MTEB: Massive Text Embedding Benchmark

Fast, Accurate, Lightweight Python library to make State of the Art Embedding

⚡ Build your chatbot within minutes on your favorite device; offer SOTA compression techniques for LLMs; run LLMs efficiently on Intel Platforms⚡

A Heterogeneous Benchmark for Information Retrieval. Easy to use, evaluate your models across 15+ diverse IR datasets.

Profile-Based Long-Term Memory for AI Applications. Memobase handles user profiles, memory events, and evolving context — perfect for chatbots, companions, tutors, customer service bots, and all chat-based agents.

The official implementation of RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval

Fast lexical search implementing BM25 in Python using Numpy, Numba and Scipy

An official implementation for "CLIP4Clip: An Empirical Study of CLIP for End to End Video Clip Retrieval"

Implementation of RETRO, Deepmind's Retrieval based Attention net, in Pytorch

Neum AI is a best-in-class framework to manage the creation and synchronization of vector embeddings at large scale.

Use late-interaction multi-modal models such as ColPali in just a few lines of code.

Parsing-free RAG supported by VLMs

Grounded search engine (i.e. with source reference) based on LLM / ChatGPT / OpenAI API. It supports web search, file content search etc.

Implementation of Memorizing Transformers (ICLR 2022), attention net augmented with indexing and retrieval of memories using approximate nearest neighbors, in Pytorch

Use ArXiv ChatGuru to talk to research papers. This app uses LangChain, OpenAI, Streamlit, and Redis as a vector database/semantic cache.

Easy-to-Use RAG Framework; CCF AIOps International Challenge 2024 Top3 Solution; CCF AIOps 国际挑战赛 2024 季军方案

🔥 Rankify: A Comprehensive Python Toolkit for Retrieval, Re-Ranking, and Retrieval-Augmented Generation 🔥. Our toolkit integrates 40 pre-retrieved benchmark datasets and supports 7+ retrieval techniques, 24+ state-of-the-art Reranking models, and multiple RAG methods.

[Pytorch] Generative retrieval model using semantic IDs from "Recommender Systems with Generative Retrieval"

Retrieve, Read and LinK: Fast and Accurate Entity Linking and Relation Extraction on an Academic Budget (ACL 2024)

In-Context Learning for eXtreme Multi-Label Classification (XMC) using only a handful of examples.