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119 lines
3.5 KiB
Python
119 lines
3.5 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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from __future__ import annotations
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from typing import Any
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from google.adk.agents import Agent
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from google.adk.agents.callback_context import CallbackContext
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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.adk.tools import BaseTool
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from google.adk.tools import ToolContext
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from google.genai import types
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def get_weather(city: str) -> str:
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return f"The weather in {city} is sunny."
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def before_tool_callback(
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tool: BaseTool,
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args: dict[str, Any],
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tool_context: ToolContext,
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) -> dict[str, Any] | None:
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"""A callback that runs before a tool is called.
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Args:
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tool: The tool instance being called.
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args: The arguments passed to the tool.
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tool_context: The context for the tool execution.
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Returns:
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A dict containing the mock response if the call should be short-circuited,
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or None to proceed with the actual tool call.
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"""
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# Intercept tool calls for London and return a mocked response
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if args.get("city") == "London":
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return {
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"result": "Weather in London is always rainy (intercepted by callback)."
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}
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return None
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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 | None:
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"""A callback that runs before the model is called.
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Args:
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callback_context: The context for the callback.
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llm_request: The request that is about to be sent to the model.
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Returns:
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An LlmResponse to short-circuit the model call, or None to proceed.
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"""
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# Short-circuit if the user simply says "Hi"
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if llm_request.contents:
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last_content = llm_request.contents[-1]
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if last_content.parts:
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last_part = last_content.parts[-1]
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if last_part.text and last_part.text.strip().lower() == "hi":
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return LlmResponse(
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content=types.Content(
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role="model",
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parts=[
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types.Part.from_text(
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text="Hello from before_model callback!"
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)
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],
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)
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)
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return None
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def after_model_callback(
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callback_context: CallbackContext,
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llm_response: LlmResponse,
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) -> LlmResponse:
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"""A callback that runs after the model is called."""
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if llm_response.usage_metadata:
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usage = llm_response.usage_metadata
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usage_text = (
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"\n\nafter_model_callback: [Token Usage:"
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f" Input={usage.prompt_token_count},"
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f" Output={usage.candidates_token_count}]"
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)
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if not llm_response.content:
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llm_response.content = types.Content(role="model", parts=[])
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llm_response.content.parts.append(types.Part.from_text(text=usage_text))
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print(llm_response.content)
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return llm_response
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root_agent = Agent(
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name="callback_demo_agent",
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tools=[get_weather],
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before_tool_callback=before_tool_callback,
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before_model_callback=before_model_callback,
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after_model_callback=after_model_callback,
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)
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