Relational Database Management Systems (RDBMS) hold the vast majority of structured enterprise data. However, applying Large Language Models (LLMs) to perform natural language querying and tabular reasoning directly over relational schemas presents major bottlenecks: full schema injection quickly exhausts context windows, complex join relationships lead to hallucinated SQL syntax, and security risks like invalid query execution constrain enterprise adoption. To overcome these limitations, we present Schema-Aware Query Translation and Tabular Reasoning for Enterprise Databases aka Inference-from-RDBMS, an open-source framework designed for schema-aware query translation, dynamic context pruning, and execution-guided tabular inference over complex RDBMS structures. The framework extracts database metadata to construct an interactive Schema Relationship Graph (SRG), prunes unreferenced tables and foreign-key joins using semantic distance metrics, and passes a minimal schema sub-graph to an execution-validated text-to-SQL generator. We evaluate Inference-from-RDBMS across standard enterprise benchmarks (including SPIDER and real-world multi-table schemas). The experimental results demonstrate that our framework achieves an 88.4% execution accuracy, reduces prompt token overhead by 52%, and cuts end-to-end query translation latency by 41% compared to standard schema-injected baseline models. These results position Inference-from-RDBMS as a robust, secure, and cost-efficient solution for natural language database interfaces in large-scale AI applications.
Harshil Lodhiya· International Journal of Res...· 0 citations
The rapid proliferation of Large Language Models (LLMs) with varying capability profiles, context window limits, execution latencies, and financial costs presents a significant operational challenge for enterprise AI deployments. Monolithic deployment strategies wherein all requests are directed to a single high-capability frontier model result in substantial compute over-provisioning and excessive operational costs for routine queries. Conversely, relying solely on lightweight models degrades output accuracy on complex multi-step reasoning tasks. To resolve this trade-off, this paper introduces LLM-Advisor, an open-source, adaptive framework designed for intelligent query categorization, dynamic model evaluation, and constraint-aware request routing across heterogeneous multi-LLM pools. LLM-Advisor analyzes incoming prompt features, structural complexity, domain requirements, and user-defined constraints (e.g., maximum cost per request, latency thresholds) to route tasks to the optimal candidate model. We evaluate LLM-Advisor using a benchmark suite of 1,000 queries across code generation, general reasoning, and contextual retrieval tasks using both proprietary and open-weight models (including GPT-4o, Claude 3.5 Sonnet, Llama 3, and Mistral). Experimental results demonstrate that LLM-Advisor achieves a 42% reduction in overall inference expenditure and a 35% decrease in average response latency while retaining 94.6% task accuracy compared to static GPT-4o baseline routing. These findings highlight LLM-Advisor as an efficient, highly scalable middleware solution for production-grade AI system deployments.
Harshil Lodhiya· International Journal of Res...· 0 citations