docs(guardrails): write how-to

docs/how-to/use_guardrails.md

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+# How-To: Use Guardrails
+
+Ragbits offers an expandable guardrails system. You can use one of the available guardrails or create your own to prevent toxic language, PII leaks etc.
+
+In this guide we will show you how to use guardrail based on OpenAI moderation and how to creat your own guardrail.
+
+
+## Using existing guardrail
+To use one of the existing guardrails you need to import it together with `GuardrailManager`. Next you simply pass a list of guardrails to the manager
+and call `verify()` function that will check the input (`str` or `Prompt`) against all provided guardrails asynchronously.
+
+```python
+import asyncio
+from ragbits.guardrails.base import GuardrailManager, GuardrailVerificationResult
+from ragbits.guardrails.openai_moderation import OpenAIModerationGuardrail
+
+
+async def verify_message(message: str) -> list[GuardrailVerificationResult]:
+    manager = GuardrailManager([OpenAIModerationGuardrail()])
+    return await manager.verify(message)
+
+
+if __name__ == '__main__':
+    print(asyncio.run(verify_message("Test message")))
+```
+
+The expected output is an object with the following properties:
+```python
+    guardrail_name: str
+    succeeded: bool
+    fail_reason: str | None
+```
+It allows you to see which guardrail was used, whether the check was successful and optionally a fail reason.
+
+## Implementing custom guardrail
+We need to create a new class that inherits from `Guardrail` and implements abstract method `verify`.
+
+```python
+from ragbits.core.prompt import Prompt
+from ragbits.guardrails.base import Guardrail, GuardrailVerificationResult
+
+class CustomGuardrail(Guardrail):
+
+    async def verify(self, input_to_verify: Prompt | str) -> GuardrailVerificationResult:
+        pass
+```
+
+With that you can pass your `CustomGuardrail` to the `GuardrailManager` as shown in [using existing guardrails section](#using-existing-guardrail).