Skip to content

gcp.md

Latest commit

 

History

History
19 lines (17 loc) · 847 Bytes

gcp.md

File metadata and controls

19 lines (17 loc) · 847 Bytes
Raw

GCP FastMemory Integration Template

Architecture Map

graph TD
    User[End User] --> GCLB[Global Cloud Load Balancer]
    GCLB --> GKE[Google Kubernetes Engine]
    GKE --> VertexAI[Vertex AI - Gemini 1.5 Pro]
    GKE --> FM_Pods[FastMemory Pods]
    FM_Pods --> GCS[Google Cloud Storage - ATF Store]
    FM_Pods --> GCE_Neo4j[(Neo4j VM / Cloud SQL)]
    FM_Pods --> Log[Cloud Logging / Trace]
Loading

Integration Plan

  1. Scalability: Deploy FastMemory as a horizontally scaled microservice on GKE.
  2. Pipeline: Trigger fastmemory build via Cloud Functions whenever new Markdowns are uploaded to GCS.
  3. Intelligence: Use Vertex AI's Gemini models for rich semantic metadata extraction to populate D_ (Data) nodes.
  4. Security: Map A_ (Access) nodes to GCP IAM Service Accounts and VPC Service Controls.