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Larkup-RAG is an open-source toolkit that takes you from zero to a running Retrieval-Augmented Generation (RAG) server in minutes. It eliminates the complexities of manual infrastructure setup, allowing you to easily configure vector stores, chunking strategies, and embedding models through a simple interface, and immediately connect your AI agents.

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Why Larkup-RAG?

Building and launching a RAG Server from scratch is complex and highly dependent on configuration choices that impact quality, latency, and cost. Usually, it means wiring up vector databases, managing embedding pipelines, handling chunking logic, and dealing with deployment environments all before you’ve written a single line of your actual application. So Consider Larkup-RAG as:
The easiest way to launch a production-ready RAG server from local to deployment in minutes.
This approach allows you to focus on building your AI application while Larkup-RAG handles ingestion from URLs, files, or search, and takes care of the retrieval pipeline under the hood.

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How it Works

Larkup-RAG simplifies the process into 6 core steps:
1

Configure Server Settings

Set up your server settings, choose a vector store, and configure your embedding models.
2

Load Data Sources

Ingest data easily from files, spreadsheets (Excel), or by scraping websites.
3

Index Data

Automatically process, chunk, and embed your loaded data into the vector store.
4

Launch Locally

Spin up the generated RAG server locally for fast testing and API integration.
5

Demo & Iterate

Test your retrieval quality using the built-in demo UI and tweak settings instantly.
6

Deploy to Cloud

Deploy your production-ready server to your favorite cloud platforms (e.g., Vercel, AWS, Azure).

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Get Started

Quick Start

Get a server running locally in under 5 minutes.

Installation Guide

Step-by-step setup and system requirements.

Configuration

Configure vector stores, embeddings, and models.

SDK Integration

Connect your AI agents using the TypeScript or Python SDK.