MozerWang/Loong
[EMNLP 2024 (Oral)] Leave No Document Behind: Benchmarking Long-Context LLMs with Extended Multi-Doc QA
SoTA production-ready AI retrieval system. Agentic Retrieval-Augmented Generation (RAG) with a RESTful API.
$ git clone https://github.com/SciPhi-AI/R2R.git[EMNLP 2024 (Oral)] Leave No Document Behind: Benchmarking Long-Context LLMs with Extended Multi-Doc QA
RAG-QA-Generator 是一个用于检索增强生成(RAG)系统的自动化知识库构建与管理工具。该工具通过读取文档数据,利用大规模语言模型生成高质量的问答对(QA对),并将这些数据插入数据库中,实现RAG系统知识库的自动化构建和管理。
A custom RAG pipeline for multi-document QA from PDF/DOCX documents, in Android
Powerful web application that combines Streamlit, LangChain, and Pinecone to simplify document analysis. Powered by OpenAI's GPT-3, RAG enables dynamic, interactive document conversations, making it ideal for efficient document retrieval and summarization.
An LLM-powered advanced RAG pipeline built from scratch
Use ArXiv ChatGuru to talk to research papers. This app uses LangChain, OpenAI, Streamlit, and Redis as a vector database/semantic cache.
Data from GitHub · snapshot Sep 24, 2026