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