LMQL

What is LMQL?

LMQL (Language Model Query Language) is a programming language designed for interacting with Large Language Models (LLMs). Developed by the SRI Lab at ETH Zurich and contributors, LMQL enables developers to construct robust and modular LLM prompts using types, templates, constraints, and an optimizing runtime. It essentially brings procedural programming paradigms to the world of LLMs, making prompt engineering more structured and manageable.

How does LMQL work?

LMQL allows developers to define queries that guide the LLM's output. Here’s a breakdown of the key concepts:

• Templates: LMQL uses templates to structure prompts. These templates can include variables that the LLM will fill in.
• Constraints: Developers can impose constraints on the generated output using where clauses. These constraints can be based on length, specific patterns, or even custom Python functions.
• Types: LMQL supports typed variables, ensuring that the LLM's output adheres to a specific format, such as integers or strings.
• Nested Queries: With the introduction of nested queries, LMQL enables modularized local instructions and re-use of prompt components. This feature makes it easier to manage complex prompts and create reusable prompt modules.

The core idea is to treat LLMs as programmable components, offering a structured approach to prompt engineering that enhances reliability and maintainability. LMQL also supports multiple backends, including llama.cpp, OpenAI, and Transformers, allowing flexibility in choosing the LLM provider.

Key Features of LMQL

• Robust Prompting: LMQL helps in creating prompts that are less prone to errors and more reliable in producing the desired output.
• Modular Prompting: The ability to create reusable prompt components and nested queries promotes modularity and maintainability.
• Constraint-Based Generation: Constraints ensure that the LLM's output meets specific requirements, such as length or format.
• Multi-Backend Support: LMQL can be used with different LLM providers, ensuring portability and flexibility.
• Python Integration: LMQL queries can be defined and executed directly from Python, making it easy to integrate with existing Python codebases.
• Type Safety: Typed variables guarantee the output format, reducing the need for post-processing and validation.

How to use LMQL?

Using LMQL involves defining queries as Python functions decorated with @lmql.query. Within these queries, you can define prompts, variables, and constraints. Here’s a simple example from the original text:

@lmql.query
def meaning_of_life():
    '''lmql
    "Q: What is the answer to life, the \
     universe and everything?"

    "A: [ANSWER]" where \
        len(ANSWER) < 120 and STOPS_AT(ANSWER, ".")

    print("LLM returned", ANSWER)
    return ANSWER
    '''

meaning_of_life() # The program returns the meaning of life.

This example demonstrates how to define a prompt, use a variable (ANSWER) to capture the LLM's output, and apply constraints to ensure that the answer is less than 120 characters and ends with a period.

Who is LMQL for?

LMQL is particularly useful for:

• Developers working with LLMs: Who need a structured way to create and manage prompts.
• Researchers: Who want to experiment with different prompting techniques and constraints.
• Teams: Who need to ensure consistency and reliability in their LLM-powered applications.

Use Cases of LMQL

• Chatbots: LMQL can be used to create more reliable and context-aware chatbot responses.
• Data Extraction: Constraints can be used to ensure that extracted data adheres to a specific format.
• Content Generation: LMQL can be used to generate content that meets specific requirements, such as length and style.
• Tool Augmentation: Used to create better output for the tool.

LMQL vs Traditional Prompting

In summary, LMQL offers a more robust, modular, and maintainable approach to LLM prompting compared to traditional methods.