pyllmtest 1.0.1

The most comprehensive LLM testing framework for Python

PyLLMTest 🚀

The Most Comprehensive LLM Testing Framework for Python

PyLLMTest is a revolutionary testing framework designed specifically for LLM applications. It provides everything you need to build, test, and optimize AI-powered applications with confidence.

🌟 Why PyLLMTest?

Testing LLM applications is fundamentally different from traditional software testing. PyLLMTest solves the unique challenges of LLM testing:

• ✅ Semantic Assertions - Test meaning, not exact strings
• ✅ Snapshot Testing - Detect regressions with semantic awareness
• ✅ Multi-Provider Support - OpenAI, Anthropic, and more
• ✅ RAG Testing - Comprehensive retrieval and generation testing
• ✅ Cost Tracking - Monitor token usage and costs
• ✅ Prompt Optimization - A/B test and optimize prompts
• ✅ Performance Benchmarking - Track latency and quality
• ✅ Async Support - Full async/await compatibility
• ✅ Beautiful Reporting - Rich test reports and metrics

📦 Installation

# Basic installation
pip install pyllmtest

# With OpenAI support
pip install pyllmtest[openai]

# With Anthropic support
pip install pyllmtest[anthropic]

# With all providers and features
pip install pyllmtest[all]

🚀 Quick Start

Basic Test

from pyllmtest import LLMTest, expect, OpenAIProvider

provider = OpenAIProvider(model="gpt-4-turbo")

@LLMTest(provider=provider)
def test_summarization(ctx):
    response = ctx.complete("Summarize: AI is transforming industries...")
    
    # Semantic assertions
    expect(response.content).to_be_shorter_than(100, unit="words")
    expect(response.content).to_contain("AI")
    expect(response.content).to_preserve_facts(["transform", "industries"])

# Run the test
result = test_summarization()
print(f"Test {'PASSED' if result.passed else 'FAILED'}")

Snapshot Testing

from pyllmtest import SnapshotManager

snapshot_mgr = SnapshotManager()

@LLMTest(provider=provider)
def test_with_snapshot(ctx):
    response = ctx.complete("What are the primary colors?")
    
    # Automatically detects semantic changes
    snapshot_mgr.assert_matches_snapshot(
        name="primary_colors",
        actual_content=response.content
    )

Async Testing

@LLMTest(provider=provider)
async def test_parallel_completions(ctx):
    tasks = [
        ctx.acomplete("Explain Python"),
        ctx.acomplete("Explain JavaScript"),
        ctx.acomplete("Explain Rust")
    ]
    
    responses = await asyncio.gather(*tasks)
    
    for resp in responses:
        expect(resp.content).to_be_longer_than(50, unit="words")

📚 Core Features

1. Semantic Assertions

Unlike traditional assertions, PyLLMTest understands meaning:

# Traditional (brittle)
assert "artificial intelligence" in response  # Fails if AI says "AI"

# PyLLMTest (semantic)
expect(response).to_match_semantic("artificial intelligence", threshold=0.9)
expect(response).to_preserve_facts(["machine learning", "neural networks"])
expect(response).not_to_hallucinate(source_text=original_document)

Available Assertions:

• to_contain() / not_to_contain() - Check for substrings
• to_match_regex() - Regex matching
• to_be_shorter_than() / to_be_longer_than() - Length checks
• to_be_concise() / to_be_detailed() - Quality checks
• to_preserve_facts() - Fact preservation
• not_to_hallucinate() - Hallucination detection
• to_be_valid_json() / to_match_schema() - Format validation
• to_match_semantic() - Semantic similarity

2. Snapshot Testing

Save "golden" outputs and detect regressions:

snapshot_mgr = SnapshotManager(
    snapshot_dir=".snapshots",
    update_mode=False,  # Set to True to update snapshots
    semantic_threshold=0.9  # Allow 90% semantic similarity
)

# First run: saves snapshot
# Subsequent runs: compares with snapshot
snapshot_mgr.assert_matches_snapshot("test_name", actual_content)

Features:

• Semantic comparison - Not just exact matching
• Version tracking - Track snapshot history
• Diff generation - See what changed
• Update mode - Review and approve changes

3. Multi-Provider Support

Seamlessly switch between providers:

from pyllmtest import OpenAIProvider, AnthropicProvider

# OpenAI
openai_provider = OpenAIProvider(
    model="gpt-4-turbo",
    api_key="your-key"  # or use OPENAI_API_KEY env var
)

# Anthropic
anthropic_provider = AnthropicProvider(
    model="claude-3-5-sonnet-20241022",
    api_key="your-key"  # or use ANTHROPIC_API_KEY env var
)

# Use in tests
@LLMTest(provider=openai_provider)
def test_openai(ctx):
    ...

@LLMTest(provider=anthropic_provider)
def test_anthropic(ctx):
    ...

