mirror of
https://github.com/encounter/adk-python.git
synced 2026-07-09 18:19:28 -07:00
Xiang (Sean) ZhouandCopybara-Service
584c8c6d91
- Allow run_async to break on partial events instead of raising ValueError - Generate aggregated streaming content regardless of finish_reason - Add error_code and error_message to final streaming responses if model response is interrupted. PiperOrigin-RevId: 781377328
1372 lines
43 KiB
Python
1372 lines
43 KiB
Python
# 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 typing import Optional
|
|
from unittest import mock
|
|
|
|
from google.adk import version as adk_version
|
|
from google.adk.models.gemini_llm_connection import GeminiLlmConnection
|
|
from google.adk.models.google_llm import _AGENT_ENGINE_TELEMETRY_ENV_VARIABLE_NAME
|
|
from google.adk.models.google_llm import _AGENT_ENGINE_TELEMETRY_TAG
|
|
from google.adk.models.google_llm import Gemini
|
|
from google.adk.models.llm_request import LlmRequest
|
|
from google.adk.models.llm_response import LlmResponse
|
|
from google.adk.utils.variant_utils import GoogleLLMVariant
|
|
from google.genai import types
|
|
from google.genai import version as genai_version
|
|
from google.genai.types import Content
|
|
from google.genai.types import Part
|
|
import pytest
|
|
|
|
|
|
@pytest.fixture
|
|
def generate_content_response():
|
|
return types.GenerateContentResponse(
|
|
candidates=[
|
|
types.Candidate(
|
|
content=Content(
|
|
role="model",
|
|
parts=[Part.from_text(text="Hello, how can I help you?")],
|
|
),
|
|
finish_reason=types.FinishReason.STOP,
|
|
)
|
|
]
|
|
)
|
|
|
|
|
|
@pytest.fixture
|
|
def gemini_llm():
|
|
return Gemini(model="gemini-1.5-flash")
|
|
|
|
|
|
@pytest.fixture
|
|
def llm_request():
|
|
return LlmRequest(
|
|
model="gemini-1.5-flash",
|
|
contents=[Content(role="user", parts=[Part.from_text(text="Hello")])],
|
|
config=types.GenerateContentConfig(
|
|
temperature=0.1,
|
|
response_modalities=[types.Modality.TEXT],
|
|
system_instruction="You are a helpful assistant",
|
|
),
|
|
)
|
|
|
|
|
|
@pytest.fixture
|
|
def mock_os_environ():
|
|
initial_env = os.environ.copy()
|
|
with mock.patch.dict(os.environ, initial_env, clear=False) as m:
|
|
yield m
|
|
|
|
|
|
def test_supported_models():
|
|
models = Gemini.supported_models()
|
|
assert len(models) == 3
|
|
assert models[0] == r"gemini-.*"
|
|
assert models[1] == r"projects\/.+\/locations\/.+\/endpoints\/.+"
|
|
assert (
|
|
models[2]
|
|
== r"projects\/.+\/locations\/.+\/publishers\/google\/models\/gemini.+"
|
|
)
|
|
|
|
|
|
def test_client_version_header():
|
|
model = Gemini(model="gemini-1.5-flash")
|
|
client = model.api_client
|
|
adk_header = (
|
|
f"google-adk/{adk_version.__version__} gl-python/{sys.version.split()[0]}"
|
|
)
|
|
genai_header = (
|
|
f"google-genai-sdk/{genai_version.__version__} gl-python/{sys.version.split()[0]} "
|
|
)
|
|
expected_header = genai_header + adk_header
|
|
|
|
assert (
|
|
expected_header
|
|
in client._api_client._http_options.headers["x-goog-api-client"]
|
|
)
|
|
assert (
|
|
expected_header in client._api_client._http_options.headers["user-agent"]
|
|
)
|
|
|
|
|
|
def test_client_version_header_with_agent_engine(mock_os_environ):
|
|
os.environ[_AGENT_ENGINE_TELEMETRY_ENV_VARIABLE_NAME] = "my_test_project"
|
|
model = Gemini(model="gemini-1.5-flash")
|
|
client = model.api_client
|
|
adk_header_base = f"google-adk/{adk_version.__version__}"
|
|
adk_header_with_telemetry = (
|
|
f"{adk_header_base}+{_AGENT_ENGINE_TELEMETRY_TAG}"
|
|
f" gl-python/{sys.version.split()[0]}"
|
|
)
|
|
genai_header = (
|
|
f"google-genai-sdk/{genai_version.__version__} "
|
|
f"gl-python/{sys.version.split()[0]} "
|
|
)
|
|
expected_header = genai_header + adk_header_with_telemetry
|
|
|
|
assert (
|
|
expected_header
|
|
in client._api_client._http_options.headers["x-goog-api-client"]
|
|
)
|
|
assert (
|
|
expected_header in client._api_client._http_options.headers["user-agent"]
|
|
)
|
|
|
|
|
|
def test_maybe_append_user_content(gemini_llm, llm_request):
|
|
# Test with user content already present
|
|
gemini_llm._maybe_append_user_content(llm_request)
|
|
assert len(llm_request.contents) == 1
|
|
|
|
# Test with model content as the last message
|
|
llm_request.contents.append(
|
|
Content(role="model", parts=[Part.from_text(text="Response")])
|
|
)
|
|
gemini_llm._maybe_append_user_content(llm_request)
|
|
assert len(llm_request.contents) == 3
|
|
assert llm_request.contents[-1].role == "user"
|
|
assert "Continue processing" in llm_request.contents[-1].parts[0].text
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_generate_content_async(
|
|
gemini_llm, llm_request, generate_content_response
|
|
):
|
|
with mock.patch.object(gemini_llm, "api_client") as mock_client:
|
|
# Create a mock coroutine that returns the generate_content_response
|
|
async def mock_coro():
|
|
return generate_content_response
|
|
|
|
# Assign the coroutine to the mocked method
|
|
mock_client.aio.models.generate_content.return_value = mock_coro()
|
|
|
|
responses = [
|
|
resp
|
|
async for resp in gemini_llm.generate_content_async(
|
|
llm_request, stream=False
|
|
)
|
|
]
|
|
|
|
assert len(responses) == 1
|
|
assert isinstance(responses[0], LlmResponse)
|
|
assert responses[0].content.parts[0].text == "Hello, how can I help you?"
