Metadata-Version: 2.2
Name: gllm-multimodal-binary
Version: 0.2.0.post1
Summary: A library containing multimodal components for Gen AI applications.
Author-email: Obryan Ramadhan <obryan.ramadhan@gdplabs.id>, Yanfa Adi Putra <yanfa.adi-putra@gdplabs.id>
Requires-Python: <3.13,>=3.11
Description-Content-Type: text/markdown
Requires-Dist: poetry<3.0.0,>=2.1.3
Requires-Dist: gllm-core-binary<0.4.0,>=0.3.0
Requires-Dist: gllm-inference-binary<0.6.0,>=0.5.0
Requires-Dist: gllm-misc-binary<0.9.0,>=0.8.0
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Requires-Dist: boto3<2.0.0,>=1.38.10
Requires-Dist: exifread<4.0.0,>=3.3.2
Requires-Dist: google-api-python-client<3.0.0,>=2.174.0
Requires-Dist: google-auth<3.0.0,>=2.26.0
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Provides-Extra: dev
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Requires-Dist: mypy<2.0.0,>=1.15.0; extra == "dev"
Requires-Dist: pytest<9.0.0,>=8.1.1; extra == "dev"
Requires-Dist: pytest-asyncio<0.24.0,>=0.23.6; extra == "dev"
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Requires-Dist: ruff<0.7.0,>=0.6.7; extra == "dev"

# GLLM Multimodal

## Description
A library containing multimodal manager modules for handling modality-specific tasks.

## Installation

### Prerequisites
1. Python 3.11+ - [Install here](https://www.python.org/downloads/)
2. Pip (if using Pip) - [Install here](https://pip.pypa.io/en/stable/installation/)
3. Poetry 2.1.4+ - [Install here](https://python-poetry.org/docs/#installation)
4. Git (if using Git) - [Install here](https://git-scm.com/downloads)
5. For artifact registry installation:
   - Google Cloud CLI - [[installed](https://cloud.google.com/sdk/docs/) and [authenticated](https://cloud.google.com/docs/authentication/gcloud)]
   - Access to the [GDP Labs SDK artifact registry](https://console.cloud.google.com/artifacts/python/gdp-labs/asia-southeast2/gen-ai?project=gdp-labs)
6. For git installation, access to the [GDP Labs SDK github repository](https://github.com/GDP-ADMIN/gl-sdk)

### 1. Installation from Artifact Registry
Choose one of the following methods to install the package:

#### Using pip
```bash
pip install keyring keyrings.gl-artifactregistry-auth
pip install gllm-multimodal-binary --index-url https://glsdk.gdplabs.id/gen-ai/simple
```

#### Using Poetry
```bash
poetry source add --priority=explicit gen-ai https://glsdk.gdplabs.id/gen-ai/simple
poetry config http-basic.gen-ai oauth2accesstoken "$(gcloud auth print-access-token)"
poetry add --source gen-ai gllm-multimodal-binary
```

### 2. Development Installation (Git)
For development purposes, you can install directly from the Git repository:
```bash
git clone git@github.com:GDP-ADMIN/gl-sdk.git
cd gl-sdk/libs/gllm-multimodal
```

## Local Development Setup

### Quick Setup (Recommended)
For local development with editable gllm packages, use the provided Makefile:

```bash
# Complete setup: installs Poetry, configures auth, installs packages, sets up pre-commit
make setup
```

The following are the available Makefile targets:

1. `make setup` - Complete development setup (recommended for new developers)
2. `make install-poetry` - Install or upgrade Poetry to the latest version
3. `make auth` - Configure authentication for internal repositories
4. `make install` - Install all dependencies
5. `make install-pre-commit` - Set up pre-commit hooks
6. `make update` - Update dependencies

### Manual Development Setup (Legacy)
If you prefer to manage dependencies manually:

1. Go to root folder of `gllm-multimodal` module, e.g. `cd libs/gllm-multimodal`.
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-multimodal` 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](https://docs.google.com/document/d/1uRggCrHnVfDPBnG641FyQBwUwLoFw0kTzNqRm92vUwM/edit?usp=sharing)
to get information about code style, documentation standard, and SCA that you need to use when contributing to this project

### Getting Started with Development
1. Clone the repository and navigate to the gllm-multimodal directory
2. Run `make setup` to set up your development environment
3. Run `which python` to get the path to be referenced at Visual Studio Code interpreter path (`Ctrl`+`Shift`+`P` or `Cmd`+`Shift`+`P`)
4. Try running the unit test to see if it's working:
```bash
poetry run pytest -s tests/unit_tests/
```
