wehub-resource-sync
ec2b666284
Continuous Integration / Pre-commit Linter (push) Has been cancelled
Continuous Integration / Mypy Check (Python 3.10) (push) Has been cancelled
Continuous Integration / Mypy Check (Python 3.11) (push) Has been cancelled
Continuous Integration / Mypy Check (Python 3.12) (push) Has been cancelled
Continuous Integration / Mypy Check (Python 3.13) (push) Has been cancelled
Continuous Integration / Unit Tests (Python 3.10) (push) Has been cancelled
Continuous Integration / Unit Tests (Python 3.11) (push) Has been cancelled
Continuous Integration / Unit Tests (Python 3.12) (push) Has been cancelled
Continuous Integration / Unit Tests (Python 3.13) (push) Has been cancelled
Continuous Integration / Unit Tests (Python 3.14) (push) Has been cancelled
Continuous Integration / A2A v0.3 Tests (Python 3.10) (push) Has been cancelled
Continuous Integration / A2A v0.3 Tests (Python 3.11) (push) Has been cancelled
Continuous Integration / A2A v0.3 Tests (Python 3.12) (push) Has been cancelled
Copybara PR Handler / close-imported-pr (push) Has been cancelled
Continuous Integration / A2A v0.3 Tests (Python 3.13) (push) Has been cancelled
Continuous Integration / A2A v0.3 Tests (Python 3.14) (push) Has been cancelled
583 lines
21 KiB
Python
583 lines
21 KiB
Python
# Copyright 2026 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 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
|
|
from google.adk.sessions.in_memory_session_service import InMemorySessionService
|
|
from google.adk.tools.tool_context import ToolContext
|
|
from google.adk.tools.vertex_ai_search_tool import VertexAiSearchTool
|
|
from google.adk.utils.model_name_utils import extract_model_name
|
|
from google.adk.utils.model_name_utils import is_gemini_1_model
|
|
from google.adk.utils.model_name_utils import is_gemini_model
|
|
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()
|
|
session = await session_service.create_session(
|
|
app_name='test_app', user_id='test_user'
|
|
)
|
|
agent = SequentialAgent(name='test_agent')
|
|
invocation_context = InvocationContext(
|
|
invocation_id='invocation_id',
|
|
agent=agent,
|
|
session=session,
|
|
session_service=session_service,
|
|
)
|
|
return ToolContext(invocation_context=invocation_context)
|
|
|
|
|
|
class TestVertexAiSearchToolHelperFunctions:
|
|
"""Test the helper functions for model name extraction and validation."""
|
|
|
|
def test_extract_model_name_simple_model(self):
|
|
"""Test extraction of simple model names."""
|
|
assert extract_model_name('gemini-2.5-pro') == 'gemini-2.5-pro'
|
|
assert extract_model_name('gemini-2.5-flash') == 'gemini-2.5-flash'
|
|
assert extract_model_name('gemini-1.0-pro') == 'gemini-1.0-pro'
|
|
assert extract_model_name('claude-3-sonnet') == 'claude-3-sonnet'
|
|
|
|
def test_extract_model_name_path_based_model(self):
|
|
"""Test extraction of path-based model names."""
|
|
path_model = 'projects/265104255505/locations/us-central1/publishers/google/models/gemini-2.5-flash'
|
|
assert extract_model_name(path_model) == 'gemini-2.5-flash'
|
|
|
|
path_model_2 = 'projects/12345/locations/us-east1/publishers/google/models/gemini-1.5-pro-preview'
|
|
assert extract_model_name(path_model_2) == 'gemini-1.5-pro-preview'
|
|
|
|
def test_extract_model_name_invalid_path(self):
|
|
"""Test that invalid path formats return the original string."""
|
|
invalid_path = 'projects/invalid/path/format'
|
|
assert extract_model_name(invalid_path) == invalid_path
|
|
|
|
def test_is_gemini_model_simple_names(self):
|
|
"""Test Gemini model detection with simple model names."""
|
|
assert is_gemini_model('gemini-2.5-pro') is True
|
|
assert is_gemini_model('gemini-2.5-flash') is True
|
|
assert is_gemini_model('gemini-1.0-pro') is True
|
|
assert is_gemini_model('claude-3-sonnet') is False
|
|
assert is_gemini_model('gpt-4') is False
|
|
assert is_gemini_model('gemini') is False # Must have dash after gemini
|
|
|
|
def test_is_gemini_model_path_based_names(self):
|
|
"""Test Gemini model detection with path-based model names."""
