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A non-official DashScope (Bailian) service SDK maintained by Cnblogs.
Note: This project is actively under development. Breaking changes may occur even in minor versions. Please review the Release Notes before upgrading.
Install NuGet package Cnblogs.DashScope.AI
var client = new DashScopeClient("your-api-key").AsChatClient("qwen-max"); var completion = await client.CompleteAsync("hello"); Console.WriteLine(completion)Install NuGet package Cnblogs.DashScope.Sdk
var client = new DashScopeClient("your-api-key"); // you could use different api base, example: // var client = new DashScopeClient("your-api-key", "https://dashscope-intl.aliyuncs.com/api/v1/"); var completion = await client.GetTextCompletionAsync( new ModelRequest<TextGenerationInput, ITextGenerationParameters>() { Model = "qwen-turbo", Input = new TextGenerationInput() { Messages = new List<TextChatMessage>() { TextChatMessage.System("You are a helpful assistant"), TextChatMessage.User("你是谁？") } }, Parameters = new TextGenerationParameters() { ResultFormat = "message" } }); Console.WriteLine(completion.Output.Choices![0].Message.Content)Install NuGet package Cnblogs.DashScope.AspNetCore
Program.cs
builder.AddDashScopeClient(builder.Configuration);appsettings.json
{ "DashScope": { "ApiKey": "your-api-key", "BaseAddress": "https://dashscope-intl.aliyuncs.com/api/v1" } }Application class:
public class YourService(IDashScopeClient client) { public async Task<string> CompletePromptAsync(string prompt) { var completion = await client.GetTextCompletionAsync( new ModelRequest<TextGenerationInput, ITextGenerationParameters>() { Model = "qwen-turbo", Input = new TextGenerationInput() { Messages = new List<TextChatMessage>() { TextChatMessage.System("You are a helpful assistant"), TextChatMessage.User("你是谁？") } }, Parameters = new TextGenerationParameters() { ResultFormat = "message" } }); return completion.Output.Choices![0].Message.Content } }- Text Generation - QWen3, DeepSeek, etc. Supports reasoning/tool calling/web search/translation scenarios
- Multimodal - QWen-VL, QVQ, etc. Supports reasoning/visual understanding/OCR/audio understanding
- Text-to-Speech - CosyVoice, Sambert, etc. For TTS applications
- Image Generation - wanx2.1, etc. For text-to-image and portrait style transfer
- Application Call
- Text Embeddings
Use dashScopeClient.GetTextCompletionAsync and dashScopeClient.GetTextCompletionStreamAsync() to access text generation APIs.
Common models: qwen-max qwen-plus qwen-flush etc.
Basic example:
var client = new DashScopeClient("your-api-key"); var completion = await client.GetTextCompletionAsync( new ModelRequest<TextGenerationInput, ITextGenerationParameters>() { Model = "qwen-turbo", Input = new TextGenerationInput() { Messages = new List<TextChatMessage>() { TextChatMessage.System("You are a helpful assistant"), TextChatMessage.User("Who are you?") } }, Parameters = new TextGenerationParameters() { ResultFormat = "message" } }); Console.WriteLine(completion.Output.Choices![0].Message.Content)The key is maintaining a TextChatMessage array as conversation history.
var messages = new List<TextChatMessage>(); messages.Add(TextChatMessage.System("You are a helpful assistant")); while (true) { Console.Write("User > "); var input = Console.ReadLine(); if (string.IsNullOrEmpty(input)) { Console.WriteLine("Using default input: Who are you?"); input = "Who are you?"; } messages.Add(TextChatMessage.User(input)); var completion = await client.GetTextCompletionAsync( new ModelRequest<TextGenerationInput, ITextGenerationParameters>() { Model = "qwen-turbo", Input = new TextGenerationInput() { Messages = messages }, Parameters = new TextGenerationParameters() { ResultFormat = "message" } }); Console.WriteLine("Assistant > " + completion.Output.Choices![0].Message.Content); var usage = completion.Usage; if (usage != null) { Console.WriteLine($"Usage: in({usage.InputTokens})/out({usage.OutputTokens})/total({usage.TotalTokens})"); } messages.Add(TextChatMessage.Assistant(completion.Output.Choices[0].Message.Content)); }The model's thinking process is stored in a separate ReasoningContent property. When saving to conversation history, ignore it and only keep the model's reply Content.
