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SuperNova RAG

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Enables semantic search over internal documentation using a local server with Retrieval-Augmented Generation (RAG).

About

SuperNova RAG is a practical demonstration of building and running a local server that uses Retrieval-Augmented Generation (RAG) for semantic search over internal documentation. Built with Node.js and TypeScript, it leverages Hugging Face embeddings and an in-memory vector store for fast, context-aware answers. This allows developers to quickly find relevant information within their documentation, directly within tools like Cursor, enhancing productivity and reducing time spent searching for answers.

Key Features

  • Hugging Face embeddings for semantic search
  • 1 GitHub stars
  • Model Context Protocol (MCP) server implementation
  • In-memory vector store for fast retrieval
  • Retrieval-Augmented Generation (RAG) pipeline
  • Integration with Cursor and other MCP-compatible tools

Use Cases

  • Semantic search over internal documentation
  • Context-aware question answering for developers
  • Integration with IDEs like Cursor for instant documentation access
SuperNova RAG: Semantic Search for Your Docs