Correct complete RAG -- built for Highway Workflow Engine

A practical POC demonstrating how to build and run a local MCP server with Retrieval-Augmented Generation (RAG) for semantic search over internal documentation. Leverages Node.js, TypeScript, Hugging Face embeddings, and an in-memory vector store to enable fast, context-aware answers in tools like Cursor.

In this we implemented RAG Architecture using MCP

📄 Build RAG applications easily with ragit, a Python toolkit for document handling, vector search, and LLM integration, streamlining your Q&A and code generation systems.