Some models accept EnableThinking to control deep thinking, which can be set in Parameters.
var messages = new List<TextChatMessage>(); messages.Add(TextChatMessage.System("You are a helpful assistant")); while (true) { Console.Write("User > "); var input = Console.ReadLine(); if (string.IsNullOrEmpty(input)) { Console.WriteLine("Please enter a user input."); return; } messages.Add(TextChatMessage.User(input)); var completion = await client.GetTextCompletionAsync( new ModelRequest<TextGenerationInput, ITextGenerationParameters>() { Model = "qwen-turbo", Input = new TextGenerationInput() { Messages = messages }, Parameters = new TextGenerationParameters() { ResultFormat = "message", EnableThinking = true } }); Console.WriteLine("Reasoning > " + completion.Output.Choices![0].Message.ReasoningContent); Console.WriteLine("Assistant > " + completion.Output.Choices![0].Message.Content); var usage = completion.Usage; if (usage != null) { Console.WriteLine( $"Usage: in({usage.InputTokens})/out({usage.OutputTokens})/reasoning({usage.OutputTokensDetails?.ReasoningTokens})/total({usage.TotalTokens})"); } messages.Add(TextChatMessage.Assistant(completion.Output.Choices[0].Message.Content)); }var request = new ModelRequest<TextGenerationInput, ITextGenerationParameters>() { Model = "qwen-turbo", Input = new TextGenerationInput() { Messages = messages }, Parameters = new TextGenerationParameters() { ResultFormat = "message", EnableThinking = true, IncrementalOutput = true } }Use client.GetTextCompletionStreamAsync for streaming output. It's recommended to enable IncrementalOutput in Parameters for incremental output.
Incremental output:
Example: ["I love","eating","apples"]
Non-incremental output:
Example: ["I love","I love eating","I love eating apples"]
Streaming output returns an IAsyncEnumerable. Use await foreach to iterate, record incremental content, then save to conversation history.
var messages = new List(); messages.Add(TextChatMessage.System("You are a helpful assistant")); while (true) { Console.Write("User > "); var input = Console.ReadLine(); if (string.IsNullOrEmpty(input)) { Console.WriteLine("Please enter a user input."); return; }
messages.Add(TextChatMessage.User(input)); var completion = client.GetTextCompletionStreamAsync( new ModelRequest<TextGenerationInput, ITextGenerationParameters>() { Model = "qwen-turbo", Input = new TextGenerationInput() { Messages = messages }, Parameters = new TextGenerationParameters() { ResultFormat = "message", EnableThinking = true, IncrementalOutput = true } }); var reply = new StringBuilder(); var reasoning = false; TextGenerationTokenUsage? usage = null; await foreach (var chunk in completion) { var choice = chunk.Output.Choices![0]; if (string.IsNullOrEmpty(choice.Message.ReasoningContent) == false) { // reasoning if (reasoning == false) { Console.Write("Reasoning > "); reasoning = true; } Console.Write(choice.Message.ReasoningContent); continue; } if (reasoning) { reasoning = false; Console.WriteLine(); Console.Write("Assistant > "); } Console.Write(choice.Message.Content); reply.Append(choice.Message.Content); usage = chunk.Usage; } Console.WriteLine(); messages.Add(TextChatMessage.Assistant(reply.ToString())); if (usage != null) { Console.WriteLine( $"Usage: in({usage.InputTokens})/out({usage.OutputTokens})/reasoning({usage.OutputTokensDetails?.ReasoningTokens})/total({usage.TotalTokens})"); } }
Use ThinkingBudget in Parameters to limit the model's thinking length.
const int budget = 10; Console.WriteLine($"Set thinking budget to {budget} tokens"); var messages = new List<TextChatMessage>(); messages.Add(TextChatMessage.System("You are a helpful assistant")); while (true) { Console.Write("User > "); var input = Console.ReadLine(); if (string.IsNullOrEmpty(input)) { Console.WriteLine("Please enter a user input."); return; } messages.Add(TextChatMessage.User(input)); var completion = client.GetTextCompletionStreamAsync( new ModelRequest<TextGenerationInput, ITextGenerationParameters>() { Model = "qwen-turbo", Input = new TextGenerationInput() { Messages = messages }, Parameters = new TextGenerationParameters() { ResultFormat = "message", EnableThinking = true, ThinkingBudget = budget, IncrementalOutput = true } }); var reply = new StringBuilder(); var reasoning = false; TextGenerationTokenUsage? usage = null; await foreach (var chunk in completion) { var choice = chunk.Output.Choices![0]; if (string.IsNullOrEmpty(choice.Message.ReasoningContent) == false) { // reasoning if (reasoning == false) { Console.Write("Reasoning > "); reasoning = true; } Console.Write(choice.Message.ReasoningContent); continue; } if (reasoning) { reasoning = false; Console.WriteLine(); Console.Write("Assistant > "); } Console.Write(choice.Message.Content); reply.Append(choice.Message.Content); usage = chunk.Usage; } Console.WriteLine(); messages.Add(TextChatMessage.Assistant(reply.ToString())); if (usage != null) { Console.WriteLine( $"Usage: in({usage.InputTokens})/out({usage.OutputTokens})/reasoning({usage.OutputTokensDetails?.ReasoningTokens})/total({usage.TotalTokens})"); } }Controlled mainly through EnableSearch and SearchOptions in Parameters.
