LMQL is a special query language created for large language models (LLMs), which combines natural language prompts with the power of Python. It offers several useful features to facilitate interaction with LLMs:
– Constraints: You can set conditions for the generated output to meet specific criteria.
– Debugging: You can analyze and understand how the LLM generates the output, making it easier to fine-tune and identify errors.
– Retrieval: You can access pre-built prompts for common tasks, providing a convenient starting point.
– Control Flow: You can use Python control flow statements to have more control over the generation process.
– Automatic Token Generation and Validation: LMQL automatically generates the required tokens and validates the produced sequence based on given constraints.
– Support for Arbitrary Python Code: You can include dynamic prompts and perform text processing using Python code.
LMQL has various use cases, including:
– Natural Language Generation: It enables users to generate natural language responses from LLMs with precise 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: LMQL can automate specific tasks like generating packing lists, summarizing text, or retrieving data 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.
Overall, LMQL empowers users to enhance their interaction with LLMs by providing a range of features that offer control, flexibility, and customization.