HAPI-SDK 0.2.10

A plug-and-play SDK for de-hallucinating outputs from LLMs using semantic entropy and trained classifiers

HAPI SDK

A plug-and-play SDK for detecting and reducing hallucinations in Large Language Model outputs using semantic entropy analysis and trained classifiers.

Features

• Hallucination Detection: Uses trained classifiers to detect hallucinations in LLM outputs
• Semantic Entropy Analysis: Advanced semantic analysis to identify uncertain or inconsistent outputs
• Easy Integration: Simple API that works with any Hugging Face model
• Multiple Detection Methods: Combines classifier-based and semantic entropy-based approaches
• Real-time Analysis: Generate and analyze outputs in real-time

Installation

pip install HAPI-SDK

Quick Start

from dehallucinate_sdk import DeHallucinationClient

# Initialize the client
client = DeHallucinationClient(
    model_id="meta-llama/Llama-2-7b-chat-hf",
    license_key="your-license-key"
)

# Generate and analyze output
prompt = "Explain quantum entanglement in simple terms."
output, flagged_sentences = client.generate_output(prompt)

print("Generated Output:", output)
print("Flagged Sentences:", flagged_sentences)

Available Methods

The DeHallucinationClient exposes the following main methods:

1. generate_output(prompt, max_tokens=512)

Generates a response and performs hallucination analysis in one step.

output, flagged_sentences = client.generate_output(
    "What is the capital of France?", 
    max_tokens=100
)

2. semantic_entropy_check(prompt, num_generations=5)

Analyzes semantic entropy to detect potential hallucinations.

entropy_score, is_hallucinated = client.semantic_entropy_check(
    "Tell me about the history of artificial intelligence"
)

3. sentence_contains_hallucination(sentence, context="")

Checks if a specific sentence contains hallucinations.

is_hallucinated = client.sentence_contains_hallucination(
    "The capital of France is Berlin.",
    context="Geography facts"
)

4. generate(prompt, max_tokens=512)

Simple text generation without hallucination analysis.

output = client.generate("Write a story about space exploration", max_tokens=200)

5. generate_sentence(prompt)

Generates a single sentence response.

sentence = client.generate_sentence("Complete this: The best way to learn programming is")

Supported Models

• Llama-2-7b-chat (meta-llama/Llama-2-7b-chat-hf)
• Falcon-40b (tiiuae/falcon-40b)
• Llama-2-7b (meta-llama/Llama-2-7b-hf)
• MPT-7b (mosaicml/mpt-7b)
• More models coming soon!

Configuration

Environment Variables

export OPENAI_API_KEY="your_openai_api_key"
export HUGGINGFACE_API_KEY="your_hf_token"

Advanced Usage

# Configure with custom parameters
client = DeHallucinationClient(
    model_id="meta-llama/Llama-2-7b-chat-hf",
    license_key="your-license-key",
    device="cuda",  # or "cpu"
    temperature=0.7,
    use_semantic_entropy=True,
    confidence_threshold=0.8
)

# Batch processing
prompts = [
    "What causes climate change?",
    "How do vaccines work?", 
    "Explain machine learning basics"
]

results = []
for prompt in prompts:
    output, flagged = client.generate_output(prompt)
    results.append({"prompt": prompt, "output": output, "flagged": flagged})

Error Handling

try:
    output, flagged = client.generate_output("Your prompt here")
except Exception as e:
    print(f"Error during generation: {e}")

Contributing

We welcome contributions! Please see our GitHub repository for more information.

License

MIT License - see LICENSE file for details.

Support

For questions, suggestions, or issues, please contact us or open an issue on GitHub.