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chore: import upstream snapshot with attribution
2026-07-13 13:25:13 +08:00

74 lines
2.5 KiB
Python

# Copyright 2026 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
from typing import Optional
from google.adk.agents import LlmAgent
from google.adk.agents.callback_context import CallbackContext
from google.adk.models import LlmResponse
from google.adk.tools.vertex_ai_search_tool import VertexAiSearchTool
from google.genai import types
VERTEXAI_DATASTORE_ID = "projects/adk-agent-builder-assistant/locations/global/collections/default_collection/dataStores/adk-agent-builder-sample-datastore_1758230446136"
def citation_retrieval_after_model_callback(
callback_context: CallbackContext,
llm_response: LlmResponse,
) -> Optional[LlmResponse]:
"""Callback function to retrieve citations after model response is generated."""
grounding_metadata = llm_response.grounding_metadata
if not grounding_metadata:
return None
content = llm_response.content
if not llm_response.content:
return None
parts = content.parts
if not parts:
return None
# Add citations to the response as JSON objects.
parts.append(types.Part(text="References:\n"))
for grounding_chunk in grounding_metadata.grounding_chunks:
retrieved_context = grounding_chunk.retrieved_context
if not retrieved_context:
continue
citation = {
"title": retrieved_context.title,
"uri": retrieved_context.uri,
"snippet": retrieved_context.text,
}
parts.append(types.Part(text=json.dumps(citation)))
return LlmResponse(content=types.Content(parts=parts))
root_agent = LlmAgent(
name="adk_knowledge_agent",
description=(
"Agent for performing Vertex AI Search to find ADK knowledge and"
" documentation"
),
instruction="""You are a specialized search agent for an ADK knowledge base.
You can use the VertexAiSearchTool to search for ADK examples and documentation in the document store.
""",
tools=[VertexAiSearchTool(data_store_id=VERTEXAI_DATASTORE_ID)],
after_model_callback=citation_retrieval_after_model_callback,
)