ADK 的知识引擎工具

Supported in ADKPython v0.1.0Java v0.2.0

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 Grounding 时，你需要预先准备一个 RAG 语料库。 请参考 RAG ADK 智能体示例或知识引擎页面进行设置。

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。

Python

# 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,
    ],
)