R2R is a prod-ready RAG (Retrieval-Augmented Generation) engine with a RESTful API. R2R includes hybrid search, knowledge graphs, and more.
-
Updated
Jul 1, 2024 - Python
R2R is a prod-ready RAG (Retrieval-Augmented Generation) engine with a RESTful API. R2R includes hybrid search, knowledge graphs, and more.
⚡ Build your chatbot within minutes on your favorite device; offer SOTA compression techniques for LLMs; run LLMs efficiently on Intel Platforms⚡
Study guides for MIT's 15.003 Data Science Tools
MTEB: Massive Text Embedding Benchmark
A Heterogeneous Benchmark for Information Retrieval. Easy to use, evaluate your models across 15+ diverse IR datasets.
Fast, Accurate, Lightweight Python library to make State of the Art Embedding
Epsilla is a high performance Vector Database Management System. Try out hosted Epsilla at https://cloud.epsilla.com/
Implementation of RETRO, Deepmind's Retrieval based Attention net, in Pytorch
SGPT: GPT Sentence Embeddings for Semantic Search
An official implementation for "CLIP4Clip: An Empirical Study of CLIP for End to End Video Clip Retrieval"
The official implementation of RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval
My personal note about local and global descriptor
A realtime and indexing and structured extraction engine for Unstructured Data to build Generative AI Applications
Implementation of Memorizing Transformers (ICLR 2022), attention net augmented with indexing and retrieval of memories using approximate nearest neighbors, in Pytorch
Grounded search engine (i.e. with source reference) based on LLM / ChatGPT / OpenAI API. It supports web search, file content search etc.
Fast lexical search library implementing BM25 in Python using Scipy (on average 2x faster than Elasticsearch in single-threaded setting)
Use ArXiv ChatGuru to talk to research papers. This app uses LangChain, OpenAI, Streamlit, and Redis as a vector database/semantic cache.
Generative Representational Instruction Tuning
Add a description, image, and links to the retrieval topic page so that developers can more easily learn about it.
To associate your repository with the retrieval topic, visit your repo's landing page and select "manage topics."