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800 lines
24 KiB
Python
800 lines
24 KiB
Python
# Copyright 2026 Google LLC
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import annotations
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import os
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from unittest import mock
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from unittest.mock import AsyncMock
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from google.adk.models.apigee_llm import ApigeeLlm
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from google.adk.models.apigee_llm import CompletionsHTTPClient
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from google.adk.models.llm_request import LlmRequest
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from google.genai import types
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from google.genai.types import Content
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from google.genai.types import Part
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import pytest
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BASE_MODEL_ID = 'gemini-2.5-flash'
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APIGEE_GEMINI_MODEL_ID = 'apigee/gemini/v1/' + BASE_MODEL_ID
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APIGEE_VERTEX_MODEL_ID = 'apigee/vertex_ai/v1beta/gemini-pro'
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VERTEX_BASE_MODEL_ID = 'gemini-pro'
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PROXY_URL = 'https://test.apigee.net'
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@pytest.fixture
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def llm_request():
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"""Provides a sample LlmRequest for testing."""
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return LlmRequest(
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model=APIGEE_GEMINI_MODEL_ID,
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contents=[
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types.Content(
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role='user', parts=[types.Part.from_text(text='Test prompt')]
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)
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],
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)
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@pytest.mark.asyncio
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@mock.patch('google.genai.Client')
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async def test_generate_content_async_non_streaming(
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mock_client_constructor, llm_request
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):
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"""Tests the generate_content_async method for non-streaming responses."""
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apigee_llm_instance = ApigeeLlm(
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model=APIGEE_GEMINI_MODEL_ID,
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proxy_url=PROXY_URL,
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)
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mock_client_instance = mock.Mock()
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mock_response = types.GenerateContentResponse(
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candidates=[
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types.Candidate(
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content=Content(
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parts=[Part.from_text(text='Test response')],
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role='model',
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)
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)
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]
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)
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mock_client_instance.aio.models.generate_content = AsyncMock(
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return_value=mock_response
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)
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mock_client_constructor.return_value = mock_client_instance
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response_generator = apigee_llm_instance.generate_content_async(llm_request)
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responses = [resp async for resp in response_generator]
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assert len(responses) == 1
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llm_response = responses[0]
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assert llm_response.content.parts[0].text == 'Test response'
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assert llm_response.content.role == 'model'
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mock_client_constructor.assert_called_once()
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_, kwargs = mock_client_constructor.call_args
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assert not kwargs['enterprise']
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http_options = kwargs['http_options']
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assert http_options.base_url == PROXY_URL
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assert http_options.api_version == 'v1'
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assert 'user-agent' in http_options.headers
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assert 'x-goog-api-client' in http_options.headers
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mock_client_instance.aio.models.generate_content.assert_called_once_with(
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model=BASE_MODEL_ID,
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contents=llm_request.contents,
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config=llm_request.config,
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)
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@pytest.mark.asyncio
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@mock.patch('google.genai.Client')
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async def test_generate_content_async_streaming(
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mock_client_constructor, llm_request
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):
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"""Tests the generate_content_async method for streaming responses."""
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apigee_llm_instance = ApigeeLlm(
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model=APIGEE_GEMINI_MODEL_ID,
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proxy_url=PROXY_URL,
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)
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mock_client_instance = mock.Mock()
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mock_responses = [
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types.GenerateContentResponse(
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candidates=[
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types.Candidate(
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content=Content(
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parts=[Part.from_text(text='Hello')],
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)
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)
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]
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),
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types.GenerateContentResponse(
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candidates=[
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types.Candidate(
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content=Content(
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parts=[Part.from_text(text=',')],
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)
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)
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]
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),
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types.GenerateContentResponse(
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candidates=[
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types.Candidate(
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content=Content(
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parts=[Part.from_text(text=' world!')],
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)
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)
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]
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),
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]
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async def mock_stream_generator():
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for r in mock_responses:
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yield r
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mock_client_instance.aio.models.generate_content_stream = AsyncMock(
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return_value=mock_stream_generator()
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)
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mock_client_constructor.return_value = mock_client_instance
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response_generator = apigee_llm_instance.generate_content_async(
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llm_request, stream=True
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)
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responses = [resp async for resp in response_generator]
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assert responses
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full_text_parts = []
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for r in responses:
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for p in r.content.parts:
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if p.text:
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full_text_parts.append(p.text)
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full_text = ''.join(full_text_parts)
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assert 'Hello, world!' in full_text
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mock_client_instance.aio.models.generate_content_stream.assert_called_once_with(
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model=BASE_MODEL_ID,
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contents=llm_request.contents,
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config=llm_request.config,
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)
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@pytest.mark.asyncio
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@mock.patch('google.genai.Client')
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async def test_generate_content_async_with_custom_headers(
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mock_client_constructor, llm_request
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):
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"""Tests that custom headers are passed in the request."""
