Asjad Abbas
42a4a5f0e7
Merge https://github.com/google/adk-python/pull/6558 PiperOrigin-RevId: 966679423
226 lines
7.3 KiB
Python
226 lines
7.3 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 google.adk import models
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from google.adk.labs.openai._openai_llm import OpenAILlm
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from google.adk.models import registry
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from google.adk.models.anthropic_llm import Claude
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from google.adk.models.apigee_llm import ApigeeLlm
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from google.adk.models.base_llm import BaseLlm
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from google.adk.models.google_llm import Gemini
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from google.adk.models.lite_llm import LiteLlm
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import pytest
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@pytest.mark.parametrize(
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'model_name',
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[
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'gemini-1.5-pro',
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'gemini-1.5-pro-001',
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'gemini-1.5-pro-002',
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'gemini-2.5-flash',
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'projects/123456/locations/us-central1/endpoints/123456', # finetuned vertex gemini endpoint
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'projects/123456/locations/us-central1/publishers/google/models/gemini-2.5-flash', # vertex gemini long name
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],
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)
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def test_match_gemini_family(model_name):
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"""Test that Gemini models are resolved correctly."""
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assert models.LLMRegistry.resolve(model_name) is Gemini
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@pytest.mark.parametrize(
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'model_name',
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[
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'claude-3-5-haiku@20241022',
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'claude-3-5-sonnet-v2@20241022',
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'claude-3-5-sonnet@20240620',
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'claude-3-haiku@20240307',
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'claude-3-opus@20240229',
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'claude-3-sonnet@20240229',
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'claude-sonnet-4@20250514',
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'claude-opus-4@20250514',
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'claude-sonnet-4-5',
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'claude-haiku-4-5',
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'claude-sonnet-4-6',
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'claude-opus-4-6',
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'claude-opus-4-7',
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'claude-opus-4-8',
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'claude-opus-5',
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'claude-sonnet-5',
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'claude-fable-5',
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'claude-opus-5@default',
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'claude-sonnet-5@default',
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],
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)
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def test_match_claude_family(model_name):
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"""Test that Claude models are resolved correctly."""
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assert models.LLMRegistry.resolve(model_name) is Claude
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@pytest.mark.parametrize(
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'model_name',
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[
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'openai/gpt-4o',
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'openai/gpt-4o-mini',
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'groq/llama3-70b-8192',
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'groq/mixtral-8x7b-32768',
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'anthropic/claude-3-opus-20240229',
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'anthropic/claude-3-5-sonnet-20241022',
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],
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)
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def test_match_litellm_family(model_name):
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"""Test that LiteLLM models are resolved correctly."""
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assert models.LLMRegistry.resolve(model_name) is LiteLlm
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@pytest.mark.parametrize(
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'model_name',
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[
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'xai/grok-4',
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'gemini/gemini-3.5-flash',
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'openrouter/anthropic/claude-opus-4',
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'cerebras/llama-3.3-70b',
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],
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)
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def test_match_litellm_provider_not_spelled_out_in_registry(model_name):
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"""Test that any provider LiteLLM knows about resolves to LiteLlm."""
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assert models.LLMRegistry.resolve(model_name) is LiteLlm
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@pytest.mark.parametrize(
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'model_name',
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[
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'apigee/gemini-2.5-flash',
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'apigee/v1/gemini-2.5-flash',
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'apigee/vertex_ai/v1beta/gemini-2.5-flash',
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],
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)
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def test_match_apigee_family(model_name):
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"""Test that Apigee models are resolved correctly."""
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assert models.LLMRegistry.resolve(model_name) is ApigeeLlm
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@pytest.mark.parametrize(
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'model_name',
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[
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'o1-preview',
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'o3-mini',
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'o4-mini',
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],
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)
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def test_match_openai_reasoning_family(model_name):
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"""Test that the OpenAI o-series resolves regardless of generation."""
