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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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private OpenAIClient _uiUiApiClient;
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private OpenAIClient _zipuApiClient;
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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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[FromKeyedServices("UiUiAPIClient")]OpenAIClient uiUiApiClient,
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[FromKeyedServices("ZipuAPIClient")]OpenAIClient zipuApiClient
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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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_uiUiApiClient = uiUiApiClient;
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_zipuApiClient = zipuApiClient;
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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);
|
|
|
|
|
|
using FileStream stream = File.OpenWrite($"{Guid.NewGuid()}.mp3");
|
|
|
speech.ToStream().CopyTo(stream);
|
|
|
}
|
|
|
|
|
|
/// <summary>
|
|
|
/// 语音转文本
|
|
|
/// </summary>
|
|
|
[Fact(Skip ="因本地Ollama测试环境，不支持OpenAI音频接口，忽略测试")]
|
|
|
//[Fact]
|
|
|
public void AudioToText_AudioClient_Test()
|
|
|
{
|
|
|
var aiClientOption = new OpenAIClientOptions()
|
|
|
{
|
|
|
Endpoint = new Uri("https://sg.uiuiapi.com/v1")
|
|
|
};
|
|
|
|
|
|
AudioClient client = new("whisper-1", new ApiKeyCredential("sk-4azuOUkbzNGP22pQkND8ad1vZl7ladwBQyqGKlWWZyxYgX1L"), aiClientOption);
|
|
|
|
|
|
string audioFilePath = Path.Combine(Environment.CurrentDirectory, "Assets", "yuxia.mp3");
|
|
|
|
|
|
AudioTranscription transcription = client.TranscribeAudio(audioFilePath);
|
|
|
|
|
|
_output.WriteLine($"{transcription.Text}");
|
|
|
}
|
|
|
#endregion
|
|
|
|
|
|
#region 如何将响应与流式处理和推理结合使用
|
|
|
[Fact(Skip ="因本地Ollama测试环境不支持，忽略测试")]
|
|
|
public void Responses_With_Streaming_Reasoning_ChatClient_Test()
|
|
|
{
|
|
|
|
|
|
}
|
|
|
#endregion
|
|
|
|
|
|
#region 如何将响应与文件搜索一起使用
|
|
|
//[Fact]
|
|
|
[Fact(Skip = "因本地Ollama测试环境不支持，忽略测试")]
|
|
|
public async Task Respones_With_FileSearch_Test()
|
|
|
{
|
|
|
#pragma warning disable OPENAI001
|
|
|
OpenAIResponseClient client = new
|
|
|
(
|
|
|
"gpt-4o-mini",
|
|
|
new ApiKeyCredential("sk-4azuOUkbzNGP22pQkND8ad1vZl7ladwBQyqGKlWWZyxYgX1L"),
|
|
|
new OpenAIClientOptions()
|
|
|
{
|
|
|
Endpoint = new Uri("https://sg.uiuiapi.com/v1")
|
|
|
}
|
|
|
);
|
|
|
|
|
|
//已经存在的文件向量IDS(提前将文件向量化)
|
|
|
ResponseTool fileSearchTool = ResponseTool.CreateFileSearchTool(vectorStoreIds: ["sssssssss"]);
|
|
|
OpenAIResponse response = await client.CreateResponseAsync
|
|
|
