feat(tools): Add debug logging to VertexAiSearchTool

Merge #3284

**Problem:**
When debugging agents that utilize the `VertexAiSearchTool`, it's currently difficult to inspect the specific configuration parameters (datastore ID, engine ID, filter, max_results, etc.) being passed to the underlying Vertex AI Search API via the `LlmRequest`. This lack of visibility can hinder troubleshooting efforts related to tool configuration.

**Solution:**
This PR enhances the `VertexAiSearchTool` by adding a **debug-level log statement** within the `process_llm_request` method. This log precisely records the parameters being used for the Vertex AI Search configuration just before it's appended to the `LlmRequest`.

This provides developers with crucial visibility into the tool's runtime behavior when debug logging is enabled, significantly improving the **debuggability** of agents using this tool. Corresponding unit tests were updated to rigorously verify this new logging output using `caplog`. Additionally, minor fixes were made to the tests to resolve Pydantic validation errors.

Co-authored-by: Xuan Yang <xygoogle@google.com>
COPYBARA_INTEGRATE_REVIEW=#3284 from omkute10:feat/add-logging-vertex-search-tool 199c12b
PiperOrigin-RevId: 836419886

Author: 2 people

src/google/adk/tools/vertex_ai_search_tool.py

@@ -14,6 +14,7 @@
 
 from __future__ import annotations
 
+import logging
 from typing import Optional
 from typing import TYPE_CHECKING
 
@@ -25,6 +26,8 @@
 from .base_tool import BaseTool
 from .tool_context import ToolContext
 
+logger = logging.getLogger('google_adk.' + __name__)
+
 if TYPE_CHECKING:
   from ..models import LlmRequest
 
@@ -102,6 +105,30 @@ async def process_llm_request(
         )
       llm_request.config = llm_request.config or types.GenerateContentConfig()
       llm_request.config.tools = llm_request.config.tools or []
+
+      # Format data_store_specs concisely for logging
+      if self.data_store_specs:
+        spec_ids = [
+            spec.data_store.split('/')[-1] if spec.data_store else 'unnamed'
+            for spec in self.data_store_specs
+        ]
+        specs_info = (
+            f'{len(self.data_store_specs)} spec(s): [{", ".join(spec_ids)}]'
+        )
+      else:
+        specs_info = None
+
+      logger.debug(
+          'Adding Vertex AI Search tool config to LLM request: '
+          'datastore=%s, engine=%s, filter=%s, max_results=%s, '
+          'data_store_specs=%s',
+          self.data_store_id,
+          self.search_engine_id,
+          self.filter,
+          self.max_results,
+          specs_info,
+      )
+
       llm_request.config.tools.append(
           types.Tool(
               retrieval=types.Retrieval(

tests/unittests/tools/test_vertex_ai_search_tool.py

@@ -12,6 +12,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import logging
+
 from google.adk.agents.invocation_context import InvocationContext
 from google.adk.agents.sequential_agent import SequentialAgent
 from google.adk.models.llm_request import LlmRequest
@@ -24,6 +26,10 @@
 from google.genai import types
 import pytest
 
+VERTEX_SEARCH_TOOL_LOGGER_NAME = (
+    'google_adk.google.adk.tools.vertex_ai_search_tool'
+)
+
 
 async def _create_tool_context() -> ToolContext:
   session_service = InMemorySessionService()
@@ -121,12 +127,34 @@ def test_init_with_data_store_id(self):
     tool = VertexAiSearchTool(data_store_id='test_data_store')
     assert tool.data_store_id == 'test_data_store'
     assert tool.search_engine_id is None
+    assert tool.data_store_specs is None
 
