Merge branch 'main' into main

.github/workflows/python-unit-tests.yml

@@ -38,6 +38,8 @@ jobs:
 
     - name: Install the latest version of uv
       uses: astral-sh/setup-uv@v6
+      with:
+        version: "latest"
 
     - name: Install dependencies
       run: |

contributing/adk_project_overview_and_architecture.md

@@ -28,20 +28,22 @@ Google Agent Development Kit (ADK) for Python
 
 Adhere to this structure for compatibility with ADK tooling.
 
-my_adk_project/ \
-└── src/ \
-    └── my_app/ \
-        ├── agents/ \
-        │   ├── my_agent/ \
+```
+my_adk_project/
+└── src/
+    └── my_app/
+        ├── agents/
+        │   ├── my_agent/
         │   │   ├── __init__.py   # Must contain: from. import agent \
         │   │   └── agent.py      # Must contain: root_agent = Agent(...) \
-        │   └── another_agent/ \
-        │       ├── __init__.py \
+        │   └── another_agent/
+        │       ├── __init__.py
         │       └── agent.py\
+```
 
 agent.py: Must define the agent and assign it to a variable named root_agent. This is how ADK's tools find it.
 
-__init__.py: In each agent directory, it must contain from. import agent to make the agent discoverable.
+`__init__.py`: In each agent directory, it must contain from. import agent to make the agent discoverable.
 
 ## Local Development & Debugging
 
@@ -108,4 +110,3 @@ Test Cases: Create JSON files with input and a reference (expected tool calls an
 Metrics: tool_trajectory_avg_score (does it use tools correctly?) and response_match_score (is the final answer good?).
 
 Run via: adk web (UI), pytest (for CI/CD), or adk eval (CLI).
-

contributing/samples/adk_answering_agent/README.md

@@ -2,7 +2,11 @@
 
 The ADK Answering Agent is a Python-based agent designed to help answer questions in GitHub discussions for the `google/adk-python` repository. It uses a large language model to analyze open discussions, retrieve information from document store, generate response, and post a comment in the github discussion.
 
-This agent can be operated in three distinct modes: an interactive mode for local use, a batch script mode for oncall use, or as a fully automated GitHub Actions workflow (TBD).
+This agent can be operated in three distinct modes:
+
+- An interactive mode for local use.
+- A batch script mode for oncall use.
+- A fully automated GitHub Actions workflow (TBD).
 
 ---
 
@@ -50,6 +54,15 @@ The `main.py` is reserved for the Github Workflow. The detailed setup for the au
 
 ---
 
+## Update the Knowledge Base
+
+The `upload_docs_to_vertex_ai_search.py` is a script to upload ADK related docs to Vertex AI Search datastore to update the knowledge base. It can be executed with the following command in your terminal:
+
+```bash
+export PYTHONPATH=contributing/samples # If not already exported
+python -m adk_answering_agent.upload_docs_to_vertex_ai_search
+```
+
 ## Setup and Configuration
 
 Whether running in interactive or workflow mode, the agent requires the following setup.
@@ -59,7 +72,7 @@ The agent requires the following Python libraries.
 
 ```bash
 pip install --upgrade pip
-pip install google-adk requests
+pip install google-adk
 ```
 
