LMQL is a query language specifically developed for large language models (LLMs) that combines natural language prompts with Python's expressiveness. It provides a wide range of features such as constraints, debugging, retri, and control flow, which help facilitate interaction with LLMs.

Key Features:

  • Constraints: Enables users to specify conditions for the generated output to meet specific criteria.
  • Debugging: Helps analyze and understand how the LLM generates the output, aiding in fine-tuning and error identification.
  • Retri: Access pre-built prompts for common tasks, simplifying the task of providing a convenient starting point.
  • Control Flow: Use Python control flow statements to have more control over the generation process.
  • Automatic Token Generation and Validation: Generates the necessary tokens automatically and validates the produced sequence based on provided constraints.
  • Support for Arbitrary Python Code: Enables dynamic prompts and text processing using Python code.

Use Cases:

  • Natural Language Generation: LMQL enables users to generate natural language responses from LLMs with fine-grained control and constraints.
  • Customized Conversational Agents: Users can create chatbot-like interactions with LLMs by leveraging the control flow and constraint features of LMQL.
  • Task Automation: Can be used to automate specific tasks such as generating packing lists, summarizing text, or performing simple data retri from online sources.
  • Advanced Text Processing: The support for arbitrary Python code in LMQL allows users to perform complex text processing tasks within the prompt.

LMQL is a powerful query language designed to enhance interaction with LLMs while offering a range of features that provide control, flexibility, and customization.