Merge pull request #705 from guardrails-ai/shreya/docs-for-onfail-enum

Update docs to use OnFailAction enum

README.md

@@ -64,11 +64,11 @@ pip install guardrails-ai
 3. Create a Guard from the installed guardrail.
 
     ```python
-    from guardrails import Guard
+    from guardrails import Guard, OnFailAction
     from guardrails.hub import RegexMatch
 
     guard = Guard().use(
-        RegexMatch, regex="\(?\d{3}\)?-? *\d{3}-? *-?\d{4}", on_fail="exception"
+        RegexMatch, regex="\(?\d{3}\)?-? *\d{3}-? *-?\d{4}", on_fail=OnFailAction.EXCEPTION
     )
 
     guard.validate("123-456-7890")  # Guardrail passes
@@ -93,12 +93,12 @@ pip install guardrails-ai
     Then, create a Guard from the installed guardrails.
 
     ```python
-    from guardrails import Guard
+    from guardrails import Guard, OnFailAction
     from guardrails.hub import CompetitorCheck, ToxicLanguage
 
     guard = Guard().use_many(
-        CompetitorCheck(["Apple", "Microsoft", "Google"], on_fail="exception"),
-        ToxicLanguage(threshold=0.5, validation_method="sentence", on_fail="exception"),
+        CompetitorCheck(["Apple", "Microsoft", "Google"], on_fail=OnFailAction.EXCEPTION),
+        ToxicLanguage(threshold=0.5, validation_method="sentence", on_fail=OnFailAction.EXCEPTION),),
     )
 
     guard.validate(

docs/how_to_guides/telemetry.mdx

@@ -49,13 +49,13 @@ Then, set up your Guard the default tracer provided in the guardrails library. Y
 
 <h5 a><strong><code>main.py</code></strong></h5>
 ```python
-from guardrails import Guard
+from guardrails import Guard, OnFailAction
 from guardrails.utils.telemetry_utils import default_otlp_tracer
 from guardrails.hub import ValidLength
 import openai
 
 guard = Guard.from_string(
-  validators=[ValidLength(min=1, max=10, on_fail="exception")],
+  validators=[ValidLength(min=1, max=10, on_fail=OnFailAction.EXCEPTION)],
   # use a descriptive name that will differentiate where your metrics are stored
   tracer=default_otlp_tracer("petname_guard")
 )
@@ -112,13 +112,13 @@ For advanced use cases (like if you have a metrics provider in a VPC), you can u
 Standard [open telemetry environment variables](https://opentelemetry-python.readthedocs.io/en/stable/getting-started.html#configure-the-exporter) are used to configure the collector. Use the default_otel_collector_tracer when configuring your guard.
 
 ```python
-from guardrails import Guard
+from guardrails import Guard, OnFailAction
 from guardrails.utils.telemetry_utils import default_otel_collector_tracer
 from guardrails.hub import ValidLength
 import openai
 
 guard = Guard.from_string(
-  validators=[ValidLength(min=1, max=10, on_fail="exception")],
+  validators=[ValidLength(min=1, max=10, on_fail=OnFailAction.EXCEPTION)],
   # use a descriptive name that will differentiate where your metrics are stored
   tracer=default_otel_collector_tracer("petname_guard")
 )

docs/hub/concepts/on_fail_policies.md

@@ -1,14 +1,14 @@
 
-# `on_fail` Policies for Validators
+# `OnFailActions` for Validators
 
-While initializing a validator, you can specify `on_fail` policies to handle the failure of the validator. The `on_fail` policy specifies the corrective action that should be taken if the quality criteria is not met. The corrective action can be one of the following:
+Guardrails provides a number of `OnFailActions` for when a validator fails. The `OnFailAction` specifies the corrective action that should be taken if the quality criteria is not met. The corrective action can be one of the following:
 
 | Action    | Behavior                                                                                                                                                                                               |
 |-----------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| `reask`   | Reask the LLM to generate an output that meets the correctness criteria specified in the validator.  The prompt used for reasking contains information about which quality criteria failed, which is auto-generated by the validator. |
-| `fix`     | Programmatically fix the generated output to meet the correctness criteria when possible. E.g. the formatter `provenance_llm` validator will remove any sentences that are estimated to be hallucinated.             |
-| `filter`  | (Only applicable for structured data validation) Filter the incorrect value. This only filters the field that fails, and will return the rest of the generated output.                                                                                  |
-| `refrain` | Refrain from returning an output. This is useful when the generated output is not safe to return, in which case a `None` value is returned instead.                                                          |
-| `noop`    | Do nothing. The failure will still be recorded in the logs, but no corrective action will be taken.                                                                                                    |
-| `exception`  | Raise an exception when validation fails.     |
-| `fix_reask` | First, fix the generated output deterministically, and then rerun validation with the deterministically fixed output. If validation fails, then perform reasking.           |
+| `OnFailAction.REASK`   | Reask the LLM to generate an output that meets the correctness criteria specified in the validator.  The prompt used for reasking contains information about which quality criteria failed, which is auto-generated by the validator. |
+| `OnFailAction.FIX`     | Programmatically fix the generated output to meet the correctness criteria when possible. E.g. the formatter `provenance_llm` validator will remove any sentences that are estimated to be hallucinated.             |
+| `OnFailAction.FILTER`  | (Only applicable for structured data validation) Filter the incorrect value. This only filters the field that fails, and will return the rest of the generated output.                                                                                  |
+| `OnFailAction.REFRAIN` | Refrain from returning an output. This is useful when the generated output is not safe to return, in which case a `None` value is returned instead.                                                          |
+| `OnFailAction.NOOP`    | Do nothing. The failure will still be recorded in the logs, but no corrective action will be taken.                                                                                                    |
+| `OnFailAction.EXCEPTION`  | Raise an exception when validation fails.     |
+| `OnFailAction.FIX_REASK` | First, fix the generated output deterministically, and then rerun validation with the deterministically fixed output. If validation fails, then perform reasking.           |

guardrails/__init__.py

@@ -6,13 +6,14 @@
 from guardrails.prompt import Instructions, Prompt
 from guardrails.rail import Rail
 from guardrails.utils import constants, docs_utils
-from guardrails.validator_base import Validator, register_validator
+from guardrails.validator_base import OnFailAction, Validator, register_validator
 
 __all__ = [
     "Guard",
     "PromptCallableBase",
     "Rail",
     "Validator",
+    "OnFailAction",
     "register_validator",
     "constants",
     "docs_utils",