🧠 AI Regulatory Compliance Assistance System

An AI-based legal compliance analysis platform integrating Alibaba Cloud Bailian AI capabilities for regulatory document processing, semantic retrieval, compliance analysis, and knowledge-graph construction.

🚀 System Overview

The AI Regulatory Compliance Assistance System automates compliance interpretation across complex legal frameworks.
It supports PDF / HTML regulation ingestion, AI-powered question answering, and explainable rule mapping through RAG + knowledge-graph techniques.

✨ Key Features

1. Document Processing Module

• 📄 Upload PDF or HTML regulatory documents
• 🔍 Automatic text extraction and segmentation
• 🧾 Metadata extraction and storage

2. Vectorization & Embedding Module

• 🧠 Uses Alibaba Cloud Bailian Embedding API for text vectorization
• 🗂 Builds vector indexes for regulations
• 🔎 Enables semantic similarity search

3. Knowledge Graph Construction

• ⚖️ Extracts legal entities (articles, violations, penalties, etc.)
• 🧩 Identifies inter-entity relationships
• 🗃 Outputs a JSON-formatted knowledge graph

4. Semantic Retrieval & Q&A (RAG)

• 📚 Regulation retrieval based on vector similarity
• 🕸 Enhances context with knowledge graphs
• 💬 Generates professional answers via Qwen-Turbo model

5. Compliance Determination Workflow

• 🧮 Multi-factor logical analysis for business compliance
• 📊 Risk-level evaluation
• ⚠️ Violation identification & recommendation generation

6. Front-End Interactive Interface

• 🗂 Document upload & management
• 🤖 Real-time Q&A interaction
• 📑 Compliance analysis report
• 🔗 Knowledge-graph query
• 📈 System statistics dashboard

🏗 Technical Architecture

Backend Stack

Frontend Stack

Data Storage

• 🗂 JSON files – document / vector / graph data
• 💾 Local file system – for uploaded files

Environment Configuration

1. Basic Environment

Python 3.8+

2. Install Dependencies

pip install -r requirements.txt

3. Environment Variable Configuration

Create a .env file and configure it as follows:

ALIBABA_API_KEY="your api key"
QWEN_MODEL=qwen-turbo
EMBEDDING_MODEL=text-embedding-v1
DATA_DIR=./data
UPLOAD_DIR=./uploads

Quick Start

Method 1: Use the Launch Script

python run_system.py

Select “4. Full Test” to perform a complete system test.

Method 2: Manual Start

Start the service

uvicorn main:app --host 0.0.0.0 --port 8000

Access the system

http://localhost:8000

## API Documentation

After the system starts, visit http://localhost:8000/docs to view the complete API documentation.

### Core API Endpoints
	•	POST /api/upload-document - Upload regulatory document
	•	GET /api/documents - Retrieve document list
	•	POST /api/build-knowledge-graph - Build knowledge graph
	•	POST /api/ask - Regulatory Q&A
	•	POST /api/compliance-analysis - Compliance analysis
	•	GET /api/search-regulations - Search regulations
	•	GET /api/knowledge-graph/query - Query knowledge graph
	•	GET /api/statistics - System statistics

## User Guide

### 1. Document Upload
1. Go to the "Document Upload" tab  
2. Select a regulatory file in **PDF** or **HTML** format  
3. Click **Upload** — the system will automatically process the document  

### 2. Regulatory Q&A
1. Go to the "Regulatory Q&A" tab  
2. Enter a regulation-related question  
3. The system will provide a professional answer based on **RAG technology**  

### 3. Compliance Analysis
1. Go to the "Compliance Analysis" tab  
2. Fill in the business type and detailed description  
3. Obtain a compliance analysis report and recommendations  

### 4. Knowledge Graph
1. Go to the "Knowledge Graph" tab  
2. Build a knowledge graph (using already uploaded documents)  
3. Query related information for specific entities  

## Testing and Validation

The system includes a complete API testing suite:

```bash
# Run all tests
python tests/test_api.py

# Run with pytest
pytest tests/test_api.py -v

## Directory Structure

AI_Regulatory_Compliance_Assistance_System/ ├── app/ # Core application module │ ├── init.py │ ├── models.py # Data models │ ├── storage.py # Data storage │ ├── document_processor.py # Document processing │ ├── ai_client.py # AI service client │ ├── vector_service.py # Vector services │ ├── knowledge_graph.py # Knowledge graph │ ├── rag_service.py # RAG service │ ├── compliance_analyzer.py # Compliance analysis │ └── api.py # API routing ├── static/ # Front-end static files │ ├── index.html │ ├── style.css │ └── script.js ├── tests/ # Test files │ └── test_api.py ├── data/ # Data storage directory ├── uploads/ # File upload directory ├── main.py # Main application entry point ├── requirements.txt # Dependency file ├── .env # Environment configuration ├── run_system.py # Launch script └── README.md # System documentation

## Notes

1. **API Key**: Ensure that your Alibaba Cloud Bailian API key is valid and has sufficient quota.  
2. **File Formats**: Currently supports PDF and HTML formats; make sure the documents are clear and readable.  
3. **Network Connection**: A stable internet connection is required to access Alibaba Cloud services.  
4. **Storage Space**: Ensure there is enough disk space to store uploaded documents and generated data.  

## Future Enhancements

- Support for additional document formats (Word, TXT, etc.)  
- User permission management  
- Integration with more AI model options  
- Database persistence  
- Distributed deployment support  

## Technical Support

If any issues occur, please check the following:  
1. Whether environment variables are configured correctly  
2. Whether the network connection is stable  
3. Whether the API key is valid  
4. Review console error logs for troubleshooting  

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