README.md

---
license: apache-2.0
language:
  - zh
size_categories:
  - 10K<n<100K
tags:
  - daoism
  - taoism
  - chinese-religion
  - rag
  - retrieval
  - dingren-daoxue
  - lius-cc
pretty_name: Daoism Knowledge RAG (鼎稔道學館館藏)
---

# Daoism Knowledge RAG

**鼎稔道學館（Dingren Daoxue Lab）館藏知識庫**——專為 RAG（Retrieval-Augmented Generation）優化的道教知識結構化資料集，95,919 條目。

## Overview

This dataset is the curated knowledge base behind [Dingren Daoxue Lab (鼎稔道學館)](https://lius.cc), released under Apache 2.0 to enable open-source RAG with the [`Daoism-Qwen3.5-9B`](https://huggingface.co/lius-cc/Daoism-Qwen3.5-9B) model.

It covers eight categories of Daoist knowledge:

| Type | Count | Content |
|------|-------|---------|
| concept | ~32,700 | 神學概念、宇宙論、修煉名相 |
| scripture | ~23,000 | 經文、戒律、科儀文獻 |
| deity | ~12,000 | 神祇、仙真、神格體系 |
| ritual | ~11,000 | 科儀、法事、節慶 |
| location | ~9,600 | 道觀、聖地、宮廟 |
| person | ~5,800 | 道士、學者、傳承者 |
| sect | ~1,278 | 流派、宗派 |
| custom | ~198 | 旗艦學術專題 |

## Schema

Each row in `train.jsonl`:
```json
{
  "id": "ckm...",
  "name": "城隍",
  "type": "deity",
  "slug": "deity/cheng_huang_ye",
  "url": "https://lius.cc/n/deity/cheng_huang_ye",
  "summary": "城隍，亦稱城隍爺、城隍神，為中國道教與民間信仰中極具代表性的地方守護神與司法神...",
  "content": "## 起源與演變\n\n城隍信仰最初源於古代對城池守護神的祭祀..."
}
```

- `name`: 條目主名（繁體中文）
- `type`: concept / scripture / deity / ritual / location / person / sect / custom
- `slug`: lius.cc 上的 URL slug
- `url`: 原始條目 URL（可作為 RAG 引用）
- `summary`: 200-500 字摘要
- `content`: 完整內容（平均 2-3 KB，部分至 30 KB）

## Quick Start (RAG with Daoism-Qwen3.5-9B)

```python
from datasets import load_dataset
from sentence_transformers import SentenceTransformer
import faiss
import numpy as np
from openai import OpenAI

# 1. 載入資料
ds = load_dataset("lius-cc/daoism-knowledge-rag", split="train")
print(f"Loaded {len(ds)} entries")

# 2. Embed (bge-m3 多語言效果最好)
encoder = SentenceTransformer("BAAI/bge-m3")
embeddings = encoder.encode(
    [f"{x['name']}：{x['summary']}" for x in ds],
    batch_size=64,
    show_progress_bar=True,
)

# 3. FAISS index
index = faiss.IndexFlatIP(embeddings.shape[1])
faiss.normalize_L2(embeddings)
index.add(embeddings)

# 4. Query
def retrieve(query, k=5):
    q_emb = encoder.encode([query])
    faiss.normalize_L2(q_emb)
    _, ids = index.search(q_emb, k)
    return [ds[int(i)] for i in ids[0]]

# 5. Call Daoism LLM with retrieved context
client = OpenAI(base_url="http://localhost:8000/v1", api_key="dummy")

def ask(question):
    hits = retrieve(question, k=5)
    context = "\n\n---\n\n".join(
        f"### {h['name']}\n{h['summary']}\n\n{h['content'][:1500]}"
        for h in hits
    )
    response = client.chat.completions.create(
        model="Daoism-Qwen3.5-9B",
        messages=[
            {"role": "system", "content": f"你是道教智慧 AI。請以下列館藏資料為基礎回答：\n\n{context}"},
            {"role": "user", "content": question},
        ],
        max_tokens=2048,
    )
    return response.choices[0].message.content

print(ask("城隍信仰與道教有什麼關係？"))
```

## Alternative: Use lius.cc RAG API (no local indexing needed)

If you don't want to build your own FAISS index, use our public RAG endpoint (rate limit 30 req/min):

```python
import requests

def retrieve(query, n=5):
    r = requests.post(
        "https://lius.cc/api/llm-rag",
        json={"q": query, "n": n},
        timeout=10,
    )
    return r.json()["hits"]
```

## Licensing & Attribution

- **License**: Apache 2.0
- **Source**: [Dingren Daoxue Lab](https://lius.cc) by Liu Chi-Ying (Daoist priest, 2026)
- **Citation**: please cite as below

```bibtex
@dataset{daoism-knowledge-rag-2026,
  author = {Liu, Chi-Ying and Dingren Daoxue Lab},
  title = {Daoism Knowledge RAG: Curated Taoist Knowledge Base for Retrieval-Augmented Generation},
  year = {2026},
  publisher = {HuggingFace},
  url = {https://huggingface.co/datasets/lius-cc/daoism-knowledge-rag},
}
```

## Related

- 🤖 **Model**: [`lius-cc/Daoism-Qwen3.5-9B`](https://huggingface.co/lius-cc/Daoism-Qwen3.5-9B) (fp16) / [GGUF](https://huggingface.co/lius-cc/Daoism-Qwen3.5-9B-GGUF)
- 🌐 **Live Demo**: [demo.lius.cc](https://demo.lius.cc) (uses this dataset via RAG)
- 📚 **Deployment Guide**: [lius.cc/llm](https://lius.cc/llm)

## Limitations

- All content is in Traditional Chinese (Taiwan)
- Some entries refer to internal lius.cc `[[wiki-link]]` syntax; treat as plain text in your RAG
- Coverage focuses on **正一道、閭山派、台灣民間信仰**; mainland Quanzhen tradition less represented
- Some concept entries may overlap; deduplicate by `name` if needed for your use case

## Disclaimer

This dataset is for research and educational purposes. Religious practices should not be undertaken solely based on AI-retrieved content; consult qualified Daoist priests for ceremonial guidance.