SPAR: Session-based Pipeline for Adaptive Retrieval on Legacy File Systems

TL;DR

SPAR is a lightweight, adaptive retrieval framework that enhances access to legacy enterprise data using LLMs, reducing computational costs and improving retrieval relevance.

Contribution

It introduces a novel two-stage retrieval process combining semantic metadata indexing with dynamic vector databases tailored for legacy systems.

Findings

Improved retrieval effectiveness in enterprise-scale file systems

Reduced computational overhead compared to traditional RAG pipelines

Enhanced relevance and controllability in data retrieval

Abstract

The ability to extract value from historical data is essential for enterprise decision-making. However, much of this information remains inaccessible within large legacy file systems that lack structured organization and semantic indexing, making retrieval and analysis inefficient and error-prone. We introduce SPAR (Session-based Pipeline for Adaptive Retrieval), a conceptual framework that integrates Large Language Models (LLMs) into a Retrieval-Augmented Generation (RAG) architecture specifically designed for legacy enterprise environments. Unlike conventional RAG pipelines, which require costly construction and maintenance of full-scale vector databases that mirror the entire file system, SPAR employs a lightweight two-stage process: a semantic Metadata Index is first created, after which session-specific vector databases are dynamically generated on demand. This design reduces computational overhead while improving transparency, controllability, and relevance in retrieval. We provide a theoretical complexity analysis comparing SPAR with standard LLM-based RAG pipelines, demonstrating its computational advantages. To validate the framework, we apply SPAR to a synthesized enterprise-scale file system containing a large corpus of biomedical literature, showing improvements in both retrieval effectiveness and downstream model accuracy. Finally, we discuss design trade-offs and outline open challenges for deploying SPAR across diverse enterprise settings.