AutoGineer
Generated banner for Agent Platform RAG Engine Management
Generated logo for Agent Platform RAG Engine Management

Agent Platform RAG Engine Management

GoogleOfficialPublished by Google

  • Skill
  • Tool integration
  • Data and AI
  • Apache-2.0
  • Low risk
Download ZIP

From the author

Manage and query Agent Platform RAG Engine Corpora and retrieve grounded contexts using the Google GenAI SDK. Use when listing RAG corpora or files, inspecting a corpus, retrieving contexts, or generating content grounded in a RAG corpus. Do not use for standard database queries (use SQL/Spanner skills), Google Workspace RAG, or other RAG products like gRAG.
— the description by Google

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill provides a structured approach to interacting with the Agent Platform RAG Engine. It enables users to list, inspect, and query RAG corpora, as well as generate grounded responses using the Google GenAI SDK. The skill includes specific safety protocols for read-only operations versus resource-intensive content generation, ensuring proper handling of Google Cloud resources.

What it does

  • Lists available RAG corpora and files within a project.
  • Retrieves metadata and details for specific RAG corpora.
  • Performs similarity-based context retrieval from RAG corpora.
  • Generates grounded content using the Google GenAI SDK and RAG tools.
  • Provides templates for Python-based interaction with the RAG Engine.
  • Enforces safety and confirmation tiers for resource-consuming operations.

When to use it

  • When you need to discover or list RAG corpora and files in your Google Cloud project.
  • When you need to inspect the configuration or contents of a specific RAG corpus.
  • When you need to retrieve relevant context snippets for a specific query.
  • When you need to generate AI responses grounded in your own RAG-indexed data.

Advantages

  • Standardizes interaction with the Agent Platform RAG Engine.
  • Provides clear safety guidelines for read-only versus compute-heavy tasks.
  • Includes built-in support for pagination when listing large datasets.
  • Facilitates grounded AI responses by integrating directly with the GenAI SDK.

Usage examples

“List all RAG corpora in my project.”
The agent executes a Python script using the vertexai SDK to retrieve and display all available RAG corpora names and display names.
“What files are inside the corpus named 'my-knowledge-base'?”
The agent lists the files within the specified corpus, identifying their types based on file extensions.
“Search for information about 'remote work policy' in my RAG corpus.”
The agent performs a retrieval query against the corpus and returns the relevant context text and source URIs.
“Generate a summary of the company's remote work policy using my RAG corpus.”
The agent presents a confirmation prompt detailing the target corpus and model before proceeding to generate the grounded response.

Install

  1. Download the skill as a ZIP:

    https://mp-staging.autogineer.com/agent-skills/google/agent-platform-rag-engine-management/download

  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip agent-platform-rag-engine-management.zip -d .agents/skills

Documentation by Google

Agent Platform RAG Engine Management

This skill provides instructions on how to interact with Agent Platform RAG Engine using the Agent Platform Python SDK. You MUST use the vertexai Python SDK to perform RAG Engine operations, rather than raw REST calls or MCP tools, because this code is intended to be run by external clients.

Attribution

MUST READ: Before executing any gcloud, REST API, or MCP command, you MUST read Usage Attribution. For commands executed directly in the terminal (never in generated scripts, Terraform, or user code), prefix gcloud with CLOUDSDK_METRICS_REQUEST_ATTRIBUTION and append the attribution suffix to User-Agent headers:

CLOUDSDK_METRICS_REQUEST_ATTRIBUTION="agent-skills/1.0 (skill:agent-platform-rag-engine-management)"
User-Agent: <existing_user_agent> agent-skills/1.0 (skill:agent-platform-rag-engine-management)

Safety & Confirmation Tiers (CRITICAL)

Before executing any commands or scripts on behalf of the user, you must adhere to the following safety tiers based on the action requested:

  1. Tier R: Read-only (list_corpora, list_files, get_corpus, retrieval_query)

    • No confirmation needed. Execute immediately to gather information or retrieve grounded contexts.

