LMQL is a unique query language specially created for Large Language Models (LLMs) that makes it simple to interact with them using natural language prompts combined with Python's expressiveness. This tool comes with several useful features like debugging constraints, controlling retrieval, flow, and support for 🤗 Transformers that simplify generating responses from the LLM. With LMQL, you get access to pre-built prompts for various tasks, including telling jokes, generating packing lists, searching Wikipedia, and chatting with a bot.
In addition to providing high-level constraints, LMQL also allows you to program the generation process using regular Python control flow statements. The tool automatically generates the required tokens and validates the produced sequence as soon as the validation condition is definitely violated. Moreover, LMQL supports arbitrary Python code in the prompt clause, allowing dynamic prompts and text processing.
The Scripted Beam Search feature jointly decodes the expert name and answer, exploring multiple possible answers. You can use Python's assert to check the accuracy of the generated output, which can be beneficial for evaluating datasets. Overall, LMQL is a powerful tool that streamlines the interaction with LLMs, making it easier for Python developers to work with natural language prompts more efficiently.
