Knowledge RAG FAQs

Question 1

How does Knowledge RAG integrate with Claude Code?

Accepted Answer

It offers native integration with Claude Code via MCP (Model Context Protocol) tools, enabling Claude to seamlessly query your indexed documents for relevant context.

Question 2

What document types can Knowledge RAG process?

Accepted Answer

Knowledge RAG includes a multi-format parser supporting PDF, Markdown, TXT, Python, and JSON files, allowing you to index a wide range of personal knowledge.

Question 3

How does Knowledge RAG ensure my data privacy?

Accepted Answer

All document processing, embeddings, and search operations are performed entirely on your local machine. No data ever leaves your environment, guaranteeing maximum privacy.

Question 4

What is Knowledge RAG?

Accepted Answer

Knowledge RAG is a 100% local Retrieval-Augmented Generation (RAG) system that allows Claude Code to search and retrieve context from your personal documents using advanced hybrid search.

Question 5

What search methods does Knowledge RAG employ?

Accepted Answer

It utilizes a sophisticated hybrid search approach, combining semantic search (conceptual similarity) with BM25 keyword search (exact term matching), fused by Reciprocal Rank Fusion (RRF) for comprehensive results.

Knowledge RAG

Knowledge RAG

소개

주요 기능

사용 사례

소개

주요 기능

사용 사례