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using System.Net.Sockets;
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using System.Threading.Tasks;
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using OpenAI;
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using OpenAI.Responses;
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namespace OllamaStudy.UseExtensionsAI;
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/// <summary>
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/// Ollama兼容OpenAI接口，可以直接使用OpenAI的SDK调用
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/// </summary>
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public class OpenAISdkTest
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{
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private ITestOutputHelper _output;
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private IOptionsMonitor<OllamaServerOption> _ollamaOptionsMonitor;
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private OpenAIClient _defaultOpenAIClient;
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private ChatClient _singtonChatClient;
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public OpenAISdkTest
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(
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ITestOutputHelper outputHelper,
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OpenAIClient defaultOpenAIClient,
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IOptionsMonitor<OllamaServerOption> ollamaOptionsMonitor,
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//使用了FromKeyedServices特性，所以需要使用IKeyedServiceCollection注册服务
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[FromKeyedServices("OpenAIChatClient")]ChatClient singtonChatClient
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)
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{
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_output = outputHelper;
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_defaultOpenAIClient = defaultOpenAIClient;
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_ollamaOptionsMonitor = ollamaOptionsMonitor;
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_singtonChatClient = singtonChatClient;
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}
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#region 使用客户端库
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/// <summary>
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/// 从OpenAIClient获取各种业务Client
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/// </summary>
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[Fact]
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public void GetClients_Test()
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{
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#pragma warning disable OPENAI001
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Assert.NotNull(_defaultOpenAIClient);
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//音频客户端
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var audioClient = _defaultOpenAIClient.GetAudioClient(_ollamaOptionsMonitor.CurrentValue.Model);
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Assert.NotNull(audioClient);
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//聊天客户端
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var chatClient = _defaultOpenAIClient.GetChatClient(_ollamaOptionsMonitor.CurrentValue.Model);
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Assert.NotNull(chatClient);
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//嵌入客户端
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var embeddingClient = _defaultOpenAIClient.GetEmbeddingClient(_ollamaOptionsMonitor.CurrentValue.Model);
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Assert.NotNull(embeddingClient);
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//图像客户端
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var imageClient = _defaultOpenAIClient.GetImageClient(_ollamaOptionsMonitor.CurrentValue.Model);
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Assert.NotNull(imageClient);
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//微调客户端
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var moderationClient = _defaultOpenAIClient.GetModerationClient(_ollamaOptionsMonitor.CurrentValue.Model);
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Assert.NotNull(moderationClient);
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//文件客户端
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var openAIFileClient = _defaultOpenAIClient.GetOpenAIFileClient();
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Assert.NotNull(openAIFileClient);
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//模型客户端
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var modelClient = _defaultOpenAIClient.GetOpenAIModelClient();
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Assert.NotNull(modelClient);
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//助手客户端(仅评估)
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var assistantClient = _defaultOpenAIClient.GetAssistantClient();
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Assert.NotNull(assistantClient);
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//批量客户端(仅评估)
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var batchClient = _defaultOpenAIClient.GetBatchClient();
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Assert.NotNull(batchClient);
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//评估客户端(仅评估)
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var evaluationClient = _defaultOpenAIClient.GetEvaluationClient();
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Assert.NotNull(evaluationClient);
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//微调客户端(仅评估)
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var FineTuningClient = _defaultOpenAIClient.GetFineTuningClient();
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Assert.NotNull(FineTuningClient);
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//响应客户端(仅评估)
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var openAIResponseClient = _defaultOpenAIClient.GetOpenAIResponseClient(_ollamaOptionsMonitor.CurrentValue.Model);
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Assert.NotNull(openAIResponseClient);
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//实时客户端(仅评估)
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#pragma warning disable OPENAI002
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var realtimeClient = _defaultOpenAIClient.GetRealtimeClient();
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Assert.NotNull(realtimeClient);
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#pragma warning restore OPENAI002
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//向量存储客户端(仅评估)
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var vectorStoreClient = _defaultOpenAIClient.GetVectorStoreClient();
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Assert.NotNull(vectorStoreClient);
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#pragma warning restore OPENAI001
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}
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/// <summary>
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/// 自定义URL和API密钥
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/// </summary>
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[Fact]
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public void Custom_OpenAIClient_Test()
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{
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var option = new OpenAIClientOptions()
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{
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OrganizationId = "TianyiJituan",
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ProjectId = "StudyProject",
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Endpoint = new Uri("http://localhost:11434/v1")
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};
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//本地Ollama服务，不需要API密钥(随便填写)
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var openAIClient = new OpenAIClient(new ApiKeyCredential("nokey"), option);
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var chatClient = openAIClient.GetChatClient(_ollamaOptionsMonitor.CurrentValue.Model);
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Assert.NotNull(openAIClient);
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Assert.NotNull(chatClient);
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}
