Metadata-Version: 2.4
Name: rai-audit-llm
Version: 0.1.7
Summary: LLM and RAG audits including safety, security, hallucination, and citation checks
Author: Sai Teja Erukude
License: MIT License
        
        Copyright (c) 2026 Sai Teja Erukude
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
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License-File: LICENSE
Keywords: hallucination,llm,prompt-injection,rag,responsible-ai
Classifier: Development Status :: 4 - Beta
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Requires-Python: >=3.10
Requires-Dist: pyyaml>=6.0
Requires-Dist: rai-audit-core>=0.1.0
Provides-Extra: anthropic
Requires-Dist: anthropic>=0.20; extra == 'anthropic'
Provides-Extra: openai
Requires-Dist: openai>=1.0; extra == 'openai'
Description-Content-Type: text/markdown

# rai-audit-llm

LLM and RAG audits for prompt injection, unsafe output, toxicity, faithfulness,
citations, and retrieval security.

## CLI

Audit captured responses from a YAML suite:

```bash
rai-audit llm run --suite packages/rai-audit-llm/examples/llm_audit_suite.yml --format html
```

Use `--audit-type rag` to run only RAG checks or `--audit-type rag-security` to
scan only retrieval security cases.

## Python API

```python
from rai_audit.llm import LLMAudit, load_test_suite

suite = load_test_suite("packages/rai-audit-llm/examples/llm_audit_suite.yml")
report = LLMAudit(suite, persist=False).run()
```

For live evaluation, pass `responder=lambda case: ...`. RAG faithfulness checks
require an LLM-as-judge verdict: provide `judge_result` in captured YAML or pass
`faithfulness_judge=lambda case, response: {"score": 0.9, "reasoning": "..."}`.

All findings include OWASP LLM Top 10 2025 references where applicable.
