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2568 lines
83 KiB
Python
2568 lines
83 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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import base64
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import json
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import os
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import re
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import sys
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from unittest import mock
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from unittest.mock import AsyncMock
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from unittest.mock import MagicMock
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from anthropic import NOT_GIVEN
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from anthropic import types as anthropic_types
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from google.adk import version as adk_version
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from google.adk.models import anthropic_llm
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from google.adk.models import AnthropicGenerateContentConfig
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from google.adk.models.anthropic_llm import AnthropicLlm
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from google.adk.models.anthropic_llm import Claude
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from google.adk.models.anthropic_llm import content_to_message_param
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from google.adk.models.anthropic_llm import function_declaration_to_tool_param
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from google.adk.models.anthropic_llm import part_to_message_block
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from google.adk.models.llm_request import LlmRequest
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from google.adk.models.llm_response import LlmResponse
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from google.genai import types
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from google.genai import version as genai_version
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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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@pytest.fixture
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def generate_content_response():
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return anthropic_types.Message(
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id="msg_vrtx_testid",
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content=[
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anthropic_types.TextBlock(
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citations=None, text="Hi! How can I help you today?", type="text"
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)
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],
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model="claude-3-5-sonnet-v2-20241022",
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role="assistant",
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stop_reason="end_turn",
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stop_sequence=None,
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type="message",
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usage=anthropic_types.Usage(
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cache_creation_input_tokens=0,
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cache_read_input_tokens=0,
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input_tokens=13,
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output_tokens=12,
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server_tool_use=None,
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service_tier=None,
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),
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)
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@pytest.fixture
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def generate_llm_response():
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return LlmResponse.create(
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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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role="model",
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parts=[Part.from_text(text="Hello, how can I help you?")],
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),
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finish_reason=types.FinishReason.STOP,
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)
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]
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)
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)
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@pytest.fixture
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def claude_llm():
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return Claude(model="claude-3-5-sonnet-v2@20241022")
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@pytest.fixture
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def llm_request():
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return LlmRequest(
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model="claude-3-5-sonnet-v2@20241022",
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contents=[Content(role="user", parts=[Part.from_text(text="Hello")])],
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config=types.GenerateContentConfig(
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temperature=0.1,
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response_modalities=[types.Modality.TEXT],
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system_instruction="You are a helpful assistant",
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),
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)
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def test_claude_anthropic_client_creation():
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# Test with environment variables
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with mock.patch.dict(
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os.environ,
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{
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"GOOGLE_CLOUD_PROJECT": "env-project",
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"GOOGLE_CLOUD_LOCATION": "env-location",
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},
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):
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model = Claude(model="claude-3-5-sonnet-v2@20241022")
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with mock.patch(
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"google.adk.models.anthropic_llm.AsyncAnthropicVertex", autospec=True
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) as mock_client_class:
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_ = model._anthropic_client
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mock_client_class.assert_called_once()
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_, kwargs = mock_client_class.call_args
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assert kwargs["project_id"] == "env-project"
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assert kwargs["region"] == "env-location"
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def test_claude_anthropic_client_creation_with_full_resource_name():
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# Test with full resource name in model string
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model = Claude(
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model="projects/test-project/locations/test-location/publishers/anthropic/models/claude-3-5-sonnet-v2@20241022"
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)
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with mock.patch(
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"google.adk.models.anthropic_llm.AsyncAnthropicVertex", autospec=True
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) as mock_client_class:
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_ = model._anthropic_client
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mock_client_class.assert_called_once()
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_, kwargs = mock_client_class.call_args
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assert kwargs["project_id"] == "test-project"
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assert kwargs["region"] == "test-location"
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def test_supported_models():
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models = Claude.supported_models()
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assert len(models) == 2
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assert models[0] == r"claude-3-.*"
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assert models[1] == r"claude-.*-4.*"
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function_declaration_test_cases = [
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(
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"function_with_no_parameters",
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types.FunctionDeclaration(
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name="get_current_time",
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description="Gets the current time.",
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),
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anthropic_types.ToolParam(
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name="get_current_time",
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description="Gets the current time.",
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input_schema={"type": "object", "properties": {}},
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),
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),
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(
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"function_with_one_optional_parameter",
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types.FunctionDeclaration(
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name="get_weather",
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description="Gets weather information for a given location.",
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parameters=types.Schema(
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type=types.Type.OBJECT,
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properties={
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"location": types.Schema(
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type=types.Type.STRING,
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description="City and state, e.g., San Francisco, CA",
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)
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},
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),
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),
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anthropic_types.ToolParam(
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name="get_weather",
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description="Gets weather information for a given location.",
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input_schema={
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"type": "object",
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"properties": {
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"location": {
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"type": "string",
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"description": (
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"City and state, e.g., San Francisco, CA"
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),
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}
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},
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},
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),
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),
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(
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"function_with_one_required_parameter",
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types.FunctionDeclaration(
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name="get_stock_price",
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description="Gets the current price for a stock ticker.",
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parameters=types.Schema(
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type=types.Type.OBJECT,
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properties={
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"ticker": types.Schema(
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type=types.Type.STRING,
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description="The stock ticker, e.g., AAPL",
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)
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},
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required=["ticker"],
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),
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),
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anthropic_types.ToolParam(
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name="get_stock_price",
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description="Gets the current price for a stock ticker.",
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input_schema={
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"type": "object",
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"properties": {
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"ticker": {
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"type": "string",
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"description": "The stock ticker, e.g., AAPL",
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}
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},
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"required": ["ticker"],
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},
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),
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),
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(
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"function_with_multiple_mixed_parameters",
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types.FunctionDeclaration(
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name="submit_order",
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description="Submits a product order.",
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parameters=types.Schema(
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type=types.Type.OBJECT,
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properties={
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"product_id": types.Schema(
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type=types.Type.STRING, description="The product ID"
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),
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"quantity": types.Schema(
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type=types.Type.INTEGER,
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description="The order quantity",
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),
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"notes": types.Schema(
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type=types.Type.STRING,
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description="Optional order notes",
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),
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},
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required=["product_id", "quantity"],
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),
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),
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anthropic_types.ToolParam(
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name="submit_order",
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description="Submits a product order.",
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input_schema={
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"type": "object",
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"properties": {
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"product_id": {
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"type": "string",
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"description": "The product ID",
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},
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"quantity": {
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"type": "integer",
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"description": "The order quantity",
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},
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"notes": {
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"type": "string",
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"description": "Optional order notes",
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},
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},
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"required": ["product_id", "quantity"],
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},
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),
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),
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(
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"function_with_complex_nested_parameter",
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types.FunctionDeclaration(
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name="create_playlist",
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description="Creates a playlist from a list of songs.",
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parameters=types.Schema(
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type=types.Type.OBJECT,
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properties={
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"playlist_name": types.Schema(
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type=types.Type.STRING,
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description="The name for the new playlist",
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),
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"songs": types.Schema(
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type=types.Type.ARRAY,
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description="A list of songs to add to the playlist",
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items=types.Schema(
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type=types.Type.OBJECT,
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properties={
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"title": types.Schema(type=types.Type.STRING),
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"artist": types.Schema(type=types.Type.STRING),
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},
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required=["title", "artist"],
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),
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),
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},
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required=["playlist_name", "songs"],
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),
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),
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anthropic_types.ToolParam(
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name="create_playlist",
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description="Creates a playlist from a list of songs.",
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input_schema={
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"type": "object",
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"properties": {
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"playlist_name": {
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"type": "string",
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"description": "The name for the new playlist",
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},
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"songs": {
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"type": "array",
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"description": "A list of songs to add to the playlist",
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"items": {
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"type": "object",
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"properties": {
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"title": {"type": "string"},
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"artist": {"type": "string"},
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},
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"required": ["title", "artist"],
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},
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},
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},
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"required": ["playlist_name", "songs"],
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},
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),
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),
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(
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"function_with_nested_object_parameter",
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types.FunctionDeclaration(
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name="update_profile",
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description="Updates a user profile.",
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parameters=types.Schema(
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type=types.Type.OBJECT,
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properties={
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"profile": types.Schema(
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type=types.Type.OBJECT,
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description="The profile data",
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properties={
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"name": types.Schema(
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type=types.Type.STRING,
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description="Full name",
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),
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"address": types.Schema(
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type=types.Type.OBJECT,
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description="Mailing address",
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properties={
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"city": types.Schema(
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type=types.Type.STRING,
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),
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"state": types.Schema(
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type=types.Type.STRING,
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),
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},
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),
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},
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),
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},
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required=["profile"],
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),
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),
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anthropic_types.ToolParam(
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name="update_profile",
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description="Updates a user profile.",
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input_schema={
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"type": "object",
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"properties": {
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"profile": {
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"type": "object",
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"description": "The profile data",
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"properties": {
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"name": {
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"type": "string",
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"description": "Full name",
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},
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"address": {
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"type": "object",
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"description": "Mailing address",
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"properties": {
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"city": {"type": "string"},
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"state": {"type": "string"},
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},
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},
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},
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},
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},
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"required": ["profile"],
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},
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),
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),
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(
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"function_with_any_of_parameter",
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types.FunctionDeclaration(
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name="set_value",
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description="Sets a value that can be a string or integer.",
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parameters=types.Schema(
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type=types.Type.OBJECT,
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properties={
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"value": types.Schema(
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description="A string or integer value",
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any_of=[
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types.Schema(type=types.Type.STRING),
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types.Schema(type=types.Type.INTEGER),
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],
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),
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},
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required=["value"],
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),
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),
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anthropic_types.ToolParam(
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name="set_value",
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description="Sets a value that can be a string or integer.",
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input_schema={
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"type": "object",
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"properties": {
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"value": {
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"description": "A string or integer value",
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"anyOf": [
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{"type": "string"},
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{"type": "integer"},
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],
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},
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},
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"required": ["value"],
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},
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),
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),
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(
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"function_with_additional_properties_parameter",
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types.FunctionDeclaration(
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name="store_metadata",
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description="Stores arbitrary key-value metadata.",
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parameters=types.Schema(
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type=types.Type.OBJECT,
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properties={
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"metadata": types.Schema(
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type=types.Type.OBJECT,
|
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description="Arbitrary metadata",
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additional_properties=types.Schema(
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type=types.Type.STRING,
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),
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),
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},
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required=["metadata"],
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),
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),
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anthropic_types.ToolParam(
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name="store_metadata",
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description="Stores arbitrary key-value metadata.",
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input_schema={
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"type": "object",
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"properties": {
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"metadata": {
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"type": "object",
|
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"description": "Arbitrary metadata",
|
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"additionalProperties": {"type": "string"},
|
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},
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},
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"required": ["metadata"],
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},
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),
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),
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(
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"function_with_parameters_json_schema_combinators",
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types.FunctionDeclaration(
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name="validate_payload",
|
|
description="Validates a payload with schema combinators.",
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parameters_json_schema={
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"type": "OBJECT",
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|
"properties": {
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|
"choice": {
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"oneOf": [
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{"type": "STRING"},
|
|
{"type": "INTEGER"},
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|
],
|
|
},
|
|
"config": {
|
|
"allOf": [
|
|
{
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|
"type": "OBJECT",
|
|
"properties": {
|
|
"enabled": {"type": "BOOLEAN"},
|
|
},
|
|
},
|
|
],
|
|
},
|
|
"blocked": {
|
|
"not": {
|
|
"type": "NULL",
|
|
},
|
|
},
|
|
"tuple_value": {
|
|
"type": "ARRAY",
|
|
"items": [
|
|
{"type": "STRING"},
|
|
{"type": "INTEGER"},
|
|
],
|
|
},
|
|
},
|
|
"required": ["choice"],
|
|
},
|
|
),
|
|
anthropic_types.ToolParam(
|
|
name="validate_payload",
|
|
description="Validates a payload with schema combinators.",
|
|
input_schema={
|
|
"type": "object",
|
|
"properties": {
|
|
"choice": {
|
|
"oneOf": [
|
|
{"type": "string"},
|
|
{"type": "integer"},
|
|
],
|
|
},
|
|
"config": {
|
|
"allOf": [
|
|
{
|
|
"type": "object",
|
|
"properties": {
|
|
"enabled": {"type": "boolean"},
|
|
},
|
|
},
|
|
],
|
|
},
|
|
"blocked": {
|
|
"not": {
|
|
"type": "null",
|
|
},
|
|
},
|
|
"tuple_value": {
|
|
"type": "array",
|
|
"items": [
|
|
{"type": "string"},
|
|
{"type": "integer"},
|
|
],
|
|
},
|
|
},
|
|
"required": ["choice"],
|
|
},
|
|
),
|
|
),
|
|
(
|
|
"function_with_parameters_json_schema",
|
|
types.FunctionDeclaration(
|
|
name="search_database",
|
|
description="Searches a database with given criteria.",
|
|
parameters_json_schema={
|
|
"type": "object",
|
|
"properties": {
|
|
"query": {
|
|
"type": "string",
|
|
"description": "The search query",
|
|
},
|
|
"limit": {
|
|
"type": "integer",
|
|
"description": "Maximum number of results",
|
|
},
|
|
},
|
|
"required": ["query"],
|
|
},
|
|
),
|
|
anthropic_types.ToolParam(
|
|
name="search_database",
|
|
description="Searches a database with given criteria.",
|
|
input_schema={
|
|
"type": "object",
|
|
"properties": {
|
|
"query": {
|
|
"type": "string",
|
|
"description": "The search query",
|
|
},
|
|
"limit": {
|
|
"type": "integer",
|
|
"description": "Maximum number of results",
|
|
},
|
|
},
|
|
"required": ["query"],
|
|
},
|
|
),
|
|
),
|
|
]
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"_, function_declaration, expected_tool_param",
|
|
function_declaration_test_cases,
|
|
ids=[case[0] for case in function_declaration_test_cases],
|
|
)
|
|
def test_function_declaration_to_tool_param(
|
|
_, function_declaration, expected_tool_param
|
|
):
|
|
"""Test function_declaration_to_tool_param."""
