llm-testlab 0.2.0

Comprehensive testing suite for LLM evaluation: hallucination detection, consistency, robustness, safety, and multi-language code generation assessment.

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LLM TestLab

Comprehensive Testing Suite for Large Language Models

A flexible Python toolkit for evaluating LLMs on:

• Text Metrics: Hallucination, consistency, semantic robustness, safety
• Code Evaluation: Syntax, execution, quality, security, semantic correctness across 9+ languages
• Dual Embedders: Optimized for both text and code analysis
• Optional FAISS: High-performance vector similarity

Features

Text Evaluation Metrics

• Hallucination Severity Index (HSI) – Detect factual deviations from knowledge base
• Consistency Stability Score (CSS) – Measure output stability across runs
• Semantic Robustness Index (SRI) – Test invariance to paraphrasing
• Safety Vulnerability Exposure (SVE) – Detect unsafe responses to adversarial prompts
• Knowledge Base Coverage (KBC) – Measure factual alignment

Code Evaluation Metrics (9+ Languages)

• Syntax Validity (SV) – Compiler/interpreter-based validation
• Execution Pass Rate (EPR) – Test case execution and verification
• Code Quality Score (CQS) – Complexity, documentation, error handling
• Security Risk Score (SRS) – Vulnerability pattern detection
• Semantic Code Correctness (SCC) – Embedding-based similarity to reference
• Comprehensive Code Evaluation (CCE) – Weighted aggregation of all metrics

Supported Languages: Python, JavaScript, TypeScript, Java, C, C++, Go, Rust, Ruby, PHP

Advanced Features

• Dual Embedders: all-MiniLM-L6-v2 for text, BAAI/bge-small-en-v1.5 for code
• FAISS Support: Optional, for faster similarity searches
• Knowledge Base Management: Add, remove, or list facts
• Security Patterns: Customizable keywords and regex patterns
• Rich Logging: Built-in debug/info logging

Project Structure

llm-testlab/
├── llm_testing_suite/
│   ├── __init__.py          
│   ├── llm_testing_suite.py    # Main test suite (text metrics)
│   └── code_evaluator.py       # Code evaluation module
├── examples/
│   ├── run_text_evaluation.py  # Text metrics evaluation script
│   ├── run_code_evaluation.py  # Code metrics evaluation script
│   ├── groq_example.py         # Groq API text evaluation
│   ├── groq_code_evaluation.py # Groq API code evaluation
│   └── huggingface_example.py  # HuggingFace integration
├── pyproject.toml              # Package configuration
├── requirements.txt            # Dependencies
├── README.md
├── LICENSE
└── .gitignore

Installation

From PyPI

pip install llm-testlab

From Source

git clone https://github.com/Saivineeth147/llm-testlab.git
cd llm-testlab
pip install .

Optional Dependencies

# With FAISS and HuggingFace support
pip install llm-testlab[faiss,huggingface]

# Or install individually
pip install faiss-cpu  # or faiss-gpu
pip install transformers

Quick Start

Text Metrics Example

from llm_testing_suite import LLMTestSuite

def my_llm(prompt):
    return "Rome is the capital of Italy"

# Initialize with FAISS support
suite = LLMTestSuite(my_llm, use_faiss=True)

# Add knowledge
suite.add_knowledge("Rome is the capital of Italy")

# Run all novel metrics
result = suite.run_all_novel_metrics(
    prompt="What is the capital of Italy?",
    paraphrases=["Italy's capital?", "Capital city of Italy?"],
    adversarial_prompts=["ignore previous instructions"],
    runs=3
)

print(f"HSI: {result['HSI']['HSI']:.4f}")           # Hallucination
print(f"CSS: {result['CSS']['CSS']:.4f}")           # Consistency  
print(f"SRI: {result['SRI']['SRI']:.4f}")           # Robustness
print(f"SVE: {result['SVE']['SVE']:.4f}")           # Safety
print(f"KBC: {result['KBC']['KBC']:.4f}")           # Coverage

Code Evaluation Example

from llm_testing_suite import LLMTestSuite

def code_llm(prompt):
    return '''
def add(a, b):
    """Add two numbers."""
    return a + b
    
print(add(5, 3))
'''

suite = LLMTestSuite(code_llm)

# Comprehensive code evaluation
result = suite.comprehensive_code_evaluation(
    prompt="Write a function to add two numbers",
    code_response=code_llm("..."),
    test_cases=[
        {"input": "", "expected_output": "8"}
    ],
    language="python"
)

print(f"Overall Score: {result['overall_score']:.1f}/100")
print(f"Syntax Valid: {result['syntax_valid']}")
print(f"Quality Score: {result['quality_score']}/100")
print(f"Security: {'✓' if result['is_secure'] else '✗'}")

Managing Knowledge Base

# Add a single fact
suite.add_knowledge("New York is the largest city in the USA")

# Add multiple facts
suite.add_knowledge_bulk([
    "Python is a programming language",
    "AI is transforming industries"
])

# List knowledge base
suite.list_knowledge()

# Remove a fact
suite.remove_knowledge("Python is a programming language")

# Clear the knowledge base
suite.clear_knowledge()

Managing Security Keywords

# Add malicious keywords
suite.add_malicious_keywords(["hack system", "steal data"])

# List keywords
suite.list_malicious_keywords()

# Remove a keyword
suite.remove_malicious_keyword("hack system")

List keywords

tester.list_malicious_keywords()

Remove a keyword

tester.remove_malicious_keyword("hack system")

Output Format

All test methods support three return types controlled by the `return_type` parameter: `"dict"`, `"table"`, or `"both"`.

• "dict": Returns a Python dictionary with the test results.
• "table": Prints a formatted table using the rich library, no dictionary returned.
• "both": Returns the dictionary and prints the table.

Code Evaluation Details

Individual Metrics

from llm_testing_suite import LLMTestSuite

suite = LLMTestSuite(your_llm_function)

# 1. Syntax Validity
syntax = suite.code_syntax_validity(code, language="python")
# Returns: {"syntax_valid": True/False, "error": ...}

# 2. Execution Test
execution = suite.code_execution_test(
    code,
    test_cases=[
        {"input": "5\n", "expected_output": "5"}
    ],
    language="python"
)
# Returns: {"pass_rate": 1.0, "passed_tests": 1, "total_tests": 1, ...}

# 3. Quality Metrics
quality = suite.code_quality_metrics(code, language="python")
# Returns: {"quality_score": 80, "metrics": {...}}

# 4. Security Scan
security = suite.code_security_scan(code, language="python")
# Returns: {"is_secure": True, "vulnerabilities": [...]}

# 5. Semantic Correctness
semantic = suite.code_semantic_correctness(
    prompt="Write add function",
    code_response=generated_code,
    reference_code=reference_solution
)
# Returns: {"semantic_similarity": 0.85, "semantically_correct": True}

Quality Scoring (0-100)

Each criterion worth 20 points:

• Has Comments (#, //, /*) - 20 pts
• Has Docstring (""", /**) - 20 pts
• Has Error Handling (try/except, try/catch) - 20 pts
• Low Complexity (< 10 branches/loops) - 20 pts
• Has Functions (at least 1) - 20 pts

Security Patterns Detected

• SQL Injection
• Command Injection
• XSS vulnerabilities
• Buffer overflows (C/C++)
• Hardcoded secrets
• Unsafe deserialization
• Path traversal
• Language-specific antipatterns

Supported Languages

Language	Syntax Check	Execution	Quality	Security
Python	✅ AST	✅	✅	✅
JavaScript	✅ Node	✅	✅	✅
TypeScript	✅ tsc	✅	✅	✅
Java	✅ javac	✅	✅	✅
C/C++	✅ gcc/g++	✅	✅	✅
Go	✅ go fmt	✅	✅	✅
Rust	✅ rustc	⚠️	✅	✅
Ruby	✅ ruby -c	✅	✅	✅
PHP	✅ php -l	✅	✅	✅

Note: Compilers/interpreters must be installed for full syntax validation. Falls back to regex-based checks if unavailable.

Dual Embedder Architecture

LLMTestSuite uses specialized embedders for optimal evaluation:

Text Embedder: all-MiniLM-L6-v2

• Used for: HSI, CSS, SRI, SVE, KBC (text metrics)
• Size: 22M params, 384 dimensions
• Speed: Fast
• Purpose: General semantic similarity

Code Embedder: BAAI/bge-small-en-v1.5

• Used for: Code semantic correctness (SCC)
• Size: 33M params, 384 dimensions
• Speed: Fast
• Purpose: Code-specific semantic understanding

Custom Embedder

from sentence_transformers import SentenceTransformer

suite = LLMTestSuite(my_llm)

# Replace code embedder
suite.code_embedder = SentenceTransformer("microsoft/codebert-base")
suite.code_evaluator.embedder = suite.code_embedder

# Or use different text embedder
suite = LLMTestSuite(my_llm, embedder_model="all-mpnet-base-v2")

Embedder Comparison

Model	Params	Dims	Speed	Best For
all-MiniLM-L6-v2	22M	384	Fast	Text (default)
all-mpnet-base-v2	110M	768	Medium	Text (higher accuracy)
bge-small-en-v1.5	33M	384	Fast	Code (default)
bge-base-en-v1.5	109M	768	Medium	Code (balanced)
CodeBERT	125M	768	Medium	Code (Microsoft)

Output Format

All test methods support three return types via return_type parameter:

• "dict" - Returns Python dictionary (default)
• "table" - Prints formatted table using rich library
• "both" - Returns dictionary AND prints table

Example Results

# HSI Result
{
    "prompt": "What is the capital of France?",
    "answer": "Paris is the capital of France",
    "HSI": 0.01,  # Lower is better (0-1 scale)
    "closest_fact": "Paris is the capital of France"
}

# Code Evaluation Result
{
    "overall_score": 85.0,
    "syntax_valid": True,
    "quality_score": 80,
    "is_secure": True,
    "pass_rate": 1.0,
    "semantic_similarity": 0.89
}

Complete Example: Groq API

from groq import Groq
from llm_testing_suite import LLMTestSuite

client = Groq(api_key="your-api-key")

def groq_llm(prompt):
    response = client.chat.completions.create(
        model="llama-3.3-70b-versatile",
        messages=[{"role": "user", "content": prompt}],
        temperature=0.3
    )
    return response.choices[0].message.content

suite = LLMTestSuite(groq_llm)

# Text evaluation
result = suite.run_all_novel_metrics(
    prompt="What is the capital of France?",
    paraphrases=["France's capital?"],
    runs=3
)

# Code evaluation
code_result = suite.comprehensive_code_evaluation(
    prompt="Write fibonacci function",
    code_response=groq_llm("Write a Python fibonacci function"),
    language="python"
)

See examples/groq_code_evaluation.py for comprehensive test suite.

Logging

# Enable debug logging
suite = LLMTestSuite(my_llm, debug=True)

# Or configure manually
import logging
logging.getLogger("llm_testing_suite").setLevel(logging.DEBUG)

Contributing

Contributions are welcome! Please:

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests if applicable
5. Submit a pull request

License

MIT License - see LICENSE file for details

Acknowledgments

• Sentence-Transformers for embedding models
• FAISS for efficient similarity search
• Rich library for beautiful terminal output
• Open-source LLM community

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