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MeowKitty Programming
MeowKitty Programming
Apr 29, 2026 · Artificial Intelligence

10 Must‑Try Open‑Source AI Projects for Java Developers: RAG, Agents, Knowledge Bases, and Text‑to‑SQL

This article curates ten open‑source AI projects on Gitee that Java developers can use to learn RAG pipelines, AI agents, knowledge‑base construction, Text‑to‑SQL, workflow orchestration, and multi‑model integration, offering concrete use cases, learning goals, and guidance on selecting a learning path.

AIJavaLangChain4j
0 likes · 13 min read
10 Must‑Try Open‑Source AI Projects for Java Developers: RAG, Agents, Knowledge Bases, and Text‑to‑SQL
Ray's Galactic Tech
Ray's Galactic Tech
Apr 9, 2026 · Backend Development

From Demo to Production: Building a Secure, Scalable Text‑to‑SQL Service with Spring AI Alibaba

This article explains how to turn a simple Text‑to‑SQL demo into a production‑grade service by covering the underlying principles, layered architecture, risk‑control mechanisms, multi‑tenant security, high‑concurrency strategies, caching, observability, and deployment practices using Spring AI Alibaba.

ObservabilityScalabilitySecurity
0 likes · 40 min read
From Demo to Production: Building a Secure, Scalable Text‑to‑SQL Service with Spring AI Alibaba
Data Party THU
Data Party THU
Feb 1, 2026 · Artificial Intelligence

How AutoLink Turns Schema Linking into an Interactive Database Exploration

AutoLink introduces an autonomous, iterative schema‑linking approach for Text‑to‑SQL that treats schema discovery as a progressive, agent‑driven exploration, dramatically improving recall while cutting token costs, and outperforms existing database‑level and element‑level methods on large benchmarks such as Spider 2.0‑Lite and BIRD.

AgentAutoLinkDatabase Exploration
0 likes · 19 min read
How AutoLink Turns Schema Linking into an Interactive Database Exploration
Amap Tech
Amap Tech
Jan 28, 2026 · Artificial Intelligence

Can Databases Teach Themselves? Exploring Agents‑Based Self‑Explaining Text‑to‑SQL

This article introduces the Agents‑Companion paradigm for Text‑to‑SQL, detailing how self‑describing database agents autonomously mine schema, statistics and semantics to generate high‑quality evidence, thereby bridging the gap between academic research and industrial deployment and significantly improving query accuracy.

AIDatabase MiningLLM
0 likes · 8 min read
Can Databases Teach Themselves? Exploring Agents‑Based Self‑Explaining Text‑to‑SQL
DataFunSummit
DataFunSummit
Jan 4, 2026 · Artificial Intelligence

How Ant Group’s DeepInsight Boosted Text‑to‑SQL Accuracy by 46% with an AI‑Driven Evaluation Framework

This article details Ant Group’s DeepInsight intelligent evaluation system for Chinese Text‑to‑SQL, describing the AI‑BI background, challenges of existing benchmarks, a feature‑annotated evaluation design, automated dataset generation, experimental results showing a 46% accuracy gain and 71% reduction in failure rate, and future research directions.

AIBenchmarkData Analytics
0 likes · 13 min read
How Ant Group’s DeepInsight Boosted Text‑to‑SQL Accuracy by 46% with an AI‑Driven Evaluation Framework
Network Intelligence Research Center (NIRC)
Network Intelligence Research Center (NIRC)
Dec 19, 2025 · Databases

DIVER: A Robust Text-to-SQL System Unveiled at SIGMOD 2026, Powering ChatBI

The paper introduces DIVER, an automated expert system that gives large language models human‑like exploration, reasoning, and verification abilities for Text‑to‑SQL, addressing the severe performance drop without expert evidence by innovating dynamic interactive value linking, multi‑agent automation, and adaptive evidence generation, and demonstrates up to 10.82% accuracy gains and strong robustness on real‑world benchmarks.

