Practical_task_1.ipynb

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   "cell_type": "markdown",
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   "source": [
    "# 1.1 🚀 Initialization and Configuration\n",
    "\n",
    "* In this section, necessary libraries are imported, and the Semantic Kernel is configured with Azure OpenAI credentials loaded from a configuration file."
   ]
  },
  {
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   "source": [
    "#!import config/Settings.cs"
   ]
  },
  {
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   "source": [
    "// Import Semantic Kernel\n",
    "#r \"nuget: Microsoft.SemanticKernel, 1.0.0-rc3\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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     "kernelName": "csharp"
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   },
   "outputs": [],
   "source": [
    "using Microsoft.SemanticKernel;\n",
    "\n",
    "var builder = new KernelBuilder();"
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "using Kernel = Microsoft.SemanticKernel.Kernel;\n",
    "\n",
    "var (model1, azureEndpoint1, apiKey1, gpt35TurboServiceId) = Settings.LoadFromFile(model: \"gpt-35-turbo\");\n",
    "builder.AddAzureOpenAIChatCompletion(model1, model1, azureEndpoint1, apiKey1, serviceId: gpt35TurboServiceId);\n",
    "\n",
    "var (model2, azureEndpoint2, apiKey2, gpt4ServiceId) = Settings.LoadFromFile(model: \"gpt-4-32k\");\n",
    "builder.AddAzureOpenAIChatCompletion(model2, model2, azureEndpoint2, apiKey2, serviceId: gpt4ServiceId);\n",
    "\n",
    "var kernel = builder.Build();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1.2 🚀 Semantic Function for Short Intent Extraction\n",
    "* This section focuses on setting up the Semantic Function for extracting a concise user intent using a specific prompt."
   ]
  },
  {
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   "source": [
    "using Microsoft.SemanticKernel.TemplateEngine;\n",
    "using Microsoft.SemanticKernel.Connectors.AI.OpenAI;\n",
    "using Microsoft.SemanticKernel.Connectors;\n",
    "using Microsoft.SemanticKernel.AI;\n",
    "using Microsoft.SemanticKernel.AI.ChatCompletion;\n",
    "\n",
    "\n",
    "var aiRequestSettings = new OpenAIPromptExecutionSettings \n",
    "{\n",
    "    ExtensionData = new Dictionary<string, object> { { \"api-version\", \"2023-03-15-preview\" } },\n",
    "    ServiceId = gpt35TurboServiceId\n",
    "};"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   },
   "outputs": [],
   "source": [
    "var input = \"I want to find top-10 books about world history\";"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "string skPrompt = @\"ChatBot: How can I help you?\n",
    "User: {{$input}}\n",
    "\n",
    "---------------------------------------------\n",
    "\n",
    "Return data requested by user: \";\n",
    "\n",
    "var getShortIntentFunction  = kernel.CreateFunctionFromPrompt(skPrompt, aiRequestSettings);\n",
    "\n",
    "var intentResult = await kernel.InvokeAsync(getShortIntentFunction, new KernelArguments(input));\n",
    "\n",
    "Console.WriteLine(intentResult);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1.3 🚀 Semantic Function for Conversational Chat\n",
    "\n",
    "* This section defines the Semantic Function for engaging in a more extended conversation with the chatbot, initializing the context, and executing the function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "skPrompt = @\"\n",
    "ChatBot can have a conversation with you about any topic.\n",
    "It can give detailed answer or say 'I don't know' if it does not have an answer.\n",
    "\n",
    "{{$history}}\n",
    "User: {{$userInput}}\n",
    "ChatBot:\";"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Chat with the chatbot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "dotnet_interactive": {
     "language": "csharp"
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     "kernelName": "csharp"
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     "languageId": "polyglot-notebook"
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   },
   "outputs": [],
   "source": [
    "var chatHistory = new ChatHistory(\"You are a librarian, expert about books\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "dotnet_interactive": {
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   },
   "outputs": [],
   "source": [
    "var message = chatHistory.Last();\n",
    "Console.WriteLine($\"{message.Role}: {message.Content}\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": [
    "chatHistory.AddUserMessage(\"Hi, I'm looking for book suggestions\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "dotnet_interactive": {
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   "outputs": [],
   "source": [
    "message = chatHistory.Last();\n",
    "Console.WriteLine($\"{message.Role}: {message.Content}\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   },
   "outputs": [],
   "source": [
    "IChatCompletionService chatService = kernel.GetRequiredService<IChatCompletionService>(gpt35TurboServiceId);\n",
    "\n",
    "var reply = await chatService.GetChatMessageContentAsync(chatHistory);\n",
    "\n",
    "Console.WriteLine(reply);\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": [
    "var message = chatHistory.Last();\n",
