LMQL is a powerful tool that enables users to query large language models (LLMs) in a more intuitive and modular way. It allows users to write queries using Python control flow and string interpolation, making it easier to construct and generate prompts. This tool supports nested queries, enabling the reuse of prompt components and the creation of more complex queries.
Major Highlights
- Modular Query Construction: LMQL supports nested queries, allowing for the reuse of prompt components and the creation of more complex queries.
- Expressive Query Language: Queries are constructed using Python control flow and string interpolation, making it easier to write and manage queries.
- Portability: LMQL code is portable across several backends, allowing users to switch between them with a single line of code.
- Automatic Generation: The tool automatically generates prompts and enforces hard constraints at runtime.
- Easy Integration: LMQL can be easily integrated into Python code, making it a versatile tool for a wide range of applications.
- Query Programs as Functions: Queries are treated as functions, making it easier to write and manage complex queries.
- Typed Variables: The tool supports typed variables, ensuring a guaranteed output format.
- Constrained Generation: LMQL enforces constraints on generated text, ensuring that the output meets specific requirements.
- Execution Trace: The tool provides an execution trace, allowing users to track the execution of their queries.
- Community-Driven: LMQL is created by the SRI Lab @ ETH Zurich and contributors, ensuring a community-driven approach to its development.
Use Cases
- Natural Language Processing: LMQL can be used for a wide range of NLP tasks, including text generation, question answering, and language translation.
- Chatbots and Virtual Assistants: The tool can be used to create more sophisticated chatbots and virtual assistants that can understand and respond to complex queries.
- Data Analysis: LMQL can be used to analyze large datasets and generate insights using natural language queries.
- Content Generation: The tool can be used to generate high-quality content, such as articles, blog posts, and social media updates.
- Education and Research: LMQL can be used in educational and research settings to explore the capabilities of large language models and develop new applications.
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