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

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 Prompter API.

from promptify import OpenAI
from promptify import Prompter

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
nlp_prompter = Prompter(model)


result       = nlp_prompter.fit('ner.jinja',
                          domain      = 'medical',
                          text_input  = sentence, 
                          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 :

Community

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

💁 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