Example request:
var request = new ModelRequest<TextGenerationInput, ITextGenerationParameters>() { Model = "qwen-turbo", Input = new TextGenerationInput() { Messages = messages }, Parameters = new TextGenerationParameters() { ResultFormat = "message", EnableThinking = true, EnableSearch = true, SearchOptions = new TextGenerationSearchOptions() { SearchStrategy = "max", // max/turbo - controls number of search results EnableCitation = true, // Add source citations to model reply CitationFormat = "[ref_<number>]", // Citation format EnableSource = true, // Return search source list in SearchInfo ForcedSearch = true, // Force model to search EnableSearchExtension = true, // Enable vertical domain search, results in SearchInfo.Extra PrependSearchResult = true // First packet contains only search results } } };Provide available tools to the model via Tools in Parameters. The model returns messages with ToolCall properties to invoke tools.
After receiving the message, the server needs to call the corresponding tools and insert results as Tool role messages into the conversation history before making another request. The model will summarize the tool call results.
By default, the model calls tools once per turn. To enable multiple tool calls, enable ParallelToolCalls in Parameters.
var tools = new List<ToolDefinition> { new( ToolTypes.Function, new FunctionDefinition( nameof(GetWeather), "Get current weather", new JsonSchemaBuilder().FromType<WeatherReportParameters>().Build())) }; var request = new ModelRequest<TextGenerationInput, ITextGenerationParameters>() { Model = "qwen-turbo", Input = new TextGenerationInput() { Messages = messages }, Parameters = new TextGenerationParameters() { ResultFormat = "message", EnableThinking = true, IncrementalOutput = true, Tools = tools, ToolChoice = ToolChoice.AutoChoice, ParallelToolCalls = true // Model can call multiple tools at once } } Set ResponseFormat (remarks: not ResultFormat) in Parameters to JSON to force JSON output.
var request = new ModelRequest<TextGenerationInput, ITextGenerationParameters>() { Model = "qwen-plus", Input = new TextGenerationInput() { Messages = messages }, Parameters = new TextGenerationParameters() { ResultFormat = "message", ResponseFormat = DashScopeResponseFormat.Json, IncrementalOutput = true } }Place the prefix to complete as an assistant message at the end of the messages array with Partial set to true. The model will complete the remaining content based on this prefix.
Deep thinking cannot be enabled in this mode.
var messages = new List<TextChatMessage> { TextChatMessage.User("Complete following C# method."), TextChatMessage.Assistant("public int Fibonacci(int n)", partial: true) }; var completion = client.GetTextCompletionStreamAsync( new ModelRequest<TextGenerationInput, ITextGenerationParameters>() { Model = "qwen-turbo", Input = new TextGenerationInput() { Messages = messages }, Parameters = new TextGenerationParameters() { ResultFormat = "message", IncrementalOutput = true } });For Qwen-Long models:
var file = new FileInfo("test.txt"); var uploadedFile = await dashScopeClient.UploadFileAsync(file.OpenRead(), file.Name); var history = new List<ChatMessage> { ChatMessage.File(uploadedFile.Id) }; var completion = await client.client.GetTextCompletionAsync( new ModelRequest<TextGenerationInput, ITextGenerationParameters>() { Model = "qwen-long", Input = new TextGenerationInput() { Messages = history }, Parameters = new TextGenerationParameters() { ResultFormat = "message", EnableThinking = false, } }); Console.WriteLine(completion.Output.Choices[0].Message.Content); // Cleanup await dashScopeClient.DeleteFileAsync(uploadedFile.Id);Use GetMultimodalGenerationAsync/GetMultimodalGenerationStreamAsync Official Documentation
You can upload file to get an oss link before multimodal usage.
