The SQLBot repository introduces an innovative, open-source, AI-powered tool designed to democratize data access by transforming natural language questions into executable SQL queries. Its core mission is to bridge the gap between business users and complex databases, enabling individuals without SQL proficiency to extract insights and perform data analysis effortlessly. By leveraging large language models (LLMs), SQLBot aims to make data exploration as simple as asking a question in plain English, thereby eliminating the traditional bottlenecks associated with data access and reporting.

At its heart, SQLBot functions by taking a user's natural language query, interpreting its intent using integrated LLMs, and then generating the appropriate SQL statement. This generated SQL is subsequently executed against the connected database, and the results are fetched and presented to the user. A key feature is its ability to not only display raw data but also to visualize it through various chart types, such as bar, line, and pie charts, making data interpretation intuitive and immediate. This end-to-end process empowers users to move from question to insight within a single, streamlined interface.

SQLBot boasts extensive compatibility, supporting a wide array of relational and analytical databases. This includes popular choices like MySQL, PostgreSQL, SQL Server, Oracle, and SQLite, alongside modern data warehouses such as ClickHouse, TiDB, Doris, StarRocks, and OceanBase. This broad support ensures its versatility across diverse data ecosystems. Furthermore, the tool offers flexibility in LLM integration, currently supporting OpenAI and Azure OpenAI, with plans to incorporate more local and open-source models via LiteLLM, providing users with options for performance, cost, and data privacy.

The architecture of SQLBot follows a client-server model, featuring a robust Go backend responsible for handling LLM interactions, managing database connections, and executing queries. The frontend, built with React, provides an intuitive and responsive web-based user interface, simplifying interaction for non-technical users. Security is a paramount concern, with recommendations for read-only database connections and mechanisms for access control, crucial for maintaining data integrity and compliance in enterprise environments. Deployment is also made straightforward through Docker Compose, allowing for quick setup and reduced operational overhead.

Beyond its core functionality, SQLBot is designed with extensibility in mind, featuring a plugin architecture that allows for future enhancements and custom integrations. Its use cases span various domains, from self-service business intelligence and rapid data exploration to reducing the reliance on dedicated data teams for routine reporting. The project is under active development, with a roadmap that includes expanding LLM support, introducing more advanced analytics capabilities, and fostering collaborative features, indicating a commitment to continuous improvement and community engagement. SQLBot represents a significant step towards making data truly accessible to everyone, fostering a data-driven culture across organizations.