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RAG Enterprise Search on GKE and AlloyDB

Guides design and deployment of a private enterprise search system on Google Cloud using AlloyDB, GKE and open models.

Source Download skill
Repository
google/skills
Stars
21,782
Licence
Apache-2.0
Files
6 files

What it does

When installed, the agent follows a structured four-phase workflow to help plan a retrieval-augmented generation (RAG) search system for a company's private documents on Google Cloud. It first asks a series of requirements questions (security, reliability, cost, performance and so on) without proposing designs, then produces a component breakdown and architecture using an AlloyDB vector-enabled SQL database, Cloud Storage, and Google Kubernetes Engine hosting an open model and open-source inference framework. It can also draft validation scripts and a consolidated solution guide.

Great for

  • Engineering teams building an internal search or question-answering system over private company documents on Google Cloud
  • Architects who want a guided requirements-to-design process for a self-hosted, SQL-based vector search stack

Skip it if

  • Teams wanting a fully managed RAG or SaaS search service, which the skill explicitly says it should not be used for
  • Projects that require a non-SQL vector database, since the skill is scoped to SQL-based vector storage
  • Anyone expecting it to deploy and run production infrastructure unattended; the guidance and generated scripts still need human review and approval before running against real cloud projects

Try asking

  • “Help me design a secure RAG search over our internal policy documents using AlloyDB and GKE.”
  • “Gather requirements for an enterprise chatbot that answers questions from our private contracts in Cloud Storage.”
  • “Generate a technical decomposition and architecture for a vector search system with an open model on GKE.”
  • “Create validation scripts and a solution guide for our RAG deployment plan.”
Step-by-step guide: how to use RAG Enterprise Search on GKE and AlloyDBInstall, first run, examples, tips and FAQs

Install in 3 steps

  1. 1

    Download the skill

    The ZIP contains the 6 original files, the Apache-2.0 licence and an attribution note.

    google-cloud-solution-rag-enterprise-search-gke-sqldb.zip
  2. 2

    Add it to your agent

    Claude Code (all projects)

    mkdir -p ~/.claude/skills && unzip google-cloud-solution-rag-enterprise-search-gke-sqldb.zip -d ~/.claude/skills/

    Claude Code / Agent SDK (this project only)

    mkdir -p .claude/skills && unzip google-cloud-solution-rag-enterprise-search-gke-sqldb.zip -d .claude/skills/

    Claude apps (web/desktop): Settings → Capabilities → Skills → Upload skill, and choose the ZIP.

  3. 3

    Ask for the task

    No special command needed — the agent reads the skill's description and loads it when your request matches. Try one of the prompts above.

Get better results

  • Answer the requirements questions fully (compliance, RTO/RPO, data types) before asking for a design, since the skill withholds architecture until ambiguities are resolved.
  • Review every gcloud or REST command the agent proposes and confirm the target project before letting it execute anything that creates or changes resources.
  • Say explicitly if a phase is already done (for example 'architecture is confirmed') so the agent skips ahead instead of repeating work.

What's inside

  • assets/output-template.md
  • references/attribution.md
  • references/design-recommendations.md
  • references/product-selection-recommendations.md
  • references/related-documentation.md
  • SKILL.md

Redistributed unmodified from google/skills under the Apache-2.0 licence. The assessment was drafted with AI from the skill's files on 2026-10-29. Skills can instruct your agent to run code — read SKILL.md and any scripts before installing.