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custom_headers = {
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'X-Custom-Header': 'custom-value',
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}
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apigee_llm = ApigeeLlm(
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model=APIGEE_GEMINI_MODEL_ID,
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proxy_url=PROXY_URL,
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custom_headers=custom_headers,
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)
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mock_client_instance = mock.Mock()
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mock_response = types.GenerateContentResponse(
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candidates=[
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types.Candidate(
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content=Content(
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parts=[Part.from_text(text='Test response')],
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role='model',
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)
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)
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]
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)
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mock_client_instance.aio.models.generate_content = AsyncMock(
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return_value=mock_response
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)
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mock_client_constructor.return_value = mock_client_instance
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response_generator = apigee_llm.generate_content_async(llm_request)
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_ = [resp async for resp in response_generator] # Consume generator
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mock_client_constructor.assert_called_once()
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_, kwargs = mock_client_constructor.call_args
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http_options = kwargs['http_options']
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assert http_options.headers['X-Custom-Header'] == 'custom-value'
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assert 'user-agent' in http_options.headers
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@pytest.mark.asyncio
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@mock.patch('google.genai.Client')
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async def test_vertex_model_path_parsing(mock_client_constructor):
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"""Tests that Vertex AI model paths are parsed correctly."""
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apigee_llm = ApigeeLlm(model=APIGEE_VERTEX_MODEL_ID, proxy_url=PROXY_URL)
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llm_request = LlmRequest(
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model=APIGEE_VERTEX_MODEL_ID,
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contents=[
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types.Content(
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role='user', parts=[types.Part.from_text(text='Test prompt')]
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)
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],
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)
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mock_client_instance = mock.Mock()
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mock_client_instance.aio.models.generate_content = AsyncMock(
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return_value=types.GenerateContentResponse(
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candidates=[
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types.Candidate(
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content=Content(
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parts=[Part.from_text(text='Test response')],
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role='model',
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)
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)
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]
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)
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)
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mock_client_constructor.return_value = mock_client_instance
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_ = [resp async for resp in apigee_llm.generate_content_async(llm_request)]
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mock_client_constructor.assert_called_once()
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_, kwargs = mock_client_constructor.call_args
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assert kwargs['enterprise']
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assert kwargs['http_options'].api_version == 'v1beta'
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mock_client_instance.aio.models.generate_content.assert_called_once()
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call_kwargs = (
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mock_client_instance.aio.models.generate_content.call_args.kwargs
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)
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assert call_kwargs['model'] == VERTEX_BASE_MODEL_ID
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@pytest.mark.asyncio
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@mock.patch('google.genai.Client')
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async def test_proxy_url_from_env_variable(mock_client_constructor):
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"""Tests that proxy_url is read from environment variable."""
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with mock.patch.dict(
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os.environ, {'APIGEE_PROXY_URL': 'https://env.proxy.url'}
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):
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apigee_llm = ApigeeLlm(model=APIGEE_GEMINI_MODEL_ID)
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llm_request = LlmRequest(
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model=APIGEE_GEMINI_MODEL_ID,
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contents=[
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types.Content(
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role='user', parts=[types.Part.from_text(text='Test prompt')]
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)
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],
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)
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mock_client_instance = mock.Mock()
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mock_client_instance.aio.models.generate_content = AsyncMock(
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return_value=types.GenerateContentResponse(
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candidates=[
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types.Candidate(
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content=Content(
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parts=[Part.from_text(text='Test response')],
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role='model',
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)
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)
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]
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)
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)
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mock_client_constructor.return_value = mock_client_instance
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_ = [resp async for resp in apigee_llm.generate_content_async(llm_request)]
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mock_client_constructor.assert_called_once()
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_, kwargs = mock_client_constructor.call_args
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assert kwargs['http_options'].base_url == 'https://env.proxy.url'
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@pytest.mark.parametrize(
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('model_string', 'env_vars'),
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[
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(
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'apigee/vertex_ai/gemini-2.5-flash',
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{'GOOGLE_CLOUD_LOCATION': 'test-location'},
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),
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(
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'apigee/vertex_ai/gemini-2.5-flash',
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{'GOOGLE_CLOUD_PROJECT': 'test-project'},
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),
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(
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'apigee/gemini-2.5-flash',
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{
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'GOOGLE_GENAI_USE_ENTERPRISE': 'true',
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'GOOGLE_CLOUD_LOCATION': 'test-location',
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},
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),
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(
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'apigee/gemini-2.5-flash',
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{
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'GOOGLE_GENAI_USE_ENTERPRISE': 'true',
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'GOOGLE_CLOUD_PROJECT': 'test-project',
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},
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),
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],
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)
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def test_vertex_model_missing_project_or_location_raises_error(
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model_string, env_vars
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):
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"""Tests that ValueError is raised for Vertex models if project or location is missing."""
