pgml.transform()

The pgml.transform() function is the most powerful feature of PostgresML. It integrates open-source large language models, like Llama, Mixtral, and many more, which allows to perform complex tasks on your data.

The models are downloaded from 🤗 Hugging Face which hosts tens of thousands of pre-trained and fine-tuned models for various tasks like text generation, question answering, summarization, text classification, and more.

API

The pgml.transform() function comes in two flavors, task-based and model-based.

Task-based API

The task-based API automatically chooses a model based on the task:

postgresql

pgml.transform(
    task TEXT,
    args JSONB,
    inputs TEXT[]
)

Argument	Description	Example	Required
task	The name of a natural language processing task.	`'text-generation'`	Required
args	Additional kwargs to pass to the pipeline.	`'{"max_new_tokens": 50}'::JSONB`	Optional
inputs	Array of prompts to pass to the model for inference. Each prompt is evaluated independently and a separate result is returned.	`ARRAY['Once upon a time...']`	Required

Examples

{% tabs %} {% tabs %} {% tab title="Text generation" %}

postgresql

SELECT *
FROM pgml.transform(
  task => 'text-generation',
  inputs => ARRAY['In a galaxy far far away']
);

{% endtab %} {% tab title="Translation" %}

postgresql

SELECT *
FROM pgml.transform(
  task => 'translation_en_to_fr',
  inputs => ARRAY['How do I say hello in French?']
);

{% endtab %} {% endtabs %}

Model-based API

The model-based API requires the name of the model and the task, passed as a JSON object. This allows it to be more generic and support more models:

postgresql

pgml.transform(
    model JSONB,
    args JSONB,
    inputs TEXT[]
)

<table class="table-sm table"> <thead> <th>Argument</th> <th>Description</th> <th>Example</th> </thead> <tbody> <tr> <td>model</td> <td>Model configuration, including name and task.</td> <td> <div class="code-multi-line font-monospace"> '{

"task": "text-generation",

"model": "mistralai/Mixtral-8x7B-v0.1"

}'::JSONB </div> </td> </tr> <tr> <td>args</td> <td>Additional kwargs to pass to the pipeline.</td> <td><code>'{"max_new_tokens": 50}'::JSONB</code></td> </tr> <tr> <td>inputs</td> <td>Array of prompts to pass to the model for inference. Each prompt is evaluated independently.</td> <td><code>ARRAY['Once upon a time...']</code></td> </tr>

</table>

Example

{% tabs %} {% tab title="PostgresML SQL" %}

postgresql

SELECT pgml.transform(
  task   => '{
    "task": "text-generation",
    "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
    "model_type": "mistral",
    "revision": "main",
    "device_map": "auto"
  }'::JSONB,
  inputs  => ARRAY['AI is going to'],
  args   => '{
    "max_new_tokens": 100
  }'::JSONB
);

{% endtab %}

{% tab title="Equivalent Python" %}

python

import transformers

def transform(task, call, inputs):
    return transformers.pipeline(**task)(inputs, **call)

transform(
    {
        "task": "text-generation",
        "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
        "model_type": "mistral",
        "revision": "main",
    },
    {"max_new_tokens": 100},
    ['AI is going to change the world in the following ways:']
)

{% endtab %} {% endtabs %}

pgml.transform()

pgml.transform()

API

Task-based API

Examples

Model-based API

Example

Guides