Optimizing Vector Embedding Storage and Indexing for AI at Scale: A Unified Framework for Progressive Quantization, Adaptive Indexing, and RAG-Aware Retrieval Evaluation

Abstract

Retrieval-Augmented Generation (RAG) systems depend on fast approximate nearest-neighbor (ANN) search over dense vector embeddings, yet the memory and compute costs of maintaining float32 vector indices grow prohibitively at scale. This paper presents three novel contributions to the vector compression and retrieval landscape. First, we propose a Progressive Quantization Pipeline (PQP) that cascades Matryoshka Representation Learning (MRL) dimensionality reduction, information-entropy-guided per-dimension bit-width allocation, and residual product quantization into a unified framework achieving up to 256× compression with less than 3% recall degradation—the first integration of all three orthogonal compression axes. Second, 
we derive distribution-dependent error bounds for binary quantization that tighten the worst-case O(1/√D) guarantees of RaBitQ by incorporating the spectral decay profile of modern embedding models, yielding practically useful thresholds for minimum dimensionality at target recall levels. Third, we present the first end-to-end evaluation of compression pipelines on downstream RAG answer quality (Exact Match and F1 on Natural Questions and HotpotQA), demonstrating that retrieval recall is a necessary but insufficient proxy for generation quality—a finding consistent with the IceBerg benchmark critique of recall-centric evaluation.

Using English Wikipedia (6.5 million articles, approximately 200 million text chunks) as our primary corpus and the SIFT-1M, Deep-1M, and Deep-100M 
benchmarks for index evaluation, we demonstrate that combining HNSW indexing with binary quantization and float32 reranking yields sub-5ms p99 latency at greater than 99% recall, reducing infrastructure costs by an order of magnitude. We further contextualize these results against recent advances in GPU-accelerated search (NVIDIA CAGRA/cuVS), disk-based billion-scale indexing (DiskANN/Vamana), and emerging quantization methods (SymphonyQG, Extended-RaBitQ, TurboQuant), providing practitioners with a comprehensive decision framework for production deployment.