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576 lines
19 KiB
Python
576 lines
19 KiB
Python
# Copyright 2025 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 os
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import sys
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from unittest import mock
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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.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.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_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_parameters_json_schema",
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types.FunctionDeclaration(
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name="search_database",
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description="Searches a database with given criteria.",
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parameters_json_schema={
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"type": "object",
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"properties": {
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"query": {
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"type": "string",
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"description": "The search query",
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},
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"limit": {
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"type": "integer",
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"description": "Maximum number of results",
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},
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},
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"required": ["query"],
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},
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),
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anthropic_types.ToolParam(
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name="search_database",
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description="Searches a database with given criteria.",
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input_schema={
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"type": "object",
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"properties": {
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"query": {
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"type": "string",
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"description": "The search query",
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},
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"limit": {
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"type": "integer",
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"description": "Maximum number of results",
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},
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},
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"required": ["query"],
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},
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),
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),
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]
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@pytest.mark.parametrize(
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"_, function_declaration, expected_tool_param",
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function_declaration_test_cases,
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ids=[case[0] for case in function_declaration_test_cases],
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)
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async def test_function_declaration_to_tool_param(
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_, function_declaration, expected_tool_param
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):
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"""Test function_declaration_to_tool_param."""
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assert (
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function_declaration_to_tool_param(function_declaration)
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== expected_tool_param
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)
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@pytest.mark.asyncio
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async def test_generate_content_async(
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claude_llm, llm_request, generate_content_response, generate_llm_response
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):
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with mock.patch.object(claude_llm, "_anthropic_client") as mock_client:
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with mock.patch.object(
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anthropic_llm,
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"message_to_generate_content_response",
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return_value=generate_llm_response,
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):
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# Create a mock coroutine that returns the generate_content_response.
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async def mock_coro():
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return generate_content_response
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# Assign the coroutine to the mocked method
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mock_client.messages.create.return_value = mock_coro()
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responses = [
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resp
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async for resp in claude_llm.generate_content_async(
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llm_request, stream=False
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)
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]
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assert len(responses) == 1
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assert isinstance(responses[0], LlmResponse)
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assert responses[0].content.parts[0].text == "Hello, how can I help you?"
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@pytest.mark.asyncio
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async def test_anthropic_llm_generate_content_async(
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llm_request, generate_content_response, generate_llm_response
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):
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anthropic_llm_instance = AnthropicLlm(model="claude-sonnet-4-20250514")
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with mock.patch.object(
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anthropic_llm_instance, "_anthropic_client"
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) as mock_client:
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with mock.patch.object(
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anthropic_llm,
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"message_to_generate_content_response",
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return_value=generate_llm_response,
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):
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# Create a mock coroutine that returns the generate_content_response.
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async def mock_coro():
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return generate_content_response
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# Assign the coroutine to the mocked method
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mock_client.messages.create.return_value = mock_coro()
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responses = [
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resp
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async for resp in anthropic_llm_instance.generate_content_async(
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llm_request, stream=False
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)
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]
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assert len(responses) == 1
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assert isinstance(responses[0], LlmResponse)
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assert responses[0].content.parts[0].text == "Hello, how can I help you?"
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@pytest.mark.asyncio
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async def test_generate_content_async_with_max_tokens(
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llm_request, generate_content_response, generate_llm_response
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):
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claude_llm = Claude(model="claude-3-5-sonnet-v2@20241022", max_tokens=4096)
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with mock.patch.object(claude_llm, "_anthropic_client") as mock_client:
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with mock.patch.object(
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anthropic_llm,
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"message_to_generate_content_response",
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return_value=generate_llm_response,
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):
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# Create a mock coroutine that returns the generate_content_response.
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async def mock_coro():
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return generate_content_response
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# Assign the coroutine to the mocked method
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mock_client.messages.create.return_value = mock_coro()
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_ = [
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resp
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async for resp in claude_llm.generate_content_async(
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llm_request, stream=False
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)
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]
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mock_client.messages.create.assert_called_once()
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_, kwargs = mock_client.messages.create.call_args
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assert kwargs["max_tokens"] == 4096
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def test_part_to_message_block_with_content():
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"""Test that part_to_message_block handles content format."""
