Promptify

Installation

With pip

Requires Python 3.9+.

pip install promptify

or

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

For evaluation metrics support:

pip install promptify[eval]

Quick Tour

3-Line NER

from promptify import NER

ner = NER(model="gpt-4o-mini", domain="medical")
result = ner("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")

Output:

NERResult(entities=[
    Entity(text="93-year-old", label="AGE"),
    Entity(text="chronic right hip pain", label="CONDITION"),
    Entity(text="osteoporosis", label="CONDITION"),
    Entity(text="hypertension", label="CONDITION"),
    Entity(text="depression", label="CONDITION"),
    Entity(text="chronic atrial fibrillation", label="CONDITION"),
    Entity(text="severe nausea and vomiting", label="SYMPTOM"),
    Entity(text="urinary tract infection", label="CONDITION"),
])

Classification

from promptify import Classify

clf = Classify(model="gpt-4o-mini", labels=["positive", "negative", "neutral"])
result = clf("Amazing product! Best purchase I've ever made.")
# Classification(label="positive", confidence=0.95)

Question Answering

from promptify import QA

qa = QA(model="gpt-4o-mini")
answer = qa("Einstein was born in Ulm in 1879.", question="Where was Einstein born?")
# Answer(answer="Ulm", evidence="Einstein was born in Ulm", confidence=0.98)

Custom Task with Any Pydantic Schema

from promptify import Task
from pydantic import BaseModel

class MovieReview(BaseModel):
    sentiment: str
    rating: float
    key_themes: list[str]

task = Task(model="gpt-4o", output_schema=MovieReview, instruction="Analyze this movie review.")
review = task("Nolan's best work. Stunning visuals but the plot drags.")
# MovieReview(sentiment="mostly positive", rating=7.5, key_themes=["visuals", "pacing"])

Any Provider - Just Change the Model String

ner_openai = NER(model="gpt-4o-mini")
ner_claude = NER(model="claude-sonnet-4-20250514")
ner_local  = NER(model="ollama/llama3")

Batch Processing

results = ner.batch(["text1", "text2", "text3"], max_concurrent=10)

Async Support

result = await ner.acall("Patient has diabetes")

Built-in Evaluation

from promptify.eval import evaluate

scores = evaluate(task=ner, dataset=labeled_data, metrics=["precision", "recall", "f1"])
# {"precision": 0.92, "recall": 0.88, "f1": 0.90}

Features

• 2-3 lines of code for any NLP task -no training data required
• Pydantic structured outputs -type-safe results, not raw strings
• Any LLM provider via LiteLLM -OpenAI, Anthropic, Google, Ollama, Azure, and 100+ more
• Built-in tasks -NER, Classification (binary/multiclass/multilabel), QA, Summarization, Relation Extraction, SQL Generation, and more
• Custom tasks -bring your own Pydantic schema for any structured output
• Few-shot examples -easily add examples to improve accuracy
• Domain specialization -pass domain="medical" or any domain for context-aware prompts
• Batch processing -async concurrency under the hood for processing multiple texts
• Async support -native await support with acall()
• Evaluation framework -precision, recall, F1, accuracy, exact match, ROUGE metrics
• Safe parser -fallback JSON completion for providers without native structured outputs (no eval())
• Cost tracking -built-in token usage and cost monitoring via get_cost_summary()

Supported NLP Tasks

Task	Class	Output Schema
Named Entity Recognition	NER	NERResult (list of Entity)
Binary Classification	Classify	Classification
Multiclass Classification	Classify	Classification
Multilabel Classification	Classify(multi_label=True)	MultiLabelResult
Question Answering	QA	Answer
Summarization	Summarize	Summary
Relation Extraction	ExtractRelations	ExtractionResult
Tabular Extraction	ExtractTable	ExtractionResult
Question Generation	GenerateQuestions	list of GeneratedQuestion
SQL Generation	GenerateSQL	SQLQuery
Text Normalization	NormalizeText	normalized text
Topic Modelling	ExtractTopics	list of topics
Custom Task	Task	any Pydantic BaseModel

Community

@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