Lmql

Natural language querying for large models.

prompts code learning model

Tool Information

Primary Task	Models
Category	ai-and-machine-learning
Pricing	Free

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.

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

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