了解的运作原理之后，就可以开始使用Semantic Kernel来制作应用了。

Semantic Kernel将embedding的功能封装到了Memory中，用来存储上下文信息，就好像电脑的内存一样，而LLM就像是CPU一样，我们所需要做的就是从内存中取出相关的信息交给CPU处理就好了。

内存配置

使用Memory需要注册 embedding模型，目前使用的就是 text-embedding-ada-002。同时需要为Kernel添加MemoryStore，用于存储更多的信息，这里Semantic Kernel提供了一个 VolatileMemoryStore，就是一个普通的内存存储的MemoryStore。

	var kernel = Kernel.Builder.Configure(c =>
	{
	c.AddOpenAITextCompletionService("openai", "text-davinci-003", Environment.GetEnvironmentVariable("MY_OPEN_AI_API_KEY"));
	c.AddOpenAIEmbeddingGenerationService("openai", "text-embedding-ada-002", Environment.GetEnvironmentVariable("MY_OPEN_AI_API_KEY"));
	})
	.WithMemoryStorage(new VolatileMemoryStore())
	.Build();

信息存储

完成了基础信息的注册后，就可以往Memroy中存储信息了。

	const string MemoryCollectionName = "aboutMe";
	await kernel.Memory.SaveInformationAsync(MemoryCollectionName, id: "info1", text: "My name is Andrea");
	await kernel.Memory.SaveInformationAsync(MemoryCollectionName, id: "info2", text: "I currently work as a tourist operator");
	await kernel.Memory.SaveInformationAsync(MemoryCollectionName, id: "info3", text: "I currently live in Seattle and have been living there since 2005");
	await kernel.Memory.SaveInformationAsync(MemoryCollectionName, id: "info4", text: "I visited France and Italy five times since 2015");
	await kernel.Memory.SaveInformationAsync(MemoryCollectionName, id: "info5", text: "My family is from New York");

SaveInformationAsync 会将text的内容通过 embedding 模型转化为对应的文本向量，存放在的MemoryStore中。其中CollectionName如同数据库的表名，Id就是Id。

语义搜索

完成信息的存储之后，就可以用来语义搜索了。

直接使用Memory.SearchAsync方法，指定对应的Collection，同时提供相应的查询问题，查询问题也会被转化为embedding，再在MemoryStore中计算查找最相似的信息。

	var questions = new[]
	{
	"what is my name?",
	"where do I live?",
	"where is my family from?",
	"where have I travelled?",
	"what do I do for work?",
	};
	foreach (var q in questions)
	{
	var response = await kernel.Memory.SearchAsync(MemoryCollectionName, q).FirstOrDefaultAsync();
	Console.WriteLine(q + " " + response?.Metadata.Text);
	}
	// output
	/*
	what is my name? My name is Andrea
	where do I live? I currently live in Seattle and have been living there since 2005
	where is my family from? My family is from New York
	where have I travelled? I visited France and Italy five times since 2015
	what do I do for work? I currently work as a tourist operator
	*/

到这个时候，即便不需要进行总结归纳，光是这样的语义查找，都会很有价值。

引用存储

除了添加信息以外，还可以添加引用，像是非常有用的参考链接之类的。

	const string memoryCollectionName = "SKGitHub";
	var githubFiles = new Dictionary<string, string>()
	{
	["https://github.com/microsoft/semantic-kernel/blob/main/README.md"]
	= "README: Installation, getting started, and how to contribute",
	["https://github.com/microsoft/semantic-kernel/blob/main/samples/notebooks/dotnet/2-running-prompts-from-file.ipynb"]
	= "Jupyter notebook describing how to pass prompts from a file to a semantic skill or function",
	["https://github.com/microsoft/semantic-kernel/blob/main/samples/notebooks/dotnet/Getting-Started-Notebook.ipynb"]
	= "Jupyter notebook describing how to get started with the Semantic Kernel",
	["https://github.com/microsoft/semantic-kernel/tree/main/samples/skills/ChatSkill/ChatGPT"]
	= "Sample demonstrating how to create a chat skill interfacing with ChatGPT",
	["https://github.com/microsoft/semantic-kernel/blob/main/dotnet/src/SemanticKernel/Memory/Volatile/VolatileMemoryStore.cs"]
	= "C# class that defines a volatile embedding store",
	["https://github.com/microsoft/semantic-kernel/tree/main/samples/dotnet/KernelHttpServer/README.md"]
	= "README: How to set up a Semantic Kernel Service API using Azure Function Runtime v4",
	["https://github.com/microsoft/semantic-kernel/tree/main/samples/apps/chat-summary-webapp-react/README.md"]
	= "README: README associated with a sample starter react-based chat summary webapp",
	};
	foreach (var entry in githubFiles)
	{
	await kernel.Memory.SaveReferenceAsync(
	collection: memoryCollectionName,
	description: entry.Value,
	text: entry.Value,
	externalId: entry.Key,
	externalSourceName: "GitHub"
	);
	}

