Nucleo AI is an alpha-stage application that integrates AI capabilities with traditional personal productivity tools, targeting users who want an AI assistant that operates beyond a simple chat interface. It aims to provide background task handling, research, and document management, enhancing personal workflows.

How It Works

Nucleo AI operates as a backend service that can be connected to various OpenAI-compatible LLM endpoints, including local setups like llama.cpp and oobabooga. It supports multiple AI interaction modes: standard chat, an "Assistant" mode that breaks down requests into executable tasks (like web searches or to-do list entries), and a "Researcher" mode for in-depth topic exploration with web scraping. The application also features Retrieval-Augmented Generation (RAG) for document Q&A, allowing users to upload PDFs or text files for the AI to reference.

Quick Start & Requirements

• Install via git clone and run ./setup.sh.
• Requires Python 3, torch (CPU-only is sufficient), and git.
• RAG requires downloading ~850MB of SentenceTransformers models.
• Connects to OpenAI-compatible API endpoints (e.g., http://localhost:5000/v1).
• Official demo video: https://github.com/AndrewVeee/nucleo-ai/assets/153542699/60526628-62f5-462f-a6a4-affdbdb6d7c1

Highlighted Details

• Supports multiple chat modes: Regular Chat, Assistant (task execution), and Researcher (in-depth topic analysis).
• RAG functionality for querying uploaded documents (PDFs, TXT, Markdown).
• API-first design allows integration with OpenAI clients.
• Features a basic to-do list and a "Stream" for logging AI actions and replaying tasks.

Maintenance & Community

• Primarily seeking user feedback for development direction.
• Community support available via r/nucleoai subreddit: https://www.reddit.com/r/nucleoai/

Licensing & Compatibility

• The repository does not explicitly state a license in the provided README.

Limitations & Caveats

This is alpha software with known bugs and areas needing improvement. The Assistant mode can consume significant tokens, and RAG performance is model-dependent. Document uploads are recommended to be under 5MB. The codebase is described as "messy" with ongoing development.