|
|
assert (
|
|
function_declaration_to_tool_param(function_declaration)
|
|
== expected_tool_param
|
|
)
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_generate_content_async(
|
|
claude_llm, llm_request, generate_content_response, generate_llm_response
|
|
):
|
|
with mock.patch.object(claude_llm, "_anthropic_client") as mock_client:
|
|
with mock.patch.object(
|
|
anthropic_llm,
|
|
"message_to_generate_content_response",
|
|
return_value=generate_llm_response,
|
|
):
|
|
# 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.messages.create.return_value = mock_coro()
|
|
|
|
responses = [
|
|
resp
|
|
async for resp in claude_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?"
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_anthropic_llm_generate_content_async(
|
|
llm_request, generate_content_response, generate_llm_response
|
|
):
|
|
anthropic_llm_instance = AnthropicLlm(model="claude-sonnet-4-20250514")
|
|
with mock.patch.object(
|
|
anthropic_llm_instance, "_anthropic_client"
|
|
) as mock_client:
|
|
with mock.patch.object(
|
|
anthropic_llm,
|
|
"message_to_generate_content_response",
|
|
return_value=generate_llm_response,
|
|
):
|
|
# 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.messages.create.return_value = mock_coro()
|
|
|
|
responses = [
|
|
resp
|
|
async for resp in anthropic_llm_instance.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?"
|
|
|
|
|
|
def test_claude_vertex_client_uses_tracking_headers():
|
|
"""Tests that Claude vertex client is called with tracking headers."""
|
|
with mock.patch.object(
|
|
anthropic_llm, "AsyncAnthropicVertex", autospec=True
|
|
) as mock_anthropic_vertex:
|
|
with mock.patch.dict(
|
|
os.environ,
|
|
{
|
|
"GOOGLE_CLOUD_PROJECT": "test-project",
|
|
"GOOGLE_CLOUD_LOCATION": "us-central1",
|
|
},
|
|
):
|
|
instance = Claude(model="claude-3-5-sonnet-v2@20241022")
|
|
_ = instance._anthropic_client
|
|
mock_anthropic_vertex.assert_called_once()
|
|
_, kwargs = mock_anthropic_vertex.call_args
|
|
assert "default_headers" in kwargs
|
|
assert "x-goog-api-client" in kwargs["default_headers"]
|
|
assert "user-agent" in kwargs["default_headers"]
|
|
assert (
|
|
f"google-adk/{adk_version.__version__}"
|
|
in kwargs["default_headers"]["user-agent"]
|
|
)
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_generate_content_async_with_max_tokens(
|
|
llm_request, generate_content_response, generate_llm_response
|
|
):
|
|
claude_llm = Claude(model="claude-3-5-sonnet-v2@20241022", max_tokens=4096)
|
|
with mock.patch.object(claude_llm, "_anthropic_client") as mock_client:
|
|
with mock.patch.object(
|
|
anthropic_llm,
|
|
"message_to_generate_content_response",
|
|
return_value=generate_llm_response,
|
|
):
|
|
# 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.messages.create.return_value = mock_coro()
|
|
|
|
_ = [
|
|
resp
|
|
async for resp in claude_llm.generate_content_async(
|
|
llm_request, stream=False
|
|
)
|
|
]
|
|
mock_client.messages.create.assert_called_once()
|
|
_, kwargs = mock_client.messages.create.call_args
|
|
assert kwargs["max_tokens"] == 4096
|
|
|
|
|
|
def test_part_to_message_block_with_content():
|
|
"""Test that part_to_message_block handles content format."""
|
|
from google.adk.models.anthropic_llm import part_to_message_block
|
|
|
|
# Create a function response part with content array.
|
|
mcp_response_part = types.Part.from_function_response(
|
|
name="generate_sample_filesystem",
|
|
response={
|
|
"content": [{
|
|
"type": "text",
|
|
"text": '{"name":"root","node_type":"folder","children":[]}',
|
|
}]
|
|
},
|
|
)
|
|
mcp_response_part.function_response.id = "test_id_123"
|
|
|
|
result = part_to_message_block(mcp_response_part)
|
|
|
|
# ToolResultBlockParam is a TypedDict.
|
|
assert isinstance(result, dict)
|
|
assert result["tool_use_id"] == "test_id_123"
|
|
assert result["type"] == "tool_result"
|
|
assert not result["is_error"]
|
|
# Verify the content was extracted from the content format.
|
|
assert (
|
|
'{"name":"root","node_type":"folder","children":[]}' in result["content"]
|
|
)
|
|
|
|
|
|
def test_part_to_message_block_with_traditional_result():
|
|
"""Test that part_to_message_block handles traditional result format."""
|
|
from google.adk.models.anthropic_llm import part_to_message_block
|
|
|
|
# Create a function response part with traditional result format
|
|
traditional_response_part = types.Part.from_function_response(
|
|
name="some_tool",
|
|
response={
|
|
"result": "This is the result from the tool",
|
|
},
|
|
)
|
|
traditional_response_part.function_response.id = "test_id_456"
|
|
|
|
result = part_to_message_block(traditional_response_part)
|
|
|
|
# ToolResultBlockParam is a TypedDict.
|
|
assert isinstance(result, dict)
|
|
assert result["tool_use_id"] == "test_id_456"
|
|
assert result["type"] == "tool_result"
|
|
assert not result["is_error"]
|
|
# Verify the content was extracted from the traditional format
|
|
assert "This is the result from the tool" in result["content"]
|
|
|
|
|
|
def test_part_to_message_block_with_multiple_content_items():
|
|
"""Test content with multiple items."""
|
|
from google.adk.models.anthropic_llm import part_to_message_block
|
|
|
|
# Create a function response with multiple content items
|
|
multi_content_part = types.Part.from_function_response(
|
|
name="multi_response_tool",
|
|
response={
|
|
"content": [
|
|
{"type": "text", "text": "First part"},
|
|
{"type": "text", "text": "Second part"},
|
|
]
|
|
},
|
|
)
|
|
multi_content_part.function_response.id = "test_id_789"
|
|
|
|
result = part_to_message_block(multi_content_part)
|
|
|
|
# ToolResultBlockParam is a TypedDict.
|
|
assert isinstance(result, dict)
|
|
# Multiple text items should be joined with newlines
|
|
assert result["content"] == "First part\nSecond part"
|
|
|
|
|
|
def test_part_to_message_block_with_pdf_document():
|
|
"""Test that part_to_message_block handles PDF document parts."""
|
|
pdf_data = b"%PDF-1.4 fake pdf content"
|
|
part = Part(
|
|
inline_data=types.Blob(mime_type="application/pdf", data=pdf_data)
|
|
)
|
|
|
|
result = part_to_message_block(part)
|
|
|
|
assert isinstance(result, dict)
|
|
assert result["type"] == "document"
|
|
assert result["source"]["type"] == "base64"
|
|
assert result["source"]["media_type"] == "application/pdf"
|
|
assert result["source"]["data"] == base64.b64encode(pdf_data).decode()
|
|
|
|
|
|
def test_part_to_message_block_with_pdf_mime_type_parameters():
|
|
"""Test that PDF parts with MIME type parameters are handled correctly."""