Automated Expert AgentChatBIDIVER
0 likes · 11 min read
DIVER: A Robust Text-to-SQL System Unveiled at SIGMOD 2026, Powering ChatBI
Aikesheng Open Source Community
Aikesheng Open Source Community
Oct 29, 2025 · Artificial Intelligence

What Makes BiomedSQL and LogicCat the Toughest Text‑to‑SQL Benchmarks for LLMs?

BiomedSQL and LogicCat are two newly released Text‑to‑SQL datasets that challenge large language models with complex biomedical reasoning, multi‑step logical inference, and domain‑specific knowledge, offering detailed analyses of query types, scientific reasoning categories, and performance gaps that highlight current LLM limitations.

BiomedicalDatasetLLM
0 likes · 9 min read
What Makes BiomedSQL and LogicCat the Toughest Text‑to‑SQL Benchmarks for LLMs?
DataFunSummit
DataFunSummit
Aug 30, 2025 · Artificial Intelligence

How Tencent’s DEA‑SQL Revolutionizes Text‑to‑SQL for Intelligent BI

This article systematically presents Tencent PGC's Text‑to‑SQL research, detailing the DEA‑SQL framework, its agent‑based architecture, extensive experiments on benchmark datasets, and real‑world deployment in the OlaChat intelligent BI product, highlighting performance gains and practical capabilities.

Agent ArchitectureDEA-SQLData Analytics
0 likes · 20 min read
How Tencent’s DEA‑SQL Revolutionizes Text‑to‑SQL for Intelligent BI
JD Tech
JD Tech
Aug 20, 2025 · Artificial Intelligence

Boosting Text-to-SQL Accuracy with J‑Schema, Iterative DPO, and Self‑Consistency

This article examines the evolution of Text-to-SQL, introduces the J‑Schema representation and chain-of-thought prompting, applies iterative DPO training and self-consistency voting, and demonstrates how these techniques raise execution accuracy on the BIRD benchmark from 56.6% to 69.2%.

BIRD benchmarkIterative DPOJ-Schema
0 likes · 11 min read
Boosting Text-to-SQL Accuracy with J‑Schema, Iterative DPO, and Self‑Consistency
JD Tech Talk
JD Tech Talk
Aug 14, 2025 · Artificial Intelligence

Boosting Text-to-SQL Accuracy: J‑Schema, Iterative DPO, and Self‑Consistency

This paper presents a comprehensive approach to improve Text‑to‑SQL performance by introducing J‑Schema for structured database representation, leveraging chain‑of‑thought prompting, applying iterative DPO training, and employing self‑consistency voting, achieving execution accuracy gains from 56.6% to 69.2% on the BIRD benchmark.

BIRD benchmarkIterative DPOSelf-Consistency
0 likes · 12 min read
Boosting Text-to-SQL Accuracy: J‑Schema, Iterative DPO, and Self‑Consistency
JD Cloud Developers
JD Cloud Developers
Aug 14, 2025 · Artificial Intelligence

Boosting Text-to-SQL Accuracy: J‑Schema, Iterative DPO, and Self‑Consistency

This article presents a comprehensive study on improving Text-to-SQL performance by introducing J‑Schema for structured schema representation, applying iterative Direct Preference Optimization (DPO) training, and leveraging self‑consistency voting mechanisms, achieving up to a 12% accuracy gain on the BIRD benchmark.

Database QAIterative DPOJ-Schema
0 likes · 10 min read
Boosting Text-to-SQL Accuracy: J‑Schema, Iterative DPO, and Self‑Consistency
JD Retail Technology
JD Retail Technology
Aug 14, 2025 · Artificial Intelligence

Boosting Text-to-SQL Accuracy: J‑Schema, Iterative DPO, and Self‑Consistency

This article surveys the evolution of Text-to-SQL, introduces the J‑Schema representation and chain-of-thought prompting, details an iterative DPO training pipeline with hyper‑parameter tuning, and demonstrates how self‑consistency voting boosts execution accuracy on the BIRD benchmark from 56.6% to 69.2%.

