semvec/pypi-packages huggingface dataset:
llmverify 0.0.1
LLM Hallucination & Drift Detection — Verify LLM outputs for accuracy, consistency, and reliability
License: MIT
Requires Python: >=3.8
Keywords: llm, llmverify, llm verify, hallucination, drift detection, ai validation, gpt check, chatgpt, claude, llm accuracy, llm reliability, llm testing, llm monitoring, model drift, llmops, ai governance

# LLMCheck

> **LLM Hallucination & Drift Detection** — Coming Soon

A Python toolkit to verify LLM outputs for hallucinations, factual accuracy, and model drift over time.

---

## What This Package Is

**LLMCheck** is an upcoming utility package designed to help developers:

- **Detect hallucinations** in LLM-generated content
- **Verify factual accuracy** against source documents
- **Monitor model drift** across deployments and versions
- **Score output reliability** for production systems
- **Alert on consistency degradation** in LLM pipelines

This package is being developed by [Haiec](https://haiec.com) as part of a broader AI governance infrastructure.

---

## Why This Namespace Exists

The `llmverify` namespace is reserved to provide developers with essential LLM quality assurance tools. As LLMs become critical infrastructure, verifying their outputs is non-negotiable.

This package will provide:

- Hallucination scoring algorithms
- Source-grounded verification
- Temporal drift analysis
- Confidence calibration utilities
- Integration with popular LLM frameworks (LangChain, LlamaIndex)
- Real-time monitoring hooks

---

## Installation

```bash
pip install llmcheck
```

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## Placeholder Example

```python
import llmcheck

# Check package status
print(llmcheck.__version__)  # '0.0.1'
print(llmcheck.__status__)   # 'placeholder'

# Detect hallucination (placeholder)
result = llmcheck.detect_hallucination(
    output="LLM generated this output",
    context="Original source context"
)
print(result["message"])

# Detect drift (placeholder)
drift_result = llmcheck.detect_drift([
    "output from day 1",
    "output from day 2",
    "output from day 3"
])
print(drift_result["message"])
```

---

## Roadmap

- [ ] Hallucination detection engine
- [ ] Source-grounded verification
- [ ] Semantic drift scoring
- [ ] Confidence calibration
- [ ] LangChain integration
- [ ] LlamaIndex integration
- [ ] Real-time monitoring API
- [ ] Alerting webhooks
- [ ] Dashboard visualization hooks

---

## License

MIT © 2025 Haiec

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## Contact

For early access or partnership inquiries, reach out to the Haiec team.