Personal RAG FAQs

Question 1

How does Personal RAG store and retrieve information?

Accepted Answer

It employs a hybrid storage solution: SQLite is used for storing full-text documents and comprehensive metadata, while Qdrant handles semantic vector storage for efficient and intelligent search. This combination ensures both robust data retention and advanced retrieval capabilities.

Question 2

What kind of personal data can I manage with Personal RAG?

Accepted Answer

You can store various forms of personal context, including notes, documents, and code snippets. The system supports rich metadata like namespaces, tags, titles, categories (work, personal, family), and content types for highly organized and retrievable information.

Question 3

What is Personal RAG?

Accepted Answer

Personal RAG is a Model Context Protocol (MCP) server that provides a personal knowledge base with Retrieval-Augmented Generation (RAG) capabilities. It allows you to store, search, and utilize personal context from documents, notes, and conversations across various AI environments and applications like Claude Desktop, VS Code, and Open WebUI.

Question 4

Is Personal RAG easy to deploy and integrate?

Accepted Answer

Yes, Personal RAG is containerized and designed to run in Docker. It easily connects to existing Qdrant, Ollama, and LiteLLM infrastructure using Docker Compose, providing a streamlined setup for developers to quickly get their personal AI knowledge base running.

Question 5

Which AI environments and LLMs does Personal RAG support?

Accepted Answer

Personal RAG offers dual transport for compatibility with Claude Desktop/VS Code (stdio) and Open WebUI (HTTP streaming). It integrates with a wide range of LLMs via LiteLLM, supporting local models (e.g., Ollama) and cloud providers like OpenAI, Anthropic, Google Gemini, and AWS Bedrock.

Personal RAG

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