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with mock.patch.dict(os.environ, env_vars, clear=True):
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with pytest.raises(ValueError, match='environment variable must be set'):
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ApigeeLlm(model=model_string, proxy_url=PROXY_URL)
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@pytest.mark.asyncio
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@pytest.mark.parametrize(
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(
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'model_string',
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'use_vertexai_env',
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'expected_is_vertexai',
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'expected_api_version',
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'expected_model_id',
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),
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[
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('apigee/gemini-2.5-flash', None, False, None, 'gemini-2.5-flash'),
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('apigee/gemini-2.5-flash', 'true', True, None, 'gemini-2.5-flash'),
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('apigee/gemini-2.5-flash', '1', True, None, 'gemini-2.5-flash'),
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('apigee/gemini-2.5-flash', 'false', False, None, 'gemini-2.5-flash'),
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('apigee/gemini-2.5-flash', '0', False, None, 'gemini-2.5-flash'),
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(
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'apigee/v1/gemini-2.5-flash',
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None,
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False,
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'v1',
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'gemini-2.5-flash',
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),
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(
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'apigee/v1/gemini-2.5-flash',
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'true',
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True,
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'v1',
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'gemini-2.5-flash',
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),
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(
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'apigee/vertex_ai/gemini-2.5-flash',
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None,
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True,
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None,
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'gemini-2.5-flash',
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),
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(
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'apigee/vertex_ai/gemini-2.5-flash',
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'false',
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True,
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None,
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'gemini-2.5-flash',
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),
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(
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'apigee/gemini/v1/gemini-2.5-flash',
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'true',
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False,
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'v1',
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'gemini-2.5-flash',
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),
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(
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'apigee/vertex_ai/v1beta/gemini-2.5-flash',
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'false',
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True,
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'v1beta',
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'gemini-2.5-flash',
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),
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],
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)
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@mock.patch('google.genai.Client')
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async def test_model_string_parsing_and_client_initialization(
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mock_client_constructor,
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model_string,
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use_vertexai_env,
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expected_is_vertexai,
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expected_api_version,
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expected_model_id,
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):
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"""Tests model string parsing and genai.Client initialization."""
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env_vars = {}
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if use_vertexai_env is not None:
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env_vars['GOOGLE_GENAI_USE_ENTERPRISE'] = use_vertexai_env
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if expected_is_vertexai:
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env_vars['GOOGLE_CLOUD_PROJECT'] = 'test-project'
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env_vars['GOOGLE_CLOUD_LOCATION'] = 'test-location'
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# The ApigeeLlm is initialized in the 'with' block to make sure that the mock
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# of the environment variable is active.
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with mock.patch.dict(os.environ, env_vars, clear=True):
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apigee_llm = ApigeeLlm(model=model_string, proxy_url=PROXY_URL)
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request = LlmRequest(model=model_string, contents=[])
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mock_client_instance = mock.Mock()
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mock_client_instance.aio.models.generate_content = AsyncMock(
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return_value=types.GenerateContentResponse(
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candidates=[
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types.Candidate(
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content=Content(parts=[Part.from_text(text='')])
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)
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]
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)
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)
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mock_client_constructor.return_value = mock_client_instance
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_ = [resp async for resp in apigee_llm.generate_content_async(request)]
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mock_client_constructor.assert_called_once()
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_, kwargs = mock_client_constructor.call_args
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assert kwargs['enterprise'] == expected_is_vertexai
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if expected_is_vertexai:
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assert kwargs['project'] == 'test-project'
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assert kwargs['location'] == 'test-location'
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http_options = kwargs['http_options']
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assert http_options.api_version == expected_api_version
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(
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mock_client_instance.aio.models.generate_content.assert_called_once_with(
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model=expected_model_id,
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contents=request.contents,
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config=request.config,
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)
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)
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|
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@pytest.mark.asyncio
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@pytest.mark.parametrize(
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'invalid_model_string',
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[
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'apigee/', # Missing model_id
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'apigee', # Invalid format
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'gemini-pro', # Invalid format
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'apigee/vertex_ai/v1/model/extra', # Too many components
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'apigee/unknown/model',
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],
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)
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async def test_invalid_model_strings_raise_value_error(invalid_model_string):
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"""Tests that invalid model strings raise a ValueError."""
