ADK 的知识引擎工具

Supported in ADKPython v0.1.0Java v0.2.0Kotlin v0.7.0

vertex_ai_rag_retrieval 工具允许智能体使用知识引擎执行私有数据检索。

使用知识引擎进行检索增强生成（Grounding）时，你需要提前准备一个 RAG 语料库。请参阅知识引擎页面了解如何设置。

警告：每个智能体单个工具限制

此工具在智能体实例中只能单独使用。 有关此限制及解决方法更多信息，请参阅 ADK 工具限制。

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

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-flash-latest",
    name="ask_rag_agent",
    instruction=(
        "You are an expert RAG documentation assistant. Use the "
        "retrieve_rag_documentation tool to fetch relevant documentation and "
        "reference materials, then answer the user's question based on them."
    ),
    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,
                ),
            ),
    )