await using var video = File.OpenRead("sample.mp4"); var ossLink = await client.UploadTemporaryFileAsync("qwen3-vl-plus", video, "sample.mp4"); Console.WriteLine($"File uploaded: {ossLink}"); var messages = new List<MultimodalMessage>(); messages.Add( MultimodalMessage.User( [ MultimodalMessageContent.VideoContent(ossLink, fps: 2), // MultimodalMessageContent.VideoFrames(links), // MultimodalMessageContent.ImageContent(link) MultimodalMessageContent.TextContent("这段视频的内容是什么？") ]));var image = await File.ReadAllBytesAsync("Lenna.jpg"); var response = dashScopeClient.GetMultimodalGenerationStreamAsync( new ModelRequest<MultimodalInput, IMultimodalParameters>() { Model = "qvq-plus", Input = new MultimodalInput() { Messages = [ MultimodalMessage.User( [ MultimodalMessageContent.ImageContent(image, "image/jpeg"), MultimodalMessageContent.TextContent("她是谁？") ]) ] }, Parameters = new MultimodalParameters { IncrementalOutput = true, // EnableThinking = true, VlHighResolutionImages = false } }); // output var reasoning = false; await foreach (var modelResponse in response) { var choice = modelResponse.Output.Choices.FirstOrDefault(); if (choice != null) { if (choice.FinishReason != "null") { break; } if (string.IsNullOrEmpty(choice.Message.ReasoningContent) == false) { if (reasoning == false) { reasoning = true; Console.WriteLine("<think>"); } Console.Write(choice.Message.ReasoningContent); continue; } if (reasoning) { reasoning = false; Console.WriteLine("</think>"); } Console.Write(choice.Message.Content[0].Text); } }Create a speech synthesis session using dashScopeClient.CreateSpeechSynthesizerSocketSessionAsync().
Note: Use the using statement to automatically dispose the session, or manually call Dispose() to release resources. Avoid reusing sessions.
Create a synthesis session:
using var tts = await dashScopeClient.CreateSpeechSynthesizerSocketSessionAsync("cosyvoice-v2"); var taskId = await tts.RunTaskAsync(new SpeechSynthesizerParameters { Voice = "longxiaochun_v2", Format = "mp3" }); await tts.ContinueTaskAsync(taskId, "Cnblogs"); await tts.ContinueTaskAsync(taskId, "Code changes the world"); await tts.FinishTaskAsync(taskId); var file = new FileInfo("tts.mp3"); using var stream = file.OpenWrite(); await foreach (var b in tts.GetAudioAsync()) { stream.WriteByte(b); } Console.WriteLine($"Audio saved to {file.FullName}");Use shortcuts for Wanx models:
var task = await dashScopeClient.CreateWanxImageSynthesisTaskAsync( WanxModel.WanxV21Turbo, "A futuristic cityscape at sunset", new ImageSynthesisParameters { Style = ImageStyles.OilPainting }); // Pull status while (true) { var result = await dashScopeClient.GetWanxImageSynthesisTaskAsync(task.TaskId); if (result.Output.TaskStatus == DashScopeTaskStatus.Succeeded) { Console.WriteLine($"Image URL: {result.Output.Results[0].Url}"); break; } await Task.Delay(500); }Use CreateWanxImageGenerationTaskAsync and GetWanxImageGenerationTaskAsync
Use CreateWanxBackgroundGenerationTaskAsync and GetWanxBackgroundGenerationTaskAsync
var request = new ApplicationRequest() { Input = new ApplicationInput() { Prompt = "Summarize this file." }, Parameters = new ApplicationParameters() { TopK = 100, TopP = 0.8f, Seed = 1234, Temperature = 0.85f, RagOptions = new ApplicationRagOptions() { PipelineIds = ["thie5bysoj"], FileIds = ["file_d129d632800c45aa9e7421b30561f447_10207234"] } } }; var response = await client.GetApplicationResponseAsync("your-application-id", request); Console.WriteLine(response.Output.Text);ApplicationRequest uses Dictionary<string, object?> as the default type for BizParams.
var request = new ApplicationRequest() { Input = new ApplicationInput() { Prompt = "Summarize this file.", BizParams = new Dictionary<string, object?>() { { "customKey1", "custom-value" } } } }; var response = await client.GetApplicationResponseAsync("your-application-id", request); Console.WriteLine(response.Output.Text);For strong typing support, you can use the generic class ApplicationRequest<TBizParams>. Note that the SDK uses snake_case for JSON serialization. If your application uses different naming conventions, manually specify the serialized property names using [JsonPropertyName("camelCase")].
public record TestApplicationBizParam( [property: JsonPropertyName("sourceCode")] string SourceCode); var request = new ApplicationRequest<TestApplicationBizParam>() { Input = new ApplicationInput<TestApplicationBizParam>() { Prompt = "Summarize this file.", BizParams = new TestApplicationBizParam("test") } }; var response = await client.GetApplicationResponseAsync("your-application-id", request); Console.WriteLine(response.Output.Text);var text = "Sample text for embedding"; var response = await dashScopeClient.GetTextEmbeddingsAsync( TextEmbeddingModel.TextEmbeddingV4, [text], new TextEmbeddingParameters { Dimension = 512 }); var embedding = response.Output.Embeddings.First().Embedding; Console.WriteLine($"Embedding vector length: {embedding.Length}");See Snapshot Files for API parameter examples.
Review Tests for comprehensive usage examples.