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87 lines
3.0 KiB
Python
87 lines
3.0 KiB
Python
# Copyright 2026 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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"""Echo agent for remote trigger integration tests.
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Uses a before_model_callback to echo user input without calling the LLM,
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making tests fast, deterministic, and free of LLM model quota usage.
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Supports optional 429 simulation via the SIMULATE_429_COUNT environment
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variable: when set to N > 0, the first N invocations per session will raise
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a RuntimeError containing "429 RESOURCE_EXHAUSTED" before succeeding.
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"""
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import os
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from google.adk.agents.callback_context import CallbackContext
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from google.adk.agents.llm_agent import Agent
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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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# Track 429 simulation state across invocations within a process.
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# Keyed by session_id to allow per-session failure counts.
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_429_counter: dict[str, int] = {}
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def before_model_callback(
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callback_context: CallbackContext,
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llm_request: LlmRequest,
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) -> LlmResponse:
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"""Echo user input back without calling the LLM.
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If SIMULATE_429_COUNT is set, raises a transient error for the first N
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invocations (per session) to exercise the retry-with-backoff logic in
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the trigger endpoints.
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"""
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fail_count = int(os.environ.get("SIMULATE_429_COUNT", "0"))
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session = callback_context.session
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session_id = session.id if session else "default"
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if fail_count > 0:
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current = _429_counter.get(session_id, 0)
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if current < fail_count:
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_429_counter[session_id] = current + 1
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raise RuntimeError("429 RESOURCE_EXHAUSTED: simulated quota exceeded")
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# Extract the most recent user message from the session events.
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user_text = ""
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if session and session.events:
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for event in reversed(session.events):
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if event.content and event.content.role == "user" and event.content.parts:
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user_text = event.content.parts[0].text or ""
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break
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# Fall back to the current LLM request contents if no session event found.
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if not user_text and llm_request.contents:
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for content in reversed(llm_request.contents):
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if content.role == "user" and content.parts:
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user_text = content.parts[0].text or ""
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break
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return LlmResponse(
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content=types.Content(
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role="model",
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parts=[types.Part(text=f"ECHO: {user_text}")],
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),
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)
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root_agent = Agent(
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model="gemini-2.5-flash",
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name="trigger_echo_agent",
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instruction="Echo agent for trigger testing.",
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before_model_callback=before_model_callback,
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)
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