 The agent also requires gcloud login:
@@ -68,6 +81,12 @@ The agent also requires gcloud login:
 gcloud auth application-default login
 ```
 
+The upload script requires the following additional Python libraries.
+
+```bash
+pip install google-cloud-storage google-cloud-discoveryengine
+```
+
 ### Environment Variables
 The following environment variables are required for the agent to connect to the necessary services.
 
@@ -75,9 +94,15 @@ The following environment variables are required for the agent to connect to the
 * `GOOGLE_GENAI_USE_VERTEXAI=TRUE`: **(Required)** Use Google Vertex AI for the authentication.
 * `GOOGLE_CLOUD_PROJECT=YOUR_PROJECT_ID`: **(Required)** The Google Cloud project ID.
 * `GOOGLE_CLOUD_LOCATION=LOCATION`: **(Required)** The Google Cloud region.
-* `VERTEXAI_DATASTORE_ID=YOUR_DATASTORE_ID`: **(Required)** The Vertex AI datastore ID for the document store (i.e. knowledge base).
+* `VERTEXAI_DATASTORE_ID=YOUR_DATASTORE_ID`: **(Required)** The full Vertex AI datastore ID for the document store (i.e. knowledge base), with the format of `projects/{project_number}/locations/{location}/collections/{collection}/dataStores/{datastore_id}`.
 * `OWNER`: The GitHub organization or username that owns the repository (e.g., `google`). Needed for both modes.
 * `REPO`: The name of the GitHub repository (e.g., `adk-python`). Needed for both modes.
 * `INTERACTIVE`: Controls the agent's interaction mode. For the automated workflow, this is set to `0`. For interactive mode, it should be set to `1` or left unset.
 
+The following environment variables are required to upload the docs to update the knowledge base.
+
+* `GCS_BUCKET_NAME=YOUR_GCS_BUCKET_NAME`: **(Required)** The name of the GCS bucket to store the documents.
+* `ADK_DOCS_ROOT_PATH=YOUR_ADK_DOCS_ROOT_PATH`: **(Required)** Path to the root of the downloaded adk-docs repo.
+* `ADK_PYTHON_ROOT_PATH=YOUR_ADK_PYTHON_ROOT_PATH`: **(Required)** Path to the root of the downloaded adk-python repo.
+
 For local execution in interactive mode, you can place these variables in a `.env` file in the project's root directory. For the GitHub workflow, they should be configured as repository secrets.

contributing/samples/adk_answering_agent/settings.py

@@ -29,6 +29,11 @@ VERTEXAI_DATASTORE_ID = os.getenv("VERTEXAI_DATASTORE_ID")
 if not VERTEXAI_DATASTORE_ID:
   raise ValueError("VERTEXAI_DATASTORE_ID environment variable not set")
 
+GOOGLE_CLOUD_PROJECT = os.getenv("GOOGLE_CLOUD_PROJECT")
+GCS_BUCKET_NAME = os.getenv("GCS_BUCKET_NAME")
+ADK_DOCS_ROOT_PATH = os.getenv("ADK_DOCS_ROOT_PATH")
+ADK_PYTHON_ROOT_PATH = os.getenv("ADK_PYTHON_ROOT_PATH")
+
 OWNER = os.getenv("OWNER", "google")
 REPO = os.getenv("REPO", "adk-python")
 BOT_RESPONSE_LABEL = os.getenv("BOT_RESPONSE_LABEL", "bot responded")

contributing/samples/adk_answering_agent/upload_docs_to_vertex_ai_search.py

@@ -0,0 +1,222 @@
+# Copyright 2025 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+import sys
+
+from adk_answering_agent.settings import ADK_DOCS_ROOT_PATH
+from adk_answering_agent.settings import ADK_PYTHON_ROOT_PATH
+from adk_answering_agent.settings import GCS_BUCKET_NAME
+from adk_answering_agent.settings import GOOGLE_CLOUD_PROJECT
+from adk_answering_agent.settings import VERTEXAI_DATASTORE_ID
+from google.api_core.exceptions import GoogleAPICallError
+from google.cloud import discoveryengine_v1beta as discoveryengine
+from google.cloud import storage
+import markdown
+
+GCS_PREFIX_TO_ROOT_PATH = {
+    "adk-docs": ADK_DOCS_ROOT_PATH,
+    "adk-python": ADK_PYTHON_ROOT_PATH,
+}
+
+
+def cleanup_gcs_prefix(project_id: str, bucket_name: str, prefix: str) -> bool:
+  """Delete all the objects with the given prefix in the bucket."""
+  print(f"Start cleaning up GCS: gs://{bucket_name}/{prefix}...")
+  try:
