LMQL is a programming language designed for interacting with Large Language Models (LLMs). It empowers developers and data scientists to express complex LLM interactions with control, constraints, and structure, enabling the creation of reliable and reproducible AI applications. LMQL bridges the gap between natural language and code, allowing you to define prompts, constraints, and logic in a single, unified language.
LMQL Key Features
Declarative Prompting
Define prompts as code, leveraging variables, control flow, and functions for dynamic prompt generation. This allows for more complex and reusable prompts compared to simple string templates.
Constraint Specification
Specify constraints on LLM outputs using logical expressions. LMQL enforces these constraints during decoding, ensuring that the generated text adheres to predefined rules and formats.
Structured Decoding
LMQL supports structured decoding, enabling you to generate outputs in specific formats like JSON or lists. This is crucial for building applications that require predictable and machine-readable results.
Cooperative Decoding
LMQL allows for cooperative decoding, where multiple LLMs work together to generate a final output. This enables more complex reasoning and decision-making processes.
Efficient Execution
LMQL optimizes LLM execution through techniques like speculative decoding and caching, reducing latency and cost.
Integration with Python
Seamlessly integrate LMQL code with existing Python projects. LMQL provides a Python API for executing queries and accessing results.
How LMQL Works
LMQL code is interpreted and executed by the LMQL runtime. The runtime interacts with LLMs, applying constraints and performing structured decoding as specified in the LMQL code. The results are then returned to the user.
LMQL Benefits
Control
Gain fine-grained control over LLM behavior through constraints and structured decoding.
Reliability
Ensure that LLM outputs meet specific requirements and formats, improving the reliability of AI applications.
Reproducibility
Define LLM interactions as code, making them reproducible and easier to debug.
Efficiency
Reduce latency and cost through optimized LLM execution.
Flexibility
Build a wide range of AI applications, from question answering to code generation, using a single language.
LMQL Use Cases
Question Answering
Build question answering systems that provide accurate and reliable answers by constraining the LLM to only generate answers based on a given context.
Code Generation
Generate code in specific programming languages by enforcing syntax rules and constraints during decoding.
Data Extraction
Extract structured data from unstructured text by specifying the desired format using LMQL's structured decoding capabilities.
Dialogue Systems
Create more engaging and coherent dialogue systems by using constraints to guide the conversation flow.
Content Generation
Generate different types of content like blog posts or product descriptions using a combination of prompting and constraints.
LMQL FAQs
What LLMs are supported?
LMQL supports OpenAI's models, local models via HuggingFace and more. See the documentation for the latest list.
How do I get started with LMQL?
Follow the installation instructions on the LMQL website and try the example notebooks.
What kind of constraints can I specify?
LMQL supports a wide range of constraints, including regular expressions, logical expressions, and type constraints.
Who Should Use LMQL
Developers and data scientists who want to build reliable and reproducible AI applications using Large Language Models. LMQL is particularly useful for those who need fine-grained control over LLM behavior and require structured outputs.
