naming change for public preview

Author: yil532

generativeai/chat.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 
 # [START generativeai_sdk_chat]
-from google.cloud.aiplatform.private_preview.language_models import ChatModel, InputOutputTextPair
+from vertex_ai.preview.language_models import ChatModel, InputOutputTextPair
 
 
 def science_tutoring(temperature=.2):

generativeai/chat_test.py

@@ -14,7 +14,7 @@
 
 import pytest
 
-from google.cloud.aiplatform.private_preview.language_models import ChatModel, InputcontentputTextPair
+from vertex_ai.preview.language_models import ChatModel, InputcontentputTextPair
 
 import chat
 

generativeai/classify_news_items.py

@@ -13,12 +13,12 @@
 # limitations under the License.
 
 # [START generativeai_sdk_classify_news_items]
-from google.cloud.aiplatform.private_preview.language_models import TextGenerationModel
+from vertex_ai.preview.language_models import TextGenerationModel
 
 
 def classify_news_items(temperature=0):
     """Text Classification Example with a Large Language Model"""
-    model = TextGenerationModel.from_pretrained("google/text-bison-001")
+    model = TextGenerationModel.from_pretrained("google/text-bison@001")
     response = model.predict(
       '''What is the topic for a given news headline?
 - business

generativeai/classify_news_items_test.py

@@ -14,7 +14,7 @@
 
 import pytest
 
-from google.cloud.aiplatform.private_preview.language_models import TextGenerationModel
+from vertex_ai.preview.language_models import TextGenerationModel
 
 import classify_news_items
 

generativeai/embedding.py

@@ -13,12 +13,12 @@
 # limitations under the License.
 
 # [START generativeai_sdk_embedding]
-from google.cloud.aiplatform.private_preview.language_models import TextEmbeddingModel
+from vertex_ai.preview.language_models import TextEmbeddingModel
 
 
 def text_embedding():
   """Text embedding with a Large Language Model."""
-  model = TextEmbeddingModel.from_pretrained("google/embedding-gecko-001")
+  model = TextEmbeddingModel.from_pretrained("google/embedding-gecko@001")
   embeddings = model.get_embeddings(["What is life?"])
   for embedding in embeddings:
       vector = embedding.values

generativeai/embedding_test.py

@@ -15,7 +15,7 @@
 import pytest
 import numpy as np
 
-from google.cloud.aiplatform.private_preview.language_models import TextEmbeddingModel
+from vertex_ai.preview.language_models import TextEmbeddingModel
 
 import embedding
 

generativeai/extraction.py

@@ -13,12 +13,12 @@
 # limitations under the License.
 
 # [START generativeai_sdk_extraction]
-from google.cloud.aiplatform.private_preview.language_models import TextGenerationModel
+from vertex_ai.preview.language_models import TextGenerationModel
 
 
 def extractive_question_answering(temperature=0):
     """Extractive Question Answering with a Large Language Model."""
-    model = TextGenerationModel.from_pretrained("google/text-bison-001")
+    model = TextGenerationModel.from_pretrained("google/text-bison@001")
     response = model.predict(
       '''Background: There is evidence that there have been significant changes \
 in Amazon rainforest vegetation over the last 21,000 years through the Last \

generativeai/extraction_test.py

@@ -14,7 +14,7 @@
 
 import pytest
 
-from google.cloud.aiplatform.private_preview.language_models import TextGenerationModel
+from vertex_ai.preview.language_models import TextGenerationModel
 
 import extraction
 

generativeai/ideation.py

@@ -13,12 +13,12 @@
 # limitations under the License.
 
 # [START generativeai_sdk_ideation]
-from google.cloud.aiplatform.private_preview.language_models import TextGenerationModel
+from vertex_ai.preview.language_models import TextGenerationModel
 
 
 def interview(temperature=.2):
     """Ideation example with a Large Language Model"""
-    model = TextGenerationModel.from_pretrained("google/text-bison-001")
+    model = TextGenerationModel.from_pretrained("google/text-bison@001")
     response = model.predict(
         'Give me ten interview questions for the role of program manager.',
         temperature=temperature,

generativeai/ideation_test.py

@@ -14,7 +14,7 @@
 
 import pytest
 
-from google.cloud.aiplatform.private_preview.language_models import TextGenerationModel
+from vertex_ai.preview.language_models import TextGenerationModel
 
