OpenAI的文本嵌入衡量文本字符串的相关性。嵌入通常用于:
- 搜索(其中结果按与查询字符串的相关性排名)
- 聚类(其中文本字符串按相似性分组)
- 推荐(推荐具有相关文本字符串的项目)
- 异常检测(识别出相关性不大的异常值)
- 多样性测量(分析相似性分布)
- 分类(其中文本字符串按其最相似的标签分类)
嵌入是浮点数的向量(列表)。两个向量之间的距离衡量它们的相关性。小距离表示高相关性,大距离表示低相关性。 但是OpenAI的文本嵌入接口对中文的支持并不好,社区经过实践,对中文支持比较好的模型是Hugging face上的 ganymedenil/text2vec-large-chinese。具体可以参见:https://huggingface.co/GanymedeNil/text2vec-large-chinese/discussions/3 ,作者采用的训练数据集是 中文STS-B数据集。它将句子映射到 768 维密集向量空间,可用于任务 如句子嵌入、文本匹配或语义搜索。
在Semantic Kernel 的Sample 下有一个 hugging-face-http-server:https://github.com/microsoft/semantic-kernel/tree/main/samples/apps/hugging-face-http-server ,通过这个示例项目,我们可以在本地运行Huggingface模型。
我们首先构建一个Docker,执行命令 docker image build -t hf_model_server . ,最新的构建会有问题,我把它独立成一个repo :https://github.com/mlnethub/hugging-face-http-server。
运行容器
docker run -p 5000:5000 -d hf_model_server
通过从0.14版本引入的 Nuget 包 Microsoft.SemanticKernel.Connectors.AI.HuggingFace:https://www.nuget.org/packages/Microsoft.SemanticKernel.Connectors.AI.HuggingFace/0.14.547.1-preview#versions-body-tab
具体用法参考单元测试代码HuggingFaceEmbeddingGenerationTests
using System;
using System.Collections.Generic;
using System.Linq;
using System.Net;
using System.Net.Http;
using System.Threading.Tasks;
using Microsoft.SemanticKernel.Connectors.AI.HuggingFace.TextEmbedding;
using Xunit;
namespace SemanticKernel.Connectors.UnitTests.HuggingFace.TextEmbedding;
/// <summary>
/// Unit tests for <see cref="HuggingFaceTextEmbeddingGeneration"/> class.
/// </summary>
public class HuggingFaceEmbeddingGenerationTests : IDisposable
{
private const string Endpoint = "http://localhost:5000/embeddings";
private const string Model = @"GanymedeNil/text2vec-large-chinese";
private readonly HttpResponseMessage _response = new()
{
StatusCode = HttpStatusCode.OK,
};
/// <summary>
/// Verifies that <see cref="HuggingFaceTextEmbeddingGeneration.GenerateEmbeddingsAsync"/>
/// returns expected list of generated embeddings without errors.
/// </summary>
[Fact]
public async Task ItReturnsEmbeddingsCorrectlyAsync()
{
// Arrange
const int ExpectedEmbeddingCount = 1;
const int ExpectedVectorCount = 8;
List<string> data = new() { "test_string_1", "test_string_2", "test_string_3" };
using var service = this.CreateService(HuggingFaceTestHelper.GetTestResponse("embeddings_test_response.json"));
// Act
var embeddings = await service.GenerateEmbeddingsAsync(data);
// Assert
Assert.NotNull(embeddings);
Assert.Equal(ExpectedEmbeddingCount, embeddings.Count);
Assert.Equal(ExpectedVectorCount, embeddings.First().Count);
}
/// <summary>
/// Initializes <see cref="HuggingFaceTextEmbeddingGeneration"/> with mocked <see cref="HttpClientHandler"/>.
/// </summary>
/// <param name="testResponse">Test response for <see cref="HttpClientHandler"/> to return.</param>
private HuggingFaceTextEmbeddingGeneration CreateService(string testResponse)
{
this._response.Content = new StringContent(testResponse);
var httpClientHandler = HuggingFaceTestHelper.GetHttpClientHandlerMock(this._response);
return new HuggingFaceTextEmbeddingGeneration(new Uri(Endpoint), Model, httpClientHandler);
}
public void Dispose()
{
this.Dispose(true);
GC.SuppressFinalize(this);
}
protected virtual void Dispose(bool disposing)
{
if (disposing)
{
this._response.Dispose();
}
}
}