AutoRAG is an open-source framework designed to automate the evaluation and optimization of Retrieval-Augmented Generation (RAG) pipelines. It targets developers and researchers seeking to identify the most effective RAG configurations for their specific data and use cases, saving significant manual effort in module selection and testing.

How It Works

AutoRAG employs an AutoML-style approach to systematically explore and evaluate various combinations of RAG modules (parsing, chunking, retrieval, prompt making, generation). Users define their desired pipeline structure and evaluation metrics via YAML configuration files. The framework then executes trials, comparing different module strategies and hyperparameters to identify the optimal pipeline based on the specified metrics.

Quick Start & Requirements

• Install via pip: pip install AutoRAG
• GPU support: pip install "AutoRAG[gpu]"
• Parsing support: pip install "AutoRAG[gpu,parse]"
• Python 3.10+ recommended.
• Requires QA and Corpus datasets in .parquet format.
• Official Docs: https://github.com/Marker-Inc-Korea/AutoRAG#document
• GUI Docs: https://github.com/Marker-Inc-Korea/AutoRAG#autorag-gui-beta

Highlighted Details

• Supports automated evaluation of diverse RAG module combinations.
• Offers a web-based GUI for simplified pipeline optimization.
• Provides comprehensive metrics for retrieval and generation performance.
• Enables deployment of optimized pipelines as code, API servers, or web interfaces.

Maintenance & Community

• Active development with contributions welcomed.
• Discord community available for support and discussion.
• Roadmap and FAQ sections are linked in the README.

Licensing & Compatibility

• The project appears to be under a permissive license, but specific details are not explicitly stated in the README. Compatibility for commercial use or closed-source linking would require clarification of the exact license.

Limitations & Caveats

• The GUI is currently in beta.
• RAG optimization trials may incur costs if using external LLM APIs like OpenAI.
• Detailed license information for commercial use is not readily available in the README.