Building a Watsonx.ai Chatbot RAG Server with MCP Server

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ruslanmv

关于 Building a Watsonx.ai Chatbot RAG Server with MCP Server

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

Building a Watsonx.ai Chatbot RAG Server with MCP Server 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"
      ]
    }
  }
}

Building a Watsonx.ai Chatbot RAG Server with MCP Server 工具

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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

常见问题

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.