(
|
|
|
userInputText: "According to available files, what's the secret number?",
|
|
|
new ResponseCreationOptions()
|
|
|
{
|
|
|
Tools = { fileSearchTool }
|
|
|
}
|
|
|
);
|
|
|
|
|
|
foreach (ResponseItem outputItem in response.OutputItems)
|
|
|
{
|
|
|
if (outputItem is FileSearchCallResponseItem fileSearchCall)
|
|
|
{
|
|
|
Console.WriteLine($"[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(Skip = "因本地Ollama测试环境不支持，忽略测试")]
|
|
|
//[Fact]
|
|
|
public async Task WebSearch_ChatClient_Test()
|
|
|
{
|
|
|
#pragma warning disable OPENAI001
|
|
|
OpenAIResponseClient client = new
|
|
|
(
|
|
|
"gpt-4o-mini",
|
|
|
new ApiKeyCredential("sk-4azuOUkbzNGP22pQkND8ad1vZl7ladwBQyqGKlWWZyxYgX1L"),
|
|
|
new OpenAIClientOptions()
|
|
|
{
|
|
|
Endpoint = new Uri("https://sg.uiuiapi.com/v1")
|
|
|
}
|
|
|
);
|
|
|
|
|
|
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
|
|
|
[Fact]
|
|
|
public void Embedding_ChatClient_Test()
|
|
|
{
|
|
|
EmbeddingClient embeddingClient = _defaultOpenAIClient.GetEmbeddingClient(ModelSelecter.ModelWithEmbedding);
|
|
|
|
|
|
string description = "Best hotel in town if you like luxury hotels. They have an amazing infinity pool, a spa,"
|
|
|
+ " and a really helpful concierge. The location is perfect -- right downtown, close to all the tourist"
|
|
|
+ " attractions. We highly recommend this hotel.";
|
|
|
|
|
|
OpenAIEmbedding embedding = embeddingClient.GenerateEmbedding(description);
|
|
|
ReadOnlyMemory<float> vector = embedding.ToFloats();
|
|
|
|
|
|
Assert.True(vector.Length>0);
|
|
|
}
|
|
|
#endregion
|
|
|
|
|
|
#region 如何生成图像
|
|
|
[Fact(Skip = "因本地Ollama测试环境不支持，忽略测试")]
|
|
|
//[Fact]
|
|
|
public void Image_ChatClient_Test()
|
|
|
{
|
|
|
ImageClient imageClient = new
|
|
|
(
|
|
|
"dall-e-3",
|
|
|
new ApiKeyCredential("sk-4azuOUkbzNGP22pQkND8ad1vZl7ladwBQyqGKlWWZyxYgX1L"),
|
|
|
new OpenAIClientOptions()
|
|
|
{
|
|
|
Endpoint = new Uri("https://sg.uiuiapi.com/v1")
|
|
|
}
|
|
|
);
|
|
|
|
|
|
string prompt = """
|
|
|
The concept for a living room that blends Scandinavian simplicity with Japanese minimalism for a serene and cozy atmosphere.
|
|
|
It's a space that invites relaxation and mindfulness, with natural light and fresh air.
|
|
|
Using neutral tones, including colors like white, beige, gray, and black, that create a sense of harmony.
|
|
|
Featuring sleek wood furniture with clean lines and subtle curves to add warmth and elegance. Plants and flowers in ceramic pots adding color and life to a space.
|
|
|
They can serve as focal points, creating a connection with nature. Soft textiles and cushions in organic fabrics adding comfort and softness to a space.
|
|
|
They can serve as accents, adding contrast and texture.
