DBGPT leverages large language models to understand user intent expressed in natural language, automatically generate accurate SQL statements, and run them against supported databases such as MySQL, PostgreSQL, SQLite, and more. It returns structured results along with charts, enabling data analysts, developers, and business users to gain insights quickly. Schema exploration, query tuning suggestions, and integration with dashboards make DBGPT a comprehensive tool for data-driven decision making.

DB-conv is a lightweight Python library designed to enable conversational AI over SQL databases. After installation, developers configure it with database connection details and LLM provider credentials. DB-conv handles schema introspection, constructs optimized SQL from user prompts, executes queries, and returns results in tables or charts. It supports multiple database engines, caching, query logging, and custom prompt templates. By abstracting prompt engineering and SQL generation, DB-conv simplifies building chatbots, voice assistants, or web interfaces for self-service data exploration.