chore: main to dev downmerge

.github/workflows/azure-dev.yml

@@ -355,7 +355,7 @@ jobs:
           echo "- 🏗️ **Microsoft Fabric capacity** and user-assigned **managed identity**" >> $GITHUB_STEP_SUMMARY
           echo "- 🧠 **Microsoft Foundry hub & project** with AI Search and Storage connections" >> $GITHUB_STEP_SUMMARY
           echo "- 🔍 **Azure AI Search** service and **Azure Storage** account for document indexing" >> $GITHUB_STEP_SUMMARY
-          echo "- 🤖 **Azure OpenAI deployments**: \`gpt-4.1-mini\` (chat) and \`text-embedding-3-small\` (embeddings)" >> $GITHUB_STEP_SUMMARY
+          echo "- 🤖 **Azure OpenAI deployments**: \`gpt-5-mini\` (chat) and \`text-embedding-3-small\` (embeddings)" >> $GITHUB_STEP_SUMMARY
           echo "- 📈 **Log Analytics workspace** and **Application Insights** for diagnostics" >> $GITHUB_STEP_SUMMARY
           echo "" >> $GITHUB_STEP_SUMMARY
           echo "### Phase 2 — Solution bootstrap (\`install_microsoft_iq_solution.py\` postprovision hook)" >> $GITHUB_STEP_SUMMARY

TRANSPARENCY_FAQ.md

@@ -22,7 +22,7 @@ Microsoft M365 Copilot users can choose one of the following generative AI model
 - Claude Sonnet 4.6
 - Claude Opus 4.6
 
-Foundry IQ component uses GPT-4.1-mini for chat and text-embedding-3-small for search. 
+Foundry IQ component uses GPT-5-mini for chat and text-embedding-3-small for search. 
 
 The Fabric IQ component uses built-in AI capabilities provided by the Fabric Platform, such as Copilot for Power BI and Fabric. The Fabric IQ component source code does not explicitly use generative AI. The source code consists of PySpark Notebooks. The source code also includes Python Programs that can be used to generate configurable sample data. 
 

docs/DeploymentGuide.md

@@ -251,7 +251,7 @@ Customize your deployment by setting `azd` environment variables before running
 | **Fabric Workspace** | `FABRIC_WORKSPACE_NAME` | Override the default Fabric workspace name | `Microsoft IQ - {suffix}` | `azd env set FABRIC_WORKSPACE_NAME "My Workspace"` |
 | | `FABRIC_WORKSPACE_ADMINISTRATORS` | Comma-separated additional workspace admins (UPNs and/or object IDs) | _(empty)_ | `azd env set FABRIC_WORKSPACE_ADMINISTRATORS "user@contoso.com,11111111-2222-3333-4444-555555555555"` |
 | **Microsoft Foundry** | `AZURE_AI_DEPLOYMENTS_LOCATION` | AI deployment region (**required**) | _(prompted)_ | `azd env set AZURE_AI_DEPLOYMENTS_LOCATION eastus` |
-| | `AZURE_OPENAI_DEPLOYMENT_MODEL` | GPT model to deploy | `gpt-4.1-mini` | `azd env set AZURE_OPENAI_DEPLOYMENT_MODEL gpt-4o` |
+| | `AZURE_OPENAI_DEPLOYMENT_MODEL` | GPT model to deploy | `gpt-5-mini` | `azd env set AZURE_OPENAI_DEPLOYMENT_MODEL gpt-4o` |
 | | `AZURE_OPENAI_MODEL_VERSION` | GPT model version | `2025-04-14` | `azd env set AZURE_OPENAI_MODEL_VERSION 2025-04-14` |
 | | `AZURE_OPENAI_DEPLOYMENT_MODEL_CAPACITY` | GPT capacity (tokens/min in thousands) | `150` | `azd env set AZURE_OPENAI_DEPLOYMENT_MODEL_CAPACITY 200` |
 | | `AZURE_OPENAI_MODEL_DEPLOYMENT_TYPE` | GPT deployment type | `GlobalStandard` | `azd env set AZURE_OPENAI_MODEL_DEPLOYMENT_TYPE Standard` |
@@ -293,7 +293,7 @@ The deployment creates two integrated components in a single Azure Resource Grou
 - **[Azure AI Search](https://learn.microsoft.com/azure/search/search-what-is-azure-search)**: Document indexing with [vector search](https://learn.microsoft.com/azure/search/vector-search-overview) and [knowledge base](https://learn.microsoft.com/en-us/azure/search/agentic-retrieval-how-to-create-knowledge-base?tabs=rbac%2C2025-11-01-preview&pivots=csharp)
 - **[Azure Storage Account](https://learn.microsoft.com/azure/storage/common/storage-account-overview)**: [Blob storage](https://learn.microsoft.com/azure/storage/blobs/storage-blobs-overview) for documents with direct citations
 - **[Azure OpenAI Models](https://learn.microsoft.com/azure/ai-services/openai/)**:
-  - [`gpt-4.1-mini`](https://learn.microsoft.com/azure/ai-services/openai/concepts/models) - Chat completion (150K TPM)
+  - [`gpt-5-mini`](https://learn.microsoft.com/azure/ai-services/openai/concepts/models) - Chat completion (150K TPM)
   - [`text-embedding-3-small`](https://learn.microsoft.com/azure/ai-services/openai/concepts/models#embeddings) - Vector embeddings (80K TPM)
 - **[Chat Agent](https://learn.microsoft.com/azure/ai-studio/how-to/develop/create-agent)**: Knowledge Base-powered agent for document Q&A
 