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with pytest.raises(
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ValueError, match=f'Invalid model string: {invalid_model_string}'
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):
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ApigeeLlm(model=invalid_model_string, proxy_url=PROXY_URL)
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|
|
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@pytest.mark.asyncio
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@pytest.mark.parametrize(
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'model',
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[
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'apigee/openai/gpt-4o',
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'apigee/openai/v1/gpt-4o',
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'apigee/openai/v1/gpt-3.5-turbo',
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],
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)
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async def test_validate_model_for_chat_completion_providers(model):
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"""Tests that new providers like OpenAI are accepted."""
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# Should not raise ValueError
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ApigeeLlm(model=model, proxy_url=PROXY_URL)
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|
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@pytest.mark.parametrize(
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('model', 'api_type', 'expected_api_type'),
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[
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# Default case (input defaults to UNKNOWN)
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(
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'apigee/openai/gpt-4o',
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ApigeeLlm.ApiType.UNKNOWN,
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ApigeeLlm.ApiType.CHAT_COMPLETIONS,
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),
|
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(
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'apigee/openai/v1/gpt-3.5-turbo',
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ApigeeLlm.ApiType.UNKNOWN,
|
|
ApigeeLlm.ApiType.CHAT_COMPLETIONS,
|
|
),
|
|
(
|
|
'apigee/gemini/v1/gemini-pro',
|
|
ApigeeLlm.ApiType.UNKNOWN,
|
|
ApigeeLlm.ApiType.GENAI,
|
|
),
|
|
(
|
|
'apigee/vertex_ai/gemini-pro',
|
|
ApigeeLlm.ApiType.UNKNOWN,
|
|
ApigeeLlm.ApiType.GENAI,
|
|
),
|
|
(
|
|
'apigee/vertex_ai/v1beta/gemini-1.5-pro',
|
|
ApigeeLlm.ApiType.UNKNOWN,
|
|
ApigeeLlm.ApiType.GENAI,
|
|
),
|
|
# Override by setting the ApiType
|
|
(
|
|
'apigee/gemini/pro',
|
|
ApigeeLlm.ApiType.CHAT_COMPLETIONS,
|
|
ApigeeLlm.ApiType.CHAT_COMPLETIONS,
|
|
),
|
|
(
|
|
'apigee/gemini/pro',
|
|
ApigeeLlm.ApiType.GENAI,
|
|
ApigeeLlm.ApiType.GENAI,
|
|
),
|
|
(
|
|
'apigee/openai/gpt-4o',
|
|
ApigeeLlm.ApiType.CHAT_COMPLETIONS,
|
|
ApigeeLlm.ApiType.CHAT_COMPLETIONS,
|
|
),
|
|
(
|
|
'apigee/openai/gpt-4o',
|
|
ApigeeLlm.ApiType.GENAI,
|
|
ApigeeLlm.ApiType.GENAI,
|
|
),
|
|
# Override by setting the ApiType as a string
|
|
(
|
|
'apigee/gemini/pro',
|
|
'chat_completions',
|
|
ApigeeLlm.ApiType.CHAT_COMPLETIONS,
|
|
),
|
|
(
|
|
'apigee/gemini/pro',
|
|
'genai',
|
|
ApigeeLlm.ApiType.GENAI,
|
|
),
|
|
(
|
|
'apigee/openai/gpt-4o',
|
|
'chat_completions',
|
|
ApigeeLlm.ApiType.CHAT_COMPLETIONS,
|
|
),
|
|
(
|
|
'apigee/openai/gpt-4o',
|
|
'genai',
|
|
ApigeeLlm.ApiType.GENAI,
|
|
),
|
|
],
|
|
)
|
|
def test_api_type_resolution(model, api_type, expected_api_type):
|
|
"""Tests that api_type is resolved correctly."""
