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Add azure-architecture-autopilot skill 🤖🤖🤖 (#1158)
* Add azure-architecture-autopilot skill E2E Azure infrastructure automation skill: - Natural language → Architecture diagram → Bicep → Deploy - 70+ service types with 605+ official Azure icons - Interactive HTML diagrams (drag, zoom, click, PNG export) - Scans existing resources or designs new architecture - Modular Bicep with RBAC, Private Endpoints, DNS - Multi-language support (auto-detects user language) - Zero dependencies (diagram engine embedded) Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Fix generator.py import for flat scripts/ structure + sync README Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> --------- Co-authored-by: whoniiii <whoniiii@users.noreply.github.com> Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
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Jeonghoon Lee
GitHub
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skills/azure-architecture-autopilot/references/ai-data.md
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skills/azure-architecture-autopilot/references/ai-data.md
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# Domain Pack: AI/Data (v1)
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Service configuration guide specialized for Azure AI/Data workloads.
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v1 scope: Foundry, AI Search, ADLS Gen2, Key Vault, Fabric, ADF, VNet/PE.
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> Required properties/common mistakes → `service-gotchas.md`
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> Dynamic information (API version, SKU, region) → `azure-dynamic-sources.md`
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> Common patterns (PE, security, naming) → `azure-common-patterns.md`
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---
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## 1. Microsoft Foundry (CognitiveServices)
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### Resource Hierarchy
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```
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Microsoft.CognitiveServices/accounts (kind: 'AIServices')
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├── /projects — Foundry Project (required for portal access)
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└── /deployments — Model deployments (GPT-4o, embedding, etc.)
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```
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### Bicep Core Structure
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```bicep
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// Foundry resource
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resource foundry 'Microsoft.CognitiveServices/accounts@<fetch>' = {
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name: foundryName
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location: location
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kind: 'AIServices'
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sku: { name: '<confirm with user>' } // ← SKU confirmed after MS Docs check in Phase 1
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identity: { type: 'SystemAssigned' }
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properties: {
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customSubDomainName: foundryName // ← Required, globally unique. Cannot change after creation — must delete and recreate if omitted
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allowProjectManagement: true
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publicNetworkAccess: 'Disabled'
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networkAcls: { defaultAction: 'Deny' }
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}
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}
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// Foundry Project — Must be created as a set with Foundry
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resource project 'Microsoft.CognitiveServices/accounts/projects@<fetch>' = {
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parent: foundry
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name: '${foundryName}-project'
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location: location
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sku: { name: '<same as parent>' }
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kind: 'AIServices'
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identity: { type: 'SystemAssigned' }
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properties: {}
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}
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// Model deployment — At Foundry resource level
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resource deployment 'Microsoft.CognitiveServices/accounts/deployments@<fetch>' = {
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parent: foundry
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name: '<model-name>' // ← Confirmed with user in Phase 1
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sku: {
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name: '<deployment-type>' // ← GlobalStandard, Standard, etc. — MS Docs fetch
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capacity: <confirm with user> // ← Capacity units — verify available range from MS Docs
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}
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properties: {
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model: {
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format: 'OpenAI'
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name: '<model-name>' // ← Must verify availability (fetch)
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version: '<fetch>' // ← Version also fetched
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}
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}
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}
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```
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> `@<fetch>`: Verify API version from the URLs in `azure-dynamic-sources.md`.
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> Model name/version/deployment type/capacity: All Dynamic — Confirmed with user after MS Docs fetch in Phase 1.