|
|
gemini_path = 'projects/265104255505/locations/us-central1/publishers/google/models/gemini-2.5-flash'
|
|
assert is_gemini_model(gemini_path) is True
|
|
|
|
non_gemini_path = 'projects/265104255505/locations/us-central1/publishers/google/models/claude-3-sonnet'
|
|
assert is_gemini_model(non_gemini_path) is False
|
|
|
|
def test_is_gemini_1_model_simple_names(self):
|
|
"""Test Gemini 1.x model detection with simple model names."""
|
|
assert is_gemini_1_model('gemini-1.5-flash') is True
|
|
assert is_gemini_1_model('gemini-1.0-pro') is True
|
|
assert is_gemini_1_model('gemini-1.5-pro-preview') is True
|
|
assert is_gemini_1_model('gemini-2.0-flash') is False
|
|
assert is_gemini_1_model('gemini-2.5-flash') is False
|
|
assert is_gemini_1_model('gemini-2.5-pro') is False
|
|
assert is_gemini_1_model('gemini-10.0-pro') is False # Only 1.x versions
|
|
assert is_gemini_1_model('claude-3-sonnet') is False
|
|
|
|
def test_is_gemini_1_model_path_based_names(self):
|
|
"""Test Gemini 1.x model detection with path-based model names."""
|
|
gemini_1_path = 'projects/265104255505/locations/us-central1/publishers/google/models/gemini-1.5-flash'
|
|
assert is_gemini_1_model(gemini_1_path) is True
|
|
|
|
gemini_2_path = 'projects/265104255505/locations/us-central1/publishers/google/models/gemini-2.5-flash'
|
|
assert is_gemini_1_model(gemini_2_path) is False
|
|
|
|
def test_edge_cases(self):
|
|
"""Test edge cases for model name validation."""
|
|
# Test with empty string
|
|
assert is_gemini_model('') is False
|
|
assert is_gemini_1_model('') is False
|
|
|
|
# Test with model names containing gemini but not starting with it
|
|
assert is_gemini_model('my-gemini-model') is False
|
|
assert is_gemini_1_model('my-gemini-1.5-model') is False
|
|
|
|
# Test with model names that have gemini in the middle of the path
|
|
tricky_path = 'projects/265104255505/locations/us-central1/publishers/gemini/models/claude-3-sonnet'
|
|
assert is_gemini_model(tricky_path) is False
|
|
|
|
|
|
class TestVertexAiSearchTool:
|
|
"""Test the VertexAiSearchTool class."""
|
|
|
|
def test_init_with_data_store_id(self):
|
|
"""Test initialization with data store ID."""
|
|
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."""
|
|
with pytest.raises(
|
|
ValueError,
|
|
match='Either data_store_id or search_engine_id must be specified',
|
|
):
|
|
VertexAiSearchTool()
|
|
|
|
def test_init_with_both_raises_error(self):
|
|
"""Test that initialization with both IDs raises ValueError."""
|
|
with pytest.raises(
|
|
ValueError,
|
|
match='Either data_store_id or search_engine_id must be specified',
|
|
):
|
|
VertexAiSearchTool(
|
|
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, caplog):
|
|
"""Test processing LLM request with simple Gemini model name."""
|
|
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(
|
|
model='gemini-2.5-pro', config=types.GenerateContentConfig()
|
|
)
|
|
|
|
await tool.process_llm_request(
|
|
tool_context=tool_context, llm_request=llm_request
|
|
)
|
|
|
|
assert llm_request.config.tools is not None
|
|
assert len(llm_request.config.tools) == 1
|
|
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, caplog):
|
|
"""Test processing LLM request with path-based Gemini model name."""
|
|
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-2.5-flash'
|
|
),
|
|
config=types.GenerateContentConfig(),
|
|
)
|
|
|
|
await tool.process_llm_request(
|
|
tool_context=tool_context, llm_request=llm_request
|
|
)
|
|
|
|
assert llm_request.config.tools is not None
|
|
assert len(llm_request.config.tools) == 1
|
|
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(
|
|
self,
|
|
):
|
|
"""Test that Gemini 1.x with other tools raises ValueError."""
|
|
tool = VertexAiSearchTool(data_store_id='test_data_store')
|
|
tool_context = await _create_tool_context()
|
|
|
|
existing_tool = types.Tool(
|
|
function_declarations=[
|
|
types.FunctionDeclaration(name='test_function', description='test')
|
|
]
|
|
)
|
|
|
|
llm_request = LlmRequest(
|
|
model='gemini-1.5-flash',
|
|
config=types.GenerateContentConfig(tools=[existing_tool]),
|
|
)
|
|
|
|
with pytest.raises(
|
|
ValueError,
|
|
match=(
|
|
'Vertex AI search tool cannot be used with other tools in'
|
|
' Gemini 1.x'
|
|
),
|
|
):
|
|
await tool.process_llm_request(
|
|
tool_context=tool_context, llm_request=llm_request
|
|
)
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_process_llm_request_with_path_based_gemini_1_and_other_tools_raises_error(
|
|
self,
|
|
):
|
|
"""Test that path-based Gemini 1.x with other tools raises ValueError."""