|
|
mock_client.aio.models.generate_content.assert_called_once()
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_generate_content_async_stream(gemini_llm, llm_request):
|
|
with mock.patch.object(gemini_llm, "api_client") as mock_client:
|
|
# Create mock stream responses
|
|
class MockAsyncIterator:
|
|
|
|
def __init__(self, seq):
|
|
self.iter = iter(seq)
|
|
|
|
def __aiter__(self):
|
|
return self
|
|
|
|
async def __anext__(self):
|
|
try:
|
|
return next(self.iter)
|
|
except StopIteration:
|
|
raise StopAsyncIteration
|
|
|
|
mock_responses = [
|
|
types.GenerateContentResponse(
|
|
candidates=[
|
|
types.Candidate(
|
|
content=Content(
|
|
role="model", parts=[Part.from_text(text="Hello")]
|
|
),
|
|
finish_reason=None,
|
|
)
|
|
]
|
|
),
|
|
types.GenerateContentResponse(
|
|
candidates=[
|
|
types.Candidate(
|
|
content=Content(
|
|
role="model", parts=[Part.from_text(text=", how")]
|
|
),
|
|
finish_reason=None,
|
|
)
|
|
]
|
|
),
|
|
types.GenerateContentResponse(
|
|
candidates=[
|
|
types.Candidate(
|
|
content=Content(
|
|
role="model",
|
|
parts=[Part.from_text(text=" can I help you?")],
|
|
),
|
|
finish_reason=types.FinishReason.STOP,
|
|
)
|
|
]
|
|
),
|
|
]
|
|
|
|
# Create a mock coroutine that returns the MockAsyncIterator
|
|
async def mock_coro():
|
|
return MockAsyncIterator(mock_responses)
|
|
|
|
# Set the mock to return the coroutine
|
|
mock_client.aio.models.generate_content_stream.return_value = mock_coro()
|
|
|
|
responses = [
|
|
resp
|
|
async for resp in gemini_llm.generate_content_async(
|
|
llm_request, stream=True
|
|
)
|
|
]
|
|
|
|
# Assertions remain the same
|
|
assert len(responses) == 4
|
|
assert responses[0].partial is True
|
|
assert responses[1].partial is True
|
|
assert responses[2].partial is True
|
|
assert responses[3].content.parts[0].text == "Hello, how can I help you?"
|
|
mock_client.aio.models.generate_content_stream.assert_called_once()
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_generate_content_async_stream_preserves_thinking_and_text_parts(
|
|
gemini_llm, llm_request
|
|
):
|
|
with mock.patch.object(gemini_llm, "api_client") as mock_client:
|
|
|
|
class MockAsyncIterator:
|
|
|
|
def __init__(self, seq):
|
|
self._iter = iter(seq)
|
|
|
|
def __aiter__(self):
|
|
return self
|
|
|
|
async def __anext__(self):
|
|
try:
|
|
return next(self._iter)
|
|
except StopIteration:
|
|
raise StopAsyncIteration
|
|
|
|
response1 = types.GenerateContentResponse(
|
|
candidates=[
|
|
types.Candidate(
|
|
content=Content(
|
|
role="model",
|
|
parts=[Part(text="Think1", thought=True)],
|
|
),
|
|
finish_reason=None,
|
|
)
|
|
]
|
|
)
|
|
response2 = types.GenerateContentResponse(
|
|
candidates=[
|
|
types.Candidate(
|
|
content=Content(
|
|
role="model",
|
|
parts=[Part(text="Think2", thought=True)],
|
|
),
|
|
finish_reason=None,
|
|
)
|
|
]
|
|
)
|
|
response3 = types.GenerateContentResponse(
|
|
candidates=[
|
|
types.Candidate(
|
|
content=Content(
|
|
role="model",
|
|
parts=[Part.from_text(text="Answer.")],
|
|
),
|
|
finish_reason=types.FinishReason.STOP,
|
|
)
|
|
]
|
|
)
|
|
|
|
async def mock_coro():
|
|
return MockAsyncIterator([response1, response2, response3])
|
|
|
|
mock_client.aio.models.generate_content_stream.return_value = mock_coro()
|
|
|
|
responses = [
|
|
resp
|
|
async for resp in gemini_llm.generate_content_async(
|
|
llm_request, stream=True
|
|
)
|
|
]
|
|
|
|
assert len(responses) == 4
|
|
assert responses[0].partial is True
|
|
assert responses[1].partial is True
|
|
assert responses[2].partial is True
|
|
assert responses[3].content.parts[0].text == "Think1Think2"
|
|
assert responses[3].content.parts[0].thought is True
|
|
assert responses[3].content.parts[1].text == "Answer."
|
|
mock_client.aio.models.generate_content_stream.assert_called_once()
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_connect(gemini_llm, llm_request):
|
|
# Create a mock connection
|
|
mock_connection = mock.MagicMock(spec=GeminiLlmConnection)
|
|
|
|
# Create a mock context manager
|
|
class MockContextManager:
|
|
|
|
async def __aenter__(self):
|
|
return mock_connection
|
|
|
|
async def __aexit__(self, *args):
|
|
pass
|
|
|
|
# Mock the connect method at the class level
|
|
with mock.patch(
|
|
"google.adk.models.google_llm.Gemini.connect",
|
|
return_value=MockContextManager(),
|
|
):
|
|
async with gemini_llm.connect(llm_request) as connection:
|
|
assert connection is mock_connection
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_generate_content_async_with_custom_headers(
|
|
gemini_llm, llm_request, generate_content_response
|
|
):
|
|
"""Test that tracking headers are updated when custom headers are provided."""
