A document question-answering application providing a RAG interface for end users and a code library for developers to build RAG pipelines. It is designed for general users seeking an accessible document chat tool and developers needing a foundation for customized RAG workflows.
Project overview
Serves a dual audience by offering both a functional RAG user interface for document QA and a customizable code foundation for developers to build their own pipelines.
Project type
RAG
Use cases
Knowledge Q&A
Deployment
Refer to project documentation
License
Apache-2.0
Best for
General users wanting an accessible UI for document QA and developers wanting a foundation to build RAG pipelines.
Key capabilities
Provides a functional RAG user interface for doing question answering on documents.
Provides a foundation for developers to build their own RAG pipeline by importing the codebase.
Limitations and risks
OS support, GPU requirements, minimum hardware specifications, databases, external services, and model requirements are not documented in the available facts.
Getting started
Users can start by accessing the hosted live demo on Hugging Face. The setup difficulty is documented as easy due to live online demos, a one-click Colab notebook, and an available Docker image.
Alternatives and comparisons
Provides deep document understanding and heterogeneous data source compatibility for transforming complex, unstructured data into production-ready AI systems.
Bridges the gap between images/PDFs and LLMs by converting unstructured documents into structured, LLM-ready data to support RAG applications.
README: An open-source clean & customizable RAG UI for chatting with your documents. Built with both end users and developers in mind.
README: This project serves as a functional RAG UI for both end users who want to do QA on their documents and developers who want to build their own RAG pipeline.