text_to_sql_ai: An Ollama-based Intelligent System for Natural Language to SQL Conversion

text_to_sql_ai is an open-source AI system that uses the local large language model Ollama to automatically convert natural language into executable SQL queries. It aims to lower the barrier for non-technical users to write SQL and enable zero-code database interaction. Its core advantages include local deployment (data privacy protection, offline availability), controllable costs, flexible models, etc., allowing business personnel, product managers, and other roles to directly interact with databases using everyday language. The project is maintained by Azeelav and was released on the GitHub platform on June 5, 2026.

Databases are the core of enterprise data storage, but the SQL learning curve is too steep for non-technical roles (business personnel, product managers, etc.). Traditional solutions have problems such as high communication costs (relying on technical teams), limited functions (graphical tools), and large time investment (learning syntax). Text-to-SQL technology reduces access barriers through NLP, and this project chooses Ollama to achieve local offline operation, avoiding the upload of sensitive data to the cloud.

The core process of the system includes: 1. Natural Language Understanding (NLU): Identify user query intent, key entities, and operation types; 2. Database Schema Awareness: Obtain table structures by preloading DDL, dynamically querying INFORMATION_SCHEMA, or using user-provided documents; 3. SQL Generation and Validation: Generate syntactically correct SQL that conforms to the target database engine; 4. Result Return and Explanation: Provide query results and explanations of the SQL conversion process.

1. Data Privacy Protection: All processing is done locally, meeting compliance requirements of industries such as finance and healthcare; 2. Controllable Costs: No API call token fees, suitable for high-frequency queries; 3. Flexible Models: Supports open-source models like Llama, Mistral, CodeLlama, etc., which can be selected as needed; 4. Offline Availability: Does not rely on the network and can run in isolated environments.

Applicable scenarios include: 1. Business Self-service Analysis: Sales/operation personnel directly query performance data; 2. Rapid Prototype Verification: Product managers quickly verify data hypotheses; 3. Education and Training: Assist SQL beginners in understanding mapping relationships; 4. Enterprise Internal Knowledge Base: Integrate into chatbots or internal tools to provide a unified query entry.

The challenges faced include: 1. Accuracy of Complex Queries: The accuracy of generating multi-table joins, nested subqueries, etc., needs to be improved; 2. Ambiguity Resolution: Natural language ambiguities need to be handled in combination with context; 3. Cross-database Compatibility: Need to adapt to differences in different SQL dialects; 4. Security Considerations: Prevent SQL injection risks and ensure data integrity.

text_to_sql_ai promotes the democratization of data access through AI technology, combining LLM language understanding with database queries to turn data querying from a professional skill into a general ability. With the improvement of model capabilities and the development of fine-tuning technology, seamless interaction between natural language and data systems may be realized in the future. This project provides a worthy open-source solution for enterprises looking to lower the barrier to data access.