RAG Application

A retrieval-augmented generation app that lets you upload documents and ask questions about them. Uses SQLite as a vector database, OpenAI for embeddings and chat completions, and a React frontend.

How It Works

1. Upload a document (.txt, .md, or .pdf)
2. The app chunks the text and generates embeddings via OpenAI
3. Chunks and embeddings are stored in SQLite using the sqlite-vec extension
4. Ask a question — the app finds relevant chunks via vector similarity search, then generates an answer with GPT-4o-mini

Tech Stack

• Server: Node.js + Express
• Vector DB: SQLite via better-sqlite3 + sqlite-vec
• Embeddings: OpenAI text-embedding-3-small (1536 dimensions)
• LLM: OpenAI gpt-4o-mini
• Frontend: React 18 (CDN, no build step)

Setup

git clone https://github.com/InkMCPDev/example-rag-app.git
cd example-rag-app
npm install

Create a .env file with your OpenAI API key:

OPENAI_API_KEY=sk-...

Start the server:

node server.js

Open http://localhost:3000.

Deploy with Ink

Connect the Ink MCP server to your AI agent and prompt:

Deploy the example-rag-app repo from github. Set the OPENAI_API_KEY environment variable.

Tutorial

Full step-by-step tutorial: docs.deployink.com/examples/ai-and-mcp/rag-app

About Ink

Ink is a deployment platform built for AI agents. Connect the Ink MCP server to any AI coding agent and deploy applications, manage domains, databases, and infrastructure — all through natural language.

• Website: deployink.com
• Documentation: docs.deployink.com
• Quick Start: docs.deployink.com/quick-start
• Examples: docs.deployink.com/examples