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<div align="center"> <img width="110px" src="https://raw.githubusercontent.com/promptslab/Promptify/main/assets/logo.png"> <h1>Promptify</h1></div> <!-- <h2 align="center">Promptify</h2> --> <p align="center"> <p align="center">Prompt Engineering, Solve NLP Problems with LLM's & Easily generate different NLP Task prompts for popular generative models like GPT, PaLM, and more with Promptify </p> </p> <h4 align="center"> <a href="https://github.com/promptslab/Promptify/blob/main/LICENSE"> <img src="https://img.shields.io/badge/License-Apache_2.0-blue.svg" alt="Promptify is released under the Apache 2.0 license." /> </a> <a href="https://pypi.org/project/promptify/"> <img src="https://badge.fury.io/py/Promptify.svg" alt="PyPI version" /> </a> <a href="http://makeapullrequest.com"> <img src="https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square" alt="http://makeapullrequest.com" /> </a> <a href="https://discord.gg/m88xfYMbK6"> <img src="https://img.shields.io/badge/Discord-Community-orange" alt="Community" /> </a> <a href="#"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="colab" /> </a> </h4> <img width="910px" src="https://raw.githubusercontent.com/promptslab/Promptify/main/assets/dark.png">

Installation

With pip

This repository is tested on Python 3.7+, openai 0.25+.

You should install Promptify using Pip command

pip3 install promptify

or

pip3 install git+https://github.com/promptslab/Promptify.git

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)


### Output

[
    {"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"},
]
<p float="left"> <img src="https://raw.githubusercontent.com/promptslab/Promptify/main/assets/ner.png" width="250" /> <img src="https://raw.githubusercontent.com/promptslab/Promptify/main/assets/multilabel.png" width="250" /> <img src="https://raw.githubusercontent.com/promptslab/Promptify/main/assets/qa_gen.png" width="250" /> </p> <h4 align="center">GPT-3 Example with NER, MultiLabel, Question Generation Task</h3> <h2>Features 🎮 </h2> <ul> <li> Perform NLP tasks (such as NER and classification) in just 2 lines of code, with no training data required</li> <li> Easily add one shot, two shot, or few shot examples to the prompt</li> <li> Handling out-of-bounds prediction from LLMS (GPT, t5, etc.)</li> <li> Output always provided as a Python object (e.g. list, dictionary) for easy parsing and filtering. This is a major advantage over LLMs generated output, whose unstructured and raw output makes it difficult to use in business or other applications.</li> <li> Custom examples and samples can be easily added to the prompt</li> <li> 🤗 Run inference on any model stored on the Huggingface Hub (see <a href="https://github.com/promptslab/Promptify/blob/main/notebooks/huggingface.ipynb">notebook guide</a>).</li> <li> Optimized prompts to reduce OpenAI token costs (coming soon)</li> </ul>

Supporting wide-range of Prompt-Based NLP tasks :

Task NameColab NotebookStatus
Named Entity RecognitionNER Examples with GPT-3✅
Multi-Label Text ClassificationClassification Examples with GPT-3✅
Multi-Class Text ClassificationClassification Examples with GPT-3✅
Binary Text ClassificationClassification Examples with GPT-3✅
Question-AnsweringQA Task Examples with GPT-3✅
Question-Answer GenerationQA Task Examples with GPT-3✅
Relation-ExtractionRelation-Extraction Examples with GPT-3✅
SummarizationSummarization Task Examples with GPT-3✅
ExplanationExplanation Task Examples with GPT-3✅
SQL WriterSQL Writer Example with GPT-3✅
Tabular Data
Image Data
More Prompts

Docs

Promptify Docs

Community

<div align="center"> If you are interested in Prompt-Engineering, LLMs, ChatGPT and other latest research discussions, please consider joining <a href="https://discord.gg/m88xfYMbK6">PromptsLab</a></div> <div align="center"> <img alt="Join us on Discord" src="https://img.shields.io/discord/1069129502472556587?color=5865F2&logo=discord&logoColor=white"> </div> 

@misc{Promptify2022,
  title = {Promptify: Structured Output from LLMs},
  author = {Pal, Ankit},
  year = {2022},
  howpublished = {\url{https://github.com/promptslab/Promptify}},
  note = {Prompt-Engineering components for NLP tasks in Python}
}

💁 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