Lmql AI

Features

• Nested Queries: LMQL introduces nested queries to your LLM prompting, allowing for modular programming and reusable prompt components, increasing efficiency and maintainability.
  
• Cross-Backend Compatibility: Ensure your LLM code is portable across multiple backends with ease, using LMQL's ability to switch environments with just a single line of code.
  
• Versatile Prompting Toolkit: Empower your development with tools like constrained LLMs, types and regex, multi-part prompts, and more for a sophisticated and controlled prompting experience.
  
• Seamless Python Integration: LMQL's Python support enables developers to harness the familiarity and power of Python for prompt construction with rich control flow and string interpolation.
  

Use Cases:

• Procedural Programming for LLMs: Leverage procedural constructs within your prompts to create complex, dynamic interactions with LLMs enabled by LMQL's nested queries.
  
• Streamlined Development Across Multiple Platforms: Build your LLM applications in LMQL and run them on various platforms such as llama.cpp, OpenAI, and Hugging Face Transformers without changing your code base.
  
• Advanced Prompt Engineering: Utilize LMQL's advanced prompt engineering capabilities for mission-critical tasks that require precision, like tool augmentation and chatbot development.
  

LMQL 0.7 is a ground-breaking update offering programmers unprecedented control, modularity, and portability for LLM prompting, thus setting a new standard in language model interaction.