该项目演示了如何将模型上下文协议（MCP）与定制的大型语言模型（如Qwen）集成，创建一个强大的聊天机器人，能够通过MCP服务器与各种工具交互。该实现展示了MCP的灵活性，使大型语言模型能够无缝使用外部工具。

[!提示] 中文版本请参阅 README_ZH.md。

概述

聊天机器人Streamlit示例

工作流程追踪示例

• 🚩 更新（2025-04-11）：
• 新增聊天机器人流式照明示例。
• 🚩 更新（2025-04-10）：
• 更复杂的LLM响应解析，支持多个MCP工具调用和多次聊天迭代。
• 增加了单一提示示例，支持常规和流式两种模式。
• 增加了交互式终端聊天机器人示例。

该项目包括：

• 简单/复杂CLI聊天机器人界面
• 与某些内置的MCP服务器集成，如（Markdown处理工具）
• 支持定制大型语言模型（如Qwen）和Ollama
• 适用于常规和流式模式的单一提示处理示例脚本
• 交互式终端聊天机器人，具备常规和流式响应模式

要求

• Python 3.10+
• 依赖关系（通过需求自动安装）：

MCPChatbot Example

This project demonstrates how to integrate the Model Context Protocol (MCP) with customized LLM (e.g. Qwen), creating a powerful chatbot that can interact with various tools through MCP servers. The implementation showcases the flexibility of MCP by enabling LLMs to use external tools seamlessly.

[!TIP] For Chinese version, please refer to README_ZH.md.

Overview

Chatbot Streamlit Example

Workflow Tracer Example

• 🚩 Update (2025-04-11):
  • Added chatbot streamlit example.
• 🚩 Update (2025-04-10):
  • More complex LLM response parsing, supporting multiple MCP tool calls and multiple chat iterations.
  • Added single prompt examples with both regular and streaming modes.
  • Added interactive terminal chatbot examples.

This project includes:

• Simple/Complex CLI chatbot interface
• Integration with some builtin MCP Server like (Markdown processing tools)
• Support for customized LLM (e.g. Qwen) and Ollama
• Example scripts for single prompt processing in both regular and streaming modes
• Interactive terminal chatbot with regular and streaming response modes

Requirements

• Python 3.10+
• Dependencies (automatically installed via requirements):
  • python-dotenv
  • mcp[cli]
  • openai
  • colorama

Installation

1. Clone the repository:

   git clone [email protected]:keli-wen/mcp_chatbot.git
   cd mcp_chatbot
   

2. Set up a virtual environment (recommended):

   cd folder
   
   # Install uv if you don't have it already
   pip install uv
   
   # Create a virtual environment and install dependencies
   uv venv .venv --python=3.10
   
   # Activate the virtual environment
   # For macOS/Linux
   source .venv/bin/activate
   # For Windows
   .venv\Scripts\activate
   
   # Deactivate the virtual environment
   deactivate
   

3. Install dependencies:

   pip install -r requirements.txt
   # or use uv for faster installation
   uv pip install -r requirements.txt
   

4. Configure your environment:

   • Copy the .env.example file to .env:

     cp .env.example .env
     

   • Edit the .env file to add your Qwen API key (just for demo, you can use any LLM API key, remember to set the base_url and api_key in the .env file) and set the paths:

     LLM_MODEL_NAME=your_llm_model_name_here
     LLM_BASE_URL=your_llm_base_url_here
     LLM_API_KEY=your_llm_api_key_here
     OLLAMA_MODEL_NAME=your_ollama_model_name_here
     OLLAMA_BASE_URL=your_ollama_base_url_here
     MARKDOWN_FOLDER_PATH=/path/to/your/markdown/folder
     RESULT_FOLDER_PATH=/path/to/your/result/folder
     

Important Configuration Notes ⚠️

Before running the application, you need to modify the following:

1. MCP Server Configuration: