Merge all the changes from main

Author: Abdul-Microsoft

docs/DeploymentGuide.md

@@ -153,7 +153,8 @@ When you start the deployment, most parameters will have **default values**, but
 | **GPT Model Capacity**          | Sets the GPT model capacity.                                 | 150        |
 | **Image Tag**                  | Docker image tag used for container deployments.                                    | latest            |
 | **Enable Telemetry**           | Enables telemetry for monitoring and diagnostics.                                    | true              |
-
+| **Existing Log Analytics Workspace**        | To reuse an existing Log Analytics Workspace ID instead of creating a new one.              | *(none)*          |
+| **Existing Azure AI Foundry Project**        | To reuse an existing Azure AI Foundry Project ID instead of creating a new one.              | *(none)*          |
 
 </details>
 
@@ -176,6 +177,14 @@ To adjust quota settings, follow these [steps](./AzureGPTQuotaSettings.md).
 
 </details>
 
+<details>
+
+  <summary><b>Reusing an Existing Azure AI Foundry Project</b></summary>
+
+  Guide to get your [Existing Project ID](/docs/re-use-foundry-project.md)
+
+</details>
+
 ### Deploying with AZD
 
 Once you've opened the project in [Codespaces](#github-codespaces), [Dev Containers](#vs-code-dev-containers), or [locally](#local-environment), you can deploy it to Azure by following these steps:
@@ -206,43 +215,9 @@ Once you've opened the project in [Codespaces](#github-codespaces), [Dev Contain
 
 5. Once the deployment has completed successfully, open the [Azure Portal](https://portal.azure.com/), go to the deployed resource group, find the App Service, and get the app URL from `Default domain`.
 
-6. If you are done trying out the application, you can delete the resources by running `azd down`.
-
-### Publishing Local Build Container to Azure Container Registry
-
-If you need to rebuild the source code and push the updated container to the deployed Azure Container Registry, follow these steps:
-
-1. Set the environment variable `USE_LOCAL_BUILD` to `True`:
-
-   - **Linux/macOS**:
-
-     ```bash
-     export USE_LOCAL_BUILD=True
-     ```
-
-   - **Windows (PowerShell)**:
-     ```powershell
-     $env:USE_LOCAL_BUILD = $true
-     ```
-
-2. Run the `az login` command
-
-   ```bash
-   az login
-   ```
-
-3. Run the `azd up` command again to rebuild and push the updated container:
-   ```bash
-   azd up
-   ```
-
-This will rebuild the source code, package it into a container, and push it to the Azure Container Registry associated with your deployment.
-
-This guide provides step-by-step instructions for deploying your application using Azure Container Registry (ACR) and Azure Container Apps.
-
-There are several ways to deploy the solution. You can deploy to run in Azure in one click, or manually, or you can deploy locally.
+6. When Deployment is complete, follow steps in [Set Up Authentication in Azure App Service](../docs/azure_app_service_auth_setup.md) to add app authentication to your web app running on Azure App Service
 
-When Deployment is complete, follow steps in [Set Up Authentication in Azure App Service](../docs/azure_app_service_auth_setup.md) to add app authentication to your web app running on Azure App Service
+7. If you are done trying out the application, you can delete the resources by running `azd down`.
 
