When using large language models (LLMs) to generate SQL queries from natural language, it’s crucial to ensure that these queries don’t inadvertently expose sensitive data or perform unintended actions. Enter HeimdaLLM — a robust static analysis framework designed to validate that LLM-generated structured output is safe. In this guide, we’ll walk you through using HeimdaLLM effectively, ensuring your AI-powered SQL queries are secure.

What is HeimdaLLM?

HeimdaLLM (pronounced HEIM-dall-EM) acts as a guard dog for your database. Imagine giving a dog specific commands on what areas of the yard to protect. In this analogy, the query you generate is a guest entering your backyard. HeimdaLLM ensures that this guest can only access the safe zones you permit, preventing any unauthorized chaos.

How HeimdaLLM Works

Here’s an analogy to help you understand the internal workings of HeimdaLLM:

• It examines the SQL query like a security guard checking each person entering a building. It looks for allowed columns, tables, and functions.
• If anything forbidden is discovered (let’s say a forbidden column), it removes the potential problem before anyone can enter.
• HeimdaLLM also ensures the guest (your query) is limited to a specific number (a LIMIT), making sure it can’t overstay their welcome and consume too much information.
• Finally, it verifies that the person entering only has access to their own data, similar to ensuring that a guest can only access their designated area of your yard.

Getting Started with HeimdaLLM

To get started quickly, head over here for a comprehensive quickstart guide.

Step-by-Step Guide

• Install HeimdaLLM from PyPI.
• Begin by formulating your natural language query.
• Allow HeimdaLLM to process the query and perform a static analysis.
• Review the fortified SQL query generated after HeimdaLLM’s validation.
• Execute the validated query to ensure safe data access.

Troubleshooting Common Issues

Even with safety measures in place, you might encounter some hiccups while using HeimdaLLM. Here are potential problems and their solutions:

• Problem: The processed SQL query is still too broad.
  Solution: Ensure that your original natural language query is specific and contains required parameters.
• Problem: HeimdaLLM refuses to validate a seemingly harmless query.
  Solution: Revisit the analysis and modify the query to align with HeimdaLLM’s safety rules.
• Problem: Difficulties integrating HeimdaLLM into your system.
  Solution: Check the documentation patiently to identify potential mismatches or coding errors that might hinder integration.

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Safety Precautions

While HeimdaLLM is a powerful tool, it’s fundamental to conduct a security audit before deploying in a production environment. Until the audit is complete, approach production use with caution.

Database Support

• SQLite
• MySQL
• Postgres

Development is actively ongoing to support more databases. Share your preferences to prioritize future releases.

Licenses

HeimdaLLM offers both open-source and commercial licenses. Consider the implications of each before deployment:

• Open-source license (AGPLv3) allows for free usage but comes with limitations regarding proprietary modifications and copyleft clauses.
• Commercial license removes such restrictions, providing operational flexibility.

If you are interested in a commercial license, please inquire about it here.

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.