|
|
# Add custom headers to the request config
|
|
custom_headers = {"custom-header": "custom-value"}
|
|
for key in gemini_llm._tracking_headers:
|
|
custom_headers[key] = "custom " + gemini_llm._tracking_headers[key]
|
|
llm_request.config.http_options = types.HttpOptions(headers=custom_headers)
|
|
|
|
with mock.patch.object(gemini_llm, "api_client") as mock_client:
|
|
# Create a mock coroutine that returns the generate_content_response
|
|
async def mock_coro():
|
|
return generate_content_response
|
|
|
|
mock_client.aio.models.generate_content.return_value = mock_coro()
|
|
|
|
responses = [
|
|
resp
|
|
async for resp in gemini_llm.generate_content_async(
|
|
llm_request, stream=False
|
|
)
|
|
]
|
|
|
|
# Verify that the config passed to generate_content contains merged headers
|
|
mock_client.aio.models.generate_content.assert_called_once()
|
|
call_args = mock_client.aio.models.generate_content.call_args
|
|
config_arg = call_args.kwargs["config"]
|
|
|
|
for key, value in config_arg.http_options.headers.items():
|
|
if key in gemini_llm._tracking_headers:
|
|
assert value == gemini_llm._tracking_headers[key]
|
|
else:
|
|
assert value == custom_headers[key]
|
|
|
|
assert len(responses) == 1
|
|
assert isinstance(responses[0], LlmResponse)
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_generate_content_async_stream_with_custom_headers(
|
|
gemini_llm, llm_request
|
|
):
|
|
"""Test that tracking headers are updated when custom headers are provided in streaming mode."""
|
|
# Add custom headers to the request config
|
|
custom_headers = {"custom-header": "custom-value"}
|
|
llm_request.config.http_options = types.HttpOptions(headers=custom_headers)
|
|
|
|
with mock.patch.object(gemini_llm, "api_client") as mock_client:
|
|
# Create mock stream responses
|
|
class MockAsyncIterator:
|
|
|
|
def __init__(self, seq):
|
|
self.iter = iter(seq)
|
|
|
|
def __aiter__(self):
|
|
return self
|
|
|
|
async def __anext__(self):
|
|
try:
|
|
return next(self.iter)
|
|
except StopIteration:
|
|
raise StopAsyncIteration
|
|
|
|
mock_responses = [
|
|
types.GenerateContentResponse(
|
|
candidates=[
|
|
types.Candidate(
|
|
content=Content(
|
|
role="model", parts=[Part.from_text(text="Hello")]
|
|
),
|
|
finish_reason=types.FinishReason.STOP,
|
|
)
|
|
]
|
|
)
|
|
]
|
|
|
|
async def mock_coro():
|
|
return MockAsyncIterator(mock_responses)
|
|
|
|
mock_client.aio.models.generate_content_stream.return_value = mock_coro()
|
|
|
|
responses = [
|
|
resp
|
|
async for resp in gemini_llm.generate_content_async(
|
|
llm_request, stream=True
|
|
)
|
|
]
|
|
|
|
# Verify that the config passed to generate_content_stream contains merged headers
|
|
mock_client.aio.models.generate_content_stream.assert_called_once()
|
|
call_args = mock_client.aio.models.generate_content_stream.call_args
|
|
config_arg = call_args.kwargs["config"]
|
|
|
|
expected_headers = custom_headers.copy()
|
|
expected_headers.update(gemini_llm._tracking_headers)
|
|
assert config_arg.http_options.headers == expected_headers
|
|
|
|
assert len(responses) == 2
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_generate_content_async_without_custom_headers(
|
|
gemini_llm, llm_request, generate_content_response
|
|
):
|
|
"""Test that tracking headers are not modified when no custom headers exist."""
|
|
# Ensure no http_options exist initially
|
|
llm_request.config.http_options = None
|
|
|
|
with mock.patch.object(gemini_llm, "api_client") as mock_client:
|
|
|
|
async def mock_coro():
|
|
return generate_content_response
|
|
|
|
mock_client.aio.models.generate_content.return_value = mock_coro()
|
|
|
|
responses = [
|
|
resp
|
|
async for resp in gemini_llm.generate_content_async(
|
|
llm_request, stream=False
|
|
)
|
|
]
|
|
|
|
# Verify that the config passed to generate_content has no http_options
|
|
mock_client.aio.models.generate_content.assert_called_once()
|
|
call_args = mock_client.aio.models.generate_content.call_args
|
|
config_arg = call_args.kwargs["config"]
|
|
assert config_arg.http_options is None
|
|
|
|
assert len(responses) == 1
|
|
|
|
|
|
def test_live_api_version_vertex_ai(gemini_llm):
|
|
"""Test that _live_api_version returns 'v1beta1' for Vertex AI backend."""
|
|
with mock.patch.object(
|
|
gemini_llm, "_api_backend", GoogleLLMVariant.VERTEX_AI
|
|
):
|
|
assert gemini_llm._live_api_version == "v1beta1"
|
|
|
|
|
|
def test_live_api_version_gemini_api(gemini_llm):
|
|
"""Test that _live_api_version returns 'v1alpha' for Gemini API backend."""
|
|
with mock.patch.object(
|
|
gemini_llm, "_api_backend", GoogleLLMVariant.GEMINI_API
|
|
):
|
|
assert gemini_llm._live_api_version == "v1alpha"
|
|
|
|
|
|
def test_live_api_client_properties(gemini_llm):
|
|
"""Test that _live_api_client is properly configured with tracking headers and API version."""