@@ -327,7 +327,7 @@ After successful deployment, you will have a single Azure Resource Group contain
 | **Fabric Capacity** (`{solution_suffix}-fabric-capacity` or your existing capacity) | Compute backing the Fabric workspace. Resumed automatically if paused. |
 | **User-assigned Managed Identity** | Identity used by deployment scripts and Foundry connections to call Azure AI Search and Storage without secrets. |
 | **Microsoft Foundry Hub & Project** | Container for AI agents, model deployments, knowledge bases, and connections. |
-| **Azure OpenAI deployments** | Two model deployments inside the Foundry project: a chat completion model (default `gpt-4.1-mini`) and an embedding model (default `text-embedding-3-small`). |
+| **Azure OpenAI deployments** | Two model deployments inside the Foundry project: a chat completion model (default `gpt-5-mini`) and an embedding model (default `text-embedding-3-small`). |
 | **Azure AI Search** | Vector + keyword search service. Backs the Foundry knowledge base; index name `{solution_suffix}-documents`. |
 | **Azure Storage Account** | Blob storage for source documents. Container `{solution_suffix}-documents` is uploaded to by `setup_knowledge_base` and referenced by AI Search citations. |
 | **Log Analytics workspace + Application Insights** | Diagnostic and monitoring sink for the Foundry project, AI Search, and the chat agent. Reused if `AZURE_EXISTING_LOG_ANALYTICS_WORKSPACE_ID` is set. |
@@ -384,7 +384,7 @@ Sourced and named by [`install_microsoft_iq_solution.py`](../infra/scripts/insta
 Open [ai.azure.com](https://ai.azure.com) and sign in with the same account used for `azd auth login`. From the landing page, select your hub and then your project (the project name is stored as `AZURE_AI_PROJECT_NAME` in your `azd` environment; the endpoint is `AZURE_AI_AGENT_ENDPOINT`). Once inside the project, confirm:
 
 1. **Knowledge Bases** → `{solution_suffix}-kb` exists, status is *Ready*, and it lists `{solution_suffix}-ks` as its source.
-2. **Agents** → an agent named `ChatAgent` exists, its model matches `AZURE_AI_AGENT_MODEL_DEPLOYMENT_NAME` / `AZURE_CHAT_MODEL` (default `gpt-4.1-mini`), and the **Tools** panel shows the `{solution_suffix}-kb-mcp-connection` MCP tool attached.
+2. **Agents** → an agent named `ChatAgent` exists, its model matches `AZURE_AI_AGENT_MODEL_DEPLOYMENT_NAME` / `AZURE_CHAT_MODEL` (default `gpt-5-mini`), and the **Tools** panel shows the `{solution_suffix}-kb-mcp-connection` MCP tool attached.
 3. **Connections** → the AI Search, Blob Storage, and KB MCP connections are all *Connected*.
 
