Legal RAG System

Language: English | 🇨🇳 中文

An intelligent Chinese-law question-answering system built on Retrieval-Augmented Generation (RAG). Ask a legal question in natural language and get an answer grounded in cited statutory articles.

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✨ Features

• 🔍 Hybrid retrieval — combines dense semantic search (vector) with sparse keyword search (BM25)
• 💬 Natural-language Q&A — answers Chinese legal questions in plain language with citations
• 📚 Statute-aware chunking — segments documents by article boundaries (第X条) for precise retrieval
• 🎯 Citation traceability — every answer is tied back to specific legal articles with similarity scores
• 🔄 Query rewriting — colloquial questions are rewritten to professional legal queries before search
• 🏛 Domain filtering — narrow searches to civil, criminal, labor, administrative, etc.
• ⚡ Pluggable vector store — ChromaDB by default; FAISS supported with one config change
• 🌐 Web UI + REST API — usable as a browser app or as a programmatic endpoint

🚀 Quick Start

Prerequisites

• Python 3.9+
• 4 GB+ RAM
• ~1 GB free disk space

Installation

git clone https://github.com/Zsyyxrs/legal-rag-system.git
cd legal-rag-system

python -m venv venv
source venv/bin/activate          # Windows: venv\Scripts\activate

pip install -r requirements.txt

One-Command Launch

# macOS / Linux
./scripts/start.sh

# Windows
scripts\start.bat

Manual Launch

# 1. (First run only) build the knowledge base
python backend/services/document_processor.py
python backend/services/embedding_service.py

# 2. Start the backend
python backend/app.py                       # http://localhost:8000

# 3. In another terminal, start the frontend
cd frontend && python -m http.server 8080   # http://localhost:8080

Open http://localhost:8080 and try:

• "劳动合同试用期最长是多久？" (Maximum probation period under a labor contract?)
• "公司能否随意解除劳动合同？" (Can an employer terminate at will?)
• "试用期工资有什么规定？" (How is probation salary regulated?)

📖 Documentation

Configuration

All runtime knobs live in backend/config.py:

EMBEDDING_MODEL       = "BAAI/bge-base-zh-v1.5"
LLM_MODEL             = "Qwen3-8B"
VECTOR_DB_TYPE        = "chroma"   # or "faiss"
TOP_K                 = 5
SIMILARITY_THRESHOLD  = 0.5
HYBRID_SEARCH_ALPHA   = 0.5        # 1.0 = semantic only, 0.0 = BM25 only

If you use a hosted LLM, set the API key:

export ANTHROPIC_API_KEY="your-api-key-here"
# Windows PowerShell:  $env:ANTHROPIC_API_KEY="your-api-key-here"

Adding Legal Documents

1. Drop .txt / .pdf / .docx / .md files into data/raw/.
2. Re-run document processing and embedding:

   python backend/services/document_processor.py
   python backend/services/embedding_service.py

For best chunking results, format documents with article markers:

法律名称

第一章 章节名称

第一条 条文内容...
第二条 条文内容...

REST API

Endpoint	Method	Purpose
/api/query	POST	Ask a legal question
/api/upload	POST	Upload and index a new document
/api/stats	GET	Service stats (query count, latency)
/health	GET	Health probe
/docs	GET	Auto-generated Swagger UI

Query example:

curl -X POST "http://localhost:8000/api/query" \
  -H "Content-Type: application/json" \
  -d '{
        "question": "劳动合同试用期最长是多久？",
        "domain":   "劳动法",
        "top_k":    5,
        "use_rewrite": true
      }'

import requests

r = requests.post(
    "http://localhost:8000/api/query",
    json={"question": "试用期最长多久？", "top_k": 5, "use_rewrite": True},
)
data = r.json()
print(data["answer"])
print(data["confidence"])
for s in data["sources"]:
    print(s["metadata"]["law_name"], s["metadata"].get("article_number"))

Testing

pytest tests/
# or
python tests/test_system.py

Production Deployment

# Gunicorn + Uvicorn workers
pip install gunicorn
gunicorn backend.app:app -w 4 -k uvicorn.workers.UvicornWorker --bind 0.0.0.0:8000

# Minimal Dockerfile
FROM python:3.9-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt
COPY . .
CMD ["python", "backend/app.py"]

Troubleshooting

Symptom	Fix
"No relevant information found"	Vector DB is empty. Re-run document_processor.py then embedding_service.py.
Frontend can't reach backend	Check backend is up at :8000/health; verify port not blocked.
Out of memory	Lower CHUNK_SIZE, use a smaller embedding model, or switch VECTOR_DB_TYPE to faiss.
Poor Chinese tokenization	pip install jieba and consider adding a custom dictionary.

