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1. Abstract
1.1 Abstract
QueryBridge is a novel language model-driven framework designed to convert natural language queries into executable SQL commands. By bridging the gap between non-technical users and complex databases, QueryBridge enables streamlined, accurate data retrieval with minimal user expertise. The system leverages a modular architecture and advanced LLM capabilities to deliver improved performance over traditional approaches (Agarwal, Rawat & Chauhan 2025).
2. Introduction
2.1 Background
The increasing volume of organizational data has escalated the need for accessible database querying solutions. QueryBridge addresses this challenge by enabling users to interact with databases using plain English queries, thereby overcoming the conventional requirement for SQL proficiency (Agarwal, Rawat & Chauhan 2025).
2.2 Motivation
Technical barriers in data access restrict analytical operations and delay decision-making processes. By providing a user-friendly natural language interface that converts everyday queries into SQL, QueryBridge reduces reliance on technical teams and accelerates data-driven insights (Agarwal, Rawat & Chauhan 2025).
2.3 Contributions
This work introduces a modular, LLM-based approach that integrates natural language processing with robust database management systems. The key contributions include an innovative model architecture, efficient data processing pipelines, and a transparent experimental framework that benchmarks the system’s performance against traditional methods.
3. Related Work
3.1 Traditional Text-to-SQL Approaches
Earlier strategies for text-to-SQL conversion relied on template-based and rule-driven systems which required extensive manual configuration. Although effective for straightforward queries, these methods often failed when handling complex or variable database schemas (Agarwal, Rawat & Chauhan 2025).
3.2 LLM Based Models
Recent advancements have shifted focus towards large language models that can intuit semantic intent and generate SQL queries without explicit rule encoding. QueryBridge exemplifies this evolution by employing context-aware LLMs coupled with schema integration to achieve higher flexibility and accuracy.
4. Methodology
4.1 Model Architecture
QueryBridge is structured around a tiered architecture comprising a Streamlit-based user interface, an application logic layer for natural language processing, and a data layer that manages database connectivity using SQLAlchemy. This modular design promotes independent enhancements and robust error handling (Agarwal, Rawat & Chauhan 2025).
4.2 Data Preparation and Processing
The system assimilates structured database schema metadata with natural language parsing techniques to ensure contextual mapping of user queries. Preprocessing routines validate schema consistency, thereby streamlining the SQL generation process.
4.3 Training and Optimization
By leveraging pre-trained language models accessed via the OpenRouter API, QueryBridge is fine-tuned on domain-specific query pairs. Iterative self-correction mechanisms and performance feedback further optimize the accuracy and reliability of its SQL outputs (Agarwal, Rawat & Chauhan 2025).
5. Experimental Setup and Results
5.1 Experimental Design
The evaluation involved deploying QueryBridge across multiple relational databases under read-only conditions. The experimental design simulated real-world query scenarios to replicate diverse user interactions.
5.2 Evaluation Metrics
Performance was assessed using metrics such as query accuracy, response latency, and overall user satisfaction. These measures provided a comprehensive view of the system’s efficacy relative to conventional approaches.
5.3 Results
Experimental findings demonstrate that QueryBridge delivers higher query accuracy and enhanced translation speed compared to traditional models, effectively simplifying database interaction for non-technical users (Agarwal, Rawat & Chauhan 2025).
6. Discussion
6.1 Interpretation of Results
The results affirm the potential of LLM-based models in democratizing data access. Improved natural language understanding and schema integration were critical factors in enhancing query performance.
6.2 Limitations
Certain challenges remain, including the system’s reliance on clear query phrasing and the complexities inherent in handling diverse and intricate database schemas.
Note: This section includes information based on general knowledge, as specific supporting data was not available.
6.3 Future Work
Future research will focus on expanding database compatibility, integrating advanced security measures, and refining error recovery protocols to manage ambiguous queries more effectively.
7. Conclusion
7.1 Summary of Findings
QueryBridge successfully bridges the gap between natural language and SQL, enabling efficient data access for non-technical users through its modular design and advanced optimization strategies.
7.2 Final Remarks
The implementation of QueryBridge marks a significant advancement in the field of natural language query processing, setting a strong foundation for future enhancements and broader applications in data-driven environments.
8. References
Agarwal, N., Rawat, A.S. and Chauhan, A.S. (2025) QUERY BRIDGE: A Text to SQL LLM Model. N/A.
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