|
|
|
""";
|
|
|
prompt = """
|
|
|
画一个中国古代的将军。身披铠甲，手持长剑，眼神坚定，表情严肃，看起来很威严。国字脸，在战场上指挥作战！
|
|
|
""";
|
|
|
|
|
|
ImageGenerationOptions options = new()
|
|
|
{
|
|
|
Quality = GeneratedImageQuality.High,
|
|
|
Size = GeneratedImageSize.W1792xH1024,
|
|
|
Style = GeneratedImageStyle.Vivid,
|
|
|
ResponseFormat = GeneratedImageFormat.Bytes
|
|
|
};
|
|
|
|
|
|
GeneratedImage image = imageClient.GenerateImage(prompt, options);
|
|
|
BinaryData bytes = image.ImageBytes;
|
|
|
|
|
|
using FileStream stream = File.OpenWrite($"{Guid.NewGuid()}.png");
|
|
|
bytes.ToStream().CopyTo(stream);
|
|
|
}
|
|
|
#endregion
|
|
|
|
|
|
#region 如何转录音频
|
|
|
[Fact(Skip = "因本地Ollama测试环境不支持，忽略测试")]
|
|
|
//[Fact]
|
|
|
public void Tranacribe_Audio_AudioClient_Test()
|
|
|
{
|
|
|
AudioClient client = new
|
|
|
(
|
|
|
"whisper-1",
|
|
|
new ApiKeyCredential("sk-4azuOUkbzNGP22pQkND8ad1vZl7ladwBQyqGKlWWZyxYgX1L"),
|
|
|
new OpenAIClientOptions()
|
|
|
{
|
|
|
Endpoint = new Uri("https://sg.uiuiapi.com/v1")
|
|
|
}
|
|
|
);
|
|
|
|
|
|
string audioFilePath = Path.Combine("Assets", "dongdong.mp3");
|
|
|
|
|
|
AudioTranscriptionOptions options = new()
|
|
|
{
|
|
|
ResponseFormat = AudioTranscriptionFormat.Verbose,
|
|
|
TimestampGranularities = AudioTimestampGranularities.Word | AudioTimestampGranularities.Segment,
|
|
|
};
|
|
|
|
|
|
AudioTranscription transcription = client.TranscribeAudio(audioFilePath, options);
|
|
|
|
|
|
_output.WriteLine("Transcription:");
|
|
|
_output.WriteLine($"{transcription.Text}");
|
|
|
|
|
|
_output.WriteLine("");
|
|
|
_output.WriteLine($"Words:");
|
|
|
foreach (TranscribedWord word in transcription.Words)
|
|
|
{
|
|
|
_output.WriteLine($" {word.Word,15} : {word.StartTime.TotalMilliseconds,5:0} - {word.EndTime.TotalMilliseconds,5:0}");
|
|
|
}
|
|
|
|
|
|
_output.WriteLine("");
|
|
|
_output.WriteLine($"Segments:");
|
|
|
foreach (TranscribedSegment segment in transcription.Segments)
|
|
|
{
|
|
|
_output.WriteLine($" {segment.Text,90} : {segment.StartTime.TotalMilliseconds,5:0} - {segment.EndTime.TotalMilliseconds,5:0}");
|
|
|
}
|
|
|
}
|
|
|
#endregion
|
|
|
|
|
|
#region 如何将助手与检索增强生成 （RAG） 结合使用
|
|
|
/// <summary>
|
|
|
/// 在此示例中，您有一个包含不同产品每月销售信息的 JSON 文档，并且您希望构建一个能够分析它并回答有关它的问题的助手
|
|
|
/// 请同时使用 OpenAI.Files 命名空间中的 OpenAIFileClient 和 OpenAI.Assistants 命名空间中的 AssistantClient
|
|
|
/// </summary>
|
|
|
//[Fact]
|
|
|
[Fact(Skip = "因本地Ollama测试环境不支持，忽略测试")]
|
|
|
public void RAG_Assistant_ChatClient_Test()
|
|
|
{
|
|
|
#pragma warning disable OPENAI001
|
|
|
|
|
|
OpenAIClientOptions clientOptions = new()
|
|
|
{
|
|
|
Endpoint = new Uri("https://sg.uiuiapi.com/v1"),
|
|
|
};
|
|
|
|
|
|
OpenAIClient openAIClient = new(new ApiKeyCredential("sk-4azuOUkbzNGP22pQkND8ad1vZl7ladwBQyqGKlWWZyxYgX1L"));
|
|
|
OpenAIFileClient fileClient = openAIClient.GetOpenAIFileClient();
|
|
|
AssistantClient assistantClient = openAIClient.GetAssistantClient();
|
|
|
|
|
|
using Stream document = BinaryData.FromBytes("""
