litmus-ai 0.2.1
A Python SDK for Hallucination Detection — powered by Litmus AI
License: MIT
Requires Python: >=3.8

# Litmus Hallucination Detector 

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Litmus Hallucination Detector is an SDK for detecting hallucinations in Large Language Model (LLM) outputs. It uses geometric algebra on embeddings to verify outputs against a provided context.

## Installation

```bash
pip install litmus-ai
```

## Quick Start

### 1. Context Grounding (Type I - Default)

Checks if claims are supported by the provided context.

```python
from litmus_ai import LitmusDetector

# Initialize grounding detector (default)
detector = LitmusDetector(type='context-grounding')

context = "The capital of France is Paris."
output = "Paris is the capital of France."

result = detector.check(context=context, output=output)
print(f"Outcome: {'Supported' if not result.is_hallucination else 'Hallucination'}")
print(f"Support Score: {result.score}")
```

**Output:**
```text
Outcome: Supported
Support Score: 1.0
```

### 2. Context Contradiction (Type II)

Checks if claims actively contradict the provided context.

```python
# Initialize contradiction detector
detector = LitmusDetector(type='context-contradiction')

context = ["Revenue increased 15%."]
output = ["Revenue decreased 15%."]

result = detector.check(context=context, output=output)
print(f"Is Hallucination: {result.is_hallucination}")
print(f"Contradiction Score: {result.score}")
```

**Output:**
```text
Is Hallucination: True
Contradiction Score: 0.9998
```

### 3. Relational Inversion (Type III)

Detects if the logical relationship between entities is flipped or if there's a direct contradiction/negation.

```python
# Initialize relational inversion detector
detector = LitmusDetector(type='relational-inversion')

context = ["Alice hired Bob."]
output = ["Bob hired Alice."] # Relational Inversion

result = detector.check(context=context, output=output)
print(f"Status: {result.details['claim_scores'][0]['type']}")
```

**Output:**
```text
Status: Relational Inversion
```

### 4. Instruction Drift (Type IV)

Detects when an answer drifts away from the query-context relationship, even if it uses similar keywords. Requires a `query` parameter.

```python
# Initialize instruction drift detector
detector = LitmusDetector(type='instruction-drift')

query = "Why did revenue decline?"
context = "Revenue declined due to supply chain issues."
output = "Revenue declined amid a challenging environment."

result = detector.check(query=query, context=context, output=output)
print(f"Drift Score: {result.score:.4f}")
print(f"Is Hallucination: {result.is_hallucination}")

# Override threshold at runtime (must be 0.0 to 1.0)
result = detector.check(query=query, context=context, output=output, threshold=0.5)
```

**Output:**
```text
Drift Score: 0.45
Is Hallucination: True
```

## Threshold Override

Every `detector.check()` call supports an optional `threshold` parameter to override the default for that call:

```python
# Uses default threshold
result = detector.check(context=context, output=output)

# Override threshold (0.0 to 1.0 inclusive)
result = detector.check(context=context, output=output, threshold=0.9)

# Invalid — raises ValueError
result = detector.check(context=context, output=output, threshold=1.5)
```

## Detection Types

| Type Value | Classification | Model | Default Threshold | Description |
|------------|---------------|-------|-------------------|-------------|
| `context-grounding` | Type I | `all-MiniLM-L6-v2` | 0.7 | Checks if claims are *supported* by evidence. |
| `context-contradiction` | Type II | `nli-deberta-v3-base` | 0.3 | Checks if claims *contradict* evidence. |
| `relational-inversion` | Type III | `nli-deberta-v3-base` | 0.5 | Checks for *entity inversions* or *contradictions*. |
| `instruction-drift` | Type IV | `all-MiniLM-L6-v2` | 0.3 | Checks if answers *drift* from query-context alignment. |

## How it works

Litmus uses mathematical projections to verify claims:
- **Type I** projects embeddings onto the subspace spanned by context facts.
- **Type II** projects cross-encoded pairs onto a contradiction discriminant.
- **Type III** compares projection magnitudes on specialized Inversion vs. Contradiction subspaces.
- **Type IV** compares bivector plane alignment between (Query ∧ Context) and (Query ∧ Answer).

## License

This project is licensed under the MIT License