This sample app demonstrates how to create technical documents for a codebase using AI. More specifically, it uses the agent framework offered by Semantic Kernel to ochestrate multiple agents to ...

Abstract: Current Text-to-SQL methods are evaluated and only focused on executable queries overlooking the semantic alignment challenge both in terms of the semantic meaning of the query and the ...

Spider is a large human-labeled dataset for complex and cross-domain semantic parsing and text-to-SQL task (natural language interfaces for relational databases). It is released along with our EMNLP ...

Every data engineering team right now is being asked the same question: "How do we build a chatbot that talks to our data?" The prototypes are deceptively simple. A developer connects GPT-5.1 to a ...

A team of AI researchers at Bloomberg have developed PExA, an agentic framework that achieved 70.2% execution accuracy, sharing one of the top positions on the Spider 2.0 (Snow) leaderboard, one of ...

In most enterprises, data access still feels like a locked room with SQL as the only key. Business teams depend on data engineers for every report, dashboard, or metric tweak. Even in the age of ...

Semantic SEO helps search engines understand context. Learn how to use entities, topics, and intent to build richer content that ranks higher. Semantic SEO aims to describe the relationships between ...

Discover how Tinker and Ray are utilized to fine-tune text-to-SQL models, enhancing AI capabilities in generating efficient SQL queries. In an innovative approach to advancing text-to-SQL models, ...