1. 簡介
總覽
本程式碼研究室將說明如何使用 Agent Development Kit (ADK) 建構可擴充的非同步代理系統。您將建立 Cloud Run 工作站集區，用於代管 ADK 快速入門天氣代理程式，處理來自 Pub/Sub 提取訂閱項目的工作。
課程內容
- 如何使用 Agent Development Kit (ADK) 建立單輪代理。
- 如何部署從 Pub/Sub 訂閱項目提取資料的 Cloud Run 工作站集區。
2. 事前準備
啟用 API
開始使用本程式碼研究室前，請先執行下列指令，啟用下列 API：
gcloud services enable \
run.googleapis.com \
cloudbuild.googleapis.com \
artifactregistry.googleapis.com \
pubsub.googleapis.com \
aiplatform.googleapis.com
3. 設定和需求
如要設定必要資源，請按照下列步驟操作：
- 為本程式碼研究室設定環境變數：
export PROJECT_ID=<YOUR_PROJECT_ID>
export REGION=europe-west1
# AR repo
export AR_REPO="codelab-agent-wp"
# Application Names
export WORKER_APP_NAME="multi-tool-agent-worker"
# Pub/Sub Resources
export MY_TOPIC="pull-pubsub-topic-agent"
export MY_SUBSCRIPTION="agent-wp-sub"
# Service Accounts
export WORKER_SA_NAME="agent-worker-sa"
export WORKER_SA_ADDRESS="${WORKER_SA_NAME}@${PROJECT_ID}.iam.gserviceaccount.com"
建立服務帳戶
為確保安全性，我們會為工作人員建立專屬服務帳戶，確保該帳戶只具備所需權限。
為工作人員建立服務帳戶：
gcloud iam service-accounts create ${WORKER_SA_NAME} \
--display-name="Service Account for ADK Agent Worker"
將必要角色授予服務帳戶。這項服務必須從 Pub/Sub 提取訊息，並叫用 ADK 使用的 Vertex AI 模型。
# Role for subscribing to Pub/Sub
gcloud projects add-iam-policy-binding ${PROJECT_ID} \
--member="serviceAccount:${WORKER_SA_ADDRESS}" \
--role="roles/pubsub.admin"
# Role for invoking Vertex AI
gcloud projects add-iam-policy-binding ${PROJECT_ID} \
--member="serviceAccount:${WORKER_SA_ADDRESS}" \
--role="roles/aiplatform.user"
建立 Pub/Sub 資源
建立 Pub/Sub 主題，做為工作佇列。
gcloud pubsub topics create $MY_TOPIC
為工作站建立 Pub/Sub 訂閱項目，以便從中提取訊息。
gcloud pubsub subscriptions create $MY_SUBSCRIPTION --topic=$MY_TOPIC
4. 建立 Cloud Run worker 集區
為專案建立名為 agents-wp 的目錄。
mkdir agents-wp && cd agents-wp
建立 Dockerfile
touch Dockerfile
並在 Dockerfile 中新增下列內容
FROM python:3.11-slim
WORKDIR /app
# Create a non-root user
RUN adduser --disabled-password --gecos "" myuser
# Switch to the non-root user
USER myuser
# Set up environment variables
ENV PATH="/home/myuser/.local/bin:$PATH"
# Copy agent files
COPY --chown=myuser:myuser multi_tool_agent/ /app/multi_tool_agent/
# Install dependencies from requirements.txt
RUN pip install -r /app/multi_tool_agent/requirements.txt
# Set the entrypoint to run the agent as a worker
CMD ["python3", "/app/multi_tool_agent/main.py"]
在其中建立名為 multi_tool_agent 的子目錄。請注意資料夾名稱 multi_tool_agent 中的底線。這個資料夾的名稱必須與稍後部署的 ADK 代理程式名稱相符。
mkdir multi_tool_agent && cd multi_tool_agent
建立 __init__.py 檔案
touch __init__.py
並在 __init__.py 檔案中加入以下內容
from . import agent
建立 agent.py 檔案
touch agent.py
並在 agent.py 檔案中新增以下內容
import datetime
from zoneinfo import ZoneInfo
from google.adk.agents.llm_agent import Agent
def get_weather(city: str) -> dict:
"""Retrieves the current weather report for a specified city.