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---
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## 2. Azure AI Search
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### Bicep Core Structure
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```bicep
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resource search 'Microsoft.Search/searchServices@<fetch>' = {
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name: searchName
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location: location
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sku: { name: '<confirm with user>' }
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identity: { type: 'SystemAssigned' }
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properties: {
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hostingMode: 'default'
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publicNetworkAccess: 'disabled'
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semanticSearch: '<confirm with user>' // disabled | free | standard — verify in MS Docs
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}
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}
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```
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### Design Notes
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- PE support: Basic SKU or higher (verify latest constraints in MS Docs)
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- Semantic Ranker: Activated via `semanticSearch` property (`disabled` | `free` | `standard`) — verify per-SKU support in MS Docs
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- Vector search: Supported on paid SKUs (verify in MS Docs)
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- Commonly used together with Foundry for RAG configurations
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---
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## 3. ADLS Gen2 (Storage Account)
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### Bicep Core Structure
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```bicep
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resource storage 'Microsoft.Storage/storageAccounts@<fetch>' = {
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name: storageName // Lowercase+numbers only, no hyphens
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location: location
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kind: 'StorageV2'
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sku: { name: 'Standard_LRS' }
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properties: {
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isHnsEnabled: true // ← Never omit this
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accessTier: 'Hot'
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allowBlobPublicAccess: false
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minimumTlsVersion: 'TLS1_2'
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publicNetworkAccess: 'Disabled'
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networkAcls: { defaultAction: 'Deny' }
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}
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}
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// Container
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resource container 'Microsoft.Storage/storageAccounts/blobServices/containers@<fetch>' = {
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name: '${storage.name}/default/raw'
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}
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```
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### Design Notes
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- `isHnsEnabled` cannot be changed after creation → Resource must be recreated if omitted
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- PE: May need both `blob` and `dfs` PEs depending on use case
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- Common containers: `raw`, `processed`, `curated`
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---
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## 4. Microsoft Fabric
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### Bicep Core Structure
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```bicep
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resource fabric 'Microsoft.Fabric/capacities@<fetch>' = {
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name: fabricName
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location: location
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sku: { name: '<confirm with user>', tier: 'Fabric' }
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properties: {
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administration: {
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members: [ '<admin-email>' ] // ← Required, deployment fails without it
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}
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}
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}
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```
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### Design Notes
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- Only Capacity can be provisioned via Bicep
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- Workspace, Lakehouse, Warehouse, etc. must be created manually in the portal
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- Confirm admin email with the user (`ask_user`)
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### Required Confirmation Items When Adding in Phase 1
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When Fabric is added during conversation, the following items must be confirmed via ask_user before updating the diagram:
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- [ ] **SKU/Capacity**: F2, F4, F8, ... — Provide choices after fetching available SKUs from MS Docs
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- [ ] **administration.members**: Admin email — Deployment fails without it
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> Do not arbitrarily include sub-workloads (OneLake, data pipelines, Warehouse, etc.) that the user did not specify. Only Capacity can be provisioned via Bicep.
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---
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## 5. Azure Data Factory
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### Bicep Core Structure
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```bicep
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resource adf 'Microsoft.DataFactory/factories@<fetch>' = {
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name: adfName
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location: location
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identity: { type: 'SystemAssigned' }
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properties: {
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publicNetworkAccess: 'Disabled'
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}
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}
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```
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### Design Notes
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- Self-hosted Integration Runtime requires manual setup outside Bicep
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- Primarily used for on-premises data ingestion scenarios
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- PE groupId: `dataFactory`
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---
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## 6. AML / AI Hub (MachineLearningServices)
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### When to Use
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```
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Decision Rule:
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├─ General AI/RAG → Use Foundry (AIServices)
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└─ ML training, open-source models needed → Consider AI Hub
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└─ Only when the user explicitly requests it
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```
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### Bicep Core Structure
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```bicep
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resource hub 'Microsoft.MachineLearningServices/workspaces@<fetch>' = {
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name: hubName
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location: location
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kind: 'Hub'
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sku: { name: '<confirm with user>', tier: '<confirm with user>' } // e.g., Basic/Basic — verify available SKUs in MS Docs
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identity: { type: 'SystemAssigned' }
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properties: {
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friendlyName: hubName
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storageAccount: storage.id
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keyVault: keyVault.id
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applicationInsights: appInsights.id // Required for Hub
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publicNetworkAccess: 'Disabled'
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}
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}
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```
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### AI Hub Dependencies
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Additional resources needed when using Hub:
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- Storage Account
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- Key Vault
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- Application Insights + Log Analytics Workspace
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- Container Registry (optional)
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---
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## 7. Common AI/Data Architecture Combinations
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### RAG Chatbot
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```
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Foundry (AIServices) + Project
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├── <chat-model> (chat) — Confirmed after availability check in Phase 1
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├── <embedding-model> (embedding) — Confirmed after availability check in Phase 1
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├── AI Search (vector + semantic)
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├── ADLS Gen2 (document store)
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└── Key Vault (secrets)
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+ Full VNet/PE configuration
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```
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### Data Platform
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```
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Fabric Capacity (analytics)
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├── ADLS Gen2 (data lake)
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├── ADF (ingestion)
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└── Key Vault (secrets)
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+ VNet/PE configuration
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```
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