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/// <summary>
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/// 自定义URL和API密钥
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/// </summary>
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[Fact]
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public void Custom_ChatClient_Test()
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{
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var option = new OpenAIClientOptions()
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{
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OrganizationId = "TianyiJituan",
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ProjectId = "StudyProject",
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UserAgentApplicationId = "StudyAgentApp",
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Endpoint = new Uri("http://localhost:11434/v1"),
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};
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var chatClient = new ChatClient(_ollamaOptionsMonitor.CurrentValue.Model,new ApiKeyCredential("nokey"),option);
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Assert.NotNull(chatClient);
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}
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/// <summary>
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/// 使用异步API
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/// 每个客户端方法在同一客户端类中都有一个异步变体
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/// </summary>
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[Fact]
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public async Task UseAsyncAPI_Test()
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{
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ChatClient chatClient = _defaultOpenAIClient.GetChatClient(_ollamaOptionsMonitor.CurrentValue.Model);
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ClientResult<ChatCompletion> result = await chatClient.CompleteChatAsync("你好，请问河南的省会是什么？");
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var responseText = result.Value.Content.First().Text;
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_output.WriteLine(responseText);
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Assert.NotNull(result);
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Assert.Contains("郑州",responseText);
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}
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#endregion
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#region 如何使用依赖注入
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/// <summary>
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/// OpenAI 客户端是线程安全的。可以在DI中安全地注册为单例.
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/// 这最大限度地提高了资源效率和 HTTP 连接重用。
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/// </summary>
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[Fact]
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public void Singleton_ChatClient_Test()
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{
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var result = _singtonChatClient.CompleteChat("你好");
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var responseText = result.Value.Content.First().Text;
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_output.WriteLine(responseText);
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Assert.NotNull(result);
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}
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#endregion
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#region 如何将聊天完成与流式处理一起使用
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/// <summary>
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/// 使用同步流式处理API，可以立即收到响应，而无需等待模型完成。
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/// </summary>
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[Fact]
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public void Streamimg_ChatClient_Test()
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{
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CollectionResult<StreamingChatCompletionUpdate> result = _singtonChatClient.CompleteChatStreaming("你好");
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var stringBuilder = new StringBuilder(500);
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foreach (StreamingChatCompletionUpdate completionUpdate in result)
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{
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if (completionUpdate.ContentUpdate.Count > 0)
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{
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stringBuilder.Append(completionUpdate.ContentUpdate[0].Text);
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}
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}
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_output.WriteLine(stringBuilder.ToString());
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}
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/// <summary>
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/// 使用异步流式处理API
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/// </summary>
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[Fact]
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public async Task Singleton_Async_ChatClient_Test()
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{
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var result = _singtonChatClient.CompleteChatStreamingAsync("你好");
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var stringBuilder = new StringBuilder(500);
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await foreach (StreamingChatCompletionUpdate completionUpdate in result)
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{
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if (completionUpdate.ContentUpdate.Count > 0)
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{
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stringBuilder.Append(completionUpdate.ContentUpdate[0].Text);
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}
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}
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_output.WriteLine(stringBuilder.ToString());
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}
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#endregion
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#region 如何将聊天完成与工具和函数调用一起使用
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/// <summary>
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/// 调用工具和函数
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/// </summary>
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[Fact]
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public void Use_FunctionCalling_ChatClient_Test()
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{
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ChatTool getCurrentLocationTool = ChatTool.CreateFunctionTool
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(
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functionName: nameof(GetCurrentLocation),
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functionDescription: "Get the user's current location"
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);
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ChatTool getCurrentWeatherTool = ChatTool.CreateFunctionTool
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(
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functionName: nameof(GetCurrentWeather),
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functionDescription: "Get the current weather in a given location",
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functionParameters: BinaryData.FromBytes("""
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{
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"type": "object",
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"properties": {
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"location": {
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"type": "string",
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"description": "The city and state, e.g. Boston, MA"
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},
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"unit": {
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"type": "string",
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"enum": [ "celsius", "fahrenheit" ],
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"description": "The temperature unit to use. Infer this from the specified location."