BIRD datasetIterative DPOLLM
0 likes · 14 min read
Boosting Text-to-SQL Accuracy: J‑Schema, Iterative DPO, and Self‑Consistency
Alibaba Cloud Big Data AI Platform
Alibaba Cloud Big Data AI Platform
Jul 10, 2025 · Databases

Boost Text-to-SQL Accuracy with Dynamic Few-Shot Learning and Alignment

At SIGMOD 2025 in Berlin, Alibaba Cloud presented its paper “OpenSearch‑SQL: Enhancing Text‑to‑SQL with Dynamic Few‑shot and Consistency Alignment,” which introduces a self‑taught few‑shot mechanism and multi‑agent alignment to significantly improve NL2SQL query accuracy, alongside a keynote on AI search trends.

Few‑Shot LearningNL2SQLOpenSearch-SQL
0 likes · 5 min read
Boost Text-to-SQL Accuracy with Dynamic Few-Shot Learning and Alignment
DataFunSummit
DataFunSummit
Jan 24, 2025 · Artificial Intelligence

Exploring LLM‑Based Generative Business Intelligence (GenBI): Architecture, Implementation, and Lessons Learned

With the rise of LLM‑based generative AI, this article examines the emerging GenBI (Generative Business Intelligence) paradigm, detailing why self‑serving analytics are needed, the progress of Text‑to‑SQL, an LLM‑driven agent architecture, practical AWS Bedrock implementation, technical choices, lessons learned, and future outlook.

AWS BedrockAgentic AIBusiness Intelligence
0 likes · 18 min read
Exploring LLM‑Based Generative Business Intelligence (GenBI): Architecture, Implementation, and Lessons Learned
Data Thinking Notes
Data Thinking Notes
Jan 7, 2025 · Databases

Unlocking LLM-Powered Text-to-SQL: From Basics to Cutting-Edge Techniques

This article provides a comprehensive overview of LLM-based Text-to-SQL technology, covering its background, evolution, challenges, various LLM-driven methods, benchmark datasets, evaluation metrics, and future research directions to guide researchers and practitioners in advancing natural language interfaces for databases.

LLMPrompt engineeringText-to-SQL
0 likes · 18 min read
Unlocking LLM-Powered Text-to-SQL: From Basics to Cutting-Edge Techniques
DaTaobao Tech
DaTaobao Tech
Mar 29, 2024 · Artificial Intelligence

Text-to-SQL with Large Language Models: DIN-SQL Approach

The DIN‑SQL approach enhances Text‑to‑SQL performance by using large language models in a decomposed in‑context learning framework with schema linking, query classification, SQL generation, and self‑correction modules, achieving state‑of‑the‑art 85.3% execution accuracy on the Spider benchmark by breaking complex queries into manageable sub‑tasks.

AI researchDatabase QueryingNLP
0 likes · 34 min read
Text-to-SQL with Large Language Models: DIN-SQL Approach
JD Retail Technology
JD Retail Technology
Oct 26, 2023 · Artificial Intelligence

Leveraging Large Language Models for Text-to-SQL: Prompt Design and End-to-End Pipeline

This article explains how large language models can be used to convert natural language queries into SQL statements, describes two main approaches—direct generation and fine‑tuned open‑source models—details prompt engineering techniques, and outlines an end‑to‑end pipeline that executes the generated SQL and summarizes results.

ChatGLMLLMPrompt engineering
0 likes · 7 min read
Leveraging Large Language Models for Text-to-SQL: Prompt Design and End-to-End Pipeline
DataFunTalk
DataFunTalk
May 12, 2020 · Artificial Intelligence

Semantic Parsing for Text-to-SQL: Datasets, Models, Evaluation, and Applications

This article reviews the Text-to-SQL semantic parsing task, covering its motivation, dataset landscape, major model architectures such as pointer networks, sequence‑to‑set, and grammar‑based approaches, evaluation metrics, the newly built DuSQL dataset and DuParser system, real‑world deployments, and remaining research challenges.

AIText-to-SQLdatabase
0 likes · 20 min read
Semantic Parsing for Text-to-SQL: Datasets, Models, Evaluation, and Applications