+    storage_client = storage.Client(project=project_id)
+    bucket = storage_client.bucket(bucket_name)
+    blobs = list(bucket.list_blobs(prefix=prefix))
+
+    if not blobs:
+      print("GCS target location is already empty, no need to clean up.")
+      return True
+
+    bucket.delete_blobs(blobs)
+    print(f"Successfully deleted {len(blobs)} objects.")
+    return True
+  except GoogleAPICallError as e:
+    print(f"[ERROR] Failed to clean up GCS: {e}", file=sys.stderr)
+    return False
+
+
+def upload_directory_to_gcs(
+    source_directory: str, project_id: str, bucket_name: str, prefix: str
+) -> bool:
+  """Upload the whole directory into GCS."""
+  print(
+      f"Start uploading directory {source_directory} to GCS:"
+      f" gs://{bucket_name}/{prefix}..."
+  )
+
+  if not os.path.isdir(source_directory):
+    print(f"[Error] {source_directory} is not a directory or does not exist.")
+    return False
+
+  storage_client = storage.Client(project=project_id)
+  bucket = storage_client.bucket(bucket_name)
+  file_count = 0
+  for root, dirs, files in os.walk(source_directory):
+    # Modify the 'dirs' list in-place to prevent os.walk from descending
+    # into hidden directories.
+    dirs[:] = [d for d in dirs if not d.startswith(".")]
+
+    # Keep only .md and .py files.
+    files = [f for f in files if f.endswith(".md") or f.endswith(".py")]
+
+    for filename in files:
+      local_path = os.path.join(root, filename)
+
+      relative_path = os.path.relpath(local_path, source_directory)
+      gcs_path = os.path.join(prefix, relative_path)
+
+      try:
+        content_type = None
+        if filename.lower().endswith(".md"):
+          # Vertex AI search doesn't recognize text/markdown,
+          # convert it to html and use text/html instead
+          content_type = "text/html"
+          with open(local_path, "r", encoding="utf-8") as f:
+            md_content = f.read()
+          html_content = markdown.markdown(
+              md_content, output_format="html5", encoding="utf-8"
+          )
+          if not html_content:
+            print("  - Skipped empty file: " + local_path)
+            continue
+          gcs_path = gcs_path.removesuffix(".md") + ".html"
+          bucket.blob(gcs_path).upload_from_string(
+              html_content, content_type=content_type
+          )
+        else:  # Python files
+          bucket.blob(gcs_path).upload_from_filename(
+              local_path, content_type=content_type
+          )
+        type_msg = (
+            f"(type {content_type})" if content_type else "(type auto-detect)"
+        )
+        print(
+            f"  - Uploaded {type_msg}: {local_path} ->"
+            f" gs://{bucket_name}/{gcs_path}"
+        )
+        file_count += 1
+      except GoogleAPICallError as e:
+        print(
+            f"[ERROR] Error uploading file {local_path}: {e}", file=sys.stderr
+        )
+        return False
+
+  print(f"Sucessfully uploaded {file_count} files to GCS.")
+  return True
+
+
+def import_from_gcs_to_vertex_ai(
+    full_datastore_id: str,
+    gcs_bucket: str,
+) -> bool:
+  """Triggers a bulk import task from a GCS folder to Vertex AI Search."""
+  print(f"Triggering FULL SYNC import from gs://{gcs_bucket}/**...")
+
+  try:
+    client = discoveryengine.DocumentServiceClient()
+    gcs_uri = f"gs://{gcs_bucket}/**"
+    request = discoveryengine.ImportDocumentsRequest(
+        # parent has the format of
+        # "projects/{project_number}/locations/{location}/collections/{collection}/dataStores/{datastore_id}/branches/default_branch"
+        parent=full_datastore_id + "/branches/default_branch",
+        # Specify the GCS source and use "content" for unstructed data.
+        gcs_source=discoveryengine.GcsSource(
+            input_uris=[gcs_uri], data_schema="content"
