Chris Christiansen
9fcb9c354a
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53 lines
1.8 KiB
Python
53 lines
1.8 KiB
Python
#!/usr/bin/env python3
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"""
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memory_setup.py — Initialize Agent Engine instance with Sessions + Memory Bank.
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Project: propane-will-491900-m5 | Region: us-central1
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SDK: google-cloud-aiplatform >= 1.111.0
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COST NOTE: Sessions + Memory Bank are metered since Jan 28, 2026.
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Trim session histories. Only persist high-value facts to long-term memory.
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"""
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import vertexai
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from vertexai import Client
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from google.adk.memory import VertexAiMemoryBankService
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from google.adk.sessions import VertexAiSessionService
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PROJECT_ID = "propane-will-491900-m5"
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LOCATION = "us-central1"
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def create_agent_engine() -> str:
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"""Create an Agent Engine instance (backing store for Sessions + Memory Bank)."""
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client = Client(project=PROJECT_ID, location=LOCATION)
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agent_engine = client.agent_engines.create()
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resource_name = agent_engine.api_resource.name
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agent_engine_id = resource_name.split("/")[-1]
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print(f"Agent Engine created: {resource_name}")
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print(f"Agent Engine ID: {agent_engine_id}")
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print("Store this in Secret Manager or env var: AGENT_ENGINE_ID")
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return agent_engine_id
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def get_services(agent_engine_id: str):
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"""Return configured session and memory services for use with ADK Runner."""
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memory_service = VertexAiMemoryBankService(
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project=PROJECT_ID,
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location=LOCATION,
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agent_engine_id=agent_engine_id,
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)
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session_service = VertexAiSessionService(
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project_id=PROJECT_ID,
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location=LOCATION,
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agent_engine_id=agent_engine_id,
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)
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return session_service, memory_service
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if __name__ == "__main__":
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agent_engine_id = create_agent_engine()
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session_svc, memory_svc = get_services(agent_engine_id)
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print("Services ready.")
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print(f"session_service: {session_svc}")
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print(f"memory_service: {memory_svc}")
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