semvec/pypi-packages huggingface dataset:
pymirage 0.1.1.dev2
高性能日本語形態素解析ライブラリ - SudachiPyとGemini AIによるテキスト分析
Requires Python: >=3.12
Keywords: ai, gemini, japanese, morphological-analysis, nlp, sudachi, text-analysis

# Mirage-Core

日本語文章解析エンジン。伝統的な自然言語処理（NLP）とLLMを組み合わせたハイブリッドアーキテクチャを採用しています。

## 🚀 クイックスタート

### Docker Hub から実行（最も簡単）

```bash
# Docker Hub から最新イメージを取得
docker pull stayforge/mirage:latest

# コンテナを起動
docker run -d -p 8000:8000 stayforge/mirage:latest

# API にアクセス
curl http://localhost:8000/analysis/health
```

### Docker Compose で実行

```bash
# リポジトリをクローン
git clone https://github.com/[your-repo]/Mirage-Core.git
cd Mirage-Core

# Docker Compose で起動
docker-compose up -d

# API にアクセス
curl http://localhost:8000/analysis/health
```

詳細は [DOCKER.md](./DOCKER.md) を参照

### ローカル環境で実行

```bash
# 依存関係をインストール
pip install -r requirements.txt

# UniDic 辞書をダウンロード（初回のみ）
python -m unidic download

# API サーバーを起動
python main.py
```

## 📚 API ドキュメント

起動後、以下の URL でアクセス：

- **Swagger UI**: http://localhost:8000/docs
- **ReDoc**: http://localhost:8000/redoc
- **API ガイド**: [API.md](./API.md)

## 🧪 テスト

```bash
# 全テストを実行
pytest tests/ -v

# API テストのみ
pytest tests/test_api.py -v

# 分析機能テストのみ
pytest tests/test_analysis.py -v
```

## 🔄 CI/CD

GitHub Actions による自動ビルド・デプロイを設定済み

- **main ブランチ**: `stayforge/mirage:latest`
- **dev ブランチ**: `stayforge/mirage:dev`
- **バージョンタグ**: `stayforge/mirage:v1.0.0`

セットアップ方法: [CI_SETUP.md](./CI_SETUP.md)

## 🛠️ Tools

A tool to convert other formats (mainly Aozora Bunko) to .MDI

Supported file(text) formats:
- Plain text (.txt)
- Markdown (.md)
- [HTML](./samples/蜃気楼/芥川龍之介-蜃気楼.html)（青空文庫）
- [illusion Markdown](https://github.com/Iktahana/illusions/blob/main/MDI.md) (.imd)
narrativegraphs 0.2.0
A package for creating and analyzing narrative graphs from text corpora
Requires Python: >=3.10
Keywords: nlp, narrative-analysis, network-analysis, computational-linguistics, text-mining

# narrativegraphs

Turn a collection of texts into an interactive narrative graph of entities and their relations and explore the structure of your corpus visually.

## Installation

```bash
pip install narrativegraphs
```

## Quick Start

```python
from narrativegraphs import NarrativeGraph

docs: list[str] = [...]  # your list of documents
model = NarrativeGraph().fit(docs)
model.serve_visualizer()
```

Open the link in your terminal to explore the graph in your browser:

![visualizer-screenshot.png](https://raw.githubusercontent.com/KasperFyhn/narrativegraphs/refs/heads/main/assets/visualizer-screenshot.png)

## Features
- **Plug'n'play solution** – get started with a few lines of code
- **Interactive browser-based visualizer** – shipped with an interactive React app which can be hosted directly from Python, no extra dependencies
- **See the original contexts** that extracted entities and relations appear in
- **Filter and query the graph** by statistics, category, or timestamps
- **Export graph and data to NetworkX and Pandas** for your own custom analyses
- **Modular structure** – customize or switch out pipeline components to accommodate _your_ use case.

## Documentation

Full documentation and tutorials: [kasperfyhn.github.io/narrativegraphs](https://kasperfyhn.github.io/narrativegraphs)

## Citation

If you use this package in academic work, please cite:

```bibtex
@software{narrativegraphs,
  author = {Fyhn, Kasper},
  title = {narrativegraphs: A Python package for narrative graph analysis},
  year = {2026},
  url = {https://github.com/kasperfyhn/narrativegraphs}
}
```