amd64.whl.

File metadata

File hashes

Hashes for gllm_core_binary-0.0.0-cp312-cp312-win_amd64.whl
Algorithm Hash digest
SHA256 b4dfc3fa8a74183f05590eea1f8ee628224c2cf04461376e47278500edbf7d1c
MD5 9104bbf0ac6edf773c8cc4f0549080d5
BLAKE2b-256 6076087ef2930d8ae1e47214bd51f90166ba30a3bc16c8ee17dcf17ff7024cf6

See more details on using hashes here.

Provenance

The following attestation bundles were made for gllm_core_binary-0.0.0-cp312-cp312-win_amd64.whl:

Publisher: build-binary.yml on GDP-ADMIN/gen-ai-internal

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