DynaQuery: A Self-Adapting Framework for Querying Structured and Multimodal Data

TL;DR

DynaQuery introduces a self-adapting framework with a novel schema linking engine that significantly improves robustness and consistency in natural language querying over complex, multimodal databases, outperforming traditional unstructured retrieval methods.

Contribution

The paper presents SILE, a new systems primitive for schema linking, and demonstrates its effectiveness in creating a more reliable, schema-aware querying architecture for hybrid databases.

Findings

SILE reduces schema hallucination failures in query generation.

The architecture outperforms unstructured RAG paradigms in robustness and reliability.

Transitioning from schema-awareness to semantics-awareness enhances generalization.

Abstract

The rise of Large Language Models (LLMs) has accelerated the long-standing goal of enabling natural language querying over complex, hybrid databases. Yet, this ambition exposes a dual challenge: reasoning jointly over structured, multi-relational schemas and the semantic content of linked unstructured assets. To overcome this, we present DynaQuery - a unified, self-adapting framework that serves as a practical blueprint for next-generation "Unbound Databases." At the heart of DynaQuery lies the Schema Introspection and Linking Engine (SILE), a novel systems primitive that elevates schema linking to a first-class query planning phase. We conduct a rigorous, multi-benchmark empirical evaluation of this structure-aware architecture against the prevalent unstructured Retrieval-Augmented Generation (RAG) paradigm. Our results demonstrate that the unstructured retrieval paradigm is architecturally susceptible to catastrophic contextual failures, such as SCHEMA_HALLUCINATION, leading to unreliable query generation. In contrast, our SILE-based design establishes a substantially more robust foundation, nearly eliminating this failure mode. Moreover, end-to-end validation on a complex, newly curated benchmark uncovers a key generalization principle: the transition from pure schema-awareness to holistic semantics-awareness. Taken together, our findings provide a validated architectural basis for developing natural language database interfaces that are robust, adaptable, and predictably consistent.