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}
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},
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"required": [ "location" ]
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}
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"""u8.ToArray())
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);
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List<OpenAI.Chat.ChatMessage> messages = [new UserChatMessage("What's the weather like beijing today?"),];
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ChatCompletionOptions options = new()
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{
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Tools = { getCurrentLocationTool, getCurrentWeatherTool },
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};
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bool requiresAction = false;
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do //实质上是手动调用函数
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{
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requiresAction = false;
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ChatCompletion completion = _singtonChatClient.CompleteChat(messages, options);
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switch (completion.FinishReason)
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{
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case OpenAI.Chat.ChatFinishReason.Stop:
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{
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// Add the assistant message to the conversation history.
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messages.Add(new AssistantChatMessage(completion));
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//输出
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foreach (var message in messages)
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{
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_output.WriteLine(message.Content.First().Text);
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}
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break;
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}
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case OpenAI.Chat.ChatFinishReason.ToolCalls:
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{
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// First, add the assistant message with tool calls to the conversation history.
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messages.Add(new AssistantChatMessage(completion));
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// Then, add a new tool message for each tool call that is resolved.
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foreach (ChatToolCall toolCall in completion.ToolCalls)
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{
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switch (toolCall.FunctionName)
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{
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case nameof(GetCurrentLocation):
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{
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string toolResult = GetCurrentLocation();
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messages.Add(new ToolChatMessage(toolCall.Id, toolResult));
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break;
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}
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case nameof(GetCurrentWeather):
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{
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// The arguments that the model wants to use to call the function are specified as a
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// stringified JSON object based on the schema defined in the tool definition. Note that
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// the model may hallucinate arguments too. Consequently, it is important to do the
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// appropriate parsing and validation before calling the function.
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using JsonDocument argumentsJson = JsonDocument.Parse(toolCall.FunctionArguments);
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bool hasLocation = argumentsJson.RootElement.TryGetProperty("location", out JsonElement location);
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bool hasUnit = argumentsJson.RootElement.TryGetProperty("unit", out JsonElement unit);
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if (!hasLocation)
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{
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throw new ArgumentNullException(nameof(location), "The location argument is required.");
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}
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string toolResult = hasUnit
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? GetCurrentWeather(location.GetString() ?? "", unit.GetString() ?? "")
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: GetCurrentWeather(location.GetString() ?? "");
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messages.Add(new ToolChatMessage(toolCall.Id, toolResult));
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break;
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}
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default:
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{
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// Handle other unexpected calls.
