# Integrations

### Guardrails

Bespoke MiniCheck is available as a Guardrails validator here: <https://hub.guardrailsai.com/validator/bespokelabs/bespoke_minicheck>

Example usage:

```python
# Import Guard and Validator
from guardrails.hub import BespokeMiniCheck
from guardrails import Guard

# Setup Guard
guard = Guard().use(
    BespokeMiniCheck,
    split_sentences=True,
    threshold=0.5,
    on_fail="fix"
)

# Validator passes
guard.validate("Alex likes cats.",
               metadata={"context": "Alex likes cats and dogs"})  
# Validator fails
guard.validate("Alex likes cats.",
               metadata={"context": "Alex likes dogs, but not cats."})  
```

### Ollama

Bespoke-MiniCheck-7B is available from Ollama [here](https://ollama.com/library/bespoke-minicheck).

More information can be found from their [blog post](https://ollama.com/blog/reduce-hallucinations-with-bespoke-minicheck).

Once you have Ollama, it is pretty straightforward to use the model. Note that Ollama doesn't yet support getting logits from the model, therefore we just output "yes" or "no".

As part of Ollama, there are two examples available:

1. [Fact checking](https://github.com/ollama/ollama/tree/main/examples/python-grounded-factuality-simple-check)
2. [RAG use case](https://github.com/ollama/ollama/tree/main/examples/python-grounded-factuality-rag-check)


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