 # Local setup
 

docs/images/re_use_foundry_project/azure_ai_foundry_list.png

@@ -0,0 +1,44 @@
+[← Back to *DEPLOYMENT* guide](/docs/DeploymentGuide.md#deployment-steps)
+
+# Reusing an Existing Azure AI Foundry Project
+To configure your environment to use an existing Azure AI Foundry Project, follow these steps:
+---
+### 1. Go to Azure Portal
+Go to https://portal.azure.com
+
+### 2. Search for Azure AI Foundry
+In the search bar at the top, type "Azure AI Foundry" and click on it. Then select the Foundry service instance where your project exists.
+
+![alt text](../docs/images/re_use_foundry_project/azure_ai_foundry_list.png)
+
+### 3. Navigate to Projects under Resource Management
+On the left sidebar of the Foundry service blade:
+
+- Expand the Resource Management section
+- Click on Projects (this refers to the active Foundry project tied to the service)
+
+### 4. Click on the Project
+From the Projects view: Click on the project name to open its details
+
+    Note: You will see only one project listed here, as each Foundry service maps to a single project in this accelerator
+
+![alt text](../docs/images/re_use_foundry_project/navigate_to_projects.png)
+
+### 5. Copy Resource ID
+In the left-hand menu of the project blade: 
+
+- Click on Properties under Resource Management
+- Locate the Resource ID field
+- Click on the copy icon next to the Resource ID value
+
+![alt text](../docs/images/re_use_foundry_project/project_resource_id.png)
+
+### 6. Set the Foundry Project Resource ID in Your Environment
+Run the following command in your terminal
+```bash
+azd env set AZURE_ENV_FOUNDRY_PROJECT_ID '<Existing Foundry Project Resource ID>'
+```
+Replace `<Existing Foundry Project Resource ID>` with the value obtained from Step 5.
+
+### 7. Continue Deployment
+Proceed with the next steps in the [deployment guide](/docs/DeploymentGuide.md#deployment-steps).

docs/images/re_use_foundry_project/navigate_to_projects.png

@@ -1,4 +1,4 @@
-[← Back to *DEPLOYMENT* guide](/docs/DeploymentGuide.md#deployment-options--steps)
+[← Back to *DEPLOYMENT* guide](/docs/DeploymentGuide.md#deployment-steps)
 
 # Reusing an Existing Log Analytics Workspace
 To configure your environment to use an existing Log Analytics Workspace, follow these steps:
@@ -28,4 +28,4 @@ azd env set AZURE_ENV_LOG_ANALYTICS_WORKSPACE_ID '<Existing Log Analytics Worksp
 Replace `<Existing Log Analytics Workspace Id>` with the value obtained from Step 3.
 
 ### 5. Continue Deployment
-Proceed with the next steps in the [deployment guide](/docs/DeploymentGuide.md#deployment-options--steps).
+Proceed with the next steps in the [deployment guide](/docs/DeploymentGuide.md#deployment-steps).

docs/images/re_use_foundry_project/project_resource_id.png

@@ -74,7 +74,7 @@
 # Add this near the top of your app.py, after initializing the app
 app.add_middleware(
     CORSMiddleware,
-    allow_origins=[frontend_url],
+    allow_origins=[frontend_url],  # Allow all origins for development; restrict in production
     allow_credentials=True,
     allow_methods=["*"],
     allow_headers=["*"],
@@ -269,7 +269,7 @@ async def input_task_endpoint(input_task: InputTask, request: Request):
         if "Rate limit is exceeded" in error_msg:
             match = re.search(r"Rate limit is exceeded\. Try again in (\d+) seconds?\.", error_msg)
             if match:
-                error_msg = f"Rate limit is exceeded. Try again in {match.group(1)} seconds."
+                error_msg = "Application temporarily unavailable due to quota limits. Please try again later."
 
         track_event_if_configured(
             "InputTaskError",
@@ -279,7 +279,7 @@ async def input_task_endpoint(input_task: InputTask, request: Request):
                 "error": str(e),
             },
         )
-        raise HTTPException(status_code=400, detail=f"Error creating plan: {error_msg}") from e
+        raise HTTPException(status_code=400, detail=f"{error_msg}") from e
 
 
 @app.post("/api/human_feedback")

docs/re-use-foundry-project.md

@@ -8,6 +8,7 @@ dependencies = [
     "azure-ai-evaluation>=1.5.0",
     "azure-ai-inference>=1.0.0b9",
     "azure-ai-projects>=1.0.0b9",
+    "azure-ai-agents>=1.2.0b1",
     "azure-cosmos>=4.9.0",
     "azure-identity>=1.21.0",
     "azure-monitor-events-extension>=0.1.0",