feat: LLM - Exposed the safety attributes

PiperOrigin-RevId: 541035595

tests/unit/aiplatform/test_language_models.py

@@ -570,6 +570,10 @@ def test_text_generation(self):
             )
 
         assert response.text == _TEST_TEXT_GENERATION_PREDICTION["content"]
+        assert (
+            response.safety_attributes["Violent"]
+            == _TEST_TEXT_GENERATION_PREDICTION["safetyAttributes"]["scores"][0]
+        )
 
     def test_text_generation_ga(self):
         """Tests the text generation model."""

vertexai/language_models/_language_models.py

@@ -15,7 +15,7 @@
 """Classes for working with language models."""
 
 import dataclasses
-from typing import Any, List, Optional, Sequence, Union
+from typing import Any, Dict, List, Optional, Sequence, Union
 
 from google.cloud import aiplatform
 from google.cloud.aiplatform import base
@@ -198,10 +198,19 @@ def tune_model(
 
 @dataclasses.dataclass
 class TextGenerationResponse:
-    """TextGenerationResponse represents a response of a language model."""
+    """TextGenerationResponse represents a response of a language model.
+    Attributes:
+        text: The generated text
+        is_blocked: Whether the the request was blocked.
+        safety_attributes: Scores for safety attributes.
+            Learn more about the safety attributes here:
+            https://cloud.google.com/vertex-ai/docs/generative-ai/learn/responsible-ai#safety_attribute_descriptions
+    """
 
     text: str
     _prediction_response: Any
+    is_blocked: bool = False
+    safety_attributes: Dict[str, float] = dataclasses.field(default_factory=dict)
 
     def __repr__(self):
         return self.text
@@ -289,13 +298,23 @@ def _batch_predict(
             parameters=prediction_parameters,
         )
 
-        return [
-            TextGenerationResponse(
-                text=prediction["content"],
-                _prediction_response=prediction_response,
+        results = []
+        for prediction in prediction_response.predictions:
+            safety_attributes_dict = prediction.get("safetyAttributes", {})
+            results.append(
+                TextGenerationResponse(
+                    text=prediction["content"],
+                    _prediction_response=prediction_response,
+                    is_blocked=safety_attributes_dict.get("blocked", False),
+                    safety_attributes=dict(
+                        zip(
+                            safety_attributes_dict.get("categories", []),
+                            safety_attributes_dict.get("scores", []),
+                        )
+                    ),
+                )
             )
-            for prediction in prediction_response.predictions
-        ]
+        return results
 
 
 _TextGenerationModel = TextGenerationModel
@@ -690,9 +709,20 @@ def send_message(
             parameters=prediction_parameters,
         )
 
+        prediction = prediction_response.predictions[0]
+        safety_attributes = prediction["safetyAttributes"]
         response_obj = TextGenerationResponse(
-            text=prediction_response.predictions[0]["candidates"][0]["content"],
+            text=prediction["candidates"][0]["content"]
+            if prediction.get("candidates")
+            else None,
             _prediction_response=prediction_response,
+            is_blocked=safety_attributes.get("blocked", False),
+            safety_attributes=dict(
+                zip(
+                    safety_attributes.get("categories", []),
+                    safety_attributes.get("scores", []),
+                )
+            ),
         )
         response_text = response_obj.text