AI’nt That Easy #21: Implementing Table Augmented Generation (TAG) with LOTUS and IBM Granite 3.0

Table Augmented Generation (TAG) is revolutionizing how we interact with structured data through AI. In this guide, we’ll explore implementing TAG using LOTUS (LLMs Over Tables of Unstructured and Structured Data) with IBM’s Granite 3.0 model, demonstrating two different approaches to data loading.

Understanding the Components

Before diving into the implementation, it’s worth understanding the core concepts. For a comprehensive understanding of Table Augmented Generation (TAG) and how it relates to Text-to-SQL and RAG, refer to this detailed article:

AI’nt That Easy #18: Text-to-SQL, RAG, and TAG Explained

LOTUS Framework

LOTUS provides a declarative programming model for building reasoning-based query pipelines over both structured and unstructured data. It offers a Pandas-like API with semantic operators that extend traditional relational operations with AI capabilities.

IBM Granite 3.0

The latest iteration in IBM’s Granite series, Granite 3.0 8B Instruct, is a powerful instruction-tuned LLM trained on over 12 trillion tokens across multiple languages. It’s specifically…