RAG FAQs

Question 1

Which AI models and databases does RAG utilize?

Accepted Answer

RAG integrates with Google Gemini for AI embeddings and text generation, and uses Qdrant as its high-performance vector database for efficient similarity search and document storage.

Question 2

How does RAG handle document processing and retrieval?

Accepted Answer

RAG ingests documents, chunks them, generates 768-dimensional vectors using Google's embedding model, stores them in Qdrant, and then performs similarity searches to retrieve context for AI-powered question answering.

Question 3

What is Model Context Protocol (MCP) compliance in RAG?

Accepted Answer

RAG is fully compliant with the Model Context Protocol (MCP), ensuring seamless integration and enhanced context management when used with compatible AI assistants and tools like Claude Desktop.

Question 4

What is the RAG server designed for?

Accepted Answer

The RAG server provides a powerful Retrieval-Augmented Generation (RAG) system for efficient document ingestion, vector storage, and AI-powered question answering capabilities, integrating seamlessly with AI assistants via MCP.

Question 5

What document formats can RAG process?

Accepted Answer

RAG supports multi-format document processing, including popular types like PDF, DOCX, and TXT files, making it versatile for various content sources.

RAG

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