|
|
with mock.patch.object(
|
|
gemini_llm, "_api_backend", GoogleLLMVariant.VERTEX_AI
|
|
):
|
|
client = gemini_llm._live_api_client
|
|
|
|
# Verify that the client has the correct headers and API version
|
|
http_options = client._api_client._http_options
|
|
assert http_options.api_version == "v1beta1"
|
|
|
|
# Check that tracking headers are included
|
|
tracking_headers = gemini_llm._tracking_headers
|
|
for key, value in tracking_headers.items():
|
|
assert key in http_options.headers
|
|
assert value in http_options.headers[key]
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_connect_with_custom_headers(gemini_llm, llm_request):
|
|
"""Test that connect method updates tracking headers and API version when custom headers are provided."""
|
|
# Setup request with live connect config and custom headers
|
|
custom_headers = {"custom-live-header": "live-value"}
|
|
llm_request.live_connect_config = types.LiveConnectConfig(
|
|
http_options=types.HttpOptions(headers=custom_headers)
|
|
)
|
|
|
|
mock_live_session = mock.AsyncMock()
|
|
|
|
# Mock the _live_api_client to return a mock client
|
|
with mock.patch.object(gemini_llm, "_live_api_client") as mock_live_client:
|
|
# Create a mock context manager
|
|
class MockLiveConnect:
|
|
|
|
async def __aenter__(self):
|
|
return mock_live_session
|
|
|
|
async def __aexit__(self, *args):
|
|
pass
|
|
|
|
mock_live_client.aio.live.connect.return_value = MockLiveConnect()
|
|
|
|
async with gemini_llm.connect(llm_request) as connection:
|
|
# Verify that the connect method was called with the right config
|
|
mock_live_client.aio.live.connect.assert_called_once()
|
|
call_args = mock_live_client.aio.live.connect.call_args
|
|
config_arg = call_args.kwargs["config"]
|
|
|
|
# Verify that tracking headers were merged with custom headers
|
|
expected_headers = custom_headers.copy()
|
|
expected_headers.update(gemini_llm._tracking_headers)
|
|
assert config_arg.http_options.headers == expected_headers
|
|
|
|
# Verify that API version was set
|
|
assert config_arg.http_options.api_version == gemini_llm._live_api_version
|
|
|
|
# Verify that system instruction and tools were set
|
|
assert config_arg.system_instruction is not None
|
|
assert config_arg.tools == llm_request.config.tools
|
|
|
|
# Verify connection is properly wrapped
|
|
assert isinstance(connection, GeminiLlmConnection)
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_connect_without_custom_headers(gemini_llm, llm_request):
|
|
"""Test that connect method works properly when no custom headers are provided."""
|
|
# Setup request with live connect config but no custom headers
|
|
llm_request.live_connect_config = types.LiveConnectConfig()
|
|
|
|
mock_live_session = mock.AsyncMock()
|
|
|
|
with mock.patch.object(gemini_llm, "_live_api_client") as mock_live_client:
|
|
|
|
class MockLiveConnect:
|
|
|
|
async def __aenter__(self):
|
|
return mock_live_session
|
|
|
|
async def __aexit__(self, *args):
|
|
pass
|
|
|
|
mock_live_client.aio.live.connect.return_value = MockLiveConnect()
|
|
|
|
async with gemini_llm.connect(llm_request) as connection:
|
|
# Verify that the connect method was called with the right config
|
|
mock_live_client.aio.live.connect.assert_called_once()
|
|
call_args = mock_live_client.aio.live.connect.call_args
|
|
config_arg = call_args.kwargs["config"]
|
|
|
|
# Verify that http_options remains None since no custom headers were provided
|
|
assert config_arg.http_options is None
|
|
|
|
# Verify that system instruction and tools were still set
|
|
assert config_arg.system_instruction is not None
|
|
assert config_arg.tools == llm_request.config.tools
|
|
|
|
assert isinstance(connection, GeminiLlmConnection)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
(
|
|
"api_backend, "
|
|
"expected_file_display_name, "
|
|
"expected_inline_display_name, "
|
|
"expected_labels"
|
|
),
|
|
[
|
|
(
|
|
GoogleLLMVariant.GEMINI_API,
|
|
None,
|
|
None,
|
|
None,
|
|
),
|
|
(
|
|
GoogleLLMVariant.VERTEX_AI,
|
|
"My Test PDF",
|
|
"My Test Image",
|
|
{"key": "value"},
|
|
),
|
|
],
|
|
)
|
|
def test_preprocess_request_handles_backend_specific_fields(
|
|
gemini_llm: Gemini,
|
|
api_backend: GoogleLLMVariant,
|
|
expected_file_display_name: Optional[str],
|
|
expected_inline_display_name: Optional[str],
|
|
expected_labels: Optional[str],
|
|
):
|
|
"""
|
|
Tests that _preprocess_request correctly sanitizes fields based on the API backend.
|
|
|
|
- For GEMINI_API, it should remove 'display_name' from file/inline data
|
|
and remove 'labels' from the config.
|
|
- For VERTEX_AI, it should leave these fields untouched.
|
|
"""
|
|
# Arrange: Create a request with fields that need to be preprocessed.
|
|
llm_request_with_files = LlmRequest(
|
|
model="gemini-1.5-flash",
|
|
contents=[
|
|
Content(
|
|
role="user",
|
|
parts=[
|
|
Part(
|
|
file_data=types.FileData(
|
|
file_uri="gs://bucket/file.pdf",
|
|
mime_type="application/pdf",
|
|
display_name="My Test PDF",
|
|
)
|
|
),
|
|
Part(
|
|
inline_data=types.Blob(
|
|
data=b"some_bytes",
|
|
mime_type="image/png",
|
|
display_name="My Test Image",
|
|
)
|
|
),
|
|
],
|
|
)
|
|
],
|
|
config=types.GenerateContentConfig(labels={"key": "value"}),
|
|
)
|
|
|
|
# Mock the _api_backend property to control the test scenario
|
|
with mock.patch.object(
|
|
Gemini, "_api_backend", new_callable=mock.PropertyMock
|
|
) as mock_backend:
|
|
mock_backend.return_value = api_backend
|
|
|
|
# Act: Run the preprocessing method
|
|
gemini_llm._preprocess_request(llm_request_with_files)
|
|
|
|
# Assert: Check if the fields were correctly processed
|
|
file_part = llm_request_with_files.contents[0].parts[0]
|
|
inline_part = llm_request_with_files.contents[0].parts[1]
|
|
|
|
assert file_part.file_data.display_name == expected_file_display_name
|
|
assert inline_part.inline_data.display_name == expected_inline_display_name
|
|
assert llm_request_with_files.config.labels == expected_labels
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_generate_content_async_stream_aggregated_content_regardless_of_finish_reason():
|
|
"""Test that aggregated content is generated regardless of finish_reason."""
