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/google-cloud-solution-rag-enterprise-search-gke-sqldb

@becc4b8
by googlegoogle/skills21k stars
1,698

Discovers requirements, and generates architectural, design, and deployment guidance for a retrieval-augmented generation (RAG)-capable enterprise search system in Google Cloud. Use when users need a vector-enabled SQL database as the store and index for the embedding vectors, an open model and open-source inferencing framework, and Kubernetes containers to host all the application components. DON'T use this skill for fully-managed RAG, or SaaS search services, or when a non-SQL vector database is required.

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Use this Skill: https://skilld.dev/gh/google/skills/google-cloud-solution-rag-enterprise-search-gke-sqldb

This session only. Nothing lands on disk.

referencesdesign-recommendations.md

≈499 tokens on demand. Your agent reads this file only when SKILL.md points to it.

Design recommendations

  • The design recommendations that you generate MUST be based on and consistent with the guidance in the resources that are listed in references/related-documentation.md.

  • When you generate design recommendations, consider the following:

    • Functional requirements that were gathered in Phase 1.
    • Non-functional requirements that were gathered in Phase 1.

  • To generate guidance for the non-functional requirements, use the following skills, as appropriate:

    • google-cloud-waf-security
    • google-cloud-waf-reliability
    • google-cloud-waf-cost-optimization
    • google-cloud-waf-operational-excellence
    • google-cloud-waf-performance-optimization
    • google-cloud-waf-sustainability

  • Make sure that your recommendations cover the following:

    • Security, privacy, and compliance:
      • Data encryption, e.g., using customer-managed keys (CMEK)
      • Access control
      • Network security
      • Protection of sensitive data
      • Security features provided by GKE Autopilot
      • Active Assist recommendations for security
    • Reliability:
      • High availability
      • Autoscaling
      • Data durability and redundancy
      • Reliability advantages of GKE Autopilot
      • Active Assist recommendations for reliability
    • Cost optimization:
      • Storage cost
      • Compute utilization
      • Database resource optimization
      • AI models token optimization if model as a service is used
      • Cost-optimization opportunities with GKE Autopilot
      • Active Assist recommendations for cost optimization
    • Performance optimization:
      • Efficiency of data ingestion
      • Efficiency of vector and hybrid search for low retrieval latency
      • Container start time
      • Active Assist recommendations for performance optimization

Source: SKILL.md on GitHub

Third-party checks

No alerts10d3 checks · Risk SAFE
  • Gen Agent Trust Hub10d

    This skill provides a structured workflow for designing and deploying RAG-enabled search solutions on Google Cloud. It includes potential security considerations such as the ingestion of user-provided requirements to generate deployment scripts and the capability to execute validation commands. These activities are managed through user-approval checkpoints and rely on official vendor resources for grounding.

  • Socket10d

    No alerts

  • Snyk10d

    Risk: LOW · No issues

Provenance

Signed by skilld at becc4b8. This ties the file your Agent reads to that commit on GitHub. It does not review the instructions.

Last checked against GitHub yesterday.

Activeupdated 2 weeks ago

Capability

metadata
{
  "version": "1.0.0",
  "category": "MultiProductSolutions"
}

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