Overview of llm-strategy
The llm-strategy project is designed to implement the Strategy Pattern using Large Language Models (LLMs) like OpenAI’s GPT-3. It introduces an innovative approach in software development by employing language models to automatically generate method implementations in interface classes. This involves using a unique decorator called llm_strategy, which connects to LLMs, forwarding method requests and converting responses into Python data using @dataclasses. Essentially, it leverages the power of LLMs to handle abstract methods, minimizing the dependency on human-generated Python code over time.
How It Works
The mechanism of the llm-strategy package is to transform interface specifications into prompts for the language model. It uses doc strings, type annotations, and the names of methods/functions, feeding them to an LLM to process. The responses received are then translated back into Python-friendly data types. As it stands, support is currently focused on @dataclasses, but there’s significant potential for expanding its capabilities to further simplify code requirements, possibly by utilizing cost-effective LLM solutions for parsing structured data.
Research Example
Incorporated in the latest release, the project includes a package that tracks hyperparameters and collects LLM traces for meta-optimization. This feature is harnessed to refine experiments, as demonstrated in a simple implementation using Generics. For example, the project provides a framework where various task parameters, hyperparameters, and their results are tracked, analyzed, and optimized using LLM feedback. This process is facilitated by specific classes dedicated to tasks and reflective evaluations—helping to derive and suggest next steps for parameter optimization.
Application Example
An exemplary use case of the llm-strategy is in managing a mock customer database. A decorated Customer class with properties such as key, first_name, last_name, birthdate, and address uses LLMs to compute the age of a customer from the birthdate—a task typically requiring manual implementation. The customer management system further includes finding customer keys using natural language queries—enabled by LLMs—which provide a semantic search capability beyond traditional SQL-like queries.
A mock database example is provided, showing how to load and store customer data while creating mock customer records. This showcases the practical application of LLMs in solving real-world tasks with minimal boilerplate code.
Documentation and Contribution
To start contributing to the project, developers are encouraged to clone the repository and install the necessary environment along with pre-commit hooks. Detailed instructions are provided for integrating CI/CD pipelines, publishing to PyPi/Artifactory, and activating automatic documentation with MkDocs and code coverage reports through provided links.
Releasing New Versions
The project also includes guidelines on releasing new versions, such as creating an API token on PyPi and configuring project secrets for automated deployment processes. This ensures a smooth flow in building and releasing improved iterations of the package.
The llm-strategy project exemplifies how LLMs can be integrated into software engineering practices to simplify the development lifecycle and explore new ways of automating code generation and optimization tasks.