|
|
gemini_llm = Gemini(model="gemini-1.5-flash")
|
|
llm_request = LlmRequest(
|
|
model="gemini-1.5-flash",
|
|
contents=[Content(role="user", parts=[Part.from_text(text="Hello")])],
|
|
config=types.GenerateContentConfig(
|
|
temperature=0.1,
|
|
response_modalities=[types.Modality.TEXT],
|
|
system_instruction="You are a helpful assistant",
|
|
),
|
|
)
|
|
|
|
with mock.patch.object(gemini_llm, "api_client") as mock_client:
|
|
|
|
class MockAsyncIterator:
|
|
|
|
def __init__(self, seq):
|
|
self.iter = iter(seq)
|
|
|
|
def __aiter__(self):
|
|
return self
|
|
|
|
async def __anext__(self):
|
|
try:
|
|
return next(self.iter)
|
|
except StopIteration:
|
|
raise StopAsyncIteration
|
|
|
|
# Test with different finish reasons
|
|
test_cases = [
|
|
types.FinishReason.MAX_TOKENS,
|
|
types.FinishReason.SAFETY,
|
|
types.FinishReason.RECITATION,
|
|
types.FinishReason.OTHER,
|
|
]
|
|
|
|
for finish_reason in test_cases:
|
|
mock_responses = [
|
|
types.GenerateContentResponse(
|
|
candidates=[
|
|
types.Candidate(
|
|
content=Content(
|
|
role="model", parts=[Part.from_text(text="Hello")]
|
|
),
|
|
finish_reason=None,
|
|
)
|
|
]
|
|
),
|
|
types.GenerateContentResponse(
|
|
candidates=[
|
|
types.Candidate(
|
|
content=Content(
|
|
role="model", parts=[Part.from_text(text=" world")]
|
|
),
|
|
finish_reason=finish_reason,
|
|
finish_message=f"Finished with {finish_reason}",
|
|
)
|
|
]
|
|
),
|
|
]
|
|
|
|
async def mock_coro():
|
|
return MockAsyncIterator(mock_responses)
|
|
|
|
mock_client.aio.models.generate_content_stream.return_value = mock_coro()
|
|
|
|
responses = [
|
|
resp
|
|
async for resp in gemini_llm.generate_content_async(
|
|
llm_request, stream=True
|
|
)
|
|
]
|
|
|
|
# Should have 3 responses: 2 partial and 1 final aggregated
|
|
assert len(responses) == 3
|
|
assert responses[0].partial is True
|
|
assert responses[1].partial is True
|
|
|
|
# Final response should have aggregated content with error info
|
|
final_response = responses[2]
|
|
assert final_response.content.parts[0].text == "Hello world"
|
|
assert final_response.error_code == finish_reason
|
|
assert final_response.error_message == f"Finished with {finish_reason}"
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_generate_content_async_stream_with_thought_and_text_error_handling():
|
|
"""Test that aggregated content with thought and text preserves error information."""
|
|
gemini_llm = Gemini(model="gemini-1.5-flash")
|
|
llm_request = LlmRequest(
|
|
model="gemini-1.5-flash",
|
|
contents=[Content(role="user", parts=[Part.from_text(text="Hello")])],
|
|
config=types.GenerateContentConfig(
|
|
temperature=0.1,
|
|
response_modalities=[types.Modality.TEXT],
|
|
system_instruction="You are a helpful assistant",
|
|
),
|
|
)
|
|
|
|
with mock.patch.object(gemini_llm, "api_client") as mock_client:
|
|
|
|
class MockAsyncIterator:
|
|
|
|
def __init__(self, seq):
|
|
self.iter = iter(seq)
|
|
|
|
def __aiter__(self):
|
|
return self
|
|
|
|
async def __anext__(self):
|
|
try:
|
|
return next(self.iter)
|
|
except StopIteration:
|
|
raise StopAsyncIteration
|
|
|
|
mock_responses = [
|
|
types.GenerateContentResponse(
|
|
candidates=[
|
|
types.Candidate(
|
|
content=Content(
|
|
role="model", parts=[Part(text="Think1", thought=True)]
|
|
),
|
|
finish_reason=None,
|
|
)
|
|
]
|
|
),
|
|
types.GenerateContentResponse(
|
|
candidates=[
|
|
types.Candidate(
|
|
content=Content(
|
|
role="model", parts=[Part.from_text(text="Answer")]
|
|
),
|
|
finish_reason=types.FinishReason.MAX_TOKENS,
|
|
finish_message="Maximum tokens reached",
|
|
)
|
|
]
|
|
),
|
|
]
|
|
|
|
async def mock_coro():
|
|
return MockAsyncIterator(mock_responses)
|
|
|
|
mock_client.aio.models.generate_content_stream.return_value = mock_coro()
|
|
|
|
responses = [
|
|
resp
|
|
async for resp in gemini_llm.generate_content_async(
|
|
llm_request, stream=True
|
|
)
|
|
]
|
|
|
|
# Should have 3 responses: 2 partial and 1 final aggregated
|
|
assert len(responses) == 3
|
|
assert responses[0].partial is True
|
|
assert responses[1].partial is True
|
|
|
|
# Final response should have aggregated content with both thought and text
|
|
final_response = responses[2]
|
|
assert len(final_response.content.parts) == 2
|
|
assert final_response.content.parts[0].text == "Think1"
|
|
assert final_response.content.parts[0].thought is True
|
|
assert final_response.content.parts[1].text == "Answer"
|
|
assert final_response.error_code == types.FinishReason.MAX_TOKENS
|
|
assert final_response.error_message == "Maximum tokens reached"
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_generate_content_async_stream_no_aggregated_content_without_text():
|
|
"""Test that no aggregated content is generated when there's no accumulated text."""
