abgulati/LARS
A local LLM application that enables RAG-based document querying with detailed citations including page numbers, text highlighting, and in-response document readers.
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LARS runs LLMs locally on the user’s device and allows document uploads for grounded AI responses. The application implements Retrieval Augmented Generation to reduce hallucinations and includes a distinctive citation system that provides specific document names, page numbers, text highlights, and images for every response. It supports a wide range of file formats including PDFs, Word documents, Excel files, PowerPoint presentations, and images, making it a comprehensive document-grounded AI tool.
Frequently asked
- What is abgulati/LARS?
- A local LLM application that enables RAG-based document querying with detailed citations including page numbers, text highlighting, and in-response document readers.
- Is LARS open source?
- Yes — abgulati/LARS is open source, released under the AGPL-3.0 license.
- What language is LARS written in?
- abgulati/LARS is primarily written in Python.
- How popular is LARS?
- abgulati/LARS has 637 stars on GitHub.
- Where can I find LARS?
- abgulati/LARS is on GitHub at https://github.com/abgulati/LARS.