reranking

MTEB: Massive Text Embedding Benchmark

🥤 RAGLite is a Python toolkit for Retrieval-Augmented Generation (RAG) with PostgreSQL or SQLite

Lite & Super-fast re-ranking for your search & retrieval pipelines. Supports SoTA Listwise and Pairwise reranking based on LLMs and cross-encoders and more. Created by Prithivi Da, open for PRs & Collaborations.

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

A curated list of awesome papers related to pre-trained models for information retrieval (a.k.a., pretraining for IR).

Is ChatGPT Good at Search? LLMs as Re-Ranking Agent [EMNLP 2023 Outstanding Paper Award]

Querying local documents, powered by LLM

Rust library for generating vector embeddings, reranking locally

YT Navigator: AI-powered YouTube content explorer that lets you search and chat with channel videos using AI agents. Extract insights from hours of content in seconds with semantic search and precise timestamps.

🔥 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.

A LLM RAG system runs on your laptop. 大模型检索增强生成系统，可以轻松部署在笔记本电脑上，实现本地知识库智能问答。

llama.cpp (GGUF LLMs) and llava.cpp (GGUF VLMs) for ROS 2

Diffusion on manifolds for image retrieval

Code, datasets, and checkpoints for the paper "Improving Passage Retrieval with Zero-Shot Question Generation (EMNLP 2022)"

A flask-based cbir system

Code and resources for papers "Generation-Augmented Retrieval for Open-Domain Question Answering" and "Reader-Guided Passage Reranking for Open-Domain Question Answering", ACL 2021

Official repository of ICCV21 paper "Viewpoint Invariant Dense Matching for Visual Geolocalization"

An Unsupervised Distance Learning Framework for Multimedia Retrieval

rerank library for easy reranking of results

Heterogenous, Task- and Domain-Specific Benchmark for Unsupervised Sentence Embeddings used in the TSDAE paper: https://arxiv.org/abs/2104.06979.