|
|
pdf_data = b"%PDF-1.4 fake pdf content"
|
|
part = Part(
|
|
inline_data=types.Blob(
|
|
mime_type="application/pdf; name=doc.pdf", data=pdf_data
|
|
)
|
|
)
|
|
|
|
result = part_to_message_block(part)
|
|
|
|
assert isinstance(result, dict)
|
|
assert result["type"] == "document"
|
|
assert result["source"]["type"] == "base64"
|
|
assert result["source"]["media_type"] == "application/pdf; name=doc.pdf"
|
|
assert result["source"]["data"] == base64.b64encode(pdf_data).decode()
|
|
|
|
|
|
content_to_message_param_test_cases = [
|
|
(
|
|
"user_role_with_text_and_image",
|
|
Content(
|
|
role="user",
|
|
parts=[
|
|
Part.from_text(text="What's in this image?"),
|
|
Part(
|
|
inline_data=types.Blob(
|
|
mime_type="image/jpeg", data=b"fake_image_data"
|
|
)
|
|
),
|
|
],
|
|
),
|
|
"user",
|
|
2, # Expected content length
|
|
None, # No warning expected
|
|
),
|
|
(
|
|
"model_role_with_text_and_image",
|
|
Content(
|
|
role="model",
|
|
parts=[
|
|
Part.from_text(text="I see a cat."),
|
|
Part(
|
|
inline_data=types.Blob(
|
|
mime_type="image/png", data=b"fake_image_data"
|
|
)
|
|
),
|
|
],
|
|
),
|
|
"assistant",
|
|
1, # Image filtered out, only text remains
|
|
"Image data is not supported in Claude for assistant turns.",
|
|
),
|
|
(
|
|
"assistant_role_with_text_and_image",
|
|
Content(
|
|
role="assistant",
|
|
parts=[
|
|
Part.from_text(text="Here's what I found."),
|
|
Part(
|
|
inline_data=types.Blob(
|
|
mime_type="image/webp", data=b"fake_image_data"
|
|
)
|
|
),
|
|
],
|
|
),
|
|
"assistant",
|
|
1, # Image filtered out, only text remains
|
|
"Image data is not supported in Claude for assistant turns.",
|
|
),
|
|
(
|
|
"user_role_with_text_and_document",
|
|
Content(
|
|
role="user",
|
|
parts=[
|
|
Part.from_text(text="Summarize this document."),
|
|
Part(
|
|
inline_data=types.Blob(
|
|
mime_type="application/pdf", data=b"fake_pdf_data"
|
|
)
|
|
),
|
|
],
|
|
),
|
|
"user",
|
|
2, # Both text and document included
|
|
None, # No warning expected
|
|
),
|
|
(
|
|
"model_role_with_text_and_document",
|
|
Content(
|
|
role="model",
|
|
parts=[
|
|
Part.from_text(text="Here is the summary."),
|
|
Part(
|
|
inline_data=types.Blob(
|
|
mime_type="application/pdf", data=b"fake_pdf_data"
|
|
)
|
|
),
|
|
],
|
|
),
|
|
"assistant",
|
|
1, # Document filtered out, only text remains
|
|
"PDF data is not supported in Claude for assistant turns.",
|
|
),
|
|
]
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"_, content, expected_role, expected_content_length, expected_warning",
|
|
content_to_message_param_test_cases,
|
|
ids=[case[0] for case in content_to_message_param_test_cases],
|
|
)
|
|
def test_content_to_message_param(
|
|
_, content, expected_role, expected_content_length, expected_warning
|
|
):
|
|
"""Test content_to_message_param handles images and documents based on role."""
|
|
with mock.patch("google.adk.models.anthropic_llm.logger") as mock_logger:
|
|
result = content_to_message_param(content)
|
|
|
|
assert result["role"] == expected_role
|
|
assert len(result["content"]) == expected_content_length
|
|
|
|
if expected_warning:
|
|
mock_logger.warning.assert_called_once_with(expected_warning)
|
|
else:
|
|
mock_logger.warning.assert_not_called()
|
|
|
|
|
|
# --- Tests for Bug #2: json.dumps for dict/list function results ---
|
|
|
|
|
|
def test_part_to_message_block_dict_result_serialized_as_json():
|
|
"""Dict results should be serialized with json.dumps, not str()."""
|
|
response_part = types.Part.from_function_response(
|
|
name="get_topic",
|
|
response={"result": {"topic": "travel", "active": True, "count": None}},
|
|
)
|
|
response_part.function_response.id = "test_id"
|
|
|
|
result = part_to_message_block(response_part)
|
|
content = result["content"]
|
|
|
|
# Must be valid JSON (json.dumps produces "true"/"null", not "True"/"None")
|
|
parsed = json.loads(content)
|
|
assert parsed["topic"] == "travel"
|
|
assert parsed["active"] is True
|
|
assert parsed["count"] is None
|
|
|
|
|
|
def test_part_to_message_block_list_result_serialized_as_json():
|
|
"""List results should be serialized with json.dumps."""
|
|
response_part = types.Part.from_function_response(
|
|
name="get_items",
|
|
response={"result": ["item1", "item2", "item3"]},
|
|
)
|
|
response_part.function_response.id = "test_id"
|
|
|
|
result = part_to_message_block(response_part)
|
|
content = result["content"]
|
|
|
|
parsed = json.loads(content)
|
|
assert parsed == ["item1", "item2", "item3"]
|
|
|
|
|
|
def test_part_to_message_block_empty_dict_result_not_dropped():
|
|
"""Empty dict results should produce '{}', not empty string."""
|
|
response_part = types.Part.from_function_response(
|
|
name="some_tool",
|
|
response={"result": {}},
|
|
)
|
|
response_part.function_response.id = "test_id"
|
|
|
|
result = part_to_message_block(response_part)
|
|
assert result["content"] == "{}"
|
|
|
|
|
|
def test_part_to_message_block_empty_list_result_not_dropped():
|
|
"""Empty list results should produce '[]', not empty string."""
|
|
response_part = types.Part.from_function_response(
|
|
name="some_tool",
|
|
response={"result": []},
|
|
)
|
|
response_part.function_response.id = "test_id"
|
|
|
|
result = part_to_message_block(response_part)
|
|
assert result["content"] == "[]"
|
|
|
|
|
|
def test_part_to_message_block_string_result_unchanged():
|
|
"""String results should still work as before (backward compat)."""
|
|
response_part = types.Part.from_function_response(
|
|
name="simple_tool",
|
|
response={"result": "plain text result"},
|
|
)
|
|
response_part.function_response.id = "test_id"
|
|
|
|
result = part_to_message_block(response_part)
|
|
assert result["content"] == "plain text result"
|
|
|
|
|
|
def test_part_to_message_block_nested_dict_result():
|
|
"""Nested dict with arrays should produce valid JSON."""
|
|
response_part = types.Part.from_function_response(
|
|
name="search",
|
|
response={
|
|
"result": {
|
|
"results": [
|
|
{"id": 1, "tags": ["a", "b"]},
|
|
{"id": 2, "meta": {"key": "val"}},
|
|
],
|
|
"has_more": False,
|
|
}
|
|
},
|
|
)
|
|
response_part.function_response.id = "test_id"
|
|
|
|
result = part_to_message_block(response_part)
|
|
parsed = json.loads(result["content"])
|
|
assert parsed["has_more"] is False
|
|
assert parsed["results"][0]["tags"] == ["a", "b"]
|
|
|
|
|
|
# --- Tests for arbitrary dict fallback (e.g. SkillToolset load_skill) ---
|
|
|
|
|
|
def test_part_to_message_block_arbitrary_dict_serialized_as_json():
|
|
"""Dicts with keys other than 'content'/'result' should be JSON-serialized.
|
|
|
|
This covers tools like load_skill that return arbitrary key structures
|
|
such as {"skill_name": ..., "instructions": ..., "frontmatter": ...}.
|
|
"""
|
|
response_part = types.Part.from_function_response(
|
|
name="load_skill",
|
|
response={
|
|
"skill_name": "my_skill",
|
|
"instructions": "Step 1: do this. Step 2: do that.",
|
|
"frontmatter": {"version": "1.0", "tags": ["a", "b"]},
|
|
},
|
|
)
|
|
response_part.function_response.id = "test_id"
|
|
|
|
result = part_to_message_block(response_part)
|
|
|
|
assert result["type"] == "tool_result"
|
|
assert result["tool_use_id"] == "test_id"
|
|
assert not result["is_error"]
|
|
parsed = json.loads(result["content"])
|
|
assert parsed["skill_name"] == "my_skill"
|
|
assert parsed["instructions"] == "Step 1: do this. Step 2: do that."
|
|
assert parsed["frontmatter"]["version"] == "1.0"
|
|
|
|
|
|
def test_part_to_message_block_run_skill_script_response():
|
|
"""run_skill_script response keys (stdout/stderr/status) should not be dropped."""
|
|
response_part = types.Part.from_function_response(
|
|
name="run_skill_script",
|
|
response={
|
|
"skill_name": "my_skill",
|
|
"file_path": "scripts/setup.py",
|
|
"stdout": "Done.",
|
|
"stderr": "",
|
|
"status": "success",
|
|
},
|
|
)
|
|
response_part.function_response.id = "test_id_2"
|
|
|
|
result = part_to_message_block(response_part)
|
|
|
|
parsed = json.loads(result["content"])
|
|
assert parsed["status"] == "success"
|
|
assert parsed["stdout"] == "Done."
|
|
|
|
|
|
def test_part_to_message_block_error_response_not_dropped():
|
|
"""Error dicts like {"error": ..., "error_code": ...} should be serialized."""
|
|
response_part = types.Part.from_function_response(
|
|
name="load_skill",
|
|
response={
|
|
"error": "Skill 'missing' not found.",
|
|
"error_code": "SKILL_NOT_FOUND",
|
|
},
|
|
)
|
|
response_part.function_response.id = "test_id_3"
|
|
|
|
result = part_to_message_block(response_part)
|
|
|
|
parsed = json.loads(result["content"])
|
|
assert parsed["error_code"] == "SKILL_NOT_FOUND"
|
|
|
|
|
|
def test_part_to_message_block_empty_response_stays_empty():
|
|
"""An empty response dict should still produce an empty content string."""
|
|
response_part = types.Part.from_function_response(
|
|
name="some_tool",
|
|
response={},
|
|
)
|
|
response_part.function_response.id = "test_id_4"
|
|
|
|
result = part_to_message_block(response_part)
|
|
|
|
assert result["content"] == ""
|
|
|
|
|
|
def test_part_to_message_block_string_content_passes_through():
|
|
"""A scalar string `content` value must not be iterated char-by-char."""
|
|
response_part = types.Part.from_function_response(
|
|
name="some_tool",
|
|
response={"content": "Hello"},
|
|
)
|
|
response_part.function_response.id = "test_id_str_content"
|
|
|
|
result = part_to_message_block(response_part)
|
|
|
|
assert result["content"] == "Hello"
|
|
|
|
|
|
def test_part_to_message_block_load_skill_resource_response():
|
|
"""LoadSkillResourceTool returns {content: <file text>} as a string."""
|
|
file_text = "Line one\nLine two\nLine three"
|
|
response_part = types.Part.from_function_response(
|
|
name="load_skill_resource",
|
|
response={
|
|
"skill_name": "my-skill",
|
|
"file_path": "references/doc.md",
|
|
"content": file_text,
|
|
},
|
|
)
|
|
response_part.function_response.id = "test_id_load_skill"
|
|
|
|
result = part_to_message_block(response_part)
|
|
|
|
assert result["content"] == file_text
|
|
|
|
|
|
def test_part_to_message_block_empty_string_content_falls_through():
|
|
"""`{"content": ""}` falls through to the JSON-dump fallback, not a crash."""