  2. Tier RC: Read-only but consumes Compute Resources (client.models.generate_content)

    • Requires interactive confirmation with 'Yes'/'No' options before executing grounded content generation. The confirmation prompt MUST clearly explain the proposed generation execution and its key parameters (e.g., target corpus ID, query text, target model). Natural-language paraphrases without specifying exact parameters are insufficient, as explicit parameter listing is required to ensure unambiguous user approval of the specific resource and configuration.

    • Same-turn restriction: Do not execute the generation code in the same turn as presenting the confirmation prompt. Stop and wait for the user's reply; only execute after explicit 'Yes' / approval.

    • Gold Standard Example:

      I will perform grounded content generation with the following parameters. Please confirm this information before I proceed:

      • Target Corpus ID: projects/123/locations/us/ragCorpora/abc

      • Target Model: gemini-2.5-pro

      • Query Text: "What are the company policies on remote work?"

      Do you confirm? [Yes/No]

Phase 0: Environment Setup

CRITICAL: Before running any of the Python snippets below, you must ensure the environment is correctly initialized by following these steps:

  1. Google Cloud Authentication: Authenticate with your Google Cloud credentials and configure active Application Default Credentials (ADC) for Agent Platform access:

    gcloud auth login
gcloud auth application-default login

  2. Python Dependencies: This skill needs google-cloud-aiplatform and google-genai, which the sandbox already provides. Do not create a virtual environment — it starts empty and hides packages the environment already provides, forcing a redundant install. Do not spend a separate command checking for them: run the snippet directly, and only if it fails with ModuleNotFoundError, install in the same command as the retry:

    pip install -q google-cloud-aiplatform google-genai && python3 - <<'PY'
...
PY

  3. Execution: Run each snippet as a quoted heredoc, python3 - <<'PY' ... PY, rather than python3 -c '...', whose nested quotes break easily. There is no environment to activate first. When one step needs several of the snippets below (for example, list the corpora and then the files in each), combine them into one script and one command. The vertexai.preview.rag deprecation warning printed on stderr is expected; keep using these snippets rather than switching clients because of it.

Workflow Decision Tree

  1. Information Gathering: Has the user provided the Project ID, Region, and Corpus ID?

    • No -> Proceed to [1. Listing Corpora and Files] to discover the necessary Resource Names and IDs. Only ask the user if discovery fails.

    • Yes -> Proceed.

  2. Task Type: What does the user want to do?

    • List Corpora and Files -> Proceed to [1. Listing Corpora and Files].

    • Inspect a Corpus -> Proceed to [2. Getting / Inspecting a RAG Engine Corpus].

    • Search for Contexts -> Proceed to [3. Retrieving Contexts].

    • Answer questions using RAG Engine -> Proceed to [4. Answering the User with Retrieved Context].

[!TIP]

Placeholder Parameter Replacement: The Python scripts below use bracketed string placeholders (like "{project_id}", "{region}", and "{corpus_id}"). You MUST dynamically replace these placeholders with the actual Project ID, Region, and Corpus ID values provided in the user's prompt (or active context) before generating, providing, or executing the scripts.

1. Listing Corpora and Files (Discovery)

If you do not know the Resource Name of the corpus or file, you MUST list them first to discover them. The SDK handles pagination automatically when converted to a list, but you can also use manual pagination for large sets.

1.1 Listing and Discovering Corpora
import vertexai
from vertexai.preview import rag

vertexai.init(project="{project_id}", location="{region}")

# Approach A: List ALL (Automatic Pagination)
# The SDK's Pager iterates through all pages for you.
all_corpora = list(rag.list_corpora())
print(f"Found {len(all_corpora)} corpora in total.")
for c in all_corpora:
    print(f"Corpus Name: {c.name} | Display Name: {c.display_name}")

# Approach B: Manual Pagination (for very large projects)
pager = rag.list_corpora(page_size=10)
# Process first page
for c in pager:
    print(f"Corpus: {c.display_name}")