|
|
|
{
|
|
|
"description": "This document contains the sale history data for Contoso products.",
|
|
|
"sales": [
|
|
|
{
|
|
|
"month": "January",
|
|
|
"by_product": {
|
|
|
"113043": 15,
|
|
|
"113045": 12,
|
|
|
"113049": 2
|
|
|
}
|
|
|
},
|
|
|
{
|
|
|
"month": "February",
|
|
|
"by_product": {
|
|
|
"113045": 22
|
|
|
}
|
|
|
},
|
|
|
{
|
|
|
"month": "March",
|
|
|
"by_product": {
|
|
|
"113045": 16,
|
|
|
"113055": 5
|
|
|
}
|
|
|
}
|
|
|
]
|
|
|
}
|
|
|
"""u8.ToArray()).ToStream();
|
|
|
//使用 OpenAIFileClient 的 UploadFile 方法将此文档上传到 OpenAI，确保使用 FileUploadPurpose.Assistants 以允许助手稍后访问它
|
|
|
OpenAIFile salesFile = fileClient.UploadFile
|
|
|
(
|
|
|
document,
|
|
|
"monthly_sales.json",
|
|
|
FileUploadPurpose.Assistants
|
|
|
);
|
|
|
|
|
|
//使用 AssistantCreationOptions 类的实例创建新助手以对其进行自定义。
|
|
|
AssistantCreationOptions assistantOptions = new()
|
|
|
{
|
|
|
Name = "Example: Contoso sales RAG",
|
|
|
Instructions ="You are an assistant that looks up sales data and helps visualize the information based on user queries. When asked to generate a graph, chart, or other visualization, use the code interpreter tool to do so.",
|
|
|
Tools =
|
|
|
{
|
|
|
new FileSearchToolDefinition(),
|
|
|
new CodeInterpreterToolDefinition(),
|
|
|
},
|
|
|
ToolResources = new()
|
|
|
{
|
|
|
FileSearch = new()
|
|
|
{
|
|
|
NewVectorStores =
|
|
|
{
|
|
|
new VectorStoreCreationHelper([salesFile.Id]),
|
|
|
}
|
|
|
}
|
|
|
},
|
|
|
};
|
|
|
|
|
|
Assistant assistant = assistantClient.CreateAssistant("gpt-4o", assistantOptions);
|
|
|
|
|
|
//接下来，创建一个新线程。出于说明目的，可以包含询问给定产品的销售信息的初始用户消息，然后使用 AssistantClient 的 CreateThreadAndRun 方法启动它：
|
|
|
ThreadCreationOptions threadOptions = new()
|
|
|
{
|
|
|
InitialMessages = { "How well did product 113045 sell in February? Graph its trend over time." }
|
|
|
};
|
|
|
|
|
|
ThreadRun threadRun = assistantClient.CreateThreadAndRun(assistant.Id, threadOptions);
|
|
|
|
|
|
//轮询运行的状态，直到它不再排队或正在进行：
|
|
|
do
|
|
|
{
|
|
|
Thread.Sleep(TimeSpan.FromSeconds(1));
|
|
|
threadRun = assistantClient.GetRun(threadRun.ThreadId, threadRun.Id);
|
|
|
} while (!threadRun.Status.IsTerminal);
|
|
|
|
|
|
//如果一切顺利，运行的终端状态将为 RunStatus.Completed
|
|
|
//最后，可以使用 AssistantClient 的 GetMessages 方法检索与此线程关联的消息，这些消息现在包括助手对初始用户消息的响应。
|
|
|
//出于说明目的，您可以将消息打印到控制台，并将助手生成的任何图像保存到本地存储：
|
|
|
|
|
|
CollectionResult<ThreadMessage> messages = assistantClient.GetMessages(threadRun.ThreadId, new MessageCollectionOptions() { Order = MessageCollectionOrder.Ascending });
|
|
|
|
|
|
foreach (ThreadMessage message in messages)
|
|
|
{
|
|
|
Console.Write($"[{message.Role.ToString().ToUpper()}]: ");
|
|
|
foreach (MessageContent contentItem in message.Content)
|
|
|
{
|
|
|
if (!string.IsNullOrEmpty(contentItem.Text))
|
|
|
{
|
|
|
Console.WriteLine($"{contentItem.Text}");
|
|
|
|
|
|
if (contentItem.TextAnnotations.Count > 0)
|
|
|
{
|
|
|
Console.WriteLine();
|
|
|
}
|
|
|
|
|
|
// Include annotations, if any.