|
|
response_part = types.Part.from_function_response(
|
|
name="some_tool",
|
|
response={"content": ""},
|
|
)
|
|
response_part.function_response.id = "test_id_empty_content_only"
|
|
|
|
result = part_to_message_block(response_part)
|
|
|
|
assert json.loads(result["content"]) == {"content": ""}
|
|
|
|
|
|
def test_part_to_message_block_empty_content_with_metadata_keeps_metadata():
|
|
"""`content: ""` is falsy; sibling keys still reach the model via JSON dump."""
|
|
response_part = types.Part.from_function_response(
|
|
name="some_tool",
|
|
response={"content": "", "extra": "keep me"},
|
|
)
|
|
response_part.function_response.id = "test_id_empty_content_with_meta"
|
|
|
|
result = part_to_message_block(response_part)
|
|
|
|
parsed = json.loads(result["content"])
|
|
assert parsed["content"] == ""
|
|
assert parsed["extra"] == "keep me"
|
|
|
|
|
|
# --- Tests for Bug #1: Streaming support ---
|
|
|
|
|
|
def _make_mock_stream_events(events):
|
|
"""Helper to create an async iterable from a list of events."""
|
|
|
|
async def _stream():
|
|
for event in events:
|
|
yield event
|
|
|
|
return _stream()
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_streaming_text_yields_partial_and_final():
|
|
"""Streaming text should yield partial chunks then a final response."""
|
|
llm = AnthropicLlm(model="claude-sonnet-4-20250514")
|
|
|
|
events = [
|
|
MagicMock(
|
|
type="message_start",
|
|
message=MagicMock(usage=MagicMock(input_tokens=10, output_tokens=0)),
|
|
),
|
|
MagicMock(
|
|
type="content_block_start",
|
|
index=0,
|
|
content_block=anthropic_types.TextBlock(text="", type="text"),
|
|
),
|
|
MagicMock(
|
|
type="content_block_delta",
|
|
index=0,
|
|
delta=anthropic_types.TextDelta(text="Hello ", type="text_delta"),
|
|
),
|
|
MagicMock(
|
|
type="content_block_delta",
|
|
index=0,
|
|
delta=anthropic_types.TextDelta(text="world!", type="text_delta"),
|
|
),
|
|
MagicMock(type="content_block_stop", index=0),
|
|
MagicMock(
|
|
type="message_delta",
|
|
delta=MagicMock(stop_reason="end_turn"),
|
|
usage=MagicMock(output_tokens=5),
|
|
),
|
|
MagicMock(type="message_stop"),
|
|
]
|
|
|
|
mock_client = MagicMock()
|
|
mock_client.messages.create = AsyncMock(
|
|
return_value=_make_mock_stream_events(events)
|
|
)
|
|
|
|
llm_request = LlmRequest(
|
|
model="claude-sonnet-4-20250514",
|
|
contents=[Content(role="user", parts=[Part.from_text(text="Hi")])],
|
|
config=types.GenerateContentConfig(
|
|
system_instruction="You are helpful",
|
|
),
|
|
)
|
|
|
|
with mock.patch.object(llm, "_anthropic_client", mock_client):
|
|
responses = [
|
|
r async for r in llm.generate_content_async(llm_request, stream=True)
|
|
]
|
|
|
|
# 2 partial text chunks + 1 final aggregated
|
|
assert len(responses) == 3
|
|
assert responses[0].partial is True
|
|
assert responses[0].content.parts[0].text == "Hello "
|
|
assert responses[1].partial is True
|
|
assert responses[1].content.parts[0].text == "world!"
|
|
assert responses[2].partial is False
|
|
assert responses[2].content.parts[0].text == "Hello world!"
|
|
assert responses[2].usage_metadata.prompt_token_count == 10
|
|
assert responses[2].usage_metadata.candidates_token_count == 5
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_streaming_tool_use_yields_function_call():
|
|
"""Streaming tool_use should accumulate args and yield in final."""
|
|
llm = AnthropicLlm(model="claude-sonnet-4-20250514")
|
|
|
|
events = [
|
|
MagicMock(
|
|
type="message_start",
|
|
message=MagicMock(usage=MagicMock(input_tokens=20, output_tokens=0)),
|
|
),
|
|
MagicMock(
|
|
type="content_block_start",
|
|
index=0,
|
|
content_block=anthropic_types.TextBlock(text="", type="text"),
|
|
),
|
|
MagicMock(
|
|
type="content_block_delta",
|
|
index=0,
|
|
delta=anthropic_types.TextDelta(text="Checking.", type="text_delta"),
|
|
),
|
|
MagicMock(type="content_block_stop", index=0),
|
|
MagicMock(
|
|
type="content_block_start",
|
|
index=1,
|
|
content_block=anthropic_types.ToolUseBlock(
|
|
id="toolu_abc",
|
|
name="get_weather",
|
|
input={},
|
|
type="tool_use",
|
|
),
|
|
),
|
|
MagicMock(
|
|
type="content_block_delta",
|
|
index=1,
|
|
delta=anthropic_types.InputJSONDelta(
|
|
partial_json='{"city": "Paris"}',
|
|
type="input_json_delta",
|
|
),
|
|
),
|
|
MagicMock(type="content_block_stop", index=1),
|
|
MagicMock(
|
|
type="message_delta",
|
|
delta=MagicMock(stop_reason="tool_use"),
|
|
usage=MagicMock(output_tokens=12),
|
|
),
|
|
MagicMock(type="message_stop"),
|
|
]
|
|
|
|
mock_client = MagicMock()
|
|
mock_client.messages.create = AsyncMock(
|
|
return_value=_make_mock_stream_events(events)
|
|
)
|
|
|
|
llm_request = LlmRequest(
|
|
model="claude-sonnet-4-20250514",
|
|
contents=[
|
|
Content(
|
|
role="user",
|
|
parts=[Part.from_text(text="Weather?")],
|
|
)
|
|
],
|
|
config=types.GenerateContentConfig(
|
|
system_instruction="You are helpful",
|
|
),
|
|
)
|
|
|
|
with mock.patch.object(llm, "_anthropic_client", mock_client):
|
|
responses = [
|
|
r async for r in llm.generate_content_async(llm_request, stream=True)
|
|
]
|
|
|
|
# 1 text partial + 1 final
|
|
assert len(responses) == 2
|
|
|
|
final = responses[-1]
|
|
assert final.partial is False
|
|
assert len(final.content.parts) == 2
|
|
assert final.content.parts[0].text == "Checking."
|
|
assert final.content.parts[1].function_call.name == "get_weather"
|
|
assert final.content.parts[1].function_call.args == {"city": "Paris"}
|
|
assert final.content.parts[1].function_call.id == "toolu_abc"
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_streaming_passes_stream_true_to_create():
|
|
"""When stream=True, messages.create should be called with stream=True."""
|
|
llm = AnthropicLlm(model="claude-sonnet-4-20250514")
|
|
|
|
events = [
|
|
MagicMock(
|
|
type="message_start",
|
|
message=MagicMock(usage=MagicMock(input_tokens=5, output_tokens=0)),
|
|
),
|
|
MagicMock(
|
|
type="content_block_start",
|
|
index=0,
|
|
content_block=anthropic_types.TextBlock(text="", type="text"),
|
|
),
|
|
MagicMock(
|
|
type="content_block_delta",
|
|
index=0,
|
|
delta=anthropic_types.TextDelta(text="Hi", type="text_delta"),
|
|
),
|
|
MagicMock(type="content_block_stop", index=0),
|
|
MagicMock(
|
|
type="message_delta",
|
|
delta=MagicMock(stop_reason="end_turn"),
|
|
usage=MagicMock(output_tokens=1),
|
|
),
|
|
MagicMock(type="message_stop"),
|
|
]
|
|
|
|
mock_client = MagicMock()
|
|
mock_client.messages.create = AsyncMock(
|
|
return_value=_make_mock_stream_events(events)
|
|
)
|
|
|
|
llm_request = LlmRequest(
|
|
model="claude-sonnet-4-20250514",
|
|
contents=[Content(role="user", parts=[Part.from_text(text="Hi")])],
|
|
config=types.GenerateContentConfig(
|
|
system_instruction="Test",
|
|
),
|
|
)
|
|
|
|
with mock.patch.object(llm, "_anthropic_client", mock_client):
|
|
_ = [r async for r in llm.generate_content_async(llm_request, stream=True)]
|
|
|
|
mock_client.messages.create.assert_called_once()
|
|
_, kwargs = mock_client.messages.create.call_args
|
|
assert kwargs["stream"] is True
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_non_streaming_does_not_pass_stream_param():
|
|
"""When stream=False, messages.create should NOT get stream param."""
|
|
llm = AnthropicLlm(model="claude-sonnet-4-20250514")
|
|
|
|
mock_message = anthropic_types.Message(
|
|
id="msg_test",
|
|
content=[
|
|
anthropic_types.TextBlock(text="Hello!", type="text", citations=None)
|
|
],
|
|
model="claude-sonnet-4-20250514",
|
|
role="assistant",
|
|
stop_reason="end_turn",
|
|
stop_sequence=None,
|
|
type="message",
|
|
usage=anthropic_types.Usage(
|
|
input_tokens=5,
|
|
output_tokens=2,
|
|
cache_creation_input_tokens=0,
|
|
cache_read_input_tokens=0,
|
|
server_tool_use=None,
|
|
service_tier=None,
|
|
),
|
|
)
|
|
|
|
mock_client = MagicMock()
|
|
mock_client.messages.create = AsyncMock(return_value=mock_message)
|
|
|
|
llm_request = LlmRequest(
|
|
model="claude-sonnet-4-20250514",
|
|
contents=[Content(role="user", parts=[Part.from_text(text="Hi")])],
|
|
config=types.GenerateContentConfig(
|
|
system_instruction="Test",
|
|
),
|
|
)
|
|
|
|
with mock.patch.object(llm, "_anthropic_client", mock_client):
|
|
responses = [
|
|
r async for r in llm.generate_content_async(llm_request, stream=False)
|
|
]
|
|
|
|
assert len(responses) == 1
|
|
mock_client.messages.create.assert_called_once()
|
|
_, kwargs = mock_client.messages.create.call_args
|
|
assert "stream" not in kwargs
|
|
|
|
|
|
def test_part_to_message_block_function_call_preserves_valid_id():
|
|
"""Valid Anthropic ids must round-trip byte-for-byte."""
|
|
part = types.Part.from_function_call(name="test_tool", args={"k": "v"})
|
|
part.function_call.id = "toolu_01abc"
|
|
|
|
result = part_to_message_block(part)
|
|
|
|
assert result["id"] == "toolu_01abc"
|
|
|
|
|
|
def test_part_to_message_block_function_response_preserves_valid_id():
|
|
"""function_response ids must round-trip byte-for-byte to tool_use_id."""
