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docs: Update README with correct CLI flag (--guard-name instead of --name) and minor formatting fixes #1251

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Apr 21, 2025
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docs: Update README with correct CLI flag (--guard-name instead of --name) and minor formatting fixes #1251

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10 changes: 5 additions & 5 deletions README.md
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Expand Up @@ -56,7 +56,7 @@ pip install guardrails-ai
### Create Input and Output Guards for LLM Validation

1. Download and configure the Guardrails Hub CLI.

```bash
pip install guardrails-ai
guardrails configure
Expand Down Expand Up @@ -96,7 +96,7 @@ pip install guardrails-ai
```

Then, create a Guard from the installed guardrails.

```python
from guardrails import Guard, OnFailAction
from guardrails.hub import CompetitorCheck, ToxicLanguage
Expand Down Expand Up @@ -161,7 +161,7 @@ raw_output, validated_output, *rest = guard(
print(validated_output)
```

This prints:
This prints:
```
{
"pet_type": "dog",
Expand All @@ -175,7 +175,7 @@ Guardrails can be set up as a standalone service served by Flask with `guardrail

1. Install: `pip install "guardrails-ai"`
2. Configure: `guardrails configure`
3. Create a config: `guardrails create --validators=hub://guardrails/two_words --name=two-word-guard`
3. Create a config: `guardrails create --validators=hub://guardrails/two_words --guard-name=two-word-guard`
4. Start the dev server: `guardrails start --config=./config.py`
5. Interact with the dev server via the snippets below
```
Expand Down Expand Up @@ -204,7 +204,7 @@ completion = openai.chat.completions.create(
)
```

For production deployments, we recommend using Docker with Gunicorn as the WSGI server for improved performance and scalability.
For production deployments, we recommend using Docker with Gunicorn as the WSGI server for improved performance and scalability.

## FAQ

Expand Down