aiaio is a lightweight, privacy-focused web UI for interacting with AI models, designed for users who want to connect to local or remote LLM deployments via OpenAI-compatible APIs. It offers a user-friendly interface for managing conversations, configuring model parameters, and uploading files, with all data stored locally.

How It Works

aiaio acts as a frontend client that communicates with any OpenAI-compatible API endpoint. It leverages a local SQLite database for storing conversation history, summaries, and user settings. The UI allows for dynamic configuration of model parameters like temperature, max tokens, and top-p, as well as API endpoint details. File uploads are handled and can be referenced within conversations, with processing capabilities dependent on the backend model.

Quick Start & Requirements

• Install via pip: pip install aiaio
• Requires Python 3.10+ and an OpenAI-compatible API endpoint.
• Docker support is available for deployment.
• Official documentation and demo are not explicitly linked in the README.

Highlighted Details

• Supports OpenAI, vLLM, Text Generation Inference (TGI), Hugging Face Inference Endpoints, LocalAI, and custom compatible APIs.
• Features dark/light mode, file upload (images, documents, text, audio, video), syntax highlighting, and one-click code copying.
• Offers real-time conversation updates via WebSockets and automatic conversation summarization.
• Customizable system prompts per conversation.

Maintenance & Community

• The project acknowledges significant assistance from GitHub Copilot in its development.
• Contribution guidelines are provided, encouraging pull requests.
• No specific community channels (Discord, Slack) or roadmap links are mentioned.

Licensing & Compatibility

• Licensed under the Apache License 2.0.
• Permissive license suitable for commercial use and integration with closed-source applications.

Limitations & Caveats

The README does not specify any known limitations, alpha status, or deprecations. The project's reliance on GitHub Copilot for code generation might warrant additional scrutiny for code quality and potential licensing implications of the generated code.