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.
面向扫地机器人售前、售后与使用报告的多工具智能客服 Agent:基于 LangChain/LangGraph、通义千问、Chroma RAG、SQLite 与 Streamlit,集成知识库安全入库、浏览器定位、实时天气、会话用户及设备使用记录查询,并提供可审阅工具追踪、动态 Prompt 与 Recall@K/MRR 检索评测。
Validate TREC qrels and runs in CI; explore a MiniLM browser lab and FAISS + DeBERTa product-search pipeline.
Hybrid RAG (BM25 + NumPy in-memory vector store) with LLM-as-a-Judge response scoring and a retrieval evaluation harness. Built with Gemini 2.5 Flash.
Published PyPI package for ArXiv embedding benchmarks, retrieval evaluation, and scientific RAG experiments.
Open-source retrieval diagnostics toolkit for enterprise RAG pipelines
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
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.
Deterministic RAG evaluation toolkit -- retrieval metrics (recall, precision, MRR), corpus overlap detection, and CI regression gating without model calls.
A minimal RAG pipeline with a labelled retrieval eval set, testing whether query rewriting and routing actually improve retrieval. They didn't.
A controlled experiment evaluating whether hybrid (dense + sparse) retrieval surfaces evidence that dense-only RAG systems misrank—without changing generation behavior.
A day-by-day hands-on journey into LLM application engineering, covering LLM APIs, prompt engineering, structured outputs, RAG, embeddings, vector databases, evaluation, agents, and production-ready AI systems.
A systems-level analysis of static RAG pipelines, isolating ingestion, retrieval, and ranking boundaries to expose structural failure modes before generation.
RAG chatbot over the WHO/PPRI pharmaceutical glossary - Gemini/Ollama behind a factory, ChromaDB, Streamlit; evaluated with hit@k vs a BM25 baseline, RAGAS, and refusal checks
Active web-search RAG workbench with provider routing, source extraction, citation verification, and extractive fallback.
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