RAG Personal Knowledge Base FAQs

Question 1

What types of documents can RAG Personal Knowledge Base index?

Accepted Answer

It can index a diverse range of document types, including PDFs, Markdown files (.md), plain text files (.txt), and various code files such as Python (.py), JavaScript (.js), TypeScript (.ts), JSON (.json), and YAML (.yaml).

Question 2

Is my data private when using this tool?

Accepted Answer

Yes, privacy is a core feature. All document embeddings are generated locally on your machine using Ollama, and your vector store data is persistently stored on disk with ChromaDB. No data leaves your local environment.

Question 3

How does RAG Personal Knowledge Base integrate with Claude?

Accepted Answer

It functions as an MCP server for Claude Desktop, exposing specific tools like `index_document`, `search_docs`, `list_indexed_docs`, and `delete_document`. This allows Claude to interact directly with your personal knowledge base for indexing and querying.

Question 4

What is RAG Personal Knowledge Base?

Accepted Answer

RAG Personal Knowledge Base is a software tool that indexes your local documents (PDFs, Markdown, code, text) to create a private, semantic search engine. It integrates with Claude to allow you to ask questions across your entire document collection.

Question 5

What are the key requirements to run RAG Personal Knowledge Base?

Accepted Answer

You need Python 3.13+, `uv` (a Python package manager), and Ollama running locally on your machine with the `nomic-embed-text` embedding model pulled.

RAG Personal Knowledge Base

RAG Personal Knowledge Base

Key Features

Use Cases

Key Features

Use Cases