 > If `setup_agent` finished with a warning during deployment, this verification is the recommended way to check whether the agent was actually created. If `ChatAgent` is missing, simply re-run `azd up`.

docs/foundry/DeploymentGuideFoundry.md

@@ -41,7 +41,7 @@ In addition to the [common requirements](../DeploymentGuide.md#deployment-enviro
 
 ### Model availability
 
-- **`gpt-4.1-mini`** (or your `AZURE_CHAT_MODEL` override) available in the target `AZURE_AI_DEPLOYMENTS_LOCATION` region.
+- **`gpt-5-mini`** (or your `AZURE_CHAT_MODEL` override) available in the target `AZURE_AI_DEPLOYMENTS_LOCATION` region.
 - **`text-embedding-3-small`** (or your `AZURE_OPENAI_EMBEDDING_MODEL` override) available in the same region.
 
 > Check model availability at [Azure OpenAI model summary](https://learn.microsoft.com/azure/ai-services/openai/concepts/models#model-summary-table-and-region-availability).
@@ -57,7 +57,7 @@ Phase 1 (Bicep) creates the following Foundry-related resources in a single reso
 | **[Azure AI Foundry Hub & Project](https://learn.microsoft.com/azure/ai-studio/concepts/ai-resources)** | Container for agents, model deployments, knowledge bases, and connections. |
 | **[Azure AI Search](https://learn.microsoft.com/azure/search/search-what-is-azure-search)** | Document indexing with vector + keyword hybrid search. |
 | **[Azure Storage Account](https://learn.microsoft.com/azure/storage/common/storage-account-overview)** | Blob storage for PDF documents with direct citation links. |
-| **Azure OpenAI deployments** | `gpt-4.1-mini` for chat (150K TPM) and `text-embedding-3-small` for embeddings (80K TPM). |
+| **Azure OpenAI deployments** | `gpt-5-mini` for chat (150K TPM) and `text-embedding-3-small` for embeddings (80K TPM). |
 | **User-assigned Managed Identity** | Secure authentication between Foundry, Search, and Storage — no secrets stored. |
 | **Log Analytics + Application Insights** | Diagnostics and monitoring for the Foundry project and AI Search. |
 | **Foundry connections** | Project connections wiring Foundry to AI Search, Blob Storage, and the Knowledge Base MCP endpoint. |
@@ -151,7 +151,7 @@ The `ChatAgent` is configured with:
 
 | Setting | Value |
 |---|---|
-| **Model** | `gpt-4.1-mini` (override with `AZURE_CHAT_MODEL` or `AZURE_AI_AGENT_MODEL_DEPLOYMENT_NAME`) |
+| **Model** | `gpt-5-mini` (override with `AZURE_CHAT_MODEL` or `AZURE_AI_AGENT_MODEL_DEPLOYMENT_NAME`) |
 | **Tool** | Knowledge Base MCP connection for document retrieval |
 | **Capabilities** | Document Q&A, semantic search, citation generation, chart creation |
 
@@ -177,7 +177,7 @@ Foundry-specific variables set via `azd env set <KEY> <VALUE>` before `azd up`:
 | Variable | Purpose | Default |
 |---|---|---|
 | `AZURE_AI_DEPLOYMENTS_LOCATION` | Region for AI model deployments (**required**) | _(prompted)_ |
-| `AZURE_CHAT_MODEL` | Chat completion model name | `gpt-4.1-mini` |
+| `AZURE_CHAT_MODEL` | Chat completion model name | `gpt-5-mini` |
 | `AZURE_OPENAI_EMBEDDING_MODEL` | Embedding model name | `text-embedding-3-small` |
 | `AZURE_AI_SEARCH_INDEX` | Search index name | `{solution_suffix}-documents` |
 | `KB_MCP_CONNECTION_NAME` | MCP connection name | `{solution_suffix}-kb-mcp-connection` |
@@ -223,7 +223,7 @@ This sends a sample question to the deployed agent and prints the response with
 