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from google.adk.models.anthropic_llm import part_to_message_block
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# Create a function response part with content array.
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mcp_response_part = types.Part.from_function_response(
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name="generate_sample_filesystem",
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response={
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"content": [{
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"type": "text",
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"text": '{"name":"root","node_type":"folder","children":[]}',
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}]
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},
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)
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mcp_response_part.function_response.id = "test_id_123"
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result = part_to_message_block(mcp_response_part)
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# ToolResultBlockParam is a TypedDict.
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assert isinstance(result, dict)
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assert result["tool_use_id"] == "test_id_123"
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assert result["type"] == "tool_result"
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assert not result["is_error"]
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# Verify the content was extracted from the content format.
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assert (
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'{"name":"root","node_type":"folder","children":[]}' in result["content"]
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)
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def test_part_to_message_block_with_traditional_result():
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"""Test that part_to_message_block handles traditional result format."""
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from google.adk.models.anthropic_llm import part_to_message_block
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# Create a function response part with traditional result format
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traditional_response_part = types.Part.from_function_response(
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name="some_tool",
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response={
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"result": "This is the result from the tool",
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},
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)
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traditional_response_part.function_response.id = "test_id_456"
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result = part_to_message_block(traditional_response_part)
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# ToolResultBlockParam is a TypedDict.
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assert isinstance(result, dict)
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assert result["tool_use_id"] == "test_id_456"
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assert result["type"] == "tool_result"
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assert not result["is_error"]
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# Verify the content was extracted from the traditional format
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assert "This is the result from the tool" in result["content"]
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def test_part_to_message_block_with_multiple_content_items():
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"""Test content with multiple items."""
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from google.adk.models.anthropic_llm import part_to_message_block
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# Create a function response with multiple content items
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multi_content_part = types.Part.from_function_response(
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name="multi_response_tool",
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response={
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"content": [
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{"type": "text", "text": "First part"},
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{"type": "text", "text": "Second part"},
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]
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},
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)
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multi_content_part.function_response.id = "test_id_789"
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result = part_to_message_block(multi_content_part)
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# ToolResultBlockParam is a TypedDict.
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assert isinstance(result, dict)
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# Multiple text items should be joined with newlines
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assert result["content"] == "First part\nSecond part"
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content_to_message_param_test_cases = [
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(
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"user_role_with_text_and_image",
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Content(
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role="user",
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parts=[
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Part.from_text(text="What's in this image?"),
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Part(
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inline_data=types.Blob(
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mime_type="image/jpeg", data=b"fake_image_data"
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)
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),
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],
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),
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"user",
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2, # Expected content length
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False, # Should not log warning
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),
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(
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"model_role_with_text_and_image",
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Content(
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role="model",
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parts=[
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Part.from_text(text="I see a cat."),
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Part(
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inline_data=types.Blob(
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mime_type="image/png", data=b"fake_image_data"
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)
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),
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],
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),
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"assistant",
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1, # Image filtered out, only text remains
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True, # Should log warning
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),
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(
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"assistant_role_with_text_and_image",
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Content(
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role="assistant",
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parts=[
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Part.from_text(text="Here's what I found."),
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Part(
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inline_data=types.Blob(
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mime_type="image/webp", data=b"fake_image_data"
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)
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),
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],
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),
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"assistant",
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1, # Image filtered out, only text remains
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True, # Should log warning
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),
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]
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|
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@pytest.mark.parametrize(
|
|
"_, content, expected_role, expected_content_length, should_log_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_with_images(
|
|
_, content, expected_role, expected_content_length, should_log_warning
|
|
):
|
|
"""Test content_to_message_param handles images correctly 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 should_log_warning:
|
|
mock_logger.warning.assert_called_once_with(
|
|
"Image data is not supported in Claude for assistant turns."
|
|
)
|
|
else:
|
|
mock_logger.warning.assert_not_called()
|