+        ),
+        reconciliation_mode=discoveryengine.ImportDocumentsRequest.ReconciliationMode.FULL,
+    )
+    operation = client.import_documents(request=request)
+    print(
+        "Successfully started full sync import operation."
+        f"Operation Name: {operation.operation.name}"
+    )
+    return True
+
+  except GoogleAPICallError as e:
+    print(f"[ERROR] Error triggering import: {e}", file=sys.stderr)
+    return False
+
+
+def main():
+  # Check required environment variables.
+  if not GOOGLE_CLOUD_PROJECT:
+    print(
+        "[ERROR] GOOGLE_CLOUD_PROJECT environment variable not set. Exiting...",
+        file=sys.stderr,
+    )
+    return 1
+  if not GCS_BUCKET_NAME:
+    print(
+        "[ERROR] GCS_BUCKET_NAME environment variable not set. Exiting...",
+        file=sys.stderr,
+    )
+    return 1
+  if not VERTEXAI_DATASTORE_ID:
+    print(
+        "[ERROR] VERTEXAI_DATASTORE_ID environment variable not set."
+        " Exiting...",
+        file=sys.stderr,
+    )
+    return 1
+  if not ADK_DOCS_ROOT_PATH:
+    print(
+        "[ERROR] ADK_DOCS_ROOT_PATH environment variable not set. Exiting...",
+        file=sys.stderr,
+    )
+    return 1
+  if not ADK_PYTHON_ROOT_PATH:
+    print(
+        "[ERROR] ADK_PYTHON_ROOT_PATH environment variable not set. Exiting...",
+        file=sys.stderr,
+    )
+    return 1
+
+  for gcs_prefix in GCS_PREFIX_TO_ROOT_PATH:
+    # 1. Cleanup the GSC for a clean start.
+    if not cleanup_gcs_prefix(
+        GOOGLE_CLOUD_PROJECT, GCS_BUCKET_NAME, gcs_prefix
+    ):
+      print("[ERROR] Failed to clean up GCS. Exiting...", file=sys.stderr)
+      return 1
+
+    # 2. Upload the docs to GCS.
+    if not upload_directory_to_gcs(
+        GCS_PREFIX_TO_ROOT_PATH[gcs_prefix],
+        GOOGLE_CLOUD_PROJECT,
+        GCS_BUCKET_NAME,
+        gcs_prefix,
+    ):
+      print("[ERROR] Failed to upload docs to GCS. Exiting...", file=sys.stderr)
+      return 1
+
+  # 3. Import the docs from GCS to Vertex AI Search.
+  if not import_from_gcs_to_vertex_ai(VERTEXAI_DATASTORE_ID, GCS_BUCKET_NAME):
+    print(
+        "[ERROR] Failed to import docs from GCS to Vertex AI Search."
+        " Exiting...",
+        file=sys.stderr,
+    )
+    return 1
+
+  print("--- Sync task has been successfully initiated ---")
+  return 0
+
+
+if __name__ == "__main__":
+  sys.exit(main())

src/google/adk/cli/cli_eval.py

@@ -15,6 +15,7 @@
 from __future__ import annotations
 
 import importlib.util
+import inspect
 import json
 import logging
 import os
@@ -31,6 +32,7 @@ from ..evaluation.eval_case import EvalCase
 from ..evaluation.eval_metrics import EvalMetric
 from ..evaluation.eval_metrics import EvalMetricResult
 from ..evaluation.eval_metrics import EvalMetricResultPerInvocation
+from ..evaluation.eval_metrics import JudgeModelOptions
 from ..evaluation.eval_result import EvalCaseResult
 from ..evaluation.evaluator import EvalStatus
 from ..evaluation.evaluator import Evaluator
@@ -42,6 +44,7 @@ logger = logging.getLogger("google_adk." + __name__)
 TOOL_TRAJECTORY_SCORE_KEY = "tool_trajectory_avg_score"
 RESPONSE_MATCH_SCORE_KEY = "response_match_score"
 SAFETY_V1_KEY = "safety_v1"
+FINAL_RESPONSE_MATCH_V2 = "final_response_match_v2"
 # This evaluation is not very stable.
 # This is always optional unless explicitly specified.
 RESPONSE_EVALUATION_SCORE_KEY = "response_evaluation_score"
@@ -191,10 +194,16 @@ async def run_evals(
         for eval_metric in eval_metrics:
           metric_evaluator = _get_evaluator(eval_metric)
 
-          evaluation_result = metric_evaluator.evaluate_invocations(
-              actual_invocations=inference_result,
-              expected_invocations=eval_case.conversation,
-          )
+          if inspect.iscoroutinefunction(metric_evaluator.evaluate_invocations):