|
|
part = types.Part.from_function_response(
|
|
name="test_tool", response={"result": "ok"}
|
|
)
|
|
part.function_response.id = "toolu_01abc"
|
|
|
|
result = part_to_message_block(part)
|
|
|
|
assert result["tool_use_id"] == "toolu_01abc"
|
|
|
|
|
|
def test_part_to_message_block_preserves_adk_fallback_id():
|
|
"""ADK-generated ``adk-<uuid>`` ids match Anthropic's regex and round-trip.
|
|
|
|
This is the path exercised by the contents.py fix: when Vertex Claude
|
|
returns id=None, ``populate_client_function_call_id`` writes ``adk-<uuid>``,
|
|
and contents.py preserves it through replay. ``part_to_message_block`` must
|
|
pass it through to Anthropic unchanged so call/response stay paired.
|
|
"""
|
|
call_part = types.Part.from_function_call(name="t", args={"a": 1})
|
|
call_part.function_call.id = "adk-12345678-1234-1234-1234-123456789012"
|
|
response_part = types.Part.from_function_response(
|
|
name="t", response={"result": "ok"}
|
|
)
|
|
response_part.function_response.id = (
|
|
"adk-12345678-1234-1234-1234-123456789012"
|
|
)
|
|
|
|
call_result = part_to_message_block(call_part)
|
|
response_result = part_to_message_block(response_part)
|
|
|
|
assert call_result["id"] == "adk-12345678-1234-1234-1234-123456789012"
|
|
assert (
|
|
response_result["tool_use_id"]
|
|
== "adk-12345678-1234-1234-1234-123456789012"
|
|
)
|
|
# The pair must remain matched after conversion.
|
|
assert call_result["id"] == response_result["tool_use_id"]
|
|
|
|
|
|
# --- Tests for extended thinking support ---
|
|
|
|
|
|
def test_build_anthropic_thinking_param_with_config():
|
|
"""When thinking_config has a positive budget, return ThinkingConfigEnabledParam."""
|
|
from google.adk.models.anthropic_llm import _build_anthropic_thinking_param
|
|
|
|
config = types.GenerateContentConfig(
|
|
thinking_config=types.ThinkingConfig(thinking_budget=5000),
|
|
)
|
|
result = _build_anthropic_thinking_param(config)
|
|
assert result == anthropic_types.ThinkingConfigEnabledParam(
|
|
type="enabled", budget_tokens=5000
|
|
)
|
|
|
|
|
|
def test_build_anthropic_thinking_param_zero_budget_disabled():
|
|
"""thinking_budget=0 maps to ThinkingConfigDisabledParam (genai DISABLED)."""
|
|
from google.adk.models.anthropic_llm import _build_anthropic_thinking_param
|
|
|
|
config = types.GenerateContentConfig(
|
|
thinking_config=types.ThinkingConfig(thinking_budget=0),
|
|
)
|
|
result = _build_anthropic_thinking_param(config)
|
|
assert result == anthropic_types.ThinkingConfigDisabledParam(type="disabled")
|
|
|
|
|
|
def test_build_anthropic_thinking_param_none_budget_raises():
|
|
"""thinking_budget=None must be set explicitly; raises ValueError."""
|
|
from google.adk.models.anthropic_llm import _build_anthropic_thinking_param
|
|
|
|
config = types.GenerateContentConfig(
|
|
thinking_config=types.ThinkingConfig(),
|
|
)
|
|
with pytest.raises(
|
|
ValueError, match="thinking_budget must be set explicitly"
|
|
):
|
|
_build_anthropic_thinking_param(config)
|
|
|
|
|
|
def test_build_anthropic_thinking_param_automatic_budget_uses_adaptive():
|
|
"""thinking_budget=-1 (genai AUTOMATIC) maps to Anthropic adaptive thinking.
|
|
|
|
Required for Claude Opus 4.7 (which rejects ``"enabled"`` with a 400 error)
|
|
and recommended for Opus 4.6 / Sonnet 4.6 where ``"enabled"`` is deprecated.
|
|
"""
|
|
from google.adk.models.anthropic_llm import _build_anthropic_thinking_param
|
|
|
|
config = types.GenerateContentConfig(
|
|
thinking_config=types.ThinkingConfig(thinking_budget=-1),
|
|
)
|
|
result = _build_anthropic_thinking_param(config)
|
|
assert result == anthropic_types.ThinkingConfigAdaptiveParam(type="adaptive")
|
|
|
|
|
|
def test_build_anthropic_thinking_param_other_negative_uses_adaptive():
|
|
"""Any negative thinking_budget (not just -1) maps to adaptive thinking."""
|
|
from google.adk.models.anthropic_llm import _build_anthropic_thinking_param
|
|
|
|
config = types.GenerateContentConfig(
|
|
thinking_config=types.ThinkingConfig(thinking_budget=-5),
|
|
)
|
|
result = _build_anthropic_thinking_param(config)
|
|
assert result == anthropic_types.ThinkingConfigAdaptiveParam(type="adaptive")
|
|
|
|
|
|
def test_build_anthropic_thinking_param_no_config():
|
|
"""Returns NOT_GIVEN when no thinking config is set."""
|
|
from anthropic import NOT_GIVEN
|
|
from google.adk.models.anthropic_llm import _build_anthropic_thinking_param
|
|
|
|
result_none = _build_anthropic_thinking_param(None)
|
|
assert result_none is NOT_GIVEN
|
|
|
|
config_no_thinking = types.GenerateContentConfig(
|
|
system_instruction="test",
|
|
)
|
|
result_no_thinking = _build_anthropic_thinking_param(config_no_thinking)
|
|
assert result_no_thinking is NOT_GIVEN
|
|
|
|
|
|
def test_content_block_to_part_thinking_block():
|
|
"""ThinkingBlock should produce Part with thought=True and signature."""
|
|
from google.adk.models.anthropic_llm import content_block_to_part
|
|
|
|
block = anthropic_types.ThinkingBlock(
|
|
thinking="Let me reason about this.",
|
|
signature="sig_abc123",
|
|
type="thinking",
|
|
)
|
|
part = content_block_to_part(block)
|
|
|
|
assert part is not None
|
|
assert part.text == "Let me reason about this."
|
|
assert part.thought is True
|
|
assert part.thought_signature == b"sig_abc123"
|
|
|
|
|
|
def test_content_block_to_part_redacted_thinking():
|
|
"""RedactedThinkingBlock should preserve the encrypted blob for round-trip."""
|
|
from google.adk.models.anthropic_llm import content_block_to_part
|
|
|
|
block = anthropic_types.RedactedThinkingBlock(
|
|
data="redacted_data",
|
|
type="redacted_thinking",
|
|
)
|
|
part = content_block_to_part(block)
|
|
|
|
assert part.thought is True
|
|
assert part.text is None
|
|
assert part.thought_signature == b"redacted_data"
|
|
|
|
|
|
def test_message_to_generate_content_response_with_thinking():
|
|
"""Message with ThinkingBlock + TextBlock yields both parts."""
|
|
from google.adk.models.anthropic_llm import message_to_generate_content_response
|
|
|
|
message = anthropic_types.Message(
|
|
id="msg_test_thinking",
|
|
content=[
|
|
anthropic_types.ThinkingBlock(
|
|
thinking="I need to think about this.",
|
|
signature="sig_xyz",
|
|
type="thinking",
|
|
),
|
|
anthropic_types.RedactedThinkingBlock(
|
|
data="hidden",
|
|
type="redacted_thinking",
|
|
),
|
|
anthropic_types.TextBlock(
|
|
text="Here is my answer.",
|
|
type="text",
|
|
citations=None,
|
|
),
|
|
],
|
|
model="claude-sonnet-4-20250514",
|
|
role="assistant",
|
|
stop_reason="end_turn",
|
|
stop_sequence=None,
|
|
type="message",
|
|
usage=anthropic_types.Usage(
|
|
input_tokens=10,
|
|
output_tokens=20,
|
|
cache_creation_input_tokens=0,
|
|
cache_read_input_tokens=0,
|
|
server_tool_use=None,
|
|
service_tier=None,
|
|
),
|
|
)
|
|
|
|
response = message_to_generate_content_response(message)
|
|
|
|
assert len(response.content.parts) == 3
|
|
|
|
thinking_part = response.content.parts[0]
|
|
assert thinking_part.text == "I need to think about this."
|
|
assert thinking_part.thought is True
|
|
assert thinking_part.thought_signature == b"sig_xyz"
|
|
|
|
redacted_part = response.content.parts[1]
|
|
assert redacted_part.thought is True
|
|
assert redacted_part.text is None
|
|
assert redacted_part.thought_signature == b"hidden"
|
|
|
|
text_part = response.content.parts[2]
|
|
assert text_part.text == "Here is my answer."
|
|
assert text_part.thought is not True
|
|
|
|
|
|
def test_message_to_generate_content_response_reports_cache_read_tokens():
|
|
"""cache_read_input_tokens maps to usage_metadata.cached_content_token_count."""
|
|
from google.adk.models.anthropic_llm import message_to_generate_content_response
|
|
|
|
message = anthropic_types.Message(
|
|
id="msg_cache_read",
|
|
content=[
|
|
anthropic_types.TextBlock(text="hi", type="text", citations=None)
|
|
],
|
|
model="claude-sonnet-4-20250514",
|
|
role="assistant",
|
|
stop_reason="end_turn",
|
|
stop_sequence=None,
|
|
type="message",
|
|
usage=anthropic_types.Usage(
|
|
input_tokens=100,
|
|
output_tokens=20,
|
|
cache_creation_input_tokens=0,
|
|
cache_read_input_tokens=75,
|
|
server_tool_use=None,
|
|
service_tier=None,
|
|
),
|
|
)
|
|
|
|
response = message_to_generate_content_response(message)
|
|
|
|
assert response.usage_metadata.cached_content_token_count == 75
|
|
|
|
|
|
def test_message_to_generate_content_response_no_cache_read_tokens():
|
|
"""Absent cache_read_input_tokens yields cached_content_token_count=None."""
|
|
from google.adk.models.anthropic_llm import message_to_generate_content_response
|
|
|
|
message = anthropic_types.Message(
|
|
id="msg_no_cache",
|
|
content=[
|
|
anthropic_types.TextBlock(text="hi", type="text", citations=None)
|
|
],
|
|
model="claude-sonnet-4-20250514",
|
|
role="assistant",
|
|
stop_reason="end_turn",
|
|
stop_sequence=None,
|
|
type="message",
|
|
usage=anthropic_types.Usage(
|
|
input_tokens=100,
|
|
output_tokens=20,
|
|
cache_creation_input_tokens=0,
|
|
cache_read_input_tokens=None,
|
|
server_tool_use=None,
|
|
service_tier=None,
|
|
),
|
|
)
|
|
|
|
response = message_to_generate_content_response(message)
|
|
|
|
assert response.usage_metadata.cached_content_token_count is None
|
|
|
|
|
|
def test_part_to_message_block_thinking_roundtrip():
|
|
"""Part with thought=True and signature creates ThinkingBlockParam."""