同样的，使用SearchAsync搜索就行。

	string ask = "I love Jupyter notebooks, how should I get started?";
	Console.WriteLine("===========================\n" +
	"Query: " + ask + "\n");
	var memories = kernel.Memory.SearchAsync(memoryCollectionName, ask, limit: 5, minRelevanceScore: 0.77);
	var i = 0;
	await foreach (MemoryQueryResult memory in memories)
	{
	Console.WriteLine($"Result {++i}:");
	Console.WriteLine(" URL: : " + memory.Metadata.Id);
	Console.WriteLine(" Title : " + memory.Metadata.Description);
	Console.WriteLine(" ExternalSource: " + memory.Metadata.ExternalSourceName);
	Console.WriteLine(" Relevance: " + memory.Relevance);
	Console.WriteLine();
	}
	//output
	/*
	===========================
	Query: I love Jupyter notebooks, how should I get started?
	Result 1:
	URL: : https://github.com/microsoft/semantic-kernel/blob/main/samples/notebooks/dotnet/Getting-Started-Notebook.ipynb
	Title : Jupyter notebook describing how to get started with the Semantic Kernel
	ExternalSource: GitHub
	Relevance: 0.8677381632778319
	Result 2:
	URL: : https://github.com/microsoft/semantic-kernel/blob/main/samples/notebooks/dotnet/2-running-prompts-from-file.ipynb
	Title : Jupyter notebook describing how to pass prompts from a file to a semantic skill or function
	ExternalSource: GitHub
	Relevance: 0.8162989178955157
	Result 3:
	URL: : https://github.com/microsoft/semantic-kernel/blob/main/README.md
	Title : README: Installation, getting started, and how to contribute
	ExternalSource: GitHub
	Relevance: 0.8083238591883483
	*/

这里多使用了两个参数，一个是limit，用于限制返回信息的条数,只返回最相似的前几条数据，另外一个是minRelevanceScore，限制最小的相关度分数，这个取值范围在0.0 ~ 1.0 之间，1.0意味着完全匹配。

语义问答

将Memory的存储、搜索功能和语义技能相结合，就可以快速的打造一个实用的语义问答的应用了。

只需要将搜索到的相关信息内容填充到 prompt中，然后将内容和问题都抛给LLM，就可以等着得到一个满意的答案了。

	const string MemoryCollectionName = "aboutMe";
	await kernel.Memory.SaveInformationAsync(MemoryCollectionName, id: "info1", text: "My name is Andrea");
	await kernel.Memory.SaveInformationAsync(MemoryCollectionName, id: "info2", text: "I currently work as a tourist operator");
	await kernel.Memory.SaveInformationAsync(MemoryCollectionName, id: "info3", text: "I currently live in Seattle and have been living there since 2005");
	await kernel.Memory.SaveInformationAsync(MemoryCollectionName, id: "info4", text: "I visited France and Italy five times since 2015");
	await kernel.Memory.SaveInformationAsync(MemoryCollectionName, id: "info5", text: "My family is from New York");
	var prompt =
	"""
	It can give explicit instructions or say 'I don't know' if it does not have an answer.
	Information about me, from previous conversations:
	{{ $fact }}
	User: {{ $ask }}
	ChatBot:
	""";
	var skill = kernel.CreateSemanticFunction(prompt);
	var ask = "Hello, I think we've met before, remember? my name is...";
	var fact = await kernel.Memory.SearchAsync(MemoryCollectionName,ask).FirstOrDefaultAsync();
	var context = kernel.CreateNewContext();
	context["fact"] = fact?.Metadata?.Text;
	context["ask"] = ask;
	var resultContext =await skill.InvokeAsync(context);
	resultContext.Result.Dump();
	//output
	/*
	Hi there! Yes, I remember you. Your name is Andrea, right?
	*/

优化搜索过程

由于这种场景太常见了，所以Semantic Kernel中直接提供了一个技能TextMemorySkill，通过Function调用的方式简化了搜索的过程。

	// .. SaveInformations
	// TextMemorySkill provides the "recall" function
	kernel.ImportSkill(new TextMemorySkill());
	var prompt =
	"""
	It can give explicit instructions or say 'I don't know' if it does not have an answer.
	Information about me, from previous conversations:
	{{ recall $ask }}
	User: {{ $ask }}
	ChatBot:
	""";
	var skill = kernel.CreateSemanticFunction(prompt);
	var ask = "Hello, I think we've met before, remember? my name is...";
	var context = kernel.CreateNewContext();
	context["ask"] = ask;
	context[TextMemorySkill.CollectionParam] = MemoryCollectionName;
	var resultContext =await skill.InvokeAsync(context);
	resultContext.Result.Dump();
	// output
	/*
	Hi there! Yes, I remember you. Your name is Andrea, right?
	*/

这里直接使用 recall 方法，将问题传给了 TextMemorySkill，搜索对应得到结果，免去了手动搜索注入得过程。

内存的持久化

VolatileMemoryStore本身也是易丢失的，往往使用到内存的场景，其中的信息都是有可能长期存储的，起码并不会即刻过期。那么将这些信息的 embedding 能够长期存储起来，也是比较划算的事情。毕竟每一次做 embedding的转化也是需要调接口，需要花钱的。

Semantic Kernel库中包含了SQLite、Qdrant和CosmosDB的实现，自行扩展的话，也只需要实现 IMemoryStore 这个接口就可以了。

至于未来，可能就是专用的 Vector Database 了。