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SAFAARI framework enhances chatbots' data access with schema linking

By PulseAugur Editorial · [1 sources] ·

A new framework called SAFAARI has been developed to improve agentic chatbots' ability to access enterprise data by addressing schema linking challenges in Natural Language to SQL systems. This framework utilizes specialized agents for content, metadata, and orchestration, and introduces a novel metric, SEAL, to evaluate performance. Experiments show SAFAARI achieves an 81.66% SEAL score, a 6.65% improvement over baselines, and reduces development time by 8x while maintaining high accuracy. AI

IMPACT Streamlines API development and enhances self-service capabilities for enterprises with complex data ecosystems.

RANK_REASON The cluster contains a research paper detailing a new framework and metric for Natural Language to SQL systems. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

SAFAARI framework enhances chatbots' data access with schema linking

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The cluster contains a research paper detailing a new framework and metric for Natural Language to SQL systems. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Bhanu Teja Rangaraju, Chandan Kumar ·

    SAFAARI: Schema-Aware Framework for Accelerated Advertiser Response Intelligence

    arXiv:2607.25042v1 Announce Type: new Abstract: 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 (Schem…

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