Args:
city (str): The name of the city for which to retrieve the weather report.
Returns:
dict: status and result or error msg.
"""
print(f"--- Entering get_weather function for city: {city} ---")
if city.lower() == "new york":
result = {
"status": "success",
"report": (
"The weather in New York is sunny with a temperature of 25 degrees"
" Celsius (77 degrees Fahrenheit)."
),
}
else:
result = {
"status": "error",
"error_message": f"Weather information for '{city}' is not available.",
}
print(f"--- Exiting get_weather function with result: {result} ---")
return result
def get_current_time(city: str) -> dict:
"""Returns the current time in a specified city.
Args:
city (str): The name of the city for which to retrieve the current time.
Returns:
dict: status and result or error msg.
"""
print(f"--- Entering get_current_time function for city: {city} ---")
if city.lower() == "new york":
tz_identifier = "America/New_York"
else:
result = {
"status": "error",
"error_message": (
f"Sorry, I don't have timezone information for {city}."
),
}
print(f"--- Exiting get_current_time function with result: {result} ---")
return result
tz = ZoneInfo(tz_identifier)
now = datetime.datetime.now(tz)
report = (
f'The current time in {city} is {now.strftime("%Y-%m-%d %H:%M:%S %Z%z")}'
)
result = {"status": "success", "report": report}
print(f"--- Exiting get_current_time function with result: {result} ---")
return result
print("--- Creating root_agent ---")
root_agent = Agent(
name="weather_time_agent",
model="gemini-2.5-flash",
description=(
"Agent to answer questions about the time and weather in a city."
),
instruction=(
"You are a helpful agent who can answer user questions about the time and weather in a city."
),
tools=[get_weather, get_current_time],
)
print("--- root_agent created ---")
建立 main.py 檔案
touch main.py
並在 main.py 檔案中加入以下內容
import asyncio
import os
from google.adk.runners import InMemoryRunner, Runner
from google.genai import types
from google.cloud import pubsub_v1
from agent import root_agent
# --- Runner-based Invocation with Proper Async Handling ---
APP_NAME = "multi_tool_agent_worker"
USER_ID = "pubsub_user"
async def process_message(runner: Runner, message_data: bytes):
"""Processes a single message using the agent runner."""
print(f"Processing message: {message_data}")
try:
prompt = message_data.decode("utf-8")
session = await runner.session_service.create_session(
app_name=APP_NAME,
user_id=USER_ID
)
final_response_text = ""
async for event in runner.run_async(
user_id=USER_ID,
session_id=session.id,
new_message=types.Content(
role="user", parts=[types.Part.from_text(text=prompt)]
),
):
if event.content and event.content.parts:
if event.author != "user":
# Filter out thought parts to get only the final response text
final_response_text += "".join(
part.text or "" for part in event.content.parts if not part.thought
)
print(f"Agent response: {final_response_text}")
except Exception as e:
print(f"Error processing message: {e}")
async def async_worker(queue: asyncio.Queue, runner: Runner):
"""Continuously gets messages from the queue and processes them."""
while True:
message = await queue.get()
if message is None: # Sentinel for stopping
break
await process_message(runner, message.data)
message.ack()
queue.task_done()
async def main():
"""Sets up the Pub/Sub subscriber and the async worker."""
project_id = os.environ.get("GOOGLE_CLOUD_PROJECT")
subscription_id = os.environ.get("SUBSCRIPTION_ID")
if not project_id or not subscription_id:
print("GOOGLE_CLOUD_PROJECT and SUBSCRIPTION_ID environment variables must be set.")
return
runner = InMemoryRunner(agent=root_agent, app_name=APP_NAME)
message_queue = asyncio.Queue()
subscriber = pubsub_v1.SubscriberClient()
subscription_path = subscriber.subscription_path(project_id, subscription_id)
loop = asyncio.get_running_loop()
callback = lambda message: loop.call_soon_threadsafe(
message_queue.put_nowait, message
)
print(f"Listening for messages on {subscription_path}...\n")
streaming_pull_future = subscriber.subscribe(subscription_path, callback=callback)
worker_task = asyncio.create_task(async_worker(message_queue, runner))
try:
# This will block until the subscription is cancelled or an error occurs.
await loop.run_in_executor(None, streaming_pull_future.result)
except KeyboardInterrupt:
print("Shutting down...")
finally:
streaming_pull_future.cancel()
await message_queue.put(None) # Stop the worker
await worker_task # Wait for the worker to finish
await runner.close()
subscriber.close()
if __name__ == "__main__":
try:
asyncio.run(main())
except KeyboardInterrupt:
print("Exiting.")
