  In recent times Large Language Models have exhibited tremendous capabilities,
especially in the areas of mathematics, code generation and general-purpose
reasoning. However for specialized domains especially in applications that
require parsing and analyzing large chunks of numeric or tabular data even
state-of-the-art (SOTA) models struggle. In this paper, we introduce a new
approach to solving domain-specific tabular data analysis tasks by presenting a
unique RAG workflow that mitigates the scalability issues of existing tabular
LLM solutions. Specifically, we present Tabular Embedding Model (TEM), a novel
approach to fine-tune embedding models for tabular Retrieval-Augmentation
Generation (RAG) applications. Embedding models form a crucial component in the
RAG workflow and even current SOTA embedding models struggle as they are
predominantly trained on textual datasets and thus underperform in scenarios
involving complex tabular data. The evaluation results showcase that our
approach not only outperforms current SOTA embedding models in this domain but
also does so with a notably smaller and more efficient model structure.