    "Console.WriteLine($\"{message.Role}: {message.Content}\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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   "outputs": [],
   "source": [
    "chatHistory.AddSystemMessage(reply);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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     "language": "csharp"
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   },
   "outputs": [],
   "source": [
    "var message = chatHistory.Last();\n",
    "Console.WriteLine($\"{message.Role}: {message.Content}\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1.4 🚀 Interactive Chat Function\n",
    "\n",
    "* In this final section, an interactive chat function is defined, and several interactions with the chatbot are simulated. The complete chat history and the final user input are then printed. *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": [
    "Func<string, Task> Chat = async (string input) => {\n",
    "    // Save new message in the context variables\n",
    "    chatHistory.AddUserMessage(input);\n",
    "\n",
    "    var reply = await chatService.GetChatMessageContentAsync(chatHistory);\n",
    "\n",
    "    chatHistory.AddSystemMessage(reply);\n",
    "    \n",
    "    Console.WriteLine(reply);\n",
    "};"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "dotnet_interactive": {
     "language": "csharp"
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   },
   "outputs": [],
   "source": [
    "await Chat(\"I would like a non-fiction book suggestion about Greece history. Please only list one book.\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "dotnet_interactive": {
     "language": "csharp"
    },
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     "kernelName": "csharp"
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    "vscode": {
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    }
   },
   "outputs": [],
   "source": [
    "await Chat(\"that sounds interesting, what are some of the topics I will learn about?\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "dotnet_interactive": {
     "language": "csharp"
    },
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     "kernelName": "csharp"
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    "vscode": {
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   },
   "outputs": [],
   "source": [
    "await Chat(\"Which topic from the ones you listed do you think the most popular?\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "dotnet_interactive": {
     "language": "csharp"
    },
    "polyglot_notebook": {
     "kernelName": "csharp"
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    "vscode": {
     "languageId": "polyglot-notebook"
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   },
   "outputs": [],
   "source": [
    "foreach (var message in chatHistory)\n",
    "{\n",
    "    Console.WriteLine($\"{message.Role}: {message.Content}\");\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1.5 🚀 Switching between models\n",
    "\n",
    "* In this final section, an interactive chat function is defined, and several interactions with the chatbot are simulated. The complete chat history and the final user input are then printed. *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Call HuggingFace model by name:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "dotnet_interactive": {
     "language": "csharp"
    },
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     "kernelName": "csharp"
    },
    "vscode": {
     "languageId": "polyglot-notebook"
    }
   },
   "outputs": [],
   "source": [
    "#r \"nuget: Microsoft.SemanticKernel.Connectors.AI.HuggingFace, 1.0.0-rc3\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "dotnet_interactive": {
     "language": "csharp"
    },
    "polyglot_notebook": {
     "kernelName": "csharp"
    },
    "vscode": {
     "languageId": "polyglot-notebook"
    }
   },
   "outputs": [],
   "source": [
    "using Kernel = Microsoft.SemanticKernel.Kernel;\n",
    "\n",
    "var (model, _, apiKey, _) = Settings.LoadFromFile(model: \"google/flan-t5-xxl\");\n",
    "\n",
    "#pragma warning disable SKEXP0020\n",
    "builder.AddHuggingFaceTextGeneration(model, apiKey: apiKey);\n",
    "\n",
    "\n",
    "var kernel = builder.Build();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "dotnet_interactive": {
     "language": "csharp"
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     "kernelName": "csharp"
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    "vscode": {
     "languageId": "polyglot-notebook"
    }
   },
   "outputs": [],
   "source": [
    "var input = \"I want to find top-10 books about world history\";"
   ]
  },
  {
   "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": [
    "string skPrompt = @\"ChatBot: How can I help you?\n",
    "User: {{$input}}\n",
    "\n",
    "---------------------------------------------\n",
    "\n",
    "Return data requested by user: \";\n",
    "\n",
    "var getShortIntentFunction  = kernel.CreateFunctionFromPrompt(skPrompt);\n",
    "\n",
    "var intentResult = await kernel.InvokeAsync(getShortIntentFunction, new KernelArguments(input));\n",
    "\n",
    "Console.WriteLine(intentResult);"
   ]
  }
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