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

• Initialize Firebase (or validate an existing repo): Hosting/Functions/Firestore as required.

• Implement backend integration:

• add a Cloud Function/HTTP endpoint that calls Gemini/Vertex AI

• validate inputs and return structured responses

• Configure data and security:

• Firestore rules + indexes

• Storage rules (if applicable)

• Auth providers and authorization checks

• Deploy and verify:

• deploy Functions/Hosting

• run smoke tests against deployed endpoints

• 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): ${CLAUDE_SKILL_DIR}/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