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Commit16f7d98

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google-genai-botcopybara-github
authored andcommitted
feat: Update for anthropic models
Enable parallel tools for anthropic models, and add agent examples, and also added functional test for anthropic models.PiperOrigin-RevId: 766703018
1 parent44f5078 commit16f7d98

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5 files changed

+312
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# 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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from .importagent
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# 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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importrandom
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fromgoogle.adkimportAgent
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fromgoogle.adk.models.anthropic_llmimportClaude
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defroll_die(sides:int)->int:
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"""Roll a die and return the rolled result.
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Args:
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sides: The integer number of sides the die has.
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Returns:
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An integer of the result of rolling the die.
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"""
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returnrandom.randint(1,sides)
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asyncdefcheck_prime(nums:list[int])->str:
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"""Check if a given list of numbers are prime.
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Args:
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nums: The list of numbers to check.
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Returns:
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A str indicating which number is prime.
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"""
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primes=set()
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fornumberinnums:
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number=int(number)
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ifnumber<=1:
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continue
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is_prime=True
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foriinrange(2,int(number**0.5)+1):
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ifnumber%i==0:
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is_prime=False
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break
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ifis_prime:
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primes.add(number)
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return (
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"No prime numbers found."
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ifnotprimes
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elsef"{', '.join(str(num)fornuminprimes)} are prime numbers."
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)
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root_agent=Agent(
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model=Claude(model="claude-3-5-sonnet-v2@20241022"),
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name="data_processing_agent",
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description=(
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"hello world agent that can roll a dice of 8 sides and check prime"
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" numbers."
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),
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instruction="""
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You roll dice and answer questions about the outcome of the dice rolls.
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You can roll dice of different sizes.
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You can use multiple tools in parallel by calling functions in parallel(in one request and in one round).
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It is ok to discuss previous dice roles, and comment on the dice rolls.
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When you are asked to roll a die, you must call the roll_die tool with the number of sides. Be sure to pass in an integer. Do not pass in a string.
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You should never roll a die on your own.
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When checking prime numbers, call the check_prime tool with a list of integers. Be sure to pass in a list of integers. You should never pass in a string.
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You should not check prime numbers before calling the tool.
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When you are asked to roll a die and check prime numbers, you should always make the following two function calls:
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1. You should first call the roll_die tool to get a roll. Wait for the function response before calling the check_prime tool.
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2. After you get the function response from roll_die tool, you should call the check_prime tool with the roll_die result.
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2.1 If user asks you to check primes based on previous rolls, make sure you include the previous rolls in the list.
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3. When you respond, you must include the roll_die result from step 1.
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You should always perform the previous 3 steps when asking for a roll and checking prime numbers.
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You should not rely on the previous history on prime results.
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""",
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tools=[
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roll_die,
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check_prime,
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],
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)
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# 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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importasyncio
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importtime
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importagent
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fromdotenvimportload_dotenv
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fromgoogle.adkimportRunner
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fromgoogle.adk.artifactsimportInMemoryArtifactService
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fromgoogle.adk.cli.utilsimportlogs
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fromgoogle.adk.sessionsimportInMemorySessionService
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fromgoogle.adk.sessionsimportSession
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fromgoogle.genaiimporttypes
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load_dotenv(override=True)
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logs.log_to_tmp_folder()
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asyncdefmain():
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app_name='my_app'
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user_id_1='user1'
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session_service=InMemorySessionService()
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artifact_service=InMemoryArtifactService()
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runner=Runner(
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app_name=app_name,
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agent=agent.root_agent,
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artifact_service=artifact_service,
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session_service=session_service,
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)
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session_11=awaitsession_service.create_session(
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app_name=app_name,user_id=user_id_1
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)
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asyncdefrun_prompt(session:Session,new_message:str):
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content=types.Content(
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role='user',parts=[types.Part.from_text(text=new_message)]
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)
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print('** User says:',content.model_dump(exclude_none=True))
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asyncforeventinrunner.run_async(
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user_id=user_id_1,
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session_id=session.id,
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new_message=content,
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):
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ifevent.content.partsandevent.content.parts[0].text:
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print(f'**{event.author}:{event.content.parts[0].text}')
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start_time=time.time()
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print('Start time:',start_time)
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print('------------------------------------')
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awaitrun_prompt(session_11,'Hi, introduce yourself.')
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awaitrun_prompt(
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session_11,
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'Run the following request 10 times: roll a die with 100 sides and check'
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' if it is prime',
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)
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end_time=time.time()
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print('------------------------------------')
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print('End time:',end_time)
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print('Total time:',end_time-start_time)
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if__name__=='__main__':
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asyncio.run(main())

