实现阿里云模型服务灵积 DashScope 的 Semantic Kernel Connector
Semantic Kernel 内置的 IChatCompletionService 实现只支持 OpenAI 与 Azure OpenAI，而我却打算结合 DashScope(阿里云模型服务灵积) 学习 Semantic Kernel。
于是决定自己动手实现一个支持 DashScope 的 Semantic Kernel Connector —— DashScopeChatCompletionService，实现的过程也是学习 Semantic Kernel 源码的过程，
而且借助 Sdcb.DashScope，实现变得更容易了，详见前一篇博文 借助 .NET 开源库 Sdcb.DashScope 调用阿里云灵积通义千问 API
这里只实现用于调用 chat completion 服务的 connector，所以只需实现 IChatCompletionService 接口，该接口继承了 IAIService 接口，一共需要实现2个方法+1个属性。
public sealed class DashScopeChatCompletionService : IChatCompletionService
{
public IReadOnlyDictionary<string, object?> Attributes { get; }
public Task<IReadOnlyList<ChatMessageContent>> GetChatMessageContentsAsync(ChatHistory chatHistory, PromptExecutionSettings? executionSettings = null, Kernel? kernel = null, CancellationToken cancellationToken = default)
{
throw new NotImplementedException();
}
public IAsyncEnumerable<StreamingChatMessageContent> GetStreamingChatMessageContentsAsync(ChatHistory chatHistory, PromptExecutionSettings? executionSettings = null, Kernel? kernel = null, CancellationToken cancellationToken = default)
{
throw new NotImplementedException();
}
}
先实现 GetChatMessageContentsAsync 方法，调用 Kernel.InvokePromptAsync 方法时会用到这个方法。
实现起来比较简单，就是转手买卖：
- 把 Semantic Kernel 的
ChatHistory转换为 Sdcb.DashScope 的IReadOnlyList<ChatMessage> - 把 Semantic Kernel 的
PromptExecutionSettings转换为 Sdcb.DashScope 的ChatParameters - 把 Sdcb.DashScope 的
ResponseWrapper<ChatOutput, ChatTokenUsage>转换为 Semantic Kernel 的IReadOnlyList<ChatMessageContent>
实现代码如下：
public async Task<IReadOnlyList<ChatMessageContent>> GetChatMessageContentsAsync(ChatHistory chatHistory, PromptExecutionSettings? executionSettings = null, Kernel? kernel = null, CancellationToken cancellationToken = default)
{
var chatMessages = chatHistory
.Where(x => !string.IsNullOrEmpty(x.Content))
.Select(x => new ChatMessage(x.Role.ToString(), x.Content!)).
ToList();
ChatParameters? chatParameters = null;
if (executionSettings?.ExtensionData?.Count > 0)
{
var json = JsonSerializer.Serialize(executionSettings.ExtensionData);
chatParameters = JsonSerializer.Deserialize<ChatParameters>(
json,
new JsonSerializerOptions { NumberHandling = JsonNumberHandling.AllowReadingFromString });
}
var response = await _dashScopeClient.TextGeneration.Chat(_modelId, chatMessages, chatParameters, cancellationToken);
return [new ChatMessageContent(new AuthorRole(chatMessages.First().Role), response.Output.Text)];
}
接下来实现 GetStreamingChatMessageContentsAsync，调用 Kernel.InvokePromptStreamingAsync 时会用到它，同样也是转手买卖。
ChatHistory 与 PromptExecutionSettings 参数的转换与 GetChatMessageContentsAsync 一样，所以引入2个扩展方法 ChatHistory.ToChatMessages 与 PromptExecutionSettings.ToChatParameters 减少重复代码，另外需要将 ChatParameters.IncrementalOutput 设置为 true。
不同之处是返回值类型，需要将 Sdcb.DashScope 的 IAsyncEnumerable<ResponseWrapper<ChatOutput, ChatTokenUsage>> 转换为 IAsyncEnumerable<StreamingChatMessageContent>
实现代码如下：
public async IAsyncEnumerable<StreamingChatMessageContent> GetStreamingChatMessageContentsAsync(
ChatHistory chatHistory,
PromptExecutionSettings? executionSettings = null,
Kernel? kernel = null,
[EnumeratorCancellation] CancellationToken cancellationToken = default)
{
var chatMessages = chatHistory.ToChatMessages();
var chatParameters = executionSettings?.ToChatParameters() ?? new ChatParameters();
chatParameters.IncrementalOutput = true;
var responses = _dashScopeClient.TextGeneration.ChatStreamed(_modelId, chatMessages, chatParameters, cancellationToken);
await foreach (var response in responses)
