raggenie

🥇Gold

RAGGENIE is an open-source, low-code platform designed for building custom Retrieval-Augmented Generation (RAG) Copilets using your own data. It simplifies AI development, making it accessible for users looking to use AI capabilities effortlessly.

180670Updated 1mo ago

Intermediate15 minutes to implementdevelopment

Saves ~90 min per use

Quick InstallView Source

claude install sirocco-ventures/raggenie

Works with:

GitHub Copilot

Overview

About This Skill

Use Cases

Building custom AI assistants

Integrating proprietary data for enhanced responses

Creating tailored content generation tools

Developing chatbots with specific knowledge bases

Best For

GrowthRevOps

Setup & Installation

Quick Install

Terminal

claude install sirocco-ventures/raggenie

Alternative Install (Git Clone)

git clone https://github.com/sirocco-ventures/raggenie

Requirements

Claude Code or compatible AI agent
Works with: GitHub Copilot

Quick Start Guide

Install the Skill

Copy the install command above and run it in your terminal.

Open Your AI Agent

Launch Claude Code, Cursor, or your preferred AI coding agent.

Try It Out

Use the prompt template or examples below to test the skill.

Customize

Adapt the skill to your specific use case and workflow.

Usage Examples

Prompt Template

I want to build a custom RAG Copilet using RAGGENIE to enhance my Etar calendar application. My goal is to create an AI assistant that can [SPECIFIC TASK], such as suggesting optimal meeting times based on historical data or providing insights into client engagement patterns. I have a dataset of [DATA TYPE], like meeting logs, client interactions, and campaign timelines. Guide me through the steps to integrate this data into RAGGENIE and train the model to perform the desired tasks.

Example Output

To build a custom RAG Copilet using RAGGENIE for your Etar calendar application, follow these steps:

1. **Data Preparation**: Start by organizing your dataset of meeting logs, client interactions, and campaign timelines. Ensure the data is clean and structured in a format compatible with RAGGENIE, such as CSV or JSON.

2. **Setting Up RAGGENIE**: Install RAGGENIE on your local machine or a cloud server. Follow the installation guide provided in the RAGGENIE documentation. This typically involves cloning the repository and setting up the necessary dependencies.

3. **Data Integration**: Use the RAGGENIE interface to upload your dataset. The platform will guide you through the process of mapping your data fields to the appropriate input fields for the RAG model.

4. **Model Training**: Configure the RAG model to perform the specific tasks you need, such as suggesting optimal meeting times or providing insights into client engagement patterns. RAGGENIE offers a user-friendly interface for setting training parameters and monitoring the training process.

5. **Integration with Etar**: Once the model is trained, use the RAGGENIE API to integrate the RAG Copilet into your Etar application. This involves embedding the API endpoints into your application's backend and ensuring seamless data flow between the RAG model and Etar.

6. **Testing and Optimization**: Test the integrated RAG Copilet to ensure it performs as expected. Monitor its performance and make adjustments as needed to improve accuracy and efficiency.

By following these steps, you can leverage RAGGENIE to enhance your Etar calendar application with custom AI capabilities tailored to your specific needs.