 import ideation
 

generativeai/list_tuned_models.py

@@ -16,14 +16,14 @@
 from typing import Union
 import pandas as pd
 
-from google.cloud.aiplatform.private_preview.language_models import TextGenerationModel, TextEmbeddingModel
+from vertex_ai.preview.language_models import TextGenerationModel, TextEmbeddingModel
 from google.cloud import aiplatform
 
 
 def list_tuned_models(project_id, location):
     """List tuned models."""
     aiplatform.init(project=project_id, location=location)
-    model = TextGenerationModel.from_pretrained("google/text-bison-001")
+    model = TextGenerationModel.from_pretrained("google/text-bison@001")
     tuned_model_names = model.list_tuned_model_names()
     print(tuned_model_names)
 # [END generativeai_sdk_list_tuned_models]

generativeai/list_tuned_models_test.py

@@ -14,7 +14,7 @@
 
 import pytest
 
-from google.cloud.aiplatform.private_preview.language_models import TextGenerationModel, TextEmbeddingModel
+from vertex_ai.preview.language_models import TextGenerationModel, TextEmbeddingModel
 
 import list_tuned_models
 

generativeai/sentiment_analysis.py

@@ -13,12 +13,12 @@
 # limitations under the License.
 
 # [START generativeai_sdk_sentiment_analysis]
-from google.cloud.aiplatform.private_preview.language_models import TextGenerationModel
+from vertex_ai.preview.language_models import TextGenerationModel
 
 
 def sentiment_analysis(temperature=0):
     """Sentiment analysis example with a Large Language Model."""
-    model = TextGenerationModel.from_pretrained("google/text-bison-001")
+    model = TextGenerationModel.from_pretrained("google/text-bison@001")
     response = model.predict(
       '''I had to compare two versions of Hamlet for my Shakespeare class and \
 unfortunately I picked this version. Everything from the acting (the actors \

generativeai/sentiment_analysis_test.py

@@ -14,7 +14,7 @@
 
 import pytest
 
-from google.cloud.aiplatform.private_preview.language_models import TextGenerationModel
+from vertex_ai.preview.language_models import TextGenerationModel
 
 import sentiment_analysis
 

generativeai/summarization.py

@@ -13,12 +13,12 @@
 # limitations under the License.
 
 # [START generativeai_sdk_summarization]
-from google.cloud.aiplatform.private_preview.language_models import TextGenerationModel
+from vertex_ai.preview.language_models import TextGenerationModel
 
 
 def text_summarization(temperature=.2):
     """Summarization Example with a Large Language Model"""
-    model = TextGenerationModel.from_pretrained("google/text-bison-001")
+    model = TextGenerationModel.from_pretrained("google/text-bison@001")
     response = model.predict(
         '''Provide a summary with about two sentences for the following article:
 The efficient-market hypothesis (EMH) is a hypothesis in financial \

generativeai/summarization_test.py

@@ -14,7 +14,7 @@
 
 import pytest
 
-from google.cloud.aiplatform.private_preview.language_models import TextGenerationModel
+from vertex_ai.preview.language_models import TextGenerationModel
 
 import summarization
 

generativeai/tuning.py

@@ -16,7 +16,7 @@
 from typing import Union
 import pandas as pd
 
-from google.cloud.aiplatform.private_preview.language_models import TextGenerationModel, TextEmbeddingModel
+from vertex_ai.preview.language_models import TextGenerationModel, TextEmbeddingModel
 from google.cloud import aiplatform
 
 
@@ -47,7 +47,7 @@ def tuning(
     train_steps: Number of training steps to use when tuning the model.
   """
   aiplatform.init(project=project_id, location=location)
-  model = TextGenerationModel.from_pretrained("google/text-bison-001")
+  model = TextGenerationModel.from_pretrained("google/text-bison@001")
 
   model.tune_model(
     training_data=training_data,

generativeai/tuning_test.py

@@ -14,7 +14,7 @@
 
 import pytest
 
-from google.cloud.aiplatform.private_preview.language_models import TextEmbeddingModel
+from vertex_ai.preview.language_models import TextEmbeddingModel
 
 import tuning