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assert models.LLMRegistry.resolve(model_name) is OpenAILlm
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def test_non_exist_model():
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with pytest.raises(ValueError) as e_info:
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models.LLMRegistry.resolve('non-exist-model')
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assert 'Model non-exist-model not found.' in str(e_info.value)
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def test_helpful_error_for_claude_without_extensions():
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"""Test that missing Claude models show helpful install instructions.
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Note: This test may pass even when anthropic IS installed, because it
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only checks the error message format when a model is not found.
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"""
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# Use a non-existent Claude model variant to trigger error
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with pytest.raises(ValueError) as e_info:
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models.LLMRegistry.resolve('claude-nonexistent-model-xyz')
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error_msg = str(e_info.value)
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# The error should mention anthropic package and installation instructions
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# These checks work whether or not anthropic is actually installed
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assert 'Model claude-nonexistent-model-xyz not found' in error_msg
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assert 'anthropic package' in error_msg
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assert 'pip install' in error_msg
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def test_helpful_error_for_litellm_without_extensions():
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"""Test that missing LiteLLM models show helpful install instructions.
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Note: This test may pass even when litellm IS installed, because it
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only checks the error message format when a model is not found.
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"""
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# Use a non-existent provider to trigger error
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with pytest.raises(ValueError) as e_info:
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models.LLMRegistry.resolve('unknown-provider/gpt-4o')
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error_msg = str(e_info.value)
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# The error should mention litellm package for provider-style models
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assert 'Model unknown-provider/gpt-4o not found' in error_msg
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assert 'litellm package' in error_msg
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assert 'pip install' in error_msg
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assert 'Provider-style models' in error_msg
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def test_resolve_with_prefix():
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"""Test that model resolution can be overridden with a prefix."""
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assert models.LLMRegistry.resolve('gemini:gemini-1.5-flash') is Gemini
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assert models.LLMRegistry.resolve('Claude:claude-3-opus@20240229') is Claude
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assert models.LLMRegistry.resolve('lite:openai/gpt-4o') is LiteLlm
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assert models.LLMRegistry.resolve('LiteLlm:openai/gpt-4o') is LiteLlm
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def test_register_after_resolve_returns_the_new_class():
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"""Test that registering over an already-resolved name takes effect."""
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model_name = 'test-registry-override-model'
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class FirstLlm(BaseLlm):
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@classmethod
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def supported_models(cls):
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return [model_name]
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class SecondLlm(BaseLlm):
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@classmethod
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def supported_models(cls):
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return [model_name]
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try:
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models.LLMRegistry.register(FirstLlm)
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assert models.LLMRegistry.resolve(model_name) is FirstLlm
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models.LLMRegistry.register(SecondLlm)
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assert models.LLMRegistry.resolve(model_name) is SecondLlm
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finally:
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registry._llm_registry_dict.pop(model_name, None)
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models.LLMRegistry.resolve.cache_clear()
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def test_new_llm_with_prefix(mocker):
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"""Test that new_llm strips prefix when creating instance if it matches class."""
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mock_class = mocker.MagicMock()
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mock_class.__name__ = 'MockLlm'
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mocker.patch.object(models.LLMRegistry, 'resolve', return_value=mock_class)
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models.LLMRegistry.new_llm('mock:gpt-4')
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mock_class.assert_called_once_with(model='gpt-4')
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mock_class.reset_mock()
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models.LLMRegistry.new_llm('MockLlm:gpt-4')
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mock_class.assert_called_once_with(model='gpt-4')
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def test_new_llm_with_non_matching_prefix(mocker):
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"""Test that new_llm keeps prefix if it does not match class."""
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mock_class = mocker.MagicMock()
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mock_class.__name__ = 'MockLlm'
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mocker.patch.object(models.LLMRegistry, 'resolve', return_value=mock_class)
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models.LLMRegistry.new_llm('custom:gpt-4')
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mock_class.assert_called_once_with(model='custom:gpt-4')
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