|
|
|
foreach (TextAnnotation annotation in contentItem.TextAnnotations)
|
|
|
{
|
|
|
if (!string.IsNullOrEmpty(annotation.InputFileId))
|
|
|
{
|
|
|
Console.WriteLine($"* File citation, file ID: {annotation.InputFileId}");
|
|
|
}
|
|
|
if (!string.IsNullOrEmpty(annotation.OutputFileId))
|
|
|
{
|
|
|
Console.WriteLine($"* File output, new file ID: {annotation.OutputFileId}");
|
|
|
}
|
|
|
}
|
|
|
}
|
|
|
if (!string.IsNullOrEmpty(contentItem.ImageFileId))
|
|
|
{
|
|
|
OpenAIFile imageInfo = fileClient.GetFile(contentItem.ImageFileId);
|
|
|
BinaryData imageBytes = fileClient.DownloadFile(contentItem.ImageFileId);
|
|
|
using FileStream stream = File.OpenWrite($"{imageInfo.Filename}.png");
|
|
|
imageBytes.ToStream().CopyTo(stream);
|
|
|
|
|
|
Console.WriteLine($"<image: {imageInfo.Filename}.png>");
|
|
|
}
|
|
|
}
|
|
|
Console.WriteLine();
|
|
|
|
|
|
}
|
|
|
|
|
|
//它会产生这样的结果
|
|
|
_ =
|
|
|
"""
|
|
|
[USER]: How well did product 113045 sell in February? Graph its trend over time.
|
|
|
|
|
|
|
|
|
[ASSISTANT]: Product 113045 sold 22 units in February【4:0†monthly_sales.json】.
|
|
|
|
|
|
Now, I will generate a graph to show its sales trend over time.
|
|
|
|
|
|
* File citation, file ID: file-hGOiwGNftMgOsjbynBpMCPFn
|
|
|
|
|
|
[ASSISTANT]: <image: 015d8e43-17fe-47de-af40-280f25452280.png>
|
|
|
The sales trend for Product 113045 over the past three months shows that:
|
|
|
|
|
|
- In January, 12 units were sold.
|
|
|
- In February, 22 units were sold, indicating significant growth.
|
|
|
- In March, sales dropped slightly to 16 units.
|
|
|
|
|
|
The graph above visualizes this trend, showing a peak in sales during February.
|
|
|
""";
|
|
|
|
|
|
#pragma warning restore OPENAI001
|
|
|
}
|
|
|
#endregion
|
|
|
|
|
|
#region 如何将助手与流媒体和视觉结合使用
|
|
|
|
|
|
//演示如何使用 v2助手 API 向助手提供图像数据，然后流式传输运行的响应。
|
|
|
//[Fact]
|
|
|
[Fact(Skip = "因本地Ollama测试环境不支持，忽略测试")]
|
|
|
public void Streaming_Vision_AssistantsClient()
|
|
|
{
|
|
|
#pragma warning disable OPENAI001
|
|
|
OpenAIClientOptions clientOptions = new()
|
|
|
{
|
|
|
Endpoint = new Uri("https://sg.uiuiapi.com/v1"),
|
|
|
};
|
|
|
|
|
|
OpenAIClient openAIClient = new(new ApiKeyCredential("sk-4azuOUkbzNGP22pQkND8ad1vZl7ladwBQyqGKlWWZyxYgX1L"));
|
|
|
OpenAIFileClient fileClient = openAIClient.GetOpenAIFileClient();
|
|
|
AssistantClient assistantClient = openAIClient.GetAssistantClient();
|
|
|
|
|
|
OpenAIFile pictureOfAppleFile = fileClient.UploadFile
|
|
|
(
|
|
|
Path.Combine("Assets", "images_apple.png"),
|
|
|
FileUploadPurpose.Vision
|
|
|
);
|
|
|
|
|
|
Uri linkToPictureOfOrange = new("https://raw.githubusercontent.com/openai/openai-dotnet/refs/heads/main/examples/Assets/images_orange.png");
|
|
|
|
|
|
//接下来，创建一个具有视觉支持模型（如 gpt-4o）的新助手和一个包含引用图像信息的线程
|
|
|
Assistant assistant = assistantClient.CreateAssistant
|
|
|
(
|
|
|
"gpt-4o",
|
|
|
new AssistantCreationOptions()
|
|
|
{
|
|
|
Instructions = "When asked a question, attempt to answer very concisely. Prefer one-sentence answers whenever feasible."
