arXiv:2511.16681v1 Announce Type: new 
Abstract: Retrieval-Augmented Generation (RAG) systems have become a dominant approach to augment large language models (LLMs) with external knowledge. However, existing vector database (VecDB) retrieval pipelines rely on flat or single-resolution indexing structures, which cannot adapt to the varying semantic granularity required by diverse user queries. This limitation leads to suboptimal trade-offs between retrieval speed and contextual relevance.
  To address this, we propose \textbf{Semantic Pyramid Indexing (SPI)}, a novel multi-resolution vector indexing framework that introduces query-adaptive resolution control for RAG in VecDBs. Unlike existing hierarchical methods that require offline tuning or separate model training, SPI constructs a semantic pyramid over document embeddings and dynamically selects the optimal resolution level per query through a lightweight classifier. This adaptive approach enables progressive retrieval from coarse-to-fine representations, significantly accelerating search while maintaining semantic coverage.
  We implement SPI as a plugin for both FAISS and Qdrant backends and evaluate it across multiple RAG tasks including MS MARCO, Natural Questions, and multimodal retrieval benchmarks. SPI achieves up to \textbf{5.7$\times$} retrieval speedup and \textbf{1.8$\times$} memory efficiency gain while improving end-to-end QA F1 scores by up to \textbf{2.5 points} compared to strong baselines. Our theoretical analysis provides guarantees on retrieval quality and latency bounds, while extensive ablation studies validate the contribution of each component. The framework's compatibility with existing VecDB infrastructures makes it readily deployable in production RAG systems. Code is availabe at \href{https://github.com/FastLM/SPI_VecDB}{https://github.com/FastLM/SPI\_VecDB}.

تم اقتراح إطار عمل جديد يسمى Semantic Pyramid Indexing (SPI) لتحسين أنظمة الجيل المعزز بالاسترجاع (RAG) من خلال السماح بفهرسة متجهات متعددة الدقة في قواعد بيانات المتجهات (VecDBs). تتناول هذه المقاربة المبتكرة قيود خطوط أنابيب الاسترجاع الحالية التي تعتمد على هياكل الفهرسة المسطحة، والتي تواجه صعوبات مع التباين الدلالي في استفسارات المستخدمين.

Se ha propuesto un nuevo marco llamado Semantic Pyramid Indexing (SPI) para mejorar los sistemas de Generación Aumentada por Recuperación (RAG) al permitir la indexación vectorial de múltiples resoluciones en bases de datos vectoriales (VecDBs). Este enfoque innovador aborda las limitaciones de los pipelines de recuperación existentes que dependen de estructuras de indexación planas, que tienen dificultades con la granularidad semántica variable en las consultas de los usuarios.

Un nouveau cadre appelé Semantic Pyramid Indexing (SPI) a été proposé pour améliorer les systèmes de génération augmentée par récupération (RAG) en permettant un indexage vectoriel multi-résolution dans les bases de données vectorielles (VecDBs). Cette approche innovante répond aux limitations des pipelines de récupération existants qui reposent sur des structures d'indexation plates, qui peinent à gérer la granularité sémantique variable dans les requêtes des utilisateurs.

A new framework called Semantic Pyramid Indexing (SPI) has been proposed to enhance Retrieval-Augmented Generation (RAG) systems by allowing for multi-resolution vector indexing in vector databases (VecDBs). This innovative approach addresses the limitations of existing retrieval pipelines that rely on flat indexing structures, which struggle with varying semantic granularity in user queries.

Towards Hyper-Efficient RAG Systems in VecDBs: Distributed Parallel Multi-Resolution Vector Search

arXiv:2510.01612v2 Announce Type: replace 
Abstract: The exponential growth of biomedical literature creates significant challenges for accessing precise medical information. Current biomedical question-answering systems primarily focus on short-form answers, failing to provide the comprehensive explanations necessary for clinical decision-making. We present RAG-BioQA, a novel framework combining retrieval-augmented generation with domain-specific fine-tuning to produce evidence-based, long-form biomedical answers. Our approach integrates BioBERT embeddings with FAISS indexing and compares various re-ranking strategies (BM25, ColBERT, MonoT5) to optimize context selection before synthesizing evidence through a fine-tuned T5 model. Experimental results on the PubMedQA dataset show significant improvements over baselines, with our best model achieving substantial gains across BLEU, ROUGE, and METEOR metrics, advancing the state of accessible, evidence-based biomedical knowledge retrieval.

تم تقديم إطار عمل RAG-BioQA لمعالجة التحديات التي يطرحها النمو الأسي للأدبيات الطبية الحيوية، مع التركيز على تقديم إجابات طويلة قائمة على الأدلة لأسئلة الطب الحيوي. يجمع هذا النظام بين الجيل المعزز بالاسترجاع والتخصيص المحدد للمجال، باستخدام تمثيلات BioBERT وفهرسة FAISS لتحسين جودة الردود. تُظهر النتائج التجريبية تحسينات كبيرة في مقاييس الأداء مقارنة بالنماذج الحالية.

Se ha presentado el marco RAG-BioQA para abordar los desafíos que plantea el crecimiento exponencial de la literatura biomédica, centrándose en proporcionar respuestas largas y basadas en evidencia para preguntas biomédicas. Este sistema integra la generación aumentada por recuperación con un ajuste específico del dominio, utilizando embeddings de BioBERT y un indexado FAISS para mejorar la calidad de las respuestas. Los resultados experimentales muestran mejoras significativas en las métricas de rendimiento en comparación con los modelos existentes.

Le cadre RAG-BioQA a été introduit pour relever les défis posés par la croissance exponentielle de la littérature biomédicale, en se concentrant sur la fourniture de réponses longues et basées sur des preuves aux questions biomédicales. Ce système intègre la génération augmentée par récupération avec un ajustement spécifique au domaine, utilisant des embeddings BioBERT et un indexage FAISS pour améliorer la qualité des réponses. Les résultats expérimentaux indiquent des améliorations significatives des métriques de performance par rapport aux modèles existants.

The RAG-BioQA framework has been introduced to address the challenges posed by the exponential growth of biomedical literature, focusing on providing long-form, evidence-based answers for biomedical questions. This system integrates retrieval-augmented generation with domain-specific fine-tuning, utilizing BioBERT embeddings and FAISS indexing to enhance the quality of responses. Experimental results indicate significant improvements in performance metrics over existing models.

Towards Hyper-Efficient RAG Systems in VecDBs: Distributed Parallel Multi-Resolution Vector Search

Was this article worth reading? Share it

Grasp.info

Scop.ai

Magicley AI