feat: LLM - Support for Batch Prediction for the textembedding mode…

…ls (preview)

Usage:
```
model = TextEmbeddingModel.from_pretrained("textembedding-gecko@001")
job = model.batch_predict(
    dataset="gs://<bicket>/dataset.jsonl",
    destination_uri_prefix="gs://<bicket>/batch_prediction/",
    # Optional:
    model_parameters={},
)
```
PiperOrigin-RevId: 546112540

tests/system/aiplatform/test_language_models.py

@@ -19,7 +19,7 @@
 
 from google.cloud import aiplatform
 from google.cloud.aiplatform.compat.types import (
-    job_state_v1beta1 as gca_job_state_v1beta1,
+    job_state as gca_job_state,
 )
 from tests.system.aiplatform import e2e_base
 from vertexai.preview.language_models import (
@@ -160,7 +160,7 @@ def test_tuning(self, shared_state):
         )
         assert tuned_model_response.text
 
-    def test_batch_prediction(self):
+    def test_batch_prediction_for_text_generation(self):
         source_uri = "gs://ucaip-samples-us-central1/model/llm/batch_prediction/batch_prediction_prompts1.jsonl"
         destination_uri_prefix = "gs://ucaip-samples-us-central1/model/llm/batch_prediction/predictions/text-bison@001_"
 
@@ -178,4 +178,24 @@ def test_batch_prediction(self):
         gapic_job = job._gca_resource
         job.delete()
 
-        assert gapic_job.state == gca_job_state_v1beta1.JobState.JOB_STATE_SUCCEEDED
+        assert gapic_job.state == gca_job_state.JobState.JOB_STATE_SUCCEEDED
+
+    def test_batch_prediction_for_textembedding(self):
+        source_uri = "gs://ucaip-samples-us-central1/model/llm/batch_prediction/batch_prediction_prompts1.jsonl"
+        destination_uri_prefix = "gs://ucaip-samples-us-central1/model/llm/batch_prediction/predictions/textembedding-gecko@001_"
+
+        aiplatform.init(project=e2e_base._PROJECT, location=e2e_base._LOCATION)
+
+        model = TextEmbeddingModel.from_pretrained("textembedding-gecko")
+        job = model.batch_predict(
+            dataset=source_uri,
+            destination_uri_prefix=destination_uri_prefix,
+            model_parameters={},
+        )
+
+        job.wait_for_resource_creation()
+        job.wait()
+        gapic_job = job._gca_resource
+        job.delete()
+
+        assert gapic_job.state == gca_job_state.JobState.JOB_STATE_SUCCEEDED

tests/unit/aiplatform/test_language_models.py

@@ -141,7 +141,7 @@
     "version_id": "001",
     "open_source_category": "PROPRIETARY",
     "launch_stage": gca_publisher_model.PublisherModel.LaunchStage.GA,
-    "publisher_model_template": "projects/{user-project}/locations/{location}/publishers/google/models/chat-bison@001",
+    "publisher_model_template": "projects/{user-project}/locations/{location}/publishers/google/models/textembedding-gecko@001",
     "predict_schemata": {
         "instance_schema_uri": "gs://google-cloud-aiplatform/schema/predict/instance/text_embedding_1.0.0.yaml",
         "parameters_schema_uri": "gs://google-cloud-aiplatfrom/schema/predict/params/text_generation_1.0.0.yaml",
@@ -1323,3 +1323,37 @@ def test_batch_prediction(self):
                 gcs_destination_prefix="gs://test-bucket/results/",
                 model_parameters={"temperature": 0.1},
             )
+
+    def test_batch_prediction_for_text_embedding(self):
+        """Tests batch prediction."""
+        aiplatform.init(
+            project=_TEST_PROJECT,
+            location=_TEST_LOCATION,
+        )
+        with mock.patch.object(
+            target=model_garden_service_client.ModelGardenServiceClient,
+            attribute="get_publisher_model",
+            return_value=gca_publisher_model.PublisherModel(
+                _TEXT_EMBEDDING_GECKO_PUBLISHER_MODEL_DICT
+            ),
+        ):
+            model = preview_language_models.TextEmbeddingModel.from_pretrained(
+                "textembedding-gecko@001"
+            )
+
+        with mock.patch.object(
+            target=aiplatform.BatchPredictionJob,
+            attribute="create",
+        ) as mock_create:
+            model.batch_predict(
+                dataset="gs://test-bucket/test_table.jsonl",
+                destination_uri_prefix="gs://test-bucket/results/",
+                model_parameters={},
+            )
+            mock_create.assert_called_once_with(
+                model_name="publishers/google/models/textembedding-gecko@001",
+                job_display_name=None,
+                gcs_source="gs://test-bucket/test_table.jsonl",
+                gcs_destination_prefix="gs://test-bucket/results/",
+                model_parameters={},
+            )

vertexai/language_models/_language_models.py

@@ -586,9 +586,7 @@ def get_embeddings(self, texts: List[str]) -> List["TextEmbedding"]:
         ]
 
 
-class _PreviewTextEmbeddingModel(TextEmbeddingModel):
-    """Preview text embedding model."""
-
+class _PreviewTextEmbeddingModel(TextEmbeddingModel, _ModelWithBatchPredict):
     _LAUNCH_STAGE = _model_garden_models._SDK_PUBLIC_PREVIEW_LAUNCH_STAGE