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throw new NotImplementedException();
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}
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}
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}
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requiresAction = true;
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break;
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}
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case OpenAI.Chat.ChatFinishReason.Length:
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throw new NotImplementedException("Incomplete model output due to MaxTokens parameter or token limit exceeded.");
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case OpenAI.Chat.ChatFinishReason.ContentFilter:
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throw new NotImplementedException("Omitted content due to a content filter flag.");
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case OpenAI.Chat.ChatFinishReason.FunctionCall:
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throw new NotImplementedException("Deprecated in favor of tool calls.");
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default:
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throw new NotImplementedException(completion.FinishReason.ToString());
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}
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} while (requiresAction);
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}
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#endregion
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#region 如何将聊天完成与结构化输出一起使用
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[Fact]
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public void StructuredOutputs_ChatClient_Test()
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{
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List<OpenAI.Chat.ChatMessage> messages =[new UserChatMessage("How can I solve 8x + 7 = -23?"),];
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ChatCompletionOptions options = new()
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{
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ResponseFormat = OpenAI.Chat.ChatResponseFormat.CreateJsonSchemaFormat(
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jsonSchemaFormatName: "math_reasoning",
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jsonSchema: BinaryData.FromBytes("""
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{
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"type": "object",
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"properties": {
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"steps": {
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"type": "array",
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"items": {
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"type": "object",
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"properties": {
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"explanation": { "type": "string" },
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"output": { "type": "string" }
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},
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"required": ["explanation", "output"],
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"additionalProperties": false
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}
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},
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"final_answer": { "type": "string" }
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},
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"required": ["steps", "final_answer"],
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"additionalProperties": false
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}
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"""u8.ToArray()),
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jsonSchemaIsStrict: true)
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};
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ChatCompletion completion = _singtonChatClient.CompleteChat(messages, options);
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using JsonDocument structuredJson = JsonDocument.Parse(completion.Content[0].Text);
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_output.WriteLine($"Final answer: {structuredJson.RootElement.GetProperty("final_answer")}");
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_output.WriteLine("Reasoning steps:");
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foreach (JsonElement stepElement in structuredJson.RootElement.GetProperty("steps").EnumerateArray())
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{
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_output.WriteLine($" - Explanation: {stepElement.GetProperty("explanation")}");
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_output.WriteLine($" Output: {stepElement.GetProperty("output")}");
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}
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}
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#endregion
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#region 如何将聊天完成与音频一起使用
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/// <summary>
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/// 生成语音
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/// </summary>
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//[Fact]
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[Fact(Skip ="因本地Ollama测试环境，不支持OpenAI音频接口，忽略测试")]
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//[Fact]
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public void GenerateSpeech_AudioClient_Test()
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{
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var aiClientOption = new OpenAIClientOptions()
