System Architecture
Ask a question in natural language, and AI will generate and execute a SQL query. The application is connected to PostgreSQL, where the ai_service table containing the parameters of 18 AI models (prices, SQL support, providers, etc.) is stored.

• Java
• Spring Boot 3.4.5
• Spring AI 1.0.0-M8
• PostgreSQL
• OpenAI GPT (gpt-3.5-turbo)
• 🐳 Docker + Docker Compose

UI Example

• UI walkthrough video (short demo, downloadable if not previewable)
• 📂 Example screenshots and results are located in the media_example/ folder

• Natural language to SQL translation using LLMs
  Integrated Spring AI with OpenAI (GPT-3.5) to translate user questions into SQL queries based on the structure of the ai_services table.

• Safe dynamic SQL execution
  Designed a secure way to execute dynamically generated SQL without injection risks, using validation and controlled execution via JdbcTemplate.

• Database bootstrapping with production-ready data
  On first startup, the PostgreSQL container automatically initializes the ai_services table with 18 real AI model entries via spring_ai_init.sql.

• Dockerized multi-container setup with persistent storage
  Used Docker Compose to orchestrate the Spring Boot app and PostgreSQL with named volumes and health checks to ensure reliable startup and data persistence.

• Flexible, language-agnostic prompting pipeline
  The LLM prompting system is designed to handle both SQL and natural questions with domain-specific context, and can be extended to other database schemas.

The Java backend is covered with both unit and integration-style tests for core logic components:

• Text-to-SQL conversion logic: Validates that natural language questions are correctly translated into SQL using mocked ChatClient (LLM).
• SQL execution layer: Verifies safety of query execution, correct result formatting, null-handling, column ordering, and exception handling using JdbcTemplate.
• Safety filters: Prevent unsafe queries (DELETE, DROP, etc.) from being executed — covered by negative test cases.

1. Open docker-compose.yml and update these environment variables:

• POSTGRES_PASSWORD=your_password
• SPRING_AI_OPENAI_API-KEY=your_openai_api_key

2. Run everything with Docker Compose:

docker-compose up --build

3. Access service:
   • http://localhost:8080/ask

• This project uses the OpenAI API, but the architecture allows for easy integration with other LLM providers (e.g. Anthropic, Google, Cohere, Mistral) depending on business needs and cost model.

• I'm always open to feedback, collaboration, or professional connections.
• Feel free to reach out!