Basic_Samples/Chat/dotnet/csharp/Chat_with_your_own_data.ipynb

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   "source": [
    "# Azure chat completion models with your own data (preview)\n",
    "This example shows how to use Azure OpenAI service models with your own data. The feature is currently in preview. \n",
    "\n",
    "Azure OpenAI on your data enables you to run supported chat models such as GPT-3.5-Turbo and GPT-4 on your data without needing to train or fine-tune models. Running models on your data enables you to chat on top of, and analyze your data with greater accuracy and speed. One of the key benefits of Azure OpenAI on your data is its ability to tailor the content of conversational AI. Because the model has access to, and can reference specific sources to support its responses, answers are not only based on its pretrained knowledge but also on the latest information available in the designated data source. This grounding data also helps the model avoid generating responses based on outdated or incorrect information.\n",
    "\n",
    "Azure OpenAI on your own data with Azure Cognitive Search provides a customizable, pre-built solution for knowledge retrieval, from which a conversational AI application can be built. To see alternative methods for knowledge retrieval and semantic search, check out the cookbook examples for [vector databases](https://github.com/openai/openai-cookbook/tree/main/examples/vector_databases)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How it works\n",
    "\n",
    "[Azure OpenAI on your own data](https://learn.microsoft.com/azure/ai-services/openai/concepts/use-your-data) connects the model with your data, giving it the ability to retrieve and utilize data in a way that enhances the model's output. Together with Azure Cognitive Search, data is retrieved from designated data sources based on the user input and provided conversation history. The data is then augmented and resubmitted as a prompt to the model, giving the model contextual information it can use to generate a response.\n",
    "\n",
    "See the [Data, privacy, and security for Azure OpenAI Service](https://learn.microsoft.com/legal/cognitive-services/openai/data-privacy?context=%2Fazure%2Fai-services%2Fopenai%2Fcontext%2Fcontext) for more information."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Chat completion model with your own data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setting the context"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this example, we want our model to base its responses on Azure AI services documentation data. Following the [Quickstart](https://learn.microsoft.com/azure/ai-services/openai/use-your-data-quickstart?tabs=command-line&pivots=programming-language-studio) shared previously, we have added the [markdown](https://github.com/MicrosoftDocs/azure-docs/blob/main/articles/ai-services/cognitive-services-and-machine-learning.md) file for the [Azure AI services and machine learning](https://learn.microsoft.com/azure/ai-services/cognitive-services-and-machine-learning) documentation page to our search index. The model is now ready to answer questions about Azure AI services and machine learning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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   "outputs": [],
   "source": [
    "#r \"nuget: Azure.AI.OpenAI, 1.0.0-beta.14\"\n",
    "#r \"nuget:Microsoft.DotNet.Interactive.AIUtilities, 1.0.0-beta.24129.1\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "dotnet_interactive": {
     "language": "csharp"
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    "polyglot_notebook": {
     "kernelName": "csharp"
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    "vscode": {
     "languageId": "polyglot-notebook"
    }
   },
   "outputs": [],
   "source": [
    "using Microsoft.DotNet.Interactive;\n",
    "using Microsoft.DotNet.Interactive.AIUtilities;\n",
    "using Azure;\n",
    "using Azure.AI.OpenAI;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run this cell, it will prompt you for the apiKey, endPoint, chatDeployment, Azure Cognitive Search Key, search enpoint, and search index name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "dotnet_interactive": {
     "language": "csharp"
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    "polyglot_notebook": {
     "kernelName": "csharp"
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    "vscode": {