 **Symptom**: Bicep deployment fails with a quota or capacity error for OpenAI model deployments.
 
-**Why**: The target region may not have sufficient quota for `gpt-4.1-mini` (150K TPM) or `text-embedding-3-small` (80K TPM).
+**Why**: The target region may not have sufficient quota for `gpt-5-mini` (150K TPM) or `text-embedding-3-small` (80K TPM).
 
 **Fix**: Either request a quota increase, or deploy to a different region:
 ```bash

infra/deploy_ai_foundry.bicep

@@ -189,6 +189,7 @@ resource aiServicesDeployments 'Microsoft.CognitiveServices/accounts/deployments
     model: {
       format: 'OpenAI'
       name: aiModeldeployment.model
+      version: aiModeldeployment.?version
     }
     raiPolicyName: aiModeldeployment.raiPolicyName
   }

infra/deploy_foundry_role_assignment.bicep

@@ -78,6 +78,7 @@ resource aiServicesDeployments 'Microsoft.CognitiveServices/accounts/deployments
     model: {
       format: 'OpenAI'
       name: aiModeldeployment.model
+      version: aiModeldeployment.?version
     }
     raiPolicyName: aiModeldeployment.raiPolicyName
   }

infra/fabric/deploy/fabric_solution_installer.ipynb

@@ -90,10 +90,9 @@
     "ONTOLOGY_TARGET_FOLDER = \"ontology\" # Target folder where ontologies will be moved after deployment\n",
     "\n",
     "# ── Source environment GUIDs (from parameter.yml) ───────────────────────────\n",
-    "# These are the hardcoded GUIDs from the source DEV workspace that need to be replaced\n",
-    "# during ontology deployment with actual target workspace/lakehouse IDs\n",
-    "SOURCE_WORKSPACE_ID = \"27818fad-0450-42b9-a4a0-db84075ac8d7\"\n",
-    "SOURCE_LAKEHOUSE_ID = \"e6cd0b76-50e8-498f-9cd0-89251fae70c2\""
+    "# ontology deployment with the actual target workspace/lakehouse IDs.\n",
+    "SOURCE_WORKSPACE_ID = \"PLACEHOLDER-WORKSPACE-ID\"\n",
+    "SOURCE_LAKEHOUSE_ID = \"PLACEHOLDER-LAKEHOUSE-ID\""
    ]
   },
   {

infra/main.bicep

@@ -85,7 +85,7 @@ param searchServiceLocation string = resourceGroup().location
   azd: {
     type: 'location'
     usageName: [
-      'OpenAI.GlobalStandard.gpt4.1-mini,100'
+      'OpenAI.GlobalStandard.gpt-5-mini,150'
       'OpenAI.GlobalStandard.text-embedding-3-small,80'
     ]
   }
@@ -102,10 +102,10 @@ param aiDeploymentsLocation string
 param deploymentType string = 'GlobalStandard'
 
 @description('Name of the GPT model to deploy')
-param gptModelName string = 'gpt-4.1-mini'
+param gptModelName string = 'gpt-5-mini'
 
 @description('Version of the GPT model to deploy')
-param gptModelVersion string = '2025-04-14'
+param gptModelVersion string = '2025-08-07'
 
 @minValue(10)
 @description('Capacity of the GPT deployment')
@@ -238,7 +238,7 @@ output AZURE_FABRIC_CAPACITY_ADMINISTRATORS array = fabricTotalAdminMembers
 
 // AI Foundry Outputs
 
-@description('GPT model deployment name (e.g., gpt-4o-mini)')
+@description('GPT model deployment name (e.g., gpt-5-mini)')
 output AZURE_OPENAI_DEPLOYMENT_MODEL string = gptModelName
 
 @description('Azure OpenAI service endpoint URL')

