A2RCHI is a retrieval-augmented generation framework for research and education teams who need a low-barrier to entry, configurable, private, and extensible assistant. The system was first developed at MIT for the SubMIT computing project, and now powers chat, ticketing, and course-support workflows across academia and research organizations.
A2RCHI provides:
- Customizable AI pipelines that combine data retrieval and LLMs (and more tools to come!).
- Data ingestion connectors: web links, git repositories, local files, JIRA, and more.
- Interfaces: chat app, ticketing assistant, email bot, and more.
- Support for running or interacting with local and API-based LLMs.
- Modular design that allows custom data sources, LLM backends, and deployment targets.
- Containerized services and CLI utilities for repeatable deployments.
The docs are organized as follows:
- Install — system requirements and environment preparation.
- Quickstart — after installation, learn how to deploy your first A2RCHI instance.
- User Guide — framework concepts for users and administrators.
- Advanced Setup & Deployment — configuring A2RCHI for GPU use, custom models, and advanced workflows.
- API Reference — programmatic interfaces and integration points.
- Developer Guide — codebase layout, contribution workflow, and extension patterns.
Follow the Install and Quickstart guide to set up prerequisites, configure data sources, and launch an instance.
We welcome fixes and new integrations—see the Developer Guide for coding standards, testing instructions, and contribution tips. Please open issues or pull requests on the GitHub repository.
A2RCHI is released under the MIT License. For project inquiries, contact [email protected].