让聊天机器人自动理解企业数据，快速生成准确查询。
SAFAARI: Schema-Aware Framework for Accelerated Advertiser Response Intelligence
- 设计多智能体系统，自动完成自然语言到SQL的语义映射。
- 在五个配置中达81.66%评估分，比基线提升6.65%。
- 适合需要快速构建智能客服的企业，尤其数据结构复杂者。
客户支持系统正快速演进为基于智能体的聊天机器人，但其在缺乏预定义API接口时难以访问企业数据。本文提出SAFAARI（Schema-Aware Framework for Accelerated Advertiser Response Intelligence），一种通过内容、元数据和编排智能体协同解决自然语言转SQL（NL-to-SQL）系统中模式链接瓶颈的多智能体框架。我们还引入SEAL（Schema Evaluation and Accuracy in Language-to-SQL）这一新型综合指标，全面评估系统性能并惩罚不一致结果。通过五种特征配置的系统性实验，SAFAARI在SEAL上取得81.66%得分（较基线提升6.65%），数据点准确率提高5.51%，模式链接精确度提升4.69%。经领域专家参与的人机协同评估验证，该框架在不同支持场景中具有高度适应性。通过自动化模式链接与查询生成流程，框架实现开发时间缩短8倍，同时保持高精度。该方案简化了API开发，提升了自助服务能力，特别适用于拥有复杂数据生态的企业客户支持系统。
原文摘要 · Abstract (English)
The evolution of customer support systems is rapidly advancing with agentic chatbots, yet these systems face significant limitations when accessing enterprise data without predefined API endpoints. This paper presents SAFAARI (Schema-Aware Framework for Accelerated Advertiser Response Intelligence), a multi-agent framework that addresses the critical bottleneck of schema linking in Natural Language to SQL (NL-to-SQL) systems through specialized content, metadata, and orchestration agents. We also introduce SEAL (Schema Evaluation and Accuracy in Language-to-SQL), a novel composite metric that holistically evaluates system performance while penalizing inconsistent results. Through systematic experimentation with five feature set configurations, SAFAARI achieves an 81.66% SEAL score (6.65% improvement over baseline), with notable gains in datapoint accuracy (5.51%) and schema-linking precision (4.69%). The framework's effectiveness is validated through human-in-the-loop evaluation with domain experts, which proves its adaptability across diverse support domains. By automating the labor-intensive process of schema linking and query generation, our framework demonstrates 8x reduction in development time while maintaining high accuracy. The solution streamlines API development and enhances self-service capabilities, particularly benefiting customer support enterprises with complex data ecosystems.
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