chore!: GenAI SDK client - remove duplicate types for Content, Part, and evals

PiperOrigin-RevId: 798282497

Author: sararob

tests/unit/vertexai/genai/replays/test_evaluate_instances.py

@@ -18,6 +18,7 @@
 
 from tests.unit.vertexai.genai.replays import pytest_helper
 from vertexai._genai import types
+from google.genai import types as genai_types
 import pandas as pd
 import pytest
 
@@ -30,7 +31,7 @@ def test_bleu_metric(client):
                 prediction="A fast brown fox leaps over a lazy dog.",
             )
         ],
-        metric_spec=types.BleuSpec(),
+        metric_spec=genai_types.BleuSpec(),
     )
     response = client.evals.evaluate_instances(
         metric_config=types._EvaluateInstancesRequestParameters(
@@ -68,7 +69,7 @@ def test_rouge_metric(client):
                 reference="The quick brown fox jumps over the lazy dog.",
             )
         ],
-        metric_spec=types.RougeSpec(rouge_type="rougeL"),
+        metric_spec=genai_types.RougeSpec(rouge_type="rougeL"),
     )
     response = client.evals.evaluate_instances(
         metric_config=types._EvaluateInstancesRequestParameters(
@@ -85,7 +86,7 @@ def test_pointwise_metric(client):
 
     test_input = types.PointwiseMetricInput(
         instance=types.PointwiseMetricInstance(json_instance=json_instance),
-        metric_spec=types.PointwiseMetricSpec(
+        metric_spec=genai_types.PointwiseMetricSpec(
             metric_prompt_template="Evaluate if the response '{response}' correctly answers the prompt '{prompt}'."
         ),
     )
@@ -109,11 +110,11 @@ def test_pairwise_metric_with_autorater(client):
 
     test_input = types.PairwiseMetricInput(
         instance=types.PairwiseMetricInstance(json_instance=json_instance),
-        metric_spec=types.PairwiseMetricSpec(
+        metric_spec=genai_types.PairwiseMetricSpec(
             metric_prompt_template="Which response is a better summary? Baseline: '{baseline_response}' or Candidate: '{candidate_response}'"
         ),
     )
-    autorater_config = types.AutoraterConfig(sampling_count=2)
+    autorater_config = genai_types.AutoraterConfig(sampling_count=2)
 
     response = client.evals.evaluate_instances(
         metric_config=types._EvaluateInstancesRequestParameters(
@@ -178,7 +179,7 @@ async def test_bleu_metric_async(client):
                 prediction="A fast brown fox leaps over a lazy dog.",
             )
         ],
-        metric_spec=types.BleuSpec(),
+        metric_spec=genai_types.BleuSpec(),
     )
     response = await client.aio.evals.evaluate_instances(
         metric_config=types._EvaluateInstancesRequestParameters(

tests/unit/vertexai/genai/replays/test_generate_agent_engine_memories.py

@@ -16,6 +16,7 @@
 
 from tests.unit.vertexai.genai.replays import pytest_helper
 from vertexai._genai import types
+from google.genai import types as genai_types
 
 
 def test_generate_memories(client):
@@ -31,10 +32,10 @@ def test_generate_memories(client):
         direct_contents_source=types.GenerateMemoriesRequestDirectContentsSource(
             events=[
                 types.GenerateMemoriesRequestDirectContentsSourceEvent(
-                    content=types.Content(
+                    content=genai_types.Content(
                         role="model",
                         parts=[
-                            types.Part(
+                            genai_types.Part(
                                 text="I am a software engineer focusing in security"
                             )
                         ],

tests/unit/vertexai/genai/replays/test_internal_generate_rubrics.py

@@ -17,6 +17,7 @@
 
 from tests.unit.vertexai.genai.replays import pytest_helper
 from vertexai._genai import types
+from google.genai import types as genai_types
 
 _TEST_RUBRIC_GENERATION_PROMPT = """SPECIAL INSTRUCTION: think silently. Silent thinking token budget: 16384.
 
@@ -146,9 +147,9 @@
 def test_internal_method_generate_rubrics(client):
     """Tests the internal _generate_rubrics method."""
     test_contents = [
-        types.Content(
+        genai_types.Content(
             parts=[
-                types.Part(
+                genai_types.Part(
                     text="Generate a short story about a friendly dragon.",
                 ),
             ],

vertexai/_genai/_evals_metric_handlers.py

@@ -529,19 +529,25 @@ def _build_pointwise_input(
                             role = msg_obj.content.role or msg_obj.author or "user"
                             history_texts.append(f"{role}: {msg_text}")
                     content_list_to_serialize = [
-                        types.Content(parts=[types.Part(text="\n".join(history_texts))])
+                        genai_types.Content(
+                            parts=[genai_types.Part(text="\n".join(history_texts))]
+                        )
                     ]
                 else:
                     content_list_to_serialize = [
-                        types.Content(parts=[types.Part(text=json.dumps(value))])
+                        genai_types.Content(
+                            parts=[genai_types.Part(text=json.dumps(value))]
+                        )
                     ]
             elif isinstance(value, dict):
                 content_list_to_serialize = [
-                    types.Content(parts=[types.Part(text=json.dumps(value))])
+                    genai_types.Content(
+                        parts=[genai_types.Part(text=json.dumps(value))]
+                    )
                 ]
             else:
                 content_list_to_serialize = [
-                    types.Content(parts=[types.Part(text=str(value))])
+                    genai_types.Content(parts=[genai_types.Part(text=str(value))])
                 ]
 
             content_map_values[key] = types.ContentMapContents(