infra/main.parameters.json

@@ -33,16 +33,16 @@
       "value": "${AZURE_SEARCH_SERVICE_LOCATION=}"
     },
     "aiDeploymentsLocation": {
-      "value": "${AZURE_AI_DEPLOYMENTS_LOCATION=westus}"
+      "value": "${AZURE_AI_DEPLOYMENTS_LOCATION=}"
     },
     "deploymentType": {
       "value": "${AZURE_OPENAI_MODEL_DEPLOYMENT_TYPE=GlobalStandard}"
     },
     "gptModelName": {
-      "value": "${AZURE_OPENAI_DEPLOYMENT_MODEL=gpt-4.1-mini}"
+      "value": "${AZURE_OPENAI_DEPLOYMENT_MODEL=gpt-5-mini}"
     },
     "gptModelVersion": {
-      "value": "${AZURE_OPENAI_MODEL_VERSION=2025-04-14}"
+      "value": "${AZURE_OPENAI_MODEL_VERSION=2025-08-07}"
     },
     "gptDeploymentCapacity": {
       "value": "${AZURE_OPENAI_DEPLOYMENT_MODEL_CAPACITY=150}"

infra/scripts/install_microsoft_iq_solution.py

@@ -54,11 +54,11 @@
     AZURE_OPENAI_EMBEDDING_MODEL         (optional) Embedding model deployment name.
                                                     Defaults to text-embedding-3-small.
     AZURE_CHAT_MODEL                     (optional) Chat model deployment name.
-                                                    Defaults to gpt-4.1-mini.
+                                                    Defaults to gpt-5-mini.
     AZURE_AI_SEARCH_INDEX                (optional) Search index name.
                                                     Defaults to <SOLUTION_SUFFIX>-documents.
     AZURE_AI_AGENT_MODEL_DEPLOYMENT_NAME (optional) Chat model deployment name for the agent.
-                                                    Falls back to AZURE_CHAT_MODEL, then gpt-4.1-mini.
+                                                    Falls back to AZURE_CHAT_MODEL, then gpt-5-mini.
     KB_MCP_CONNECTION_NAME               (optional) Project connection name for the Knowledge Base MCP tool.
                                                     Defaults to <SOLUTION_SUFFIX>-kb-mcp-connection.
 """
@@ -159,7 +159,7 @@ def main() -> None:
         or agent_endpoint.split("/api/projects")[0]
     )
     embedding_model = os.getenv("AZURE_OPENAI_EMBEDDING_MODEL", "text-embedding-3-small")
-    chat_model = os.getenv("AZURE_CHAT_MODEL", "gpt-4.1-mini")
+    chat_model = os.getenv("AZURE_CHAT_MODEL", "gpt-5-mini")
     search_index_name = os.getenv("AZURE_AI_SEARCH_INDEX", f"{solution_suffix}-documents")
     blob_container_name = f"{solution_suffix}-documents"
     knowledge_base_name = f"{solution_suffix}-kb"
@@ -168,7 +168,7 @@ def main() -> None:
     # Agent model selection
     agent_model = (
         os.getenv("AZURE_CHAT_MODEL")
-        or os.getenv("AZURE_AI_AGENT_MODEL_DEPLOYMENT_NAME", "gpt-4.1-mini")
+        or os.getenv("AZURE_AI_AGENT_MODEL_DEPLOYMENT_NAME", "gpt-5-mini")
     )
     kb_mcp_connection_name = os.getenv("KB_MCP_CONNECTION_NAME", f"{solution_suffix}-kb-mcp-connection")
 

infra/scripts/utils/Get-FabricOntologyDefinition.ps1

@@ -537,3 +537,4 @@ catch {
 }
 
 Write-Log "Script completed successfully"