建立 requirements.txt 檔案
touch requirements.txt
並在 requirements.txt 檔案中加入以下內容
google-adk
google-cloud-pubsub
google-cloud-aiplatform
您的資料夾結構應如下所示
agents-wp
- multi_tool_agent
- __init__.py
- agent.py
- main.py
- requirements.txt
- Dockerfile
5. 建構及部署
建立 Artifact Registry 存放區
您需要儲存容器映像檔的位置。
gcloud artifacts repositories create codelab-agent-wp \
--repository-format=docker \
--location=${REGION} \
--description="Repo for Cloud Run source deployments"
建構容器映像檔
前往 Dockerfile 所在的根 agents-wp 目錄
cd ..
並執行下列建構指令。
gcloud builds submit . --tag \
${REGION}-docker.pkg.dev/${PROJECT_ID}/${AR_REPO}/${WORKER_APP_NAME}:latest
部署至 Cloud Run
部署代理程式工作站映像檔。
gcloud beta run worker-pools deploy ${WORKER_APP_NAME} \
--image=${REGION}-docker.pkg.dev/${PROJECT_ID}/${AR_REPO}/${WORKER_APP_NAME}:latest \
--service-account=${WORKER_SA_ADDRESS} \
--region=${REGION} \
--set-env-vars="SUBSCRIPTION_ID=${MY_SUBSCRIPTION}" \
--set-env-vars="PYTHONUNBUFFERED=1" \
--set-env-vars="GOOGLE_GENAI_USE_VERTEXAI=1" \
--set-env-vars="GOOGLE_CLOUD_PROJECT=${PROJECT_ID}" \
--set-env-vars="GOOGLE_CLOUD_LOCATION=${REGION}"
6. 測試代理程式
您可以直接將訊息發布至 Pub/Sub 主題，藉此測試工作站。
gcloud pubsub topics publish ${MY_TOPIC} --message="What is the weather in New York?"
您可以執行這項指令，在 Google Cloud 控制台中查看 multi-tool-agent-worker 服務的記錄。
gcloud logging read 'resource.type="cloud_run_worker_pool" AND resource.labels.worker_pool_name="'$WORKER_APP_NAME'" AND resource.labels.location="'$REGION'"' --limit 10 --format="value(textPayload)"
您應該會看到輸出內容，指出系統已收到並處理訊息，接著是代理程式的回應。
Agent response: The weather in New York is sunny with a temperature of 25 degrees Celsius (77 degrees Fahrenheit).
7. 恭喜！
恭喜您完成本程式碼研究室！
建議您參閱 Cloud Run 說明文件，瞭解工作站集區和主機代理程式。
涵蓋內容
- 如何使用 Agent Development Kit (ADK) 建立單輪代理。
- 如何部署從 Pub/Sub 訂閱項目提取資料的 Cloud Run 工作站集區。
8. 清除
為避免產生任何費用，請刪除您建立的資源。
刪除 Cloud Run worker 集區
gcloud beta run worker-pools delete ${WORKER_APP_NAME} --region=${REGION}
刪除 Pub/Sub 資源
gcloud pubsub subscriptions delete ${MY_SUBSCRIPTION}
gcloud pubsub topics delete ${MY_TOPIC}
刪除 Artifact Registry 存放區
gcloud artifacts repositories delete ${AR_REPO} --location=$REGION
刪除服務帳戶
gcloud iam service-accounts delete ${WORKER_SA_ADDRESS}
如要刪除整個專案，請前往「管理資源」，選取您在步驟 2 中建立的專案，然後選擇「刪除」。刪除專案後，您必須在 Cloud SDK 中變更專案。如要查看所有可用專案的清單，請執行 gcloud projects list。