ADK용 지식 엔진 도구

ADK에서 지원Python v0.1.0Java v0.2.0Kotlin v0.7.0

vertex_ai_rag_retrieval 도구를 사용하면 에이전트가 개인 데이터를 수행할 수 있습니다. 지식 엔진을 사용하여 검색합니다.

Knowledge Engine으로 Grounding을 사용할 경우 미리 RAG Corpus를 준비해야 합니다. 설정 방법은 Knowledge Engine 페이지를 참조하십시오.

경고: 에이전트당 단일 도구 제한

이 도구는 에이전트 인스턴스 내에서 자체적으로만 사용할 수 있습니다. 이 제한 사항 및 해결 방법에 대한 자세한 내용은 다음을 참조하세요. Limitations for ADK tools.

PythonKotlin

# Copyright 2025 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 os

from google.adk.agents import Agent
from google.adk.tools.retrieval.vertex_ai_rag_retrieval import VertexAiRagRetrieval
from vertexai.preview import rag

from dotenv import load_dotenv
from .prompts import return_instructions_root

load_dotenv()

ask_vertex_retrieval = VertexAiRagRetrieval(
    name="retrieve_rag_documentation",
    description=(
        "Use this tool to retrieve documentation and reference materials for the question from the RAG corpus,"
    ),
    rag_resources=[
        rag.RagResource(
            # please fill in your own rag corpus
            # here is a sample rag corpus for testing purpose
            # e.g. projects/123/locations/us-central1/ragCorpora/456
            rag_corpus=os.environ.get("RAG_CORPUS")
        )
    ],
    similarity_top_k=10,
    vector_distance_threshold=0.6,
)

root_agent = Agent(
    model="gemini-2.0-flash-001",
    name="ask_rag_agent",
    instruction=return_instructions_root(),
    tools=[
        ask_vertex_retrieval,
    ],
)

import com.google.adk.kt.agents.Instruction
import com.google.adk.kt.agents.LlmAgent
import com.google.adk.kt.models.Gemini
import com.google.adk.kt.tools.VertexAiRagRetrieval
import com.google.adk.kt.types.VertexRagStoreRagResource

/**
 * An agent that answers from a Vertex AI RAG corpus.
 *
 * Retrieval happens inside the model through the Gemini-native `vertexRagStore`
 * kind, so the tool never runs locally.
 */
val ragAgent =
    LlmAgent(
        name = "rag_agent",
        model = Gemini(name = "gemini-flash-latest"),
        instruction =
            Instruction(
                "Answer questions using the documents in the RAG corpus. " +
                    "If the corpus does not cover the question, say so.",
            ),
        tools =
            listOf(
                VertexAiRagRetrieval(
                    name = "retrieve_docs",
                    description = "Retrieve reference material from the Vertex AI RAG corpus.",
                    // One corpus, or specific files from one corpus.
                    ragResources =
                        listOf(
                            VertexRagStoreRagResource(
                                ragCorpus =
                                    "projects/PROJECT_ID/locations/LOCATION/" +
                                        "ragCorpora/CORPUS_ID",
                            ),
                        ),
                    similarityTopK = 3,
                    vectorDistanceThreshold = 0.5,
                ),
            ),
    )