|
|
tool = VertexAiSearchTool(data_store_id='test_data_store')
|
|
tool_context = await _create_tool_context()
|
|
|
|
existing_tool = types.Tool(
|
|
function_declarations=[
|
|
types.FunctionDeclaration(name='test_function', description='test')
|
|
]
|
|
)
|
|
|
|
llm_request = LlmRequest(
|
|
model='projects/265104255505/locations/us-central1/publishers/google/models/gemini-1.5-pro-preview',
|
|
config=types.GenerateContentConfig(tools=[existing_tool]),
|
|
)
|
|
|
|
with pytest.raises(
|
|
ValueError,
|
|
match=(
|
|
'Vertex AI search tool cannot be used with other tools in'
|
|
' Gemini 1.x'
|
|
),
|
|
):
|
|
await tool.process_llm_request(
|
|
tool_context=tool_context, llm_request=llm_request
|
|
)
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_process_llm_request_with_non_gemini_model_raises_error(self):
|
|
"""Test that non-Gemini model raises ValueError."""
|
|
tool = VertexAiSearchTool(data_store_id='test_data_store')
|
|
tool_context = await _create_tool_context()
|
|
|
|
llm_request = LlmRequest(
|
|
model='claude-3-sonnet', config=types.GenerateContentConfig()
|
|
)
|
|
|
|
with pytest.raises(
|
|
ValueError,
|
|
match=(
|
|
'Vertex AI search tool is not supported for model claude-3-sonnet'
|
|
),
|
|
):
|
|
await tool.process_llm_request(
|
|
tool_context=tool_context, llm_request=llm_request
|
|
)
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_process_llm_request_with_non_gemini_model_and_disabled_check(
|
|
self, monkeypatch
|
|
):
|
|
"""Test non-Gemini model can pass when model-id check is disabled."""
|
|
monkeypatch.setenv('ADK_DISABLE_GEMINI_MODEL_ID_CHECK', 'true')
|
|
tool = VertexAiSearchTool(data_store_id='test_data_store')
|
|
tool_context = await _create_tool_context()
|
|
|
|
llm_request = LlmRequest(
|
|
model='internal-model-v1', config=types.GenerateContentConfig()
|
|
)
|
|
|
|
await tool.process_llm_request(
|
|
tool_context=tool_context, llm_request=llm_request
|
|
)
|
|
|
|
assert llm_request.config.tools is not None
|
|
assert len(llm_request.config.tools) == 1
|
|
retrieval_tool = llm_request.config.tools[0]
|
|
assert retrieval_tool.retrieval is not None
|
|
assert retrieval_tool.retrieval.vertex_ai_search is not None
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_process_llm_request_with_path_based_non_gemini_model_raises_error(
|
|
self,
|
|
):
|
|
"""Test that path-based non-Gemini model raises ValueError."""
|
|
tool = VertexAiSearchTool(data_store_id='test_data_store')
|
|
tool_context = await _create_tool_context()
|
|
|
|
non_gemini_path = 'projects/265104255505/locations/us-central1/publishers/google/models/claude-3-sonnet'
|
|
llm_request = LlmRequest(
|
|
model=non_gemini_path, config=types.GenerateContentConfig()
|
|
)
|
|
|
|
with pytest.raises(
|
|
ValueError,
|
|
match=(
|
|
'Vertex AI search tool is not supported for model'
|
|
f' {non_gemini_path}'
|
|
),
|
|
):
|
|
await tool.process_llm_request(
|
|
tool_context=tool_context, llm_request=llm_request
|
|
)
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_process_llm_request_with_gemini_2_and_other_tools_succeeds(
|
|
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()
|
|
|
|
existing_tool = types.Tool(
|
|
function_declarations=[
|
|
types.FunctionDeclaration(name='test_function', description='test')
|
|
]
|
|
)
|
|
|
|
llm_request = LlmRequest(
|
|
model='gemini-2.5-pro',
|
|
config=types.GenerateContentConfig(tools=[existing_tool]),
|
|
)
|
|
|
|
await tool.process_llm_request(
|
|
tool_context=tool_context, llm_request=llm_request
|
|
)
|
|
|
|
# Should have both the existing tool and the new vertex AI search tool
|
|
assert llm_request.config.tools is not None
|
|
assert len(llm_request.config.tools) == 2
|
|
assert llm_request.config.tools[0] == existing_tool
|
|
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
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_subclass_with_dynamic_filter(self):
|
|
"""Test subclassing to provide dynamic filter based on context."""