4. Metrics Tracking

Track everything:

from pyllmtest import MetricsTracker

metrics = MetricsTracker()

# Automatic tracking in tests
@LLMTest(provider=provider)
def test_with_metrics(ctx):
    response = ctx.complete("query")  # Automatically tracked

# Print comprehensive report
metrics.print_summary()

# Export to JSON/CSV
metrics.export_json("metrics.json")
metrics.export_csv("requests.csv")

Tracked Metrics:

• Total requests and tokens
• Prompt vs completion tokens
• Cost breakdown by model/provider
• Latency percentiles (p50, p95, p99)
• Per-model and per-provider stats

5. RAG Testing

Test retrieval-augmented generation:

from pyllmtest import RAGTester, RetrievedDocument

def my_retrieval_fn(query: str):
    # Your retrieval logic
    return [
        RetrievedDocument(
            content="Document content",
            score=0.95,
            metadata={"source": "doc.txt"}
        )
    ]

def my_generation_fn(query: str, docs: list):
    # Your generation logic
    return "Generated response"

rag_tester = RAGTester(
    retrieval_fn=my_retrieval_fn,
    generation_fn=my_generation_fn
)

result = rag_tester.test_query(
    query="What is AI?",
    expected_facts=["artificial", "intelligence"]
)

# Assertions
rag_tester.assert_retrieval_quality(result, min_docs=3, min_relevance=0.8)
rag_tester.assert_context_used(result)
rag_tester.assert_no_hallucination(result)
rag_tester.assert_performance(result, max_total_ms=1000)

6. Prompt Optimization

A/B test and optimize prompts:

from pyllmtest import PromptOptimizer, PromptVariant

optimizer = PromptOptimizer(provider=provider, quality_fn=my_quality_fn)

variants = [
    PromptVariant(
        id="detailed",
        template="Provide a detailed explanation of {topic}",
        description="Detailed prompt"
    ),
    PromptVariant(
        id="concise",
        template="Briefly explain {topic}",
        description="Concise prompt"
    )
]

test_inputs = [
    {"topic": "machine learning"},
    {"topic": "neural networks"}
]

# Compare prompts
results = optimizer.compare_prompts(variants, test_inputs)
optimizer.print_comparison(results)

# Find best prompt
best_id = optimizer.find_best_prompt(
    results,
    optimize_for="balanced",  # "quality", "cost", "latency", or "balanced"
    quality_threshold=0.8
)

print(f"Best prompt: {best_id}")

7. Test Suites

Organize tests into suites:

@LLMTest(provider=provider, suite="nlp_tests", name="test_sentiment")
def test_sentiment(ctx):
    ...

@LLMTest(provider=provider, suite="nlp_tests", name="test_translation")
def test_translation(ctx):
    ...

# Run all tests
test_sentiment()
test_translation()

# Get suite summary
suite = LLMTest.get_suite("nlp_tests")
summary = suite.get_summary()

print(f"Pass rate: {summary['pass_rate']:.1f}%")
print(f"Total cost: ${summary['total_cost_usd']:.4f}")

🎯 Advanced Features

Streaming Support

@LLMTest(provider=provider)
async def test_streaming(ctx):
    full_content = ""
    
    async for chunk in provider.stream("Explain quantum computing"):
        full_content += chunk.content
        
        if chunk.is_final:
            expect(full_content).to_be_detailed()

Custom Assertions

def is_valid_email(text: str) -> bool:
    import re
    pattern = r'^[\w\.-]+@[\w\.-]+\.\w+$'
    return bool(re.match(pattern, text))

expect(response.content).to_satisfy(
    is_valid_email,
    message="Response must be a valid email"
)

Semantic Deduplication

from pyllmtest.utils.semantic import semantic_deduplication

texts = [
    "Machine learning is a subset of AI",
    "ML is part of artificial intelligence",  # Similar to above
    "Deep learning uses neural networks"
]

unique_texts = semantic_deduplication(texts, provider, threshold=0.95)
# Returns: ["Machine learning is a subset of AI", "Deep learning uses neural networks"]

Semantic Clustering

from pyllmtest.utils.semantic import cluster_texts

texts = [
    "Python is great for AI",
    "JavaScript is used for web dev",
    "TensorFlow is an ML framework",
    "React is a web framework"
]

clusters = cluster_texts(texts, provider, num_clusters=2)
# Groups similar texts together

📊 Reporting

Console Reports

# Automatic beautiful console output
metrics.print_summary()

Output:

============================================================
METRICS SUMMARY
============================================================
Total Requests: 10
Total Tokens: 5,420
  Prompt Tokens: 2,100
  Completion Tokens: 3,320
Total Cost: $0.0542

Latency:
  Average: 1,234.56ms
  Min: 890.12ms
  Max: 2,100.45ms
  P50: 1,200.00ms
  P95: 1,800.00ms
  P99: 2,000.00ms
============================================================

Export Options

# JSON export
metrics.export_json("report.json")

# CSV export (detailed request log)
metrics.export_csv("requests.csv")

🔧 Configuration

Environment Variables

# OpenAI
export OPENAI_API_KEY=your-key

# Anthropic
export ANTHROPIC_API_KEY=your-key

Provider Configuration

provider = OpenAIProvider(
    model="gpt-4-turbo",
    timeout=60,
    max_retries=3,
    temperature=0.7
)

📖 Examples

Check out the examples/ directory for:

• comprehensive_example.py - All features demonstrated
• basic_testing.py - Simple getting started
• rag_testing.py - RAG system testing
• prompt_optimization.py - Prompt A/B testing
• async_testing.py - Async patterns

🤝 Contributing

We welcome contributions! Please see CONTRIBUTING.md for guidelines.

🙏 Acknowledgments

Built with ❤️ for the AI community.

Special thanks to:

• OpenAI for their amazing APIs
• Anthropic for Claude
• The Python testing community

Rahul Malik

• Email: [e-mail]
• GitHub: @RahulMK22
• LinkedIn:(https://www.linkedin.com/in/rahul-malik-b0791a1a7/)

📞 Support

• 📧 Email: [e-mail]
• 🐛 Issues: GitHub Issues

⭐ Star History

If you find PyLLMTest useful, please consider giving it a star on GitHub!

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📄 License

MIT License - see LICENSE file for details.

Copyright (c) 2024 Rahul Malik

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Made with 🚀 by Rahul Malik

Making LLM testing as easy as it should be.