Overview

LMQL is a query language designed specifically for large language models (LLMs). It combines the natural language prompts with the expressiveness of Python to facilitate the interaction with LLMs.

The tool provides various features such as constraints, debugging, retrieval, control flow, and support for 🤗 Transformers, which make it easier to prompt responses from the LLM.

LMQL offers a broad range of pre-built prompts for tasks such as telling a joke, generating a packing list, searching Wikipedia, and chatting with a bot.

In addition to providing high-level constraints, LMQL also allows users to control the generation process programmatically by supporting regular Python control flow statements.

The tool generates the required tokens automatically and validates the produced sequence as soon as the provided validation condition is definitively violated.LMQL also supports arbitrary Python code in the prompt clause, enabling dynamic prompts and text processing.

The Scripted Beam Search feature decodes the expert name and answer jointly, exploring multiple possible answers. LMQL supports Python's assert to check the correctness of the generated output, which can be useful for evaluating data sets.

Overall, LMQL is a powerful tool that simplifies the interaction with LLMs and enables Python developers to work with natural language prompts more efficiently.

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Initial release

April 5, 2023

Initial release of Lmql.

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5.0

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Comments(1)

Rustle

Aug 8, 2023

@ initial release

Rated it

I have used this for several personal assistant applications and it works amazingly. Obviously it is still restricted to the flaws of any model you are using it with, but the ability to constrain prompts is incredibly helpful (especially when it comes to getting things like JSON format from the AI).

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Pros and Cons

Pros

Natural language querying

Designed for LLMs

Python expressiveness

Supports constraints

Offers debugging

Supports retrieval

Flow control support

Supports Transformers

Pre-built programmers

Regular control flow support

Automatic token generation

Sequence validity checks

Supports Python code

Scripted Beam Search support

Supports correctness checks

High-level constraint support

Control over generation process

Python control-flow integration

Fixed set value enforcement

Python assert support

Supports decoding parameters

Interactive query execution

Supports constraint clauses

Utility function integration

Efficient LLM interaction

Web service interaction support

Simple key-value storage

Integration of model reasoning

Output distribution computation

Supports Chat models

Markup integration in prompts

Consistent interaction with LLMs

Supports interactive queries

Supports special marker tokens

Enable user input integration

Mutate state during decoding

Supports arithmetic evaluation

Can query external services

Dynamic prompt handling

Dynamic context integration

Supports async functions

Robust parsed response

Standardized LLM interaction

Web-based Playground IDE

Aligns with Python packaging

Supports conditional reasoning

Prompt clause role marking

Early release provided

Integrates user input

Ensures result assignation

Control over decoding parameters

Built operation support

Encourages user feedback

View 48 more pros

Cons

Requires Python knowledge

May have learning curve

Limited inbuilt tasks

Limited interaction flow

Possible troubleshooting complexity

Dependent on prompt efficiency

No mobile version

Validation happens post-violation

Limited debugging tools

Lacks multi-language support

View 5 more cons

Included in lists

Prompt Engineering

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