MTEB: Massive Text Embedding Benchmark

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

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

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

User Profile-Based Long-Term Memory for AI Chatbot Applications.

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

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

Data for the MTEB leaderboard

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

Deep Recommenders

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

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

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

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

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

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

Parsing-free RAG supported by VLMs

The official implementation of ICLR 2020, "Learning to Retrieve Reasoning Paths over Wikipedia Graph for Question Answering".

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