Qdrant, the leading provider of high-performance, open source vector search, is debuting Qdrant Cloud Inference, a new solution for generating text and image embeddings directly within managed Qdrant ...

The Text-to-SQL task has significant application prospects in automating relational database query interfaces. It can reduce user learning costs and improve data query efficiency. However, in ...

Conventional Text-to-SQL research tackles the problem of solving user questions in natural language by generating the corresponding SQL queries. Most of the recent works are dedicated to improving ...

NVIDIA's NIM microservices accelerate Vanna's text-to-SQL model, enhancing analytics by reducing latency and improving performance for natural language database queries.

New open-source efforts from Snowflake aim to help solve that unsolved challenges of text-to-SQL and inference performance for enterprise AI.

"I’ve developed a text-to-SQL application using Gemini, which converts natural language input into SQL queries. By leveraging the power of Gemini, the application efficiently understands and processes ...

XiYan-SQL employs a three-stage process to generate and refine SQL queries. First, schema linking identifies relevant database elements, reducing extraneous information and focusing on key structures.

As the demand for natural language data queries continues to grow, so does the need for a standardized way to evaluate Text-to-SQL (T2SQL) solutions.

AtScale launches public leaderboard for Text-to-SQL, offering a standardized framework to enhance transparency, competition & collaboration in T2SQL.