wehub-resource-sync
98 lines
3.9 KiB
C#
98 lines
3.9 KiB
C#
// Copyright (c) Microsoft. All rights reserved.
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using Microsoft.SemanticKernel;
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using Microsoft.SemanticKernel.ChatCompletion;
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using Microsoft.SemanticKernel.Connectors.OpenAI;
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namespace ChatCompletion;
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/// <summary>
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/// This example shows a way of using OpenAI connector with other APIs that supports the same ChatCompletion API standard from OpenAI.
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/// <list type="number">
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/// <item>Install LMStudio Platform in your environment (As of now: 0.3.10)</item>
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/// <item>Open LM Studio</item>
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/// <item>Search and Download Llama2 model or any other</item>
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/// <item>Update the modelId parameter with the model llm name loaded (i.e: llama-2-7b-chat)</item>
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/// <item>Start the Local Server on http://localhost:1234</item>
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/// <item>Run the examples</item>
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/// </list>
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/// </summary>
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public class LMStudio_ChatCompletionStreaming(ITestOutputHelper output) : BaseTest(output)
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{
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/// <summary>
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/// Sample showing how to use <see cref="IChatCompletionService"/> streaming directly with a <see cref="ChatHistory"/>.
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/// </summary>
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[Fact]
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public async Task UsingServiceStreamingWithLMStudio()
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{
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Console.WriteLine($"======== LM Studio - Chat Completion - {nameof(UsingServiceStreamingWithLMStudio)} ========");
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var modelId = "llama-2-7b-chat"; // Update the modelId if you chose a different model.
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var endpoint = new Uri("http://localhost:1234/v1"); // Update the endpoint if you chose a different port.
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var kernel = Kernel.CreateBuilder()
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.AddOpenAIChatCompletion(
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modelId: modelId,
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apiKey: null,
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endpoint: endpoint)
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.Build();
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OpenAIChatCompletionService chatCompletionService = new(modelId: modelId, apiKey: null, endpoint: endpoint);
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Console.WriteLine("Chat content:");
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Console.WriteLine("------------------------");
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var chatHistory = new ChatHistory("You are a librarian, expert about books");
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OutputLastMessage(chatHistory);
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// First user message
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chatHistory.AddUserMessage("Hi, I'm looking for book suggestions");
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OutputLastMessage(chatHistory);
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// First assistant message
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await StreamMessageOutputAsync(chatCompletionService, chatHistory, AuthorRole.Assistant);
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// Second user message
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chatHistory.AddUserMessage("I love history and philosophy, I'd like to learn something new about Greece, any suggestion?");
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OutputLastMessage(chatHistory);
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// Second assistant message
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await StreamMessageOutputAsync(chatCompletionService, chatHistory, AuthorRole.Assistant);
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}
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/// <summary>
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/// This example shows how to setup LMStudio to use with the Kernel InvokeAsync (Streaming).
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/// </summary>
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[Fact]
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public async Task UsingKernelStreamingWithLMStudio()
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{
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Console.WriteLine($"======== LM Studio - Chat Completion - {nameof(UsingKernelStreamingWithLMStudio)} ========");
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var modelId = "llama-2-7b-chat"; // Update the modelId if you chose a different model.
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var endpoint = new Uri("http://localhost:1234/v1"); // Update the endpoint if you chose a different port.
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var kernel = Kernel.CreateBuilder()
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.AddOpenAIChatCompletion(
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modelId: modelId,
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apiKey: null,
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endpoint: endpoint)
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.Build();
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var prompt = @"Rewrite the text between triple backticks into a business mail. Use a professional tone, be clear and concise.
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Sign the mail as AI Assistant.
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Text: ```{{$input}}```";
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var mailFunction = kernel.CreateFunctionFromPrompt(prompt, new OpenAIPromptExecutionSettings
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{
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TopP = 0.5,
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MaxTokens = 1000,
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});
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await foreach (var word in kernel.InvokeStreamingAsync(mailFunction, new() { ["input"] = "Tell David that I'm going to finish the business plan by the end of the week." }))
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{
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Console.WriteLine(word);
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}
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}
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}
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