Task Description
Develop a basic NLP-powered query builder that allows users to write natural language queries and converts them into analytics tables. The system should use a rule-based parsing approach to interpret user intent, map keywords to the underlying database schema, and generate safe, validated SELECT queries. A query preview mechanism should allow users to review and confirm the generated SQL before execution. The design should be modular to support future ML-based or AI-driven enhancements.
Key Outcomes:
- Rule-based NLP parsing engine.
- Mapping logic between user keywords and database schema (tables, columns, relationships).
- Support for basic SELECT queries with WHERE conditions.
- Query preview before execution.
- SQL injection prevention mechanisms (strict validation and parameterized queries).
- Clear error handling and user-friendly feedback for unsupported or ambiguous queries.
- Modular and extensible architecture for future ML/NLP model integration.
Task Description
Develop a basic NLP-powered query builder that allows users to write natural language queries and converts them into analytics tables. The system should use a rule-based parsing approach to interpret user intent, map keywords to the underlying database schema, and generate safe, validated SELECT queries. A query preview mechanism should allow users to review and confirm the generated SQL before execution. The design should be modular to support future ML-based or AI-driven enhancements.
Key Outcomes: