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Dhruvkumar-MicrosoftDhruvkumar-Microsoft
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added the environment varibale values
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Lines changed: 75 additions & 239 deletions

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‎azure.yaml‎

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@@ -1,7 +1,7 @@
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# yaml-language-server: $schema=https://raw.githubusercontent.com/Azure/azure-dev/main/schemas/v1.0/azure.yaml.json
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name: multi-agent-custom-automation-engine-solution-accelerator
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# metadata:
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# template: multi-agent-custom-automation-engine-solution-accelerator@1.0
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metadata:
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template: multi-agent-custom-automation-engine-solution-accelerator@1.0
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requiredVersions:
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azd: ">=1.15.0 !=1.17.1"
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hooks:

‎docs/DeploymentGuide.md‎

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@@ -370,16 +370,24 @@ The files for the dev container are located in `/.devcontainer/` folder.
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- If you are using `venv`, create and activate your virtual environment for both the frontend and backend folders.
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8. **Install requirements - frontend:**
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8. **Install requirements - Backend:**
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- To install the requirement for backend -
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Open a terminal in the `src/backend` folder and run:
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- In each of the frontend and backend folders -
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Open a terminal in the `src` folder and run:
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```bash
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pip install -r requirements.txt
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pip install uv
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uv sync
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```
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9. **Build the frontend (important):**
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- To install the requirement for frontend -
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Open a terminal in the `src/frontend` folder and run:
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```bash
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pip install -r requirements.txt
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```
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- Before running the frontend server, you must build the frontend to generate the necessary `build/assets` directory.
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From the `src/frontend` directory, run:
@@ -391,7 +399,7 @@ The files for the dev container are located in `/.devcontainer/` folder.
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10. **Run the application:**
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- From the src/backend directory:
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- From the src/backend directory activate the virtual environment created through step 8:
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```bash
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python app_kernel.py
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python frontend_server.py
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```
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10. Open a browser and navigate to `http://localhost:3000`
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11. To see swagger API documentation, you can navigate to `http://localhost:8000/docs`
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11. Open a browser and navigate to `http://localhost:3000`
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12. To see swagger API documentation, you can navigate to `http://localhost:8000/docs`
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## Deploy Your local changes
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To Deploy your local changes rename the below files.
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1. Rename `azure.yaml` to `azure_custom2.yaml` and `azure_custom.yaml` to `azure.yaml`.
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2. Go to `infra` directory
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- Remove `main.bicep` to `main_custom2.bicep` and `main_custom.bicep` to `main.bicep`.
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Continue with the [deploying steps](#deploying-with-azd).
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## Debugging the solution locally
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‎infra/main.bicep‎

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@@ -1455,19 +1455,19 @@ module containerApp 'br/public:avm/res/app/container-app:0.18.1' = {
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}
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{
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name: 'REASONING_MODEL_NAME'
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value: ''
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value: 'o3'
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}
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{
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name: 'MCP_SERVER_ENDPOINT'
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value: ''
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value: 'https://${containerAppMcp.outputs.fqdn}/mcp'
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}
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{
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name: 'MCP_SERVER_NAME'
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value: 'MACAE MCP Server'
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}
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{
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name: 'MCP_SERVER_DESCRIPTION'
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value: ''
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value: 'MACAE MCP Server Description'
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}
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{
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name: 'AZURE_TENANT_ID'
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}
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{
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name: 'SUPPORTED_MODELS'
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value: ''
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value: '["o3","o4-mini","gpt-4.1","gpt-4.1-mini"]'
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}
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{
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name: 'AZURE_AI_SEARCH_API_KEY'
@@ -1881,3 +1881,12 @@ output AZURE_AI_AGENT_ENDPOINT string = aiFoundryAiProjectEndpoint
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output APP_ENV string = 'Prod'
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output AI_FOUNDRY_RESOURCE_ID string = !useExistingAiFoundryAiProject ? aiFoundryAiServices.outputs.resourceId : existingAiFoundryAiProjectResourceId
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output COSMOSDB_ACCOUNT_NAME string = cosmosDbResourceName
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output AZURE_SEARCH_ENDPOINT string =searchService.outputs.endpoint
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output AZURE_CLIENT_ID string = userAssignedIdentity!.outputs.clientId
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output AZURE_TENANT_ID string = tenant().tenantId
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output AZURE_AI_SEARCH_CONNECTION_NAME string = searchService.outputs.name
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output AZURE_COGNITIVE_SERVICES string = 'https://cognitiveservices.azure.com/.default'
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output REASONING_MODEL_NAME string = 'o3'
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output MCP_SERVER_NAME string = 'MACAE MCP Server'
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output MCP_SERVER_DESCRIPTION string = 'MACAE MCP Server Description'
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output SUPPORTED_MODELS string = '["o3","o4-mini","gpt-4.1","gpt-4.1-mini"]'