|
|
|
}
|
|
|
);
|
|
|
|
|
|
AssistantThread thread = assistantClient.CreateThread
|
|
|
(
|
|
|
new ThreadCreationOptions()
|
|
|
{
|
|
|
InitialMessages =
|
|
|
{
|
|
|
new ThreadInitializationMessage(
|
|
|
OpenAI.Assistants.MessageRole.User,
|
|
|
[
|
|
|
"Hello, assistant! Please compare these two images for me:",
|
|
|
MessageContent.FromImageFileId(pictureOfAppleFile.Id),
|
|
|
MessageContent.FromImageUri(linkToPictureOfOrange),
|
|
|
]),
|
|
|
}
|
|
|
}
|
|
|
);
|
|
|
|
|
|
CollectionResult<StreamingUpdate> streamingUpdates = assistantClient.CreateRunStreaming
|
|
|
(
|
|
|
thread.Id,
|
|
|
assistant.Id,
|
|
|
new RunCreationOptions()
|
|
|
{
|
|
|
AdditionalInstructions = "When possible, try to sneak in puns if you're asked to compare things.",
|
|
|
}
|
|
|
);
|
|
|
|
|
|
foreach (StreamingUpdate streamingUpdate in streamingUpdates)
|
|
|
{
|
|
|
if (streamingUpdate.UpdateKind == StreamingUpdateReason.RunCreated)
|
|
|
{
|
|
|
Console.WriteLine($"--- Run started! ---");
|
|
|
}
|
|
|
if (streamingUpdate is MessageContentUpdate contentUpdate)
|
|
|
{
|
|
|
Console.Write(contentUpdate.Text);
|
|
|
}
|
|
|
}
|
|
|
|
|
|
|
|
|
#pragma warning restore OPENAI001
|
|
|
}
|
|
|
|
|
|
|
|
|
#endregion
|
|
|
|
|
|
#region 高级方案
|
|
|
|
|
|
/// <summary>
|
|
|
/// 使用协议方法
|
|
|
/// 除了使用强类型请求和响应对象的客户端方法外，.NET 库还提供协议方法 ，以便更直接地访问 REST API。
|
|
|
/// 协议方法是“二进制输入，二进制输出”，接受 BinaryContent 作为请求体，并提供 BinaryData 作为响应体。
|
|
|
/// </summary>
|
|
|
[Fact]
|
|
|
public void Using_Protocol_Methods_Test()
|
|
|
{
|
|
|
//若要使用 ChatClient 的 CompleteChat 方法的协议方法变体，请将请求正文作为 BinaryContent 传递
|
|
|
//ChatClient client = new("gpt-4o", Environment.GetEnvironmentVariable("OPENAI_API_KEY"));
|
|
|
|
|
|
ChatClient client = _defaultOpenAIClient.GetChatClient(ModelSelecter.ModelWithToolAndThing);
|
|
|
|
|
|
BinaryData input = BinaryData.FromBytes("""
|
|
|
{
|
|
|
"model": "gemma3:4b",
|
|
|
"messages": [
|
|
|
{
|
|
|
"role": "user",
|
|
|
"content": "Say 'this is a test.'"
