Omama GUI

✨ Overview

Omama GUI is a sleek and modern graphical interface that helps you manage and interact with local Ollama models. Whether you're a developer or an AI enthusiast, Omama simplifies your workflow with a powerful general-purpose Retrieval-Augmented Generation (RAG) system — no deep technical knowledge required.

🔗 Watch the Demo: Overview Video

🚀 Features

• ⚙️ Model Management Browse, download, and manage Ollama models with ease using a beautiful, intuitive interface.

• 💬 Chat Experience Seamlessly chat with your favorite models in real time through a responsive, clean interface.

• 🧠 General-Purpose RAG Integration Responses are enhanced by an integrated RAG system that brings context-awareness and document-grounded answers.

• 🧩 Modular Architecture Built to work with Omama Manager, supporting:

  • Indexing
  • Retrieval
  • Query Orchestration
  • File or SurrealDB storage

• 🌙 Offline-First Omama works fully offline with locally run LLMs using Ollama, ensuring data privacy.

🛠️ Getting Started

⚠️ Note: Full setup automation is in progress — follow these manual steps for now.

🔧 Requirements

• ✅ Ollama installed and running in the background.
• ✅ Omama GUI Release downloaded and extracted.
• ✅ Omama CLI downloaded and extracted into a folder named omama_cli, then placed inside the Omama GUI directory.

⚡ First Run Instructions

1. Open Omama GUI
2. Click on the Models Store icon.
3. Wait ~2 minutes while model data is cached from the web.
4. Close and reopen Omama GUI.
5. You're ready to chat and explore!

🧩 Related Projects

• omama-cli – Command-line tool that bridges GUI with the backend.
• omama-manager – Core backend for RAG, retrieval, orchestration, and model logic.
• ollama-models-info-fetcher – Library for fetching model metadata.
• ollama-td – Utility for downloading Ollama clients (not used in production due to issues).

📚 References & Inspiration

The RAG engine in Omama GUI is inspired by cutting-edge research:

• 📘 Rag book - quick book guide.
• 📖 RAG Survey (2023) – Overview of RAG methods and design choices.
• 🧱 Modular RAG (2024) – Best practices for building modular RAG systems.
• 🧠 RAG Best Practices (2024) – Techniques for scalable and robust RAG architectures.

🙌 Contributing

Contributions, feature ideas, and feedback are welcome! Feel free to open issues or pull requests if you'd like to help improve Omama GUI.

📄 License

This project is licensed under the MIT License. See the LICENSE file for more details.