‎infra/main_custom.bicep‎

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@@ -1466,6 +1466,18 @@ module containerApp 'br/public:avm/res/app/container-app:0.18.1' = {
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name: 'APP_ENV'
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value: 'Prod'
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}
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{
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name: 'AZURE_AI_SEARCH_CONNECTION_NAME'
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value: searchService.outputs.name
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}
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{
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name: 'AZURE_AI_SEARCH_INDEX_NAME'
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value: aiSearchIndexName
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}
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{
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name: 'AZURE_AI_SEARCH_ENDPOINT'
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value: searchService.outputs.endpoint
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}
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{
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name: 'AZURE_COGNITIVE_SERVICES'
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value: 'https://cognitiveservices.azure.com/.default'
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}
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{
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name: 'REASONING_MODEL_NAME'
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value: ''
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value: 'o3'
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}
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{
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name: 'MCP_SERVER_ENDPOINT'
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value: ''
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value: 'https://${containerAppMcp.outputs.fqdn}/mcp'
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}
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{
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name: 'MCP_SERVER_NAME'
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value: 'MACAE MCP Server'
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}
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{
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name: 'MCP_SERVER_DESCRIPTION'
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value: ''
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value: 'MACAE MCP Server Description'
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}
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{
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name: 'AZURE_TENANT_ID'
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}
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{
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name: 'SUPPORTED_MODELS'
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value: ''
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}
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{
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name: 'AZURE_AI_SEARCH_CONNECTION_NAME'
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value: aiSearchConnectionName
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}
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{
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name: 'AZURE_AI_SEARCH_INDEX_NAME'
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value: aiSearchIndexName
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}
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{
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name: 'AZURE_AI_SEARCH_ENDPOINT'
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value: searchService.outputs.endpoint
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value: '["o3","o4-mini","gpt-4.1","gpt-4.1-mini"]'
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}
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{
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name: 'AZURE_AI_SEARCH_API_KEY'
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name: 'AZURE_STORAGE_CONTAINER_NAME'
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value: storageContainerName
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}
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{
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name: 'AZURE_SEARCH_ENDPOINT'
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value: searchService.outputs.endpoint
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}
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]
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}
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]
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output APP_ENV string = 'Prod'
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output AI_FOUNDRY_RESOURCE_ID string = !useExistingAiFoundryAiProject ? aiFoundryAiServices.outputs.resourceId : existingAiFoundryAiProjectResourceId
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output COSMOSDB_ACCOUNT_NAME string = cosmosDbResourceName
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output AZURE_SEARCH_ENDPOINT string =searchService.outputs.endpoint
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output AZURE_CLIENT_ID string = userAssignedIdentity!.outputs.clientId
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output AZURE_TENANT_ID string = tenant().tenantId
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output AZURE_AI_SEARCH_CONNECTION_NAME string = searchService.outputs.name
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output AZURE_COGNITIVE_SERVICES string = 'https://cognitiveservices.azure.com/.default'
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output REASONING_MODEL_NAME string = 'o3'
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output MCP_SERVER_NAME string = 'MACAE MCP Server'
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output MCP_SERVER_DESCRIPTION string = 'MACAE MCP Server Description'
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output SUPPORTED_MODELS string = '["o3","o4-mini","gpt-4.1","gpt-4.1-mini"]'

‎src/backend/common/config/app_config.py‎

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@@ -69,15 +69,15 @@ def __init__(self):
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self.AZURE_AI_PROJECT_ENDPOINT = self._get_optional("AZURE_AI_PROJECT_ENDPOINT")
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# Azure Search settings
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self.AZURE_SEARCH_ENDPOINT = self._get_optional("AZURE_SEARCH_ENDPOINT")
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self.AZURE_SEARCH_ENDPOINT = self._get_optional("AZURE_AI_SEARCH_ENDPOINT")
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# Optional MCP server endpoint (for local MCP server or remote)
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# Example: http://127.0.0.1:8000/mcp
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self.MCP_SERVER_ENDPOINT = self._get_optional("MCP_SERVER_ENDPOINT")
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self.MCP_SERVER_NAME = self._get_optional("MCP_SERVER_NAME", "MCPGreetingServer")
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self.MCP_SERVER_DESCRIPTION = self._get_optional("MCP_SERVER_DESCRIPTION", "MCP server with greeting and planning tools")
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self.TENANT_ID = self._get_optional("TENANT_ID")
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self.CLIENT_ID = self._get_optional("CLIENT_ID")
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self.TENANT_ID = self._get_optional("AZURE_TENANT_ID")
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self.CLIENT_ID = self._get_optional("AZURE_CLIENT_ID")
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self.AZURE_AI_SEARCH_CONNECTION_NAME = self._get_optional("AZURE_AI_SEARCH_CONNECTION_NAME")
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self.AZURE_AI_SEARCH_INDEX_NAME = self._get_optional("AZURE_AI_SEARCH_INDEX_NAME")
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self.AZURE_AI_SEARCH_ENDPOINT = self._get_optional("AZURE_AI_SEARCH_ENDPOINT")

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