Promptify

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

Installation

With pip

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

You should install Promptify using Pip command

pip3 install promptify

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"},
]

GPT-3 Example with NER, MultiLabel, Question Generation Task

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
Handling out-of-bounds prediction from LLMS (GPT, t5, etc.)
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.
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)

Supporting wide-range of Prompt-Based NLP tasks :

Task Name	Colab Notebook	Status
Named Entity Recognition	NER Examples with GPT-3	✅
Multi-Label Text Classification	Classification Examples with GPT-3	✅
Multi-Class Text Classification	Classification Examples with GPT-3	✅
Binary Text Classification	Classification Examples with GPT-3	✅
Question-Answering	QA Task Examples with GPT-3	✅
Question-Answer Generation	QA Task Examples with GPT-3	✅
Relation-Extraction	Relation-Extraction Examples with GPT-3	✅
Summarization	Summarization Task Examples with GPT-3	✅
Explanation	Explanation Task Examples with GPT-3	✅
SQL Writer	SQL Writer Example with GPT-3	✅
Tabular Data
Image Data
More Prompts

Docs

Promptify Docs

Community

If you are interested in Prompt-Engineering, LLMs, ChatGPT and other latest research discussions, please consider joining PromptsLab


@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