# Get next page if needed
if pager.next_page_token:
    second_page = rag.list_corpora(
        page_size=10, page_token=pager.next_page_token
    )
1.2 Listing and Discovering Files

To understand what files (and types) are in a corpus, list them and inspect the display_name (usually includes the extension).

import vertexai
from vertexai.preview import rag

vertexai.init(project="{project_id}", location="{region}")
corpus_name = (
    "projects/{project_id}/locations/{region}/ragCorpora/{corpus_id}"
)

# List files with automatic pagination
files = list(rag.list_files(corpus_name=corpus_name))
print(f"Found {len(files)} files.")

for f in files:
    # High-level SDK RagFile objects usually have name, display_name,
    # description
    print(f"File: {f.display_name} | Resource: {f.name}")
    # Tip: Check extension to understand file type (PDF, TXT, etc.)
    if f.display_name.lower().endswith(".pdf"):
        print("  Type: PDF")
    elif f.display_name.lower().endswith(".txt"):
        print("  Type: Plain Text")

2. Getting / Inspecting an Agent Platform RAG Engine Corpus

To retrieve details about an existing Agent Platform RAG Engine corpus:

import vertexai
from vertexai.preview import rag

vertexai.init(project="{project_id}", location="{region}")

# To get details of a specific corpus
corpus_name = (
    "projects/{project_id}/locations/{region}/ragCorpora/{corpus_id}"
)
corpus = rag.get_corpus(name=corpus_name)
print(f"Corpus Name: {corpus.name}")
print(f"Display Name: {corpus.display_name}")

3. Retrieving Contexts

To retrieve relevant contexts from a RAG Engine corpus based on a query. If no contexts come back, that is a valid answer: report it with the corpus and query you used. If a broader search is reasonable, try it in the same script (for example a larger similarity_top_k) rather than as a separate command.

import vertexai
from vertexai.preview import rag

vertexai.init(project="{project_id}", location="{region}")

corpus_name = (
    "projects/{project_id}/locations/{region}/ragCorpora/{corpus_id}"
)
query = "What is the speed of light?"

# Retrieve contexts
response = rag.retrieval_query(
    rag_corpora=[corpus_name],
    text=query,
    similarity_top_k=3
)

for context in response.contexts.contexts:
    print(f"Context text: {context.text}")
    print(f"Source: {context.source_uri}")

4. Answering the User with Retrieved Context

To use the retrieved context alongside an Agent Platform model to generate a grounded response:

from google import genai
from google.genai import types

client = genai.Client(enterprise=True, project="{project_id}", location="{region}")
corpus_name = (
    "projects/{project_id}/locations/{region}/ragCorpora/{corpus_id}"
)

# Define the Agent Platform RAG Engine tool pointing to the corpus
rag_tool = types.Tool(
    retrieval=types.Retrieval(
        vertex_rag_store=types.VertexRagStore(
            rag_resources=[types.VertexRagStoreRagResource(rag_corpus=corpus_name)],
            rag_retrieval_config=types.RagRetrievalConfig(
                top_k=3,
                filter=types.RagRetrievalConfigFilter(
                    vector_similarity_threshold=0.5,
                ),
            ),
        )
    )
)

# Generate content using the RAG Engine tool
response = client.models.generate_content(
    model="gemini-2.5-flash",
    contents="What is the speed of light?",
    config=types.GenerateContentConfig(
        tools=[rag_tool]
    )
)
print(response.text)

Files

LICENSE


                                 Apache License
                           Version 2.0, January 2004
                        http://www.apache.org/licenses/

   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION

   1. Definitions.

      "License" shall mean the terms and conditions for use, reproduction,
      and distribution as defined by Sections 1 through 9 of this document.

      "Licensor" shall mean the copyright owner or entity authorized by
      the copyright owner that is granting the License.