|
|
gemini_llm = Gemini(model="gemini-1.5-flash")
|
|
llm_request = LlmRequest(
|
|
model="gemini-1.5-flash",
|
|
contents=[Content(role="user", parts=[Part.from_text(text="Hello")])],
|
|
config=types.GenerateContentConfig(
|
|
temperature=0.1,
|
|
response_modalities=[types.Modality.TEXT],
|
|
system_instruction="You are a helpful assistant",
|
|
),
|
|
)
|
|
|
|
with mock.patch.object(gemini_llm, "api_client") as mock_client:
|
|
|
|
class MockAsyncIterator:
|
|
|
|
def __init__(self, seq):
|
|
self.iter = iter(seq)
|
|
|
|
def __aiter__(self):
|
|
return self
|
|
|
|
async def __anext__(self):
|
|
try:
|
|
return next(self.iter)
|
|
except StopIteration:
|
|
raise StopAsyncIteration
|
|
|
|
# Mock response with no text content
|
|
mock_responses = [
|
|
types.GenerateContentResponse(
|
|
candidates=[
|
|
types.Candidate(
|
|
content=Content(
|
|
role="model",
|
|
parts=[
|
|
Part(
|
|
function_call=types.FunctionCall(
|
|
name="test", args={}
|
|
)
|
|
)
|
|
],
|
|
),
|
|
finish_reason=types.FinishReason.STOP,
|
|
)
|
|
]
|
|
),
|
|
]
|
|
|
|
async def mock_coro():
|
|
return MockAsyncIterator(mock_responses)
|
|
|
|
mock_client.aio.models.generate_content_stream.return_value = mock_coro()
|
|
|
|
responses = [
|
|
resp
|
|
async for resp in gemini_llm.generate_content_async(
|
|
llm_request, stream=True
|
|
)
|
|
]
|
|
|
|
# Should have only 1 response (no aggregated content generated)
|
|
assert len(responses) == 1
|
|
# Verify it's a function call, not text
|
|
assert responses[0].content.parts[0].function_call is not None
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_generate_content_async_stream_mixed_text_function_call_text():
|
|
"""Test streaming with pattern: [text, function_call, text] to verify proper aggregation."""
|
|
gemini_llm = Gemini(model="gemini-1.5-flash")
|
|
llm_request = LlmRequest(
|
|
model="gemini-1.5-flash",
|
|
contents=[Content(role="user", parts=[Part.from_text(text="Hello")])],
|
|
config=types.GenerateContentConfig(
|
|
temperature=0.1,
|
|
response_modalities=[types.Modality.TEXT],
|
|
system_instruction="You are a helpful assistant",
|
|
),
|
|
)
|
|
|
|
with mock.patch.object(gemini_llm, "api_client") as mock_client:
|
|
|
|
class MockAsyncIterator:
|
|
|
|
def __init__(self, seq):
|
|
self.iter = iter(seq)
|
|
|
|
def __aiter__(self):
|
|
return self
|
|
|
|
async def __anext__(self):
|
|
try:
|
|
return next(self.iter)
|
|
except StopIteration:
|
|
raise StopAsyncIteration
|
|
|
|
# Create responses with pattern: text -> function_call -> text
|
|
mock_responses = [
|
|
# First text chunk
|
|
types.GenerateContentResponse(
|
|
candidates=[
|
|
types.Candidate(
|
|
content=Content(
|
|
role="model", parts=[Part.from_text(text="First text")]
|
|
),
|
|
finish_reason=None,
|
|
)
|
|
]
|
|
),
|
|
# Function call interrupts the text flow
|
|
types.GenerateContentResponse(
|
|
candidates=[
|
|
types.Candidate(
|
|
content=Content(
|
|
role="model",
|
|
parts=[
|
|
Part(
|
|
function_call=types.FunctionCall(
|
|
name="test_func", args={}
|
|
)
|
|
)
|
|
],
|
|
),
|
|
finish_reason=None,
|
|
)
|
|
]
|
|
),
|
|
# More text after function call
|
|
types.GenerateContentResponse(
|
|
candidates=[
|
|
types.Candidate(
|
|
content=Content(
|
|
role="model",
|
|
parts=[Part.from_text(text=" second text")],
|
|
),
|
|
finish_reason=types.FinishReason.STOP,
|
|
)
|
|
]
|
|
),
|
|
]
|
|
|
|
async def mock_coro():
|
|
return MockAsyncIterator(mock_responses)
|
|
|
|
mock_client.aio.models.generate_content_stream.return_value = mock_coro()
|
|
|
|
responses = [
|
|
resp
|
|
async for resp in gemini_llm.generate_content_async(
|
|
llm_request, stream=True
|
|
)
|
|
]
|
|
|
|
# Should have multiple responses:
|
|
# 1. Partial text "First text"
|
|
# 2. Aggregated "First text" when function call interrupts
|
|
# 3. Function call
|
|
# 4. Partial text " second text"
|
|
# 5. Final aggregated " second text"
|
|
assert len(responses) == 5
|
|
|
|
# First partial text
|
|
assert responses[0].partial is True
|
|
assert responses[0].content.parts[0].text == "First text"
|
|
|
|
# Aggregated first text (when function call interrupts)
|
|
assert responses[1].content.parts[0].text == "First text"
|
|
assert (
|
|
responses[1].partial is None
|
|
) # Aggregated responses don't have partial flag
|
|
|
|
# Function call
|
|
assert responses[2].content.parts[0].function_call is not None
|
|
assert responses[2].content.parts[0].function_call.name == "test_func"
|
|
|
|
# Second partial text
|
|
assert responses[3].partial is True
|
|
assert responses[3].content.parts[0].text == " second text"
|
|
|
|
# Final aggregated text with error info
|
|
assert responses[4].content.parts[0].text == " second text"
|
|
assert responses[4].error_code == types.FinishReason.STOP
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_generate_content_async_stream_multiple_text_parts_in_single_response():
|
|
"""Test streaming with multiple text parts in a single response."""
