Local RAG FAQs

Question 1

What is Local RAG and how does it protect my data?

Accepted Answer

Local RAG is a privacy-first server that runs entirely on your machine, enabling AI coding assistants to perform semantic search on your personal documents. It ensures data privacy by keeping all documents, embeddings, and searches local; no data is ever sent to external APIs or cloud services.

Question 2

What types of files can Local RAG index and search?

Accepted Answer

Local RAG supports automatic ingestion and semantic search across PDF, DOCX, TXT, and Markdown files. It handles text extraction, chunking, and stores embeddings in a local vector database.

Question 3

Can I use Local RAG without an internet connection?

Accepted Answer

Yes. After the initial download of the embedding model (approx. 90MB), Local RAG operates completely offline for both document ingestion and semantic search, making it reliable in any environment.

Question 4

Which AI coding assistants are compatible with Local RAG?

Accepted Answer

Local RAG is built for the Model Context Protocol (MCP) and seamlessly integrates with MCP clients such as Cursor, Codex, and Claude Code, allowing your AI assistant to leverage your local document context.

Question 5

What are the main benefits of using Local RAG?

Accepted Answer

The primary benefits include enhanced privacy for sensitive documents, elimination of external API costs, and reliable offline functionality. It provides fast, accurate semantic search using a local vector database and embedding models.

Local RAG

Local RAG

Key Features

Use Cases

Key Features

Use Cases