      "Legal Entity" shall mean the union of the acting entity and all
      other entities that control, are controlled by, or are under common
      control with that entity. For the purposes of this definition,
      "control" means (i) the power, direct or indirect, to cause the
      direction or management of such entity, whether by contract or
      otherwise, or (ii) ownership of fifty percent (50%) or more of the
      outstanding shares, or (iii) beneficial ownership of such entity.

      "You" (or "Your") shall mean an individual or Legal Entity
      exercising permissions granted by this License.

      "Source" form shall mean the preferred form for making modifications,
      including but not limited to software source code, documentation
      source, and configuration files.

      "Object" form shall mean any form resulting from mechanical
      transformation or translation of a Source form, including but
      not limited to compiled object code, generated documentation,
      and conversions to other media types.

      "Work" shall mean the work of authorship, whether in Source or
      Object form, made available under the License, as indicated by a
      copyright notice that is included in or attached to the work
      (an example is provided in the Appendix below).

      "Derivative Works" shall mean any work, whether in Source or Object
      form, that is based on (or derived from) the Work and for which the
      editorial revisions, annotations, elaborations, or other modifications
      represent, as a whole, an original work of authorship. For the purposes
      of this License, Derivative Works shall not include works that remain
      separable from, or merely link (or bind by name) to the interfaces of,
      the Work and Derivative Works thereof.

      "Contribution" shall mean any work of authorship, including
      the original version of the Work and any modifications or additions
      to that Work or Derivative Works thereof, that is intentionally
      submitted to Licensor for inclusion in the Work by the copyright owner
      or by an individual or Legal Entity authorized to submit on behalf of
      the copyright owner. For the purposes of this definition, "submitted"
      means any form of electronic, verbal, or written communication sent
      to the Licensor or its representatives, including but not limited to
      communication on electronic mailing lists, source code control systems,
      and issue tracking systems that are managed by, or on behalf of, the
      Licensor for the purpose of discussing and improving the Work, but
      excluding communication that is conspicuously marked or otherwise
      designated in writing by the copyright owner as "Not a Contribution."

      "Contributor" shall mean Licensor and any individual or Legal Entity
      on behalf of whom a Contribution has been received by Licensor and
      subsequently incorporated within the Work.

   2. Grant of Copyright License. Subject to the terms and conditions of
      this License, each Contributor hereby grants to You a perpetual,
      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
      copyright license to reproduce, prepare Derivative Works of,
      publicly display, publicly perform, sublicense, and distribute the
      Work and such Derivative Works in Source or Object form.

   3. Grant of Patent License. Subject to the terms and conditions of
      this License, each Contributor hereby grants to You a perpetual,
      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
      (except as stated in this section) patent license to make, have made,
      use, offer to sell, sell, import, and otherwise transfer the Work,
      where such license applies only to those patent claims licensable
      by such Contributor that are necessarily infringed by their
      Contribution(s) alone or by combination of their Contribution(s)
      with the Work to which such Contribution(s) was submitted. If You
      institute patent litigation against any entity (including a
      cross-claim or counterclaim in a lawsuit) alleging that the Work
      or a Contribution incorporated within the Work constitutes direct
      or contributory patent infringement, then any patent licenses
      granted to You under this License for that Work shall terminate
      as of the date such litigation is filed.

   4. Redistribution. You may reproduce and distribute copies of the
      Work or Derivative Works thereof in any medium, with or without
      modifications, and in Source or Object form, provided that You
      meet the following conditions:

      (a) You must give any other recipients of the Work or
          Derivative Works a copy of this License; and

      (b) You must cause any modified files to carry prominent notices
          stating that You changed the files; and

      (c) You must retain, in the Source form of any Derivative Works
          that You distribute, all copyright, patent, trademark, and
          attribution notices from the Source form of the Work,
          excluding those notices that do not pertain to any part of
          the Derivative Works; and

      (d) If the Work includes a "NOTICE" text file as part of its
          distribution, then any Derivative Works that You distribute must
          include a readable copy of the attribution notices contained
          within such NOTICE file, excluding those notices that do not
          pertain to any part of the Derivative Works, in at least one
          of the following places: within a NOTICE text file distributed
          as part of the Derivative Works; within the Source form or
          documentation, if provided along with the Derivative Works; or,
          within a display generated by the Derivative Works, if and
          wherever such third-party notices normally appear. The contents
          of the NOTICE file are for informational purposes only and
          do not modify the License. You may add Your own attribution
          notices within Derivative Works that You distribute, alongside
          or as an addendum to the NOTICE text from the Work, provided
          that such additional attribution notices cannot be construed
          as modifying the License.

