Query Expension for Better Query Embedding using LLMs

Code and models for the paper "Questions Are All You Need to Train a Dense Passage Retriever (TACL 2023)"

SPRINT Toolkit helps you evaluate diverse neural sparse models easily using a single click on any IR dataset.

Evaluation of BEIR Datasets using ColBERT retrieval model

A genral RAG Search chatbot, with SoTA RAG techniques such as HyDE, Hybrid retrieval with BM25 + RRF and Cross encoder reranking. Evaluated on the BEIR scifact dataset and compared all the different pipelines i tried along the way

Physics-Inspired Reranking via Token-Level Point Clouds & PDE Fusion | NFCorpus NDCG@10 = 0.3232 (+47.2%) | 26ms CPU | Zero training

Scripts to convert the LegalBench-RAG dataset into the standard IR format

A RAG system that replaces standard BM25/FAISS retrieval with a fully learned neural retrieval stack - including a fine-tuned bi-encoder, a cross-encoder reranker, ColBERT-style late interaction scoring, and a locally hosted LLM generator. Built entirely with free and open-source tools.

Given a set of documents and the minimum required similarity threshold find the number of document pairs that exceed the threshold

Retrieve the top-𝑘 documents with respect to a given query by maximal inner product over dense and sparse vectors