Model generation 2023-04-05
Lmql icon


Natural language querying for large models.
Generated by ChatGPT

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 ๐Ÿค— 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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Aug 8, 2023
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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Lmql was manually vetted by our editorial team and was first featured on April 6th 2023.
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Pros and Cons


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


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

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