Integration of LLM and RVAT Database: Prototype Exploration of Natural Language Database Querying

This proof-of-concept project explores how to use large language models (LLMs) to convert natural language questions into database queries, enabling intelligent retrieval of the RVAT database. The project aims to lower the barrier for non-technical users to access the database, verify the technical feasibility of LLM-assisted database querying, and point the way for subsequent development.

RVAT represents a type of structured data storage solution with complex table structures and relationship definitions. Users familiar with its schema can query efficiently, but external users face a cognitive barrier. Traditional solutions (predefined reports, visual query builders) struggle to balance flexibility and ease of use, and the core challenge is enabling users without database knowledge to effectively access information from the RVAT database.

The core idea of the project is to use the code generation capability of LLMs to convert natural language questions into executable SQL queries, involving three stages: 1. Intent Understanding: Parse the user's question, identify key entities (table names, field names, values) and operations (filtering, aggregation, sorting); 2. Schema Mapping: Map natural language concepts to the actual database schema (table relationships, field types, foreign key constraints, etc.); 3. Query Generation: Generate syntactically correct SQL statements, execute them after verification, and return the results.

As a proof-of-concept project, its goal is to verify technical feasibility rather than provide a production solution. Its value lies in demonstrating the basic process of LLM-assisted database querying and identifying technical challenges. The project focuses on simple query scenarios (single-table filtering, basic aggregation), where the success rate of LLM-generated correct SQL is relatively high; however, complex scenarios (multi-table joins, subqueries, specific business logic) require more complex solutions.

Using LLMs for database query generation faces three major challenges: 1. Accuracy: May generate incorrect SQL or queries that do not match the semantics, with high error costs; 2. Security: There is a risk of SQL injection, and countermeasures include strict input validation, query sandboxes, and read-only execution environments; 3. Context Management: Token consumption and information omission issues in the transmission of database schema information.

Application scenarios include internal enterprise data democratization (business personnel directly querying data warehouses), customer service (intelligent customer service retrieving order inventory), and development tools (AI-assisted SQL editors). Future expansion directions: Introduce RAG to optimize schema transmission, establish a query result verification and correction mechanism, support multi-turn dialogue to clarify requirements, and integrate domain knowledge to improve accuracy.

This project touches on the technical trend of AI acting as a translator between humans and complex systems. Databases are representative of structured systems, and similar ideas can be applied to scenarios such as API calls, configuration management, and code repository queries. As LLM capabilities improve, natural language is expected to become the standard way of interacting with information systems. Technical knowledge will no longer be a barrier to accessing information; users only need to ask questions, and the details are handled by AI.