Lmql ✓
Supercharge your LLM prompting with code
Verdict
A quick read before you dig into the full page.
- Best if
- Supercharge your LLM prompting with code
- Skip if
- May have learning curve
- Pick this when
- Natural language querying
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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.
Access & connections
Where you can use Lmql, and what it plugs into.
Platforms
Integrations
Capabilities
What this tool accepts, produces, and is strongest at.
Input
- Code
- Text
Output
- Code
- Text
Pros & cons
Pros
- Natural language querying
- Designed for LLMs
- Python expressiveness
- Supports constraints
- Offers debugging
- Supports retrieval
- Flow control support
- Supports Transformers
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- 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
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
Show 2 more
- Limited debugging tools
- Lacks multi-language support
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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).