|
|
gemini_llm = Gemini(model="gemini-1.5-flash")
|
|
llm_request = LlmRequest(
|
|
model="gemini-1.5-flash",
|
|
contents=[Content(role="user", parts=[Part.from_text(text="Hello")])],
|
|
config=types.GenerateContentConfig(
|
|
temperature=0.1,
|
|
response_modalities=[types.Modality.TEXT],
|
|
system_instruction="You are a helpful assistant",
|
|
),
|
|
)
|
|
|
|
with mock.patch.object(gemini_llm, "api_client") as mock_client:
|
|
|
|
class MockAsyncIterator:
|
|
|
|
def __init__(self, seq):
|
|
self.iter = iter(seq)
|
|
|
|
def __aiter__(self):
|
|
return self
|
|
|
|
async def __anext__(self):
|
|
try:
|
|
return next(self.iter)
|
|
except StopIteration:
|
|
raise StopAsyncIteration
|
|
|
|
# Create a response with multiple text parts
|
|
mock_responses = [
|
|
types.GenerateContentResponse(
|
|
candidates=[
|
|
types.Candidate(
|
|
content=Content(
|
|
role="model",
|
|
parts=[
|
|
Part.from_text(text="First part"),
|
|
Part.from_text(text=" second part"),
|
|
],
|
|
),
|
|
finish_reason=types.FinishReason.STOP,
|
|
)
|
|
]
|
|
),
|
|
]
|
|
|
|
async def mock_coro():
|
|
return MockAsyncIterator(mock_responses)
|
|
|
|
mock_client.aio.models.generate_content_stream.return_value = mock_coro()
|
|
|
|
responses = [
|
|
resp
|
|
async for resp in gemini_llm.generate_content_async(
|
|
llm_request, stream=True
|
|
)
|
|
]
|
|
|
|
# Should handle only the first text part in current implementation
|
|
# Note: This test documents current behavior - the implementation only
|
|
# looks at parts[0].text, so it would only process "First part"
|
|
assert len(responses) >= 1
|
|
assert responses[0].content.parts[0].text == "First part"
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_generate_content_async_stream_complex_mixed_thought_text_function():
|
|
"""Test complex streaming with thought, text, and function calls mixed."""
|
|
gemini_llm = Gemini(model="gemini-1.5-flash")
|
|
llm_request = LlmRequest(
|
|
model="gemini-1.5-flash",
|
|
contents=[Content(role="user", parts=[Part.from_text(text="Hello")])],
|
|
config=types.GenerateContentConfig(
|
|
temperature=0.1,
|
|
response_modalities=[types.Modality.TEXT],
|
|
system_instruction="You are a helpful assistant",
|
|
),
|
|
)
|
|
|
|
with mock.patch.object(gemini_llm, "api_client") as mock_client:
|
|
|
|
class MockAsyncIterator:
|
|
|
|
def __init__(self, seq):
|
|
self.iter = iter(seq)
|
|
|
|
def __aiter__(self):
|
|
return self
|
|
|
|
async def __anext__(self):
|
|
try:
|
|
return next(self.iter)
|
|
except StopIteration:
|
|
raise StopAsyncIteration
|
|
|
|
# Complex pattern: thought -> text -> function_call -> thought -> text
|
|
mock_responses = [
|
|
# Thought
|
|
types.GenerateContentResponse(
|
|
candidates=[
|
|
types.Candidate(
|
|
content=Content(
|
|
role="model",
|
|
parts=[Part(text="Thinking...", thought=True)],
|
|
),
|
|
finish_reason=None,
|
|
)
|
|
]
|
|
),
|
|
# Regular text
|
|
types.GenerateContentResponse(
|
|
candidates=[
|
|
types.Candidate(
|
|
content=Content(
|
|
role="model",
|
|
parts=[Part.from_text(text="Here's my answer")],
|
|
),
|
|
finish_reason=None,
|
|
)
|
|
]
|
|
),
|
|
# Function call
|
|
types.GenerateContentResponse(
|
|
candidates=[
|
|
types.Candidate(
|
|
content=Content(
|
|
role="model",
|
|
parts=[
|
|
Part(
|
|
function_call=types.FunctionCall(
|
|
name="lookup", args={}
|
|
)
|
|
)
|
|
],
|
|
),
|
|
finish_reason=None,
|
|
)
|
|
]
|
|
),
|
|
# More thought
|
|
types.GenerateContentResponse(
|
|
candidates=[
|
|
types.Candidate(
|
|
content=Content(
|
|
role="model",
|
|
parts=[Part(text="More thinking...", thought=True)],
|
|
),
|
|
finish_reason=None,
|
|
)
|
|
]
|
|
),
|
|
# Final text
|
|
types.GenerateContentResponse(
|
|
candidates=[
|
|
types.Candidate(
|
|
content=Content(
|
|
role="model",
|
|
parts=[Part.from_text(text=" and conclusion")],
|
|
),
|
|
finish_reason=types.FinishReason.STOP,
|
|
)
|
|
]
|
|
),
|
|
]
|
|
|
|
async def mock_coro():
|
|
return MockAsyncIterator(mock_responses)
|
|
|
|
mock_client.aio.models.generate_content_stream.return_value = mock_coro()
|
|
|
|
responses = [
|
|
resp
|
|
async for resp in gemini_llm.generate_content_async(
|
|
llm_request, stream=True
|
|
)
|
|
]
|
|
|
|
# Should properly separate thought and regular text across aggregations
|
|
assert len(responses) > 5 # Multiple partial + aggregated responses
|
|
|
|
# Verify we get both thought and regular text parts in aggregated responses
|
|
aggregated_responses = [
|
|
r
|
|
for r in responses
|
|
if r.partial is None and r.content and len(r.content.parts) > 1
|
|
]
|
|
assert (
|
|
len(aggregated_responses) > 0
|
|
) # Should have at least one aggregated response with multiple parts
|
|
|
|
# Final aggregated response should have both thought and text
|
|
final_response = responses[-1]
|
|
assert final_response.error_code == types.FinishReason.STOP
|
|
assert len(final_response.content.parts) == 2 # thought part + text part
|
|
assert final_response.content.parts[0].thought is True
|
|
assert "More thinking..." in final_response.content.parts[0].text
|
|
assert final_response.content.parts[1].text == " and conclusion"
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_generate_content_async_stream_two_separate_text_aggregations():
|
|
"""Test that [text, function_call, text] results in two separate text aggregations."""
