RAG Application FAQs

Question 1

What is the RAG Application?

Accepted Answer

The RAG Application is a powerful AI tool that integrates a Retrieval-Augmented Generation (RAG) system with the Model Context Protocol (MCP). It enables the creation of modular, interactive AI chatbots capable of querying custom PDF documents and interacting with exposed tools.

Question 2

What are the core capabilities of this application?

Accepted Answer

Its core capabilities include custom PDF document querying using RAG, a modular architecture via MCP for exposing tools, configurable LLM providers (Ollama, Google Gemini), and an interactive Gradio-based web UI for chatbot interactions. It also includes an example weather tool integration.

Question 3

How does it handle custom PDF documents?

Accepted Answer

You can place your PDF documents in the designated 'data/' directory. An ingestion script ('ingest.py') then processes these PDFs, creating a vector store (ChromaDB) that the RAG system uses for efficient, context-aware querying.

Question 4

Which Large Language Models (LLMs) are supported?

Accepted Answer

The RAG Application is highly configurable, allowing you to use various LLM providers. It supports local models via Ollama (e.g., qwen3:1.7b) and cloud-based options like Google Gemini, which can be used for both the RAG system and LLM orchestration.

Question 5

What is the Model Context Protocol (MCP) and how is it used here?

Accepted Answer

The Model Context Protocol (MCP) is central to the application's modular design. It allows the RAG capabilities and other tools (like the weather tool) to be exposed as services. An orchestrator LLM in the client UI then uses MCP to discover and interact with these tools seamlessly, enabling a flexible and expandable AI application.

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