   def test_init_with_search_engine_id(self):
     """Test initialization with search engine ID."""
     tool = VertexAiSearchTool(search_engine_id='test_search_engine')
     assert tool.search_engine_id == 'test_search_engine'
     assert tool.data_store_id is None
+    assert tool.data_store_specs is None
+
+  def test_init_with_engine_and_specs(self):
+    """Test initialization with search engine ID and specs."""
+    specs = [
+        types.VertexAISearchDataStoreSpec(
+            dataStore=(
+                'projects/p/locations/l/collections/c/dataStores/spec_store'
+            )
+        )
+    ]
+    engine_id = (
+        'projects/p/locations/l/collections/c/engines/test_search_engine'
+    )
+    tool = VertexAiSearchTool(
+        search_engine_id=engine_id,
+        data_store_specs=specs,
+    )
+    assert tool.search_engine_id == engine_id
+    assert tool.data_store_id is None
+    assert tool.data_store_specs == specs
 
   def test_init_with_neither_raises_error(self):
     """Test that initialization without either ID raises ValueError."""
@@ -146,10 +174,34 @@ def test_init_with_both_raises_error(self):
           data_store_id='test_data_store', search_engine_id='test_search_engine'
       )
 
+  def test_init_with_specs_but_no_engine_raises_error(self):
+    """Test that specs without engine ID raises ValueError."""
+    specs = [
+        types.VertexAISearchDataStoreSpec(
+            dataStore=(
+                'projects/p/locations/l/collections/c/dataStores/spec_store'
+            )
+        )
+    ]
+    with pytest.raises(
+        ValueError,
+        match=(
+            'search_engine_id must be specified if data_store_specs is'
+            ' specified'
+        ),
+    ):
+      VertexAiSearchTool(
+          data_store_id='test_data_store', data_store_specs=specs
+      )
+
   @pytest.mark.asyncio
-  async def test_process_llm_request_with_simple_gemini_model(self):
+  async def test_process_llm_request_with_simple_gemini_model(self, caplog):
     """Test processing LLM request with simple Gemini model name."""
-    tool = VertexAiSearchTool(data_store_id='test_data_store')
+    caplog.set_level(logging.DEBUG, logger=VERTEX_SEARCH_TOOL_LOGGER_NAME)
+
+    tool = VertexAiSearchTool(
+        data_store_id='test_data_store', filter='f', max_results=5
+    )
     tool_context = await _create_tool_context()
 
     llm_request = LlmRequest(
@@ -162,17 +214,56 @@ async def test_process_llm_request_with_simple_gemini_model(self):
 
     assert llm_request.config.tools is not None
     assert len(llm_request.config.tools) == 1
-    assert llm_request.config.tools[0].retrieval is not None
-    assert llm_request.config.tools[0].retrieval.vertex_ai_search is not None
+    retrieval_tool = llm_request.config.tools[0]
+    assert retrieval_tool.retrieval is not None
+    assert retrieval_tool.retrieval.vertex_ai_search is not None
+    assert (
+        retrieval_tool.retrieval.vertex_ai_search.datastore == 'test_data_store'
+    )
+    assert retrieval_tool.retrieval.vertex_ai_search.engine is None
+    assert retrieval_tool.retrieval.vertex_ai_search.filter == 'f'
+    assert retrieval_tool.retrieval.vertex_ai_search.max_results == 5
+
+    # Verify debug log
+    debug_records = [
+        r
+        for r in caplog.records
+        if 'Adding Vertex AI Search tool config' in r.message
+    ]
+    assert len(debug_records) == 1
+    log_message = debug_records[0].getMessage()
+    assert 'datastore=test_data_store' in log_message
+    assert 'engine=None' in log_message
+    assert 'filter=f' in log_message
+    assert 'max_results=5' in log_message
+    assert 'data_store_specs=None' in log_message
 
   @pytest.mark.asyncio
-  async def test_process_llm_request_with_path_based_gemini_model(self):
+  async def test_process_llm_request_with_path_based_gemini_model(self, caplog):
     """Test processing LLM request with path-based Gemini model name."""
-    tool = VertexAiSearchTool(data_store_id='test_data_store')
+    caplog.set_level(logging.DEBUG, logger=VERTEX_SEARCH_TOOL_LOGGER_NAME)
+
+    specs = [
+        types.VertexAISearchDataStoreSpec(
+            dataStore=(
+                'projects/p/locations/l/collections/c/dataStores/spec_store'
+            )
+        )
+    ]
+    engine_id = 'projects/p/locations/l/collections/c/engines/test_engine'
+    tool = VertexAiSearchTool(
+        search_engine_id=engine_id,
+        data_store_specs=specs,
+        filter='f2',
+        max_results=10,
+    )
     tool_context = await _create_tool_context()
 
