RAG Assistant FAQs

Question 1

Can I integrate other LLM applications with my RAG Assistant knowledge base?

Accepted Answer

Absolutely. The included MCP server exposes your knowledge base as tools via the stdio transport, allowing external MCP-compatible applications to search, list, add, and delete documents programmatically.

Question 2

Is my document data stored locally?

Accepted Answer

Yes, RAG Assistant utilizes a persistent local ChromaDB vector store for efficient storage and retrieval of your documents, ensuring your data remains on your machine.

Question 3

What types of documents can I upload and chat with?

Accepted Answer

You can upload and manage various document types including PDF, DOCX, TXT, MD, and CSV files through its intuitive web-based GUI.

Question 4

What is RAG Assistant?

Accepted Answer

RAG Assistant is a local project that combines Retrieval-Augmented Generation (RAG) with the Model Context Protocol (MCP) to enable interactive chat with your personal documents using various LLMs.

Question 5

How does RAG Assistant connect to Large Language Models (LLMs)?

Accepted Answer

It uses OpenRouter for direct chat functionality within the GUI, supporting diverse LLM models. Additionally, it features an MCP server to connect external MCP-compatible LLMs (like Claude Desktop or Cursor) to your personal knowledge base.

RAG Assistant

RAG Assistant

主要功能

使用案例

主要功能

使用案例