powerml-app 0.0.17

PowerML python package

PowerML Python Package

Installation

pip install powerml_app

Configure

In order to use this library, first create a config file at ~/.powerml/configure.yaml. Here's an example:

powerml:
    key: "<POWERML-KEY>"
openai:
    key: "<OPENAI-KEY>"

You may also configure the PowerML class by passing in a dictionary with the following specified format

from powerml import PowerML
config = {"powerml": {"key": "<POWERML-KEY>"}}
powerml = PowerML(config)

PowerML Key

To get a powerml key, go to https://staging.powerml.co/ and log in with your email. Contact our team if you are unable to log in and we'll add you!

Usage

How to use:

After configuring PowerML, we can use its member functions fit and predict

from powerml import PowerML
config = {"powerml": {"key": "<POWERML-KEY>"}}
powerml = PowerML(config)

testPrompt = "hello there"
response = powerml.predict(prompt=testPrompt)
data = ["item2", "item3"]
model_details = powerml.fit(data)

You may further calibrate any model using PowerML.fit

model_details = powerml.fit(data, model="<MODEL_NAME>")

Currently the default model is openai's text-davinci-003.

Fit

fit will return a dictionary object in the following format:

{
    "model_id":"23",
    "project_id":"None",
    "user_id":"12",
    "job_id":"89",
    "model_name":"be894276039088c5f8db3f6bfaeb19953ed9ffe55f37a847a58f9fb320d307bc",
    "job_config":"{\"type\": \"prompt_tune\", \"model_name\": \"llama\"}",
    "prompt":"item2item3{{input}}",
    "creation_time":"2022-12-20 02:19:36.519260",
    "job":{
        "job_id":"89",
        "project_id":"None",
        "user_id":"12",
        "config":"{\"type\": \"prompt_tune\", \"model_name\": \"llama\"}",
        "status":"COMPLETED",
        "name":"be894276039088c5f8db3f6bfaeb19953ed9ffe55f37a847a58f9fb320d307bc",
        "metric":"None",
        "history":"None",
        "start_time":"2022-12-20 02:19:36.369450",
        "end_time":"2022-12-20 02:19:35.837668"
    }
}

Predict

In the dictionary object returned from Powerml.fit, model_name is the name of your newly fit model. The PowerML class will immediately start using this model in predictions, so all you need to do now is to call predict:

response = powerml.predict("test")

Alternatively, you may use any model of a model you've trained before

response = powerml.predict("test", model="<MODEL_NAME>")

Currently the default model is openai's text-davinci-003.

PowerML Class

The PowerML class has member functions fit and predict.

predict accepts the following arguments:

def predict(self,
            prompt: str,
            model: str = "",
            stop: str = "",
            max_tokens: int = 128,
            temperature: int = 0,
            ) -> str:

fit accepts the following arguments:

def fit(self,
        data: list[str],
        model: str = ""):

PowerMLTopicModel Class

The PowerMLTopicModel class is an example class designed to extract topics from the prompt.

Usage

def get_examples():
    examples_path = os.path.join(os.path.dirname(__file__), "examples.json")
    with open(examples_path) as examples_file:
        examples = json.load(examples_file)
    return examples

def get_topics():
    return ["vscode","web","dashboard"]

config = {"powerml": {"key": "<POWERML-KEY>"}}
model = PowerMLTopicModel(get_topics(), config)
examples = get_examples()
model.fit(examples)
topics = model.predict("Move invite teammates page to its own base route . per designs:   This PR just moves existing views around and adds a new base route (i.e. no new functionality)")
print("topics:", topics)

Methods

__init__ is defined as follows:

def __init__(self, topics: list[str], config={}):

fit is defined as follows:

def fit(self, 
        examples: list[
            {"example": str, "labels": list[str]}
        ]):

where examples is a list of dictionaries with format {"example": str, "labels": list[str]}.

predict is defined as follows:

def predict(self, prompt: str):

PowerMLLearnTopics Class

The PowerMLLearnTopics class is an example class designed to generate topics from a list of documents.

Usage

def preprocess_data_into_messages(data):
    data = data.split('\n')
    return [d for d in data if d]

with open('data.txt', 'r') as f:
    data = f.read()
docs = preprocess_data_into_messages(data)
model = PowerMLLearnTopics()
model.add_data(docs)

topics = model.get_topics()
print(topics)

Methods

__init__ is defined as follows:

def __init__(self, config={}, num_subsamples=100, sample_size=50):

add_data is defined as follows:

def add_data(self, documents):

where documents is a list of strings.

get_topics is defined as follows:

def get_topics(self):

and returns a set of strings