+# Script ends here

src/fabric/fabric_workspace/ontology/RetailSupplyChainOntologyModel.Ontology/EntityTypes/107549390124998/DataBindings/688600ec-8c75-4910-940a-23c2e23bd144.json

@@ -56,7 +56,7 @@
     "sourceTableProperties": {
       "sourceType": "LakehouseTable",
       "workspaceId": "00000000-0000-0000-0000-000000000000",
-      "itemId": "1fae70c2-8925-9cd0-498f-50e8e6cd0b76",
+      "itemId": "PLACEHOLDER-LAKEHOUSE-ID",
       "sourceTableName": "suppliers",
       "sourceSchema": "supplychain"
     }

src/fabric/fabric_workspace/ontology/RetailSupplyChainOntologyModel.Ontology/EntityTypes/113325854257683/DataBindings/271405da-1e26-4b22-a9d3-dc2c8297ce85.json

@@ -72,7 +72,7 @@
     "sourceTableProperties": {
       "sourceType": "LakehouseTable",
       "workspaceId": "00000000-0000-0000-0000-000000000000",
-      "itemId": "1fae70c2-8925-9cd0-498f-50e8e6cd0b76",
+      "itemId": "PLACEHOLDER-LAKEHOUSE-ID",
       "sourceTableName": "inventorytransactions",
       "sourceSchema": "inventory"
     }

src/fabric/fabric_workspace/ontology/RetailSupplyChainOntologyModel.Ontology/EntityTypes/120698763730570/DataBindings/f9fabcfe-54ac-45a4-be7b-18f3c3e3f115.json

@@ -96,7 +96,7 @@
     "sourceTableProperties": {
       "sourceType": "LakehouseTable",
       "workspaceId": "00000000-0000-0000-0000-000000000000",
-      "itemId": "1fae70c2-8925-9cd0-498f-50e8e6cd0b76",
+      "itemId": "PLACEHOLDER-LAKEHOUSE-ID",
       "sourceTableName": "warehouses",
       "sourceSchema": "inventory"
     }

src/fabric/fabric_workspace/ontology/RetailSupplyChainOntologyModel.Ontology/EntityTypes/122254761916513/DataBindings/aaf05d1e-94bb-4fed-92d2-0a240f58f55c.json

@@ -64,7 +64,7 @@
     "sourceTableProperties": {
       "sourceType": "LakehouseTable",
       "workspaceId": "00000000-0000-0000-0000-000000000000",
-      "itemId": "1fae70c2-8925-9cd0-498f-50e8e6cd0b76",
+      "itemId": "PLACEHOLDER-LAKEHOUSE-ID",
       "sourceTableName": "productsuppliers",
       "sourceSchema": "supplychain"
     }

src/fabric/fabric_workspace/ontology/RetailSupplyChainOntologyModel.Ontology/EntityTypes/130227575921799/DataBindings/2196afb7-ce18-4508-ac00-fa279f1c3b02.json

@@ -68,7 +68,7 @@
     "sourceTableProperties": {
       "sourceType": "LakehouseTable",
       "workspaceId": "00000000-0000-0000-0000-000000000000",
-      "itemId": "1fae70c2-8925-9cd0-498f-50e8e6cd0b76",
+      "itemId": "PLACEHOLDER-LAKEHOUSE-ID",
       "sourceTableName": "inventory",
       "sourceSchema": "inventory"
     }

src/fabric/fabric_workspace/ontology/RetailSupplyChainOntologyModel.Ontology/EntityTypes/150097852588650/DataBindings/05bd70ed-8dcf-4b67-be34-99b8b4674b84.json

@@ -28,7 +28,7 @@
     "sourceTableProperties": {
       "sourceType": "LakehouseTable",
       "workspaceId": "00000000-0000-0000-0000-000000000000",
-      "itemId": "1fae70c2-8925-9cd0-498f-50e8e6cd0b76",
+      "itemId": "PLACEHOLDER-LAKEHOUSE-ID",
       "sourceTableName": "productline",
       "sourceSchema": "product"
