school-of-prompt 0.4.0

🎸 Rock your prompts! Enterprise-grade prompt optimization with statistical rigor and production features

School of Prompt

Prompt optimization framework with smart defaults and enterprise features.

Quick Start

from school_of_prompt import optimize

results = optimize(
    data="reviews.csv",
    task="classify sentiment", 
    prompts=["Analyze sentiment: {text}", "Is this positive or negative: {text}"],
    api_key="sk-..."
)

print(f"Best prompt: {results['best_prompt']}")
print(f"Accuracy: {results['best_score']:.2f}")

Installation

pip install school-of-prompt

Usage Patterns

Level 0: Simple API

results = optimize(
    data="data.csv",
    task="classify sentiment",
    prompts=["Prompt 1", "Prompt 2"],
    api_key="sk-..."
)

Level 1: Configuration-Driven

# YAML configuration
results = optimize(config="config.yaml")

# Enhanced API
results = optimize(
    data="data.csv",
    task="regression",
    prompts=["prompt1.txt", "prompt2.txt"],
    model={"name": "gpt-4", "temperature": 0.1},
    metrics=["mae", "within_1", "within_2"],
    sampling_strategy="stratified",
    cross_validation=True,
    cache_enabled=True,
    api_key="sk-..."
)

Level 2: Enterprise Features

from school_of_prompt import optimize
from school_of_prompt.data.registry import get_data_registry

# Multi-dataset workflow
results = optimize(
    data={
        "train": "train.csv",
        "validation": "val.csv", 
        "test": "test.csv"
    },
    task="classification",
    prompts=["template1", "template2"],
    metrics=["accuracy", "f1_score", "valid_rate"],
    enrichers=["text_length", "readability"],
    preprocessors=["clean_text", "normalize"],
    cross_validation=True,
    comprehensive_analysis=True,
    parallel_evaluation=True,
    api_key="sk-..."
)

Core Features

Advanced Metrics

• Tolerance-based: within_1, within_2, within_3
• Domain-specific: valid_rate, token_efficiency, response_quality
• Statistical: r2_score, prediction_confidence, error_std

Production Features

• Intelligent caching: Configurable expiry and size management
• Batch processing: Parallel evaluation with progress tracking
• Multi-dataset workflows: Train/validation/test dataset support
• Cross-validation: K-fold cross-validation support
• Error handling: Retry logic and graceful degradation

Data Loading

• File formats: CSV, JSONL, pandas DataFrames
• Multi-dataset: {"train": "train.csv", "test": "test.csv"}
• Custom sources: Extensible data source registry
• Preprocessing: Text cleaning, normalization, enrichment

Configuration

YAML configuration example:

task:
  name: "classification_task"
  type: "classification"

datasets:
  training: "data/train.csv"
  validation: "data/val.csv"
  test: "data/test.csv"

evaluation:
  metrics: ["accuracy", "f1_score", "within_1"]
  sampling_strategy: "stratified"
  cross_validation: true
  k_fold: 5

cache:
  enabled: true
  expiry: "24h"

batch_processing:
  parallel_evaluation: true
  chunk_size: 100

Examples

Classification

results = optimize(
    data="reviews.csv",
    task="classify sentiment",
    prompts=[
        "Sentiment: {review}",
        "Classify sentiment of: {review}",
        "Is this positive or negative: {review}"
    ],
    model="gpt-3.5-turbo",
    metrics=["accuracy", "f1_score"]
)

Regression

results = optimize(
    data="ratings.csv",
    task="rate from 1-10",
    prompts=[
        "Rate this from 1-10: {content}",
        "Score (1-10): {content}",
        "Rating for {content}:"
    ],
    metrics=["mae", "within_1", "within_2"]
)

Content Moderation

results = optimize(
    data="content.csv",
    task="classify as safe or unsafe",
    prompts="templates/safety_prompts.txt",
    model={"name": "gpt-4", "temperature": 0.0},
    metrics=["accuracy", "precision", "recall"]
)

API Reference

optimize()

Main optimization function.

Parameters:

• data (str|DataFrame|dict): Dataset or file path
• task (str): Task description
• prompts (list): Prompt variants to evaluate
• model (str|dict): Model configuration
• metrics (list): Evaluation metrics
• api_key (str): OpenAI API key
• config (str): Path to YAML configuration file
• sample_size (int): Limit evaluation samples
• cross_validation (bool): Enable k-fold cross-validation
• cache_enabled (bool): Enable response caching
• comprehensive_analysis (bool): Enable detailed analysis

Returns:

{
    "best_prompt": str,
    "best_score": float,
    "prompts": dict,
    "summary": dict,
    "comprehensive_analysis": dict  # if enabled
}

Environment Setup

export OPENAI_API_KEY="sk-your-key-here"

Data Format

Expected data structure:

• Input columns: Text or features to process
• Label column: Ground truth (label, target, class, etc.)

CSV example:

text,label
"Great product",positive
"Poor quality",negative
"Average item",neutral

JSONL example:

{"text": "Great product", "label": "positive"}
{"text": "Poor quality", "label": "negative"}

Extension Points

from school_of_prompt import CustomMetric, CustomDataSource

# Custom metrics
class CustomAccuracy(CustomMetric):
    name = "custom_accuracy"
    def calculate(self, predictions, actuals):
        return custom_accuracy_logic(predictions, actuals)

# Custom data sources
class APIDataSource(CustomDataSource):
    def load(self):
        return fetch_from_api()

# Usage
results = optimize(
    data=APIDataSource(),
    metrics=[CustomAccuracy(), "f1_score"],
    task="classification",
    prompts=["template1", "template2"]
)

Version 0.3.0 Features

• Advanced metrics with tolerance and statistical analysis
• YAML configuration system
• Production caching and batch processing
• Multi-dataset workflows
• Cross-validation support
• Comprehensive error analysis
• Data enrichment pipeline

Contributing

See CONTRIBUTING.md for development setup and guidelines.

License

MIT License