Implementation of Rule-Based NLP Query Builder

[Vasanthi1723]

NLP-Powered Query Builder Implementation :-
This pull request introduces a rule-based NLP query builder that allows users to write natural language queries and converts them into structured SQL queries for analytics. The implementation focuses on simplicity, safety, and extensibility so that it can later be enhanced with ML/NLP models.

Overview :-
The system interprets user input using a rule-based parsing mechanism, maps detected keywords to the underlying database schema, and generates validated SELECT queries with optional filtering conditions. A preview mechanism is provided so users can review the generated SQL query before execution.

Key Features :-

1. Rule-Based NLP Parsing Engine :-
   Implemented a lightweight parser that analyzes natural language input and extracts key entities such as table names, column references, and filter conditions.

2. Schema Mapping Layer :-
   Added mapping logic between user keywords and database schema elements (tables, columns, and relationships) to ensure accurate query generation.

3. SQL Query Builder :-
   Developed a query builder that constructs safe SELECT statements and supports WHERE clause conditions based on parsed user intent.

4. Query Preview Mechanism :-
   Before executing any query, the system generates and displays a preview of the SQL query, allowing users to review and confirm it.

5. SQL Injection Prevention :-
   Implemented strict validation and parameterized query patterns to ensure only safe queries are generated and executed.

6. Error Handling and User Feedback :-
   Added validation checks and clear error responses for unsupported queries or ambiguous input, improving usability and reliability.

7. Modular Architecture :-
   The implementation follows a modular design separating parsing, schema mapping, query generation, and validation layers. This structure allows easy integration of future ML or AI-based NLP models.

Example Workflow :-

1. User enters a natural language query.
2. The NLP parser identifies relevant keywords and intent.
3. Schema mapping links keywords to database tables and columns.
4. The query builder generates a validated SQL query.
5. The system displays a query preview before execution.

future Improvements :-

• Integration of machine learning–based NLP models for improved query interpretation

• Support for more complex SQL queries (joins, aggregations, grouping)

• Enhanced intent recognition and semantic understanding

         This implementation provides a solid foundation for **natural language-driven analytics** while   maintaining security, modularity, and scalability.