ChatBot-QwenAgent

A powerful AI chatbot system built with Qwen Agent framework, featuring RAG (Retrieval-Augmented Generation) capabilities and multiple integrated tools.

Features

• 🤖 Powered by Qwen Agent framework
• 🔍 RAG (Retrieval-Augmented Generation) system for knowledge-based responses
• 🛠️ Multiple integrated tools:
  • Image generation
  • MySQL database querying
  • Code interpretation
  • Knowledge base management
• ⚡ MCP (Model Control Protocol) Services:
  • Real-time time service with timezone support
  • Server-Sent Events (SSE) for live data streaming
• 🌐 FastAPI-based RAG service
• 📚 Vector-based knowledge storage using FAISS

Project Structure

.
├── assets/              # Static assets
├── docs/               # Documentation
├── knowledge_base/     # Vector store for RAG
├── scripts/           # Utility scripts
├── workspace/         # Working directory
├── client.py          # Main client implementation
└── rag_service.py     # RAG service implementation

Prerequisites

• Python 3.10+
• DashScope API key

Installation

1. Clone the repository:

git clone https://github.com/yourusername/ChatBot-QwenAgent.git
cd ChatBot-QwenAgent

2. Install dependencies:

pip install -r requirements.txt

3. Configure your environment:

• Set up your DashScope API key
• Configure MySQL database connection in client.py

Usage

1. Start the RAG service:

python rag_service.py

2. Run scripts to add documents to knowledge base

python scripts/add_document.py docs/我的世界观.txt

3. Run the client:

python client.py

Features in Detail

RAG Service

• FastAPI-based service for document retrieval
• Uses HuggingFace embeddings for semantic search
• Supports document addition and retrieval
• Configurable chunk size and overlap

Tools

• Image Generation: Generate images from text descriptions
• MySQL Query: Execute SQL queries on your database
• RAG Search: Search through your knowledge base
• Knowledge Base Management: Add and manage documents in your knowledge base
• MCP Services:
  • Time Service: Real-time time information with timezone support (Asia/Shanghai), enabling accurate time-based operations
  • Fetch Service: Advanced Server-Sent Events (SSE) integration for real-time data streaming and live updates

Configuration

RAG Service Configuration

• Default port: 8000
• Embedding model: shibing624/text2vec-base-chinese
• Chunk size: 500
• Chunk overlap: 50

Client Configuration

• Model: Qwen3-235b-a22b (DashScope)
• Customizable tools and system instructions

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is licensed under the MIT License - see the LICENSE file for details.