前言

本文通过Codeblaze.SemanticKernel这个项目，学习如何实现ITextEmbeddingGenerationService接口，接入本地嵌入模型。

项目地址：https://github.com/BLaZeKiLL/Codeblaze.SemanticKernel

实践

SemanticKernel初看以为只支持OpenAI的各种模型，但其实也提供了强大的抽象能力，可以通过自己实现接口，来实现接入不兼容OpenAI格式的模型。

Codeblaze.SemanticKernel这个项目实现了ITextGenerationService、IChatCompletionService与ITextEmbeddingGenerationService接口，由于现在Ollama的对话已经支持了OpenAI格式，因此可以不用实现ITextGenerationService和IChatCompletionService来接入Ollama中的模型了，但目前Ollama的嵌入还没有兼容OpenAI的格式，因此可以通过实现ITextEmbeddingGenerationService接口，接入Ollama中的嵌入模型。

查看ITextEmbeddingGenerationService接口：

代表了一种生成浮点类型文本嵌入的生成器。

再看看IEmbeddingGenerationService<string, float>接口：

[Experimental("SKEXP0001")]
public interface IEmbeddingGenerationService<TValue, TEmbedding> : IAIService where TEmbedding : unmanaged
{Task<IList<ReadOnlyMemory<TEmbedding>>> GenerateEmbeddingsAsync(IList<TValue> data, Kernel? kernel = null, CancellationToken cancellationToken = default(CancellationToken));
}

再看看IAIService接口：

说明我们只要实现了

Task<IList<ReadOnlyMemory<TEmbedding>>> GenerateEmbeddingsAsync(IList<TValue> data, Kernel? kernel = null, CancellationToken cancellationToken = default(CancellationToken));IReadOnlyDictionary<string, object?> Attributes { get; }

这个方法和属性就行。

学习Codeblaze.SemanticKernel中是怎么做的。

添加OllamaBase类：

 public interface IOllamaBase{Task PingOllamaAsync(CancellationToken cancellationToken = new());}public abstract class OllamaBase<T> : IOllamaBase where T : OllamaBase<T>{public IReadOnlyDictionary<string, object?> Attributes => _attributes;private readonly Dictionary<string, object?> _attributes = new();protected readonly HttpClient Http;protected readonly ILogger<T> Logger;protected OllamaBase(string modelId, string baseUrl, HttpClient http, ILoggerFactory? loggerFactory){_attributes.Add("model_id", modelId);_attributes.Add("base_url", baseUrl);Http = http;Logger = loggerFactory is not null ? loggerFactory.CreateLogger<T>() : NullLogger<T>.Instance;}/// <summary>/// Ping Ollama instance to check if the required llm model is available at the instance/// </summary>/// <param name="cancellationToken"></param>public async Task PingOllamaAsync(CancellationToken cancellationToken = new()){var data = new{name = Attributes["model_id"]};var response = await Http.PostAsJsonAsync($"{Attributes["base_url"]}/api/show", data, cancellationToken).ConfigureAwait(false);ValidateOllamaResponse(response);Logger.LogInformation("Connected to Ollama at {url} with model {model}", Attributes["base_url"], Attributes["model_id"]);}protected void ValidateOllamaResponse(HttpResponseMessage? response){try{response.EnsureSuccessStatusCode();}catch (HttpRequestException){Logger.LogError("Unable to connect to ollama at {url} with model {model}", Attributes["base_url"], Attributes["model_id"]);}}}

注意这个

public IReadOnlyDictionary<string, object?> Attributes => _attributes;

实现了接口中的属性。

添加OllamaTextEmbeddingGeneration类：

#pragma warning disable SKEXP0001public class OllamaTextEmbeddingGeneration(string modelId, string baseUrl, HttpClient http, ILoggerFactory? loggerFactory): OllamaBase<OllamaTextEmbeddingGeneration>(modelId, baseUrl, http, loggerFactory),ITextEmbeddingGenerationService{public async Task<IList<ReadOnlyMemory<float>>> GenerateEmbeddingsAsync(IList<string> data, Kernel? kernel = null,CancellationToken cancellationToken = new()){var result = new List<ReadOnlyMemory<float>>(data.Count);foreach (var text in data){var request = new{model = Attributes["model_id"],prompt = text};var response = await Http.PostAsJsonAsync($"{Attributes["base_url"]}/api/embeddings", request, cancellationToken).ConfigureAwait(false);ValidateOllamaResponse(response);var json = JsonSerializer.Deserialize<JsonNode>(await response.Content.ReadAsStringAsync().ConfigureAwait(false));var embedding = new ReadOnlyMemory<float>(json!["embedding"]?.AsArray().GetValues<float>().ToArray());result.Add(embedding);}return result;}}

注意实现了GenerateEmbeddingsAsync方法。实现的思路就是向Ollama中的嵌入接口发送请求，获得embedding数组。

为了在MemoryBuilder中能用还需要添加扩展方法：

#pragma warning disable SKEXP0001public static class OllamaMemoryBuilderExtensions{/// <summary>/// Adds Ollama as the text embedding generation backend for semantic memory/// </summary>/// <param name="builder">kernel builder</param>/// <param name="modelId">Ollama model ID to use</param>/// <param name="baseUrl">Ollama base url</param>/// <returns></returns>public static MemoryBuilder WithOllamaTextEmbeddingGeneration(this MemoryBuilder builder,string modelId,string baseUrl){builder.WithTextEmbeddingGeneration((logger, http) => new OllamaTextEmbeddingGeneration(modelId,baseUrl,http,logger));return builder;}       }

开始使用

 public async Task<ISemanticTextMemory> GetTextMemory3(){var builder = new MemoryBuilder();var embeddingEndpoint = "http://localhost:11434";var cancellationTokenSource = new System.Threading.CancellationTokenSource();var cancellationToken = cancellationTokenSource.Token;builder.WithHttpClient(new HttpClient());builder.WithOllamaTextEmbeddingGeneration("mxbai-embed-large:335m", embeddingEndpoint);IMemoryStore memoryStore = await SqliteMemoryStore.ConnectAsync("memstore.db");builder.WithMemoryStore(memoryStore);var textMemory = builder.Build();return textMemory;}

  builder.WithOllamaTextEmbeddingGeneration("mxbai-embed-large:335m", embeddingEndpoint);

实现了WithOllamaTextEmbeddingGeneration这个扩展方法，因此可以这么写，使用的是mxbai-embed-large:335m这个向量模型。

我使用WPF简单做了个界面，来试试效果。

找了一个新闻嵌入：

文本向量化存入数据库中：

现在测试RAG效果：

回答的效果也还可以。

大模型使用的是在线api的Qwen/Qwen2-72B-Instruct，嵌入模型使用的是本地Ollama中的mxbai-embed-large:335m。