Google Cloud Agent Starter Pack

   git clone https://github.com/GoogleCloudPlatform/agent-starter-pack.git   cd agent-starter-pack

   make install

   export GOOGLE_CLOUD_PROJECT=your-project-id   gcloud config set project $GOOGLE_CLOUD_PROJECT

   make enable-apis

   make deploy-infra

make playground

export PROJECT_ID="your-project-id"make data-ingestion

make deploy-cloud-run

from app.agent import root_agent
response = root_agent.invoke("What's the weather in San Francisco?")print(response)

import datetimeimport osfrom zoneinfo import ZoneInfo
import google.authfrom google.adk.agents import Agent
_, project_id = google.auth.default()os.environ.setdefault("GOOGLE_CLOUD_PROJECT", project_id)os.environ.setdefault("GOOGLE_CLOUD_LOCATION", "global")os.environ.setdefault("GOOGLE_GENAI_USE_VERTEXAI", "True")
def get_weather(query: str) -> str:    """模拟天气查询"""    if "sf" in query.lower() or "san francisco" in query.lower():        return "It's 60 degrees and foggy."    return "It's 90 degrees and sunny."
def get_current_time(query: str) -> str:    """模拟时间查询"""    if "sf" in query.lower() or "san francisco" in query.lower():        tz_identifier = "America/Los_Angeles"    else:        return f"Sorry, I don't have timezone information for query: {query}."
    tz = ZoneInfo(tz_identifier)    now = datetime.datetime.now(tz)    return f"The current time for query {query} is {now.strftime('%Y-%m-%d %H:%M:%S %Z%z')}"
root_agent = Agent(    name="root_agent",    model="gemini-2.5-flash",    instruction="You are a helpful AI assistant designed to provide accurate and useful information.",    tools=[get_weather, get_current_time],)

from kfp import dsl
@dsl.pipeline(description="Data ingestion pipeline into datastore")def pipeline(    project_id: str,    location: str,    is_incremental: bool = True,    look_back_days: int = 1,    chunk_size: int = 1500,    chunk_overlap: int = 20,    destination_table: str = "incremental_questions_embeddings",    deduped_table: str = "questions_embeddings",    destination_dataset: str = "stackoverflow_data",    data_store_region: str = "",    data_store_id: str = "",) -> None:    """Processes data and ingests it into a datastore for RAG Retrieval"""    processed_data = process_data(        project_id=project_id,        schedule_time=dsl.PIPELINE_JOB_SCHEDULE_TIME_UTC_PLACEHOLDER,        is_incremental=is_incremental,        look_back_days=look_back_days,        chunk_size=chunk_size,        chunk_overlap=chunk_overlap,        destination_dataset=destination_dataset,        destination_table=destination_table,        deduped_table=deduped_table,        location=location,        embedding_column="embedding",    ).set_retry(num_retries=2)        ingest_data(        project_id=project_id,        data_store_region=data_store_region,        input_files=processed_data.output,        data_store_id=data_store_id,        embedding_column="embedding",    ).set_retry(num_retries=2)