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Building a Watsonx.ai Chatbot RAG Server with MCP

@ruslanmv

代码仓库

关于 Building a Watsonx.ai Chatbot RAG Server with MCP

Building a Watsonx.ai Chatbot RAG Server with MCP compatible with Claude Desktop.

基本信息

分类

记忆与知识

运行时

python

传输方式

stdio

发布者

ruslanmv

配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "watsonx-rag-mcp-server": {
      "command": "python",
      "args": [
        "-m",
        "venv",
        ".venv"
      ]
    }
  }
}

工具

未检测到工具

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。

概览

What is Building a Watsonx.ai Chatbot RAG Server with MCP?

A Retrieval-Augmented Generation (RAG) server that uses IBM Watsonx.ai for LLM inference, ChromaDB for vector indexing, and exposes a chat tool via the Model Context Protocol (MCP) Python SDK. It processes PDF documents and answers queries based on their content, and can integrate with Claude Desktop.

How to use Building a Watsonx.ai Chatbot RAG Server with MCP?

Set up a Python 3.8+ virtual environment, install dependencies, create a .env file with Watsonx.ai credentials, place PDF files in the documents/ folder, then run server.py to start the MCP server. A client.py script is provided for testing queries, and the server can also be plugged into Claude Desktop.

Key features of Building a Watsonx.ai Chatbot RAG Server with MCP

  • Uses IBM Watsonx.ai for large language model inference
  • Indexes PDF documents in ChromaDB vector database
  • Exposes a single MCP tool (chat_with_manual) for RAG queries
  • Supports configurable chunk size and overlap for text splitting
  • Integrates directly with Claude Desktop via MCP

Use cases of Building a Watsonx.ai Chatbot RAG Server with MCP

  • Answering questions from technical manuals (e.g., drone documentation)
  • Building a chatbot grounded in a company’s internal PDF documents
  • Enabling Claude Desktop to retrieve information from local PDF files
  • Rapid prototyping of a RAG pipeline without custom infrastructure

FAQ from Building a Watsonx.ai Chatbot RAG Server with MCP

What are the runtime requirements?

Python 3.8+ and an IBM Cloud account with provisioned Watsonx.ai services. You need a Watsonx API key and project ID.

Where are the documents and vector data stored?

PDFs are kept in the local documents/ folder (configurable via DOCS_FOLDER). The ChromaDB vector index persists to the chroma_db_data/ directory.

How does the RAG process work?

When a user asks a question, the server retrieves relevant text chunks from ChromaDB, concatenates them with the query, and sends the combined prompt to Watsonx.ai for answer generation.

What transport does the MCP server use?

The server is built with FastMCP, which defaults to stdio transport; it is configured to work with Claude Desktop’s MCP integration.

Is authentication required?

Yes, the server reads WATSONX_APIKEY, WATSONX_URL, and PROJECT_ID from a .env file and validates them at startup.

评论

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