Unlocking Powerful Interactions with Large Language Models

LMQL is a game-changing query language designed specifically for large language models (LLMs). By combining natural language prompts with the expressiveness of Python, it simplifies the interaction process and opens up new possibilities for developers. With features like constraints, debugging, retrieval, and control flow, LMQL empowers users to seamlessly work with LLMs.

Transformers Support: Enhanced Prompt Responses

The integration of Transformers support takes prompt responses to the next level. This enables tasks such as generating packing lists, searching Wikipedia, and even chatting with a bot. The possibilities are endless!

Programmatic Control Flow

With LMQL, users can programmatically control the generation process using regular Python control flow statements. This ensures that generated sequences meet specific requirements and accuracy is guaranteed.

Dynamic Prompts and Text Processing

The tool also supports arbitrary Python code in the prompt clause, allowing for dynamic prompts and text processing. This feature enables developers to create custom solutions tailored to their needs.

Scripted Beam Search: Exploring Multiple Answers

LMQL's Scripted Beam Search feature decodes expert names and answers jointly, exploring multiple possible answers. This innovative approach revolutionizes the way we interact with LLMs.

Python's Assert: Evaluating Data Sets for Correctness

The support for Python's assert aids in evaluating data sets for correctness, ensuring that results are reliable and accurate. This feature saves developers time and effort when working with large datasets.

Simplifying Interactions and Empowering Developers

Overall, LMQL simplifies the interaction process with LLMs and empowers Python developers to efficiently work with natural language prompts. By streamlining the process, it unlocks new possibilities for innovation and creativity.