Starter Tutorial

You can also check out the full documentation here. Here is a starter example for using Promptify. Make sure you’ve followed the installation steps first.

Quick Tour

To immediately use a LLM model for your NLP task, we provide the Pipeline API.

from promptify import Prompter,OpenAI, Pipeline

sentence     =  """The patient is a 93-year-old female with a medical
                history of chronic right hip pain, osteoporosis,
                hypertension, depression, and chronic atrial
                fibrillation admitted for evaluation and management
                of severe nausea and vomiting and urinary tract
                infection"""

model        = OpenAI(api_key) # or `HubModel()` for Huggingface-based inference or 'Azure' etc
prompter     = Prompter('ner.jinja') # select a template or provide custom template
pipe         = Pipeline(prompter , model)


result = pipe.fit(sentence, domain="medical", labels=None)

Note

The labels parameter is optional. If you don’t provide it, the model will automatically infer the labels from the text input. If you do provide it, the model will use the labels you provide. The output should be similar to the following:

[
    {"E": "93-year-old", "T": "Age"},
    {"E": "chronic right hip pain", "T": "Medical Condition"},
    {"E": "osteoporosis", "T": "Medical Condition"},
    {"E": "hypertension", "T": "Medical Condition"},
    {"E": "depression", "T": "Medical Condition"},
    {"E": "chronic atrial fibrillation", "T": "Medical Condition"},
    {"E": "severe nausea and vomiting", "T": "Symptom"},
    {"E": "urinary tract infection", "T": "Medical Condition"},
    {"Branch": "Internal Medicine", "Group": "Geriatrics"},
]

Features

Perform NLP tasks (such as NER and classification) in just 2 lines of code, with no training data required
Easily add one shot, two shot, or few shot examples to the prompt
Output always provided as a Python object (e.g. list, dictionary) for easy parsing and filtering
Custom examples and samples can be easily added to the prompt
Run inference on any model stored on the Huggingface Hub (see notebook guide)
Optimized prompts to reduce OpenAI token costs (coming soon)

Community

If you are interested in prompt engineering, LLMs, ChatGPT, and other latest research discussions, please consider joining PromptsLab. You can also join their Discord community.

Contributing

We welcome any contributions to our open source project, including new features, improvements to infrastructure, and more comprehensive documentation. Please see the contributing guidelines..

License

This project is licensed under the terms of the Apache 2.0 license.