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   },
   "outputs": [],
   "source": [
    "var azureOpenAIKey = await Kernel.GetPasswordAsync(\"Provide your OPEN_AI_KEY\");\n",
    "\n",
    "// Your endpoint should look like the following https://YOUR_OPEN_AI_RESOURCE_NAME.openai.azure.com/\n",
    "var azureOpenAIEndpoint = await Kernel.GetInputAsync(\"Provide the OPEN_AI_ENDPOINT\");\n",
    "\n",
    "// Enter the deployment name you chose when you deployed the model.\n",
    "var chatDeployment = await Kernel.GetInputAsync(\"Provide chat deployment name\");\n",
    "\n",
    "var searchKey = await Kernel.GetPasswordAsync(\"Provide the search Key\");\n",
    "\n",
    "var searchEndpoint = await Kernel.GetInputAsync(\"Provide the search endpoint\");\n",
    "\n",
    "var searchIndex = await Kernel.GetInputAsync(\"Provide the search index name\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "dotnet_interactive": {
     "language": "csharp"
    },
    "polyglot_notebook": {
     "kernelName": "csharp"
    },
    "vscode": {
     "languageId": "polyglot-notebook"
    }
   },
   "outputs": [],
   "source": [
    "OpenAIClient client = new (new Uri(azureOpenAIEndpoint), new AzureKeyCredential(azureOpenAIKey.GetClearTextPassword()));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "dotnet_interactive": {
     "language": "csharp"
    },
    "polyglot_notebook": {
     "kernelName": "csharp"
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    "vscode": {
     "languageId": "polyglot-notebook"
    }
   },
   "outputs": [],
   "source": [
    "var contosoExtensionConfig = new AzureCognitiveSearchChatExtensionConfiguration()\n",
    "{\n",
    "    SearchEndpoint = new Uri(searchEndpoint),\n",
    "    IndexName = searchIndex,\n",
    "};\n",
    "\n",
    "contosoExtensionConfig.SetSearchKey(searchKey.GetClearTextPassword());"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "dotnet_interactive": {
     "language": "csharp"
    },
    "polyglot_notebook": {
     "kernelName": "csharp"
    },
    "vscode": {
     "languageId": "polyglot-notebook"
    }
   },
   "outputs": [],
   "source": [
    " var chatCompletionsOptions = new ChatCompletionsOptions()\n",
    "            {\n",
    "                DeploymentName = chatDeployment,\n",
    "                Messages =\n",
    "                {\n",
    "                    new ChatMessage(\n",
    "                        ChatRole.System,\n",
    "                        \"You are a helpful assistant that answers questions about the Contoso product database.\"),\n",
    "                    new ChatMessage(ChatRole.User, \"What are the best-selling Contoso products this month?\")\n",
    "                },\n",
    "\n",
    "                // The addition of AzureChatExtensionsOptions enables the use of Azure OpenAI capabilities that add to\n",
    "                // the behavior of Chat Completions, here the \"using your own data\" feature to supplement the context\n",
    "                // with information from an Azure Cognitive Search resource with documents that have been indexed.\n",
    "                AzureExtensionsOptions = new AzureChatExtensionsOptions()\n",
    "                {\n",
    "                    Extensions = { contosoExtensionConfig }\n",
    "                }\n",
    "            };\n",
    "\n",
    "var response = await client.GetChatCompletionsAsync(chatCompletionsOptions);\n",
    "\n",
    "var message = response.Value.Choices[0].Message;\n",
    "\n",
    "// The final, data-informed response still appears in the ChatMessages as usual\n",
    "Console.WriteLine($\"{message.Role}: {message.Content}\");\n",
    "\n",
    "// Responses that used extensions will also have Context information that includes special Tool messages\n",
    "// to explain extension activity and provide supplemental information like citations.\n",
    "Console.WriteLine($\"Citations and other information:\");\n",
    "\n",
    "foreach (var contextMessage in message.AzureExtensionsContext.Messages)\n",
    "{\n",
    "    // Note: citations and other extension payloads from the \"tool\" role are often encoded JSON documents\n",
    "    // and need to be parsed as such; that step is omitted here for brevity.\n",
    "    Console.WriteLine($\"{contextMessage.Role}: {contextMessage.Content}\");\n",
    "}\n"
   ]
  }
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