【Semantic Kernel】RAG（检索增强生成）-编程知识

【Semantic Kernel】RAG（检索增强生成）

news/2024/11/7 16:14:39/文章来源:https://www.cnblogs.com/fanfan-90/p/18532503

RAG（Retrieval-Augmented Generation）是一种结合信息检索和生成模型的自然语言处理方法。它通过检索相关文档片段作为生成模型的上下文，提高生成文本的准确性和相关性。RAG广泛应用于问答系统、对话系统和文本摘要等领域，兼具高效性和灵活性。公司的客户机器人都特别适合。下面的案例是使用GPT的embedding来向量化相关信息，然后通过关键字检索，最后把这些信息，结合用户问题送给gpt-4o，得到一个相对友好的回复结果。

代码：

/// <summary>
/// RAG(Retrieval Augmented Generation)检索增强生成
/// </summary>
public static class RAGSample
{public static async Task Exec(){#pragma warning disable SKEXP0001
#pragma warning disable SKEXP0010
#pragma warning disable SKEXP0050ISemanticTextMemory memory = new MemoryBuilder().WithOpenAITextEmbeddingGeneration("text-embedding-ada-002", Config.OpenAiKey).WithMemoryStore(new VolatileMemoryStore())//内存数据库，生产环境不能用.Build();#region 数据导入Memory
#pragma warning restore SKEXP0050
#pragma warning restore SKEXP0010
#pragma warning restore SKEXP0001var sampleData = new Dictionary<string, string>{["info4"]= "3年级2班有56人",["info5"]= "小明在3年级2班",["info6"]= "小明在第二中学",["info7"]= "第二中学有1500人"};var i = 0;foreach (var entry in sampleData){await memory.SaveReferenceAsync(collection: "个人信息",externalSourceName: "GitHub",externalId: entry.Key,description: entry.Value,text: entry.Value);}#endregionvar builder = Kernel.CreateBuilder().AddOpenAIChatCompletion(modelId: Config.OpenAiChatModel, Config.OpenAiKey);var kernel = builder.Build();var chatHistory = new ChatHistory(); var chat = kernel.GetRequiredService<IChatCompletionService>();while (true){Console.ResetColor(); Console.WriteLine("----------学生提问：----------"); var ask = Console.ReadLine(); chatHistory.Clear();chatHistory.AddSystemMessage("基于下面的信息回复问题："); await foreach (var answer in memory.SearchAsync(collection: "个人信息", query: ask, limit: 3, minRelevanceScore: 0.65d, withEmbeddings: true)){ chatHistory.AddSystemMessage(answer.Metadata.Description); }chatHistory.AddUserMessage(ask); Console.WriteLine(); Console.ForegroundColor = ConsoleColor.Green; Console.WriteLine("==========讲师回答：=========="); AuthorRole? role = AuthorRole.Assistant; var contentBuilder = new StringBuilder(); await foreach (var reply in chat.GetStreamingChatMessageContentsAsync(chatHistory)) { if (reply.Role.HasValue && role != reply.Role) { role = reply.Role; } Console.Write(reply.Content);contentBuilder.Append(reply.Content); }chatHistory.AddMessage(role.Value, contentBuilder.ToString()); Console.WriteLine();}}
}