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docs: Updated Workshop folder: Clearer Docs, Refactored Plugin, and Better Quota Instructions
docs: Updated Workshop folder: Clearer Docs, Refactored Plugin, and Better Quota Instructions
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workshop/docs/workshop/.env.sample

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AZURE_AI_SEARCH_API_KEY=""
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AZURE_OPENAI_API_KEY=""
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workshop/docs/workshop/Challenge-3-and-4/knowledge_mining_api.ipynb

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"outputs": [],
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"source": [
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"\"\"\"\n",
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"The following sample demonstrates how to create an Azure AI agent that answers\n",
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"questions about a sample menu using a Semantic Kernel Plugin.\n",
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"The following sample demonstrates how to create an Azure AI agent that answers \n",
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"questions about conversational data using a Semantic Kernel Plugin.\n",
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"\"\"\"\n",
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"\n",
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"async def get_db_connection():\n",
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" cursor.close()\n",
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" conn.close()\n",
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"\n",
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"# Define a sample plugin for the sample\n",
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"class MenuPlugin:\n",
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"# Define a chat with data plugin for the conversational data\n",
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"class ChatWithDataPlugin:\n",
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" def __init__(self):\n",
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" self.azure_openai_deployment_model = os.getenv(\"AZURE_OPEN_AI_DEPLOYMENT_MODEL\")\n",
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" self.azure_openai_endpoint = os.getenv(\"AZURE_OPEN_AI_ENDPOINT\")\n",
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" self.use_ai_project_client = os.getenv(\"USE_AI_PROJECT_CLIENT\", \"False\").lower() == \"true\"\n",
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" self.azure_ai_project_conn_string = os.getenv(\"AZURE_AI_PROJECT_CONN_STRING\")\n",
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" \n",
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" \"\"\"A sample Menu Plugin used for the concept sample.\"\"\"\n",
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"\n",
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" \n",
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" @kernel_function(name=\"Greeting\",\n",
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" description=\"Respond to any greeting or general questions\")\n",
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" )\n",
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" sql_query = completion.choices[0].message.content\n",
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" sql_query = sql_query.replace(\"```sql\", '').replace(\"```\", '')\n",
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" print(\"SQL Query: \", sql_query, flush=True)\n",
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" # print(\"SQL Query: \", sql_query, flush=True)\n",
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"\n",
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" answer = await execute_sql_query(sql_query)\n",
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" answer = answer[:20000] if len(answer) > 20000 else answer\n",
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" agent = AzureAIAgent(\n",
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" client=client,\n",
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" definition=agent_definition,\n",
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" plugins=[MenuPlugin()], # Add the plugin to the agent\n",
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" plugins=[ChatWithDataPlugin()], # Add the plugin to the agent\n",
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" )\n",
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"\n",
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" # 3. Create a thread for the agent\n",
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" await thread.delete() if thread else None\n",
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" await client.agents.delete_agent(agent.id)\n",
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"\n",
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" \"\"\"\n",
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" Sample Output:\n",
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" # User: Hello\n",
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" # Agent: Hello! How can I assist you today?\n",
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" # User: What is the special soup?\n",
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" # ...\n",
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" \"\"\"\n",
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"\n",
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"\n",
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"if __name__ == \"__main__\":\n",
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" await main()"
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]

workshop/docs/workshop/Challenge-5/docs/create_azure_ai_service.md

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# How To Create Azure AI Service
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1. Navigate to https://aka.ms/CognitiveServicesAIServices .
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1. Navigate to https://portal.azure.com/#create/Microsoft.CognitiveServicesAIServices .
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2. Select your Azure subscription.
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3. Select the available Resource group.
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4. Choose the region which support Content Understanding service from below tables.
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## How to Check & Update Quota
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1. **Navigate** to the [Azure AI Foundry portal](https://ai.azure.com/).
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2. **Select** the AI Project associated with this accelerator.
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3. **Go to** the `Management Center` from the bottom-left navigation menu.
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4. Select `Quota`
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- Click on the `GlobalStandard` dropdown.
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- Select the required **GPT model** (`GPT-4, GPT-4o, GPT-4o Mini`) or **Embeddings model** (`text-embedding-ada-002`).
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- Choose the **region** where the deployment is hosted.
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5. Request More Quota or delete any unused model deployments as needed.
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1. Go to the [Azure Portal](https://portal.azure.com).
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2. In the **search bar**, type the name of the **Resource Group** you created during **Challenge 1**.
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3. Within the resource group, look for the **Azure AI services** ending in -aiservices.
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4. In the AI services, Click on **Go to Azure AI Foundry portal**.
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4. **Navigate** to `Shared resources` in the bottom-left menu.
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---
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### 🔍 To Check Quota
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- Click on the `Quota` tab.
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- In the `GlobalStandard` dropdown:
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- Select the desired model (e.g., **GPT-4**, **GPT-4o**, **GPT-4o Mini**, or **text-embedding-ada-002**).
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- Choose the **region** where your deployment is hosted.
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- You can:
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**Request more quota**, or **Delete unused deployments** to free up capacity.
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---
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### ✏️ To Update Quota
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- Go to the `Deployments` tab.
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- Select the deployment of the desired model.
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- Click **Edit**, update the **Tokens per Minute (TPM) Rate Limit**, then **Submit Changes**.

workshop/docs/workshop/support-docs/quota_check.md

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Use one of the following scripts based on your needs:
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- **`quota_check_params.sh`** → If you **know the model and capacity** required.
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- **`quota_check_all_regions.sh`** → If you **want to check available capacity across all regions** for supported models.
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---
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## **If using Azure Portal and Cloud Shell**
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chmod +x quota_check_params.sh
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./quota_check_params.sh <model_name:capacity> [<model_region>] (e.g., gpt-4o-mini:30,text-embedding-ada-002:20 eastus)
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```
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**To check available quota across all regions for supported models:**
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```sh
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curl -L -o quota_check_all_regions.sh "https://raw.githubusercontent.com/microsoft/Conversation-Knowledge-Mining-Solution-Accelerator/main/infra/scripts/quota_check_all_regions.sh"
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chmod +x quota_check_all_regions.sh
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./quota_check_all_regions.sh
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```
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## **If using VS Code or Codespaces**
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./quota_check_params.sh <model_name:capacity> [<model_region>] (e.g., gpt-4o-mini:30,text-embedding-ada-002:20 eastus)
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```
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**To check available quota across all regions for supported models:**
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```sh
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./quota_check_all_regions.sh
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```
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2. If you see the error `_bash: az: command not found_`, install Azure CLI:
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```sh

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