AI Transforms

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Overview

AI Transforms can be used to augment data models using large language models in either the cloud or locally. An LLMTransform handles token management, column metadata context and more that is provided via Vinyl internals.

Lets take some user reviews of movies from the IMDB dataset. Suppose we want to tag each review with sentiment analysis using the latest LLM models available.

We can import the LLMTransform and OpenAIProvider from Vinyl to run an LLM-powered transform on a specific column of our dataset.

from my_project.sources.taxi_sample import TaxiSample
from vinyl.ai import LLMTransform, OpenAIProvider

@model(deps=Movies)
def movie_sentiment(m):
    infer_sentiment = LLMTransform(
        OpenAIProvider,
        cols=[
            m.comment,
        ]
        prompt="Rate the sentiment of this movie review comment column either 'positive', 'neutral' or 'negative'"
    )

    m.mutate({
        "sentiment": infer_sentiment()
    })

    return t

Vinyl handles concurrency, caching and incremental updates. Vinyl will only run the transform on new rows. The OpenAIProvider allows you to bring your own OpenAI API key.

We can preview the results of this using:

vinyl preview model movie_sentiment

Here are 4 rows of revised example data with movie titles and comments that better align with their respective sentiment labels:

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Local AI Inference

Working with data workflows comes with a host of privacy challenges. If you’re interested in running AI Transforms using self-hosted models and GPU inference reach out to us.