Firebase Vertex AI

Firebase Vertex AI

Operate Firebase projects end-to-end (Auth, Firestore, Functions, Hosting) and integrate Gemini/​Vertex AI safely for AI-powered features.

Overview

Use this skill to design, implement, and deploy Firebase applications that call Vertex AI/​Gemini from Cloud Functions (or other GCP services) with secure secrets handling, least-privilege IAM, and production-ready observability.

Prerequisites

• Node.js runtime and Firebase CLI access for the target project
• A Firebase project (billing enabled for Functions/​Vertex AI as needed)
• Vertex AI API enabled and permissions to call Gemini/​Vertex AI from your backend
• Secrets managed via env vars or Secret Manager (never in client code)

Instructions

1. Initialize Firebase (or validate an existing repo): Hosting/​Functions/​Firestore as required.
2. Implement backend integration:
   • add a Cloud Function/​HTTP endpoint that calls Gemini/​Vertex AI
   • validate inputs and return structured responses
3. Configure data and security:
   • Firestore rules + indexes
   • Storage rules (if applicable)
   • Auth providers and authorization checks
4. Deploy and verify:
   • deploy Functions/​Hosting
   • run smoke tests against deployed endpoints
5. Add ops guardrails:
   • logging/​metrics
   • alerting for error spikes
   • basic cost controls (budgets/​quotas) where appropriate

Output

• A deployable Firebase project structure (configs + Functions/​Hosting as needed)
• Secure backend code that calls Gemini/​Vertex AI (with secrets handled correctly)
• Firestore/​Storage rules and index guidance
• A verification checklist (local + deployed) and CI-ready commands

Error Handling

• Auth failures: identify the principal and missing permission/​role; fix with least privilege.
• Billing/​API issues: detect which API or quota is blocking and provide remediation steps.
• Firestore rule/​index problems: provide minimal repro queries and rule fixes.
• Vertex AI call failures: surface model/​region mismatches and add retries/​backoff for transient errors.

Examples

Example: Gemini-backed chat API on Firebase

• Request: “Deploy Hosting + a Function that powers a Gemini chat endpoint.”
• Result: /​api/​chat function, Secret Manager wiring, and smoke tests.

Example: Firestore-powered RAG

• Request: “Build a RAG flow that embeds docs and answers with citations.”
• Result: ingestion plan, embedding + index strategy, and evaluation prompts.

Resources

• Full detailed guide (kept for reference): {baseDir}/​references/​SKILL.full.md
• Firebase docs: https://firebase.google.com/docs
• Cloud Functions for Firebase: https://firebase.google.com/docs/functions
• Vertex AI docs: https://cloud.google.com/vertex-ai/docs