@Shantanu1711
基本信息
暂无概览
使用下面的配置,将此服务器添加到你的 MCP 客户端。
{
"mcpServers": {
"mcp-server-shantanu1711": {
"command": "python",
"args": [
"-m",
"venv",
"venv"
]
}
}
}工具列表暂未提供
当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。
工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。
Customer Support RAG Chatbot is a retrieval-augmented generation (RAG) chatbot that answers queries based on provided customer support documentation. It uses semantic search to find relevant information and generates responses using the Hugging Face API, responding with “I don’t know” for questions outside its documentation scope.
Clone the repository, set up a Python virtual environment, install dependencies, and create a .env file with your Hugging Face API key. Gather documentation (scrape AngelOne docs or place insurance PDFs in docs/insurance), then run process_documents.py. Start the backend with python mcp_server.py and the frontend with streamlit run app.py. Open the deployed URL in a browser and type questions in the chat interface.
It uses customer support documentation you provide: scraped AngelOne web pages (via `scrape_angelone.py`) and any insurance PDFs placed in the `docs/insurance` directory. The system only answers from these documents.
Python, FastAPI (backend), Streamlit (frontend), Hugging Face API (LLM), ChromaDB (vector database), Sentence Transformers (embeddings), PyPDF, BeautifulSoup4, and Requests. Install with `pip install -r requirements.txt`.
Processed documents are stored in a ChromaDB vector database in the `chroma_db/` directory. The raw documents reside in the `docs/` folder.
It responds with “I don’t know” to any question that cannot be answered from the provided documentation.
The frontend communicates with the backend via a standard HTTP API (FastAPI). Authentication is not described; the only external credential is the Hugging Face API key stored in the `.env` file.
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