text-to-sql

To convert natural language to SQL, this Skill translates your plain-text questions into executable SQL queries and runs them against your database. It scaffolds a project, extracts the schema, and generates context to guide the LLM query conversion.

Yes, you can query PostgreSQL and MySQL databases using natural language. This Skill supports SQLite, PostgreSQL, and MySQL engines by configuring the connection and extracting the database schema for accurate SQL generation.

You extract a database schema for LLM context generation by initializing a project and running the schema extraction tool. This process produces a markdown context file that guides the language model in generating accurate SQL queries.

The best way to set up a text-to-SQL project is through its guided workflow that initializes scaffolding, configures your environment, and extracts the schema. This allows non-technical users to run ad hoc queries and export database results quickly.

Yes, natural language to SQL conversion works with server databases. The Python-based toolchain supports connections to server databases like PostgreSQL and MySQL, enabling you to query them directly using natural language inputs.

Yes, you need specific Python dependencies like psycopg2 and mysql-connector-python to run natural language SQL queries. These packages enable the toolchain to connect to databases, extract schemas, and execute the generated SQL commands.