Task-based NLP engine with Pydantic structured outputs, built-in evaluation, and LiteLLM as the universal LLM backend. Think "scikit-learn for LLM-powered NLP".
## Installation
### With pip
Requires Python 3.9+.
```bash
pip install promptify
```
or
```bash
pip install git+https://github.com/promptslab/Promptify.git
```
For evaluation metrics support:
```bash
pip install promptify[eval]
```
## Quick Tour
### 3-Line NER
```python
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:**
```python
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
```python
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
```python
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
```python
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
```python
ner_openai = NER(model="gpt-4o-mini")
ner_claude = NER(model="claude-sonnet-4-20250514")
ner_local = NER(model="ollama/llama3")
```
### Batch Processing
```python
results = ner.batch(["text1", "text2", "text3"], max_concurrent=10)
```
### Async Support
```python
result = await ner.acall("Patient has diabetes")
```
### Built-in Evaluation
```python
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
```
---
**Machine-readable endpoints**
- JSON: [`/api/graphcanon/tools/promptslab-promptify`](/api/graphcanon/tools/promptslab-promptify)
- LLM index: [/llms.txt](/llms.txt)
- Full corpus: [/llms-full.txt](/llms-full.txt)
_GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_