     }

src/fabric/fabric_workspace/ontology/RetailSupplyChainOntologyModel.Ontology/EntityTypes/161336334468822/DataBindings/f2755cb0-640a-4097-a064-20313f55af09.json

@@ -40,7 +40,7 @@
     "sourceTableProperties": {
       "sourceType": "LakehouseTable",
       "workspaceId": "00000000-0000-0000-0000-000000000000",
-      "itemId": "1fae70c2-8925-9cd0-498f-50e8e6cd0b76",
+      "itemId": "PLACEHOLDER-LAKEHOUSE-ID",
       "sourceTableName": "productcategory",
       "sourceSchema": "product"
     }

src/fabric/fabric_workspace/ontology/RetailSupplyChainOntologyModel.Ontology/EntityTypes/177023137565323/DataBindings/afc3f38a-1ee5-4f4f-a258-e9e95a13b9bb.json

@@ -92,7 +92,7 @@
     "sourceTableProperties": {
       "sourceType": "LakehouseTable",
       "workspaceId": "00000000-0000-0000-0000-000000000000",
-      "itemId": "1fae70c2-8925-9cd0-498f-50e8e6cd0b76",
+      "itemId": "PLACEHOLDER-LAKEHOUSE-ID",
       "sourceTableName": "product",
       "sourceSchema": "product"
     }

src/fabric/fabric_workspace/ontology/RetailSupplyChainOntologyModel.Ontology/EntityTypes/212018112555376/DataBindings/fcf4e257-aa96-40e4-96a5-11461e2e40fd.json

@@ -68,7 +68,7 @@
     "sourceTableProperties": {
       "sourceType": "LakehouseTable",
       "workspaceId": "00000000-0000-0000-0000-000000000000",
-      "itemId": "1fae70c2-8925-9cd0-498f-50e8e6cd0b76",
+      "itemId": "PLACEHOLDER-LAKEHOUSE-ID",
       "sourceTableName": "purchaseorders",
       "sourceSchema": "inventory"
     }

src/fabric/fabric_workspace/ontology/RetailSupplyChainOntologyModel.Ontology/EntityTypes/224432779600142/DataBindings/ef7b8f02-1a0f-4ffd-811c-7edcb61da809.json

@@ -68,7 +68,7 @@
     "sourceTableProperties": {
       "sourceType": "LakehouseTable",
       "workspaceId": "00000000-0000-0000-0000-000000000000",
-      "itemId": "1fae70c2-8925-9cd0-498f-50e8e6cd0b76",
+      "itemId": "PLACEHOLDER-LAKEHOUSE-ID",
       "sourceTableName": "purchaseorderitems",
       "sourceSchema": "inventory"
     }

src/fabric/fabric_workspace/ontology/RetailSupplyChainOntologyModel.Ontology/EntityTypes/263228276373168/DataBindings/8cac8ae8-94e7-4857-85ea-bd5b3fef59d2.json

@@ -76,7 +76,7 @@
     "sourceTableProperties": {
       "sourceType": "LakehouseTable",
       "workspaceId": "00000000-0000-0000-0000-000000000000",
-      "itemId": "1fae70c2-8925-9cd0-498f-50e8e6cd0b76",
+      "itemId": "PLACEHOLDER-LAKEHOUSE-ID",
       "sourceTableName": "demandforecast",
       "sourceSchema": "inventory"
     }

src/fabric/fabric_workspace/ontology/RetailSupplyChainOntologyModel.Ontology/EntityTypes/85700348291860/DataBindings/6726d420-45ab-4273-b263-3e17fee34d6f.json

@@ -76,7 +76,7 @@
     "sourceTableProperties": {
       "sourceType": "LakehouseTable",
       "workspaceId": "00000000-0000-0000-0000-000000000000",
-      "itemId": "1fae70c2-8925-9cd0-498f-50e8e6cd0b76",
+      "itemId": "PLACEHOLDER-LAKEHOUSE-ID",
       "sourceTableName": "supplychainevents",
       "sourceSchema": "supplychain"
     }