|
|
|
|
class DynamicFilterSearchTool(VertexAiSearchTool):
|
|
"""Custom search tool with dynamic filter."""
|
|
|
|
def _build_vertex_ai_search_config(self, ctx):
|
|
user_id = ctx.state.get('user_id', 'default_user')
|
|
return types.VertexAISearch(
|
|
datastore=self.data_store_id,
|
|
engine=self.search_engine_id,
|
|
filter=f"user_id = '{user_id}'",
|
|
max_results=self.max_results,
|
|
)
|
|
|
|
tool = DynamicFilterSearchTool(data_store_id='test_data_store')
|
|
tool_context = await _create_tool_context()
|
|
tool_context.state['user_id'] = 'test_user_123'
|
|
|
|
llm_request = LlmRequest(
|
|
model='gemini-2.5-pro', config=types.GenerateContentConfig()
|
|
)
|
|
|
|
await tool.process_llm_request(
|
|
tool_context=tool_context, llm_request=llm_request
|
|
)
|
|
|
|
assert llm_request.config.tools is not None
|
|
assert len(llm_request.config.tools) == 1
|
|
retrieval_tool = llm_request.config.tools[0]
|
|
assert retrieval_tool.retrieval is not None
|
|
assert retrieval_tool.retrieval.vertex_ai_search is not None
|
|
# Verify the filter was dynamically set
|
|
assert (
|
|
retrieval_tool.retrieval.vertex_ai_search.filter
|
|
== "user_id = 'test_user_123'"
|
|
)
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_subclass_with_dynamic_max_results(self):
|
|
"""Test subclassing to provide dynamic max_results based on context."""
|
|
|
|
class DynamicMaxResultsSearchTool(VertexAiSearchTool):
|
|
"""Custom search tool with dynamic max_results."""
|
|
|
|
def _build_vertex_ai_search_config(self, ctx):
|
|
# Use a larger max_results for premium users
|
|
is_premium = ctx.state.get('is_premium', False)
|
|
dynamic_max_results = 20 if is_premium else 5
|
|
return types.VertexAISearch(
|
|
datastore=self.data_store_id,
|
|
engine=self.search_engine_id,
|
|
filter=self.filter,
|
|
max_results=dynamic_max_results,
|
|
)
|
|
|
|
tool = DynamicMaxResultsSearchTool(
|
|
data_store_id='test_data_store', max_results=10
|
|
)
|
|
tool_context = await _create_tool_context()
|
|
tool_context.state['is_premium'] = True
|
|
|
|
llm_request = LlmRequest(
|
|
model='gemini-2.5-pro', config=types.GenerateContentConfig()
|
|
)
|
|
|
|
await tool.process_llm_request(
|
|
tool_context=tool_context, llm_request=llm_request
|
|
)
|
|
|
|
retrieval_tool = llm_request.config.tools[0]
|
|
# Verify max_results was dynamically set to premium value
|
|
assert retrieval_tool.retrieval.vertex_ai_search.max_results == 20
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_subclass_receives_readonly_context(self):
|
|
"""Test that subclass receives the context correctly."""
|
|
received_contexts = []
|
|
|
|
class ContextCapturingSearchTool(VertexAiSearchTool):
|
|
"""Custom search tool that captures the context."""
|
|
|
|
def _build_vertex_ai_search_config(self, ctx):
|
|
received_contexts.append(ctx)
|
|
return types.VertexAISearch(
|
|
datastore=self.data_store_id,
|
|
engine=self.search_engine_id,
|
|
filter=self.filter,
|
|
max_results=self.max_results,
|
|
)
|
|
|
|
tool = ContextCapturingSearchTool(data_store_id='test_data_store')
|
|
tool_context = await _create_tool_context()
|
|
|
|
llm_request = LlmRequest(
|
|
model='gemini-2.5-pro', config=types.GenerateContentConfig()
|
|
)
|
|
|
|
await tool.process_llm_request(
|
|
tool_context=tool_context, llm_request=llm_request
|
|
)
|
|
|
|
# Verify the context was passed to _build_vertex_ai_search_config
|
|
assert len(received_contexts) == 1
|
|
assert received_contexts[0] is tool_context
|