gllm-core-binary 0.4.16

A library containing core components for Gen AI applications.

GLLM Core

Description

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

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Installation

Prerequisites

Mandatory:

1. Python 3.11+ — Install here
2. pip — Install here
3. uv — Install here

Extras (required only for Artifact Registry installations):

1. gcloud CLI (for authentication) — Install here, then log in using:

   gcloud auth login
   

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Option 1: Install from Artifact Registry

This option requires authentication via the gcloud CLI.

uv pip install \
  --extra-index-url "https://oauth2accesstoken:$(gcloud auth print-access-token)@glsdk.gdplabs.id/gen-ai-internal/simple/" \
  gllm-core

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Option 2: Install from PyPI

This option requires no authentication. However, it installs the binary wheel version of the package, which is fully usable but does not include source code.

uv pip install gllm-core-binary

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Local Development Setup

Prerequisites

1. Python 3.11+ — Install here

2. pip — Install here

3. uv — Install here

4. gcloud CLI — Install here, then log in using:

   gcloud auth login
   

5. Git — Install here

6. Access to the GDP Labs SDK GitHub repository

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1. Clone Repository

git clone [e-mail]:GDP-ADMIN/gl-sdk.git
cd gl-sdk/libs/gllm-core

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2. Setup Authentication

Set the following environment variables to authenticate with internal package indexes:

export UV_INDEX_GEN_AI_INTERNAL_USERNAME=oauth2accesstoken
export UV_INDEX_GEN_AI_INTERNAL_PASSWORD="$(gcloud auth print-access-token)"
export UV_INDEX_GEN_AI_USERNAME=oauth2accesstoken
export UV_INDEX_GEN_AI_PASSWORD="$(gcloud auth print-access-token)"

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3. Quick Setup

Run:

make setup

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4. Activate Virtual Environment

source .venv/bin/activate

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Local Development Utilities

The following Makefile commands are available for quick operations:

Install uv

make install-uv

Install Pre-Commit

make install-pre-commit

Install Dependencies

make install

Update Dependencies

make update

Run Tests

make test

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Contributing

Please refer to the Python Style Guide for information about code style, documentation standards, and SCA requirements.