|
|
gemini_llm = Gemini(model="gemini-1.5-flash")
|
|
llm_request = LlmRequest(
|
|
model="gemini-1.5-flash",
|
|
contents=[Content(role="user", parts=[Part.from_text(text="Hello")])],
|
|
config=types.GenerateContentConfig(
|
|
temperature=0.1,
|
|
response_modalities=[types.Modality.TEXT],
|
|
system_instruction="You are a helpful assistant",
|
|
),
|
|
)
|
|
|
|
with mock.patch.object(gemini_llm, "api_client") as mock_client:
|
|
|
|
class MockAsyncIterator:
|
|
|
|
def __init__(self, seq):
|
|
self.iter = iter(seq)
|
|
|
|
def __aiter__(self):
|
|
return self
|
|
|
|
async def __anext__(self):
|
|
try:
|
|
return next(self.iter)
|
|
except StopIteration:
|
|
raise StopAsyncIteration
|
|
|
|
# Create responses: multiple text chunks -> function_call -> multiple text chunks
|
|
mock_responses = [
|
|
# First text accumulation (multiple chunks)
|
|
types.GenerateContentResponse(
|
|
candidates=[
|
|
types.Candidate(
|
|
content=Content(
|
|
role="model", parts=[Part.from_text(text="First")]
|
|
),
|
|
finish_reason=None,
|
|
)
|
|
]
|
|
),
|
|
types.GenerateContentResponse(
|
|
candidates=[
|
|
types.Candidate(
|
|
content=Content(
|
|
role="model", parts=[Part.from_text(text=" chunk")]
|
|
),
|
|
finish_reason=None,
|
|
)
|
|
]
|
|
),
|
|
# Function call interrupts
|
|
types.GenerateContentResponse(
|
|
candidates=[
|
|
types.Candidate(
|
|
content=Content(
|
|
role="model",
|
|
parts=[
|
|
Part(
|
|
function_call=types.FunctionCall(
|
|
name="divide", args={}
|
|
)
|
|
)
|
|
],
|
|
),
|
|
finish_reason=None,
|
|
)
|
|
]
|
|
),
|
|
# Second text accumulation (multiple chunks)
|
|
types.GenerateContentResponse(
|
|
candidates=[
|
|
types.Candidate(
|
|
content=Content(
|
|
role="model", parts=[Part.from_text(text="Second")]
|
|
),
|
|
finish_reason=None,
|
|
)
|
|
]
|
|
),
|
|
types.GenerateContentResponse(
|
|
candidates=[
|
|
types.Candidate(
|
|
content=Content(
|
|
role="model", parts=[Part.from_text(text=" chunk")]
|
|
),
|
|
finish_reason=types.FinishReason.STOP,
|
|
)
|
|
]
|
|
),
|
|
]
|
|
|
|
async def mock_coro():
|
|
return MockAsyncIterator(mock_responses)
|
|
|
|
mock_client.aio.models.generate_content_stream.return_value = mock_coro()
|
|
|
|
responses = [
|
|
resp
|
|
async for resp in gemini_llm.generate_content_async(
|
|
llm_request, stream=True
|
|
)
|
|
]
|
|
|
|
# Find the aggregated text responses (non-partial, text-only)
|
|
aggregated_text_responses = [
|
|
r
|
|
for r in responses
|
|
if (
|
|
r.partial is None
|
|
and r.content
|
|
and r.content.parts
|
|
and r.content.parts[0].text
|
|
and not r.content.parts[0].function_call
|
|
)
|
|
]
|
|
|
|
# Should have two separate text aggregations: "First chunk" and "Second chunk"
|
|
assert len(aggregated_text_responses) >= 2
|
|
|
|
# First aggregation should contain "First chunk"
|
|
first_aggregation = aggregated_text_responses[0]
|
|
assert first_aggregation.content.parts[0].text == "First chunk"
|
|
|
|
# Final aggregation should contain "Second chunk" and have error info
|
|
final_aggregation = aggregated_text_responses[-1]
|
|
assert final_aggregation.content.parts[0].text == "Second chunk"
|
|
assert final_aggregation.error_code == types.FinishReason.STOP
|
|
|
|
# Verify the function call is preserved between aggregations
|
|
function_call_responses = [
|
|
r
|
|
for r in responses
|
|
if (r.content and r.content.parts and r.content.parts[0].function_call)
|
|
]
|
|
assert len(function_call_responses) == 1
|
|
assert (
|
|
function_call_responses[0].content.parts[0].function_call.name
|
|
== "divide"
|
|
)
|