apconw/Aix-DB

Aix-DB - LLM Data Assistant

An intelligent data analytics system powered by Large Language Models and RAG technology, enabling conversational data analysis (ChatBI) for rapid data extraction and visualization

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Aix-DB is built on the LangChain/LangGraph framework, combined with MCP Skills multi-agent collaboration architecture, enabling end-to-end transformation from natural language to data insights.

Core Capabilities: General Q&A · Data Q&A (Text2SQL) · Spreadsheet Q&A · Deep Research · Data Visualization · MCP Multi-Agent

Product Features: 📦 Ready to Use · 🔒 Secure & Controllable · 🔌 Easy Integration · 🎯 Increasingly Accurate

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Demo Video

🎯 Skill Mode	💬 Standard Mode

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System Architecture

Layered Architecture Design:

• Frontend Layer: Modern web interface built with Vue 3 + TypeScript, integrated with ECharts and AntV visualization components
• API Gateway Layer: High-performance async API service based on Sanic, providing RESTful interfaces and JWT authentication
• Intelligent Service Layer: LLM services, Text2SQL Agent, RAG retrieval engine, MCP multi-agent collaboration
• Data Storage Layer: Support for multiple database types including relational databases, vector databases, graph databases, and file storage

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Supported Data Sources

Step	Module	Description
1	User Input	User asks data query questions in natural language
2	LLM Intent Understanding	LLM parses question intent, extracts key entities and query conditions
3	RAG Knowledge Retrieval	Embedding + BM25 hybrid retrieval, combined with Neo4j graph to obtain relevant table structures and business knowledge
4	SQL Generation	Text2SQL engine generates SQL statements with syntax validation and optimization
5	Database Execution	Execute SQL on target data source, supporting 8+ database types
6	Visualization	Automatically generate ECharts/AntV charts to present analysis results

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Quick Start

Deploy with Docker (Recommended)

docker run -d \
  --name aix-db \
  --restart unless-stopped \
  -e TZ=Asia/Shanghai \
  -e JWT_SECRET_KEY=<your-generated-secret> \
  -e SERVER_HOST=0.0.0.0 \
  -e SERVER_PORT=8088 \
  -e SERVER_WORKERS=2 \
  -e LANGFUSE_TRACING_ENABLED=false \
  -e LANGFUSE_SECRET_KEY= \
  -e LANGFUSE_PUBLIC_KEY= \
  -e LANGFUSE_BASE_URL= \
  -p 18080:80 \
  -p 18088:8088 \
  -p 15432:5432 \
  -p 9000:9000 \
  -p 9001:9001 \
  -v ./volume/pg_data:/var/lib/postgresql/data \
  -v ./volume/minio/data:/data \
  -v ./volume/logs/supervisor:/var/log/supervisor \
  -v ./volume/logs/nginx:/var/log/nginx \
  -v ./volume/logs/aix-db:/var/log/aix-db \
  -v ./volume/logs/minio:/var/log/minio \
  -v ./volume/logs/postgresql:/var/log/postgresql \
  --add-host host.docker.internal:host-gateway \
  crpi-7xkxsdc0iki61l0q.cn-hangzhou.personal.cr.aliyuncs.com/apconw/aix-db:1.2.3

Note: To enable Langfuse full-chain tracing, set LANGFUSE_TRACING_ENABLED=true and configure the corresponding keys and URL.

Deploy with Docker Compose

git clone https://github.com/apconw/Aix-DB.git
cd Aix-DB/docker
cp .env.template .env  # Copy env template, modify as needed

Edit docker/.env and set the required JWT_SECRET_KEY (leaving it blank makes docker-compose up fail immediately):

# Generate a random secret
python3 -c "import secrets; print(secrets.token_hex(32))"

Note: all workers/replicas must use the same JWT_SECRET_KEY; rotating it invalidates every previously issued login token, requiring all users to log in again.

docker-compose up -d

Access the System

Web Management Interface

• URL: http://localhost:18080
• Username: admin
• Password: 123456

PostgreSQL Database

• Connection: localhost:15432
• Database: aix_db
• Username: aix_db
• Password: 1

Local Development

① Clone the Repository

git clone https://github.com/apconw/Aix-DB.git
cd Aix-DB

② Start Middleware Dependencies (PostgreSQL, MinIO, etc.)

This container also runs a backend process that needs JWT_SECRET_KEY; configure docker/.env as described in the "Deploy with Docker Compose" section above before running:

cd docker
docker-compose up -d

③ Configure Environment Variables

Edit .env.dev in the project root to set database connection, MinIO address, etc. (default config works out of the box)

④ Install Python Dependencies (requires Python 3.11)

# Option 1: pip
pip install -r requirements.txt

# Option 2: uv (recommended, faster)
uv venv --python 3.11
source .venv/bin/activate
uv sync

⑤ Start Backend Service

python serv.py

⑥ Start Frontend Dev Server (in another terminal)

cd web
npm install
npm run dev

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Tech Stack

Backend: Sanic · SQLAlchemy · LangChain/LangGraph · Neo4j · FAISS/Chroma · MinIO

Frontend: Vue 3 · TypeScript · Vite 5 · Naive UI · ECharts · AntV

AI Models: OpenAI · Anthropic · DeepSeek · Qwen · Ollama

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Documentation

• Configuration Guide
• API Documentation (available after startup)

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Contributing

We welcome Issues and Pull Requests!

1. Fork this repository
2. Create a feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

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Contact Us

If you have any questions, feel free to reach out:

• GitHub Issues

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Star History

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License

This project is licensed under the Apache License 2.0.