TIGER-AI-Lab/LongRAG

Official repo for "LongRAG: Enhancing Retrieval-Augmented Generation with Long-context LLMs".

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Emerging

This project helps researchers and developers improve how large language models (LLMs) answer complex questions using external information. It takes a collection of documents (like Wikipedia articles) and user questions, then produces more accurate and comprehensive answers by finding and using longer, more relevant document sections. This is ideal for anyone building advanced question-answering systems.

245 stars. No commits in the last 6 months.

Use this if you are developing AI applications that need to answer detailed questions accurately by drawing information from a large pool of documents, especially when short snippets of text aren't sufficient.

Not ideal if you are looking for a pre-built, production-ready question-answering application rather than a research framework for enhancing RAG systems.

question-answering information-retrieval natural-language-processing LLM-development AI-research
Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 10 / 25
Maturity 16 / 25
Community 13 / 25

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Stars

245

Forks

19

Language

Python

License

MIT

Last pushed

Aug 25, 2024

Commits (30d)

0

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