fix: GenAI SDK client(multimodal) - Fix Pydantic validation errors when using create_* in some cases

PiperOrigin-RevId: 906376276

Author: cleop-google

vertexai/_genai/_datasets_utils.py

@@ -21,7 +21,7 @@
 
 import google.auth.credentials
 from vertexai._genai.types import common
-from pydantic import BaseModel
+from google.genai import _common
 
 
 METADATA_SCHEMA_URI = (
@@ -31,18 +31,27 @@
 _DEFAULT_BQ_DATASET_PREFIX = "vertex_datasets"
 _DEFAULT_BQ_TABLE_PREFIX = "multimodal_dataset"
 
-T = TypeVar("T", bound=BaseModel)
+T = TypeVar("T", bound=_common.BaseModel)
 
 
-def create_from_response(model_type: Type[T], response: dict[str, Any]) -> T:
+def create_from_response(
+    model_type: Type[T],
+    response: dict[str, Any],
+    config: Any | None = None,
+) -> T:
     """Creates a model from a response."""
-    model_field_names = model_type.model_fields.keys()
-    filtered_response = {}
-    for key, value in response.items():
-        snake_key = common.camel_to_snake(key)
-        if snake_key in model_field_names:
-            filtered_response[snake_key] = value
-    return model_type(**filtered_response)
+    kwargs = (
+        {
+            "config": {
+                "response_schema": getattr(config, "response_schema", None),
+                "response_json_schema": getattr(config, "response_json_schema", None),
+                "include_all_fields": getattr(config, "include_all_fields", None),
+            }
+        }
+        if config
+        else {}
+    )
+    return model_type._from_response(response=response, kwargs=kwargs)
 
 
 def validate_multimodal_dataset_bigquery_uri(

vertexai/_genai/datasets.py

@@ -963,7 +963,9 @@ def create_from_bigquery(
             operation=multimodal_dataset_operation,
             timeout_seconds=config.timeout,
         )
-        return _datasets_utils.create_from_response(types.MultimodalDataset, response)
+        return _datasets_utils.create_from_response(
+            types.MultimodalDataset, response, config
+        )
 
     def create_from_pandas(
         self,
@@ -1302,6 +1304,7 @@ def assess_tuning_resources(
         return _datasets_utils.create_from_response(
             types.TuningResourceUsageAssessmentResult,
             response["tuningResourceUsageAssessmentResult"],
+            config,
         )
 
     def assess_tuning_validity(
@@ -1368,6 +1371,7 @@ def assess_tuning_validity(
         return _datasets_utils.create_from_response(
             types.TuningValidationAssessmentResult,
             response["tuningValidationAssessmentResult"],
+            config,
         )
 
     def assess_batch_prediction_resources(
@@ -1430,7 +1434,7 @@ def assess_batch_prediction_resources(
         )
         result = response["batchPredictionResourceUsageAssessmentResult"]
         return _datasets_utils.create_from_response(
-            types.BatchPredictionResourceUsageAssessmentResult, result
+            types.BatchPredictionResourceUsageAssessmentResult, result, config
         )
 
     def assess_batch_prediction_validity(
@@ -1493,7 +1497,7 @@ def assess_batch_prediction_validity(
         )
         result = response["batchPredictionValidationAssessmentResult"]
         return _datasets_utils.create_from_response(
-            types.BatchPredictionValidationAssessmentResult, result
+            types.BatchPredictionValidationAssessmentResult, result, config
         )
 
 
@@ -2231,7 +2235,9 @@ async def create_from_bigquery(
             operation=multimodal_dataset_operation,
             timeout_seconds=config.timeout,
         )
-        return _datasets_utils.create_from_response(types.MultimodalDataset, response)
+        return _datasets_utils.create_from_response(
+            types.MultimodalDataset, response, config
+        )
 
     async def create_from_pandas(
         self,
@@ -2568,6 +2574,7 @@ async def assess_tuning_resources(
         return _datasets_utils.create_from_response(
             types.TuningResourceUsageAssessmentResult,
             response["tuningResourceUsageAssessmentResult"],
+            config,
         )
 
     async def assess_tuning_validity(
@@ -2634,6 +2641,7 @@ async def assess_tuning_validity(
         return _datasets_utils.create_from_response(
             types.TuningValidationAssessmentResult,
             response["tuningValidationAssessmentResult"],
+            config,
         )
 
     async def assess_batch_prediction_resources(
@@ -2696,7 +2704,7 @@ async def assess_batch_prediction_resources(
         )
         result = response["batchPredictionResourceUsageAssessmentResult"]
         return _datasets_utils.create_from_response(
-            types.BatchPredictionResourceUsageAssessmentResult, result
+            types.BatchPredictionResourceUsageAssessmentResult, result, config
         )
 
     async def assess_batch_prediction_validity(
@@ -2759,5 +2767,5 @@ async def assess_batch_prediction_validity(
         )
         result = response["batchPredictionValidationAssessmentResult"]
         return _datasets_utils.create_from_response(
-            types.BatchPredictionValidationAssessmentResult, result
+            types.BatchPredictionValidationAssessmentResult, result, config
         )