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Examples

Real-world usage patterns and code examples for SteadyText.

Overview

This section demonstrates practical applications of SteadyText across different use cases:

All examples showcase SteadyText's core principle: same input → same output, every time.

Quick Examples

Basic Usage

import steadytext

# Deterministic text generation
code = steadytext.generate("implement binary search in Python")
assert "def binary_search" in code  # Always passes!

# Streaming generation
for token in steadytext.generate_iter("explain quantum computing"):
    print(token, end="", flush=True)

# Deterministic embeddings  
vec = steadytext.embed("Hello world")  # 1024-dim numpy array
print(f"Shape: {vec.shape}, Norm: {np.linalg.norm(vec):.6f}")

Testing Applications

def test_ai_code_generation():
    """Test that never flakes - same input, same output."""
    prompt = "write a function to reverse a string"
    result = my_ai_function(prompt)
    expected = steadytext.generate(prompt)
    assert result == expected  # Deterministic comparison!

def test_embedding_similarity():
    """Reliable similarity testing."""
    vec1 = steadytext.embed("machine learning")
    vec2 = steadytext.embed("artificial intelligence")
    similarity = np.dot(vec1, vec2)  # Already normalized
    assert similarity > 0.7  # Always passes with same threshold

CLI Tool Building

import click
import steadytext

@click.command()
@click.argument('topic')
def motivate(topic):
    """Generate motivational quotes about any topic."""
    prompt = f"Write an inspiring quote about {topic}"
    quote = steadytext.generate(prompt)
    click.echo(f"💪 {quote}")

# Usage: python script.py "programming"
# Always generates the same motivational quote for "programming"

Use Case Categories

🧪 Testing & Quality Assurance

Perfect for: - Unit tests with AI components - Integration testing with deterministic outputs - Regression testing for AI features - Mock AI services for development

🛠️ Developer Tools

Ideal for: - Code generation tools - Documentation generators
- CLI utilities with AI features - Build system integration

📊 Data & Content Generation

Great for: - Synthetic data generation - Content templates - Data augmentation for testing - Reproducible research datasets

🔍 Search & Similarity

Excellent for: - Semantic search systems - Document clustering - Content recommendation - Duplicate detection

Getting Started

  1. Browse examples - Check out Testing and CLI Tools
  2. Run the code - All examples are fully executable
  3. Adapt for your use case - Copy and modify patterns that fit your needs

Example Repository

All examples are available in the examples/ directory of the SteadyText repository:

git clone https://github.com/julep-ai/steadytext.git
cd steadytext/examples
python basic_usage.py
python testing_with_ai.py  
python cli_tools.py

Deterministic Outputs

Remember: all examples produce identical outputs every time you run them. This predictability is SteadyText's core feature and what makes it perfect for testing and tooling applications.