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{
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Endpoint = new Uri("https://sg.uiuiapi.com/v1")
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};
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AudioClient client = new("tts-1-1106", new ApiKeyCredential("sk-4azuOUkbzNGP22pQkND8ad1vZl7ladwBQyqGKlWWZyxYgX1L"), aiClientOption);
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string input = """
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对于那些照顾室内植物的人来说，过度浇水是一个常见的问题。
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为了防止这种情况，让土壤在两次浇水之间变干至关重要。
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与其按照固定的时间表浇水，不如考虑使用水分计来准确测量土壤的湿度。
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如果土壤保持水分，明智的做法是再推迟几天浇水。
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如有疑问，“节约用水，保持少即是多”的方法通常更安全。
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""";
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BinaryData speech = client.GenerateSpeech(input, GeneratedSpeechVoice.Alloy);
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using FileStream stream = File.OpenWrite($"{Guid.NewGuid()}.mp3");
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speech.ToStream().CopyTo(stream);
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|
}
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/// <summary>
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/// 语音转文本
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/// </summary>
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[Fact(Skip ="因本地Ollama测试环境，不支持OpenAI音频接口，忽略测试")]
|
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|
//[Fact]
|
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public void AudioToText_AudioClient_Test()
|
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|
{
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var aiClientOption = new OpenAIClientOptions()
|
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|
{
|
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|
Endpoint = new Uri("https://sg.uiuiapi.com/v1")
|
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|
};
|
|
|
|
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AudioClient client = new("whisper-1", new ApiKeyCredential("sk-4azuOUkbzNGP22pQkND8ad1vZl7ladwBQyqGKlWWZyxYgX1L"), aiClientOption);
|
|
|
|
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string audioFilePath = Path.Combine(Environment.CurrentDirectory, "Assets", "yuxia.mp3");
|
|
|
|
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|
AudioTranscription transcription = client.TranscribeAudio(audioFilePath);
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|
|
|
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|
_output.WriteLine($"{transcription.Text}");
|
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|
}
|
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|
#endregion
|
|
|
|
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|
#region 如何将响应与流式处理和推理结合使用
|
|
|
[Fact(Skip ="因本地Ollama测试环境不支持，忽略测试")]
|
|
|
public void Responses_With_Streaming_Reasoning_ChatClient_Test()
|
|
|
{
|
|
|
|
|
|
}
|
|
|
#endregion
|
|
|
|
|
|
[Fact]
|
|
|
#region 如何将响应与文件搜索一起使用
|
|
|
public async Task Respones_With_FileSearch_Test()
|
|
|
{
|
|
|
#pragma warning disable OPENAI001
|
|
|
OpenAIResponseClient client = new(
|
|
|
model: "gpt-4o-mini",
|
|
|
apiKey: Environment.GetEnvironmentVariable("OPENAI_API_KEY"));
|
|
|
|
|
|
ResponseTool fileSearchTool = ResponseTool.CreateFileSearchTool(vectorStoreIds: ["sssssssss"]);
|
|
|
OpenAIResponse response = await client.CreateResponseAsync
|
|
|
(
|
|
|
userInputText: "According to available files, what's the secret number?",
|
|
|
new ResponseCreationOptions()
|
|
|
{
|
|
|
Tools = { fileSearchTool }
|
|
|
}
|
|
|
);
|
|
|
|
|
|
foreach (ResponseItem outputItem in response.OutputItems)
|
|
|
{
|
|
|
if (outputItem is FileSearchCallResponseItem fileSearchCall)
|
|
|
{
|
|
|
Console.WriteLine($"[file_search] ({fileSearchCall.Status}): {fileSearchCall.Id}");
|
|
|
foreach (string query in fileSearchCall.Queries)
|
|
|
{
|
|
|
Console.WriteLine($" - {query}");
|
|
|
}
|
|
|
}
|
|
|
else if (outputItem is MessageResponseItem message)
|
|
|
{
|
|
|
Console.WriteLine($"[{message.Role}] {message.Content.FirstOrDefault()?.Text}");
|
|
|
}
|
|
|
}
|
|
|
#pragma warning restore OPENAI001
|
|
|
}
|
|
|
#endregion
|
|
|
|
|
|
#region 如何将响应与网络搜索结合使用
|
|
|
[Fact]
|
|
|
public async Task WebSearch_ChatClient_Test()
|
|
|
{
|
|
|
#pragma warning disable OPENAI001
|
|
|
OpenAIResponseClient client = _defaultOpenAIClient.GetOpenAIResponseClient(ModelSelecter.ModelWithRawmodel);
|
|
|
|
|
|
OpenAIResponse response = await client.CreateResponseAsync
|
|
|
(
|
|
|
userInputText: "What's a happy news headline from today?",
|
|
|
new ResponseCreationOptions()
|
|
|
{
|
|
|
Tools = { ResponseTool.CreateWebSearchTool() },
|
|
|
}
|
|
|
);
|
|
|
|
|
|
foreach (ResponseItem item in response.OutputItems)
|
|
|
{
|
|
|
if (item is WebSearchCallResponseItem webSearchCall)
|
|
|
{
|
|
|
Console.WriteLine($"[Web search invoked]({webSearchCall.Status}) {webSearchCall.Id}");
|
|
|
}
|
|
|
else if (item is MessageResponseItem message)
|
|
|
{
|
|
|
Console.WriteLine($"[{message.Role}] {message.Content?.FirstOrDefault()?.Text}");
|
|
|
}
|
|
|
}
|
|
|
#pragma warning restore OPENAI001
|
|
|
}
|
|
|
#endregion
|
|
|
|
|
|
#region 如何生成文本嵌入
|
|
|
#endregion
|
|
|
|
|
|
#region 如何生成图像
|
|
|
#endregion
|
|
|
|
|
|
#region 如何转录音频
|
|
|
#endregion
|
|
|
|
|
|
#region 如何将助手与检索增强生成 （RAG） 结合使用。
|
|
|
#endregion
|
|
|
|
|
|
#region 如何将助手与流媒体和视觉结合使用
|
|
|
#endregion
|
|
|
|
|
|
#region 高级方案
|
|
|
#endregion
|
|
|
|
|
|
#region 私有方法
|
|
|
private static string GetCurrentLocation()
|
|
|
{
|
|
|
// Call the location API here.
|
|
|
return "San Francisco";
|
|
|
}
|
|
|
|
|
|
private static string GetCurrentWeather(string location, string unit = "celsius")
|
|
|
{
|
|
|
// Call the weather API here.
|
|
|
return $"31 {unit}";
|
|
|
}
|
|
|
#endregion
|
|
|
}
|