|
|
part = Part(
|
|
text="My reasoning steps.",
|
|
thought=True,
|
|
thought_signature=b"roundtrip_sig",
|
|
)
|
|
|
|
result = part_to_message_block(part)
|
|
|
|
assert isinstance(result, dict)
|
|
assert result["type"] == "thinking"
|
|
assert result["thinking"] == "My reasoning steps."
|
|
assert result["signature"] == "roundtrip_sig"
|
|
|
|
|
|
def test_part_to_message_block_redacted_thinking_roundtrip():
|
|
"""Part with thought=True, no text, signature -> RedactedThinkingBlockParam."""
|
|
part = Part(thought=True, thought_signature=b"encrypted_blob")
|
|
|
|
result = part_to_message_block(part)
|
|
|
|
assert isinstance(result, dict)
|
|
assert result["type"] == "redacted_thinking"
|
|
assert result["data"] == "encrypted_blob"
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_non_streaming_passes_thinking_param():
|
|
"""When thinking_config is set, messages.create gets thinking kwarg."""
|
|
llm = AnthropicLlm(model="claude-sonnet-4-20250514")
|
|
|
|
mock_message = anthropic_types.Message(
|
|
id="msg_think",
|
|
content=[
|
|
anthropic_types.TextBlock(text="Answer.", type="text", citations=None)
|
|
],
|
|
model="claude-sonnet-4-20250514",
|
|
role="assistant",
|
|
stop_reason="end_turn",
|
|
stop_sequence=None,
|
|
type="message",
|
|
usage=anthropic_types.Usage(
|
|
input_tokens=5,
|
|
output_tokens=2,
|
|
cache_creation_input_tokens=0,
|
|
cache_read_input_tokens=0,
|
|
server_tool_use=None,
|
|
service_tier=None,
|
|
),
|
|
)
|
|
|
|
mock_client = MagicMock()
|
|
mock_client.messages.create = AsyncMock(return_value=mock_message)
|
|
|
|
request = LlmRequest(
|
|
model="claude-sonnet-4-20250514",
|
|
contents=[Content(role="user", parts=[Part.from_text(text="Think")])],
|
|
config=types.GenerateContentConfig(
|
|
system_instruction="Test",
|
|
thinking_config=types.ThinkingConfig(thinking_budget=8000),
|
|
),
|
|
)
|
|
|
|
with mock.patch.object(llm, "_anthropic_client", mock_client):
|
|
_ = [r async for r in llm.generate_content_async(request, stream=False)]
|
|
|
|
mock_client.messages.create.assert_called_once()
|
|
_, kwargs = mock_client.messages.create.call_args
|
|
assert kwargs["thinking"] == anthropic_types.ThinkingConfigEnabledParam(
|
|
type="enabled", budget_tokens=8000
|
|
)
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_non_streaming_no_thinking_param_without_config():
|
|
"""Without thinking_config, thinking kwarg should be NOT_GIVEN."""
|
|
from anthropic import NOT_GIVEN
|
|
|
|
llm = AnthropicLlm(model="claude-sonnet-4-20250514")
|
|
|
|
mock_message = anthropic_types.Message(
|
|
id="msg_no_think",
|
|
content=[
|
|
anthropic_types.TextBlock(text="Hello!", type="text", citations=None)
|
|
],
|
|
model="claude-sonnet-4-20250514",
|
|
role="assistant",
|
|
stop_reason="end_turn",
|
|
stop_sequence=None,
|
|
type="message",
|
|
usage=anthropic_types.Usage(
|
|
input_tokens=5,
|
|
output_tokens=2,
|
|
cache_creation_input_tokens=0,
|
|
cache_read_input_tokens=0,
|
|
server_tool_use=None,
|
|
service_tier=None,
|
|
),
|
|
)
|
|
|
|
mock_client = MagicMock()
|
|
mock_client.messages.create = AsyncMock(return_value=mock_message)
|
|
|
|
request = LlmRequest(
|
|
model="claude-sonnet-4-20250514",
|
|
contents=[Content(role="user", parts=[Part.from_text(text="Hi")])],
|
|
config=types.GenerateContentConfig(
|
|
system_instruction="Test",
|
|
),
|
|
)
|
|
|
|
with mock.patch.object(llm, "_anthropic_client", mock_client):
|
|
_ = [r async for r in llm.generate_content_async(request, stream=False)]
|
|
|
|
mock_client.messages.create.assert_called_once()
|
|
_, kwargs = mock_client.messages.create.call_args
|
|
assert kwargs["thinking"] is NOT_GIVEN
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_streaming_thinking_yields_partial_and_final():
|
|
"""Streaming with thinking blocks yields partial thought then final."""
|
|
llm = AnthropicLlm(model="claude-sonnet-4-20250514")
|
|
|
|
events = [
|
|
MagicMock(
|
|
type="message_start",
|
|
message=MagicMock(usage=MagicMock(input_tokens=15, output_tokens=0)),
|
|
),
|
|
# Thinking block start
|
|
MagicMock(
|
|
type="content_block_start",
|
|
index=0,
|
|
content_block=anthropic_types.ThinkingBlock(
|
|
thinking="", signature="", type="thinking"
|
|
),
|
|
),
|
|
# Thinking deltas
|
|
MagicMock(
|
|
type="content_block_delta",
|
|
index=0,
|
|
delta=anthropic_types.ThinkingDelta(
|
|
thinking="Step 1: ", type="thinking_delta"
|
|
),
|
|
),
|
|
MagicMock(
|
|
type="content_block_delta",
|
|
index=0,
|
|
delta=anthropic_types.ThinkingDelta(
|
|
thinking="analyze.", type="thinking_delta"
|
|
),
|
|
),
|
|
MagicMock(type="content_block_stop", index=0),
|
|
# Text block start
|
|
MagicMock(
|
|
type="content_block_start",
|
|
index=1,
|
|
content_block=anthropic_types.TextBlock(text="", type="text"),
|
|
),
|
|
MagicMock(
|
|
type="content_block_delta",
|
|
index=1,
|
|
delta=anthropic_types.TextDelta(
|
|
text="The answer is 42.", type="text_delta"
|
|
),
|
|
),
|
|
MagicMock(type="content_block_stop", index=1),
|
|
MagicMock(
|
|
type="message_delta",
|
|
delta=MagicMock(stop_reason="end_turn"),
|
|
usage=MagicMock(output_tokens=10),
|
|
),
|
|
MagicMock(type="message_stop"),
|
|
]
|
|
|
|
mock_client = MagicMock()
|
|
mock_client.messages.create = AsyncMock(
|
|
return_value=_make_mock_stream_events(events)
|
|
)
|
|
|
|
request = LlmRequest(
|
|
model="claude-sonnet-4-20250514",
|
|
contents=[Content(role="user", parts=[Part.from_text(text="What?")])],
|
|
config=types.GenerateContentConfig(
|
|
system_instruction="Think carefully",
|
|
thinking_config=types.ThinkingConfig(thinking_budget=5000),
|
|
),
|
|
)
|
|
|
|
with mock.patch.object(llm, "_anthropic_client", mock_client):
|
|
responses = [
|
|
r async for r in llm.generate_content_async(request, stream=True)
|
|
]
|
|
|
|
# 2 thinking partials + 1 text partial + 1 final = 4 responses
|
|
assert len(responses) == 4
|
|
|
|
# First two partials are thinking chunks.
|
|
assert responses[0].partial is True
|
|
assert responses[0].content.parts[0].thought is True
|
|
assert responses[0].content.parts[0].text == "Step 1: "
|
|
|
|
assert responses[1].partial is True
|
|
assert responses[1].content.parts[0].thought is True
|
|
assert responses[1].content.parts[0].text == "analyze."
|
|
|
|
# Third partial is text.
|
|
assert responses[2].partial is True
|
|
assert responses[2].content.parts[0].text == "The answer is 42."
|
|
|
|
# Final aggregated response has both thinking and text parts.
|
|
final = responses[3]
|
|
assert final.partial is False
|
|
assert len(final.content.parts) == 2
|
|
|
|
thinking_part = final.content.parts[0]
|
|
assert thinking_part.thought is True
|
|
assert thinking_part.text == "Step 1: analyze."
|
|
|
|
text_part = final.content.parts[1]
|
|
assert text_part.text == "The answer is 42."
|
|
|
|
assert final.usage_metadata.prompt_token_count == 15
|
|
assert final.usage_metadata.candidates_token_count == 10
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_streaming_passes_thinking_param():
|
|
"""When thinking_config is set and stream=True, thinking kwarg is passed."""
|
|
llm = AnthropicLlm(model="claude-sonnet-4-20250514")
|
|
|
|
events = [
|
|
MagicMock(
|
|
type="message_start",
|
|
message=MagicMock(usage=MagicMock(input_tokens=5, output_tokens=0)),
|
|
),
|
|
MagicMock(
|
|
type="content_block_start",
|
|
index=0,
|
|
content_block=anthropic_types.TextBlock(text="", type="text"),
|
|
),
|
|
MagicMock(
|
|
type="content_block_delta",
|
|
index=0,
|
|
delta=anthropic_types.TextDelta(text="Ok", type="text_delta"),
|
|
),
|
|
MagicMock(type="content_block_stop", index=0),
|
|
MagicMock(
|
|
type="message_delta",
|
|
delta=MagicMock(stop_reason="end_turn"),
|
|
usage=MagicMock(output_tokens=1),
|
|
),
|
|
MagicMock(type="message_stop"),
|
|
]
|
|
|
|
mock_client = MagicMock()
|
|
mock_client.messages.create = AsyncMock(
|
|
return_value=_make_mock_stream_events(events)
|
|
)
|
|
|
|
request = LlmRequest(
|
|
model="claude-sonnet-4-20250514",
|
|
contents=[Content(role="user", parts=[Part.from_text(text="Hi")])],
|
|
config=types.GenerateContentConfig(
|
|
system_instruction="Test",
|
|
thinking_config=types.ThinkingConfig(thinking_budget=3000),
|
|
),
|
|
)
|
|
|
|
with mock.patch.object(llm, "_anthropic_client", mock_client):
|
|
_ = [r async for r in llm.generate_content_async(request, stream=True)]
|
|
|
|
mock_client.messages.create.assert_called_once()
|
|
_, kwargs = mock_client.messages.create.call_args
|
|
assert kwargs["thinking"] == anthropic_types.ThinkingConfigEnabledParam(
|
|
type="enabled", budget_tokens=3000
|
|
)
|
|
assert kwargs["stream"] is True
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_streaming_redacted_thinking_block_preserved_in_final():
|
|
"""Streaming RedactedThinkingBlock arrives at start and ends up in final."""
