Open-Source Databases · whoiskatrin

sql-translator

SQL Translator is an open-source web tool that converts natural language queries to SQL and vice versa, powered by OpenAI's API. It eliminates the need to manually write SQL by allowing users to describe what they want in plain English and receive executable SQL code.

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Source: GitHub — github.com/whoiskatrin/sql-translator

4.3k

GitHub stars

376

Forks

TypeScript

Primary language

MIT

License (OSI-approved)

Key facts

Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.

Field	Value
Repository	whoiskatrin/sql-translator
Owner	whoiskatrin
Primary language	TypeScript
License	MIT — OSI-approved
Stars	4.3k
Forks	376
Open issues	7
Latest release	Unknown
Last updated	2025-07-06
Source	https://github.com/whoiskatrin/sql-translator

What sql-translator is

TypeScript-based single-page application that leverages OpenAI's language models to bidirectionally translate between natural language and SQL. The tool supports schema awareness (beta), syntax highlighting, and integrates with PostgreSQL; deployment via npm or Docker Compose with mandatory OPENAI_API_KEY configuration.

Quickstart

Get the sql-translator source

Clone the repository and explore it locally.

terminalbash

1git clone https://github.com/whoiskatrin/sql-translator.git2cd sql-translator3# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

Self-service analytics for non-technical users

Business analysts and stakeholders without SQL expertise can formulate database queries in natural language, reducing dependency on data engineering teams for ad-hoc reporting.

SQL learning and documentation

Developers and students can reverse-translate SQL queries to natural language for understanding complex query logic, and validate their own SQL syntax against AI-generated equivalents.

Internal data exploration and query generation

Teams running self-hosted instances can integrate the tool into internal workflows for rapid prototyping of database queries against private schemas without exposing data to third-party APIs.

Implementation considerations

Mandate review and validation of all AI-generated SQL before execution against production databases; treat output as draft, not final.
Implement API rate limiting and cost controls on OpenAI key to prevent unexpected billing from heavy usage or misconfiguration.
Ensure schema metadata provided to the tool is current and does not leak sensitive column names, constraints, or business logic.
Plan for offline or alternative fallback workflows if OpenAI API becomes unavailable, rate-limited, or pricing changes.
Test generated SQL against representative data sets and edge cases; LLM outputs can be plausible but incorrect.

When to avoid it — and what to weigh

Sensitive production data cannot be exposed to OpenAI — The tool requires sending query context and schema information to OpenAI's API. Any organization with strict data residency or confidentiality requirements must evaluate API data handling policies or run a fully disconnected instance.
Complex multi-step or domain-specific SQL logic required — LLM-generated SQL may not reliably produce correct queries for advanced use cases (window functions, recursive CTEs, vendor-specific extensions). Manual review and testing are mandatory.
No versioning or release cycle — The project shows no tagged releases and relies on continuous HEAD deployment. Teams requiring stable, pinned versions should evaluate alternatives or maintain internal forks.
Support or SLA expectations — This is a community-maintained open-source project with no formal support, SLAs, or guaranteed bug-fix timelines. Production systems should not depend on this tool without in-house maintenance capability.

License & commercial use

MIT License is a permissive, OSI-approved license allowing commercial use, modification, and distribution with minimal restrictions (retain license and copyright notice).

MIT License explicitly permits commercial use. However, this project is community-maintained with no warranties or support guarantees. Commercial deployments should: (1) conduct security and compliance review, (2) maintain internal forks or patches if needed, (3) document your own liability and support model, (4) ensure OpenAI API terms align with your commercial use case. Consult legal counsel for production commercial deployments.

DEV.co evaluation signals

Editorial assessment — not user reviews. Directional, with an explicit confidence level.

Signal	Assessment
Maintenance	Active
Documentation	Adequate
License clarity	Clear
Deployment complexity	Low
DEV.co fit	Good
Assessment confidence	High

Security considerations

Key considerations: (1) All queries and schema metadata are transmitted to OpenAI; ensure compliance with data residency and confidentiality policies. (2) OPENAI_API_KEY must be protected (use secrets management, not version control). (3) LLM output injection risks if generated SQL is executed without parameterization or validation. (4) UI runs client-side; input/output handling should prevent XSS. (5) No documented security audit or vulnerability disclosure process. (6) Schema awareness feature (beta) may leak unintended metadata. (7) Assume no warranty or security guarantees from the maintainer.

Alternatives to consider

Text2SQL / Defog

Specialized commercial offerings for NL-to-SQL with fine-tuned models, SLA support, and enterprise compliance features. Better for production deployments requiring guarantees.

Custom OpenAI/Claude integration

Rolling your own prompt-engineering solution gives full control over API selection, data handling, and model tuning. Suitable if you have engineering capacity and strict data governance.

BI tools with copilot (Tableau, Looker, Power BI)

Integrated NL-to-SQL features within established BI platforms eliminate separate tool management, offer enterprise support, and provide schema governance. Trade-off: higher cost, vendor lock-in.

Software development agency

Build on sql-translator with DEV.co software developers

Evaluate this tool for self-service analytics, data exploration, or internal SQL learning. Ensure your team conducts security and compliance review, especially if handling sensitive data. Contact us to discuss deployment options, self-hosting, or custom AI integrations.

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sql-translator FAQ

Can I use this tool without sending data to OpenAI?

Not with the current implementation. The tool requires an active OpenAI API key and sends query context to OpenAI's servers. For fully offline operation, you would need to fork and integrate a local LLM (e.g., Ollama, LLaMA) or a self-hosted model, which is non-trivial.

Is the generated SQL always correct?

No. LLM outputs are plausible but not guaranteed to be correct. Always review and test generated SQL, especially for production queries. Complex logic, edge cases, and database-specific syntax may be misinterpreted.

Can I deploy this on Kubernetes or serverless platforms?

Yes, the Docker image can be deployed to Kubernetes, AWS ECS, or other container orchestration systems. Serverless deployment (Lambda, Cloud Run) is possible but requires stateless adaptation and environment variable injection for the OpenAI API key.

How do I contribute or report security issues?

Standard GitHub fork-and-PR workflow is documented. No dedicated security policy or responsible disclosure process is mentioned. Report security issues via GitHub issues or contact the maintainer directly (check the repository for contact details).

Software developers & web developers for hire

Need help beyond evaluating sql-translator? DEV.co is a software development agency offering software development services and web development for teams of every size. Our software developers and web developers build custom software, web applications, APIs, and open-source databases integrations — and maintain them long-term.

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