chore(governance): ruff and ty checks passed

src/va_agent/governance.py

@@ -1,8 +1,9 @@
+# ruff: noqa: E501
 """GovernancePlugin: Guardrails for VAia, the virtual assistant for VA."""
 import json
 import logging
 import re
-from typing import Literal, Optional
+from typing import Literal
 
 from google.adk.agents.callback_context import CallbackContext
 from google.adk.models import LlmRequest, LlmResponse
@@ -35,11 +36,11 @@ class GuardrailOutput(BaseModel):
         ...,
         description="Decision for the user prompt",
     )
-    reasoning: Optional[str] = Field(
+    reasoning: str | None = Field(
         default=None,
         description="Optional reasoning for the decision"
     )
-    blocking_response: Optional[str] = Field(
+    blocking_response: str | None = Field(
         default=None,
         description="Optional custom blocking response to return to the user if unsafe"
     )
@@ -50,7 +51,6 @@ class GovernancePlugin:
 
     def __init__(self) -> None:
         """Initialize guardrail model (structured output), prompt and emojis patterns."""
-
         self.guardrail_llm = Client(
             vertexai=True,
             project=settings.google_cloud_project,
@@ -94,24 +94,23 @@ Devuelve un JSON con la siguiente estructura:
 
         self._combined_pattern = self._get_combined_pattern()
 
-    def _get_combined_pattern(self):
+    def _get_combined_pattern(self) -> re.Pattern:
         person_pattern = r"(?:🧑|👩|👨)"
         tone_pattern = r"[\U0001F3FB-\U0001F3FF]?"
 
-        # Unique pattern that combines all forbidden emojis, including complex ones with skin tones
-        combined_pattern = re.compile(
-            rf"{person_pattern}{tone_pattern}\u200d❤️?\u200d💋\u200d{person_pattern}{tone_pattern}"  # kiss
+        # Unique pattern that combines all forbidden emojis, including skin tones and compound emojis
+        return re.compile(
+            rf"{person_pattern}{tone_pattern}\u200d❤️?\u200d💋\u200d{person_pattern}{tone_pattern}" # kissers
             rf"|{person_pattern}{tone_pattern}\u200d❤️?\u200d{person_pattern}{tone_pattern}"  # lovers
             rf"|{'|'.join(map(re.escape, sorted(FORBIDDEN_EMOJIS, key=len, reverse=True)))}"  # simple emojis
             rf"|🖕{tone_pattern}"  # middle finger with all skin tone variations
         )
-        return combined_pattern
-    
+
     def _remove_emojis(self, text: str) -> tuple[str, list[str]]:
         removed = self._combined_pattern.findall(text)
         text = self._combined_pattern.sub("", text)
         return text.strip(), removed
-    
+
     def before_model_callback(
         self,
         callback_context: CallbackContext | None = None,
@@ -124,6 +123,10 @@ Devuelve un JSON con la siguiente estructura:
         if callback_context is None:
             error_msg = "callback_context is required"
             raise ValueError(error_msg)
+        
+        if llm_request is None:
+            error_msg = "llm_request is required"
+            raise ValueError(error_msg)
 
         try:
             resp = self.guardrail_llm.models.generate_content(
@@ -134,7 +137,10 @@ Devuelve un JSON con la siguiente estructura:
             data = json.loads(resp.text or "{}")
             decision = data.get("decision", "safe").lower()
             reasoning = data.get("reasoning", "")
-            blocking_response = data.get("blocking_response", "Lo siento, no puedo ayudarte con esa solicitud 😅")
+            blocking_response = data.get(
+                "blocking_response",
+                "Lo siento, no puedo ayudarte con esa solicitud 😅"
+            )
 
             if decision == "unsafe":
                 callback_context.state["guardrail_blocked"] = True