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Supercharge your LLM prompting with code
Most popular alternative: H2O.ai (63 saves)
By unverified author Claim this AI
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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Releases
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April 5, 2023
Initial release of Lmql.
Pricing
Pricing model
Free
Paid options from
Free
Prompts & Results
Add your own prompts and outputs to help others understand how to use this AI.
Lmql was manually vetted by our editorial team and was first featured on April 6th 2023.
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880,459644v1.6 released 27d agoFree + from $12/mo
Reducing manual efforts in first-pass during code-review process helps speed up the "final check" before merging PRs -
13,476111v2.1 released 2mo agoFree + from $45/mo
This is the first AI marketing tool I’ve used that actually helps me get things done, not just suggest generic ideas. The mix of AI-powered strategy and real experts executing the work makes it feel like having a high-performance marketing team without the overhead
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
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