Many production RAG systems implement retrieval abstention by thresholding raw similarity scores, implicitly treating score magnitude as a confidence signal. We demonstrate that this practice degrades systematically as queries require reasoning beyond semantic matching. Across 11 retrieval architectures and 28 datasets, neural retrievers consistently assign high similarity scores to semantically related but constraint-violating documents, causing magnitude-based thresholds to collapse toward near-random abstention performance on logical and temporal reasoning tasks—a failure we term the Magnitude Mirage. To address this without computationally expensive alternatives, we conduct a large-scale empirical study of six zero-cost Query Performance Prediction (QPP) metrics across three cognitive tiers: semantic matching (BEIR), logical reasoning (BRIGHT), and temporal reasoning (TEMPO). Our central finding is that the key improvement comes from abandoning magnitude in favor of score-distribution signals: the gain from this shift exceeds the differences among distributional alternatives by a factor of 5–10×. In particular, Score Gap (s₁ - s_k) and a practical adaptation of Score Magnitude and Variance (LSMV) improve abstention AUROC by up to 0.16 in settings where magnitude-based confidence provides little discriminative power. These methods require no additional inference, retraining, or latency, making them a practical zero-cost replacement for magnitude thresholding in deployed RAG systems.

Building retrieval-augmented generation (RAG) systems often requires combining separate tools for retrieval, re-ranking, and generation, with incompatible data formats, evaluation pipelines, and deployment workflows. We present Rankify, an open-source Python toolkit that unifies these stages in a single modular framework. Rankify provides 42 benchmark datasets with pre-retrieved documents and pre-built indices, 15 retrievers (sparse, dense, and reasoning-augmented), and 24 re-ranking models spanning 41 pointwise, pairwise, and listwise variants. It also supports 6 RAG strategies across four inference backends (Hugging Face, vLLM, LiteLLM, and OpenAI), enabling consistent experimentation from local models to hosted APIs. A unified pipeline interface allows users to compose retrieve–rerank–generate workflows in a few lines of code, while an agentic assistant (RankifyAgent), aREST server (RankifyServer), and an interactive web playground support deployment and non-programmatic exploration. Across 200+ configurations on QA and BEIR/TREC benchmarks with six generator LLMs, re-ranking consistently improves downstream performance, yielding gains of 5–15 points in Exact Match and up to 8.5 points in RAGAS context precision across diverse retriever–generator combinations. Rankify is released under the Apache-2.0 License.