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Using Large Language Models with Grist
In this experimental Grist feature, originally developed by Alex Hall, you can hook up an AI model such as OpenAI's Codex to write formulas for you. Here's how.
First, you need an API key. You'll have best results currently with an
OpenAI model. Visit https://openai.com/api/ and prepare a key, then
store it in an environment variable OPENAI_API_KEY.
Alternatively, there are many non-proprietary models hosted on Hugging Face.
At the time of writing, none can compare with OpenAI for use with Grist.
Things can change quickly in the world of AI though. So instead of OpenAI,
you can visit https://huggingface.co/ and prepare a key, then
store it in an environment variable HUGGINGFACE_API_KEY.
That's all the configuration needed!
Currently it is only a backend feature, we are still working on the UI for it.
Trying other models
The model used will default to text-davinci-002 for OpenAI. You can
get better results by setting an environment variable COMPLETION_MODEL to
code-davinci-002 if you have access to that model.
The model used will default to NovelAI/genji-python-6B for
Hugging Face. There's no particularly great model for this application,
but you can try other models by setting an environment variable
COMPLETION_MODEL to codeparrot/codeparrot or
NinedayWang/PolyCoder-2.7B or similar.
If you are hosting a model yourself, host it as Hugging Face does,
and use COMPLETION_URL rather than COMPLETION_MODEL to
point to the model on your own server rather than Hugging Face.