+            evaluation_result = await metric_evaluator.evaluate_invocations(
+                actual_invocations=inference_result,
+                expected_invocations=eval_case.conversation,
+            )
+          else:
+            evaluation_result = metric_evaluator.evaluate_invocations(
+                actual_invocations=inference_result,
+                expected_invocations=eval_case.conversation,
+            )
 
           overall_eval_metric_results.append(
               EvalMetricResult(
@@ -260,6 +269,7 @@ async def run_evals(
 
 def _get_evaluator(eval_metric: EvalMetric) -> Evaluator:
   try:
+    from ..evaluation.final_response_match_v2 import FinalResponseMatchV2Evaluator
     from ..evaluation.response_evaluator import ResponseEvaluator
     from ..evaluation.safety_evaluator import SafetyEvaluatorV1
     from ..evaluation.trajectory_evaluator import TrajectoryEvaluator
@@ -276,5 +286,8 @@ def _get_evaluator(eval_metric: EvalMetric) -> Evaluator:
     )
   elif eval_metric.metric_name == SAFETY_V1_KEY:
     return SafetyEvaluatorV1(eval_metric)
+  elif eval_metric.metric_name == FINAL_RESPONSE_MATCH_V2:
+    eval_metric.judge_model_options = JudgeModelOptions()
+    return FinalResponseMatchV2Evaluator(eval_metric)
 
   raise ValueError(f"Unsupported eval metric: {eval_metric}")

src/google/adk/cli/cli_tools_click.py

@@ -461,7 +461,10 @@ def adk_services_options():
         "--session_service_uri",
         help=(
             """Optional. The URI of the session service.
-          - Use 'agentengine://<agent_engine_resource_id>' to connect to Agent Engine sessions.
+          - Use 'agentengine://<agent_engine>' to connect to Agent Engine
+            sessions. <agent_engine> can either be the full qualified resource
+            name 'projects/abc/locations/us-central1/reasoningEngines/123' or
+            the resource id '123'.
           - Use 'sqlite://<path_to_sqlite_file>' to connect to a SQLite DB.
           - See https://docs.sqlalchemy.org/en/20/core/engines.html#backend-specific-urls for more details on supported database URIs."""
         ),
@@ -487,11 +490,12 @@ def adk_services_options():
     @click.option(
         "--memory_service_uri",
         type=str,
-        help=(
-            """Optional. The URI of the memory service.
+        help=("""Optional. The URI of the memory service.
             - Use 'rag://<rag_corpus_id>' to connect to Vertex AI Rag Memory Service.
-            - Use 'agentengine://<agent_engine_resource_id>' to connect to Vertex AI Memory Bank Service. e.g. agentengine://12345"""
-        ),
+            - Use 'agentengine://<agent_engine>' to connect to Agent Engine
+              sessions. <agent_engine> can either be the full qualified resource
+              name 'projects/abc/locations/us-central1/reasoningEngines/123' or
+              the resource id '123'."""),
         default=None,
     )
     @functools.wraps(func)

src/google/adk/cli/fast_api.py

@@ -297,6 +297,31 @@ def get_fast_api_app(
     eval_sets_manager = LocalEvalSetsManager(agents_dir=agents_dir)
     eval_set_results_manager = LocalEvalSetResultsManager(agents_dir=agents_dir)
 
+  def _parse_agent_engine_resource_name(agent_engine_id_or_resource_name):
+    if not agent_engine_id_or_resource_name:
+      raise click.ClickException(
+          "Agent engine resource name or resource id can not be empty."
+      )
+
+    # "projects/my-project/locations/us-central1/reasoningEngines/1234567890",
+    if "/" in agent_engine_id_or_resource_name:
+      # Validate resource name.
+      if len(agent_engine_id_or_resource_name.split("/")) != 6:
+        raise click.ClickException(
+            "Agent engine resource name is mal-formatted. It should be of"
+            " format :"
+            " projects/{project_id}/locations/{location}/reasoningEngines/{resource_id}"