|
|
llm = ApigeeLlm(
|
|
model=model,
|
|
proxy_url=PROXY_URL,
|
|
api_type=api_type,
|
|
)
|
|
assert llm._api_type == expected_api_type
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
('input_value', 'expected_type'),
|
|
[
|
|
('chat_completions', ApigeeLlm.ApiType.CHAT_COMPLETIONS),
|
|
('genai', ApigeeLlm.ApiType.GENAI),
|
|
('unknown', ApigeeLlm.ApiType.UNKNOWN),
|
|
('', ApigeeLlm.ApiType.UNKNOWN),
|
|
(None, ApigeeLlm.ApiType.UNKNOWN),
|
|
],
|
|
)
|
|
def test_apitype_creation(input_value, expected_type):
|
|
"""Tests the creation of ApiType enum members."""
|
|
assert ApigeeLlm.ApiType(input_value) == expected_type
|
|
|
|
|
|
def test_apitype_creation_invalid():
|
|
"""Tests that invalid ApiType raises ValueError."""
|
|
with pytest.raises(ValueError):
|
|
ApigeeLlm.ApiType('invalid')
|
|
|
|
|
|
def test_invalid_api_type_raises_error():
|
|
"""Tests that invalid string for api_type raises ValueError."""
|
|
with pytest.raises(ValueError):
|
|
ApigeeLlm(
|
|
model='apigee/gemini-pro',
|
|
proxy_url=PROXY_URL,
|
|
api_type='invalid_type',
|
|
)
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_generate_content_async_dispatch_to_completions_client(
|
|
llm_request,
|
|
):
|
|
"""Tests that generate_content_async uses CompletionsHTTPClient for OpenAI models."""
|
|
llm_request.model = 'apigee/openai/gpt-4o'
|
|
with (
|
|
mock.patch.object(
|
|
CompletionsHTTPClient,
|
|
'generate_content_async',
|
|
) as mock_completions_generate_content,
|
|
mock.patch('google.genai.Client') as mock_genai_client,
|
|
):
|
|
apigee_llm = ApigeeLlm(model='apigee/openai/gpt-4o', proxy_url=PROXY_URL)
|
|
_ = [
|
|
r
|
|
async for r in apigee_llm.generate_content_async(
|
|
llm_request, stream=False
|
|
)
|
|
]
|
|
mock_completions_generate_content.assert_called_once()
|
|
mock_genai_client.assert_not_called()
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
@pytest.mark.parametrize(
|
|
'model',
|
|
[
|
|
'apigee/openai/gpt-4o',
|
|
'apigee/openai/v1/gpt-3.5-turbo',
|
|
],
|
|
)
|
|
async def test_api_key_injection_openai(model):
|
|
"""Tests that api_key is injected for OpenAI models."""
|
|
apigee_llm = ApigeeLlm(
|
|
model=model,
|
|
proxy_url=PROXY_URL,
|
|
custom_headers={'Authorization': 'Bearer sk-test-key'},
|
|
)
|
|
client = apigee_llm._completions_http_client
|
|
assert client._headers['Authorization'] == 'Bearer sk-test-key'
|
|
|
|
|
|
def test_parse_response_usage_metadata():
|
|
"""Tests that CompletionsHTTPClient parses usage metadata correctly including reasoning tokens."""
|
|
client = CompletionsHTTPClient(base_url='http://test')
|
|
response_dict = {
|
|
'choices': [{
|
|
'message': {'role': 'assistant', 'content': 'hello'},
|
|
'finish_reason': 'stop',
|
|
}],
|
|
'usage': {
|
|
'prompt_tokens': 10,
|
|
'completion_tokens': 5,
|
|
'total_tokens': 15,
|
|
'completion_tokens_details': {'reasoning_tokens': 4},
|
|
},
|
|
}
|
|
llm_response = client._parse_response(response_dict)
|
|
assert llm_response.usage_metadata.prompt_token_count == 10
|
|
assert llm_response.usage_metadata.candidates_token_count == 5
|
|
assert llm_response.usage_metadata.total_token_count == 15
|
|
assert llm_response.usage_metadata.thoughts_token_count == 4
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
@mock.patch('google.genai.Client')
|
|
async def test_api_client_passes_credentials_when_provided(
|
|
mock_client_constructor, llm_request
|
|
):
|
|
"""Tests that credentials passed to __init__ are forwarded to genai.Client."""
