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README.md
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---
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license: apache-2.0
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language:
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- zh
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size_categories:
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- 10K<n<100K
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tags:
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- daoism
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- taoism
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- chinese-religion
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- rag
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- retrieval
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- dingren-daoxue
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- lius-cc
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pretty_name: Daoism Knowledge RAG (鼎稔道學館館藏)
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---
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# Daoism Knowledge RAG
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**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。
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## Overview
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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.
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It covers eight categories of Daoist knowledge:
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| Type | Count | Content |
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|------|-------|---------|
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| concept | ~32,700 | 神學概念、宇宙論、修煉名相 |
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| scripture | ~23,000 | 經文、戒律、科儀文獻 |
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| deity | ~12,000 | 神祇、仙真、神格體系 |
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| ritual | ~11,000 | 科儀、法事、節慶 |
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| location | ~9,600 | 道觀、聖地、宮廟 |
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| person | ~5,800 | 道士、學者、傳承者 |
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| sect | ~1,278 | 流派、宗派 |
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| custom | ~198 | 旗艦學術專題 |
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## Schema
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Each row in `train.jsonl`:
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```json
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{
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"id": "ckm...",
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"name": "城隍",
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"type": "deity",
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"slug": "deity/cheng_huang_ye",
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"url": "https://lius.cc/n/deity/cheng_huang_ye",
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"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...",
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"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..."
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}
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```
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- `name`: 條目主名(繁體中文)
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- `type`: concept / scripture / deity / ritual / location / person / sect / custom
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- `slug`: lius.cc 上的 URL slug
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- `url`: 原始條目 URL(可作為 RAG 引用)
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- `summary`: 200-500 字摘要
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- `content`: 完整內容(平均 2-3 KB,部分至 30 KB)
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## Quick Start (RAG with Daoism-Qwen3.5-9B)
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```python
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from datasets import load_dataset
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from sentence_transformers import SentenceTransformer
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import faiss
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import numpy as np
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from openai import OpenAI
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# 1. 載入資料
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ds = load_dataset("lius-cc/daoism-knowledge-rag", split="train")
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print(f"Loaded {len(ds)} entries")
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# 2. Embed (bge-m3 多語言效果最好)
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encoder = SentenceTransformer("BAAI/bge-m3")
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embeddings = encoder.encode(
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[f"{x['name']}:{x['summary']}" for x in ds],
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batch_size=64,
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show_progress_bar=True,
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)
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# 3. FAISS index
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index = faiss.IndexFlatIP(embeddings.shape[1])
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faiss.normalize_L2(embeddings)
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index.add(embeddings)
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# 4. Query
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def retrieve(query, k=5):
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q_emb = encoder.encode([query])
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faiss.normalize_L2(q_emb)
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_, ids = index.search(q_emb, k)
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return [ds[int(i)] for i in ids[0]]
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# 5. Call Daoism LLM with retrieved context
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client = OpenAI(base_url="http://localhost:8000/v1", api_key="dummy")
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def ask(question):
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hits = retrieve(question, k=5)
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context = "\n\n---\n\n".join(
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f"### {h['name']}\n{h['summary']}\n\n{h['content'][:1500]}"
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for h in hits
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)
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response = client.chat.completions.create(
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model="Daoism-Qwen3.5-9B",
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messages=[
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{"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}"},
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{"role": "user", "content": question},
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],
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max_tokens=2048,
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)
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return response.choices[0].message.content
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print(ask("城隍信仰與道教有什麼關係?"))
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```
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## Alternative: Use lius.cc RAG API (no local indexing needed)
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If you don't want to build your own FAISS index, use our public RAG endpoint (rate limit 30 req/min):
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```python
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import requests
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def retrieve(query, n=5):
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r = requests.post(
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"https://lius.cc/api/llm-rag",
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json={"q": query, "n": n},
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timeout=10,
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)
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return r.json()["hits"]
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```
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## Licensing & Attribution
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- **License**: Apache 2.0
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- **Source**: [Dingren Daoxue Lab](https://lius.cc) by Liu Chi-Ying (Daoist priest, 2026)
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- **Citation**: please cite as below
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```bibtex
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@dataset{daoism-knowledge-rag-2026,
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author = {Liu, Chi-Ying and Dingren Daoxue Lab},
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title = {Daoism Knowledge RAG: Curated Taoist Knowledge Base for Retrieval-Augmented Generation},
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year = {2026},
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publisher = {HuggingFace},
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url = {https://huggingface.co/datasets/lius-cc/daoism-knowledge-rag},
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}
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```
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## Related
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- 🤖 **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)
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- 🌐 **Live Demo**: [demo.lius.cc](https://demo.lius.cc) (uses this dataset via RAG)
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- 📚 **Deployment Guide**: [lius.cc/llm](https://lius.cc/llm)
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## Limitations
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- All content is in Traditional Chinese (Taiwan)
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- Some entries refer to internal lius.cc `[[wiki-link]]` syntax; treat as plain text in your RAG
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- Coverage focuses on **正一道、閭山派、台灣民間信仰**; mainland Quanzhen tradition less represented
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- Some concept entries may overlap; deduplicate by `name` if needed for your use case
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## Disclaimer
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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.
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