+        )
+      project = agent_engine_id_or_resource_name.split("/")[1]
+      location = agent_engine_id_or_resource_name.split("/")[3]
+      agent_engine_id = agent_engine_id_or_resource_name.split("/")[-1]
+    else:
+      envs.load_dotenv_for_agent("", agents_dir)
+      project = os.environ["GOOGLE_CLOUD_PROJECT"]
+      location = os.environ["GOOGLE_CLOUD_LOCATION"]
+      agent_engine_id = agent_engine_id_or_resource_name
+    return project, location, agent_engine_id
+
   # Build the Memory service
   if memory_service_uri:
     if memory_service_uri.startswith("rag://"):
@@ -308,13 +333,13 @@ def get_fast_api_app(
           rag_corpus=f'projects/{os.environ["GOOGLE_CLOUD_PROJECT"]}/locations/{os.environ["GOOGLE_CLOUD_LOCATION"]}/ragCorpora/{rag_corpus}'
       )
     elif memory_service_uri.startswith("agentengine://"):
-      agent_engine_id = memory_service_uri.split("://")[1]
-      if not agent_engine_id:
-        raise click.ClickException("Agent engine id can not be empty.")
-      envs.load_dotenv_for_agent("", agents_dir)
+      agent_engine_id_or_resource_name = memory_service_uri.split("://")[1]
+      project, location, agent_engine_id = _parse_agent_engine_resource_name(
+          agent_engine_id_or_resource_name
+      )
       memory_service = VertexAiMemoryBankService(
-          project=os.environ["GOOGLE_CLOUD_PROJECT"],
-          location=os.environ["GOOGLE_CLOUD_LOCATION"],
+          project=project,
+          location=location,
           agent_engine_id=agent_engine_id,
       )
     else:
@@ -327,14 +352,13 @@ def get_fast_api_app(
   # Build the Session service
   if session_service_uri:
     if session_service_uri.startswith("agentengine://"):
-      # Create vertex session service
-      agent_engine_id = session_service_uri.split("://")[1]
-      if not agent_engine_id:
-        raise click.ClickException("Agent engine id can not be empty.")
-      envs.load_dotenv_for_agent("", agents_dir)
+      agent_engine_id_or_resource_name = session_service_uri.split("://")[1]
+      project, location, agent_engine_id = _parse_agent_engine_resource_name(
+          agent_engine_id_or_resource_name
+      )
       session_service = VertexAiSessionService(
-          project=os.environ["GOOGLE_CLOUD_PROJECT"],
-          location=os.environ["GOOGLE_CLOUD_LOCATION"],
+          project=project,
+          location=location,
           agent_engine_id=agent_engine_id,
       )
     else:

src/google/adk/evaluation/eval_metrics.py

@@ -36,6 +36,10 @@ class PrebuiltMetrics(Enum):
 
   RESPONSE_MATCH_SCORE = "response_match_score"
 
+  SAFETY_V1 = "safety_v1"
+
+  FINAL_RESPONSE_MATCH_V2 = "final_response_match_v2"
+
 
 MetricName: TypeAlias = Union[str, PrebuiltMetrics]
 

src/google/adk/evaluation/final_response_match_v2.py

@@ -21,7 +21,7 @@ from typing import Optional
 from typing_extensions import override
 
 from ..models.llm_response import LlmResponse
-from ..utils.feature_decorator import working_in_progress
+from ..utils.feature_decorator import experimental
 from .eval_case import Invocation
 from .eval_metrics import EvalMetric
 from .evaluator import EvalStatus
@@ -125,7 +125,7 @@ def _parse_critique(response: str) -> Label:
   return label
 
 
-@working_in_progress
+@experimental
 class FinalResponseMatchV2Evaluator(LlmAsJudge):
   """V2 final response match evaluator which uses an LLM to judge responses.
 

src/google/adk/evaluation/metric_evaluator_registry.py

@@ -21,7 +21,9 @@ from .eval_metrics import EvalMetric
 from .eval_metrics import MetricName
 from .eval_metrics import PrebuiltMetrics
 from .evaluator import Evaluator
+from .final_response_match_v2 import FinalResponseMatchV2Evaluator
 from .response_evaluator import ResponseEvaluator
+from .safety_evaluator import SafetyEvaluatorV1