|
|
mock_credentials = mock.Mock()
|
|
|
|
mock_client_instance = mock.Mock()
|
|
mock_client_instance.aio.models.generate_content = AsyncMock(
|
|
return_value=types.GenerateContentResponse(
|
|
candidates=[
|
|
types.Candidate(
|
|
content=Content(
|
|
parts=[Part.from_text(text='Test response')],
|
|
role='model',
|
|
)
|
|
)
|
|
]
|
|
)
|
|
)
|
|
mock_client_constructor.return_value = mock_client_instance
|
|
|
|
apigee_llm = ApigeeLlm(
|
|
model=APIGEE_GEMINI_MODEL_ID,
|
|
proxy_url=PROXY_URL,
|
|
credentials=mock_credentials,
|
|
)
|
|
_ = [resp async for resp in apigee_llm.generate_content_async(llm_request)]
|
|
|
|
_, kwargs = mock_client_constructor.call_args
|
|
assert kwargs['credentials'] is mock_credentials
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
@mock.patch('google.genai.Client')
|
|
async def test_api_client_omits_credentials_when_not_provided(
|
|
mock_client_constructor, llm_request
|
|
):
|
|
"""Tests that credentials kwarg is not forwarded when not supplied."""
|
|
mock_client_instance = mock.Mock()
|
|
mock_client_instance.aio.models.generate_content = AsyncMock(
|
|
return_value=types.GenerateContentResponse(
|
|
candidates=[
|
|
types.Candidate(
|
|
content=Content(
|
|
parts=[Part.from_text(text='Test response')],
|
|
role='model',
|
|
)
|
|
)
|
|
]
|
|
)
|
|
)
|
|
mock_client_constructor.return_value = mock_client_instance
|
|
|
|
apigee_llm = ApigeeLlm(
|
|
model=APIGEE_GEMINI_MODEL_ID,
|
|
proxy_url=PROXY_URL,
|
|
)
|
|
_ = [resp async for resp in apigee_llm.generate_content_async(llm_request)]
|
|
|
|
_, kwargs = mock_client_constructor.call_args
|
|
assert 'credentials' not in kwargs
|
|
|
|
|
|
def test_parse_response_with_refusal():
|
|
"""Tests that CompletionsHTTPClient parses refusal correctly."""
|
|
client = CompletionsHTTPClient(base_url='http://test')
|
|
|
|
response_dict = {
|
|
'choices': [{
|
|
'message': {
|
|
'role': 'assistant',
|
|
'refusal': 'I refuse to answer',
|
|
},
|
|
'finish_reason': 'stop',
|
|
}],
|
|
}
|
|
llm_response = client._parse_response(response_dict)
|
|
assert len(llm_response.content.parts) == 1
|
|
assert llm_response.content.parts[0].text == '[[REFUSAL]]: I refuse to answer'
|
|
|
|
response_dict_mixed = {
|
|
'choices': [{
|
|
'message': {
|
|
'role': 'assistant',
|
|
'content': 'Here is some content',
|
|
'refusal': 'But I refuse to answer the rest',
|
|
},
|
|
'finish_reason': 'stop',
|
|
}],
|
|
}
|
|
llm_response_mixed = client._parse_response(response_dict_mixed)
|
|
assert len(llm_response_mixed.content.parts) == 1
|
|
assert (
|
|
llm_response_mixed.content.parts[0].text
|
|
== 'Here is some content\n[[REFUSAL]]: But I refuse to answer the rest'
|
|
)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
('parts', 'expected_message'),
|
|
[
|
|
(
|
|
[
|
|
types.Part.from_text(text='[[REFUSAL]]: I refuse to answer'),
|
|
types.Part.from_text(text='normal content'),
|
|
],
|
|
{
|
|
'role': 'assistant',
|
|
'refusal': 'I refuse to answer',
|
|
'content': 'normal content',
|
|
},
|
|
),
|
|
(
|
|
[
|
|
types.Part.from_text(
|
|
text=(
|
|
'Here is some content\n[[REFUSAL]]: But I refuse to'
|
|
' answer the rest'
|
|
)
|
|
),
|
|
],
|
|
{
|
|
'role': 'assistant',
|
|
'refusal': 'But I refuse to answer the rest',
|
|
'content': 'Here is some content',
|
|
},
|
|
),
|
|
],
|
|
)
|
|
def test_construct_payload_with_refusal(parts, expected_message):
|
|
"""Tests that CompletionsHTTPClient constructs payload with refusal correctly."""
|
|
client = CompletionsHTTPClient(base_url='http://test')
|
|
req = LlmRequest(
|
|
model='apigee/openai/gpt-4o',
|
|
contents=[
|
|
types.Content(
|
|
role='model',
|
|
parts=parts,
|
|
)
|
|
],
|
|
)
|
|
payload = client._construct_payload(req, stream=False)
|
|
messages = payload['messages']
|
|
assert messages == [expected_message]
|