Rag Context FAQs

Question 1

How do I integrate Rag Context with my AI assistant?

Accepted Answer

Rag Context can be integrated with AI assistants like Claude Desktop and Cursor by configuring the MCP server settings. You'll need to specify the command and arguments to run the Rag Context server, along with the data directory.

Question 2

How does Rag Context ensure privacy?

Accepted Answer

Rag Context prioritizes privacy by storing all data locally on your machine. It uses a local embedding model (Xenova/all-MiniLM-L6-v2) for semantic search, eliminating the need for external API calls.

Question 3

What are the benefits of using Rag Context over other memory solutions?

Accepted Answer

Rag Context offers a privacy-focused and lightweight solution with local data storage, hybrid retrieval for optimal performance, and easy integration with various AI assistants. It's ideal for developers who prioritize data security and control.

Question 4

What are the key features of Rag Context?

Accepted Answer

Key features include local vector storage for efficient similarity search, persistent memory with SQLite, semantic search using local embeddings, hybrid retrieval combining semantic and indexed queries, and a simple API with `setContext` and `getContext` tools.

Question 5

What is Rag Context?

Accepted Answer

Rag Context is a lightweight MCP (Model Context Protocol) server that provides AI assistants with persistent memory and context management using local vector storage and a SQLite database. It allows AI to efficiently store and retrieve information across conversations.

Rag Context

Rag Context

Key Features

Use Cases

Key Features

Use Cases