llm-strategy

llm-strategy

Implementing the Strategy Pattern using LLMs.

Also, please see https://blog.blackhc.net/2022/12/llm_software_engineering/ for a wider perspective on why this could be important in the future.

This package adds a decorator llm_strategy that connects to an LLM (such as OpenAI’s GPT-3) and uses the LLM to "implement" abstract methods in interface classes. It does this by forwarding requests to the LLM and converting the responses back to Python data using Python's @dataclasses.

It uses the doc strings, type annotations, and method/function names as prompts for the LLM, and can automatically convert the results back into Python types (currently only supporting @dataclasses). It can also extract a data schema to send to the LLM for interpretation. While the llm-strategy package still relies on some Python code, it has the potential to reduce the need for this code in the future by using additional, cheaper LLMs to automate the parsing of structured data.

• Github repository:
• Documentation

Research Example

The latest version also includes a package for hyperparameter tracking and collecting traces from LLMs.

This for example allows for meta optimization. See examples/research for a simple implementation using Generics.

You can find an example WandB trace at: https://wandb.ai/blackhc/blackboard-pagi/reports/Meta-Optimization-Example-Trace--Vmlldzo3MDMxODEz?accessToken=p9hubfskmq1z5yj1uz7wx1idh304diiernp7pjlrjrybpaozlwv3dnitjt7vni1j

The prompts showing off the pattern using Generics are straightforward:

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Application Example

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See examples/mock_app/customer_database_search.py for a full example.

Getting started with contributing

Clone the repository first. Then, install the environment and the pre-commit hooks with

make install

The CI/CD pipeline will be triggered when you open a pull request, merge to main, or when you create a new release.

To finalize the set-up for publishing to PyPi or Artifactory, see here. For activating the automatic documentation with MkDocs, see here. To enable the code coverage reports, see here.

Releasing a new version

• Create an API Token on Pypi.
• Add the API Token to your projects secrets with the name PYPI_TOKEN by visiting this page.
• Create a new release on Github. Create a new tag in the form *.*.*.

For more details, see here.

Repository initiated with fpgmaas/cookiecutter-poetry.