What does the RAG Application do?
It is a demo of Retrieval-Augmented Generation that uses an MCP server to answer questions about a company by retrieving relevant documents and generating context‑aware responses.
@hulk-pham
Basic information
A demo of Retrieval-Augmented Generation (RAG) application with MCP server integration.
No standard config provided
This server doesn't expose a parseable MCP config block in its README. See the repository for install instructions.
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RAG Application is a demo of Retrieval-Augmented Generation (RAG) that integrates an MCP server. It retrieves relevant documents using vector search with ChromaDB, builds context-aware prompts, and sends them to an LLM API to answer questions about a company.
Install dependencies with pip install -r requirements.txt, set the OPENAI_API_KEY in a .env file, then connect the MCP server via Claude Desktop, Cursor, or another compatible IDE. Use the process_query tool to ask questions about the company.
It is a demo of Retrieval-Augmented Generation that uses an MCP server to answer questions about a company by retrieving relevant documents and generating context‑aware responses.
Run `pip install -r requirements.txt` in the project directory to install all dependencies.
Create a `.env` file and set your `OPENAI_API_KEY` to authenticate with the LLM API.
Connect the MCP server to a supported client (Claude Desktop, Cursor, etc.) and use the `process_query` tool to ask questions about the company.
The MCP server can be connected with Claude Desktop, Cursor, or any other IDE that supports the Model Context Protocol.
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