HeyTrisha is an open-source Natural Language Interface to Databases (NLIDB) framework designed to address a persistent challenge in applied AI research: enabling natural language access to heterogeneous, production-scale databases without tight coupling to schema or business logic.

The core innovation of HeyTrisha lies in its Adapter Pattern–driven architecture, which decouples natural language understanding from database-specific implementations. Instead of relying on monolithic query translation pipelines, the framework introduces a modular adapter layer that translates user intent into structured queries across multiple data sources, including relational databases and domain-specific schemas commonly found in e-commerce systems.

This architectural approach allows researchers and engineers to experiment with different NLP models, prompt-engineering strategies, and query planners without modifying downstream database logic. By isolating intent interpretation, schema mapping, and execution planning into discrete adapters, HeyTrisha improves extensibility, explainability, and system robustness—three critical requirements for trustworthy AI systems.

Unlike traditional NLIDB implementations that are tightly bound to a single schema or dataset, HeyTrisha supports rapid adaptation to evolving data models, making it suitable for longitudinal research, comparative evaluation of language models, and applied studies in AI-assisted decision systems. The framework is intentionally designed for research reproducibility, enabling controlled experimentation across domains such as sales analytics, inventory intelligence, and operational reporting.

HeyTrisha contributes to ongoing research in human–AI interaction, database systems, and applied machine learning, demonstrating how classic software design patterns can be leveraged to improve the reliability and scalability of AI-driven interfaces in real-world environments.