{
yield return new StreamingChatMessageContent(new AuthorRole(chatMessages[0].Role), response.Output.Text);
}
}
到这里2个方法就实现好了，还剩下很容易实现的1个属性，轻松搞定
public sealed class DashScopeChatCompletionService : IChatCompletionService
{
private readonly DashScopeClient _dashScopeClient;
private readonly string _modelId;
private readonly Dictionary<string, object?> _attribues = [];
public DashScopeChatCompletionService(
IOptions<DashScopeClientOptions> options,
HttpClient httpClient)
{
_dashScopeClient = new(options.Value.ApiKey, httpClient);
_modelId = options.Value.ModelId;
_attribues.Add(AIServiceExtensions.ModelIdKey, _modelId);
}
public IReadOnlyDictionary<string, object?> Attributes => _attribues;
}
到此，DashScopeChatCompletionService 的实现就完成了。
接下来，实现一个扩展方法，将 DashScopeChatCompletionService 注册到依赖注入容器
public static class DashScopeServiceCollectionExtensions
{
public static IKernelBuilder AddDashScopeChatCompletion(
this IKernelBuilder builder,
string? serviceId = null,
Action<HttpClient>? configureClient = null,
string configSectionPath = "dashscope")
{
Func<IServiceProvider, object?, DashScopeChatCompletionService> factory = (serviceProvider, _) =>
serviceProvider.GetRequiredService<DashScopeChatCompletionService>();
if (configureClient == null)
{
builder.Services.AddHttpClient<DashScopeChatCompletionService>();
}
else
{
builder.Services.AddHttpClient<DashScopeChatCompletionService>(configureClient);
}
builder.Services.AddOptions<DashScopeClientOptions>().BindConfiguration(configSectionPath);
builder.Services.AddKeyedSingleton<IChatCompletionService>(serviceId, factory);
return builder;
}
}
为了方便通过配置文件配置 ModelId 与 ApiKey，引入了 DashScopeClientOptions
public class DashScopeClientOptions : IOptions<DashScopeClientOptions>
{
public string ModelId { get; set; } = string.Empty;
public string ApiKey { get; set; } = string.Empty;
public DashScopeClientOptions Value => this;
}
最后就是写测试代码验证实现是否成功，为了减少代码块的长度，下面的代码片段只列出其中一个测试用例
public class DashScopeChatCompletionTests
{
[Fact]
public async Task ChatCompletion_InvokePromptAsync_WorksCorrectly()
{
// Arrange
var builder = Kernel.CreateBuilder();
builder.Services.AddSingleton(GetConfiguration());
builder.AddDashScopeChatCompletion();
var kernel = builder.Build();
var prompt = @"<message role=""user"">博客园是什么网站</message>";
PromptExecutionSettings settings = new()
{
ExtensionData = new Dictionary<string, object>()
{
{ "temperature", "0.8" }
}
};
KernelArguments kernelArguments = new(settings);
// Act
var result = await kernel.InvokePromptAsync(prompt, kernelArguments);
// Assert
Assert.Contains("博客园", result.ToString());
Trace.WriteLine(result.ToString());
}
private static IConfiguration GetConfiguration()
{
return new ConfigurationBuilder()
.SetBasePath(Directory.GetCurrentDirectory())
.AddJsonFile("appsettings.json")
.AddUserSecrets<DashScopeChatCompletionTests>()
.Build();
}
}
最后的最后就是运行测试，在 appsettings.json 中添加模型Id
{
"dashscope": {
"modelId": "qwen-max"
}
}
注：qwen-max 是通义千问千亿级大模型
通过 user-secrets 添加 api key
dotnet user-secrets set "dashscope:apiKey" "sk-xxx"
dotnet test 命令运行测试
A total of 1 test files matched the specified pattern.
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Passed! - Failed: 0, Passed: 1, Skipped: 0, Total: 1, Duration: < 1 ms - SemanticKernel.DashScope.IntegrationTest.dll (net8.0)
测试通过！连接 DashScope 的 Semantic Kernel Connector 初步实现完成。
完整实现代码放在 github 上，详见 https://github.com/cnblogs/semantic-kernel-dashscope/tree/v0.1.0
针对这个实现发布了 nuget 包 Cnblogs.SemanticKernel.Connectors.DashScope
