Enables configuration of environment variables for the server through a .env file to store API keys and other settings
Provides the backend framework for the MCP server, handling API endpoints and request processing
Used for processing and converting PPT documents to enable content extraction for the RAG engine
Utilizes OpenAI Embeddings for vectorizing document content to enable semantic search capabilities
Supports project configuration through TOML files as indicated by the pyproject.toml in the project structure
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@ChatPPT-MCP summarize the key points from the quarterly presentation"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
一个基于MCP（Model Context Protocol）的多文档RAG（Retrieval-Augmented Generation）引擎应用，支持PPT文档的智能问答和分析。
🔍 多文档处理: 支持索引多个PPT文档，基于视觉模型
🤖 智能问答: 基于RAG技术的文档问答
🔄 MCP集成: 使用Model Context Protocol进行工具调用
📊 交互式测试: 提供命令行交互测试界面
Related MCP server: RAG-Anything MCP Server
后端框架: FastAPI
向量数据库: ChromaDB
LLM: Doubao Vision
文档处理: LibreOffice, pypdfium2
协议: Model Context Protocol (MCP)
向量化: OpenAI Embeddings
pip install -e .复制环境变量模板文件，并修改：
cp .env.example .envpython rag_interactive_test.pypython mcp_interactive_test.pyapp_chatppt/
├── src/ # 源代码目录
│ ├── multi_doc_rag_engine.py # RAG引擎核心
│ ├── document_processor.py # 文档处理器
│ └── ...
├── data/ # 示例数据
├── rag_interactive_test.py # 交互式测试入口
├── mcp_interactive_test.py # MCP集成测试
├── pyproject.toml # 项目配置
├── .env.example # 环境变量模板
└── README.md # 项目文档MIT License
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DocBase MCP server for AI agents
MCP server for building and testing AI agents with multi-model experimentation and insights.
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