LMQL is a programming language designed for large language models, enabling robust and modular prompting through types, templates, and constraints.

LMQL was developed by the SRI Lab at ETH Zurich along with various contributors.

Can LMQL handle nested queries?

Yes, LMQL supports nested queries, which allow for modularized local instructions and the re-use of prompt components.

What is scripted prompting in LMQL?

Scripted prompting in LMQL allows for control flow and branching behavior in prompt construction, enabling more sophisticated prompt designs.

What constraints can be applied in LMQL?

LMQL allows for high-level constraints such as length restrictions and logical conditions to be applied to prompt outputs.

Is there an IDE available for LMQL?

Yes, LMQL provides a Playground IDE which can be used for writing and testing queries.

Can I use local models with LMQL?

Yes, LMQL supports self-hosted models via frameworks like 🤗 Transformers and llama.cpp.

What are some use cases of LMQL?

LMQL can be used for complex prompt generation, chatbots, interactive systems, and any scenario requiring structured prompting and constrained generation.

Does LMQL support integration with other tools?

Yes, LMQL supports tool augmentation and has integrations with LangChain, LlamaIndex, and Pandas among others.

How does LMQL optimize LLM queries?

LMQL uses an optimizing runtime to efficiently handle constraints and improve the performance of large language model queries.

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LMQL

Name: LMQL
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Last updated: August 8, 2024

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What is LMQL?

LMQL is a programming language tailored for large language models (LLMs). It offers robust and modular LLM prompting through the use of types, templates, constraints, and an optimizing runtime. It simplifies the creation of complex prompts by allowing procedural programming techniques in a query-like syntax. Created by the SRI Lab at ETH Zurich, LMQL supports features such as nested queries, scripted prompting, and custom constraints. It also provides a Playground IDE for ease of use.