      You may add Your own copyright statement to Your modifications and
      may provide additional or different license terms and conditions
      for use, reproduction, or distribution of Your modifications, or
      for any such Derivative Works as a whole, provided Your use,
      reproduction, and distribution of the Work otherwise complies with
      the conditions stated in this License.

   5. Submission of Contributions. Unless You explicitly state otherwise,
      any Contribution intentionally submitted for inclusion in the Work
      by You to the Licensor shall be under the terms and conditions of
      this License, without any additional terms or conditions.
      Notwithstanding the above, nothing herein shall supersede or modify
      the terms of any separate license agreement you may have executed
      with Licensor regarding such Contributions.

   6. Trademarks. This License does not grant permission to use the trade
      names, trademarks, service marks, or product names of the Licensor,
      except as required for reasonable and customary use in describing the
      origin of the Work and reproducing the content of the NOTICE file.

   7. Disclaimer of Warranty. Unless required by applicable law or
      agreed to in writing, Licensor provides the Work (and each
      Contributor provides its Contributions) on an "AS IS" BASIS,
      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
      implied, including, without limitation, any warranties or conditions
      of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
      PARTICULAR PURPOSE. You are solely responsible for determining the
      appropriateness of using or redistributing the Work and assume any
      risks associated with Your exercise of permissions under this License.

   8. Limitation of Liability. In no event and under no legal theory,
      whether in tort (including negligence), contract, or otherwise,
      unless required by applicable law (such as deliberate and grossly
      negligent acts) or agreed to in writing, shall any Contributor be
      liable to You for damages, including any direct, indirect, special,
      incidental, or consequential damages of any character arising as a
      result of this License or out of the use or inability to use the
      Work (including but not limited to damages for loss of goodwill,
      work stoppage, computer failure or malfunction, or any and all
      other commercial damages or losses), even if such Contributor
      has been advised of the possibility of such damages.

   9. Accepting Warranty or Additional Liability. While redistributing
      the Work or Derivative Works thereof, You may choose to offer,
      and charge a fee for, acceptance of support, warranty, indemnity,
      or other liability obligations and/or rights consistent with this
      License. However, in accepting such obligations, You may act only
      on Your own behalf and on Your sole responsibility, not on behalf
      of any other Contributor, and only if You agree to indemnify,
      defend, and hold each Contributor harmless for any liability
      incurred by, or claims asserted against, such Contributor by reason
      of your accepting any such warranty or additional liability.

   END OF TERMS AND CONDITIONS

   APPENDIX: How to apply the Apache License to your work.

      To apply the Apache License to your work, attach the following
      boilerplate notice, with the fields enclosed by brackets "[]"
      replaced with your own identifying information. (Don't include
      the brackets!)  The text should be enclosed in the appropriate
      comment syntax for the file format. We also recommend that a
      file or class name and description of purpose be included on the
      same "printed page" as the copyright notice for easier
      identification within third-party archives.

   Copyright [yyyy] [name of copyright owner]

   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.

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs a command-line tool, Needs Python

Security

What our scanner noticed· Low risk

  • Instructions that use the network

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Published by
Google
Publisher
Google (Official)
Source
google/skills · skills/cloud/agent-platform-rag-engine-management · commit 2194d3b
Licence
Apache License 2.0 (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
Synced

AutoGineer is not affiliated with or endorsed by Google.

Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download agent-platform-rag-engine-management.zip