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RAG Chatbot on Vertex AI — Google Cloud Architecture Template
A retrieval-augmented chatbot: documents are embedded via Vertex AI into a vector cache, and a Cloud Run chat API grounds LLM answers in retrieved context.
Why this architecture works
- Grounding LLM responses in retrieved documents slashes hallucination versus raw prompting.
- The embedding ingest function keeps the vector index fresh as the corpus changes.
- Memorystore-backed vector lookups keep retrieval latency in single-digit milliseconds.
- Chat history in Firestore enables multi-turn context without stuffing whole transcripts into prompts.
- API keys for the model endpoint live in Secret Manager, never in app config.
What's inside (10 resources)
HTTPS LB
gcp-cloud-load-balancing
Document Corpus
gcp-cloud-storage
Chat API
gcp-cloud-run
Embedding Ingest Fn
gcp-cloud-functions
Vertex AI LLM
gcp-vertex-ai
Vector Cache
gcp-memorystore
Chat History
gcp-firestore
Cloud Logging
gcp-logging
Secret Manager
gcp-secret-manager
Cloud Monitoring
gcp-monitoring
From template to running infrastructure
- Open this template in the CloudForge visual designer (free account, no credit card).
- Customize resources, names, and connections on the drag-and-drop canvas — or ask Vani, the AI architect, to adapt it.
- Generate production-ready Terraform or Pulumi in one click.
- Review the plan diff and security scan, then deploy with human approval.