 from .trajectory_evaluator import TrajectoryEvaluator
 
 logger = logging.getLogger("google_adk." + __name__)
@@ -71,16 +73,24 @@ def _get_default_metric_evaluator_registry() -> MetricEvaluatorRegistry:
   metric_evaluator_registry = MetricEvaluatorRegistry()
 
   metric_evaluator_registry.register_evaluator(
-      metric_name=PrebuiltMetrics.TOOL_TRAJECTORY_AVG_SCORE,
-      evaluator=type(TrajectoryEvaluator),
+      metric_name=PrebuiltMetrics.TOOL_TRAJECTORY_AVG_SCORE.value,
+      evaluator=TrajectoryEvaluator,
   )
   metric_evaluator_registry.register_evaluator(
-      metric_name=PrebuiltMetrics.RESPONSE_EVALUATION_SCORE,
-      evaluator=type(ResponseEvaluator),
+      metric_name=PrebuiltMetrics.RESPONSE_EVALUATION_SCORE.value,
+      evaluator=ResponseEvaluator,
   )
   metric_evaluator_registry.register_evaluator(
-      metric_name=PrebuiltMetrics.RESPONSE_MATCH_SCORE,
-      evaluator=type(ResponseEvaluator),
+      metric_name=PrebuiltMetrics.RESPONSE_MATCH_SCORE.value,
+      evaluator=ResponseEvaluator,
+  )
+  metric_evaluator_registry.register_evaluator(
+      metric_name=PrebuiltMetrics.SAFETY_V1.value,
+      evaluator=SafetyEvaluatorV1,
+  )
+  metric_evaluator_registry.register_evaluator(
+      metric_name=PrebuiltMetrics.FINAL_RESPONSE_MATCH_V2.value,
+      evaluator=FinalResponseMatchV2Evaluator,
   )
 
   return metric_evaluator_registry

src/google/adk/memory/vertex_ai_memory_bank_service.py

@@ -16,13 +16,15 @@ from __future__ import annotations
 
 import json
 import logging
+from typing import Any
+from typing import Dict
 from typing import Optional
 from typing import TYPE_CHECKING
 
+from google.genai import Client
+from google.genai import types
 from typing_extensions import override
 
-from google import genai
-
 from .base_memory_service import BaseMemoryService
 from .base_memory_service import SearchMemoryResponse
 from .memory_entry import MemoryEntry
@@ -84,7 +86,7 @@ class VertexAiMemoryBankService(BaseMemoryService):
           path=f'reasoningEngines/{self._agent_engine_id}/memories:generate',
           request_dict=request_dict,
       )
-      logger.info(f'Generate memory response: {api_response}')
+      logger.info('Generate memory response: %s', api_response)
     else:
       logger.info('No events to add to memory.')
 
@@ -106,7 +108,7 @@ class VertexAiMemoryBankService(BaseMemoryService):
         },
     )
     api_response = _convert_api_response(api_response)
-    logger.info(f'Search memory response: {api_response}')
+    logger.info('Search memory response: %s', api_response)
 
     if not api_response or not api_response.get('retrievedMemories', None):
       return SearchMemoryResponse()
@@ -117,10 +119,8 @@ class VertexAiMemoryBankService(BaseMemoryService):
       memory_events.append(
           MemoryEntry(
               author='user',
-              content=genai.types.Content(
-                  parts=[
-                      genai.types.Part(text=memory.get('memory').get('fact'))
-                  ],
+              content=types.Content(
+                  parts=[types.Part(text=memory.get('memory').get('fact'))],
                   role='user',
               ),
               timestamp=memory.get('updateTime'),
@@ -137,13 +137,13 @@ class VertexAiMemoryBankService(BaseMemoryService):
     Returns:
       An API client for the given project and location.
     """
-    client = genai.Client(
+    client = Client(
         vertexai=True, project=self._project, location=self._location
     )
     return client._api_client
 
 
-def _convert_api_response(api_response):
+def _convert_api_response(api_response) -> Dict[str, Any]:
   """Converts the API response to a JSON object based on the type."""
   if hasattr(api_response, 'body'):
     return json.loads(api_response.body)