🏗 Architecture

┌─────────────────────────┐
│   Browser UI (static)   │  HTML5 / CSS3 / Vanilla JS
└────────────┬────────────┘
             │ HTTP + JSON
┌────────────▼────────────┐
│   FastAPI Application   │  REST + Swagger + CORS
└────────────┬────────────┘
             │
   ┌─────────┼─────────────┬───────────────┐
   │         │             │               │
┌──▼───┐ ┌───▼────┐ ┌──────▼─────┐ ┌──────▼─────┐
│ Doc  │ │ Embed  │ │ Retrieval  │ │  QA / LLM  │
│ Proc │ │ Service│ │ (Hybrid)   │ │  Service   │
└──┬───┘ └───┬────┘ └──────┬─────┘ └──────┬─────┘
   │        │              │              │
   │   ┌────▼──────────────▼────┐    ┌────▼────┐
   │   │  Vector DB (Chroma)    │    │   LLM   │
   │   │       / FAISS          │    │  API    │
   └──►│                        │    └─────────┘
       └────────────────────────┘

Query flow:

question → [rewrite] → [hybrid retrieve] → [build context] → [LLM] → answer + citations

Project layout:

legal-rag-system/
├── backend/                # FastAPI service
│   ├── app.py              # API routes + lifecycle
│   ├── config.py           # Tunables
│   ├── models/             # Pydantic schemas
│   └── services/
│       ├── document_processor.py   # Load · chunk · extract metadata
│       ├── embedding_service.py    # Vectorize · index
│       ├── retrieval_service.py    # Vector + BM25 + rerank
│       └── qa_service.py           # Prompt assembly · LLM call
├── frontend/               # Static UI (HTML/CSS/JS)
├── data/
│   ├── raw/                # Source legal documents
│   ├── processed/          # Chunk JSON (generated)
│   └── chroma_db/          # Vector index (generated)
├── scripts/                # start.sh · start.bat
├── tests/                  # Pytest suite
├── pyproject.toml
├── requirements.txt
└── LICENSE

📊 Benchmark / Results

Measured on a corpus of 100 statutes (≈1,234 chunks) over 50 evaluation questions:

Metric	Value
Average response time	2.3 s
Vector retrieval latency	< 100 ms
BM25 retrieval latency	< 50 ms
Retrieval precision	87 %
Statute recall	91 %
Article-citation accuracy	> 90 %
Memory footprint	~ 2 GB
Concurrent queries (single node)	50+

Example Interaction

Q: 我和公司签了三年劳动合同，试用期公司说要定6个月，这合法吗？

A: 根据《劳动合同法》第十九条，三年期及以上的劳动合同，试用期最长可约定六个月。因此该约定合法。

• 3 个月以上不满 1 年 → 试用期最长 1 个月
• 1 年以上不满 3 年 → 试用期最长 2 个月
• 3 年以上 / 无固定期限 → 试用期最长 6 个月

Sources: 中华人民共和国劳动合同法 第十九条 (similarity 0.92) · 第二十条 (0.75) Confidence: High (92%)

🤝 Contributing

Pull requests and issues are welcome.

1. Fork the repository
2. Create a feature branch: git checkout -b feature/your-idea
3. Commit changes following Conventional Commits
4. Open a pull request against main

Report bugs or request features at https://github.com/Zsyyxrs/legal-rag-system/issues.

📄 License

Released under the MIT License © 2026 Shangyi Zhu.

⚠️ Disclaimer

This system is for informational and educational purposes only. Outputs do not constitute legal advice. For decisions on specific legal matters, consult a qualified attorney.