     llm_request = LlmRequest(
-        model='projects/265104255505/locations/us-central1/publishers/google/models/gemini-2.0-flash-001',
+        model=(
+            'projects/265104255505/locations/us-central1/publishers/'
+            'google/models/gemini-2.0-flash-001'
+        ),
         config=types.GenerateContentConfig(),
     )
 
@@ -182,8 +273,28 @@ async def test_process_llm_request_with_path_based_gemini_model(self):
 
     assert llm_request.config.tools is not None
     assert len(llm_request.config.tools) == 1
-    assert llm_request.config.tools[0].retrieval is not None
-    assert llm_request.config.tools[0].retrieval.vertex_ai_search is not None
+    retrieval_tool = llm_request.config.tools[0]
+    assert retrieval_tool.retrieval is not None
+    assert retrieval_tool.retrieval.vertex_ai_search is not None
+    assert retrieval_tool.retrieval.vertex_ai_search.datastore is None
+    assert retrieval_tool.retrieval.vertex_ai_search.engine == engine_id
+    assert retrieval_tool.retrieval.vertex_ai_search.filter == 'f2'
+    assert retrieval_tool.retrieval.vertex_ai_search.max_results == 10
+    assert retrieval_tool.retrieval.vertex_ai_search.data_store_specs == specs
+
+    # Verify debug log
+    debug_records = [
+        r
+        for r in caplog.records
+        if 'Adding Vertex AI Search tool config' in r.message
+    ]
+    assert len(debug_records) == 1
+    log_message = debug_records[0].getMessage()
+    assert 'datastore=None' in log_message
+    assert f'engine={engine_id}' in log_message
+    assert 'filter=f2' in log_message
+    assert 'max_results=10' in log_message
+    assert 'data_store_specs=1 spec(s): [spec_store]' in log_message
 
   @pytest.mark.asyncio
   async def test_process_llm_request_with_gemini_1_and_other_tools_raises_error(
@@ -291,9 +402,11 @@ async def test_process_llm_request_with_path_based_non_gemini_model_raises_error
 
   @pytest.mark.asyncio
   async def test_process_llm_request_with_gemini_2_and_other_tools_succeeds(
-      self,
+      self, caplog
   ):
     """Test that Gemini 2.x with other tools succeeds."""
+    caplog.set_level(logging.DEBUG, logger=VERTEX_SEARCH_TOOL_LOGGER_NAME)
+
     tool = VertexAiSearchTool(data_store_id='test_data_store')
     tool_context = await _create_tool_context()
 
@@ -316,5 +429,23 @@ async def test_process_llm_request_with_gemini_2_and_other_tools_succeeds(
     assert llm_request.config.tools is not None
     assert len(llm_request.config.tools) == 2
     assert llm_request.config.tools[0] == existing_tool
-    assert llm_request.config.tools[1].retrieval is not None
-    assert llm_request.config.tools[1].retrieval.vertex_ai_search is not None
+    retrieval_tool = llm_request.config.tools[1]
+    assert retrieval_tool.retrieval is not None
+    assert retrieval_tool.retrieval.vertex_ai_search is not None
+    assert (
+        retrieval_tool.retrieval.vertex_ai_search.datastore == 'test_data_store'
+    )
+
+    # Verify debug log
+    debug_records = [
+        r
+        for r in caplog.records
+        if 'Adding Vertex AI Search tool config' in r.message
+    ]
+    assert len(debug_records) == 1
+    log_message = debug_records[0].getMessage()
+    assert 'datastore=test_data_store' in log_message
+    assert 'engine=None' in log_message
+    assert 'filter=None' in log_message
+    assert 'max_results=None' in log_message
+    assert 'data_store_specs=None' in log_message