Text-to-SQL Interface – Natural Language Query Engine

I developed a natural language interface at Reazon Holdings that allows non-technical users to query BigQuery databases without writing SQL. This system transforms natural language questions into accurate SQL queries, making data accessible to business users and analysts who lack SQL expertise.

Project Overview

BigQuery is a powerful data warehouse, but accessing it requires SQL knowledge. Many business users and analysts have valuable questions about data but lack the technical skills to write SQL queries. This project bridges that gap by enabling natural language queries.

Problem Statement

Challenges

Technical Barrier: SQL requires technical expertise
Time Consumption: Writing SQL queries is time-consuming
Error-Prone: Incorrect SQL can produce wrong results
Accessibility: Data remains inaccessible to non-technical users

Solution

A natural language interface that:

Accepts questions in plain English (or Japanese)
Converts them to accurate SQL queries
Executes queries and returns results
Provides explanations of the results

System Architecture

Components

1. Natural Language Understanding

Parses user questions
Identifies intent and entities
Extracts query requirements

2. Schema Understanding

Accesses BigQuery schema metadata
Understands table relationships
Maps natural language to database structures

3. SQL Generation

Generates SQL queries from natural language
Validates query syntax
Optimizes query performance

4. Query Execution

Executes queries on BigQuery
Handles errors gracefully
Returns formatted results

5. Result Presentation

Formats results for readability
Provides explanations
Suggests follow-up questions

Technology Stack

Vertex AI

Hosts LLM models for natural language understanding
Provides scalable inference
Manages model versions

LangChain

Orchestrates the text-to-SQL pipeline
Manages prompt engineering
Handles context and memory

OpenAI API

GPT-4 for high-quality SQL generation
Advanced reasoning capabilities
Multilingual support

Gemini API

Alternative model for comparison
Cost-effective option
Different reasoning patterns

BigQuery

Target database for queries
Schema metadata access
Query execution engine

Implementation Details

Prompt Engineering

Schema Context

The system includes relevant schema information in prompts:

Table names and structures
Column names and types
Relationships between tables
Sample data for context

Query Templates

Common query patterns
Best practices for BigQuery
Optimization hints

Error Handling

Validates SQL before execution
Provides helpful error messages
Suggests corrections

Multilingual Support

English

Full support for English queries
Natural conversation flow
Context understanding

Japanese

Japanese query support
Cultural context awareness
Technical term handling

Use Cases

Business Analytics

Sales performance queries
Customer behavior analysis
Revenue trend analysis

Reporting

Automated report generation
Ad-hoc data exploration
Dashboard data queries

Data Exploration

Discovering data patterns
Answering business questions
Validating hypotheses

Results

Business Impact

Data-Driven Decisions: More frequent data-driven decision making
Self-Service: Reduced dependency on data engineers
Productivity: Analysts can focus on analysis rather than query writing

Challenges and Solutions

BigQuery Schema Limitations

Challenge: BigQuery is not a traditional relational database, which created significant challenges:

Missing Relationships: Unlike relational databases, BigQuery doesn't explicitly store foreign key relationships, making it difficult for the LLM to understand how tables connect
Lack of Metadata: Many tables and columns had no descriptions or documentation, leaving the LLM without context to generate meaningful queries
Schema Ambiguity: Without relationship information, the system couldn't determine which tables to join or how to correctly structure queries

Solution:

Production Database Analysis: I cross-referenced the production relational database to identify and document all table relationships, foreign keys, and data dependencies
Enhanced BigQuery Metadata: Added comprehensive descriptions and relationship information directly to BigQuery:
- Table-level descriptions explaining purpose and use cases
- Column-level descriptions with data types and examples
- Relationship documentation showing how tables connect
- Business context for each table explaining when and why to use it
Schema Enrichment: Created a metadata layer that maps BigQuery tables to their relational equivalents, providing the LLM with the relationship context it needs

This approach transformed BigQuery from a schema-less data warehouse into a well-documented system that the LLM could understand and query effectively.

Schema Complexity

Challenge: Complex schemas with many tables and relationships.

Solution: Implemented intelligent schema selection, only including relevant schema information in prompts.

Multilingual Handling

Challenge: Different languages express queries differently.

Solution: Language-specific prompt engineering and cultural context awareness.

Key Learnings

Schema Context: Including relevant schema information is crucial for accuracy
Prompt Engineering: Careful prompt design significantly improves results
Error Handling: Graceful error handling improves user experience
User Feedback: Continuous feedback loop improves system performance

Future Enhancements

Visual query builder integration
Query explanation and learning features
Advanced analytics and insights
Integration with BI tools
Support for more databases
Query caching and optimization
Natural language result explanations

Conclusion

The Text-to-SQL interface successfully democratizes data access by enabling non-technical users to query BigQuery databases using natural language. By combining advanced LLMs with careful prompt engineering and schema understanding, we've created a system that makes data accessible to everyone in the organization.