README.md

Generative Stubs Examples

This directory contains examples demonstrating the @generative decorator for creating type-safe, composable LLM functions.

Files

generative_stubs.py

Basic introduction to generative stubs with sentiment classification and text summarization.

Key Features:

• Using @generative decorator for type-safe LLM functions
• Literal types for constrained outputs
• Docstrings as prompts for the LLM
• Simple function composition

generate_with_context.py

Shows how to use generative stubs with custom context and grounding information.

generative_gsm8k.py

Demonstrates using generative stubs for mathematical reasoning tasks (GSM8K dataset).

generative_stubs_with_requirements.py

Combines generative stubs with requirements for validated outputs.

investment_advice.py

A more complex example using generative stubs for financial analysis.

inter_module_composition/

Subdirectory with examples of composing multiple generative functions together.

Concepts Demonstrated

• Type Safety: Using Python type hints for structured outputs
• Docstring Prompts: Leveraging docstrings as instructions to the LLM
• Composition: Building complex workflows from simple generative functions
• Requirements: Adding validation to generative functions
• Context Management: Using grounding context with generative stubs

Basic Usage

from mellea import generative, start_session
from typing import Literal

@generative
def classify_sentiment(text: str) -> Literal["positive", "negative"]:
    """Classify the sentiment of the given text."""

@generative
def generate_summary(text: str) -> str:
    """Generate a concise summary under 20 words."""

with start_session() as m:
    sentiment = classify_sentiment(m, text="I love this!")
    summary = generate_summary(m, text="Long document...")

Related Documentation

• See mellea/stdlib/components/genstub.py for implementation
• See docs/dev/mellea_library.md for design philosophy