Query Expension for Better Query Embedding using LLMs
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Updated
Feb 18, 2025 - Python
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
LoRA fine-tuning of bi-encoder retrievers with hard negatives and cross-encoder distillation, evaluated on NFCorpus.
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
A decentralized cooperative memory & research layer for AI agents — collectively and cooperatively learning and advancing as a community.
Research-grade hybrid retrieval API — BM25 + FAISS + CDF calibration + entropy-weighted fusion + cross-encoder reranking. Benchmarked on BEIR SciFact with bootstrap significance tests.
Rigorous retrieval evaluation over BEIR FiQA-2018: hand-written nDCG/recall/MRR/MAP validated against pytrec_eval to 1e-6, hybrid BM25+dense fusion, cross-encoder reranking, paired-bootstrap significance, and a single-use held-out test split.
Retrieval evaluation that tells you what it can't resolve — intervals, significance, anytime-valid CI gates, and distribution-free guarantees. Zero dependencies, any retriever.
Reproducible retrieval quality benchmark methods and curated result bundles for Cortrix semantic storage.
Hybrid retrieval built to be measured, not believed — BM25 + dense + HNSW + fusion + re-ranking in pure numpy. Hand-rolled BM25 reproduces published BEIR baselines to 0.0066 MAD.
Retrieval benchmark that runs in CI without an API key: 4 strategies over BEIR corpora, scored with IR metrics, bootstrap intervals and paired significance tests
RAG evaluation on BEIR SciFact: BM25, dense and hybrid retrieval with LLM answers.
Hybrid RAG pipeline combining BM25, dense retrieval, quantum kernels, Grover amplification and QAOA reranking.
FlashRank-Pro: 149M-parameter ModernBERT cross-encoder reranker for RAG. Apache 2.0. Measured 0.3314 five-set BEIR-style NDCG@10 (+22.6% vs run-init). CPU-friendly, 8K context.
SERA-VQ: Discrete codes for extreme embedding compression — outperforms PCA+int8 at low memory budgets on BEIR/SciFact
BM25 (Elasticsearch) vs dense (FAISS + MiniLM) vs hybrid RRF retrieval on BEIR SciFact, behind one Retriever protocol, scored on Recall, Precision, MRR and nDCG.
Retrieval bench: BM25, LSA and a dense bi-encoder behind one interface, evaluated on real BEIR benchmarks with nDCG/MRR and a per-query-type breakdown.
Hybrid search benchmark for BM25 + dense vector + cross-encoder rerank on distractor-augmented BEIR scifact.
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