|
|
llm = AnthropicLlm(model="claude-sonnet-4-20250514")
|
|
|
|
events = [
|
|
MagicMock(
|
|
type="message_start",
|
|
message=MagicMock(usage=MagicMock(input_tokens=8, output_tokens=0)),
|
|
),
|
|
MagicMock(
|
|
type="content_block_start",
|
|
index=0,
|
|
content_block=anthropic_types.RedactedThinkingBlock(
|
|
data="encrypted_blob", type="redacted_thinking"
|
|
),
|
|
),
|
|
MagicMock(type="content_block_stop", index=0),
|
|
MagicMock(
|
|
type="content_block_start",
|
|
index=1,
|
|
content_block=anthropic_types.TextBlock(text="", type="text"),
|
|
),
|
|
MagicMock(
|
|
type="content_block_delta",
|
|
index=1,
|
|
delta=anthropic_types.TextDelta(text="Done.", type="text_delta"),
|
|
),
|
|
MagicMock(type="content_block_stop", index=1),
|
|
MagicMock(
|
|
type="message_delta",
|
|
delta=MagicMock(stop_reason="end_turn"),
|
|
usage=MagicMock(output_tokens=4),
|
|
),
|
|
MagicMock(type="message_stop"),
|
|
]
|
|
|
|
mock_client = MagicMock()
|
|
mock_client.messages.create = AsyncMock(
|
|
return_value=_make_mock_stream_events(events)
|
|
)
|
|
|
|
request = LlmRequest(
|
|
model="claude-sonnet-4-20250514",
|
|
contents=[Content(role="user", parts=[Part.from_text(text="Hi")])],
|
|
config=types.GenerateContentConfig(
|
|
system_instruction="Test",
|
|
thinking_config=types.ThinkingConfig(thinking_budget=3000),
|
|
),
|
|
)
|
|
|
|
with mock.patch.object(llm, "_anthropic_client", mock_client):
|
|
responses = [
|
|
r async for r in llm.generate_content_async(request, stream=True)
|
|
]
|
|
|
|
final = responses[-1]
|
|
assert final.partial is False
|
|
assert len(final.content.parts) == 2
|
|
|
|
redacted_part = final.content.parts[0]
|
|
assert redacted_part.thought is True
|
|
assert redacted_part.text is None
|
|
assert redacted_part.thought_signature == b"encrypted_blob"
|
|
|
|
text_part = final.content.parts[1]
|
|
assert text_part.text == "Done."
|
|
|
|
|
|
def test_part_to_message_block_function_call_none_id():
|
|
"""Function call with None ID should get a valid generated ID."""
|
|
part = types.Part.from_function_call(name="test_tool", args={"key": "value"})
|
|
part.function_call.id = None
|
|
|
|
result = part_to_message_block(part)
|
|
assert result["id"].startswith("toolu_")
|
|
assert re.fullmatch(r"[a-zA-Z0-9_-]+", result["id"])
|
|
|
|
|
|
def test_part_to_message_block_function_call_empty_id():
|
|
"""Function call with empty string ID should get a valid generated ID."""
|
|
part = types.Part.from_function_call(name="test_tool", args={"key": "value"})
|
|
part.function_call.id = ""
|
|
|
|
result = part_to_message_block(part)
|
|
assert result["id"].startswith("toolu_")
|
|
assert re.fullmatch(r"[a-zA-Z0-9_-]+", result["id"])
|
|
|
|
|
|
def test_part_to_message_block_function_call_invalid_chars_id():
|
|
"""Function call with invalid chars in ID should get a valid generated ID."""
|
|
part = types.Part.from_function_call(name="test_tool", args={"key": "value"})
|
|
part.function_call.id = "invalid id with spaces!"
|
|
|
|
result = part_to_message_block(part)
|
|
assert result["id"].startswith("toolu_")
|
|
assert re.fullmatch(r"[a-zA-Z0-9_-]+", result["id"])
|
|
|
|
|
|
def test_part_to_message_block_function_response_none_id():
|
|
"""Function response with None ID should get a valid generated ID."""
|
|
part = types.Part.from_function_response(
|
|
name="test_tool", response={"result": "ok"}
|
|
)
|
|
part.function_response.id = None
|
|
|
|
result = part_to_message_block(part)
|
|
assert result["tool_use_id"].startswith("toolu_")
|
|
assert re.fullmatch(r"[a-zA-Z0-9_-]+", result["tool_use_id"])
|
|
|
|
|
|
def test_part_to_message_block_function_response_empty_id():
|
|
"""Function response with empty ID should get a valid generated ID."""
|
|
part = types.Part.from_function_response(
|
|
name="test_tool", response={"result": "ok"}
|
|
)
|
|
part.function_response.id = ""
|
|
|
|
result = part_to_message_block(part)
|
|
assert result["tool_use_id"].startswith("toolu_")
|
|
assert re.fullmatch(r"[a-zA-Z0-9_-]+", result["tool_use_id"])
|
|
|
|
|
|
def _make_tool_call_part(name: str, call_id: str | None) -> Part:
|
|
part = types.Part.from_function_call(name=name, args={})
|
|
part.function_call.id = call_id
|
|
return part
|
|
|
|
|
|
def _make_tool_response_part(name: str, response_id: str | None) -> Part:
|
|
part = types.Part.from_function_response(name=name, response={"result": "ok"})
|
|
part.function_response.id = response_id
|
|
return part
|
|
|
|
|
|
async def _capture_anthropic_messages(
|
|
llm: AnthropicLlm,
|
|
contents: list[Content],
|
|
generate_content_response,
|
|
generate_llm_response,
|
|
) -> list[dict]:
|
|
llm_request = LlmRequest(
|
|
model="claude-sonnet-4-20250514",
|
|
contents=contents,
|
|
config=types.GenerateContentConfig(system_instruction="You are helpful"),
|
|
)
|
|
with mock.patch.object(llm, "_anthropic_client") as mock_client:
|
|
with mock.patch.object(
|
|
anthropic_llm,
|
|
"message_to_generate_content_response",
|
|
return_value=generate_llm_response,
|
|
):
|
|
|
|
async def mock_coro():
|
|
return generate_content_response
|
|
|
|
mock_client.messages.create.return_value = mock_coro()
|
|
_ = [
|
|
r async for r in llm.generate_content_async(llm_request, stream=False)
|
|
]
|
|
|
|
_, kwargs = mock_client.messages.create.call_args
|
|
return kwargs["messages"]
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"case_id,call_ids,response_ids,expected_unique",
|
|
[
|
|
(
|
|
"distinct_invalid_pair_uniquely",
|
|
["bad A!", "bad B!"],
|
|
["bad A!", "bad B!"],
|
|
2,
|
|
),
|
|
("matching_empty_ids_pair", [""], [""], 1),
|
|
("none_and_empty_collapse", [None], [""], 1),
|
|
("repeated_invalid_id_consistent", ["bad!"], ["bad!"], 1),
|
|
],
|
|
ids=lambda v: v if isinstance(v, str) else None,
|
|
)
|
|
@pytest.mark.asyncio
|
|
async def test_generate_content_async_pairs_invalid_tool_ids(
|
|
case_id,
|
|
call_ids,
|
|
response_ids,
|
|
expected_unique,
|
|
generate_content_response,
|
|
generate_llm_response,
|
|
):
|
|
"""Anthropic requests have matching, properly-counted tool_use/tool_result IDs."""
|
|
llm = AnthropicLlm(model="claude-sonnet-4-20250514")
|
|
contents = [
|
|
Content(role="user", parts=[Part.from_text(text="Hi")]),
|
|
Content(
|
|
role="model",
|
|
parts=[
|
|
_make_tool_call_part(f"tool_{i}", cid)
|
|
for i, cid in enumerate(call_ids)
|
|
],
|
|
),
|
|
Content(
|
|
role="user",
|
|
parts=[
|
|
_make_tool_response_part(f"tool_{i}", rid)
|
|
for i, rid in enumerate(response_ids)
|
|
],
|
|
),
|
|
]
|
|
|
|
messages = await _capture_anthropic_messages(
|
|
llm, contents, generate_content_response, generate_llm_response
|
|
)
|
|
|
|
use_ids = [b["id"] for b in messages[1]["content"] if b["type"] == "tool_use"]
|
|
result_ids = [
|
|
b["tool_use_id"]
|
|
for b in messages[2]["content"]
|
|
if b["type"] == "tool_result"
|
|
]
|
|
assert len(set(use_ids)) == expected_unique
|
|
assert set(use_ids) == set(result_ids)
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_non_streaming_no_system_instruction_passes_not_given():
|
|
"""system=NOT_GIVEN when LlmRequest has no system_instruction."""
|
|
llm = AnthropicLlm(model="claude-sonnet-4-20250514")
|
|
|
|
mock_message = anthropic_types.Message(
|
|
id="msg_test",
|
|
content=[
|
|
anthropic_types.TextBlock(text="ok", type="text", citations=None)
|
|
],
|
|
model="claude-sonnet-4-20250514",
|
|
role="assistant",
|
|
stop_reason="end_turn",
|
|
stop_sequence=None,
|
|
type="message",
|
|
usage=anthropic_types.Usage(
|
|
input_tokens=1,
|
|
output_tokens=1,
|
|
cache_creation_input_tokens=0,
|
|
cache_read_input_tokens=0,
|
|
server_tool_use=None,
|
|
service_tier=None,
|
|
),
|
|
)
|
|
|
|
mock_client = MagicMock()
|
|
mock_client.messages.create = AsyncMock(return_value=mock_message)
|
|
|
|
request = LlmRequest(
|
|
model="claude-sonnet-4-20250514",
|
|
contents=[Content(role="user", parts=[Part.from_text(text="Hi")])],
|
|
)
|
|
assert request.config.system_instruction is None
|
|
|
|
with mock.patch.object(llm, "_anthropic_client", mock_client):
|
|
_ = [r async for r in llm.generate_content_async(request, stream=False)]
|
|
|
|
mock_client.messages.create.assert_called_once()
|
|
_, kwargs = mock_client.messages.create.call_args
|
|
assert kwargs["system"] is NOT_GIVEN
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_streaming_no_system_instruction_passes_not_given():
|
|
"""system=NOT_GIVEN on the streaming path when no system_instruction."""
