Official code for ACL2025 "🔍 Retrieval Models Aren’t Tool-Savvy: Benchmarking Tool Retrieval for Large Language Models"
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Updated
Dec 22, 2025 - JavaScript
Official code for ACL2025 "🔍 Retrieval Models Aren’t Tool-Savvy: Benchmarking Tool Retrieval for Large Language Models"
smallevals — CPU-fast, GPU-blazing fast offline retrieval evaluation for RAG systems with tiny QA models.
Official codebase for the ACL 2025 Findings paper: Optimized Text Embedding Models and Benchmarks for Amharic Passage Retrieval.
Validate TREC qrels and runs in CI; explore a MiniLM browser lab and FAISS + DeBERTa product-search pipeline.
Published PyPI package for ArXiv embedding benchmarks, retrieval evaluation, and scientific RAG experiments.
RAG retrieval benchmark runner with JSON reports, Pareto plots, and regression gates for retrieval quality changes.
Bilingual RAG evaluation benchmark for culturally grounded English/Uzbek retrieval
Open-source retrieval diagnostics toolkit for enterprise RAG pipelines
Deterministic RAG evaluation toolkit -- retrieval metrics (recall, precision, MRR), corpus overlap detection, and CI regression gating without model calls.
Local-first YouTube transcript retrieval lab comparing E5 + FAISS, Japanese BM25, hybrid, and agentic search with timestamped evidence and evaluation.
Local-first memory infrastructure for coding workflows: deterministic retrieval, explainable traces, MCP/REST/SDK interfaces, and standalone browser-first operation.
Human-verified evaluation for RAG, policies, search quality, model versions, and AI agents.
A controlled experiment evaluating whether hybrid (dense + sparse) retrieval surfaces evidence that dense-only RAG systems misrank—without changing generation behavior.
A systems-level analysis of static RAG pipelines, isolating ingestion, retrieval, and ranking boundaries to expose structural failure modes before generation.
Open multilingual RAG benchmark for retrieval-grounded educational question answering
Benchmarking BM25, dense retrieval, hybrid search, and re-rankers with gold-label evaluation and query-level failure analysis.
Research-grade neuro-symbolic RAG framework where retrieval is a policy, not a vector search, built for evaluation, ablation, and reliability analysis.
CI gate for RAG retrieval quality. Declares retrievers (TF-IDF, FAISS dense, BM25, hybrid RRF) in config, evaluates recall/MRR/nDCG with bootstrap CIs against a committed labeled dataset, and fails the build on statistically significant drops vs a git-versioned baseline. Caught a 7-point recall regression; 97% test coverage.
Visual RAPTOR ColBERT Integration System - Multimodal document retrieval with SigLIP, PyMuPDF, and evaluation metrics.
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