isage-data 0.2.3.2
SAGE Data - Unified data loaders for memory benchmark datasets (LongMemEval, Locomo, MemAgentBench, etc.)
License: MIT
Requires Python: ==3.11.*
Keywords: dataset, benchmark, memory, ai, longmemeval, locomo, memagentbench, sage

# SAGE Data ��

**Dataset management module for SAGE benchmark suite**

Provides unified access to multiple datasets through a two-layer architecture:

- **Sources**: Physical datasets (qa_base, bbh, mmlu, gpqa, locomo, orca_dpo)
- **Usages**: Logical views for experiments (rag, libamm, neuromem, agent_eval)

## Quick Start

### Automatic Setup (Recommended)

```bash
# Clone the repository
git clone https://github.com/intellistream/sageData.git
cd sageData

# Run quickstart script (handles everything including Git LFS)
./quickstart.sh
source .venv/bin/activate
```

The `quickstart.sh` script will:

- ✅ Detect and install Git LFS if needed (for dataset files)
- ✅ Pull LFS-tracked data files automatically
- ✅ Create Python virtual environment
- ✅ Install all dependencies

**Note**: Some datasets (like LibAMM benchmark files) use Git LFS. The quickstart script will handle
this automatically, but you can also manually install Git LFS:

- Ubuntu/Debian: `sudo apt install git-lfs`
- macOS: `brew install git-lfs`
- Windows: Download from [git-lfs.github.com](https://git-lfs.github.com/)

### Manual Setup

```bash
# Install Git LFS (if needed)
git lfs install

# Pull LFS data files
git lfs pull

# Setup Python environment
python -m venv .venv
source .venv/bin/activate
pip install -e ".[dev]"
```

```python
from sage.data import DataManager

manager = DataManager.get_instance()

# Access datasets by logical usage profile
rag = manager.get_by_usage("rag")
qa_loader = rag.load("qa_base")  # already instantiated
queries = qa_loader.load_queries()

# Or fetch a specific data source directly
bbh_loader = manager.get_by_source("bbh")
tasks = bbh_loader.get_task_names()
```

## 🛠️ CLI 使用方式（精简版）

安装后可直接使用 `sage-data` 命令：

```bash
sage-data list               # 显示数据源状态（已下载/缺失/远程）
sage-data usage rag          # 查看某个 usage 的数据映射
sage-data download locomo    # 下载指定数据源（仅支持部分源）

# 选项
sage-data list --json        # JSON 输出，便于脚本处理
sage-data --data-root /path  # 指定自定义数据根目录
```

当前支持自动下载的源：`locomo`, `longmemeval`, `memagentbench`, `mmlu`。 其他如 `gpqa`, `orca_dpo` 采用按需在线加载（Hugging
Face），`qa_base`/`bbh` 等随包内置。

## Available Datasets

| Dataset      | Description                              | Download Required                                      | Storage                     |
| ------------ | ---------------------------------------- | ------------------------------------------------------ | --------------------------- |
| **qa_base**  | Question-Answering with knowledge base   | ❌ No (included)                                       | Local files                 |
| **locomo**   | Long-context memory benchmark            | ✅ Yes (`python -m locomo.download`)                   | Local files (2.68MB)        |
| **bbh**      | BIG-Bench Hard reasoning tasks           | ❌ No (included)                                       | Local JSON files            |
| **mmlu**     | Massive Multitask Language Understanding | 📥 Optional (`python -m mmlu.download --all-subjects`) | On-demand or Local (~160MB) |
| **gpqa**     | Graduate-Level Question Answering        | ✅ Auto (Hugging Face)                                 | On-demand (~5MB cached)     |
| **orca_dpo** | Preference pairs for alignment/DPO       | ✅ Auto (Hugging Face)                                 | On-demand (varies)          |

See `examples/` for detailed usage examples.

## 📖 Examples

```bash
python examples/qa_examples.py            # QA dataset usage
python examples/locomo_examples.py        # LoCoMo dataset usage
python examples/bbh_examples.py           # BBH dataset usage
python examples/mmlu_examples.py          # MMLU dataset usage
python examples/gpqa_examples.py          # GPQA dataset usage
python examples/orca_dpo_examples.py      # Orca DPO dataset usage
python examples/integration_example.py    # Cross-dataset integration
```

## License

MIT License - see [LICENSE](LICENSE) file.

## 🔗 Links

- **Repository**: https://github.com/intellistream/sageData
- **Issues**: https://github.com/intellistream/sageData/issues

## ❓ Common Issues

**Q: Where's the LoCoMo data?**\
A: Run `python -m locomo.download` to download it (2.68MB from Hugging Face).

**Q: How to download MMLU for offline use?**\
A: Run `python -m mmlu.download --all-subjects` to download all subjects (~160MB).

**Q: GPQA access error?**\
A: You need to accept the dataset terms on Hugging Face:
https://huggingface.co/datasets/Idavidrein/gpqa

**Q: How to use Orca DPO for alignment research?**\
A: Use `DataManager.get_by_source("orca_dpo")` to get the loader, then use `format_for_dpo()` to
prepare data for training.

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**Version**: 0.1.0 | **Last Updated**: December 2025