|
|
llm = AnthropicLlm(model="claude-sonnet-4-20250514")
|
|
|
|
events = [
|
|
MagicMock(
|
|
type="message_start",
|
|
message=MagicMock(usage=MagicMock(input_tokens=1, output_tokens=0)),
|
|
),
|
|
MagicMock(
|
|
type="content_block_start",
|
|
index=0,
|
|
content_block=anthropic_types.TextBlock(text="", type="text"),
|
|
),
|
|
MagicMock(
|
|
type="content_block_delta",
|
|
index=0,
|
|
delta=anthropic_types.TextDelta(text="ok", type="text_delta"),
|
|
),
|
|
MagicMock(type="content_block_stop", index=0),
|
|
MagicMock(
|
|
type="message_delta",
|
|
delta=MagicMock(stop_reason="end_turn"),
|
|
usage=MagicMock(output_tokens=1),
|
|
),
|
|
MagicMock(type="message_stop"),
|
|
]
|
|
|
|
mock_client = MagicMock()
|
|
mock_client.messages.create = AsyncMock(
|
|
return_value=_make_mock_stream_events(events)
|
|
)
|
|
|
|
request = LlmRequest(
|
|
model="claude-sonnet-4-20250514",
|
|
contents=[Content(role="user", parts=[Part.from_text(text="Hi")])],
|
|
)
|
|
assert request.config.system_instruction is None
|
|
|
|
with mock.patch.object(llm, "_anthropic_client", mock_client):
|
|
_ = [r async for r in llm.generate_content_async(request, stream=True)]
|
|
|
|
mock_client.messages.create.assert_called_once()
|
|
_, kwargs = mock_client.messages.create.call_args
|
|
assert kwargs["system"] is NOT_GIVEN
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_generate_content_async_with_generation_config(
|
|
generate_content_response, generate_llm_response
|
|
):
|
|
claude_llm = Claude(model="claude-3-5-sonnet-v2@20241022")
|
|
llm_request = LlmRequest(
|
|
model="claude-3-5-sonnet-v2@20241022",
|
|
contents=[Content(role="user", parts=[Part.from_text(text="Hello")])],
|
|
config=types.GenerateContentConfig(
|
|
temperature=0.7,
|
|
top_p=0.9,
|
|
top_k=50,
|
|
stop_sequences=["##"],
|
|
max_output_tokens=1024,
|
|
),
|
|
)
|
|
with mock.patch.object(claude_llm, "_anthropic_client") as mock_client:
|
|
with mock.patch.object(
|
|
anthropic_llm,
|
|
"message_to_generate_content_response",
|
|
return_value=generate_llm_response,
|
|
):
|
|
|
|
async def mock_coro():
|
|
return generate_content_response
|
|
|
|
mock_client.messages.create.return_value = mock_coro()
|
|
|
|
_ = [
|
|
resp
|
|
async for resp in claude_llm.generate_content_async(
|
|
llm_request, stream=False
|
|
)
|
|
]
|
|
mock_client.messages.create.assert_called_once()
|
|
_, kwargs = mock_client.messages.create.call_args
|
|
assert kwargs["temperature"] == 0.7
|
|
assert kwargs["top_p"] == 0.9
|
|
assert kwargs["top_k"] == 50
|
|
assert kwargs["stop_sequences"] == ["##"]
|
|
assert kwargs["max_tokens"] == 1024
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_generate_content_streaming_with_generation_config(
|
|
generate_content_response,
|
|
):
|
|
claude_llm = Claude(model="claude-3-5-sonnet-v2@20241022")
|
|
llm_request = LlmRequest(
|
|
model="claude-3-5-sonnet-v2@20241022",
|
|
contents=[Content(role="user", parts=[Part.from_text(text="Hello")])],
|
|
config=types.GenerateContentConfig(
|
|
temperature=0.7,
|
|
top_p=0.9,
|
|
top_k=50,
|
|
stop_sequences=["##"],
|
|
max_output_tokens=1024,
|
|
),
|
|
)
|
|
with mock.patch.object(claude_llm, "_anthropic_client") as mock_client:
|
|
|
|
async def mock_coro(*args, **kwargs):
|
|
async def async_gen():
|
|
if False:
|
|
yield None
|
|
|
|
return async_gen()
|
|
|
|
mock_client.messages.create.side_effect = mock_coro
|
|
|
|
_ = [
|
|
resp
|
|
async for resp in claude_llm.generate_content_async(
|
|
llm_request, stream=True
|
|
)
|
|
]
|
|
mock_client.messages.create.assert_called_once()
|
|
_, kwargs = mock_client.messages.create.call_args
|
|
assert kwargs["temperature"] == 0.7
|
|
assert kwargs["top_p"] == 0.9
|
|
assert kwargs["top_k"] == 50
|
|
assert kwargs["stop_sequences"] == ["##"]
|
|
assert kwargs["max_tokens"] == 1024
|
|
assert kwargs["stream"]
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_generate_content_async_with_thinking_level_warns_and_ignores(
|
|
generate_content_response,
|
|
generate_llm_response,
|
|
):
|
|
"""Tests that generate_content_async with standard thinking_level warns and ignores it."""
|
|
claude_llm = AnthropicLlm(model="claude-sonnet-4-20250514")
|
|
llm_request = LlmRequest(
|
|
model="claude-sonnet-4-20250514",
|
|
contents=[Content(role="user", parts=[Part.from_text(text="Hello")])],
|
|
config=types.GenerateContentConfig(
|
|
thinking_config=types.ThinkingConfig(
|
|
thinking_budget=-1,
|
|
thinking_level=types.ThinkingLevel.MINIMAL,
|
|
)
|
|
),
|
|
)
|
|
with mock.patch.object(claude_llm, "_anthropic_client") as mock_client:
|
|
with mock.patch.object(
|
|
anthropic_llm,
|
|
"message_to_generate_content_response",
|
|
return_value=generate_llm_response,
|
|
):
|
|
|
|
async def mock_coro():
|
|
return generate_content_response
|
|
|
|
mock_client.messages.create.return_value = mock_coro()
|
|
|
|
with pytest.warns(
|
|
UserWarning,
|
|
match="Standard thinking_config.thinking_level is not supported",
|
|
):
|
|
_ = [
|
|
resp
|
|
async for resp in claude_llm.generate_content_async(
|
|
llm_request, stream=False
|
|
)
|
|
]
|
|
mock_client.messages.create.assert_called_once()
|
|
_, kwargs = mock_client.messages.create.call_args
|
|
# Verify that thinking_level was ignored (but budget -1 still enabled adaptive thinking).
|
|
assert kwargs["thinking"] == {"type": "adaptive"}
|
|
assert "output_config" not in kwargs
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_generate_content_async_anthropic_config_with_thinking_level_raises_error():
|
|
"""Tests that AnthropicGenerateContentConfig with standard thinking_level raises ValueError."""
|
|
with pytest.raises(
|
|
ValueError,
|
|
match="thinking_level is not supported in AnthropicGenerateContentConfig",
|
|
):
|
|
_ = AnthropicGenerateContentConfig(
|
|
effort="xhigh",
|
|
thinking_config=types.ThinkingConfig(
|
|
thinking_budget=-1,
|
|
thinking_level=types.ThinkingLevel.MINIMAL,
|
|
),
|
|
)
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_generate_content_async_with_anthropic_config_effort(
|
|
generate_content_response,
|
|
generate_llm_response,
|
|
):
|
|
"""Tests generate_content_async with Anthropic-specific effort configuration."""
|
|
claude_llm = AnthropicLlm(model="claude-sonnet-4-20250514")
|
|
llm_request = LlmRequest(
|
|
model="claude-sonnet-4-20250514",
|
|
contents=[Content(role="user", parts=[Part.from_text(text="Hello")])],
|
|
config=AnthropicGenerateContentConfig(
|
|
effort="xhigh",
|
|
),
|
|
)
|
|
with mock.patch.object(claude_llm, "_anthropic_client") as mock_client:
|
|
with mock.patch.object(
|
|
anthropic_llm,
|
|
"message_to_generate_content_response",
|
|
return_value=generate_llm_response,
|
|
):
|
|
|
|
async def mock_coro():
|
|
return generate_content_response
|
|
|
|
mock_client.messages.create.return_value = mock_coro()
|
|
|
|
_ = [
|
|
resp
|
|
async for resp in claude_llm.generate_content_async(
|
|
llm_request, stream=False
|
|
)
|
|
]
|
|
mock_client.messages.create.assert_called_once()
|
|
_, kwargs = mock_client.messages.create.call_args
|
|
assert kwargs["output_config"] == {"effort": "xhigh"}
|
|
assert kwargs["thinking"] is NOT_GIVEN
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_generate_content_async_excludes_sampling_when_thinking(
|
|
generate_content_response,
|
|
generate_llm_response,
|
|
):
|
|
"""Tests that sampling parameters are excluded when thinking is enabled."""
|
|
claude_llm = AnthropicLlm(model="claude-sonnet-4-20250514")
|
|
llm_request = LlmRequest(
|
|
model="claude-sonnet-4-20250514",
|
|
contents=[Content(role="user", parts=[Part.from_text(text="Hello")])],
|
|
config=types.GenerateContentConfig(
|
|
temperature=0.7,
|
|
top_p=0.9,
|
|
top_k=50,
|
|
thinking_config=types.ThinkingConfig(
|
|
thinking_budget=1024,
|
|
),
|
|
),
|
|
)
|
|
with mock.patch.object(claude_llm, "_anthropic_client") as mock_client:
|
|
with mock.patch.object(
|
|
anthropic_llm,
|
|
"message_to_generate_content_response",
|
|
return_value=generate_llm_response,
|
|
):
|
|
|
|
async def mock_coro():
|
|
return generate_content_response
|
|
|
|
mock_client.messages.create.return_value = mock_coro()
|
|
|
|
with pytest.warns(
|
|
UserWarning, match="Sampling parameters .* are ignored"
|
|
):
|
|
_ = [
|
|
resp
|
|
async for resp in claude_llm.generate_content_async(
|
|
llm_request, stream=False
|
|
)
|
|
]
|
|
mock_client.messages.create.assert_called_once()
|
|
_, kwargs = mock_client.messages.create.call_args
|
|
assert "temperature" not in kwargs
|
|
assert "top_p" not in kwargs
|
|
assert "top_k" not in kwargs
|
|
assert kwargs["max_tokens"] == 8192
|
|
assert kwargs["thinking"] == {"type": "enabled", "budget_tokens": 1024}
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_generate_content_async_excludes_sampling_when_effort(
|
|
generate_content_response,
|
|
generate_llm_response,
|
|
):
|
|
"""Tests that sampling parameters are excluded when effort is enabled."""
|
|
claude_llm = AnthropicLlm(model="claude-sonnet-4-20250514")
|
|
llm_request = LlmRequest(
|
|
model="claude-sonnet-4-20250514",
|
|
contents=[Content(role="user", parts=[Part.from_text(text="Hello")])],
|
|
config=AnthropicGenerateContentConfig(
|
|
temperature=0.7,
|
|
top_p=0.9,
|
|
top_k=50,
|
|
effort="xhigh",
|
|
),
|
|
)
|
|
with mock.patch.object(claude_llm, "_anthropic_client") as mock_client:
|
|
with mock.patch.object(
|
|
anthropic_llm,
|
|
"message_to_generate_content_response",
|
|
return_value=generate_llm_response,
|
|
):
|
|
|
|
async def mock_coro():
|
|
return generate_content_response
|
|
|
|
mock_client.messages.create.return_value = mock_coro()
|
|
|
|
with pytest.warns(
|
|
UserWarning, match="Sampling parameters .* are ignored"
|
|
):
|
|
_ = [
|
|
resp
|
|
async for resp in claude_llm.generate_content_async(
|
|
llm_request, stream=False
|
|
)
|
|
]
|
|
mock_client.messages.create.assert_called_once()
|
|
_, kwargs = mock_client.messages.create.call_args
|
|
assert "temperature" not in kwargs
|
|
assert "top_p" not in kwargs
|
|
assert "top_k" not in kwargs
|
|
assert kwargs["output_config"] == {"effort": "xhigh"}
|