Rag FAQs

Question 1

What is ChromaDB and how does Rag use it?

Accepted Answer

Rag uses ChromaDB v2 API for semantic similarity search. ChromaDB is a vector database that stores document embeddings, allowing Rag to find relevant information based on meaning rather than keywords.

Question 2

How does Rag handle large documents?

Accepted Answer

Rag intelligently chunks large documents into smaller pieces and reconstructs them on demand, ensuring efficient processing and retrieval even for extensive content.

Question 3

What file types does Rag support?

Accepted Answer

Rag supports PDF, Markdown, and text files with enhanced parsing capabilities to extract meaningful information.

Question 4

What is Rag?

Accepted Answer

Rag is a software tool that provides a Model Context Protocol (MCP) server for Retrieval-Augmented Generation. It helps you easily parse documents, store them in a vector database, and retrieve them based on semantic similarity.

Question 5

Does Rag require any specific setup?

Accepted Answer

Yes, Rag requires Node.js 18+ and a running ChromaDB server. You'll also need network access to your ChromaDB instance.

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