‎src/google/adk/models/anthropic_llm.py‎

Lines changed: 6 additions & 9 deletions
Original file line numberDiff line numberDiff line change
@@ -135,6 +135,10 @@ def content_block_to_part(
135135
defmessage_to_generate_content_response(
136136
message:anthropic_types.Message,
137137
)->LlmResponse:
138+
logger.info(
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"Claude response: %s",
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message.model_dump_json(indent=2,exclude_none=True),
141+
)
138142

139143
returnLlmResponse(
140144
content=types.Content(
@@ -229,14 +233,11 @@ async def generate_content_async(
229233
fortoolinllm_request.config.tools[0].function_declarations
230234
]
231235
tool_choice= (
232-
anthropic_types.ToolChoiceAutoParam(
233-
type="auto",
234-
# TODO: allow parallel tool use.
235-
disable_parallel_tool_use=True,
236-
)
236+
anthropic_types.ToolChoiceAutoParam(type="auto")
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ifllm_request.tools_dict
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elseNOT_GIVEN
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)
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# TODO(b/421255973): Enable streaming for anthropic models.
240241
message=self._anthropic_client.messages.create(
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model=llm_request.model,
242243
system=llm_request.config.system_instruction,
@@ -245,10 +246,6 @@ async def generate_content_async(
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tool_choice=tool_choice,
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max_tokens=MAX_TOKEN,
247248
)
248-
logger.info(
249-
"Claude response: %s",
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message.model_dump_json(indent=2,exclude_none=True),
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)
252249
yieldmessage_to_generate_content_response(message)
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@cached_property
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# 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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importos
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importsys
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fromunittestimportmock
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fromanthropicimporttypesasanthropic_types
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fromgoogle.adkimportversionasadk_version
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fromgoogle.adk.modelsimportanthropic_llm
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fromgoogle.adk.models.anthropic_llmimportClaude
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fromgoogle.adk.models.llm_requestimportLlmRequest
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fromgoogle.adk.models.llm_responseimportLlmResponse
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fromgoogle.genaiimporttypes
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fromgoogle.genaiimportversionasgenai_version
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fromgoogle.genai.typesimportContent
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fromgoogle.genai.typesimportPart
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importpytest
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@pytest.fixture
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defgenerate_content_response():
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returnanthropic_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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56+
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@pytest.fixture
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defgenerate_llm_response():
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returnLlmResponse.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?")],
66+
),
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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
75+
defclaude_llm():
76+
returnClaude(model="claude-3-5-sonnet-v2@20241022")
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@pytest.fixture
80+
defllm_request():
81+
returnLlmRequest(
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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")])],
84+
config=types.GenerateContentConfig(
85+
temperature=0.1,
86+
response_modalities=[types.Modality.TEXT],
87+
system_instruction="You are a helpful assistant",
88+
),
89+
)
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deftest_supported_models():
93+
models=Claude.supported_models()
94+
assertlen(models)==2
95+
assertmodels[0]==r"claude-3-.*"
96+
assertmodels[1]==r"claude-.*-4.*"
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98+
99+
@pytest.mark.asyncio
100+
asyncdeftest_generate_content_async(
101+
claude_llm,llm_request,generate_content_response,generate_llm_response
102+
):
103+
withmock.patch.object(claude_llm,"_anthropic_client")asmock_client:
104+
withmock.patch.object(
105+
anthropic_llm,
106+
"message_to_generate_content_response",
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return_value=generate_llm_response,
108+
):
109+
# Create a mock coroutine that returns the generate_content_response.
110+
asyncdefmock_coro():
111+
returngenerate_content_response
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113+
# Assign the coroutine to the mocked method
114+
mock_client.messages.create.return_value=mock_coro()
115+
116+
responses= [
117+
resp
118+
asyncforrespinclaude_llm.generate_content_async(
119+
llm_request,stream=False
120+
)
121+
]
122+
assertlen(responses)==1
123+
assertisinstance(responses[0],LlmResponse)
124+
assertresponses[0].content.parts[0].text=="Hello, how can I help you?"

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