clean output

Author: ahibrahimm

scenarios/evaluate/Supported_Evaluation_Metrics/Agent_Evaluation/Evaluate_SK_Chat_Completion_Agent.ipynb

@@ -3,11 +3,7 @@
   {
    "cell_type": "markdown",
    "id": "bf5280e2",
-   "metadata": {
-    "vscode": {
-     "languageId": "plaintext"
-    }
-   },
+   "metadata": {},
    "source": [
     "# Evaluate Semantic Kernel AI (ChatCompletion) Agents in Azure AI Foundry"
    ]
@@ -66,11 +62,7 @@
   {
    "cell_type": "markdown",
    "id": "ba1d6576",
-   "metadata": {
-    "vscode": {
-     "languageId": "plaintext"
-    }
-   },
+   "metadata": {},
    "source": [
     "### Create a AzureChatCompletion service - [reference](https://learn.microsoft.com/en-us/semantic-kernel/concepts/ai-services/chat-completion/?tabs=csharp-AzureOpenAI%2Cpython-AzureOpenAI%2Cjava-AzureOpenAI&pivots=programming-language-python)"
    ]
@@ -106,6 +98,7 @@
     "from semantic_kernel.functions import kernel_function\n",
     "from typing import Annotated\n",
     "\n",
+    "\n",
     "# This is a sample plugin that provides tools\n",
     "class MenuPlugin:\n",
     "    \"\"\"A sample Menu Plugin used for the concept sample.\"\"\"\n",
@@ -122,13 +115,13 @@
     "    def get_item_price(\n",
     "        self, menu_item: Annotated[str, \"The name of the menu item.\"]\n",
     "    ) -> Annotated[str, \"Returns the price of the menu item.\"]:\n",
-    "        _ = menu_item # This is just to simulate a function that uses the input.\n",
+    "        _ = menu_item  # This is just to simulate a function that uses the input.\n",
     "        return \"$9.99\""
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "d6abead3",
    "metadata": {},
    "outputs": [],
@@ -146,29 +139,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "id": "3b7b9ba3",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "## User: Hello\n",
-      "## Chef: Hello! How can I assist you today? If you have any questions about the menu, feel free to ask!\n",
-      "\n",
-      "## User: What is the special drink today?\n",
-      "## Chef: The special drink today is Chai Tea. Would you like to know more about other specials or the menu?\n",
-      "\n",
-      "## User: What does that cost?\n",
-      "## Chef: The Chai Tea costs $9.99. Is there anything else you would like to know?\n",
-      "\n",
-      "## User: Thank you\n",
-      "## Chef: You're welcome! If you have any more questions or need assistance in the future, feel free to ask. Enjoy your day!\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "thread = None\n",
     "\n",
@@ -199,74 +173,47 @@
    "execution_count": null,
    "id": "fcd6ac41",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Available turn indices: [0, 1, 2, 3]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from azure.ai.evaluation import SKAgentConverter\n",
     "\n",
-    "# Get the avaiable turn indices for the thread, \n",
+    "# Get the avaiable turn indices for the thread,\n",
     "# useful for selecting a specific turn for evaluation\n",
     "turn_indices = await SKAgentConverter._get_thread_turn_indices(thread=thread)\n",
     "print(f\"Available turn indices: {turn_indices}\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "id": "d1d4ae12",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Class SKAgentConverter: This is an experimental class, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "converter = SKAgentConverter()\n",
     "\n",
     "# Get a single agent run data\n",
     "evaluation_data_single_run = await converter.convert(\n",
     "    thread=thread,\n",
-    "    turn_index=2, # Specify the turn index you want to evaluate\n",
-    "    agent=agent # Pass it to include the instructions and plugins in the evaluation data\n",
+    "    turn_index=2,  # Specify the turn index you want to evaluate\n",
+    "    agent=agent,  # Pass it to include the instructions and plugins in the evaluation data\n",
     ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "id": "7813b5eb",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "4"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "import json\n",
     "\n",
     "file_name = \"evaluation_data.jsonl\"\n",
     "# Save the agent thread data to a JSONL file (all turns)\n",
     "evaluation_data = await converter.prepare_evaluation_data(threads=[thread], filename=file_name, agent=agent)\n",
     "# print(json.dumps(evaluation_data, indent=4))\n",
-    "len(evaluation_data) # number of turns in the thread"
+    "len(evaluation_data)  # number of turns in the thread"
    ]
   },
   {
@@ -288,23 +235,7 @@
    "execution_count": null,
    "id": "e6ee09df",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Class IntentResolutionEvaluator: This is an experimental class, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Class ToolCallAccuracyEvaluator: This is an experimental class, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.\n",
-      "Class TaskAdherenceEvaluator: This is an experimental class, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import os\n",
     "from pprint import pprint\n",
@@ -332,29 +263,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "id": "80bd50ff",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{\n",
-      "    \"tool_call_accuracy\": 1.0,\n",
-      "    \"tool_call_accuracy_result\": \"pass\",\n",
-      "    \"tool_call_accuracy_threshold\": 0.8,\n",
-      "    \"per_tool_call_details\": [\n",
-      "        {\n",
-      "            \"tool_call_accurate\": true,\n",
-      "            \"tool_call_accurate_reason\": \"The tool call is relevant to the user's request for the price of the special drink, has appropriate parameters that match the tool definition, and uses a parameter value that is present in the conversation. Therefore, it is likely to help resolve the user's need.\",\n",
-      "            \"tool_call_id\": \"call_4aGdX7zYtMV28tjngPTTvyXx\"\n",
-      "        }\n",
-      "    ]\n",
-      "}\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Test a single evaluation run\n",
     "evaluator = ToolCallAccuracyEvaluator(model_config=model_config)\n",
@@ -437,8 +349,7 @@
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "pygments_lexer": "ipython3"
   }
  },
  "nbformat": 4,