|
|
|
}
|
|
|
]
|
|
|
}
|
|
|
"""u8.ToArray());
|
|
|
|
|
|
using BinaryContent content = BinaryContent.Create(input);
|
|
|
ClientResult result = client.CompleteChat(content);
|
|
|
BinaryData output = result.GetRawResponse().Content;
|
|
|
|
|
|
using JsonDocument outputAsJson = JsonDocument.Parse(output.ToString());
|
|
|
string message = outputAsJson.RootElement
|
|
|
.GetProperty("choices"u8)[0]
|
|
|
.GetProperty("message"u8)
|
|
|
.GetProperty("content"u8)
|
|
|
.GetString() ?? "";
|
|
|
|
|
|
_output.WriteLine($"[ASSISTANT]: {message}");
|
|
|
|
|
|
//请注意，然后如何调用生成的 ClientResult 的 GetRawResponse 方法，并通过 PipelineResponse 的 Content 属性将响应正文检索为 BinaryData。
|
|
|
}
|
|
|
|
|
|
/// <summary>
|
|
|
/// 模拟客户端
|
|
|
/// OpenAI .NET 库旨在支持模拟，所有命名空间都有相应的模型工厂来支持模拟，但 OpenAI.Assistants 和 OpenAI.VectorStores 命名空间除外，模型工厂即将推出。
|
|
|
/// </summary>
|
|
|
[Fact]
|
|
|
public void MocK_Client_Test()
|
|
|
{
|
|
|
#pragma warning disable OPENAI001
|
|
|
Mock<AudioClient> mockClient = new();
|
|
|
Mock<ClientResult<AudioTranscription>> mockResult = new(new List<object>(), Mock.Of<System.ClientModel.Primitives.PipelineResponse>());
|
|
|
AudioTranscription transcription = OpenAIAudioModelFactory.AudioTranscription(text: "I swear I saw an apple flying yesterday!");
|
|
|
|
|
|
// Set up mocks' properties and methods.
|
|
|
|
|
|
mockResult
|
|
|
.SetupGet(result => result.Value)
|
|
|
.Returns(transcription);
|
|
|
|
|
|
mockClient
|
|
|
.Setup
|
|
|
(client => client.TranscribeAudio
|
|
|
(
|
|
|
It.IsAny<string>(),
|
|
|
It.IsAny<AudioTranscriptionOptions>()
|
|
|
)
|
|
|
)
|
|
|
.Returns(mockResult.Object);
|
|
|
|
|
|
// Perform validation.
|
|
|
|
|
|
AudioClient client = mockClient.Object;
|
|
|
bool containsSecretWord = ContainsSecretWord(client, "<audioFilePath>", "apple");
|
|
|
|
|
|
Assert.True(containsSecretWord);
|
|
|
|
|
|
bool ContainsSecretWord(AudioClient client, string audioFilePath, string secretWord)
|
|
|
{
|
|
|
AudioTranscription transcription = client.TranscribeAudio(audioFilePath);
|
|
|
return transcription.Text.Contains(secretWord);
|
|
|
}
|
|
|
#pragma warning restore OPENAI001
|
|
|
}
|
|
|
#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
|
|
|
|
|
|
#region 其它平台测试
|
|
|
|
|
|
[Fact]
|
|
|
public void UiUiAPI_Test()
|
|
|
{
|
|
|
var chatClient = _uiUiApiClient.GetChatClient("gpt-3.5-turbo");
|
|
|
|
|
|
var r = chatClient.CompleteChat("你好");
|
|
|
|
|
|
_output.WriteLine(r.Value.Content.First().Text);
|
|
|
}
|
|
|
|
|
|
[Fact]
|
|
|
public void Zipu_Test()
|
|
|
{
|
|
|
var chatClient = _zipuApiClient.GetChatClient("GLM-4.5-Flash");
|
|
|
|
|
|
var r = chatClient.CompleteChat("你好");
|
|
|
|
|
|
_output.WriteLine(r.Value.Content.First().Text);
|
|
|
}
|
|
|
|
|
|
#endregion
|
|
|
}
|