Herramienta Vertex AI RAG Engine para ADK

Compatible con ADKPython v0.1.0Java v0.2.0

La herramienta vertex_ai_rag_retrieval permite al agente realizar recuperación de datos privados usando Vertex AI RAG Engine.

Cuando utilices grounding con Vertex AI RAG Engine, necesitas preparar un corpus RAG de antemano. Por favor consulta el ejemplo de agente RAG ADK o la página de Vertex AI RAG Engine para configurarlo.

Advertencia: Limitación de una sola herramienta por agente

Esta herramienta solo puede ser usada por sí misma dentro de una instancia de agente. Para más información sobre esta limitación y soluciones alternativas, consulta Limitaciones para las herramientas de ADK.

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,
    ]
)