LMQL is a programming language for LLM interaction.

Discover the Power of LMQL for Language Model Prompting

What is LMQL?

LMQL stands for Language Model Query Language, a groundbreaking programming language designed specifically to interact with large language models (LLMs). Developed by the SRI Lab at ETH Zurich along with various contributors, it offers a robust and modular approach to creating prompts, integrating types, templates, and constraints, all enhanced by an optimizing runtime.

Key Features of LMQL

• Robust Prompting: Create prompts with structured queries utilizing a combination of types, templates, constraints, and variables.

• Nested Queries: Implement procedural programming within your prompts for complex querying.

• Backend Flexibility: LMQL supports portability across diverse backends, allowing seamless transition from one LLM provider to another.

• Python Integration: Directly call and execute LMQL functions from within a Python environment.

• Expressive Control Flow: Utilize Python’s control flow and string interpolation capabilities for more expressive and dynamic prompt construction.

How Does LMQL Work?

The beauty of LMQL lies in its simplicity and power. For example, to find the meaning of life, you would write a function like this:

@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)
    "The answer is [NUM: int]"
    return NUM
    '''

When you call meaning_of_life() in Python, if properly prompted, the answer you would expect (based on Douglas Adams' humor) would be 42.

LMQL also allows you to utilize loops, variable constraints, and more to construct prompts that can help with tasks like creating a packing list.

Nested Queries and Procedural Prompting

One of the latest exciting features is the introduction of nested queries. This allows users to structure their prompts more like traditional computer programs, where local instructions can be modularized and reused. Here’s an example that utilizes nested queries to determine the youngest of three individuals:

Q: When was Obama born?
ANSWER 04/08/1961
Q: When was Bruno Mars born?
ANSWER 08/10/1985
Q: When was Dua Lipa born?
ANSWER 22/08/1995
Out of these, who was born last?
LAST Dua Lipa

Advantages and Limitations

While LMQL introduces an innovative way to interact with LLMs, there are some considerations to keep in mind:

Pros:

• Offers a controlled environment for prompt generation with constraints.
• Enhances reusability and modularity of code.
• Can easily switch between LLM backends without changing the source code.
• Integrates seamlessly with Python, leveraging its syntax and capabilities.

Cons:

• There is a learning curve associated with a new programming language.
• The complexity of queries may increase with advanced usage.
• Users may need to adapt to the evolving features and updates.

Final Thoughts

LMQL has changed the game when it comes to interacting with language models. Its structured approach to prompting enables developers to craft high-quality, efficient prompts, ensuring more predictable and reliable outputs.

For further information on LMQL and to explore its functionalities, you can visit their official documentation.

Whether one is experienced or new to the world of language models, LMQL offers a promising platform to explore the power of LLMs with precision and sophistication.