pytest-semantic 0.1.0

A pytest plugin for testing LLM outputs using semantic similarity matching

pytest-semantic

A pytest plugin for testing LLM outputs using semantic similarity matching instead of exact string comparison.

Installation

pip install pytest-semantic

Or with uv:

uv pip install pytest-semantic

Quick Start

def test_llm_greeting(semantic_matcher):
    """Test that LLM generates appropriate greetings."""
    llm_response = my_llm("Say hello")

    matcher = semantic_matcher(
        valid=["Hello!", "Hi there!", "Greetings!"],
    )

    assert llm_response == matcher

Features

• Semantic Matching: Compare text responses based on meaning, not exact strings
• Flexible Configuration: Configure thresholds globally or per-test
• Custom Embeddings: Use your own embedding models or functions
• Offline-First: Works locally with included sentence-transformers model
• Clear Error Messages: Detailed failure messages with similarity scores
• Easy Integration: Simple pytest fixture-based API

Usage

Basic Usage

Use the semantic_matcher fixture to create matchers with valid examples:

def test_llm_response(semantic_matcher):
    response = generate_llm_response("What is the capital of France?")

    matcher = semantic_matcher(
        valid=["Paris", "The capital is Paris", "Paris is the capital"],
    )

    assert response == matcher

With Invalid Examples

Provide invalid examples to strengthen the matching:

def test_sentiment_classification(semantic_matcher):
    result = classify_sentiment("I love this product!")

    matcher = semantic_matcher(
        valid=["positive", "good", "happy"],
        invalid=["negative", "bad", "sad", "neutral"],
    )

    assert result == matcher

If you don't provide invalid examples, random word combinations are automatically generated as a baseline.

Custom Thresholds

Adjust matching sensitivity per test:

def test_with_custom_threshold(semantic_matcher):
    matcher = semantic_matcher(
        valid=["Python programming"],
        threshold=0.2,        # Difference between valid/invalid similarity
        min_similarity=0.6,   # Minimum absolute similarity to valid examples
    )

    assert "Python coding" == matcher

Reusable Matchers

Create reusable matchers with fixtures:

import pytest

@pytest.fixture
def greeting_matcher(semantic_matcher):
    return semantic_matcher(
        valid=["Hello!", "Hi there!", "Hey!"],
    )

@pytest.fixture
def farewell_matcher(semantic_matcher):
    return semantic_matcher(
        valid=["Goodbye!", "See you!", "Bye!"],
    )

def test_conversation(greeting_matcher, farewell_matcher):
    assert llm.greet() == greeting_matcher
    assert llm.say_goodbye() == farewell_matcher

Custom Embedding Functions

Use your own embedding function:

def test_with_custom_embeddings(semantic_matcher):
    def my_embed_function(text: str) -> list:
        # Your custom embedding logic
        return openai.embeddings.create(input=text, model="text-embedding-3-small")

    matcher = semantic_matcher(
        valid=["Hello"],
        custom_embed_fn=my_embed_function,
    )

    assert "Hi" == matcher

Configuration

Configure default values in pytest.ini:

[pytest]
semantic_threshold = 0.15
semantic_min_similarity = 0.5
semantic_model = all-MiniLM-L6-v2

Or in pyproject.toml:

[tool.pytest.ini_options]
semantic_threshold = 0.15
semantic_min_similarity = 0.5
semantic_model = "all-MiniLM-L6-v2"

Configuration Options

• semantic_threshold (default: 0.15): Minimum difference between similarity to valid examples vs invalid examples
• semantic_min_similarity (default: 0.5): Minimum absolute similarity score to valid examples (0-1 range)
• semantic_model (default: "all-MiniLM-L6-v2"): Sentence-transformers model name

How It Works

1. Embeddings: Text is converted to vector embeddings using sentence-transformers
2. Similarity Calculation: Cosine similarity is computed between response and examples
3. Dual Criteria:
   • Response must be at least min_similarity similar to valid examples
   • Response must be at least threshold more similar to valid vs invalid examples

This dual-criteria approach prevents false positives while ensuring meaningful matches.

Error Messages

When a test fails, you get detailed information:

AssertionError: Semantic similarity check failed
  Response: "Bonjour"
  Similarity to valid examples: 0.342
  Similarity to invalid examples: 0.156
  Difference: 0.186 (threshold: 0.150)
  Failure reason: Response similarity (0.342) is below minimum threshold (0.500)
  Closest valid example: 'Hello!' (similarity: 0.389)
  Valid examples:
    - 'Hello!'
    - 'Hi there!'
    - 'Greetings!'

API Reference

semantic_matcher(valid, invalid=None, threshold=None, min_similarity=None, model_name=None, custom_embed_fn=None)

Creates a semantic matcher for comparing text responses.

Parameters:

• valid (List[str]): List of valid example responses (required)
• invalid (List[str], optional): List of invalid examples (random words if not provided)
• threshold (float, optional): Override default threshold (0-1 range)
• min_similarity (float, optional): Override default minimum similarity (0-1 range)
• model_name (str, optional): Override default sentence-transformers model
• custom_embed_fn (Callable, optional): Custom embedding function (str) -> List[float]

Returns: SemanticMatcher instance that can be used with == operator

SemanticMatcher.check(response: str) -> bool

Explicitly check if a response matches. Raises SemanticAssertionError on failure.

Examples

Testing LLM Text Generation

def test_story_generation(semantic_matcher):
    """Test that LLM generates creative stories."""
    story = llm.generate_story(prompt="A robot learning to paint")

    matcher = semantic_matcher(
        valid=[
            "A robot discovers art and creativity",
            "An AI learns to express itself through painting",
            "A mechanical being explores artistic expression",
        ],
        threshold=0.1,  # Allow more variation for creative content
    )

    assert story == matcher

Testing Classification

def test_intent_classification(semantic_matcher):
    """Test intent classification accuracy."""
    intent = classify_intent("I want to cancel my subscription")

    matcher = semantic_matcher(
        valid=["cancel", "cancellation", "unsubscribe"],
        invalid=["help", "question", "purchase", "upgrade"],
    )

    assert intent == matcher

Testing Summarization

def test_summarization(semantic_matcher):
    """Test that summaries capture key points."""
    long_text = "..." # Long article
    summary = llm.summarize(long_text)

    matcher = semantic_matcher(
        valid=[
            "Article discusses climate change impacts",
            "The text is about environmental challenges",
        ],
        min_similarity=0.4,  # Lower threshold for summaries
    )

    assert summary == matcher

Development

Setup

git clone https://github.com/tombedor/pytest-semantic.git
cd pytest-semantic
uv sync

Running Tests

uv run pytest tests/

Contributing

Contributions welcome! Please:

1. Fork the repository
2. Create a feature branch
3. Add tests for new functionality
4. Ensure all tests pass
5. Submit a pull request

License

MIT License - see LICENSE file for details.

Credits

Built with:

• sentence-transformers for embeddings
• pytest testing framework