---
license: apache-2.0
language:
- id
tags:
- legal
- indonesia
- regulations
- knowledge-graph
- rag
task_categories:
- text-retrieval
- question-answering
size_categories:
- 100K<n<1M
---

📧 [Github](https://github.com/azzindani)
🔗 [LinkedIn](https://www.linkedin.com/in/azzindan1/)

# 🏛️ Indonesian Legal RAG Dataset with Embeddings & TF-IDF Vectors

## 📖 What is this dataset?

This dataset contains **Indonesian legal documents** with **pre-computed embeddings** and **TF-IDF vectors** for building RAG (Retrieval-Augmented Generation) systems. It includes regulations, laws, and legal documents from Indonesia with ready-to-use vector representations.

🎯 **Perfect for**: Legal AI, Indonesian NLP, RAG systems, semantic search, and legal chatbots

## ✨ Key Features

- 📚 **~10,000 Indonesian regulations**
- 🎯 **Dense embeddings** (1024-dimensional vectors)
- 📊 **TF-IDF vectors** (5000-dimensional sparse vectors)
- ⚖️ **Complete legal metadata** (regulation type, year, authority, etc.)
- 🔍 **Ready for semantic search** - no preprocessing needed!
- 🚀 **Optimized for RAG** retrieval systems
- 📜 **Full document text** included
- 🏛️ **Indonesian legal hierarchy** preserved

## 📊 What's Inside?

### 📋 Document Fields
- **Document content**: Full text of legal documents
- **Metadata**: Regulation type, number, year, issuing body
- **Structure**: Chapters, articles, and sections
- **Embeddings**: 1024-dim dense vectors for semantic similarity
- **TF-IDF**: 5000-dim sparse vectors for keyword matching

### 🏛️ Legal Document Types
- 📜 **UUD** - Constitution (Undang-Undang Dasar)
- 📋 **UU** - Laws (Undang-Undang)
- 🏛️ **PP** - Government Regulations (Peraturan Pemerintah)
- 🎯 **PERPRES** - Presidential Regulations (Peraturan Presiden)
- 📑 **PERMEN** - Ministerial Regulations (Peraturan Menteri)
- 🏙️ **PERDA** - Regional Regulations (Peraturan Daerah)

## 📋 Complete Data Schema

| Field | Type | Dimension | Description | Example |
|-------|------|-----------|-------------|---------|
| 🆔 `global_id` | string | - | Unique document identifier | "legal_doc_001" |
| 🏷️ `local_id` | string | - | Source-specific ID | "local_001" |
| 📜 `regulation_type` | string | - | Type of regulation | "UU", "PP", "PERPRES" |
| 🏛️ `enacting_body` | string | - | Government entity | "Kementerian Hukum" |
| 🔢 `regulation_number` | string | - | Official number | "No. 8 Tahun 1999" |
| 📅 `year` | string | - | Year enacted | "1999" |
| 📝 `about` | string | - | Document topic | "Perlindungan Konsumen" |
| 📅 `effective_date` | string | - | When it takes effect | "1999-04-20" |
| 📖 `chapter` | string | - | Document chapter | "BAB I" |
| 📄 `article` | string | - | Specific article | "Pasal 1" |
| 📄 `content` | string | - | Full document text | "Pasal 1. Dalam Undang-undang..." |
| 🎯 `embedding` | list[float] | **1024** | Dense semantic vectors | [0.1, 0.2, -0.3, ...] |
| 📊 `tfidf_vector` | list[float] | **5000** | Sparse keyword vectors | [0.0, 0.15, 0.0, 0.8, ...] |
| 🔢 `chunk_id` | int | - | Document chunk number | 1 |

## ⚠️ Important Notes

### ✅ What This Dataset Is Good For:
- 🤖 Building Indonesian legal RAG systems
- 🔍 Semantic search in legal documents
- 📊 Legal document clustering and analysis
- 🎯 Training legal AI models
- 📚 Legal research and information retrieval

### ❌ What This Dataset Is NOT:
- ⚖️ **Not legal advice** - for research/education only!
- 🎯 **Not 100% accurate** - always verify with official sources
- 🌍 **Not for other legal systems** - specific to Indonesia
- ⏰ **Not always current** - laws change over time
- 🔍 **Not query-ready** - you need to encode your queries

### 🚨 Limitations:
- 📏 **Fixed dimensions** - embeddings are 1024-dim, TF-IDF is 5000-dim
- 🎯 **Model dependency** - embeddings quality depends on the model used
- 📊 **TF-IDF vocabulary** - limited to 5000 most common terms
- ⏰ **Point-in-time** - vectors represent documents at creation time
- 🔍 **No query encoder included** - you need your own query encoding

## 🔄 Preprocessing Pipeline

The dataset was created using this pipeline:

1. 📥 **Data Collection** - Indonesian legal documents gathered
2. 🧹 **Text Cleaning** - Remove noise, normalize formatting
3. 📊 **TF-IDF Vectorization** - Create 5000-dim sparse vectors
4. 🎯 **Embedding Generation** - Create 1024-dim dense vectors
5. ✅ **Quality Control** - Validate dimensions and formats
6. 💾 **Dataset Creation** - Package into HuggingFace format

---

**🚀 Happy Legal AI Building!** 🏛️✨

*Ready-to-use vectors for Indonesian legal RAG systems* 🎯📊